Water Utilities x Open Data: The Conundrum 

While Open Data is all the talk in the water sector, it’s facing a conundrum. To demonstrate the real-world value of Open Data, utilities companies must first release enough of it for researchers, analysts, and the public to use. However, evidence of this value doesn’t exist yet, so securing internal buy-in to release data can be a challenge.

What’s the Value of Open Data in Water Utilities?

Open Data is still in the early stages of maturity across the sector, which makes it difficult to prove its value.

“It’s like Schrödinger’s cat but for data. There needs to be enough data available to see its value, but showing value is impossible without that data already being there.”

Zoe Shaughnessy, Business Analyst at Oakland

Value is hard to measure – but not impossible. It’s an ongoing process to understand what water utilities as a whole sees as ‘value’ in the data. Is it the number of visits to the Stream website? The number of clicks on datasets? 

“At this stage of data maturity process, it’s a work in progress. But by working with organisations like the Open Data Institute (ODI), we’re able to develop value frameworks aligned to KPIs to start showcasing the impact of the open water data initiative.”

Zoe

Challenges to Open Data in the Water Sector

Most of the major water companies in the UK are involved in Stream. It’s a strong turnout, but not without its challenges. Barriers to involvement in Open Data initiatives include:

• Resource constraints
• Costs
• Reputational concerns
• Worries about releasing incorrect data
• Data comparisons
• Wrong conclusions being drawn from the data by the press and public

There’s also the huge expectation of what’s needed from the data, ranging from its formatting to its accessibility. For instance, data may be technically ‘open’, but if it’s in a tricky-to-use format (e.g. a series of PDFs), it’s not the easiest to extract and analyse. 

One of the roles of data consultants, like Oakland Everything Data, is transforming data so it’s set up for success. From machine-readable tables to APIs, the easier the data is to access and analyse, the more value there is to derive from it.

How Open Data Could Improve Water Services

Like with any major change, there are challenges. But they’re massively outweighed by the opportunities sparked by the open water data initiative. These include the National Storm Overflow Hub, infrastructure planning, and public awareness. 

The National Storm Overflow Hub

In a world-first, near real-time discharge data for nearly 14,000 storm overflows in England is brought together in one interactive map. The hub enables:

• Users to see which storm overflows are discharging
• Water users (swimmers and kayakers, for instance) to make informed decisions about whether or not to enter the water
• Access to improvement plans for every single storm overflow
• Water companies to access support from an independent steering group (including the Environment Agency and Ofwat)

Pretty useful, if you ask us!

Infrastructure Planning 

Water company boundaries are now together in one public place, meaning households, developers, planners, and policymakers can view and access accurate, localised information to support decision-making. For example, the data can be used to:

• Check who supplies your water
• Plan new infrastructure
• Evaluate service performance

Public Awareness

During water shortages, high spikes in data usage occur. When the news is full of reports that reservoir levels are low, people have a tendency to do their own ‘sanity checks’. If the public has easier access to more water services data, their sense of ownership over it should grow.

Why Work with a Data Consultant on Your Open Data Strategy?

Look, we know that getting your data into a fit state for the public domain is daunting. But data fitness is our bread and butter. As a trusted data consultant for some of the UK’s biggest utilities companies, we’ve got a track record of turning raw data into structured datasets for open water data initiatives like Stream. 

From creating roadmaps and value frameworks to establishing rigorous sign-off procedures, we guide you through the whole process. Ultimately, we ensure your data is clean, accurate, and secure before it’s released to the public. 

How Water Companies Should Prepare Data for Open Data

So if you’re a water company preparing for the transition to Open Data, reach out to our team for personalised support. We’ve also detailed the critical steps you should take below to make the process as straightforward and rewarding as possible.

1. Put a strategic roadmap in place

Ad-hoc data sharing is to be avoided. Open Data success begins with a clear strategy and a release roadmap. Utilities should:

• Develop internal principles for how data will be accessed and used
• Create internal and external data hubs to manage the publication process
• Assess each potential dataset against a fixed set of criteria to avoid bias and ensure consistency

Discover our Data Strategy services.

2. Implement rigorous safe release protocols

One of the primary barriers to Open Data is the fear of reputational damage or the release of incorrect information. To mitigate these risks, utilities must build a robust sign-off process. We recommend one with:

• Cross-department sign-off

Data should move through stages involving legal, communications, customer service, and data owners.

• Data integrity

Before release, data must be cleaned, accurately formatted, and paired with comprehensive metadata.

• Contextual clarity

Providing metadata ensures the public and the press don’t draw incorrect or misleading conclusions from raw figures.

