The Enterprise AI Reality Check

You were probably expecting another AI primer. A year ago, any half decent ‘Guide To AI’ would start with explaining generative models, throwing around buzzwords like ‘agentic’, and promising plug-and-play transformation.

But here’s the thing: you already know all that. You’ve seen the demos. You’ve read the headlines. Your parents have stopped Googling and started GPTing instead.

So, let’s skip the tech talk. The question isn’t ‘What can AI do?’ anymore. It’s ‘why does AI work brilliantly everywhere except inside your business?’

Your phone’s camera makes you look like a professional photographer. Netflix knows exactly what you want to watch. Even your car parks itself. But ask AI to help with actual work (reading project reports, spotting risks, finding expertise) and suddenly it struggles with the complexity hidden in our everyday work. A recent MIT study reported by Forbes found that 95% of AI proof-of-concepts fail.

That’s not a technology problem. It’s an application problem. The 95% fail because they ignore what actually matters: getting their data ready for AI and picking the right projects.

The 5% that succeed. They do the hard work first. They design for actual workflows. They focus on the data and integration. They make AI fit their messy, complex, decidedly human business reality.

Here’s what we’ve learned at Oakland and what our experts (not marketers or ChatGPT) wanted to share in this guide: AI success follows the same rules it always has. Get the right tools to extract the right insights from the right data. Make better decisions. Generative AI is powerful, yes. But it’s still just another tool.

Success depends as much on your data as on the AI you choose.

If you want to win with AI, you need to get your data talking first.

1. The Moonshot Mistake

AI is expected to solve everything: business efficiency, customer service, competitive edge, and even make better coffee.

Some enterprises continue to use AI for the wrong things. They want it to deliver blue-sky strategies, innovate independently, and transform
at the click of a button. Big moonshot projects that look impressive in presentations often fail to deliver.

Many large companies have faced the harsh reality of AI’s limitations when expecting it to revolutionise everything simultaneously. The pattern is consistent: ambitious AI projects aimed at replacing high-level thinking and strategic decision making fail. Meanwhile, back-office automation quietly succeeds.

Here’s the truth: AI excels at specific, repetitive tasks. It’s brilliant at reading documents (even those 97-page ones nobody else will), spotting patterns, and automating boring work. It won’t replace your strategy team. It won’t revolutionise creativity. But it will handle the tedious work that makes people groan. It will deliver efficiency and allow your people to do what you’ve always wanted them to do: solve the big problems and innovate.

2. Proof-of-Concept Paralysis

These high expectations, combined with cheap AI access, tempt businesses to try everything at once. They often get bogged down spinning up endless proof-of-concepts, which spreads resources too thin and fail to deliver.

With Gen AI, the most powerful machine learning models are available for fractions of a cent per API call. With such a cheap, mighty hammer,
everything looks like a nail. The temptation becomes irresistible implement AI everywhere, for everything, all at once.

This creates the proof-of-concept trap. Projects get proposed, built, and then fail to impress. Resources scatter. Nothing gets proper attention. Frustration and disillusionment follow.

3. Getting Lost in the Technical Jungle

Making matters worse, the AI landscape changes weekly. New models, new frameworks, new vendors, all claiming breakthrough results. Attend
any AI conference and you’ll face 400 sessions and 180 vendor booths, all promising to transform your business.

Teams freeze, paralysed by choice. What if we pick the wrong tool? What if something better launches tomorrow? So, they wait. And wait. Meanwhile, competitors who pressed ahead are already seeing results.

The lowered barrier to entry has also created a forest of startups promising useful AI wrappers. Sure, new options launch monthly. But
while you’re waiting for the perfect solution, your competitors are already automating their back office with tools that work today. The best AI solution is the one that solves problems today, not the one that might exist next year.

4. Data quality: A fork in a world of soup

Think of AI as a powerful lens for viewing your data. Let’s start with the obvious truth: your AI is only as good as your data. If that data is wrong, incomplete, or outdated, AI just magnifies those problems.

But … what does ‘data quality’ even mean in the world of Gen AI and unstructured data? It’s hard to see how traditional data quality methods apply to the unstructured data sources that Gen AI can unlock.

Try tackling unstructured data armed only with a typical data quality toolkit, and you’ll feel like a fork in a world of soup. Suddenly, you’re juggling blurrier, slippery concepts.

With ‘relevance’, ‘meaningfulness’ and ‘conformity’ living alongside the comforting, clean lines of ‘completeness’, ‘validity’ and ‘accuracy’, that have been the guiding lights of data quality for decades.

At Oakland, we see this constantly. Teams rush to implement AI, then wonder why it’s not delivering. They blame the technology when, in reality, they skipped the hard part: thinking deeply about how they gather, assure and protect the information the AI will use to create its own store of knowledge.

5. The Integration Nightmare

Even with the right tool, focused use case, and clean data, there’s one last hurdle. Integrating AI with your existing systems and processes.

Too often, AI gets crowbarred into workflows without the right information, tools, or context. It can’t access key systems. It doesn’t understand how your business operates. People forget to adjust processes or train users properly.

The result? Friction, frustration, and failed projects. Another AI initiative that proves the sceptics right. Gartner’s seen this film before. Every technology follows its ‘hype cycle’: massive excitement, crushing disappointment, and then muted success for those who stick with it.

We’re entering the ‘trough of disillusionment’ for AI right now. But that is great news. It means the tourists are leaving, and the serious work can begin.

The businesses that adapt their processes, train their people, and properly integrate AI are the ones that will succeed. They’re the ones who’ll be quietly winning while everyone else is still arguing about which model to use.

As we know, the pragmatists always win. Eventually!

So, those are the traps. The moonshots that crash. The proof-of-concepts that multiply like rabbits but never grow up. The paralysis, the data chaos, the integration nightmares.

But here’s what’s interesting: once you accept that AI won’t solve everything, you can focus on what it does well. Boring, repetitive, high-volume tasks that waste human talent.

We’ve found three specific problems where AI consistently delivers value. Not because they’re glamorous. Because they’re painful, universal, and perfectly suited to what AI does well. Every business faces them. Most are drowning in them. And unlike moonshot transformations, solving these problems delivers immediate, measurable results.

Here’s the thing. AI excels at specific, repetitive tasks and handling high volumes of information. Not replacing human creativity. The boring stuff that eats your day, the things that make a task take five hours, not two. Or the task that takes so long you’ll never get around to doing it.

1. The Information Tsunami

Let me take a wild guess: your business is drowning in incoming data. Support tickets pile up faster than teams can read them. Sensors and logs generate thousands of alerts that nobody has time to investigate. Emails sit unread while critical issues hide in all that noise.

AI can handle this flood for you. Every message gets read, categorised, and routed instantly. Priority items surface immediately. Routine queries handle themselves. Your team’s focus is on complex problems and deep work, rather than trying to tune out the noise.

Who benefits most:

• IT departments managing system alerts and monitoring
• Customer service operations processing high ticket volumes
• Operations teams handling IoT and sensor data
• Procurement teams processing supplier communications
• HR departments managing employee queries

But information isn’t just external. For example, product catalogues become their own tsunami. Take one of our clients: they had eight million SKUs accumulated over decades, with thousands of new products hitting the catalogue daily. No consistent categorisation. No way to analyse what really sells.

Here’s what we did:
We built AI agents that read every product description, compared attributes, and created consistent taxonomies.

When uncertain, they searched external sources for validation. Result: over 90% of revenue mapped to clear product categories for the first time in 30 years.

This wasn’t a chatbot answering questions about products. We deployed a swarm of 300,000 AI agents working in parallel, each one reasoning through product codes,
descriptions, and attributes like a human analyst would.

The agents didn’t just match keywords. They understood context. When they encountered ambiguous products, they researched online for specifications and cross-referenced industry standards. They learned the client’s specific terminology and adapted to 30 years of inconsistent naming conventions.

Most importantly, this created permanent value. The master product catalogue now underpins all sales analytics. Revenue analysis by category? Previously impossible. Now routine.

For the first time, they could see which product lines were failing. The AI didn’t just process data. It built the foundation for decades of better decisions.

We didn’t stop there. Oakland’s machine learning team built a buying propensity model on top. Result: millions in new sales opportunities. ROI of over 150% in six months.

That’s the difference between generic AI and carefully crafted, process-native solutions. We didn’t force the business to adapt to the AI. We built an AI that understood three decades of messy human reality and turned it into strategic insight.

Could a human have done this? Eventually, sure. Could they have achieved the same speed, consistency, and cost? Not even close. We see AI doing tagging up to 1,000 times faster and over 90% cheaper.

But here’s the question nobody asks: who exactly dreams of spending months categorising eight million products? Reading endless SKU descriptions? Matching product
codes to catalogues on repeat?

This is exactly the valuable-but-mundane work AI should handle. High impact, low reward. Critical for the business, crushingly boring for humans.

When looking for AI projects, ask yourself: Is this task essential but essentially thankless, boring but important? That’s your sweet spot. Let AI handle the repetitive grind. Save your people for work that really uses their brains.

2. The Document Mountain

Your organisation has the answer to every question you want to ask. They’re buried somewhere in thousands of incident reports, lessons learned documents, and project reviews. But nobody can find them. The same mistakes are repeated. The same questions get asked.

This is where AI shines. Natural language processing means users can query years of documentation conversationally. “What went wrong on similar projects?” gets instant, relevant answers. We aren’t talking keyword matching but semantic understanding, where the AI understands what you mean.

Who benefits most:

• Project management offices needing historical insights
• Compliance teams trying to make sense of regulatory documentation
• Engineering teams needing fast access to technical specifications
• Legal departments reviewing contract histories – All of them
• Quality teams analysing incident patterns to spot the ones that matter

Here’s a real example: one of the UK’s largest infrastructure organisations, running capital projects worth tens of millions of pounds, faced exactly this challenge. Thousands of lessons were documented over the years, but the scale and complexity meant nobody could access or get value from this insight. They knew previous mistakes kept repeating.

We built an AI solution that reads and interrogates these lessons semantically. Users ask questions in plain English and get relevant insights instantly. The AI summarises complex lessons, categorises them against regulatory frameworks, and surfaces patterns nobody knew existed.

This wasn’t about digitising documents or building a better search engine. The AI understands context and intent. Ask about “contractor delays in weather-affected regions” and it finds relevant lessons even if they never use those exact words. It connects insights across decades of documentation, identifying patterns human readers would never spot.

The system actively pushes insights to relevant projects. When a new proposal matches historical failure patterns, stakeholders get warned. Time-tofind for critical lessons went from hours to seconds.

Most telling: adoption was immediate. No training needed. Engineers simply asked questions and got answers. That’s what happens when AI is integrated into existing workflows rather than creating new ones.

3. The Expert Shortage

Data often lacks meaning without context. Critical warning signs often go unnoticed because their relevance isn’t recognised. You need experts to
interpret, analyse, and explain. But experts are always in demand, often expensive, and usually stuck answering the same basic questions repeatedly. Your best analysts become human FAQ machines instead of strategic advisors.

AI excels at applying consistent expertise at scale. It can prepare reports, highlight anomalies, and answer complex queries 24/7. Not replacing experts but amplifying them. One specialist’s knowledge becomes accessible to hundreds of users.

Who benefits most:

• Finance teams drowning in complex analysis and reporting requests
• Technical support requiring specialist diagnostics
• Risk management teams monitoring multiple indicators
• Sales teams needing competitive intelligence, yesterday ideally
• Operations teams requiring predictive maintenance insights
  

We worked with a finance department drowning in ad hoc queries. Every request meant hours of Excel wizardry and SQL gymnastics. Each request delivered value, but it wasn’t maximising the experts’ potential, as it was still repetitive. It meant they couldn’t focus on the big strategic decisions.

We built an AI agent framework that could answer bespoke queries across their data and automate routine daily analysis. Data could be interrogated through follow-up questions or exported for local analysis. This framework required very specific domain knowledge and tight security controls because of the sensitive financial content.

The solution reduced the time-to-insight for routine daily tasks. Previously inaccessible data within the financial ecosystem became available to nonSQL users. Irregularities could be spotted early and rectified, creating real cost savings.

This wasn’t about replacing the finance team. It was about augmenting their capabilities. They spend less time extracting data and more time interpreting it. The AI handles the repetitive SQL queries and data gathering. Humans focus on strategy and decision-making. That’s practical augmentation, giving experts the tools to work at a higher level, to extract more value.

Look at all three problems. The information tsunami. Mountains of documents.The expert shortage. Every business faces them. The solutions share a crucial aspect: they all involve AI handling the repetitive, time-consuming tasks that humans shouldn’t waste their expertise on.

This isn’t about moonshots or transformation. It’s about removing friction. Let AI read those emails. Let it categorise those products. Let it surface those buried lessons. Your people can then do what only humans can: make complex decisions, build relationships, be creative and drive innovation.

Think of it as augmentation, not automation. Your experts become cyborgs, with the same human judgment and creativity, but with perfect recall, infinite patience, and the ability to process information at machine speed. The finance analyst who can query decades of data in seconds. The project manager who remembers every lesson from every project. The engineer who instantly identifies patterns across thousands of incidents.

We’re not building AI to replace your people. We’re giving them superpowers. Turning million-dollar experts into billion-dollar decision-makers.

That’s Oakland’s approach. We don’t ask if you’re ready for AI. We build AI that’s ready for your reality; messy, complex, and decidedly human.

In the AI gold rush, everyone’s obsessed with the models. More parameters. More powerful LLMs. The next breakthrough that’ll change everything!

Our perspective has always been different; the value isn’t in the AI models themselves, but in your data that AI can unlock.

Pause for a second and think about it. This changes everything about how you approach AI projects. You stop asking, “How can we use AI?” and start asking, “How do we get our data talking?”

Four Principles That Really Work

We’ve battle-tested these principles with clients facing the messiest, most complex data challenges. They work because they focus on the reality we see every day.

1. The Data and AI Toolbox

We desperately want you to think first about the insights that would drive better decisions. Picking the tool comes later.

Think of it this way: Generative AI is just another tool for extracting value from data. Powerful, yes. But still just a tool.

The magic happens when you combine AI with other analytical techniques. That well-designed dashboard isn’t going away. But augment it with AI that can explain anomalies? Now you’re talking. Use AI to clean and label messy, unstructured data so machine learning can work? That’s where value lives.

Look, we love Gen AI. But it’s just another tool in the box. Use it when it’s the right tool. Not because it’s the newest one.

2. Grab a Machete and Hack Through the Jungle

Once you see AI as a tool, not a religion, choosing the right technology gets easier.

But easier doesn’t mean easy. The AI landscape changes weekly. New models, new frameworks, new vendors. All promising to transform your business. It’s exhausting just keeping track.

Here’s what matters: there are two ways of embedding AI into your organisation.

Want widespread but generalised productivity gains across your organisation? That’s one path.

Want deep transformation of specific processes? That’s another.

You can have both, but they won’t come from a single solution.

The productivity play:

Microsoft Copilot and similar tools work well here. Off-the-shelf, decent integration with SharePoint and Outlook, minimal training needed. Your organisation gets a general lift. Hard to calculate, but real. Emails write themselves. Meetings get summarised. Documents improve. Death by a thousand digital paper cuts, reversed.

The transformation play:

This is where things get interesting. And where most fail.

They take general productivity tools, like chatbots, and cram them into complex process changes. Like using a hammer to make a soufflé. Wrong tool, messy results.

Public ChatGPT doesn’t understand your thirty-year-old naming conventions. Desktop copilots can’t navigate your specific compliance requirements. They don’t know that Yany should be copied on all procurement emails and that your risk scores are calculated using a proprietary method.

For real transformation, you need AI built for your reality. Custom apps with your training data, your specific tools, your domain knowledge baked in. Not generic. Process-native.

At Oakland, we build exactly that. And here’s the fantastic news: when you define clear goals upfront, transformative change becomes measurable. Real ROI, not just “productivity vibes.”

3. Build Process-Native Solutions

Technology is great, but AI needs to fit into the complexity and pace of your organisation.

Any system that can’t cope with real-world complexity won’t survive. AI is no different. Advanced agentic solutions can excel at transformation, but only with
the right tools, training, and contextual awareness.

They need to understand your specific reality. The workarounds that keep things running. The exceptions that are now the rules. The human messiness accumulated over decades. Not the clean processes in your documentation. The actual way work gets done.

That’s why at Oakland, we don’t ask, ‘Are you ready for AI?’ We ask, ‘Is the AI ready for you?’

Your AI solutions must be more than add-ons. They should feel like natural parts of your workflows and decision chains.

This requires careful model training and task orientation. Plus, business adaptation and user upskilling. Technology alone won’t cut it.

For AI to land well, it needs deep integration into existing processes. Don’t invent new workflows. Don’t force people to change habits built over years. Projects only stick when AI is embedded into what already exists. Augments, not replaces.

The best AI is often invisible. Working alongside employees to make their jobs easier. Nobody notices the AI. They just notice they leave work on time.

To find these applications, think beyond chatbots. Where else can LLMs add value? Reading contracts? Categorising incidents? Spotting patterns across thousands of documents? Connecting dots humans would never see?

The power exists. Apply it where it can really help.

4. Firm Foundations (But Not Perfect Ones)

Gen AI needs data to work. We have said it throughout this guide, and it sounds obvious, but you’d be amazed at how many projects ignore this basic fact.

Many projects struggle to transition from proof-of-concept to production because of shaky data foundations. To succeed long-term, you need reliable data pipelines. Think of it as
building a supply chain for insights.

You do, of course, have data processes today. You’re doing something similar for reports and analytics. We’re just extending it to handle the messy, unstructured data that Gen AI can now process.

We use a simple framework to help our clients think through the challenges. Four steps for extracting insights from data: Ingest, Store, Process and Serve.

For Gen AI and unstructured data, we use a parallel framework:

• Collect – Gather unstructured data from various sources
  • Identify what data solves your problem
  • Build pipelines to get data where AI can read it
• Embed – Convert data to AI-readable formats
  • Break down data so AI can interpret it
  • Build tools to surface the right information
• Interrogate – Process and summarise the data
  • Equip and prompt the AI to assemble insights
  • Interpret information in the users’ context
• Interact – Deliver value to users
  • Choose an interface: chatbot, alerts, automated actions
  • Ensure insights reach the right people at the right time

Then add the wraparound to keep everything running: long-term strategy, compliance, assurance, platform, monitoring, optimisation. These might take time to build, but they aren’t optional extras. They’re what make AI part of your organisation, not just another tool gathering dust.

But here’s the crucial bit: don’t demand perfection.

Twenty years of data projects taught us this lesson. Try reforming everything from scratch? You’ll get stuck cleaning databases forever. Endless governance committees. Zero actual insights. If you wait for perfection, you’ll never start.

Pick processes you understand but know could be better. Get wins. Build momentum. Then strengthen the foundations.

That’s how progress happens.

Those four principles aren’t just theory.

We’ve applied them with clients facing the messiest data challenges imaginable. They work because they acknowledge reality: AI success isn’t just about the technology. It’s about understanding your data, your processes, and your people.

So, how do you put this into practice?

How do you move from principles to progress?

Start focused. Stay practical. Don’t go it alone.

Focus is a Superpower

So, what have we learned? The organisations winning with AI aren’t the ones with fifty proof-of-concepts. They’re the ones who picked their battles and nailed them.

Eight million products categorised. Thousands of lessons made searchable. Finance teams freed from SQL purgatory. These weren’t moonshots. They were focused, practical solutions to specific problems.

We call them Lighthouse Projects. Not because they’re flashy but because they show the way.

Here’s what works:

Pick one painful problem. The information tsunami drowning your customer service team. The document mountain hiding critical lessons. The expertise bottleneck slowing every decision.

Go deep. Really understand the process, the people, and most importantly, the data. Build AI that fits your specific reality. Not generic tools. Process-native solutions that stick.

Build it to last. No throwaway demos. No technology tourism. Actual solutions solving actual problems. Saving hours daily is not a proof-of-concept for the finance team. That’s value.

Here’s what doesn’t work:

The scattergun approach always fails. Twenty half-finished projects. Teams stretched thin. Budget scattered. Everyone frustrated. This is what drives organisations headlong into Gartner’s AI trough of disillusionment.

‘Big bang’ transformations don’t work either. Nobody’s waiting five years for your AI strategy to deliver. By then, the technology will have moved on three times over.

The compound effect:

Your first Lighthouse Project does more than solve one problem. It builds belief. It creates advocates. It develops capability. Most importantly, it teaches you what AI can do for your specific business.

The finance team’s success with AI becomes the blueprint for operations. The lessons-learned system becomes the model for customer service. Each success makes the next one easier, faster, cheaper.

That’s how you build momentum. Not with grand strategies and transformation roadmaps. Your focused wins will compound into a competitive advantage and efficiency gains.

In the fast-moving world of AI, perfect is the enemy of good. Start narrow. Go deep. Build something real. The rest will follow.

Oakland and Softcat: Two Powerhouses, One Purpose

We’ve spent 40 years helping businesses unlock serious value from their data. Now as part of the Softcat family, we’re combining Oakland’s deep data expertise with Softcat’s unrivalled IT infrastructure and vendor relationships.

We’re still the same straight-talking, client-obsessed Oakland team, operating independently with our own voice and values. But backed by one of the UK’s most trusted tech partners, we deliver even more firepower for your data and AI challenges.

Start with Our AI Discovery Workshop

Not sure where to begin? In three hours, we’ll cut through the noise to identify real AI opportunities for your business. No jargon, no vendor pitches, just practical exploration that moves you from strategy to action.

You’ll walk away with prioritised use cases, clarity on ROI, and concrete next steps.

Book your workshop today: hello@weareoakland.com | 0113 234 1944

The post The Business Guide to Generative AI (Let’s get your data talking) appeared first on Oakland.

Accelerate your journey to valuable business insights, whether you’re building or buying. If you’re planning to build a modern data platform, this guide is your blueprint. It’s designed to help you navigate the critical architecture decisions, trade-offs, and tooling choices that will shape a scalable, secure, and future-ready platform.

You’ll also see why many organisations choose not to build from scratch. Traditional options like out-of-the-box platforms or fully bespoke solutions come with serious compromises: limited flexibility, long lead times, high costs, or technical debt.

So whether you’re designing your platform in-house, or looking for a partner to deliver it faster you’re in the right place.

“As I’ve just started a new CDO role, one of the things I was keen to get hold of was some practical advice on creating a data platform strategy, as it’s not just about technology.  I was therefore delighted to come across the data platform guide at Oakland.  They have managed to create something which is a must read for anyone thinking about their data platform. It’s clear, concise, insightful and best of all, practical. I highly recommend getting your hands on this!

Lynne Bailey, CDO, Robert Walters

The post The Ultimate Guide to Building A Data Platform appeared first on Oakland.

A Pragmatic View of the Generative AI Gold Rush

Generative AI is the buzzword of the moment. Attend any industry conference or skim through the latest Gartner hype cycle, and you’d be forgiven for thinking we’re on the cusp of a generative AI utopia. Everyone’s doing it. Everyone’s winning. Everyone’s transforming their businesses at warp speed.

Except, well… they’re not.

Here at Oakland, we’ve had some revealing conversations while compiling this report on the readiness of major organisations to implement generative AI. Spoiler alert: the reality isn’t quite keeping up with the hype.

Generative AI could be game-changing. But as this report reveals, getting there will require focus, courage, and a clear view of both the opportunities.

Read our full guide on generative AI below.

Generative AI has been billed as a ‘digital workforce that doesn’t need vacations or other benefits’ (Gartner, 2024). A new team member who can do things with or without us. Able to upskill your existing workforce and quite frankly do things that no human would have the time to do.

This all sounds like a productivity gold rush, but do business leaders, by now accustomed to the reality of generative AI, trust it to deliver these promises?

When asked to rate their overall trust in generative AI technology, almost 63% of leaders had either a moderate or high level of trust in the technology. This is largely down to a combination of technological advancements, use cases, and shifting perspectives about generative AI capabilities.

However, this wasn’t the case for all, and a significant proportion, 37%, still have a way to go before they can completely trust the technology.

”Generative AI is transforming the way we work, with data flowing from every direction—spanning business apps to previously inaccessible unstructured data. The key challenge is determining who controls and accesses this wealth of information, driving a boom in generative AI governance.” – Oakland Generative AI Report 2024

Overcoming barriers to adoption can be one of the toughest challenges when selecting and implementing new technologies within an organisation. Readiness is one of the most crucial to consider: an effective and beneficial transformation requires firm foundations or projects can easily overrun or underdeliver. After years of digital and data transformation, companies are questioning the return on their technology investments. Now, it’s up to generative AI to prove its value.

To work out how ready decision-makers feel, we asked them to assess their organisation’s readiness for generative AI adoption. On the whole, leaders took a pragmatic view of generative AI technologies. 5% reported being very prepared with a clear strategy and resources to hand (not surprisingly given the number of organisations without a data strategy) while 29% were somewhat ready, exploring options and building capabilities.

Just over 31% were in the research phase and were looking for information and guidance, which highlights the need for trusted advisors who put the customer before the tech vendors.
Almost a third (30%) said they were simply not interested in generative AI adoption at the moment, which is interesting given the amount of noise in the market.

Since the generative AI boom kicked into overdrive half a decade ago, many organisations have been experimenting. However, since the much more recent introduction of generative AI capabilities; are these same organisations successfully navigating the new challenges of implementing generative AI, or are obstacles slowing down adoption?

Asking respondents at what stage their organisation was on its “generative AI” journey, over half recognise they are already on the generative AI journey, whether that be utilising existing tools like ChatGPT and Co-Pilot (22%), exploring options (26%) or building custom solutions (6%). But, with this market changing so quickly, this is likely to change by the time you’ve finished reading this report!

With just over a quarter of businesses still exploring generative AI options, the complexity and choice between vendors, capabilities, and pricing appear to be barriers to confident adoption.
Many are dipping a toe in the water by using entry-level generative AI tools, such as ChatGPT or Microsoft’s Copilot, with few committing to custom generative AI solutions that deliver more transformative change. This is not unusual in our experience: larger, tech-centric businesses tend to explore custom solutions, while SMEs and non-tech businesses are more likely to wait and see what works for others.

Find what Oakland thinks about Gen AI, when you download the full report.

Whilst the newer generative AI can drive efficiencies and create opportunities to improve revenue, these will always require investment to make sure they have an impact. With the technology no longer in its infancy, are businesses investing in it? And if so, how are decision-makers carving out funds for initiatives?

When these questions were put to our respondents, almost two-thirds (63%) had not yet allocated any budget for generative AI development in their organisations.

Of those that had assigned a budget, nearly a fifth (17%) were using existing technology budgets, 5% had a dedicated generative AI budget, and another 5% were reallocating budget from elsewhere. The overwhelming majority had not allocated any specific generative AI funding which goes back to our earlier point around data leaders being asked to deliver more with existing budgets.

The hype surrounding generative AI has been at a fever pitch for some time now, but has the positive press and excitable generative AI evangelism on social media translated into increased investment? Have the benefits of generative AI earned the technology a right to a greater slice of the pie?

When asked, half of respondents had no specific budget allocated for generative AI.

A very small proportion of organisations (2%) had a significant generative AI budget (more than 20%), which aligns with what we are seeing in the current market.

For organisations looking to get started and begin to productionise generative AI solutions there are several ways forward. Developing in-house capabilities with existing teams, hiring in additional generative AI expertise, or using external consultancy services are all possibilities, each suiting different organisations and use cases. We asked organisations which they were pursuing.

Of the 54% of respondents in the process of developing generative AI, 24% had tasked an existing internal team with the project. 10% were using external consultancy services. And another 6% had hired generative AI specialists to guide development.

Rather than creating separate generative AI departments, most organisations are embedding generative AI capabilities within their existing data teams. This aligns with our belief that generative AI is most effective as part of a broader data toolkit.

However, a significant challenge arises from the shortage of generative AI experts, particularly for organisations looking to implement generative AI solutions. The rapid evolution of this technology has outpaced the supply of skilled professionals, creating a highly competitive market for talent.

Generative AI technologies and capabilities are developing at a breathtaking pace. What was only recently possible with generative AI only a few years ago has today transformed entire industries.

Do businesses intend to focus on generative AI over the near term to stay ahead of the curve? If so, what do they think this change will look like?

When polled, only a fifth (21%) were planning to use generative AI to either significantly or transformatively change their business, contradicting many online articles and publications intimating industry scale change is upon us.

The majority (37%) of respondents today said they were planning to implement minimal change using off-the-shelf tools like ChatGPT and Microsoft Copilot.

Finding the right use cases for generative AI is critical to ensuring success. Yet, while all businesses are unique, generative AI uses typically fall into a limited range of categories.
When asked which was their organisation’s primary use case, knowledge management was by far the most popular, chosen by 36% of respondents.

Operations and process optimisation (31%) was the second-most popular use case, followed by marketing and sales (30%), customer service (28%), and research and development (24%). 14% reported another use case.

Read our blog, on how to find generative AI use cases for enterprise.

Our research confirms the importance of clear use cases and capabilities in generative AI deployment, highlighting the need for organisations to enlist expertise that guides them towards specific, value-driven applications.

For once, companies with extensive historical unstructured data are well-positioned to leverage generative AI in knowledge management. Legacy data, previously viewed as an impediment, can now serve as a key differentiator for organisations in
traditionally non-digital industries.

As seen throughout the survey, many businesses have had trouble adopting generative AI or remain unconvinced. To understand what has been stopping them, we asked leaders what barriers they felt were holding them back the most.

A majority (41%) felt that a lack of expertise and skills was the main barrier to adopting generative AI in their organisation. Just over a third (35%) had data and privacy concerns, while 31% were concerned with a lack of proven ROI.

A quarter (25%) had encountered resistance to change within their organisations, while 22% saw high implementation costs as the greatest hurdle.

Many organisations find it challenging to start and implement generative AI initiatives. In our experience, 5 initial steps are crucial to create a strong foundation for responsibly launching and scaling generative AI in a way that is both strategic and impactful.

Find out more in our full Generative AI Guide above.

Right now, knowledge management is a key focus area for generative AI implementation. Within the topic, there are three key problem areas generative AI applications are particularly effective at solving:

1. Unlocking your document mountain
2. Organising the information tsunami
3. Expert shortage

Find out more in our blog, What is Knowledge Management?

Oakland is a consultancy focused exclusively on liberating and activating data. We help transform and grow businesses in sectors from Water Utilities and Rail and Transport to Media and Telecoms by giving them access to the latest skills and technology, leaving them free to grow with the confidence that their data is continuously working for them.

The speed of technology presents a dizzying array of choices and Oakland helps some of the UK’s largest companies navigate these in an informed and measured way. Importantly, we draw on our teams of strategy, governance, engineering, AI and analytics experts.

Engineers at heart, we are hands-on partners right through the data lifecycle, achieving powerful results for our clients and giving them the freedom to focus on their business goal.

• Data Strategy
• Data Governance
• Data Engineering & Platform
• Analytics & BI
• Generative AI consulting and solutions

The post Plugged In or Left Out? Navigating the Emerging Generative AI Gap appeared first on Oakland.

Introduction

How can Data Governance Tooling help me? 

The Data Governance landscape is constantly changing. As business direction shifts, data technologies advance and new regulations come into force – governance has to change with them. Delivering Data Governance at scale and keeping up is a constant challenge.  

Having the right tooling and technology to make sure data is in the hands of the right people has never been more essential. So, it’s no wonder that Data Governance tooling like Alation, Atlan, Collibra, Informatica, and Purview (just to name a few) has never been so popular. We can bet that you have not only heard of these names, but also you are probably on every salesperson’s speed dial. So why is Data Governance tooling so popular, and is it the silver bullet to fixing all of your Data Governance challenges? 

Our guide will help you ask the right questions, whether you are currently in the selection process or need help with implementation.

Jeff Gilley
Head of Sales
The Oakland Group
Email: jeff.gilley@weareoakland.com

Data Governance tooling has gained popularity for several reasons. Many organisations consider it a plug-and-play solution to a wide range of data-related problems due to its comprehensive functionalities and the importance (not to mention volume) of data management to the enterprise.  

Here at Oakland, we have seen many of these tooling products sold as a solution that addresses EVERY Data and Analytics Governance need, which has led to confusion in the market. We believe tooling plays a huge part in delivering effective Data Governance, and there is the right option out there for the job, but there is work involved in finding it, integrating it, and getting value from it.  

Data Governance tooling is so popular because it can address numerous problems: 

Increased Data Volume and Complexity: 

The explosion of data from various sources has made it challenging for organisations to manage and utilise data effectively. Data Governance tooling helps manage large volumes of complex data efficiently. 

Regulatory and Standards Compliance: 

With stringent data security and privacy laws and standards like GDPR, CCPA, PCI, ISO27001, and others, organisations need to ensure compliance to avoid the ire of regulators and customers. Data Governance tooling provide features to help manage compliance requirements effectively. 

Data Quality Improvement: 

High-quality data is crucial for accurate analysis and decision-making. Data Governance tooling helps ensure data accuracy, completeness, and consistency, which improves the overall quality of data. 

Enhanced Data Security: 

Protecting sensitive data from breaches and unauthorised access is a major concern. Data Governance tooling offer robust security features to understand the access landscape for data and protect data. 

Operational Efficiency: (one of our favourites and the foundation Oakland was built on): 

Automating Data Governance processes reduces manual efforts and increases operational efficiency, allowing organisations to focus on strategic activities. 

Better Decision-Making: 

With reliable and well-governed data, organisations can make informed decisions, leading to better business outcomes. Data Governance tooling provides the necessary infrastructure to support data-driven decision-making. 

Good Data Governance is an essential component for organisational growth and resilience in an increasingly data-driven world. The challenge is how to support your organisation along the journey to better data maturity and effective governance.  

As you address your data issues and start your Data Governance journey, you will start to create resources that benefit your whole organisation, from glossaries to lineage models. Tooling helps create a structured way for these resources to be shared and evolved. You might want to use Governance tooling for some or all the following purposes. 

• Data Catalogue – defining available data. A data catalogue works alongside glossaries and dictionaries to create transparency around the available data across your organisation, what it means, and how to access it within your organisation. 
• Business Glossary – defining key terms. The business glossary communicates what data means using business language. It contains common business terms used throughout the organisation and their associated definition with additional fields, including synonyms, acronyms, and status. 
• Data Profiling – understanding the data within systems. Data profiling allows users to run analysis of the data in is current state, highlighting gaps, inconsistencies and other quality issues as well as giving more data about what the data means and how it is and could be used in your organisation. 
• Data Dictionary – defining what makes up data elements. It defines the data in technical terms providing a definition of data sets and associated fields, with descriptive fields including data type, size, value, purpose, and relationship with other data elements. 
• Data Lineage – understanding the journey data takes across an organisation. Data Lineage provides information around the lifecycle of your data. It provides a visual representation of each stage that data goes through, from creation through to presentation. 
• Documentation – policies, standards, rules, controls and classifications. The ability to document and apply standards, rules, controls and classifications against data enables quality and other metrics to be measured and monitored ensuring trust, transparency and understanding of your data is maintained.
• Data Quality Management – visualising the adherence of data in your enterprise to documented policies, standards, rules, controls, and classifications. The ability to show compliance with this agreed set of documents and to identify, manage, and remediate issues is a powerful feature of Data Governance tooling. This is sometimes considered a separate category to Data Governance tooling; however, for the purposes of this guide, we have included it. 

Focusing on the pains, difficulties, and issues you are currently facing with your data is the key part to understanding which tooling will provide you with the most value.  

Starting here also enables you to build use cases for tooling, which will support the piloting and testing phases of your later implementation.  

The most common pain points that organisations looking at Data Governance tooling to solve are set out below. 

Here at Oakland, we work with our clients to help them to select, implement, and optimise the right Data Governance tooling. We believe that the tooling you select should fulfil three key requirements:  

1. Address your organisation’s most important priority and highest level of risk 
2. Be easy to use by EVERYONE who needs to use it! 
3. Integrate with your existing data landscape 

The fundamental part of the selection process is clearly defining the ‘why’ you need Data Governance tooling. Tooling can be very expensive and need to be backed by a robust business case. We recommend you:  

1. Take your time to speak to the different solution providers (it’s easy to be seduced by those charismatic sales folk)  
2. Outline your vision – clearly identify what good looks like for you? 
3. Be clear on your brief – what are your expectations, what problems are you trying to solve?  

An essential part of the selection process should be how your vendor helps you ensure the end users of the tools are engaged in providing data and how they will interact with the tools. Your tooling needs to be easier to use than the alternatives, enrich and engage all stakeholders and users, and provide value. It needs to become a supportive resource, not a hindrance that everyone rolls their eyes at.  

Buyer beware!  

If you are just starting out on your Data Governance journey, tooling probably isn’t going to solve a lot of your problems. We see many organisations with low data maturity jumping straight into tooling. This can be very expensive, and without a better understanding of what you’re going to govern and how you’re planning to govern it, tooling is a red herring to actually making the change that is necessary in your organisation happen.  

For those organisations who have a clear view of how they will operate Data Governance, tooling can provide significant benefits of speed and scalability that will be needed to help drive governance adoption. 

Data Governance tools can help with this journey, but with so many different tooling providers, selecting the right one for your organisation can be difficult. 

When it comes to tools there is not one option that can fix everything. Data Governance tools aren’t the holy grail, so make your selection based on the pains the tooling solves. It sounds obvious but we see many organisations choosing tools which, 6 months down the line, aren’t delivering on the organisation’s requirements.  

Examples: 

Do you have pain points from a lack of understanding around your data? – Then look for a data catalogue with data dictionary capacities. Or is your issue around not knowing where your data comes from or its journey through your organisation? – Data lineage tooling would be more suited to this case. 

Ensuring the tooling connects to the systems, platforms, and databases that make up your data estate and that it can understand the data within them is fundamental for easy implementation.   

Do your homework to ensure the tooling you select has all the right connectors to enable easy integration and ingestion of data; otherwise, you might have to purchase or build additional connectors, and that is where costs start to mount.  

Consider who you are trying to empower with the toolset and the skills they have, and make sure the user interface is simple enough that everyone who needs to use it can! You don’t want to select tooling that requires every user to understand SQL when you anticipate that everyone in your business should be able to access it. And conversely you don’t want tooling with a restricted front end which means that power users can’t use it to the best of their capabilities to get at information. 

There is a lot of talk about the promise of Generative AI (GenAI) to revolutionise many parts of the business landscape in the coming years. We see significant potential for automation within existing processes and help in managing large volumes of data, high velocities of data, and identifying patterns in data. This shift brings with it a need for expanding Data Governance to include AI Data Governance. The good news is that most of the work you’ve done to put Data Governance in place will have good applicability for AI Data Governance. From a tooling perspective, most of the major players are touting their ability to do AI Data Governance or that they’ve incorporated GenAI into their toolsets. Don’t let these features be the sole reason you choose to go with a provider. As we have mentioned before, the core use cases you are focused on and your non-functional requirements should be driving selection, not a beta-release feature that is probably going to change several times before it demonstrates value. If you do have strong use cases for AI Data Governance that you need tooling to work with, we recommend the following: 

• Understand the data sources for these use cases. These are likely to be unstructured sources including text, video, pictures, and sounds, which may not be on your basic data governance landscape. 
• Understand how data will be used. How is the data going to be ingested and interpreted by the GenAI tools, and to what degree does the organisation need to attest to its validity? 
• Identify your policies, standards, and classifications. Without the necessary guiding documentation in place as to how your organisation will use data within AI, it will be difficult to govern. 

Ask yourself the following questions in order to work through the process of selecting tooling: 

• What use cases does the tooling needs to solve? 
• What systems it is critical for the tooling to connect to? 
• What systems it would be nice for the tooling to connect to? 
• Who is going to be using the tools?  
• What are their current skillsets from a governance and technology perspective? 
• What level of support do you anticipate needing to help you to use the tooling effectively? 
• If you need to change or add a feature over time, how difficult is this to do? 
• Have we had a demo of the tooling aligned with our specific use cases? 
• Have we been able to pilot the tooling in a critical data set or area of the business? 

This then gives you a clear set of requirements for you to align your tool selection against. Based on this, you may need to do a build versus buy analysis. Some solutions offer a cost-effective pay per use model, others have high licencing fees which can be prohibitively expensive, whilst your requirements might be simple enough to repurpose existing productivity tooling in your organisation. 

Remember just because the tooling is highly reviewed does not mean that it will solve the problems you have, connect to your current data landscape, or be able to be used by everyone who you want to use it. 