3. Prioritise machine-readable formats

The technical definition of ‘open’ is changing. While a series of PDFs may be ‘technically’ accessible, they offer little use for modern analysis. Transition from static documents to machine-readable tables and APIs, and ensure data is interoperable. This way, it can be easily ingested into tools like PowerBI or spreadsheets for immediate use by third parties (who’ll appreciate you for it).

Explore our Data Analytics & Insights services.

4. Align with regulatory and value frameworks

At the moment, Open Data participation is largely voluntary. But we know that regulators (e.g. Ofwat) already expect companies to publish strategies and show progress.

“Though it’s currently a voluntary requirement rather than an obligation (or fined requirement) for water utilities to provide Open Data, utilities should look to other sectors with mandated requirements in place – like energy – for a preview of potential future regulations.”

Zoe

Realising the Value of Open Data

To prove the impact of all the efforts outlined above, utilities can work with organisations like the ODI to develop value frameworks. When data publication is aligned with specific KPIs, companies can measure success – for instance, data downloads, API usage, and the development of public-facing tools (e.g. the National Storm Overflow Hub). From here, the bigger data picture can start to come together – and the true value of open water data realised.

For advice or support with anything Open Data, please get in touch with our team.

The post Open Data in Water Utilities: How to Set Data Up for Success appeared first on Oakland.

Defining your future is a strategy challenge, not a technology one

The session kicked off by talking about what it means to ‘define your future’. Joe stated that we’re fundamentally looking at data strategy – I.E. how an organisation should look to unlock value from data, automation, and AI. While technologies are more powerful and accessible than ever, value doesn’t emerge by chance.

It’s not enough to simply ‘get started’ with tools, because impact is rarely delivered without a clear plan. That’s where strategy comes in. It provides direction, prioritisation, and credibility, three elements at the core of moving any organisation forward. So wherever yours is on its maturity journey, value should be the primary driver.

“The context around defining your future is that we live in an incredibly exciting era in the field of data automation and AI. 

“We’ve never had more technologies available to us. We’ve never had more skills and awareness of this. There’s never been more opportunity. 

“But that comes with a double-edged sword – there’s also disruption. We know that our business models need to change and we need to bring our people along on that change curve. 

“So when I say ‘define your future’ I’m really talking about strategy – and how we drive value from data and AI.”

Joe Horgan, Principal Consultant – Data Strategy and Digital Transformation at Oakland Everything Data

Our artificial intelligence services

Value can’t be broadly defined – it’s context specific 

There’s no one size fits all or universal definition of value in data and AI. What matters depends on an organisation’s purpose, industry, and strategic goals. 

For instance, financial regulators focus on risk, harm prevention, service quality, and scale, whereas financial organisations prioritise fraud, risk, and customer insight. Universities emphasise student experience and research impact, which differs from the focus on asset optimisation, predictive maintenance, and digital twins in utilities.

How to make meaningful change for data and AI success

Rather than merely ‘aligning’ data and AI initiatives to business strategy, organisations should aim to actively drive strategy through data and AI. Increasingly, these capabilities are at the foundation of wider business activities, like digital transformation, customer experience, and operating models. But you can’t get these off the ground without two important elements: organisational outcomes, which require clarity and planning, and leadership and investment reality.

“Having a joined up plan to unlock value is one thing. Being able to communicate that outwards is crucial if you want to get the agenda moving at scale.”

Joe Horgan

Learn about our custom AI solutions

Remember: A complete data and AI strategy is more than a vision slide

The panellists went on to highlight one of the most common recurring failures in data and AI roadmapping: mistaking a high-level vision for a full strategy.

Organisations must account for several elements to define the future, including:

1. Vision – where the organisation wants to go and why, i.e. the value you think it’ll bring.
2. Case for change – the gaps and constraints the organisation has, which show why investment is needed.
3. Use cases – what are the tangible problems you can solve?
4. Strategy and roadmap – how you’re going to make this change happen, what will be delivered and when.
5. Target operating model – translating the strategy into reliability via people, processes, and technology.

Without this full answer, you risk losing momentum and confidence – fast.

Challenges are normal and to be expected

No organisation has ever started with perfect data or platforms – so don’t pressure yours to, either.

“I’ve never met an organisation who says ‘we are absolutely happy with every aspect of our data’. (And I’ve met literally hundreds!)” Joe Horgan

Joe spoke of a range of typical challenges encountered on the road to AI and data success, from data silos and fragmented systems to platform or infrastructure limitations, cost concerns (especially around cloud and GenAI), and data quality, security, and trust issues. 

The advice? It’s absolutely fine to have challenges. Just remember they aren’t failures, but the very reason to define a strategy. A clear baseline enables realistic expectations and justifies investment.

Strategic pitfalls to avoid for data and AI success

However, there are mistakes to be mindful of – ones that we’ve seen derail the success of a strategy, time and time again.

1. Unclear value – we’re talking vague benefits with no measurable outcomes rather than what metrics will move and what use cases we can solve, for example.
2. Fragmented strategies – ones that are disconnected from other initiatives involved in digital, operating models, or user experiences.
3. Partial answers – presenting a vision without a plan/substance behind it, like having a business case without a narrative.
4. ‘Jam tomorrow’ roadmaps – where technical leaders evangelise a strategy that loses sight of what the wider organisation is interested in.
5. Technology bias – strategies that read like shopping lists rather than transformation plans that account for people, processes, and use-cases.

If you take anything away from this webinar round-up, it should be this: successful strategies address people, process, culture, and experience, not just tools.

“Putting a comprehensive and compelling strategy together isn’t easy. That’s one of the reasons people turn to Oakland Everything Data for help.” Joe Horgan

Five principles for AI and data success

Joe moved on to translate what this looks like in real life and explain our approach to defining a data and AI future. It’s based on five principles:

1. Value first, value fast. Anchor everything in value and deliver early ‘reasons to believe’.
2. Build capabilities, not just solutions. Remember that sustainable value comes from people, process, and technology working together. If you want your strategy to stick and add value for the long run, think about the foundations and capabilities you’re building in the organisation.
3. Integrated transformation. We don’t give soloed answers and data and AI automation – they must connect with, and fit into, wider digital, customer, and organisational change.
4. Balanced delivery. Combine short-term value with long-term foundational improvement – AKA value first, and value fast.
5. Don’t forget the story. Data strategy is a storytelling challenge because we’re trying to explain the relevance of complex technical activities with fast-moving technologies in a jargon-heavy field. You need to spend time thinking about the best way to narrate this journey so it resonates beyond technical teams.

Causes of data and AI failure

The panel went on to talk about how taking a balanced approach is better than an extreme one. For instance, years-long ‘big bang’ programmes that are delivered too late, which leaves everyone fed up. Or endless proofs of concept that never actually influence business reality.

What you’re after is a more balanced approach, one that shows value in there here and now. When your people realise the value early on, it’s far easier to build momentum and get buy-in from stakeholders to gain the steady investment you need in core capabilities.

Real world experience from Softcat

The difference between poor, average, and great data strategies

So, how do you know if the data strategy you’ve been working on is actually set up for success? Joe stated that most organisations ‘do data, but only the great ones turn it into a competitive advantage that feels effortless to the end user’. He went on to share what differentiates a poor strategy from an exceptional one.

Poor data strategies tend to be very reactive to one problem. They’re siloed, low trust, spreadsheet-driven, and without much thought about long-term outcomes. They become better/’good’ with centralised platforms, better visibility, and some governance. But there’ll be uncertainty about next steps – a core differentiator between a ‘good’ and ‘great’ data strategy, which can be characterised with:

• Intuitive, trusted, and embedded data
• Data-literate users
• Decisions powered by AI and automation

Ultimately, a great strategy turns data into the engine of the business. (See how we designed a data strategy and roadmap for RAW Charging that empowered automation and scale, improved data culture, and drove timely, consolidated insights: RAW Charging case study.)

To wrap up

As the webinar came to a close, Joe Ryan, and James reiterated the key takeaways from the session on data and AI success. 

1. Data and AI are now core organisational capabilities, not optional or niche.
2. All data and AI decisions should be guided by value first and value fast.
3. A successful roadmap balances quick wins with long-term foundations.
4. Defining the future requires a complete, credible story, not just a vision.
5. The biggest risk is doing nothing. So if you’re in this boat, reach out to our team for friendly guidance on where – and how – to get started.

Q&A

How to prove AI ROI?

Speaking pragmatically, remember that not all users want to understand the technology. They want to understand the impact on them and what they’re going to get out of it, so try and relate it back to them and their strategic intent as much as you can. Early AI ROI can usually be found in/proven by:

• Focusing on areas with better data to unlock some early value
• Using GenAI to highlight and fill data gaps, so the data can be put to better use
• Fixing data that undermines high-value processes

What’s the role of citizen development in data and AI success?

Citizen development should be enabled, not blocked! James says, “What we’ve started to do at Softcat is bring in the concept of a ‘centre of excellence’.” It’s where Softcat unites overall governance, principles, and ownership in a central place to allow users (citizen developers) within the business to work with the excellence team to make sure they’re abiding by Softcat’s principles.