Being able to connect your Data Governance tooling to your data estate reduces manual work of populating information into the tooling, which is often the longest step in the implementation process. So, finding tooling that connects as smoothly as possible to all the systems where data is stored across your organisation is fundamental. To do this you need to answer the following questions: 

• Where is the organisation’s data stored?  Identifies the landscape of connections that you will have to make from the tooling. 
• What type of system it is stored in? So that you can confirm that the tooling has the appropriate connectors or if custom connectors will need to be built.  
• What is the architecture surrounding the systems? Can you easily get at the data, or is there complexity that you will need to address and resolve that could prevent easy implementation? 
• What types of data are stored in these systems? Structured data provides a happy path, whilst unstructured data can create challenges. 
• How critical is the data to the organisation? This helps you know where to focus your efforts and not spend all your time working on data that provides no or little value to your organisation. 
• What is the security posture around the system? The security of the systems, including whether the tool will be able to scan the data within it or if it will not be made accessible. 
• What are the systems overall criticality to your organisation? Identifying the risks associated with integrating the tool with your existing technology will be important, as some systems are too critical to fail and likely won’t allow for integration with Data Governance tooling. 

Use this checklist during the tooling selection process to ensure that the tooling you selected connects to most of your systems, and all the systems containing critical data. 

The features and functionality of Data Governance tooling is often what it is sold upon, with teams becoming enamoured with capabilities embedded within the tooling. Unfortunately, those features are not typically what goes wrong with Data Governance tooling. Too often, non-functional requirements are overlooked during the purchase process, and can cause the biggest hiccups post implementation. For example: 

• Information security will not give the tooling access to critical systems due to the nature of the data stored within those systems, thus limiting the estate that the tooling can cover. 
• The tooling isn’t scalable enough to support the volumes of data you have, so it either operates slowly or only covers a limited portion of your data estate. 
• The maintenance of the tooling is difficult or requires specialist knowledge that you have to seek outside help on, thereby limiting effective wider deployment of the tooling. 
• There is a lack of training and support from your solution provider, which hinders adoption by users and lowers the value of tooling. 
• It is more difficult to connect to your data sources than you thought (either due to a lack of connectors or internal requirements that were not thought of during the purchase process). 
• Reporting on issues such as data quality versus attempting to fix it are very different things, as processes are in place to take identified issues, determine root cause, and implement a remediation plan. 
• The cost is higher than expected. For consumption-based tools, this is often the case as the scale of governance widens across the enterprise.  
• The tooling impacts source system performance, which causes system administrators to ask for it to be disconnected and limits coverage of the data estate.  

Some of these issues can be addressed through a structured pilot; others involve you ensuring that all the right people across your organisation are involved in the conversation to select your ideal tooling. 

Once you have selected your tooling you now get to the hard part of making sure the tooling adds value. To do this you need to create a robust roadmap that answers the following questions: 

• When data is going to be ingested, and are there any issues with systems that need to be addressed? 
• What connectors are needed, are they all available, and what permissions are needed to use them?  
• Who needs to be involved? 
• Make sure you have everyone involved in the conversation as early as possible from you Information Security, DPO, Architects, IT teams, data owners, and HR if employee data will be used within the tool  
• How is information about data going to be added and enriched? 
• By a person, through automation, or some other tool  
• Who is going to validate the definitions, rules, controls etc? 
• How are you going to manage your vendors? (especially if you are planning on bringing in multiple tooling solutions) 
• How will we deliver? 
• Use a pilot to understand how much time each step will take 
• Identify and address quick wins as early as possible in the process 
• How will we engage users? 
• Ensure you have a training plan for everyone who is going to be using the tooling 
• And remember to communicate to all stakeholders frequently and engage your whole organisation with what is coming and how it will help them 

Align the steps you take to your use cases for the tooling and focus on the data most critical to your organisation to ensure you add value. 

It is always worth remembering that technology is just one facet of delivering and maintaining good Data Governance. Your people and processes are equally, if not more important. If you’d like to learn more about ways to implement and improve your data governance, download our Ultimate Guide to Data Governance (insert link). 

What data means and how it is used will often only be in minds of a select few key individuals across your organisation. A fundamental part of bringing in tooling will be ensuring that you can document this knowledge within the tooling as quickly as possible. 

Already having this information documented will allow you to get going quickly. If you must wait for key personnel to document their knowledge, then by the time they are ready to use your shiny new piece of tech, you’ll have lost months, and people will start to ask about its value. 

While Data Governance tooling is brilliant at many things, it will not do Data Governance for you. In fact, it might end up hindering your progress if you do not have the right capabilities and information to use it effectively. Any tooling you use is only as useful as the knowledge of the people creating information within it and the capabilities of business users to use it. So, you need a network of people with a deep understanding of data who are engaged and ready when you bring on your tool supported by your data stewards and owners. 

You also need to consider who you plan on using the tooling and their current skill set and be aware of what skills are needed to use the tooling effectively. Don’t bring in tooling that requires extensive training. Group the different types of users who you anticipate using the tooling together and research their skill sets, e.g. can they code, could they profile data, and how those align with the skills needed to use the tooling effectively. 

For example, you might have three groups for a data catalogue tool: 

• ‘Developers’ – who are needed to implement the tooling, connect it to systems, ensure data is ingested, and support its maintenance  
• ‘Documenters’ – who are needed to add definitions to the data, and ensure that the data is linked together 
• ‘Designers’ – who need to be able to understand what the data means to be able to select the right data to design reports 

But remember, the more people that can use your tooling, the better outcomes you are likely to create; giving people access to well-defined data removes silos and reduces the risks of local out-of-date spreadsheets and the wrong data being selected to answer questions and inconsistencies in reporting. 

One of your goals should be to improve accountability so that people can take ownership of the data that they work with. 

Don’t underestimate the challenge of getting people to engage with your new Data Governance tooling. 

It may not be obvious, but tooling work best when organisational culture can support it because using tooling effectively means having people capable of interacting with it in a meaningful way. 

At the start of the Data Governance journey, most information created about data can be comfortably documented within readily available productivity tooling. You will also find that the skills and knowledge of your key users at this stage often mean that they need significant support to use any tooling. 

Tooling comes into its own when you have significant detail at a granular level across entire data sets, with multiple functions across an organisation represented. Your users will need to have significant capability in both enterprise and data knowledge to use the tool to provide value, which may involve upskilling your existing team. 

Populating tooling should not be the end goal of any Data Governance tooling project, but the first stage of providing meaningful, timely, accurate information about the data across your organisation which helps support the Data Governance journey. This ensures data becomes a valuable asset and not a blocker to growth. 

At Oakland we offer a variety of services to help you with Data Governance Tooling: 

• Rapid assessment of your data governance and data quality tooling with recommendations to improve capability, increase business uptake, and deliver ROI 
• New data governance and data quality tooling selection 
• Data governance and data quality tooling implementation 

Contact us for more information. 

Wherever you are on your Data Governance journey, Oakland can help you make the next step.

The post A Guide to selecting the right Data Governance Tooling appeared first on Oakland.

Introduction

There’s a good chance that data underpins every product, service, or capability in your organisation.

But organisations have been managing data for decades, so why do we need data governance?

At Oakland, we’ve been delivering data governance initiatives to some of the UK’s leading organisations for years.

The primary goal of data governance is to ensure that data assets are managed in a way that aligns with business goals, regulatory requirements, and industry best practices. These extra pressures and demands drive the need to go beyond traditional data management.

We have created this guide to provide you with the practical and pragmatic advice you need to elevate your data governance activities from something that is often done begrudgingly to something everyone in your organisation can get behind.

We’ll talk you through the different factors that must come into play to ensure your data serves its purpose effectively.

And we’ll touch upon the ubiquitous Generative AI shaking the world of DG. It can transform how you handle data, making your operations smarter, faster, and more efficient. Whether you’re looking to automate tasks, enhance decision-making, or innovate your products and services, Gen AI offers an opportunity to unlock these opportunities.

It is very much a ‘get on board or be left behind’ moment for businesses. But where does one start? Not all organisations are the same, and every business will have nuances in requirements and culture, so off the-shelf solutions are unlikely to be a true fit.

Our guide will help you ask the right questions, whether you are currently building your DG strategy or have already begun implementation and consolidation.

Jeff Gilley
Head of Sales
The Oakland Group
Email: jeff.gilley@weareoakland.com

One of the recurring themes we’ve observed is organisations trying to drive Data Governance through the focus on technology.

Attracted by the allure of modern Data Governance tools, organisations are investing in what they perceive as a fast track to data and analytics governance maturity through data cataloguing, lineage and glossary solutions.

The problem is these tools will fail in organisations lacking the foundational data behaviours, culture and core Data Governance capabilities.

When forming your Data Governance initiative, it’s essential to incorporate all facets depicted in the framework below. People, processes, and technology constitute integral components, but their success depends on behaviours and culture.

Success in your governance programs can only be achieved by benchmarking and perpetually enhancing all of these elements.

Experience

Beyond knowledge and skills, it’s crucial that your teams grasp how their work contributes to the flow of data across your organisation so they’re able to address data issues. Their familiarity with processes and systems should facilitate easy access to knowledge and tools that help to define and create good-quality data.

People

Data doesn’t magically appear. It demands skilled individuals for its effective creation, curation, and use. Your governance program must prioritise training and education to deliver the required capabilities and knowledge. It’s crucial that they understand the significance of their role and how they support organisational objectives and possess the skills to excel in their tasks.

Processes

Effective processes are crucial for your team to collect, transform, and utilise data. These processes guide data handling, ensuring consistency, quality, and usability throughout its lifecycle. Establishing clear guidelines on data collection, including standardised methods for accuracy and completeness, is a vital aspect of Data Governance process creation. Robust data collection processes minimise errors and inconsistencies, enhancing data reliability.

Behaviours

Changing data behaviours is a gradual process requiring continual effort and commitment to strategic planning, communication, training, and the establishment of effective processes. It involves changing individual habits and transforming the organisational culture to prioritise data quality and effective data management.

Culture

Data culture is both an independent activity and a result of the mentioned interactions. Integral to the Data Governance journey, cultivating a data culture involves managing organisational change and guiding data behaviours from their current state to the desired state. Proactively addressing people, processes, and technology dimensions fosters a culture that transforms data into a valuable organisational asset.

Policies

Effective policies formalise Data Governance best practices, outlining processes for measuring, monitoring, maintaining, and enhancing Data Quality. Policies should also cover how data standards are established, revised and, when necessary, retired. Another critical aspect of policy adoption is defining the roles and responsibilities of key data personnel and stakeholders, ensuring clear lines of responsibility throughout the organisation.

Data Standards

Data standards are guidelines and specifications that define how data should be structured, formatted, and handled within your organisation. These standards ensure data consistency, accuracy, and interoperability across different systems, processes, and applications. Data standards cover various aspects of data management, including naming conventions, data formats, coding schemes, and data classifications.

Technology

Data Governance programs can interact with two types of technology. The first is the everyday technology directly impacting data creation, transformation, and utilisation. The second category is specialised data governance tools that perform various tasks, including documenting data definitions, standards, stewardship, ownership, compliance and movement across the organisation.

At Oakland, we take a six-step approach to building robust capabilities for your Data Governance Strategy.

Phase 1 – Discovery

1. Build a picture of your data landscape, especially your organisational culture and appetite for change.
2. Assess your Data Governance maturity to understand how maturity differs across the organisation.
3. Evaluate your key stakeholders to understand their current awareness and education levels.

Phase 2 – Education and Training

1. Create a communications and training plan for your whole organisation – start small, then build out.
2. Develop awareness materials for your whole organisation. This will support further phases by creating a foundational understanding of Data Governance, enabling future stakeholder conversations.
3. Education is a continuous part of the Data Governance journey, so consider what training is required at each phase. Staff regularly join and change roles, so engage and educate continuously.

Phase 3 – Roles and Responsibilities

1. Define the required governance roles, including accountabilities and responsibilities.
2. Engage with all stakeholders to validate the responsibilities and accountabilities assigned to their roles.
3. Identify the role holders, assess their training needs, and empower them to undertake new duties.

Phase 4 – Definition

1. Collaborate with stakeholders to establish data standards, ensuring that data adheres to them and remains fit for purpose throughout its lifecycle.
2. Establish a governance structure to oversee the implementation and adherence to data standards, but opt for an open and inclusive model, never centralised and bureaucratic.

Phase 5 – Assess, Improve and Monitor

1. Assess data quality to pinpoint areas of adherence and non-compliance with established standards.
2. Focus on improving data quality where it does not meet required standards, ideally through eliminating root causes.
3. Facilitate ongoing monitoring and control of data quality, ensuring consistent adherence to data standards and specifications.
4. Promote transparency to stakeholders with a shared view of data quality across the entire data lifecycle.

Phase 6 – Technology and Tooling

1. Gen AI tools need to be managed as data tools. To make the tool fit for purpose, companies must have rigid oversight on the processes, policies and governance of the data involved (both at ingress and egress) and the tool performance itself.
2. Evaluate current and potential technologies that can enhance the management and visibility of data standards, definitions, and quality whilst driving greater automation and continuous improvement.
3. Technology is the final piece in your Data Governance puzzle because new tools need the right foundation of culture, capabilities and behaviours to succeed.

Transforming a data culture is not a quick fix – it requires incremental slices of value to be delivered throughout the Data Governance journey, particularly in the earliest phases when you’re moving largely by stealth.

Learn how to launch a successful data governance initiative by delivering rapid value to the business from high-impact data initiatives.

The key to advancing your data culture is identifying the optimal starting point by addressing the greatest areas of pain and reward. To do this, you need a deep understanding of the relative levels of Data Governance maturity across key parts of your organisation.

Let’s explore how to achieve this in the next section.

Building a Data Governance Program by Stealth:The Lighthouse Concept | Oakland

Executing a Data Governance Maturity Assessment

A data maturity assessment systematically evaluates your organisation’s ability to manage and leverage data effectively. Its goal is to provide a repeatable framework for evaluating maturity across areas such as data governance, quality, integration, and utilisation.

There are five levels of Data Governance maturity:

Key objectives of a data maturity assessment include:

1. Understanding Current State:

Assessing existing data management practices, policies, and processes.

2. Defining Data Maturity Levels:

Establishing a framework to categorise your organisation’s maturity levels in different aspects of data management.

3. Identifying Strengths and Weaknesses:

Pinpointing areas of strength and areas that need improvement in data management capabilities.

4. Setting Data Maturity Targets:

Defining goals and priorities for enhancing data management practices and moving towards higher maturity levels.

5. Data Strategy Planning:

Informing strategic decisions and investments in data management by aligning with the organisation’s maturity levels and improvement goals.

6. Benchmarking:

Comparing data management practices against industry best practices and standards.

Initiate your Data Governance journey by benchmarking maturity, gaining insights into the current state, and identifying initial steps for a robust foundation. This may include raising stakeholder awareness or addressing critical data set issues. Assessment results will guide key focus areas and priorities for your Data Governance initiatives.

Different business areas or data sets may need varying maturity levels to align with the organisation’s business plans and objectives. Recognising these nuances helps you tailor your Data Governance efforts and allocate resources effectively, ensuring each area reaches the required level of maturity to support broader organisational goals.

Monitoring progress, establishing KPIs, and regularly evaluating achievements and challenges are crucial as your journey advances. This reflective approach enables informed decision-making for refining and enhancing your Data Governance practices.

Promoting awareness and cultivating data maturity among employees is pivotal, with education and communication at the forefront. These initiatives foster a deeper understanding of the importance of data, actively developing a robust data culture.

Furthermore, it is crucial for individuals to take accountability and responsibility for the data within their respective areas, particularly with key data stakeholders.

Building Stakeholder Engagement

The approach you take in identifying and involving stakeholders depends on your organisation’s culture, maturity level, and specific data challenges.

Here are a few strategies to consider:

Problem-driven approach

Identify stakeholders facing data-related challenges. Conduct interviews or focus groups in key areas, particularly those heavily reliant on data (e.g. Business Intelligence/Data Analytics). Understanding their issues gives you insights into specific problems Data Governance can address. This approach aids in building a case for sponsorship by demonstrating the tangible benefits of governance.

Network-driven approach

Leverage personal connections and referrals to expand stakeholder interaction. Start by interviewing individuals with a deep understanding of the data landscape (and their role within it). Extend your network of stakeholders by seeking recommendations for other key players, ensuring a thorough exploration of the organisation’s data culture.

Organisational chart approach

Identify various business areas and their stakeholders by navigating the organisational chart. Choose representatives from each area for interviews to ensure diverse representation. While this method helps cover all business areas and prevent overlooking crucial stakeholders, it may demand more effort to initially engage individuals without direct personal connections.

Combined approach

Begin by interviewing stakeholders facing data challenges, then utilise the organisational chart to identify additional stakeholders across different areas. This method provides a balance between personal connections and comprehensive organisational coverage. Keeping thorough records of interactions facilitates progress tracking, dependency identification, and a thorough understanding of data flow.

Practical Advice for Stakeholder Collaboration

Regardless of your stakeholder engagement approach, ongoing communication and awarenessbuilding are essential.

You will need to regularly update stakeholders on the progress of the Data Governance journey and the insights gained through interviews and assessments.

By fostering open communication channels and demonstrating the value of Data Governance, you can increase engagement and create a shared understanding of the importance of data across your organisation.

While talking to your stakeholders, document data awareness gaps and training needs. This information will inform the creation of targeted training materials to address specific needs.

Providing training resources in advance can help prepare stakeholders and improve their understanding of Data Governance concepts and practices. Remember to keep engaging and communicating with your stakeholders as you progress through your Data Governance journey.

Communication is a key pillar of your Data Governance initiative.

Here are some considerations when developing your communication strategy:

Audience-focused approach

Tailor communication to the needs and interests of your audience. Consider their current level of data literacy, their roles within the organisation, and their stake in the Data Governance journey. This will help you deliver information in a way that resonates.

Transparency

Foster a culture of transparency and openness in your communications. Share successes and challenges. Be honest about the progress and outcomes of the Data Governance initiative. Encourage feedback from stakeholders and actively listen to their questions and suggestions. Two-way communication creates a sense of ownership and involvement among stakeholders.

Learning and improvement

Share lessons learned and best practices. Showcase the impact of Data Governance through stories. By sharing insights, you will foster a learning culture and encourage stakeholders to embrace Data Governance.

Timeliness

Communicate regularly and proactively throughout your Data Governance initiative. Keep stakeholders informed about upcoming activities, progress updates, and milestones. Timely communication helps maintain engagement, manages expectations, and builds trust.

Accessibility

Ensure that your communications are easily accessible to all stakeholders. Use a variety of communication channels, such as email, newsletters, intranet portals, and team meetings, to reach different audiences effectively. We’ve even seen cartoons and music being used to help bring Data Governance to life. Always make sure the information is presented in a clear and understandable manner, avoiding excessive technical jargon.

Change readiness

Use communication and information to address concerns and resistance, preparing the ground for change. Articulate the benefits of implementing Data Governance and the impact it brings to people’s roles.

Effective communication is an ongoing process integrated into every stage of your Data Governance initiative. Regularly assess communication effectiveness and adjust to stakeholder feedback and evolving needs.

Chances are Data Governance is an alien concept to your organisation, so you will need to build awareness and skills through continuous education and training.

Here are some considerations to keep in mind when planning and implementing your activities:

Grouping different sets of people:

Grouping people based on their roles and responsibilities is a practical approach to tailor the training content to their specific needs. Identify different groups, such as data stewards, data owners, and general data users, and determine the skills and knowledge expected from each group.

Soft and hard skills:

Soft skills, such as collaboration, communication, and change readiness, are essential for effective Data Governance. Hard skills encompass specific technical competencies, such as data analysis tools and understanding data definitions.

Awareness and education:

Awareness education ensures that everyone understands your Data Governance initiative, terminology, and its benefits to the organisation. Regular communication, focused sessions with key stakeholders, and change readiness training create awareness and preparedness for any changes.

General training:

Focus on providing training that incrementally lifts your organisation’s overall maturity. The training should cover core knowledge and best practices in managing data. Consider integrating this training with other annual training programmes to reinforce the importance of Data Governance.

Skills training:

Provide targeted training to enhance the skills required to use data effectively. We’ve seen the fallacy of adopting complex tools which only the most technically advanced can use. Therefore, if you plan to implement an advanced tool, you may need to look at more advanced training on analytics tools and data analysis techniques to ensure your people understand data definitions and lineage more deeply. It goes without saying that the training should align with the tools and technologies used within your organisation.

Role holder training:

For people with formal roles within the governance structure, provide training to support them in carrying out their responsibilities effectively. This training should cover collaboration skills, data issue management processes, root cause analysis, and clear guidance on their accountabilities and responsibilities. Emphasise the interconnectedness of roles within the organisation’s Data Governance structure.

Training delivery and timing:

Plan the timing and delivery of training programs strategically. Start with awareness education to prepare the organisation for change, and then gradually expand into other training programs as the Data Governance journey progresses. Provide content and guidance to role holders before their formal appointment, enabling them to understand the expectations and requirements of their roles.

Remember that effective communication and engagement are key to building capabilities and showcasing the benefits of good governance.

Continuously assess the effectiveness of your training programme, making adjustments to ensure continuous improvement.

Defining Data Governance roles and responsibilities is essential to ensure data is managed effectively, securely, and aligned with your organisational goals.

Effective role assignment creates accountability, promoting a culture of data responsibility and compliance.
Here are some common types of alignment with pros and cons to consider:

When determining the alignment of roles, consider the structure and culture of your organisation as it relates to your data landscape. Involve the HR function to discuss implications for job descriptions, responsibilities, and reporting lines.

Regular communication and collaboration among role-holders is key to successful Data Governance.

Example roles within a Data Governance framework:

Assigning roles requires careful consideration of not only the seniority of a role holder but also their Data Governance mindset and related skillset.

Mindset

• Executive and senior leaders champion and own Data Quality as a critical indicator of performance.
• Data Governance is seen as a core pillar of success.
• All colleagues recognise the importance of data and contribute to its value creation.
• Leaders push for new projects to build in data management by design.
• Passionate about developing a Data Governance community and data-centric culture.

Skillset

• Data creators have the skills to define and implement data standards.
• People have the knowledge to use tools and technologies to manage data and its quality.
• Training is designed to give people role-based skills and capabilities to successfully drive Data Quality.
• People have the knowledge and skills to follow business processes ensuring accurate data capture and usage.

Data definitions and standards are the guidelines and specifications that define how data should be structured, formatted, and handled within your organisation.

They ensure data consistency, accuracy, and interoperability across different systems, processes, and applications.

Data standards can span many aspects of data management, such as naming conventions, data formats, coding schemes, and data classifications.

To begin defining your data, you need to start by understanding where data exists within your organisation. It is best to start with a single part of your organisation or a single set of data.

• Identify any existing business terms in use.
• Identify the types and data stored and any classifications if available.
• Link the data classification with the data attributes.
• List out the data attributes.
• Collate the data specifications for the data attributes.
• Where there are available documented sources – validate their use and use them where applicable.
• Understand the relationships between the source(s) and the data to be analysed.

Any available data artefacts may support this phase of work, as they provide the documented definitions. Ensure that any analysis undertaken is to the level required to expose the processes relevant to the business issue.

Sharing and communicating definitions

As you engage with stakeholders and build your Data Governance foundations, creating and populating key documents will help facilitate effective data management and decision-making.

Here are some essential artifacts to consider:

Business Glossary:

Develop a comprehensive business glossary that defines key terms and concepts used in your organisation. This helps standardise terminology, align
understanding, and reduce confusion or misinterpretation of data. It fosters collaboration and ensures consistent interpretation of data across teams
and departments.

Data Dictionary:

Develop a comprehensive data dictionary that defines key data and calculations used in your organisation. This helps standardise calculations and repeatable data creation. It fosters collaboration and ensures consistent usage of data across teams and departments.

Data Standards:

Compile and consolidate existing data rules, standards, and guidelines within your organisation. Evaluate their effectiveness, identify gaps, and determine if they adequately support data usage throughout its lifecycle. This serves as a reference for Data Quality and consistency and supports decision-making regarding Data Governance policies and practices.

By creating and populating these artefacts, you establish a foundation for effective Data Governance, promote collaboration, standardise understanding, and enable targeted actions to address data-related challenges within your organisation.

Understanding data criticality

Assigning criticality levels to data is a valuable approach for prioritising data improvement efforts and ensuring appropriate levels of quality based on the data’s importance and associated risks.

The following levels of criticality can serve as an example to help assess and manage data criticality within your organisation:

Data with a very high criticality exposes the organisation to significant consequences when it is inaccurate.

This encompass data that is made public and in which the public and the press have significant interest for example Sensitive / Personally Identifiable Information.

The impact of this data being inaccurate would have significant reputational damage.

High criticality data covers all data which is used to make critical board decisions and any data covered by, or reported to regulators, including Personal Identifiable Information.

The impact of this data being inaccurate is significant, and this data needs to meet a high standard, but issues with its quality do not have the reputational effect of very high criticality data.

Data with medium criticality is used for daily operations.

This data still needs to meet high-standards, but risks associated with issues are lower.

With this type of data there is often a need to balance more quality dimensions to ensure it meets levels required for trustworthy usage.

Low criticality data provides business benefits but making the data quality high does not add more value to the data.

This type of data is mostly used for infrequent operating purposes rather than decision making.

With this type of data “Timeliness” will often need to be prioritised over other quality dimensions.

By assigning criticality levels to your data, organisations can make informed decisions regarding the allocation of resources, prioritise data improvement initiatives, and focus on the appropriate Data Quality dimensions that are most relevant to each criticality level. This approach ensures that efforts are directed where they are most needed and where the potential risks and value associated with the data are the greatest.

Mapping the data lifecycle

Understanding how data moves through your organisation ensures the appropriate controls and standards can be applied at the right time.

An example could be the lifecycle of customer data from “Business Development” to “After-sales”.

A typical data lifecycle will look like this:

Here is a breakdown of the stages and their associated activities:

Create, collect, acquire and ingest:

This stage involves the creation or collection of data, either internally or from external sources. The focus is to ensure that the data meets the required standards and is fit for its intended purpose. This may involve data validation, data capture guidelines and Data Quality checks during the acquisition process. The acquired or collected data is ingested into the organisation’s systems or data repositories. It is important to maintain data integrity during the ingestion process, including data cleansing, data mapping and data transformation if necessary.

Prepare, store and maintain:

Once data is ingested, it needs to be prepared, stored, and maintained in a controlled and organised manner. This includes activities such as data profiling, data integration, data storage optimisation, data security measures and maintaining metadata about the data.

Transform and process:

This stage involves transforming and processing the data to make it usable for various purposes. It includes activities such as data cleansing, data enrichment, data integration, data aggregation and applying business rules or calculations. Clear documentation of the transformations and calculations is important for transparency and data lineage.

Share and publish:

Data should be shared and published in a way that is accessible and understandable to the intended users. This stage involves providing access to glossaries, dictionaries, and metadata to help users interpret and utilise the data effectively. Quality metrics and information about data sources should also be made available to support informed decision-making.

Archive or destroy:

Data retention and disposal policies should be established to determine when data should be archived or destroyed. Clear documentation and adherence to regulatory considerations are essential in this stage to ensure data is retained or disposed of appropriately.

When progressing through these stages, it is still vital to maintain Data Governance principles; this includes Data Quality monitoring, Data Lineage tracking and Continuous Improvement.

Communication and collaboration across different roles and stakeholders involved in each stage are crucial for effective Data Governance. A Change Management Process will make sure changes to data requirements are reviewed and approved by, as well as communicated to, the appropriate teams for implementation.

Data quality management

Earlier, we highlighted that a key business driver to embed Data Governance is to make sure that the data quality in the organisation is fit for purpose.

Data Quality rules are required, but it is unlikely that one set of rules will apply across all your data sets. Since different types of data will serve different purposes, it is important to consider data quality metrics specific to your dataset`s purpose.

Balancing the quality metrics applied to data is crucial for effective Data Governance.

Here’s a summary of the key quality dimensions (or data quality measurements) and their significance:

Accuracy (e.g. Telephone number)

Determines if the recorded value aligns with the actual value. Data accuracy is essential for making informed decisions and avoiding errors or misinterpretations.

Completeness (e.g. Email missing on customer records)

Examines if any expected data elements are missing. Complete data provides a comprehensive view and supports accurate analysis and reporting.

Auditability (e.g. Errors can be traced back to root cause)

Considers if the data flow is welldocumented to identify potential issues throughout its lifecycle. Auditability ensures traceability, accountability and the ability to investigate data-related problems.

Consistency (e.g. First name and surname in the right order)

Checks for discrepancies or conflicting values within the data. Consistent data ensures reliable and coherent information across different sources or systems.

Timeliness (e.g. Customer billing address is up-to-date.)

Assesses whether the data is up-to-date and arriving within the required timeframe. Timely data is crucial for making real-time or time-sensitive decisions.

Validity (e.g. Date of birth format matches business requirement)

Verifies if the data adheres to the defined business rules and expectations in terms of format and value. Valid data is reliable and suitable for its intended use.

When building the foundations of good Data Governance, it’s important to have a prioritised approach to understanding, documenting, monitoring, and improving Data Quality because the scale of the challenge can be daunting.

By leveraging the criticality framework introduced earlier, you can prioritise areas that require higher Data Quality levels based on business needs and benefits. With a focus on the appropriate Data Quality dimensions and realistic metrics, you can raise Data Quality to meet the varied needs of your stakeholders and use cases.

Prioritised data quality allows for a more targeted and efficient approach to Data Governance, ensuring your data delivers better decision-making, operational efficiency and customer satisfaction.

Your first Data Quality assessment will likely focus on your organisation’s key issues. As organisation maturity develops, you should aim to create a repeatable assessment of the quality of your data using a combination of the above metrics and your data standards.

As your Data Quality process discovers issues, your next task will be to log the most serious issues via a central register.

Building the Data Issue Register

A Data Issue Register provides a coordinated method of documenting and tracking your organisation’s most serious data issues.

Data-related problems, inconsistencies, errors, and concerns are logged, monitored, and managed via a central repository that ensures transparency, accountability, and effective resolution of data issues throughout their lifecycle.

Here are some key elements of a Data Issue Register:

Issue Identification

• Description: A clear and concise description of the data issue.
• Source: Indicating the issue’s origin (e.g., specific data source, process, application).

Classification

• Type: Categorisation of the issue (e.g., data quality, accuracy, completeness, timeliness).
• Severity: Evaluation of the impact or severity of the issue on business operations.

Metadata

• Date Detected: The date the issue was first identified.
• Detected By: Person or team responsible for identifying the issue.

Status Tracking

• Current Status: The current state of the issue (e.g., open, in progress, resolved).
• Assigned To: The individual or team responsible for resolving the issue.
• Target Resolution Date: The expected or agreed-upon date for resolving the issue.

Root Cause Analysis

• Investigation Notes: Detailed information about the steps taken to analyse and understand the root cause of the data issue.

Resolution Details

• Corrective Actions: Specific steps or measures taken to address and resolve the data issue.
• Verification Steps: Procedures to confirm that the resolution is effective and the issue is fully resolved.

Communication Log

• Updates: Records of any communications, updates, or discussions related to the data issue.
• Notifications: Information about any notifications sent to stakeholders regarding the issue and its resolution.

Audit Trail

• History: A chronological record of changes to the issue status, resolutions, and other relevant details.

Governance and Compliance

• Compliance Requirements: Indicating whether the resolution aligns with regulatory or organisational compliance standards.
• Documentation: Links or references to relevant policies, procedures, or guidelines.

Reporting and Analysis

• Metrics: Key performance indicators (KPIs) related to issue resolution and data quality improvement.
• Trends: Analysis of recurring issues or patterns that may indicate underlying systemic problems.

The issue register helps prioritise and remediate data issues, quantify the need for Data Governance investment and identify areas that require immediate attention. It provides insights into common pain points and informs decision-making regarding resource allocation and improvement initiatives. The register also allows you to document who is responsible and accountable for resolving data issues.

Once you have a prioritised list of issues, focus on root cause analysis to resolve the issue at source rather than downstream. With data owner support, ensure staff have the right data, people, process and technology skills to resolve the causes of the most important issues.

Finally, by leveraging your standards and metrics for data quality, you can build a repeatable assessment and monitoring capability that can instantly report and track future data quality issues.

Data Governance Tooling

Do you need a Data Governance tool?

Once you have created the Data Governance fundamentals, you can explore how Data Governance technology could extend your foundational gains.

Here are some useful pointers when considering tooling options:

Define the purpose

Gather a clear consensus on the challenges and goals you wish to address with a Data Governance tool. This clarity will guide your selection process and help you find a tool that aligns with your objectives.

User engagement and experience

Involve end users in the selection process and ensure engagement through the implementation. Consider how they need to interact with the tool. The tool should be user-friendly and intuitive, providing a seamless experience to encourage adoption.

Stakeholder engagement

Look for a tool that facilitates collaboration and engagement among stakeholders. It should allow for efficient communication, feedback, and knowledge sharing, enhancing the overall Data Governance practices within your organisation.

Move beyond storage

Avoid treating the tool as a storage solution. While some Data Governance tools contain business glossaries and data dictionaries, the benefit should go beyond storage. Look for a tool that enables collaboration, engagement, and active use of the data and resources it contains, and ensure it integrates to your system architecture and not siloed.

Ease of use

The tool should be easier to use than alternative methods of managing Data Governance. It should simplify tasks, streamline processes, and make it easier for stakeholders to contribute, access, and derive value from the information within the tool.

Value-driven solution

Assess how the tool can provide tangible value. Consider how it can support decisionmaking, improve data understanding, streamline processes, enhance Data Quality, and contribute to overall Data Governance maturity.

By focusing on these aspects, you can select a Data Governance tool that not only meets your needs but also empowers users, fosters collaboration, and adds value to your Data Governance initiatives.

How should the technology work with your people and culture?

Data Governance tools can’t replace the people element of effective Data Governance – the key is to use technology to enhance the capabilities and performance of existing data stakeholders, knowledge workers and processes.

Here are some key points to consider:

People capabilities

The success of a Data Governance tool relies on having a network of people with a deep understanding of data and its governance. Ensure you have the right people in roles and provide adequate training to build the necessary knowledge and capabilities to use the tool effectively.

Organisational culture

Evaluate if your organisational culture is ready to support the use of a Data Governance tool. The tool requires active engagement and participation from users across the organisation. If the culture is not receptive to using technology for Data Governance or lacks the necessary data literacy, it may hinder the tool’s effectiveness.

Start with familiar tools

In the early stages of your Data Governance journey, consider using readily available productivity tools to document information about data. This allows for a simpler and more familiar approach, especially when the skills and knowledge of key users are still developing. Gradually, as you scale your Data Governance capability and identify the need for more advanced functions, the need for a dedicated Data Governance tool may become apparent.

Meaningful information and value

Remember that simply gathering vast amounts of information into the tool should not be the focus. The tool should enable the provision of meaningful, timely, accurate and useful information about your data across the organisation. It should facilitate data understanding, decision-making and support the overall Data Governance journey.

Ultimately, Data Governance tools should align with your organisation’s specific needs and readiness. They should complement your Data Governance efforts and empower users to manage and exploit data as an asset for growth and success.

Remember that what’s right for one organisation may not be right for you. By discovering current and future use cases, you can build a transparent, unbiased assessment of the tool’s capabilities against your own selection criteria.

Game changer: Generative AI shaking the Data Governance world

Generative AI is here to stay. Whether you are already investing in Gen AI or still exploring your options, it is a game changer for how business manage their data assets.

Challenges for businesses

Generative AI introduces unique challenges that demand vigilance at input, processing and output. With the introduction of AI regulations and laws, there is no getting around that this technology is more ‘demanding’ than other traditional data solutions, and as such will require more complex governance.

• The ‘black box’ concept makes governance tricky! We might know what the AI is ingesting and producing as output or predictions but we don’t know the “why” of its predictions. This makes traceability way more complicated.
• It brings with it increased data documentation to teach context and accuracy while keeping with privacy and security requirements.
• And getting concrete value out of AI and GenAI is very dependent of asking the right business questions and building the right prompts.

The holy grail is good data. Or else, your Gen AI will be contextless without structured prompting and will be EXACTLY representative of your data – you need to be ready.

AI makes your data speak to you – Data Governance makes it say the right thing

Your AI-powered solutions will be ingesting the data, making decisions and acting autonomously to achieve specific objectives. For minimal business disruption, this ideally needs to be seamlessly embedded in your day-to-day processes.

AI agents are software programs or entities designed to do exactly that. These agents can vary widely in complexity, from simple rule-based systems to sophisticated machine learning algorithms.

The core idea of agents is to use a language model to choose a sequence of actions to take – the language model is used as a reasoning engine to determine which actions to take and in which order; not very different to human reasoning. The eureka moment comes when we give the agents the tools to act. Good data is non-negotiable.

A robust Data Governance framework is your guiding light. It’s the unsung hero that ensures that your AI-powered agents will ACCURATELY understand your prompts, ingest the CORRECT data, in the right format and make the RIGHT decisions or predictions.

How does a Data Governance framework make your AI journey seamless? It’s all about considerations – data quality, ‘human’ roles and responsibilities, education, monitoring and compliance – which allows for trust, ethical use and bias removal.

Starter for ten checklist:

• Understand your business landscape for Gen AI improvement.
• Map your information flow and data dependencies.
• Transform your Data Strategy from a document to action.
• Elevate your data platforms to meet Gen AI standards.
• Uncover and address biases lurking in your data.
• Assess the quality of data powering your Gen AI models.

Addressing challenges while implementing your Data Governance journey is crucial to ensuring a successful outcome.

Here are some strategies to tackle common challenges:

Lack of awareness:

Despite regular communications, some of your people may still be unaware of Data Governance activities due to organisational silos. To address this, have easily accessible and understandable material that clearly communicates the why, how, and what of Data Governance. This material can be readily shared to provide instant information and raise awareness about the importance and benefits of Data Governance.

Existing systems and processes:

During your data landscape analysis, you may encounter systems and processes that pose challenges to your Data Governance implementation. If attempting to solve issues in these areas would create more problems, work closely with the affected teams to identify pain points and explore alternative solutions. Collaboration and open communication will be essential in finding viable approaches.

Difficult stakeholders:

Dealing with stakeholders resistant to change requires a focus on active listening and responsiveness. Take the time to understand their concerns and address them effectively. Additionally, connect them with other people involved in different parts of the data lifecycle who can provide firsthand experiences and demonstrate the need for change. This humanises the process and fosters a sense of collaboration.

Tools as solutions:

It can be tempting to bring in a tool or start a Gen AI program to solve your Data Governance problems, but without the right foundations these can hinder rather than help. A tool needs people with the skills to use it across your organisation; otherwise the tool will end up only being added to and used by a select few. You also need to select the tool based on specific use cases, which is easier to do once Data Governance foundations are built. For Gen AI, if you have poor data quality or siloed data you will not get the best out of it and could end up making the wrong decisions. Ultimately tools should be enablers not solutions in your data governance journey.

Historic failures:

Past unsuccessful attempts at Data Governance can lead to apathy and scepticism among stakeholders. Address this directly by acknowledging the previous failures, emphasising the continued need for Data Governance, and explaining why the current implementation is different and more likely to succeed.

Lack of sponsorship:

Data Governance is a change management endeavour. Having a sponsor who is at a very senior position helps the programme get traction where things might stall. They do not necessarily actively get involved in the delivery of the Data Governance programme, but they act as a leadership authority who champion data governance and own the budget for its implementation.

Learn from past mistakes and clearly articulate the changes and improvements in the current approach. Maintain open channels for feedback, listen actively, and be prepared to pivot if necessary, based on stakeholder input.

By proactively addressing these challenges and tailoring communication and engagement strategies, you can help overcome resistance and create a more receptive environment for your Data Governance initiatives.

Wherever you are on your Data Governance journey, Oakland can help you make the next step.

We offer a range of custom services spanning the entire Data Governance lifecycle. You can choose to engage us at specific points on your journey, or we can partner you through the entire process of defining and implementing Data Governance.

A – Data Ownership Design

Have you ever noticed how people can be sensitive about their prized possessions being tinkered with? Owning something can make you feel really protective of it, especially when it’s an asset that increases in value over time.

At Oakland, we have distilled the collective knowledge from our experts and in depth industry knowledge into a rounded service offering to help you design and build an Ownership model to suit your organisation This ensures data accountabilities and responsibilities are in place from day one to drive a data-centric culture.

This is a key aspect of Data Ownership – if someone is accountable and responsible for a piece of data, they’ll ensure that it is defined accurately, maintained correctly, and used responsibly. More importantly, there’s a go-to person or team that can be approached with questions or changes that need to be made to the data. Ownership means accountability. By implementing an Ownership model for data within your organisation you create a focus on responsibilities and accountabilities that support a value-creating data culture.