The post How to Build a Roadmap for Data & AI Success  appeared first on Oakland.

At Oakland, we believe that data is not just a useful tool but a critical element in every aspect of business, from quality management to operational improvements. Whether you’re in the early stages of digital transformation or already reaping the rewards of data-informed decision-making, leveraging data effectively is essential for staying relevant and successful within your industry.

In this guide, we’ll delve into why data is so important for business, and how you can harness it to unlock its true potential.

Measuring Success with Business Data

One of the most fundamental reasons businesses should care about data is its role in measuring performance. At its core, data allows businesses to track key performance indicators (KPIs) and identify areas for improvement. As the old saying goes, “You can’t improve what you don’t measure.” This is particularly relevant in a world where businesses need to stay agile and adapt quickly to the ever-changing market around them.

Andy Crossley, Director at Oakland, tells us more about the continuous need to measure business data.

“Gathering accurate business data types, you can assess where your company stands, benchmark against competitors, and map out pathways for growth. As we’ve seen with several of our clients, like Yorkshire Water,, modern data tools can now deliver faster, more reliable insights to inform strategic direction.”

Using these tools, you can constantly refine and improve your processes. At Oakland, we help organisations integrate their data to measure improvements and align efforts with broader strategic objectives. To support this, we offer tailored services focused on strategy, analytics and business intelligence, and platform development.

Linking Data to Strategy

Data isn’t valuable in isolation, it’s valuable when linked to a business strategy. Too often, companies gather data for its own sake without clearly aligning it with their goals. By focusing on data that directly informs your strategic direction, you can ensure your efforts contribute meaningfully to the organisation’s mission.

A well-constructed data strategy is essential. It provides a roadmap for how data will be collected, analysed, and used to deliver business value. Whether you’re looking to improve customer experiences, drive operational efficiencies, or uncover new market opportunities, your data strategy should be tied directly to business goals and outcomes. 

To get started on developing your data strategy, check out our guide on how to write your data strategy and learn more about crafting a data-product focused strategy.

Data-Driven Decision Making

By now everyone knows, and we’ve said it before data plays a crucial role in decision-making. In the past, many decisions were based on intuition or experience. While this is still a valid approach, data enables companies to make faster, more informed decisions with less room for error.

The value of data in business is amplified when decision-makers can quickly access accurate information. Businesses that harness data effectively can reduce guesswork and take decisive action that drives results. Modern data science tools, like data warehouses and analytics platforms, can help speed up this process by organising vast amounts of information and making it actionable.

If you are looking to leverage the power of advanced analytics and artificial intelligence, Oakland offers AI services to help businesses transform raw data into actionable insights.

When discussing how data becomes business value, it’s important to consider how you’re collecting, managing, and using that data. Companies that excel are often those that understand their processes deeply and can translate insights into actions. Without well-defined processes and data governance, efforts to extract business value from data can falter. To learn more about ensuring proper governance, explore our governance services.

The Importance of Data Quality

Another key factor is data quality. Measuring the business value of data quality is crucial for ensuring that decisions are based on accurate, reliable information. Poor data quality can lead to costly mistakes, inefficiencies, and missed opportunities.

Oakland works with organisations to assess and improve data quality as part of a broader commitment to continuous improvement. A key part of this is ensuring that data is properly governed, standardised, and accessible to those who need it. Companies that invest in improving their data quality often see significant returns, from better decision-making to enhanced operational efficiency. 

For guidance on this process, take a look at our guide on mastering successful data projects.

Staying Competitive in a Data-Driven World

In today’s fast-paced business environment, staying ahead of the game means making the most of your data. It’s not just about collecting it—it’s about putting it to work. Data is the foundation of innovation, growth, and the ability to adapt to market changes. The fact is, if you’re not using your data effectively, you’re leaving opportunities on the table.

The truth is, businesses that don’t embrace a data-driven approach risk becoming obsolete. Everyone else is using data to streamline operations, uncover wastage, and risk, deliver personalised experiences, and uncover new growth opportunities. Staying competitive means you need to make your data work for you; unfortunately, hoarding it and hoping for the best isn’t going to cut it, particularly in the new world of Generative AI.

For more insights into how to build a robust data strategy that supports your organisation’s goals, explore our blog on the purpose of a company’s data strategy.

Our Verdict? Data as the Key to Continuous Improvement

Here’s the thing about data: it’s not just numbers on a screen—it’s your best friend for continuous improvement. You can’t fix what you don’t understand. To improve, you need to know where you’re starting from, and that’s where data comes in. Once you’ve got your baseline, you can make changes, track what’s working, and keep improving.