B – Data Governance Maturity Assessment

Most organisations recognise the value of more general governance and this translates directly into Data Governance. Data Governance should help you manage your data more efficiently and effectively, instil an ethos of data ownership and stewardship, and progressively work to improve the quality and reduce the risks surrounding data and its use.

Our Data Governance Maturity Assessment reviews the state of your current organisational Data Governance against a detailed framework of industry standards to understand strengths and weaknesses, and help you decide where to target your improvement efforts.

C – Data Quality Assessment

Some people would say that trust is everything. Data is a business asset that needs to be trusted for an organisation to use it effectively. But, not all data is equal and, ultimately, all data will have its trustworthiness questioned. Failing the test can be expensive, either measured in organisational outcomes or in terms of duplicating or repeating of effort.

Our Data Quality Assessment provides a framework for assessing your data in terms of its completeness, accuracy, consistency, integrity, timeliness and auditability. All the essential benchmarks of Data Quality.

It will provide recommendations on the appropriate Data Quality framework(s) to fix issues and suggest the right Data Quality business rules and thresholds.

We will talk you through the assessment results to help you fix the issues identified and help you move forward.

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Foreword

Data: it’s the best of times and the worst of times.

Over the last two decades, there’s been a huge surge in interest, innovation, and investment in data. It’s hard to find a company that doesn’t want to be more data-driven or a CEO who isn’t interested. Data professionals have never been in higher demand, and new ideas, technologies, and career paths are hitting the market at warp speed. Sounds amazing, doesn’t it? But remember: the data industry is really good at gloss.

The reality for most companies is very different. The chasm between the hype and the reality has probably never been wider. Many businesses are still grappling with the basics.

What can data do for us? Where does this data come from? Can I trust it? Why do we never have the data we need?

If you’re reading this, you probably know what happens when expectations are inflated, or investments are made in tech or talent without anyone stopping to think why.

It’s a dangerous path and it won’t take long for the excitement to give way to frustration. The reality is, you won’t get a return on investment unless you’re clear about what you want to achieve.

Which is why you need a Data Strategy.

If you get it right, a Data Strategy will unify data activity behind a clear vision and case for change which are grounded in the objectives and priorities of your whole organisation.

Drawing on decades of expertise, we’ve honed a practical and successful approach to creating and implementing a data strategy that puts data at the heart of your organisation’s future. Our Oakland approach has been successful across different industries and client organisations, so we’re delighted to be able to share it with you.

If you would like to discuss how we could help you plan, deliver or implement your data strategy, please get in touch, but in the meantime, enjoy the guide!

Joe Horgan
Data Strategy Lead
The Oakland Group
Email: joe.horgan@weareoakland.com

In simple terms, a Data Strategy sets a vision, detailed strategy and roadmap which explains how an organisation will use data and analytics to realise its strategic objectives.

A Data Strategy is not about drawing a different future for your organisation, or a standalone strategy that is purely about data. Remember, the wider future of the organisation is (or should be) already imagined through the overall organisational strategy. herefore, a Data Strategy is really about explaining how data can play a key role in the achievement of the organisational future envisaged in the business strategy, and what changes, investments and capabilities are needed to make that happen.

For some businesses their organisational strategy is not well documented or in a state of flux, which can make the process of defining a data strategy a more iterative process involving discovery and re-confirmation of organisational goals. But crucially, this doesn’t change the overall purpose of writing a data strategy.

Done properly, a Data Strategy will unite data-driven activity throughout the host organisation behind a clear set of business-aligned goals, with a compelling vision and case for change to drive engagement and adoption.

This quickly becomes a complex exercise covering a huge range of topics and themes, but when you’re writing a data strategy it’s vital to stick close to this simple purpose.

Remember, it’s data for strategy, not a ‘strategy for data’!

Until recently, many businesses didn’t consider the need for a data strategy. However, the explosion in data availability and processing power has created an impetus for organisations to ‘up their game.’

A recurring theme is organisations skipping over data strategy and leaping straight into technical implementations. This results in technical and business upheaval as new technology get introduced with a localised short-term mindset instead of a longer-term strategic perspective.

If you want to succeed, it’s vital you start by thinking about the challenges and opportunities you’re facing.

Do any of these statements sound familiar?

“There’s an opportunity in here somewhere”

Very often organisations have a sense that they are missing opportunities to use data better, but are struggling to identify how. A structured data strategy design will uncover and prioritise the relevant opportunities.

“We need to set a vision”

One powerful benefit of a data strategy is that it creates a clear future to unite efforts and guide decision-making. Sometimes this needs creating from scratch; but in other scenarios we find it’s more about helping to better structure and articulate existing thoughts.

“Everyone’s doing their own thing”

Many organisations suffer from disjointed data functions and activity. Accountability may be disperse or budgets and decision-making rights are withheld or non-existent. A data strategy will align priorities, resources and roadmaps behind a clear vision.

“Where’s the ROI?”

A very common challenge. Either investments have been made which are not showing the expected benefits, or data sponsors and leaders are struggling to secure budget and resources to execute their ideas. The root cause is often a lack of a clear underpinning strategy.

“The customers have had enough”

Scratch below the surface and data ‘customers’ (internal or external) are often a frustrated bunch. Availability, access rights, quality and timeliness of data are all perennial frustrations. An effective data strategy harnesses these pains, identifies root causes and mobilises investment in solutions.

“Our house has no foundations”

A frequent refrain from anguished data leaders. The key challenge is helping link customer frustration (“the dashboard is wrong”) to root causes (“nobody owns the data”) to solutions (“we need data governance”). This is about linking data management best practise with a narrative that is grounded in day-to-day data customer challenges.

“We’re stuck in a reactive cycle”

Many data teams become trapped in urgent demands, re-work and improvisation. This is a draining environment and can prevent any long-term capabilities or enduring corporate knowledge from emerging. A collaborative process to define and implement data strategy helps break this cycle.

Every business will have their own reasons, but if some of what you’ve just read sounds familiar, there’s a good chance you need to consider creating or updating a data strategy for your organisation.

What’s next? Well, starting with the right mindset and approach is crucial. In the next section we’ll take you through one fatal mistake to avoid and show you how to find a better way to tackle writing your data strategy.

What not to do

Bear with us. It sounds strange, but a data strategy shouldn’t be a strategy for data.

It’s so easy to fall into the trap of thinking of data as something separate from the wider organisation, with its own priorities, objectives and concerns. But this is a fatal error.

Take a look at this. Where would you say you are today?

Sometimes we meet organisations with no real data strategy at all. But most of the clients we work with do have a data strategy. The problem is they are stuck in the ‘Emerging’ state.

This means they’ve put a strategic lens on data, but only from the perspective of data and the data team(s). This usually results in a data strategy that’s internally facing, tech-heavy and leaves the rest of the organisation grasping for the ‘so what?’.

This means interest, momentum and (crucially) resources are never secured. The data strategy goes on a shelf and things carry on exactly as before. The organisation might be able to say ‘we have a strategy for data’, but none of the expected benefits have materialised.

A better way

So how do you avoid the ‘strategy for data’?

Well, nothing will change until you put the organisation and its overall strategy before the data. Data needs to be re-cast as a means to meet organisational needs, not an end in itself.

Many organisations struggle with this shift. Even when you have moved your mindset, you can easily get stuck.

It’s complex because every data strategy needs to be custom-shaped to your organisation. You can’t cut corners with ‘cookie-cutter’ blueprints. (Well you can, but it won’t end well.)

However, you can apply a structured process to help you and your team discover, design and deploy a compelling data strategy that creates a path from where you are today to a data-driven future.

Sadly, most data strategies fail to create that path.

If you want yours to be one of the ones that succeed, here are four key principles to stick to.

1. Data is business and your business is data

The question is not “what’s our strategy for data?”, it’s “how do we use our data to achieve our strategy?”. In this context data becomes the ‘how’, or one of the planks in the bridge between the your today and the future envisioned in your organisational strategy.

The starting point for your data strategy is to therefore understand:

1. What outcome(s) does our organisation need to achieve?
2. What is the role of data in that journey?
3. How will business value be created?

With business value and critical results in sight, you can identify and prioritise the required capabilities.

2. People, Process and Technology

The modern data strategy requires a balanced view incorporating people and process elements, such as culture, experience, policies, and behaviour alongside technology.

Historically, many organisations have over-invested in data technology and talent without enough emphasis on culture, governance and process.

A better way of looking at the problem is through the lens of capabilities. What does your business need to be able to do with its data?

Buying technology or talent is inherently transactional and narrow in scope. Creating capability requires a carefully balanced orchestration of people, processes and technology. It opens up a much wider perspective for a data strategy and avoids the pitfalls of scattergun hiring or pursuing technology for technology’s sake.

3. Stories not sermons.

‘Oh fantastic a fifty-slide presentation about data mesh, let’s extend the meeting!’ Said no CEO. Ever.

Too many data strategies are long, technically focused and leave their audiences cold.

Any effective data strategy must have a compelling narrative. Writing and implementing a data strategy is, fundamentally, a storytelling challenge.

It’s easy to get lost in a maze of frameworks, jargon and detailed arguments. But for the non-technical folk who are the majority of your audience, you need to find a compelling way to tell three interrelated stories:

1. The value story: how will the organisation create concrete value from data against its strategic objectives? What are the opportunities people should be excited about? It’s a realistic but exciting vision of how data can drive a better future rooted in the concerns and self-image of the host organisation.
2. The data management story: this knits a vision for how the organisation will collect, store, organise and safeguard its data. It’s as much about ownership, care and ethics as it is about lakehouses and ELT. It’s crucial to link in to the data value story and position data management as key to realising that value.
3. The data culture story: this is so often overlooked, but oh so vital. How do we cultivate the right behaviours and values towards data in the host organisation? What should people think, feel, role model and advocate? What are the different organisational roles and personas?

The point here is not to produce a mini-series of paperbacks and leave them strategically placed around the office canteen and boardroom.

It’s about ensuring there are compelling, accessible and coherent narratives woven through the whole design, presentation and implementation of a data strategy.

Data strategy storytelling could be a guide like this all on its own. But, briefly, at Oakland we like to follow an approach based around what we call ‘the 7Cs’. Follow these and you shouldn’t go far wrong:

• Customer-Centric: it starts with the customer’s pains, and ends with their benefits
• Commercial: we focus on the tangible value we can add, not complex methods
• Context: a vision grounded in organisational reality, not the textbook
• Concise: short, focused messages that are easily consumable
• Concrete: practical examples or case studies in accessible language
• Creative: visual representation of complex concepts and relationships
• Conversational: stories emerge from a two-way dialogue with stakeholders

4. Co-discovery and creation

At Oakland, we think data strategy should be an outward-facing, whole-company effort.

Your data strategy is a shared document that sets out how the organisation plans to use one of its most important assets. It should grow out of workshops, customer engagement and deep analysis of the needs and personas of the host organisation. The final presentation should not be a ‘big reveal’; you MUST work collaboratively.

A different way

When you put those four principles together, a very different way of looking at creating a data strategy emerges. This is what allows you to escape the ‘Emerging’ state that many data strategies become stuck in. It’s an approach that unlocks a whole new range of possibilities for data, and positions it as a critical organisational capability, not a standalone technical domain.

Many data strategies are only partial answers. So you need to make sure you have everything covered. When crafting your data strategy, you need five major components:

1. Strategic vision – the broad brush of what you want to achieve and the high-level changes that are needed to make that happen. This needs to be a compelling vision of a better future and the big-picture changes needed to make it happen.
2. Case for change – the challenges with your current state and what’s in it for the enterprise if you deliver on the vision. This needs to strike a balance between the challenges of today and the benefits of the future painted in the strategic vision.
3. Detailed strategy – a richer picture of what the future will look like covering: enterprise value delivered, target state capability, alignment of delivery resources, and governance of implementation. Turn the vision into detailed components for implementation.
4. Strategic roadmap – the transition phases and change activities your organisation will undergo as it moves through implementation and stages of change
5. Target Operating Model (TOM) – the build-out and organisation of data capabilities to deliver the strategy. The translation of the strategy into organisational reality.

Each component plays a crucial role in driving the realisation of the value of data in your organisation.

1. Strategic vision

Your data strategy should provide a clear view of how data will become a key engine for the wider organisation as it drives towards its strategy. It’s where you paint the big picture of a different, data-driven future for your company. The three organisational data stories and our ‘7C’ storytelling principles have a critical role to play in this part of your strategy.

Key elements to include in your vision are:

Purpose

What place does data have in your organisation, and why is it vital you think about it differently? Finding ways to connect this back to your organisation’s broader business purpose and strategy will be critical, as it cements the foundation of data.

Scope

What’s the scale and content of your strategy? Your scope definition should also clarify what the broader organisation understands by the term ‘data’.

Future

What are the big-picture changes and benefits you want to bring about through the realisation of the strategy? What will be different when the implementation has occurred?

Objectives

What things will we be able to achieve as an organisation as a result of the data strategy? Goals should be tangible for the business and aligned with overall business objectives. Expressing (in simple terms) how the data strategy achieves each business objective will aid the communication and buy-in of the roadmap.

Key results

When we have achieved (or are approaching) our objectives, what metrics will we influence, and what will those look like? As with objectives, it is best to express these with key business metrics.

Capabilities

What (at a high level) will we be building or enhancing in our organisation? This section sketches out the capabilities that need to be created, developed or overhauled. How will people, process and technology be combined to create new possibilities for the organisation?

Every vision will be different, depending on how ambitious or transformational you to be. To find the right balance, you must be mindful of your current data maturity. It’s tempting to shoot for the moon but winning support by tackling an immediate problem can help you create a successful launchpad for a more ambitious data strategy.

2. The case for change

Your case for change should present a straightforward and compelling rationale for why your organisation should pursue the strategic vision of your data strategy.

Translating the vision into action demands significant buy-in. The case for change will make or break your data strategy. So take the time to get it right.

Your case for change must meet people where they are at and how they think. It must resonate equally with the ‘big picture’ visionary, the execution-oriented process manager, the numbers geek, and the people person. This is a storytelling challenge: the narrative must be strong.

The following elements are critical:

Clear view of today

This should be an unbiased and critical look at how your organisation delivers data and analytics today. When doing this for our clients, we typically leverage a maturity assessment against our data and analytics capability model. The critical question is: “is this fit for purpose?” If not, why not?

Voice of the customer

Maturity assessments are only part of the puzzle, so we enrich them with clear explanations of real-world data problems and aspirations from the customer community. Customer-engagement is non-negotiable when creating your case for change.

View of target

Leveraging the capability model above, we can set a data maturity target for the future. In most cases, organisations will look to move up 1 or 2 levels in maturity over the lifespan of a data strategy. Crucially, maturity increases need to clearly tie back to the underlying business needs identified in your data strategy.

Business and financial rationale

This is essential but, we know, often difficult. Sometimes, there may be a directly identifiable uplift in profitability based on the data strategy. In other cases, data can support benefits realised elsewhere. However you do it, make sure your assumptions are clear and agreed on.

The depth of your case for change depends on the size of transformation you are aiming for as well as the number (and type) of stakeholders you want to get on board.

Data is a nebulous space for many stakeholders. It can feel confusing, disruptive and uncomfortable for those who don’t understand its aims. So, think of your case for change as a process of education and joint discussion rather than just a one-off document. Whilst it can feel frustrating if stakeholders don’t ‘get it’ at first, the effort and patience you put in at this stage will pay huge dividends later on. If you keep listening and keep responding, you will get there!

When you have a strong coalition on board to deliver on your vision, it’s time to get into the detail.

3. Detailed Data Strategy

This is about adding the detail to your strategic vision so that it becomes implementable into your organisation. You need to paint a more specific picture of how the future will be different, and the key shifts involved in getting there. So, you will want to focus on a few areas of detail to gain alignment and agreement on key questions:

Future state definition – the forward-looking view of the maturity assessment and wider diagnostic work you did as part of your case for change. What are the specific ways in which the data future of your organisation is different from today? How do they deliver the benefits laid out in your vision and case for change? In some cases, you may want to grow maturity in one aspect or capability of data more than others, so identify where those differences exist and call out the rationale. Be sure the improvement you want to develop matches your organisation’s ambition. The future state definition should relate back to your vision and case for change, but also build out the next level of detail. Once you have that definition in place, you can move on to…

Concept design(s) –these are high-level views of the future from a data and analytics operation, data governance, and technology point of view. Providing a clear view of the future and getting alignment early helps to drive out more detailed design. This can take multiple forms depending on the focus areas for your strategy. To help bring this to life, below is a concept design which aligns components of a data organisation behind clear delivery streams and consumption methods which tracks value delivery right through to end users.

You can also imagine concept designs of future data flows, data governance frameworks or platform architecture. The point here is to put together some clear pictures behind the future state definition so that stakeholders can see how the components of the future fit together.

Levers of influence– an overlooked aspect of most data strategies. You need to identify what the strategy can influence and, crucially, how it will be implemented. Think of this as a template for how you will deliver organisational change, with a heavy focus on aligning people. A figure of 8 change model like this one below will help guide your thinking and, crucially, shine a light on the content and deliverability of your future state vision.

Key gaps to close – By now, you will have charted out your organisation’s future state data and analytics capabilities. Still, there will be gaps that need to be closed before you can deliver on the strategy. Documenting these gaps within your strategy is essential for starting a chain of accountability for getting each gap closed. You will need to establish what gaps will be addressed within the scope of the data strategy and what is out of scope but dependent on other parts of the organisation.

Building upon the items above to create a detailed data strategy is a valuable tool to take the high-level vision you have for your organisation and set out the practical components necessary to realise that vision.

Then it’s time to think about the planning necessary to move your strategy from a paper-based concept to organisational reality.

4. The Strategic Roadmap

By this stage, you’ve pulled together:

• Your vision
• Why your organisation should implement the vision
• The details of what the vision entails

The inevitable response will be:

“How long and how much?”

The cornerstone of your data strategy will be the strategic roadmap for developing the capabilities provided as an outcome of your data strategy.

The detail in your roadmap components will vary depending on the scale of intended change.  Regardless of the detail, here are some areas you may want to include:

• Overarching 2-3 year roadmap – It’s difficult to predict the future, so your roadmap needs to be ambitious enough to account for the likely shifts in your organisation’s various operational and strategic contexts.
• Transitional states – How does your strategy build over time? Clear transitions help to create buy-in for change and make it easier to sequence your roadmap.
• Projects on a page – Implementation will likely take the form of various distinct projects within the roadmap. Crafting a high-level view of scope, activity, and potential resource requirements on a single page will bring these efforts to life.
• Governance – Outlining who will oversee the data strategy, the reporting structure, and how they intend to govern the program is crucial

By this stage, you have outlined your data strategy roadmap and explained the value of each component.

The next step is to build a Target Operating Model (TOM) – the blueprint for how the business will operate with its new data capabilities.

5. Target Operating Model (TOM)

A data strategy provides the shape of where you are going, and you roadmap should include specific changes or programmes to deliver on your strategic agenda. These strategic projects implement your strategy as a series of key initiatives, targeted at major gaps or key themes.

However, these projects might not touch on every area of a data organisation. Further, they’re unlikely to fully define how the organisation operates its new data capabilities as BAU.

Further, without careful alignment, much of your current organisational set up and ways of working might actually be working against your strategy. There might be a strategic vision, but if the day to day reality is one where ways of working are unchanged, then delays and internal friction will dominate the data customer and employee experience.

The solution is to define a new Target Operating Model to support your data strategy.

Far more than an organisational chart, your TOM will describe the people, processes and technologies required for delivering value from data within your organisation. If describes how you will organise to deliver the data and analytics capabilities in your strategy through a number of critical components:

In our experience, you need a joined up design spanning all of these for you to fully design a TOM that delivers on your strategic vision.

We recommend starting each at a conceptual level to gain buy-in. Once you’ve got broad agreement, you can move into detailed design and execution of your future operating model f. If you get this right, you will have deeply embedded your data strategy in to the very fabric of your data organisation.

So far, we’ve outlined what to include in your data strategy. It’s a big task. Breaking it down is key to success.

This section summarises how to do that and work through the process of designing a data strategy, along with important questions to consider at each stage.

Phase 1: Discover

The goal of the discovery phase is to understand the baseline of where you are today and where you want to be in the future with data and analytics.

Critical questions include:

• What is the current maturity of your data capabilities? What is the perception of maturity across different stakeholder groups?
• What is the experience of data customers and what are they looking for in the future?
• What strengths and weaknesses exist in the current operating model?
• What are the organisation’s key strategic goals, and how can data and analytics be leveraged to drive towards them?
• What is the vision for the future of data and analytics? How can that be translated into specific, measurable goals?
• What is the case for change? How should it be communicated?

Phase 2: Define

In the define phase, you will develop the outline of the data capabilities that align with the vision for data across the organisation.

You will be defining the concepts and interactions that shape the future of your data strategy and operating model (e.g. strategy and purpose, value streams, process architecture, ownership and roles, functional boundaries, leadership and culture).

You will also define the target and key results from a business perspective.

Common questions are:

• What future state design principles will act as a yardstick for the success of the strategy?
• What strategic questions and trade-offs will shape the future data strategy?

Phase 3: Plan

In the planning phase, you will establish the processes, standards, technology, and organisational changes required to deliver the vision.

You will also create a supporting transformation programme to deliver on the ambition, including a detailed design of the future strategy and operating model for data.

Essential to the planning activity is understanding what enablers need to be in place to execute your vision (e.g. stakeholder support, investment cases, technology implementations, training).

Some of the standard planning deliverables will include:

• Strategic roadmap and transition states
• Success factors and measurement framework
• Programme mobilisation
• Change management and risk control

Phase 4: Execute

Here you will deliver the transformation across the enterprise in an iterative fashion, gathering lessons along the way to adjust delivery.

The typical programme execution activities we execute during this phase include:

• Programme management and change delivery
• Communication and training
• Project control and assurance
• Implementation monitoring and feedback loops

Phase 5: Adapt

Data strategy programmes are never a straight line – there are always twists and turns along the way.

We recommend adding an adapt phase that can flex to meet the shifting demands of the business and the many changes, opportunities, and obstacles your data strategy programme will invariably encounter.

You can sustain the delivery of change in the organisation by embedding principles of continuous improvement aligned with key performance indicators, such as:

• Continual monitoring and adaptation of implementation
• Transition and support for a sustainable future (not cut and run)
• Monitoring and assurance of delivery and benefits

Here are the most common challenges we have learned to expect and prepare for through our many years of building data strategies:

Understanding

Many stakeholders’ hopes and dreams (or their doom and gloom) will go into your data strategy. Not to mention their expectations for what outcomes the programme will deliver. So, when communicating, make sure you set realistic expectations.

The many misconceptions and misunderstandings about data will likely be your biggest people hurdle.  Data and analytics is a broad profession, so you’ll need to create a comprehensive education plan. A robust current state assessment can help, but you still have to deliver considerable educational resources and stakeholder sessions to build up the baseline of knowledge.

Engagement

This can manifest in several different ways:

1. You need to speak with stakeholders, but they aren’t available.
2. You want to build buy-in for the strategy but cannot get time with resistant individuals.
3. You need external support to build the strategy but cannot get the budget.

Even before the strategy development effort begins, there is a lot of groundwork to ensure you will get the right resources at the right time. One tactic we find successful is to chunk up the work (with our phased approach) and not move on to the next phase until you have the necessary engagement.

Demonstrating tangible ROI

Business case assumptions can be rock solid or flimsy as paper.

The data strategy capabilities you create are typically enablers of the wider organisation rather than drivers of increased revenue or massive cost reduction.  Be upfront about these challenges with stakeholders and focus on where better data and analytics can best drive financial improvement.

The implementation long haul

You will need to lay the groundwork of expectation that there will not be a quick fix, especially if your strategy is transformative.  Planning should look to define clear transition states, as these offer an opportunity for the organisation to adjust the roadmap and celebrate how far it has come.

Data Strategy Experts

Wherever you are on your data strategy journey, Oakland can help you make the next step.

We offer a range of customisable services which span the entire data strategy lifecycle.

You can choose to engage us at specific points on your journey, or we can partner with you through the entire process of defining and implementing your data strategy.

1. Diagnostic services

An effective data strategy design must have a clear picture of the current state of an organisation’s data capability.

Our diagnostic services are designed to carefully analyse and summarise the current state of data at your organisation.

These services are available as part of the ‘Discover’ phase of a wider data strategy design or as standalone advisory services:

1. Data Maturity Assessment. Using Oakland’s detailed data capability framework we can provide a complete evaluation of your current state data capabilities Summarised in a detailed report, we provide maturity scores and supporting evidence as well as a clear assessment of whether your current capabilities are fit for purpose to serve your expected organisational needs.
2. Data Culture Assessment: using our detailed data culture framework, we take a detailed look at the wider environment and ecosystem for data in your business. This is about understanding the beliefs, skills and decision making styles that exist in your company and considering their fitness-for-purpose in the context of your strategic objectives. We’ll translate that analysis into a vision and supporting action plan to drive a data-driven culture in your organisation.
3. Data Strategy Review: if your organisation already has a current or planned data strategy, our experts will provide a complete end-to-end review of your current data strategy to highlight gaps, opportunities and implementation risks. These reviews are benchmarked, peer-reviewed and assessed against our suite of data frameworks and delivery tools as well as years of experience and industry best practices.

It’s important that diagnostic service(s) are tailored to your needs. These services can be bought individually, in combination with each other or even as a complement to other focused diagnostic studies (e.g. a data platform assessment).

2. Data strategy design and implementation

If you need us, we’ll support you throughout the discovery, definition, design, implementation and monitoring of your data strategy. Or, if you’d prefer, our experts will help you flesh out specific parts of your data strategy: for example, we can help you craft a compelling strategic vision for data to mobilise investment, or build out a strategic roadmap to support your existing strategy.

We can tailor scope, timelines and team composition to your needs because we know that every organisation is different. Our experts put decades of experience at your disposal, alongside the powerful frameworks we’ve shared in this guide.

3. Target Operating Model (TOM)

Without a supporting operating model design, data strategies can struggle to deliver sustainable change as long term BAU.

Not all “operating models” are created equal. When we say TOM, we mean much more than drawing up some organisational charts or dashing off a quick architecture sketch. It’s about defining the fundamental way in which your data team(s) deliver value to the organisation and the capabilities you need to do that. Using tried and tested frameworks and delivery tools, our experts can support your data strategy with complete end-to-end design and implementation of a TOM to make your data strategy an organisational reality.

4. Strategic advisory

For clients who want access to insights and expertise in data leadership and strategy outside of creating or refreshing their data strategy, we offer a flexible strategic advisory service which puts our experts at your disposal for as long as you need them. Whatever your needs, our experts will still bring the tools, frameworks and experience that they would to any other data strategy engagement.

Yorkshire Water

Enterprise Data Transformation

The challenge

Yorkshire Water has historically faced similar pressures and demands to many other utilities, such as aging infrastructure, cost pressures and high demand for reliability in their core services.

What they needed

Yorkshire Water has long realised that data is a core asset in helping to address the above demands. This need to utilise data assets to help exploit and capitalise their physical assets has led Yorkshire Water to undertake a complex, multi-year data transformation.

What we did

Based on our track record of successful delivery, Yorkshire Water has invited Oakland into a minimum one-year agreement help them to plan and execute across a broad set of areas within their data transformation:

1. Data strategy evolution and enterprise-wide Target Operating Model (TOM) for data and analytics.
2. Defining and implementing an enterprise-wide data governance framework and associated capability.
3. Extending and evolving Yorkshire Water enterprise architecture to help support and deliver enterprise-wide data analytics.
4. Provide architectural, engineering and data science capability to build a telemetry platform and associated predictive models to forecast and predict leakage.

The results

Oakland is busy delivering critical milestones against each of the data transformation streams in what has been a cultural sea change for data within Yorkshire Water, as Andy Crossley explains:

“A lot of the earlier data work undertaken within Yorkshire Water was tactical, but as a result of the data transformation journey, the internal business, data and technical communities have rightly recognised that Yorkshire Water urgently needed a more mature, strategic view of data.

The data transformation streams are rapidly delivering the mature data fundamentals critical to the growth of an innovative and progressive information-driven enterprise.”

–Andy Crossley, Oakland Group Director

Initiatives such as data quality improvement, data governance and enterprise data architecture, not to mention the strategic data platforms and data science solutions, are transforming data capabilities into positive business outcomes for Yorkshire Water.

Infrastructure management, cost control, and reliability performance have benefitted dramatically from the results of the data transformation program, with data enablement activities helping to drive an overall business transformation ROI of £150m+.

Kaplan

Improving Data Capabilities

The challenge

Seeking to quickly build up a new Apprenticeship business to complement their existing offering, our client faced a pressure on data, from entry to usage. To achieve their business aims, the client acknowledged the need to improve their data capabilities to allow them to:

• Combine more data from siloed specialised systems
• Clarify sources of truth and define a common language and glossaries for their data
• Improve data quality and control
• Manage data demands

Before Oakland engaged, existing discussions were focusing on systems such as: “we need a CRM”. We helped our client to look through the lens of data: what do we have? Where’s it stored? Can we use it? What capabilities do we have?

What we did

Phase 1: Data Discovery

Oakland started by helping the client with a comprehensive assessment of the current situation:

• Mapping existing data alongside business capabilities
• Rapid data capability assessment: using our Oakland maturity model, we positioned each capability against an as-is baseline and quickly assessed data quality for the largest critical data sets.

This as-is picture was based on interviews and reviews with stakeholders across departments. From this current picture, we derived a set of themes to explore. Hosting a workshop per theme, such as ‘Data Entry’, ‘Data Architecture’, ‘Data Governance’, or ‘Data Offense’ we raised awareness, assessed needs and set shared priorities

Through these engagements, we did not just shape the vision, we also created a strong consensus between team members. We were then ready to move in to Design and Plan activity for the future data strategy.

Phase 2: Data Strategy Design and Planning

It was clear that the data strategy had to be comprehensive. But before going in the details of what a target should look like, we wanted to be led by value.

In a joint working session, we worked as a team to define the expected results and contribution over multiple time horizons and business outcomes.

Then, for each strategic theme there was a phase of deep dive investigation, options identification, and creation of recommendations. As we progressed on each topic, we constantly tailored principles with our client: dropping elements that were not a priority and aligning the roadmap with the sense of urgency and appetite for investment.

At this stage, we identified and addressed all the little things that makes a strategy a success. Elements such as business glossary templates, roles description, steering terms of reference, or illustrative processes were shared to make the strategy real. With clarity in mind, the client identified quickly which model was the most appropriate for them – a centralised model with domain driven data governance.

The results

Following our strategy engagement, our client now has a vision, a target and a plan. Just as importantly, the client gained understanding and knowledge of how to work with data and what are data capabilities required. With the key results in sight, there is a shared understanding of what can be achieved, which defined the level of capabilities and investment required for our client to fully exploit their data.

Crucially, the Data Strategy has been approved by our client’s CEO and board, with the recruitment of new roles and the implementation of strategic initiatives to make this strategy a reality now underway.

Creating a unified vision

Having a data strategy allows you to unite and catalyse activity behind a single, compelling vision for how data can be leveraged to meet your organisational goals.

Yes, it’s hard, but it’s worth it.

Here’s some final pointers to consider before moving forward on your data strategy journey:

Collaborate, collaborate, collaborate: your data strategy should reflect the goals and ideas of your entire organisation. Fundamentally, it’s a shared document that sets out how the organisation plans to use one of its most important assets to achieve its critical goals. Working collaboratively and giving your stakeholders a chance to shape what’s emerging will really pay back in terms of engagement.

Take your time: the opportunity you’re seeking is huge, but a good data strategy needs time and space to emerge. A poorly crafted strategy will do more harm than good. It will use up finite patience and sow confusion. So, make sure you have the time and resources ready before committing.

Focus on what your company needs: there’s a lot of people who will tell you (usually from the safety of the internet) that they have the one big idea that will solve all your data problems. Our advice would be: avoid those people like the plague. All organisations are unique, and all data strategies should be unique to their host organisations. Focus on what you know about your organisation and where you need to get to, not trying to bend the facts to fit a pre-conceived silver bullet.

Keep it real: writing a data strategy can seem like a tall order. It makes some people think they must come up with something startlingly original or devastatingly abstract. Well, the good news is you don’t have to do that at all. Unless you are working at the absolute cutting edge, then most of the ideas you need are already out there. The key is to select the right concepts and approaches and stitch them together into a well-grounded plan than can win organisational support.

Keep the value flowing: stakeholder patience is finite. You won’t get 3 years to come up with the right answer and build it. Engagement and momentum are the fuel of data-led transformation. So, finding ways to deliver value along the journey of transformation is crucial.

The post How to write your data strategy appeared first on Oakland.

Introduction

Chances are, you’re reading this guide because you’ve been asked to launch, support or accelerate some form of data governance initiative.

Congratulations – you’re embarking on a worthy endeavour.

Here at Oakland, our Data Governance team have trodden a similar path as we helped guide our clients across multiple data governance implementations. The results have been profoundly beneficial; often in ways the organisation had never considered at the outset.

Having delivered a number of large data governance programs, we’re in the fortunate position to look in the rear-view mirror and spot those critical activities that held the key to success.

We’ve curated those learnings into this guide to provide a ‘warts and all’ account of what you can expect as you embark on your own data governance journey.

Finally, as you’ve no doubt experienced, every data initiative presents its own distinct challenges. If you would like to discuss how our approach could help your organisation deliver a more successful data initiative, please book a discovery call to discuss further.

What motivates the next generation of data governance stakeholders?

At Oakland, we typically see the demand for an enterprise-wide data governance process initiated as part of a broader data foundation or enterprise-wide digital transformation. Many clients are keen to embark on their data journey in a quest for monetisation and the creation of new opportunities through innovation.

There is a growing realisation that better enterprise data leads to increased business performance, smarter decision-making and significantly reduced risk. As a result of these strategic triggers, we increasingly find executive stakeholders reaching out for guidance.

Making enterprise data management changes centrally in these organisations can be a challenge because they are often organised around different segments or geographies. There is also a lot of autonomy in large organisations, so to make changes centrally you’ve got to be business-led.

People have to see the value in the changes you want to make.

The data governance process: ‘What vs How’

If you’ve spent time sifting through the vast amount of books, articles, webinars and frameworks on the topic of data governance, you’ll soon realise that the ‘whats’ of data governance are reasonably well understood and readily available. Most organisations we speak to know what is required to get the basics of data governance in place.

However, particularly with larger, federated businesses, the translation from the ‘what’ into the ‘how’ is where many data governance programs begin to struggle.

This question of how to deliver data governance in a way that engages and motivates the business, whilst simultaneously laying a future data governance foundation, is a challenge we’ve obsessed over for many years at Oakland.

It’s through this obsession that led us to the realisation that many business cases are not positioned correctly to launch a successful platform for data governance.

Re-thinking the data governance business case

In the early beginnings of our data governance practice, we would collaborate with clients to build defensible business cases for data governance, often as a request to help construct a large data transformation program.

The data management fundamentals

Large-scale data and digital transformation programs typically incorporate a broad swathe of data fundamentals such as:

• Data Strategy
• Master Data Management
• Data Analytics (Business Intelligence, Data Warehouse, Data Lakes)
• Data Quality Management
• Metadata Management and Data Lineage Data Stewardship
• Data Protection and Regulatory Compliance
• Big Data

If you’re embarking on a similar data transformation, there are comprehensive frameworks such as DAMA DMBOKv2 that provide a comprehensive ‘what’ list of data elements required for the ‘mega-transformation’ projects.

Whilst it seems logical to combine data governance with all the other elements of a large-scale data transformation journey, you will often bump into challenges when constructing the data governance business case and getting it approved.

The four pillars of a successful data governance business case

To address the previous challenges, we discovered that a more pragmatic approach is to build a data governance business case with a simpler, four pillar approach:

Limit the early investment

Plan a modest level of investment, resisting the urge to build an ‘all-encompassing’ data governance program

Focus on value creation

Focus on value creation – deliver a series of tactical ‘Lighthouse Projects’ that create value for the business

Fly under the radar

Gradually establish your data governance program and enabling activity in ‘stealth mode’

Lock in the gains

Create the ‘foundational cornerstones’ for Data Governance as each Lighthouse Project is delivered

Launching your data governance framework with stealth: Applying the ‘Lighthouse Project’ approach

When we switched to helping clients implement data governance through ‘Lighthouse Projects’, we found that executives were more likely to get on board when they were investing in quick wins that delivered faster returns, as opposed to much larger data governance programs that often grapple for attention amongst so many other transformation programs.

Larger data governance programs often run into additional problems once they get past the business case phase, such as:

• Interest stagnation: Too much emphasis is placed on building the data governance foundation, at the expense of solving problems that are attractive to the business.
• Lack of ownership: Data cuts across organisational boundaries, so this requires new data ownership and accountability structures that are often lacking from data governance initiatives.
• Hidden data risks: It can be challenging figuring out where the risks are within your present data landscape, and which ones require attention.
• Lack of adaptation: You can’t copy and paste a data governance framework in the hope that ‘one-size-fits-all’. Instead, you need to adapt your implementation with a mix of different tactics over the long-term.

Through the delivery of high-value Lighthouse Projects, the foundations of your data governance framework can start to be deployed, but without the burden and resistance associated with a much larger program.

Setting up your data governance strategy for success

We typically aim to apply a 3-stage process when our data governance team works with clients:

• Stage 1: Set the Direction (Defining the Data Governance Strategy)
• Stage 2: Plan the Execution (Implement the Data Governance Strategy)
• Stage 3: Executing your Lighthouse Projects (Proofs of Concept)

Stage 1: Set the Direction (Defining the Data Governance Strategy)

The best approaches to data governance and the ‘exploitation’ of data must first seek to capture ‘management intent’. Once this is understood, you can then ‘smuggle’ data governance into the business using projects that engage and excite people whilst laying the necessary real-world data governance policies and supporting structures, such as data stewardship and accountability frameworks for data stakeholders.

Technical skills and tools are important but should support, not lead, your data governance strategy. For example, a data governance tool is meaningless if you don’t have committed and trained group of data stewards capable of building out a corporate data stewardship capability.

Key Dimensions of Data Governance Strategy Readiness

There are three key capability dimensions that will provide the focus for defining the data governance strategy:

Business Capabilities:

Understand current business priorities for data and test coherence through policies, governance and leadership structures.

Operational Capabilities

Sample key processes and data lifecycles to determine alignment with key data management principles. Gather evidence of operational behaviours, both positive and negative.

Data Capabilities:

Understand the data architecture and its footprint, together with any existing data management capabilities (e.g. tooling for data quality, master data management, metadata, data maturity metrics, data dictionary of data assets, data definitions completeness, data domains under governance, data owners assigned to existing data stewardship controls etc.)

Stage 2: Plan the Execution (Implement the Data Governance Strategy)

By applying some intentional planning in a multi-faceted approach, you will be able to:

• Balance the various leadership (and political) drivers, whilst establishing clear ownership of the key levers of change (e.g. financial approvals)
• Manage the narrative, particularly around the tendency for inflated expectations that are so often associated with large data initiatives Curate and support your portfolio of ‘Lighthouse Projects’ (POCs) on two-fronts:
  • Business outcome success stories that everyone can get excited about
  • Targeted interventions to address areas of poor practice/high risk (e.g. regulatory compliance and data protection weaknesses)
• Drive the necessary data governance policy, standards, frameworks and overall data governance process, that support the strategic direction of travel

Inputs to Stage 2

To prepare your Stage 2 planning activity, you will typically require various supporting documentation and insights such as:

Existing data strategy and data foundation plans

The known ‘whats’ of a preferred data governance process/strategy Previous data maturity assessments, baseline reviews and data audits Any other supporting documentation

Preparing the Data Governance Strategy Plan

During this phase, we would typically expect to carry out the following activities:

• In-depth interviews: Exploring possible root-causes of issues and areas of opportunity
• Landscape review: Investigate known data-related initiatives to identify focus areas and avenues for collaboration/harmonisation
• Deep dives and sampling: Reviewing the key data lifecycles and data management processes to identify good/poor practices as a reference point Data management maturity review: Provide a baseline data maturity assessment through quantitative and qualitative metrics, covering all aspects of people, process, systems, technology and data governance
• Central capability review: You will need to review the available capacity and capability of your central data function against their ability to deliver on your vision for data.

Following this initial preparation work, you will have some clear indication of where the major gaps, opportunities and readiness exists within your current data landscape.

Creating the ‘Commissioning Document’

At Oakland, we recommend creating a ‘commissioning document’ that aligns senior management and various stakeholders around a set of common goals and plan to move forward.