At Oakland, helping businesses do this is what we’re all about. We started out 40 years ago in quality and continuous improvement and we wrote the book on SPC (literally) so there is no-one that knows more about driving business improvement whether it’s building a strategy, setting up data governance, creating powerful platforms, or diving into advanced analytics, we make sure your data efforts align with your big-picture goals. It’s not just about collecting data—it’s about making it work for you.

If you’re ready to start treating data as the strategic asset it is, you’ll be unlocking real business value and setting your organisation up for long-term success. Curious how we can help? Check out our services page and let’s start the conversation!

The post Why Is Data Important for Business? appeared first on Oakland.

But what does a TOM entail? How can it help complex, enterprise-level organisations achieve their goals? And, if your specific focus is an organisational subset, like Data & Analytics, how can a specific functional strategy, like data strategy, help define the roadmap to provide localised impactful results whilst also aligning with the wider Operational Model of the wider business? 

Understanding how to implement a TOM is essential if you want to drive meaningful organisational change and ensure different functional areas, like Data, share capabilities and consistently set and align to common principles. This guide explores the fundamentals, their importance, applications, and examples. We will also delve into specialised frameworks like the Data Target Operating Model and the role of capabilities, like Data Governance, in shaping a model.

Our data strategy experts can help you understand your current operating model, pains and problems within it, and help you generate the vision and strategy that will inform the design of your Target Operating Model and make it a success. Get in touch and see how we can help.

A target operating model is a blueprint that defines the desired future or target state of your organisation’s operations. It serves as a guide for how your business will work to achieve its strategic objectives. By detailing the interplay of strategy, people, processes, technology, and data, a target operating model provides clarity on what needs to change to align day-to-day activities with long-term goals. It should be an essential tool in engaging with leaders within the business to get buy-in for business transformation initiatives. Your target operating model supports the development of roadmaps that help create a culture of continuous improvement delivering your corporate strategy more efficiently.

You’ll often hear Architects talk at length about People, Process, Technology, and Data as key components or domains; but at Oakland, we feel that talking about them in such a siloed way can somewhat miss the point in terms of unlocking true business value. You can very rarely solve real problems by focusing on them independently and the reality of modern business is that technology over-indexes as the focus of investment and delivery.

Our point of view is that for TOM, the value is not in the domains themselves, but where they overlap. You can unlock competitive advantage from Enterprise Architecture to:

• ENGAGE customer and colleagues with better process experiences.
• EMPOWER your people to deliver through better tools and systems.
• SCALE your business through data-driven processes, underpinned by carefully chosen technologies
• AUTOMATE key processes, enabled by process controls and data insights.

So, what should your target operating model focus on? 

Well, it should outline how your organisation’s structure and the interplay between people, process, technology, and data can support its strategy; how processes can be streamlined for efficiency; and how technology will enable those processes. It also addresses how your team is organised and empowered to work more effectively. With a well-defined model, you can ensure your operations are aligned with your objectives and optimised for scalability and resilience.

If you think about your critical business processes, they revolve around creating and managing data around objects and entities, like Assets, Products, Employees, and Customers.  These physical, and sometimes abstract, entities exist as logical activities linked together to generate organisational value – we call these connected up activities “Value Chains” and they deliver the purpose of your business. A Target Operating Model seeks to understand the composition and organisation of people, processes, technology and data to maximise efficiency and reduce disconnects within a function and between your business functions.

A robust strategy that delivers your organisational vision is essential in creating and implementing a Target Operating Model, particularly if it is intended to inform a wider digital transformation of your business. Learn more about our data strategy consulting below.

What Is a Data Target Operating Model?

It’s exactly what it sounds like – a TOM, but specifically targeted at a more ring-fenced and focused understanding of delivering your Data Strategy. Therefore, as a subset, a Data TOM is a vital component of and must be aligned with your broader organisational TOM, even if it is designed in isolation from it. 

Data is perhaps your business’s most valuable asset. If you think about your critical business processes, they revolve around creating and managing critical data about objects and entities, like Assets, Products, Employees, and Customers. 

These physical, and sometimes abstract, entities exist as logical relationships linked together through process to generate organisational value – we call these connected up activities “Value Chains”. Value chains, and the groups of co-ordinated processes generating value are, in turn, supported by capabilities that consider a range of components, including people skills and training, culture, roles and relationship definitions in addition to the more standard process, technology and data considerations.