This document is likely to include:

• Components of the overall data governance strategy with an order and intensity in which they should be progressed
• Assessment of the central support required (e.g. additional comms, policy development, standards)
• Identification of any technical needs, beyond the POCs (e.g. Master Data Management (MDM), Data Quality, Metadata and Data Dictionary for Data Definitions and Data Lineage etc.)
• Key delivery risks and issues

The plan needs to be resilient enough to cope with any organisational or leadership changes, and coordinated in a way that is most likely to succeed in the medium-term.

Typically, a core team will continue to support delivery of the plan as it rolls on into future phases.

Stage 3: Executing your Lighthouse Projects (Proofs of Concept)

For this stage, we’re going to focus on the process of launching ‘Lighthouse Projects’ because we find that when executed correctly, they form the driving force for an effective data governance strategy.

Lighthouse Projects Proof of Concepts (POCs) form a crucial component of your overall data governance strategy as they engage the business in tactical improvements that capture attention. These improvements create success stories that build momentum, reduce risk and establish confidence in your approach.

Overcoming resistance to data management change

Many organisations suffer from ‘change fatigue’.

When starting out, you’re going to be ‘smuggling’ data governance into the organisation so make sure your project’s mission is 100% focused on the ‘pains and gains’ that those in the business already recognise.

The first rule of Data Governance – don’t talk about Data Governance

You don’t always want to label your first Lighthouse Project as a ‘Data Governance Initiative’ because you’ll often face enough resistance as it is, without introducing a phrase that many have never heard before!

For example, with one client, we selected a project that addressed an asset data quality headache that had been frustrating the business for some time.

We could have adopted a top-down approach and waited to build out accountability structures for data governance, create data quality assessment frameworks, train the business in data literacy and select a team of data stewards to manage the information chain.

Instead, we addressed the root-cause of why the data asset system was creating poor data quality.

We then introduced a system for onboarding data stewards, discovering metadata, defining data quality metrics and controls – all the elements of a data governance capability, but in a much shorter timeframe than if we had waited for a full data governance program to be rolled out

Which data management improvements should you consider for a Lighthouse Project?

When considering the monetisation opportunities of your Lighthouse Projects, we recommend ‘two waves of attack’.

Wave 1: Initial assessment against an agreed project selection criterion

This phase should identify a prioritised list of candidate POCs drawing on the selection criteria previously discussed. For example, you may collapse a list of candidate projects from 20 down to 5 possible options.

Sample criteria and considerations for Lighthouse Project selection

Every organisation has scope for data improvement, so your initial challenge will be determining which Lighthouse Projects should be candidates for consideration.

Having delivered many of these projects, we’ve found the following advice to be a good starting point for selecting the best data management/business improvement projects to kick off your strategy of ‘data governance by stealth’:

1. Identify which projects are most likely to succeed – reduce the scope rather than fail an overly ambitious target
2. Clearly define the scope – understand the specifics of the business problem being solved and its technical / data viability
3. Focus on business-led projects – look for projects that rely on active contribution from the business
4. Look for balance across the POCs – shortlist a ‘project portfolio’ that will nurture good ideas and opportunities, whilst tackling areas of poor data practice
5. Consider what happens at the end of the POC – is the purpose to prove viability (and perhaps discard afterwards) or build a fully supported solution?

Wave 2: Investigation of each candidate POC

In this phase, you will start by validating project feasibility and scope, before arriving at an attractive set of options for more detailed investigation that will be completed at the end of Wave 2.

Investigation focus areas

Each investigation will typically focus on areas such as:

• Use case definition
• Business processes Technology
• Data availability
• Data quality and associated metrics
• Risks and challenges for delivery
• Delivery planning

Upon completion, Wave 2 will select a focused set of projects that fully address several frustrating business problems. These projects will allow you to introduce key elements of your data governance roadmap as a side-benefit, but without the fanfare and resistance so often associated with a major data governance program.

Depending on the scale of each candidate project, it typically requires 10-20 days to complete the POC investigation.

What team will you need for POC investigation?

Within our data strategy and governance consulting practice, we typically deploy a core investigation team consisting of:

• Business translator (SME)
• Business analyst
• Technical data architect

The exact make-up of the investigation team needs to adapt to the specifics of your data, process, and technology landscape.

For example, some projects may be deeply technical in nature, requiring expertise in data interface accessibility, workflow automation and Big Data platforms.

Other times, the project requires ‘softer’ skills in the form of data literacy at the grassroots level, educating a team of stakeholders to take accountability for decision-making, or simply coordinating a team of data stewards to set more robust data definitions and assessment metrics for their data assets.

The key is to work with a data governance team who have the experience to address the complex and varied types of data challenges that typify the modern enterprise.

One challenge you’re likely to face is the issue of alignment of a data strategy to transition your first Lighthouse projects into a full-blown data governance capability. It’s not enough to deliver lots of successful Lighthouse projects, they still need to fit within a coordinated ‘master plan’.

The following diagram highlights some of the essential considerations when expanding Lighthouse projects into a broader data strategy and governance framework:

This approach relies on 3 vital building blocks that are essential to the longer term progress of your data strategy and governance roadmap:

1. Creating a business-led authority
2. Establishing your ‘Controlling Mind’ for data
3. Launching ‘Data Management-Enabling’ projects

Building Block #1: Creating a business-led authority

In this phase, you will be establishing the subordinate organisation required to manage the accountabilities for delivering the anticipated outcomes of your data governance initiative.

Key activities are setting up the coordination, assurance and ownership of data domains within each directorate across the organisation.

In particular, you’ll need to train the business on what it means to be the accountable owner/steward of a data domain, particularly with data domains that span multiple departments and functions within the company.

Building Block #2: Establishing your ‘Controlling Mind’ for data

As you deliver more Lighthouse Projects, you’ll develop the need for a ‘Controlling Mind’ to help coordinate and operationalise your ongoing data- related activities so that your data is treated as an asset.

For example, in order to retain the necessary skills, you’ll be looking to transition the required data capabilities as each Lighthouse Project is completed.

There are three specific requirements for building a ‘Controlling Mind’ and the required supporting capability:

• Setting the Direction: Defining a data/governance strategy and ensuring alignment with relevant strategic initiatives.
• Controlling/Assuring Data Assets: Managing the availability and quality of your data, including its integrity and security. Included in this step is the development of your core data governance processes, including relevant metrics and measurement of key behaviours.
• Building the Capability: Depending on the preferred deployment model, you will develop a Centre of Excellence (CoE), offering the necessary training and support foundations.

Building Block #3: Launching ‘Data Management-Enabling’ Projects

As each Lighthouse Project gets completed, you’ll find yourself iteratively building deeper foundations of data management capability, albeit fully aligned to business outcomes.

By taking the outcome-driven approach, you can advance data maturity and data capability one project at a time, but in alignment with any goals for your other areas of focus such as:

• Data Capability Target Operating Model (ToM)
• Data Architecture
• Technology Strategy
• Enterprise Data Strategy

Success depends on getting the business engaged and excited about the art of the possible as each ‘slice’ of data capability increases in maturity following each project win.

When the business experiences the positive change resulting from data, they start to drive and take ownership of the ‘data conversation’, which is preferable to relying on IT and technical teams to guide the way.

We’ve found this Agile approach delivers far more impact against strategic and tactical drivers whilst incrementally building out the key elements of a progressive Chief Data Office (CDO) function.

The diagram below illustrates how, with a recent client, we iteratively rolled out layers of data maturity and function, by executing one ‘enabling project’ at a time:

The key to this approach is to assure the completion of the most critical projects, i.e. those with executive attention.

How do we overcome a perception that data governance means more paperwork and slower projects?

One of the biggest challenges faced with data governance is the negative association people have with ‘governance’ in general.

Many people assume that governance slows projects and initiatives down, ‘gumming up the works’ with bureaucracy and unnecessary controls, stifling creativity with endless approvals and administrative paperwork.

The reality couldn’t be any more different in fact.

Let’s take an example of managing data lineage across critical data assets, a common data governance activity.

Demanding that all projects have their data lineage documented prior to release, may feel like a tedious, unnecessary task. And you’d be right.

Fully documented data lineage is an end-goal for data governance, but we don’t start with a mandated policy at the beginning, that would be seen as draconian, and as a result no-one would follow it.

Instead, we adopt data lineage on a needs basis. When carrying out a Lighthouse Project, we begin to document the data lineage as we go, thus ensuring that the next project which needs our data has a 100% complete and accurate of where our data was sourced from and which parts of the business it now services.

With each Lighthouse Project completed, another piece of the data lineage puzzle is completed.

If resource is scarce, you can even prioritise the most critical data assets and ignore the rest (for now). The key here is to get started and demonstrate the benefits.

Having a reference source of data lineage provides huge value to the business:

• Data migrations and system consolidations take far less time
• Regulatory audits quickly find the data they need
• Customer data becomes accessible for subject access requests
• Management information become more trusted
• Defects are more easily identified and resolved

There are hundreds of benefits that accrue from robust data lineage, but you have to start gradually, winning support with each Lighthouse Project that you deliver.

Finally, remember that the role of data governance is not to deploy ‘the data police’ and inhibit projects. The goal should be to accelerate projects, improve their outcomes and reduce their costs. To achieve this, look at where your organisation currently struggles with its projects and identify which Data Governance elements could be deployed to turn things around.

When you do this, you’ll find it much easier to get buy-in for future data governance support.

How do we find the right data owners and stakeholders to support our data governance initiative?

Firstly, you need look at the lifecycle of your most important data.

Where is your data created? What systems and applications does the data originate from?

Think about who is responsible for the data management processes.

The starting point for your data will give you clues as to which teams and departments need to be considered for data ownership. From there, simply scale the management tree until you find a suitable candidate.

And don’t forget the business stakeholders, because ultimately data governance should be driving positive business outcomes.

Assigning data owners is critical. If you don’t have an owner for your data it’s very difficult to reach agreement on what needs to get done, and who should do it.

How do we determine the most critical data elements to include in a data governance program?

‘Where do we start’ is a common challenge we hear from our customers. There are typically thousands of data elements that are critical to any organisation, so boiling them down to the critical few can be a challenge.

As you’ve learned in this guide, at Oakland, our preference is to embark on Lighthouse Projects. These are small, benefits-focused, Proofs of Concept initiatives that tackle a specific problem. As we’re solving the problem, we begin to build the foundations of data governance.

These Lighthouse Projects drive the scope around what Critical Data Elements need to be included, but we often look for other signals and indicators that a data element is critical to the organisation.

For example, some of our clients have a master issues log. This is a register of all the most serious data issues within the organisation and the underlying data sets that are contributing to the problem.

Examining regulatory requirements can also surface the most important data elements for your data governance initiative.

Other examples can include key data that feeds into senior management reports or other important decision-making resources. For example, some organisations build complex financial models that rely on underlyling data, so it makes sense to include this foundational data in scope for data governance and data quality assurance.

Building out a data governance program for the first time is challenging.

Our recommendation is to first gain an overview of the ‘whats’ of data governance and then seek an experienced implementation partner who can guide you through the process of delivering smaller, business-driven, ‘Lighthouse Projects’ that deliver incremental data governance milestones.

Don’t be taken in by the industry trend of building out all- encompassing data governance frameworks until you’ve at least demonstrated repeated value from lightweight data improvement projects that help you ‘smuggle’ in the fundamentals of data governance.

You’ll find that when you come to expand your reach with data governance, you already have a captive executive audience who are primed and eager to support your continued success.

The post Data Governance by Stealth: The Lighthouse Concept appeared first on Oakland.

Introduction

Data Platforms are EVERYWHERE but which is right for you? When Gartner names one of its top strategic trends for 2022 as Cloud-Native Platforms you know this isn’t a trend that is here today and gone tomorrow.

By 2025, cloud-native platforms will serve as the foundation for more than 95% of new digital initiatives – up from less than 40% in 2021.

At Oakland we know a thing or two about data platforms. Having built many different variations of them for numerous clients we now have a solid framework for efficiently creating or enhancing your data capabilities.

We have created this guide to provide practical and pragmatic advice so you can see what it takes to deliver a modern data platform, complete with patterns of success to follow and common pitfalls to avoid. Our underlying philosophy is that focus must target the critical data capabilities rather than worry about technologies alone. We’ll come to this later.

It is impossible to deliver technical projects by technology alone. Your data platform must be delivered as part of a wider change programme where people and process are equally as important as the tech.

From our experience each project is completely unique and challenging. If you would like to discuss how you can deliver a successful data platform project, please book a discovery call to find out more.

Cloud-native platforms are an essential tool to help accelerate the execution of enterprises digitisation plans over the next 2-3 years. They are essential because improved access to cloud services enable the introduction of modern technologies with less operational burden than with legacy systems. They will expedite and facilitate the creation of innovative business solutions.

Adopting cloud-native platforms will provide the primary means for enterprises to execute their digital strategies. Thus enabling business growth, customer retention and efficiency.

Source: Gartner

In very simple terms a data platform enables data access, governance, delivery, and security. It brings together the technology needed to collect, transform, unify and govern the data you need to support users, applications, models and data products.

In today’s world, it also needs to be cost-effective, highly scalable, and have security designed in from the outset. It will also need to consider how you will enable the ability to ingest data from multiple sources (including other data platforms) and be flexible enough to deal with system changes in the future. Your data platform architecture therefore needs to consider what data models you will need to support your business outcomes – but we’ll come onto that later.

The relentless pace of advancements in technology has introduced many solutions that claim to be the latest tool to solve everything. They have their uses, but alone will not be a silver bullet, and your data platform has to keep up.

We believe a successful data platform can only be achieved where cloud capabilities are complemented with an aligned data governance approach. Without this component, too often issues of the past leading to poor data quality, availability, and capitalisation, raise their heads. This leads to both the existing performance of the organisation being impacted and future growth opportunities not being exploited.

Moreover any data platform has to directly target the capabilities an organisation needs, for example, a highly complex organisation with many data sources will need data conformity as a critical capability.

Here at Oakland, we see a data platform as a layered set of capabilities that build on each other to enable organisations to realise value using known quality data managed through governance processes, empowering confidence in data and decision making.

We believe any modern day data platform should be cloud-native.

By this we mean: Cloud-native platforms enable organisations to deliver solutions that are scalable to the enterprise without being heavily reliant on managing the infrastructure that they run on. These platforms are sourced from various public cloud services (e.g. Amazon Web Services, Microsoft Azure, Google Cloud Platform) or can be created utilising software that can structure a fully private cloud environment for added security and control. Cloud-native platforms use core pieces of functionality such as container management, infrastructure-as-code, and serverless functions while allowing for teams to deliver through continuous integration and delivery pipelines. These platforms can work with other cloud tools, SaaS tools, or on-premise applications and offer a speedier alternative to some traditional on-premise solutions.

Through provision of all three layers (reference the diagram above) – infrastructure, services and governance – the true value of cloud can be realised by enabling core capabilities (shown in the diagram above, under the themes of Knowledge, Insight and Awareness).

When conceived correctly, consolidation and standardisation of your data should be a critical step to success (this is where MDM can become important – but that’s for another day). Being able to create data which conforms to a standard format, structure or logic, helps to calm and remove all the “noisy” data that many organisations have built up over time. These concepts sit at the heart of everything we do and should form the solid foundations from which your data products can be built.

As you seek to progress from knowledge to insight and awareness, the capabilities must evolve to meet your ambitions. The component parts of a data platform can be seen below.

Now consider this: when we attempt to build a house (data platform) we don’t just start laying bricks (ingesting data) – we need to know the measurements of the rooms (data subject areas), the layout of them (the data model(s)), and there needs to be adherence to building regulations (governance). This is how you design a data platform which delivers on your ambitions.

By 2025, cloud-native platforms will serve as the foundation for more than 95% of new digital initiatives — up from less than 40% in 2021.

Source: Gartner

The reason Oakland, and the rest of the planet, it seems, are so focused on building data platforms in the cloud is due to the many benefits provided over the more traditional, on-premise alternatives, particularly in the following areas:

• Reduced TCO: The overall cost of ownership for cloud data platform deployment and operation are, in our experience, considerably lower than most traditional on-premise implementations.
• Service resiliency and management: Managing a cloud data platform requires far less effort and complexity to meet spikes and rising demand while keeping the lights on.
• Speed and service agility: Our clients have experienced a considerable reduction in delivery timescales since using our cloud-based platform delivery process, leveraging our reusable cloud blueprint architectures and components over the years.
• Business model transformation/optimisation: Speed, cost and scale are some of the more apparent benefits, but our advice is not to ‘lift and shift’ what you’ve already been doing in your legacy data landscape and dump it on the cloud. Instead, consider the broader range of benefits the cloud affords.
• Cloud-native platforms assemble, develop, integrate and operate solutions that use the inherent capabilities of cloud computing, accelerating their transformation by making IT a core component of their strategies.
• Legacy application backlogs can be addressed and enhanced with modern technologies offered through cloud-native platforms, enabling organisations to react quickly, and thus be more competitive.
• The managed aspects of cloud-native platforms help shift IT resources towards value-added outcomes by reducing the infrastructure burden.

Before discussing the challenges and process of launching a data platform, let’s consider why you actually need it.

At its simplest you need a ‘platform’ to help your audience find its data and the data find its audience.

Most organisations that have a desire to get more from their data will at some point require a ‘data platform’ of some sort. This is driven by the need to solve these common challenges:

• Disparate ways of working or engaging, leading to additional costs and timeframes in delivery of solutions
• Difficult to access the data that is often spread across multiple systems and needs ‘stitching together’ to yield meaningful results
• Systems are often either too complex or complicated by legacy design meaning that manually ‘stitching together’ isn’t viable
• Confusing and cumbersome processes to engage IT and technology solutions
• Inability to influence the design and service architecture driving poor customer and colleague experience
• Lack of trust across data outputs – “my dashboard is right, yours is wrong”
• Skills and knowledge leading to increased technical and resource costs
• Data is unstructured, not commonly understood and then misused
• The need to centralise ‘data platform’ capabilities where the business has ‘bought the next shiny solution’

To survive and evolve in this technology-led world, you need to be able to change and adapt at a pace like never before, and this is relentless. You need to be all over your insights, you need to be able to get to them quickly, and you need to be able to present them in the right way.

A data platform fundamentally enables you to surface those insights and make the right decisions to transform your business. Your data is the raw material you need to introduce new services and products, wow your customers, and differentiate you from your competitors.

The modern enterprise data platform will allow you to…

• Improve data governance – Done right we feel the modern data platform should reduce the data governance debt
• Accelerate speed to value – access to accurate and timely data
• Simplify your data estate, enabling more people to get access to it (democratise some may say!)
• Centralise and standardise a very complex and disparate set of data sources
• Add the governance needed to ensure the trust and accuracy and the technology handles the timeliness and completeness
• Reduce the growing cost to support IT systems, replacing with resilient, future-proof, flexible solutions
• Improve ‘developer’ experience, delivering compliance in line with industry good practice
• Leveraging the data for future change
• Simplify reporting process ensuring your data products are easy to create

The diagram above represents many data and analytics capabilities coming together to unlock value from data assets in the organisation. A data platform alone does not create value but requires an intersection of process, people, and technology conceived around the platform to provide its actual value. Foundational elements of data ownership in the business set a baseline for platform success. Marrying up good governance with a strong data platform enables the data backbone for an organisation. Finally, building processes and organisational structures to deliver compelling data products to users is the realisation of what the platform aims to provide. Bringing these together is the basis for creating value from the data held within your organisation.

However, don’t believe all you read either – data platforms are not the holy grail to answer all your problems in one go.

For example: “If you ingest all of your data into a data platform technology (Databricks, Synapse, Snowflake etc) then you have a data platform” – not true!

Only when there is sufficient governance as well as standardisation, consolidation and conformity of the data to enterprise architectural standards, will the desired benefits come to fruition.

When considering your data platform, we recognise that most businesses operate from a ‘Brownfield’ state. They often already possess some of the resources and assets they need to implement a data platform. Your business case, therefore, needs to identify what you can reuse – be that people, processes, technology or partners.

But beware, even with these internal capabilities, many in-house teams struggle because they:

• Overlook the connecting ‘glue’ that pulls people, processes and technologies together
• Struggle to unlock the core data that cuts across the business and data landscape
• Try to adopt first principles instead of using established blueprints to accelerate and streamline the development process

You can learn from these observations by asking the following questions during your business case planning phase:

• What are some known data platform ‘prefabrications’ that will help cut development time and costs, whilst still delivering what we need in accordance with company data architecture, security and other related policies?
• Which business outcomes and functions are best served by the data platform, and what data will be required to meet those needs?
• What has to change for alignment across the business, data and technology communities critical to the delivery and ongoing success of the data platform?