A Data TOM specifically focuses on how data is managed, governed, and leveraged to support the achievement of wider business goals. For example, there is little benefit in generating a whole suite of sales insight data unless that insight is provided in a way that can be used by the Sales function. Unlike traditional operating models that concentrate on your organisation’s overall functions, a Data TOM concerns the frameworks and systems that support data-driven decision-making. Data & Analytics is a core value-chain enabling activity that supports successful decision-making in other parts of the business.

Importantly, activities within these value chains may fall within the accountability sphere of people in other business functions – all of whom likely have their own “operating models” – think of operational platforms that are often the primary source of data your Data Organisation operates on. This is why a Data TOM is a vital component of your broader target operating model and why conscious effort must be put in to align with the “bigger picture”. It’s difficult to achieve operational excellence when everyone is doing their own thing in an uncoordinated way.

A well-designed Data TOM aligns data initiatives with your organisation’s strategic objectives, ensuring that data is both effectively collected, stored, and utilised. It includes robust governance frameworks that establish policies, roles, and responsibilities for managing data securely. Moreover, it incorporates the right technologies and tools to enable seamless data integration, storage, and analysis. It also acts as a feedback loop, providing insight to the business about issues in the way that data is defined, collected and managed in source platforms, which creates all sorts of issues well before the Data function touches it. 

Lastly, a Data TOM also considers the cultural aspects of a data-driven organisation, identifying the skills and mindsets required to embed data as a core asset and supporting better ownership and management of data as a holistic strategic imperative.

For further insight into crafting a data strategy that integrates seamlessly with your operating model, read our guides below.

What Are Data Governance Target Operating Models?

Like most things, Operating Models can be layered and complicated and it really depends on the language your business wants to align to. Some may call it a Data Governance Operating Model, which underpins your organisation’s ability to manage data effectively.  Others may call it a Data Governance Data Capability definition – as a component of the Data Target Operating Model. Po-ta-to, Po-ta-to.

Either way, this a prescriptive (for your organisation) model that establishes the policies, processes, and technologies necessary to ensure that data is accurate, secure, and accessible, and well managed consistently in all areas of your business. Again, Data reaches beyond the boundaries other functions may recognise to ensure everyone manages the life-blood of the business (data) in a consistent way, and largely to reduce the effort of wrestling the data into a valuable resource for data-driven decision making. 

Governance frameworks define who is owns and responsible for what types and categories of data, ensuring clear accountability and established methods of raising quality issues and ensuring ongoing improvement of data over time. They also set out policies for data business and technical, definition, usage, privacy, and compliance: critical if you operate in a regulated industry.

In addition to governance frameworks, A Data Governance Operating Model considers which are the right technologies to enable effective monitoring and control. Tools for data cataloguing, lineage tracking, and compliance reporting play a vital role in maintaining data quality. 

However, technology alone is not enough – your organisation must also foster a culture where data is seen as an essential-for-life asset. This cultural shift and shared accountability is often the most politically challenging aspect of implementing Data Governance but is crucial for long-term success.

For actionable insights into developing governance frameworks, explore our data governance services below.

What Does a Target Operating Model Comprise of?

A Target Operating Model should benefit operations across your entire organisation. Typically, it should describe each capability in terms of a consistent set of components and co-ordinate with each other to generate collective value outcomes:

• Strategy & Purpose: what is the vision and mission and what’s the driving force and intended outcomes and how it will evolve positively over time
• People: Humans and hierarchies are complex, so it should come as no surprise that there are many aspects to consider including:
  • Roles & Responsibilities – who is responsible for doing what and when, and how events and issues are escalated and resolved.
  • Leadership & Talent – how is control exerted across the organisation and ensuring succession is actively considered and planned for in colleague development.
  • Structures – how capabilities are brought together to produce effective functions, completing specific activity.
  • Culture & Ways of Working – the creation of a methodologies for co-ordinating activity within teams, and to ensure teams can interact with each other collaboratively. Governance is central in providing standardised “rules of engagement”.
• Processes: The workflows upon which your organisation operates.
• Technology: The technological infrastructure that augments with the workforce, provides efficiency through automation, and can easily be scaled as the business grows. Consistent principles help to provide better alignment between tools.
• Data: Data and its management is central to understanding of all other components, considering this is the key resource generated and managed in workflows, supports customer outcomes and the business’s understanding of how well all the components are operating with each other.
• Customer experience: How the above impacts the customer experience.

Why Are Target Operating Models Important?

The importance of a target operating model lies in its ability to translate your vision for your organisation into a successful plan of action. It creates alignment between your strategic goals and operational execution, ensuring that every function and process contributes to and drives forward success. 

By providing clarity, TOM lets you identify inefficiencies, eliminate redundancies, and optimise workflows. And if you’re preparing for growth or transformation, a target operating model acts as a target state that can be compared against the current state to identify capability development areas that can be prioritised into a roadmap of continuous change initiatives outlining how to scale your operations effectively and safely.