Before we share our best thinking around data platform delivery, it’s worth shining a light on some of the challenges you can expect along the way so you can be well prepared. Introducing a modern data platform to the enterprise is not easy.

Challenge 1: Excessive Tech-Centric Focus

It’s easy to think of your data platform initiative as a technical project; after all, you’ll soon be designing, launching and modifying an expensive chunk of technology real estate.

But think back to the failed data management initiatives you’ve observed in past organisations – what did they have in common?

Chances are, they got bogged down in the tech at the expense of the business strategy.

Technical teams often prefer to solve technical problems rather than get involved in the messy business of persuading people with different objectives to collaborate.

The big risk in being overly tech-focused is that if your data platform does not meet user needs (such as data availability and data platform usability) they won’t use it. You will achieve minimal adoption and the platform will fail to deliver tangible business outcomes.

Your data platform aspirations should therefore form the ‘pointy end’ of a data strategy – it’s where the rubber hits the roadmap of your digital transformation.

Whenever you feel the narrative swinging too far over to the tech, bring it back with questions such as:

• What does this tech mean to our business model and value proposition?
• How will the technical direction impact our strategic goals?
•  How will the tech impact the customer experience?

Without this, you run the risk of low adoption, low involvement from business users, and ultimately low value delivered, if any.

Challenge 2: Departmental Data Silos

In an attempt to solve a tactical or near-term challenge, departments or cross-business functions can often be swayed by the allure of a solution vendor’s shiny offering. The department then commissions a localised solution that seemingly fits their needs but doesn’t take stock of the wider data strategy or needs of the business.

Other teams then struggle to extract this new data, particularly if the department has used off-the-shelf solutions.

The result is an ever-increasing technical burden that becomes difficult to unravel and migrate in the future.

Challenge 3: Poor Quality Data

One of the most common issues we have to deal with is poor quality data. Your data platform will only deliver your business goals if the data ingested is of a high enough quality, otherwise your platform could sink without a trace.

Challenge 4: Lack of Data Governance

Data governance is the specification of decision rights and an accountability framework to ensure the appropriate behaviour in the valuation, creation, consumption and control of data and analytics.

Source: Gartner

The demand for data governance originally emerged from the shift toward more robust regulatory controls in the banking and insurance sectors.

Today, data governance is pervasive across all industries; you can now even buy data governance platforms! Yet many data platform initiatives stutter or fail when data governance is immature or lacks key components suited to data platform strategy and management.

When data governance is missing from your data platform initiative, you create a situation where:

• Business and technical users lose trust in the data
• Designing and maintaining the platform takes far longer
• Frustrating ‘Turf wars’ over data ownership erupt or remain unresolved

To find out more about data governance, check out our guide: ‘How to launch a Data Governance Initiative by Stealth’.

Challenge 5: Failing to consider the complexity and cost implications of the legacy data landscape

Your data platform is not an island; it needs careful integration with existing systems and processes.

At Oakland, we’ve been around a long time (three decades and counting), and one of the recurring trends we’ve seen is the case of the ‘overoptimistic’ target vendor.

Despite the glossy marketing blurb, no data platform is a true plug and play solution. The amount of times we have heard vendors describe how their solution seamlessly integrates with existing tech but then can’t explain in any detail how.

Many aspects of vendor-lock are inevitable and using a vendors portfolio of cloud-native services increases lock-in although access to integrated services and increased discounts can be a plus. To prevent lock-in, (eg open source software solutions) make decisions on a case by case basis and vet the total cost of ownership (TCO of solutions intended).

Creating a new data platform into any enterprise requires a careful analysis of what approaches have gone before and now require direct integration with your new data architecture. (Overlooking the need for a robust data architecture is something we’ll cover in another guide).

We’ve parachuted into several data platform recoveries where the complexity of integration was overlooked and soon became the mother of all obstacles to going live.

In short, don’t overlook the essential brownfield discovery tasks that some vendors like to gloss over in their haste to get you over the finishing line.

Challenge 6: Not prioritising requirements

“So, what are we building again?” can become a common challenge as you get deeper into data platform delivery.

The problem is the modern data platform can support a plethora of use cases including:

• BI/MI capabilities
• Self-service reporting
• Integration
• Data catalogue
• Single source of truth e.g. single voice of the customer, employee, product, service or asset
• Executive or regulatory reporting
• Advanced analytics and AI
• Real time analytics
• Assessment management
• Adjunct to a core system

The list goes on and on…

It’s easy for your data platform to lack clarity and prioritisation around its core function, especially as different groups begin to see your platform as a data ‘dumping ground’. The practice of “let’s keep it in case we need it” can lead to a bloated data platform, further complicating the task of getting insights out of your data.

To prevent a toxic swamp of data, we prefer to phase the delivery of a data platform with a regular cycle of ‘Lighthouse Projects’ that solve burning issues within the business but still align to an overarching data strategy and architecture with a clear transition to an enterprise solution.

Start small, think big, and act fast.

You get to demonstrate the benefits of each release, garnering support as you deliver each successful project, helping to justify the investment of further initiatives.

Challenge 7: Creating the case for change

Creating a compelling business case for a modern data platform can be challenging for many organisations, particularly when faced with a legacy of delivery struggles.

As you’ll read later in this guide, there are multiple benefits and use cases for deploying the next generation of data platforms that will appeal to a range of leadership sponsors.

We’ve found that the key is to deliver smaller, faster pilot projects that deliver rapid and sustained gains without over-investment and risk, whilst building data capabilities at the same time.

What does the business really want to know? Where do they need insights?

By focusing on delivering the right data at the right time to support specific business outcomes, you’ll quickly gain support for the future of your data platform.

Over the years we’ve also assembled a portfolio of transformation stories that highlight the impact of data platform introduction, so feel free to reach out and learn more.

No guide to a data platform could be complete without a framework diagram, so here we go.

This is Oakland’s Enterprise Data Platform blueprint

At this stage we would assume the requirements for your data platform are now known and documented. So, where do we go from here… (consider this is from a technical value)

We believe there is a logical order to tackle your data platform delivery. Yes, every organisation is different, every challenge unique, all frought with idiosyncrasies and quirks, but all things considered, our 6 step approach provides a pragmatic guide from which you can start…

1. Catalogue sources

we know your data is available in your technical estate. A key activity to building a data platform is to gather a list of those data sources and what type of process they support and information they store.

2. Design output model

to get good value from a data platform the information in it must be presented in a way which can meet the needs of the platform – you do this by creating a data model. This way you can conform your source data to the data model providing a reliable confident information repository.

3. Define your data governance needs (lightweight vs hardcore)

like all strategies/methodologies and frameworks, data governance must be applied contextually. Failing to build data governance as a foundational component of the data platform effectively means you have an ungoverned data, source which damages the validity of data reducing it as a trustworthy source. Pull together your data owners, stewards, processes and standards. Work out what business change is needed to support your data governance approach. Decide on where you are going to start and what your governance road map looks like – remember don’t attempt to boil the ocean; data governance takes time, effort and iteration.

4. Consider tools, technology, and resources

(let’s get our implementation strategy nailed). Often data platforms lose their way because of technology, either because a technology dictates a way of working or the technology doesn’t do what you need without complex work arounds. We have found componentisation to offer a good way to navigate the technology minefield enabling compartmentation of capability and managed interfaces to ensure successful interoperation. Tools, technology and resource mean capability, capacity and supportability all critical elements of successful delivery and operation.

5. Build your capabilities

whilst iterating your use case achievements. Through the combined activities of understanding your sources, developing your data model, aligning data governance and establishing a technology/ way of working strategy, building the platform becomes both more predictable around co ordination of delivery but also from the perspective of what is delivered is of value and quality.

6. Realise value

with a repository of data presented in a structure (model), where the quality is known because of aligned data governance, you are enabled to provide multiple uses from this one source. Confident that your data is valid and consistent across all use cases.

Building a data platform will require you to assemble a varied cross-section of skills, technologies and leadership so the below highlights how we approach data platform service delivery.

When considering building an internal team, this can also be a useful cross-reference as you develop your approach.

As well as the above step-by-step guide, it can often help to leverage delivery blueprints that will accelerate the data platform delivery, inject some much-needed governance, and help accelerate any aspirations for a digital strategy in a shortened timeframe.

Data platform standard patterns are a simple way to speed up the overall delivery process and inject some repeatable architectures and delivery processes into your organisation.

Having delivered so many data platform initiatives, we’ve adopted a pragmatic view on the technologies and practices that consistently deliver rapid results and those that don’t.

We have developed standard patterns to integrate across many core architectures. As an example, we utilise patterns from within existing ecosystems as illustrated on the next page.

Oakland has standard patterns to integrate across many core architectures:

We relish the opportunity to help deliver data platform initiatives for new and existing clients.

We have remained technology agnostic for over 35 years and are able to develop the right solution regardless of your existing technology estate.

Those 35+ years operating across tech, data, people and process, have taught us how to navigate and bring together those functions in a more coherent way. BUT, not one size fits all – there is no silver bullet. Our playbook is tech, people and process (full E2E to get the change to stick). We offer an agile, tailored and flexible approach which is able to move at pace and change direction when required.

While a data platform is the basis of the technology investment you will be making for data in your organisation, it alone will not move the organisation forward. You must also invest in building capabilities in the data and analytics space that can leverage the platform to drive value. Your capability solution should give equal priority to the technology stack that you are developing, the data product management process, or the organisation of data stewards across functions. This will form the basis of lasting value delivery for the organisation, as you
can move forward with changes in technology more easily in the future while continuing to answer the compelling questions that are demanded of data within the organisation.

We help deliver the standard technical and delivery services you would expect from a premier partner:

• Assessing and developing operating models
• Creating a cloud data platform from scratch
• Data modelling / data architecture / data solution architecture
• Developing data functions
• Re-engineering and improving business processes reliant on the platform
• Developing the supporting ‘non-technical’ infrastructure to ensure long term success

We’ve been around long enough to know that delivering a modern data platform (with all the machine learning, automation and data science capabilities you demand) is pointless if your
organisation isn’t ready to embrace this level of change.

We’re not a mega-firm with bus loads of consultants and offshore delivery staff, because that model, in our experience, quickly becomes excessive and self-defeating. You need to deliver a data platform that fits your existing capabilities once the experts leave the building.

Delivery success lies in ownership engagement and transition

Our approach is to actively engage and transition the business/tech/data communities to take ownership of the platform at all levels through the following:

• Coaching client-side data and product teams to put in place the right operating model
• Enabling data and product teams with the right technology and development methodology to scale future product team resourcing and data processing demands
• Assuring the launch and long-term future of the data platform through data governance

Remember, we focus on value creation through three key delivery routes:

Role 1: Strategic Data Product Delivery

Responsive delivery of data products with high strategic value directly to end users

• High profile, high value data, reporting and analytics to serve enterprise needs
• Machine learning and AI
• Complex, high volume data or end to end data views

Role 2: Data Backbone

Underpin data-driven value delivery across the enterprise

• Strategic data assets for the enterprise, across use cases
• Incorporate data standards and deliver data of high quality
• Infrastructure and tooling for data manipulation across the enterprise

Role 3: Data Ownership

Business-driven data governance creating step change in quality and assurance

• Definition and ownership of clear standards, data lifecycle and data quality
• Coordination on rights for access to and management of data
• Consistent interpretation, usage and action based on insights delivered

Our clients feel comfortable and reassured enough to sustain the data platform once our partner role comes to a close.

Building out a data platform for the first time can be challenging, but so is moving from a first generation to a second generation…
Oakland can help in a number of ways:

• Data Platform Capability Assessment: Have you already got something, but it’s not delivering value?
• Data Platform Accelerator: you want to dip your toe in, don’t want to commit to a multi ££ programme, but need to demonstrate capability/ value quickly
• Strategic Data Platform Delivery Partner: you’re looking to deliver a large enterprise data platform programme and need a partner to design, deliver and run the programme
• Data Architecture Assurance: Is the enterprise data architecture fit for purpose? Do you need an independent view to confirm your plans and designs?

This guide has provided conceptual guidance and approaches, but there’s nothing better than seeing real world examples of this work in action. Here we explore some of the real-life challenges and positive outcomes our clients experienced during their data platform journey.

A water utility – Improve trust, insight and efficiency of projects and regulated reporting

Challenges

The existing reporting infrastructure impacted a water utility’s ability to deliver timely reports that met the needs of various finance, projects and regulatory stakeholders.
There were a variety of challenges to address:

Speed and value of insight:

Basic project data analytics and reporting had become slow and laborious, whilst advanced analytics was challenging.

Lack of easy accessibility:

Reporting teams were physically unable to reach many datasets. This lack of reach resulted in wasted resources, manually linking and merging disparate data to ensure each report was fit for purpose.

Portfolio complexity:

Legacy reporting practices had made it harder to manage the project portfolio and ensure project and regulatory milestones would be met, or at least flagged as a warning if they were drifting off-target.

Demand for standards:

The CIO had expanded a new data infrastructure that meant any new project analytics capabilities would need to conform to the revised company standards for data architecture.

Use Case:

They needed a way to maintain reporting quality but using a faster, more scalable and cost-effective system than ever before whilst meeting stringent company standards for architecture, performance and regulatory compliance.

Business Outcomes

There were many outcomes, but here are some of the most impactful:

• Reduced risk: They can now rapidly pinpoint potential capital projects failure points in a way it couldn’t achieve before.
• Innovation use case: The project delivered a defensible use case for implementing data solutions according to the corporate data architecture standard.
• Operational effectiveness: Project managers can run the business and projects more effectively than previously.
• Greater trust: The data platform has improved the confidence and relationship between key stakeholders and regulators.

Network Rail – Intelligent forecasting and analytics for complex capital projects

Challenges

Rail projects need to be delivered faster, at less cost and with greater value to the taxpayer than ever before.

In a bid to ‘put passengers first’ and create a more customer-focused, service-driven organisation, Network Rail devolved the delivery of capital projects to the regional level, which put pressure on the existing capital projects reporting platform:

Legacy obsolescence:

The existing business intelligence and forecasting technology used for Regional Capital Delivery reporting became dated and out of touch with the modern demands of complex engineering and infrastructure programs.

Lack of unified data strategy:

Whilst each region had its own reporting needs, Network Rail required coordinated governance of the data definitions, data structures and dictionaries across the group. Regions needed local autonomy but within the constraints of a common data strategy.

A shift in reporting models:

Network Rail had transformed its process for major engineering projects, from the GRIP (Governance for Railway Investment Projects) process to the new PACE (Project Acceleration in a Controlled Environment) approach. This step-change in reporting policy meant added pressure for the legacy data platform to service new capabilities.

Data Platform Use Case:

Develop an agile data platform supporting state-of-the-art intelligent forecasting and reporting, capable of rapidly adapting to the complex and changing needs of capital projects reporting across the entire business.

Data Platform Design:

Network Rail was keen to mature towards open technology and move away from ‘lock-ins’ with a particular vendor or supplier. As a result, Oakland opted for standard ‘off-the-shelf’ tools readily available in the marketplace, such as Microsoft Azure and DataBricks.

Business Outcomes

Total evolution in capital projects decision support within Network Rail. Here are just some of the benefits this project has delivered:

• Smarter decisions: Machine learning and advanced analytics tools were deployed, offering greater forecasting and decision analytics than previously possible, potentially saving millions of pounds. These insights are now just another information source that users can incorporate into their reports.
• Faster decisions: Reporting processes have been streamlined, resulting in faster, better quality decisions without the manual cost and ‘Excel-hell’ of the previous system.
• Capital savings: Intelligent project analytics has delivered far greater transparency into projects that have misallocated contingencies and can be flushed out of the portfolio roadmap, leading to the potential for substantial savings each year.
• Self-service: Each region can now ‘do their own thing’ and leverage multiple reporting levels, including static reports and custom reports (on static models). Users can execute ‘build your own models’, featuring data pipelines with curated data, giving them full customisation of data processing.

Income Analytics – Creating a catalyst for growth

Challenges

Income Analytics was established in 2019 by three entrepreneurs with a history of working in global real estate investment markets.

Their goal was to leverage twenty years of planning and development expertise to create an online platform that would enable real estate professionals, investors, owners and lenders to make better informed data-driven letting, investment and lending decisions.

The problem was their legacy (on-premise) data platform was creaking at the seams:

Outdated data processing:

Getting quality data into the system was time-consuming and clunky. Staff had to manually input client portfolio information and asset data, then integrate with the latest information from Dun & Bradstreet.

Ineffective customer experience:

Clients got weekly updates, so manual data validation was slow and laborious. When source data changed, the Income Analytics team had to email details of the change to clients manually.

Lack of visibility:

Many of Income Analytics’ clients had hundreds of assets, so they needed to quickly and visually access their information, putting added pressure on a cumbersome data operation behind the scenes.

Reduced morale

The laborious method of inputting data led to an overworked team which meant Income Analytics struggled to concentrate on growing the business.

Use Case:

The Income Analytics leadership team asked Oakland to re-architect their data platform by leveraging the Oakland data strategy, cloud engineering and process transformation expertise.

Data Platform Design:

We started with a small proof of concept that began transforming their complex legacy system into a modern application, leveraging a cloud architecture to create a platform for continued innovations such as machine learning and predictive analytics.

The following diagram explains our final design:

Business Outcomes:

Let’s explore some of the business outcomes from this design.

Faster automation drives more intelligent decision making

The Oakland-built data platform now provides real-time, continuous monitoring of various Dun and Bradstreet data sources such as:

• Tenant Data
• Company Data
• Historical Scores
• Parent Data
• Financial Data

With seamless integration and intelligent workflows, the data platform automatically feeds the source datasets directly into the Income Analytics service offering, a game-changer for a market that thrives on making quick decisions.

The result was a highly differentiated offering that delighted their customers and a great example of leveraging data platforms to create a competitive advantage, not just operational cost savings.

Delivering rapid and incremental commercial value

Building in the cloud meant Oakland could design a solution roadmap that ensured the business could benefit commercially from each significant release instead of waiting long periods to recoup their investment.

Leveraging the speed and agility of the cloud (with our cloud architecture blueprints) meant we could consistently hit operational milestones faster than anything we’ve experienced in a purely on-premise environment.

Driving down costs

Oakland designed a solution to leverage AWS serverless technology. This decision helped speed up the development process (and reduce technology
costs for the client).

This decision means the client only paid for the limited usage required during the build phase and now only pays for whatever resources they consume.

Hopefully, this guide has answered many of the questions you or your team have been wrestling with as you consider the many options associated with data platform implementation.

Finally, we’ve provided some final pointers to consider before moving forward on your data platform journey.

• Platforms are ecosystems: In effect, you’re building a series of integrated solutions that constitutes a ‘platform’ when complete. If you refer to our data platform case studies, you’ll notice we constructed each data platform with various underpinning applications, all requiring seamless integration.
• Mitigate hidden costs: When building a cloudbased data platform, the allure of reduced implementation and ownership costs is compelling. However, many unexpected costs can creep in and derail the success of your project. One of the ways we’ve found to reduce these costs at Oakland is to rely on our standard blueprints for data platform delivery and architecture. Don’t pay for consultancies to ‘learn the ropes’ of data platform implementation with your time and budget.
• Reimagine the customer and employee experience: Don’t just consider cost and speed improvements; think about how the cloud can transform every facet of the customer experience with your core services through a modern data platform.
• Go lean, go fast: Plan with the end in mind, but build an early prototype to validate expectations and offer something to the business to foster a clear vision and buy-in. Business models can adapt and shift quickly, so rapid delivery is vital for staying aligned.

• Success lies in transition: Building a data platform is only one part of the puzzle. You need the business to engage and buy into the platform’s goals. Think beyond technical transition. The success of your data platform stems from culture and behaviour change delivered through programs of coaching and training.
• Align with corporate standards: Don’t fly solo. Build a solution that meets the varied security, governance, quality and architectural standards of each governing body within your organisation because this will deliver far greater business value over time.
• Forget people at your peril: Don’t forget that successful adoption of your data platform will only happen if you’ve aligned the people and process elements. Using the platform and using it in the right way will often require a shift in mindset (get ready for a cultural change), structure (who’s going to own the DevOps team going forward!) and the introduction of new processes not only to manage the operation of the platform, but also in educating the business in how to make best use of the data products it produces.

As we always say there are no silver bullets and building a data platform can be a hugely challenging endeavour, but when you get it right the results are truly transformational.

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