Going further, Target Operating Models also help mitigate risks by establishing clear roles and responsibilities, ownership of processes and data, governance structures – essential in today’s complex regulatory environment. They also help your company become more data-driven, integrating data and performance metrics into daily business discussions, facilitating better decision-making by integrating analytics into everyday operations. 

For business leaders, a TOM is not merely a tool for operational efficiency – it is a foundation for sustainable growth, innovation and continuous improvement.

“Target operating models are important because they provide a tangible description of your future organisation that can help underpin stakeholder discussions and navigate organisational politics, supports identification of key next steps and their priority, and provide a high-level technical blueprint enabling design and delivery functions.”

Alex Guy – Group Enterprise Architect

What Are Some Examples of Target Operating Models?

To understand how a target operating model works in practice, consider the example of a large public sector organisation undergoing transformation. 

The organisation, reliant on legacy systems and suffering from poor knowledge management, wants to implement new systems and behaviours to use this data. The aim is to improve its service levels for the public, become more efficient, and use its data for continual improvement, providing lasting improvements.

In developing its target operating model, the organisation identifies its strategic objectives, such as reducing operational costs by 20%, while improving customer satisfaction scores by 30%. It analyses its current operations to work out what is a barrier to these improvements – legacy systems, siloed data, gaps in knowledge management, or staff unfamiliarity with modern systems.

A plan is then created to address each of these challenges, including:

• A data strategy to support the organisation’s overall digital transformation.
• A cloud-based knowledge management platform utilising generative AI. 
• New data governance processes. 
• Staff training programmes and forums. 
• The creation of a central team to oversee the improvements and ensure implementation.

The organisation creates a roadmap to make the changes and sets about implementing them. It charts progress, holds itself accountable, and iterates upon the target operating model once it is live, focusing on new challenges.

How to Build a Target Operating Model

Developing a target operating model is a structured process.

1. Strategy: It begins with defining your strategic intent. By clearly articulating the business mission and vision, the outcomes that need to be true, you provide a baseline of what the target operating model will ultimately need to achieve. Strategy sets the foundation for aligning operational action with overarching business goals. 
2. Analysis: The next step involves assessing the business’s current state operating model to identify pain points, gaps, and inefficiencies. This diagnostic phase provides a baseline for designing the organisation’s future state. It’s not all bad, you should also identify what is good and needs to be retained as you enter business transformation mode.
3. Recommendations: Once the desired current and future states are defined, you can begin to map out gaps between the two states and understand better the scale of change required across the business or its functions. Options can be developed to inform critical decisions, and prioritisation can be driven by insight rather than best-guessing or gut feel.
4. Roadmap: Creating a detailed roadmap is essential once gaps, dependencies, and priorities are understood.  Roadmaps help with broader engagement across the business, can help sign-post change to areas of the business change will be co-ordinated with, and helps prioritise initiatives and set realistic timelines. 
5. Implementation: This involves executing planned changes while monitoring progress to ensure the target operating model delivers its intended outcomes. The process doesn’t end with implementation; you need to continuously evaluate and refine your model to adapt to evolving market conditions.

Explore our case studies, such as Yorkshire Water’s transformation journey, to see how this process has worked for leading organisations.

Common Challenges in Target Operating Modelling

Despite its benefits, developing and implementing a Target Operating Model is not without challenges. 

One common obstacle we often come across is resistance to change, as your employees and stakeholders may be hesitant to adopt new ways of working. Cultural inertia can be a significant barrier, particularly in organisations with deeply entrenched processes.

Organisational politics, prioritisation of funding, and the individual plans of the stakeholders you need support from can be extremely difficult to navigate, especially if it involves gaining financial support and borrowing resource, making delivery of their agenda more challenging. The worst case is obviously where your agenda is moving in a completely different direction – which is why co-ordinating and dovetailing functional operating models into the Master Organisational TOM is important.

Another challenge is aligning multiple business units and functions, which can be complex. Coordinating efforts across departments requires strong leadership, clear communication, and the ability to spot the right compromises at the right time. It’s important to ensure that there is an identified “ultimate arbiter” that can fairly engage with and help resolve disputes and blockages quickly. Resource constraints, such as budget, time, or expertise, can also impede progress, as can “progress” that results in constraints for other stakeholders or functions who are actually responsible for impacted processes, platforms, people and data. 

Finally, fragmented data systems can create silos, making it difficult to achieve the level of integration required for a successful target operating model. Data held by functional areas is not inherently bad – it’s the idea behind concepts like Mesh and Fabric.  However, things can start to fall apart quite rapidly without consistent standards, policies and principles.  

How Can Data Strategy Consulting Improve Target Operating Models for Large Enterprise Organisations?

Implementing target operating models can be troublesome for large organisations. Thankfully, data strategy consulting can significantly enhance the process by providing the necessary inputs about organisational issues and, key objectives, gaps in ability, future focus and an understanding of valuable capabilities that can be leveraged.  Data Strategy consulting provides expertise in methodology, frameworks, and tools to effectively integrate data-driven decision-making to clearly articulate vision, mission, and objectives, that ultimately inform the operating model. Here’s how you can improve TOM development and execution with data strategy.

Aligning Data Strategy with Business Objectives

Data strategy consulting ensures that data initiatives are closely aligned with the organisation’s strategic goals. This is where data consultancies like Oakland come in. We can help identify how data can enable your target operating model by supporting key priorities like customer insights, operational efficiencies, and new sources of revenue, and dovetailing capability developments into your business without upsetting the wider organisational operating model. This alignment ensures the model is not just operationally sound and practical to implement, but also designed to leverage data as a strategic asset.

For example, our work with RAW Charging helped them chart a vision, strategy and roadmap for data to transform their business.

Optimising Data Management 

Effective data management is foundational for a successful target operating model. A data consultancy can bring best practices for data collection, storage, integration, and analysis, ensuring data is accessible, secure, and reliable. This helps eliminate silos, reduce inefficiencies, and simplify operations across your business, or better support more complex and fragmented organisations where consistent standards and principles are needed for more federated, independent functions. Learn how we helped UK Power Networks gain all these benefits and more.

Enhancing Data Governance 

Data governance is critical to ensuring that the data used in the model is accurate, compliant, and trustworthy – particularly in heavily regulated industries like finance or healthcare. 

Our experts can be invaluable here, designing and implementing governance frameworks that define clear roles, responsibilities, and policies for data ownership and management. This includes establishing processes for data quality monitoring, security protocols, and regulatory compliance, all of which are essential. 

Learn how we set data use standards for the Information Commissioner’s Office.

Choosing the Right Technology

Choosing the right tools and technologies to support the model is a complex task, especially if you work in a large organisation with diverse needs.

Consultants evaluate and recommend the right technologies for your business, such as cloud platforms, analytics tools, and machine learning solutions, making sure they align with your goals.

Enabling Data-Driven Decision-Making

Data strategy experts ensure that data is positioned to enable better decision-making within the target operating model. By developing analytics capabilities, data consultants can help you derive useful insights from your data to optimise operations, forecast trends, and identify opportunities.

In a manufacturing enterprise, for example, predictive analytics powered by a well-designed data strategy can optimise supply chain processes, reducing costs and improving efficiency as part of the model.

Creating a Data Culture

Data experts can be invaluable in upskilling your teams and fostering a data-driven culture within the wider organisation. This ensures that employees at all levels understand how to use data to achieve their target operating model objectives. 

Training programs, workshops, and change management initiatives can all also be tailored to embed data literacy and drive the adoption of data-focused practices.

Supporting Scalability and Future Growth

A data strategy designed with scalability in mind ensures that the target operating model can evolve alongside the organisation. Our team can help you assess future requirements, such as handling larger datasets, integrating advanced technologies, or expanding into new markets, and build these considerations into the model’s framework.

Measuring and Optimising Performance

Finally, data strategy consulting integrates performance measurement into the target operating model, using key performance indicators (KPIs) and metrics to track progress and identify areas for improvement. Our experts provide ongoing support to refine data strategies and operational practices, ensuring the model continues to deliver value over time.

We Can Help Your Target Operating Model Become Reality

A Target Operating Model is a powerful tool if your organisation wants to align its operations with strategic objectives. Whether it’s a comprehensive model or a specialised one that targets just your data and technology, these frameworks let you optimise performance, enhance scalability, and drive innovation. For leaders and decision-makers, investing in a well-designed model is not just an operational necessity – it’s a strategic imperative to optimise the value delivery from business investment.

Our expertise in data strategy and data governance can help your organisation craft and implement an effective target operating model tailored to its unique needs. Visit our case studies to learn how we’ve helped leading enterprises achieve transformational success. 

Ready to take the next step? Contact our team today to start building your Target Operating Model.

The post Target Operating Model: Delivering your Business Strategy appeared first on Oakland.

With an effective acknowledgement management strategy, you can unlock your organisation’s knowledge and put it to work across your teams. Learn how below, then get in touch with our experts to learn how we can transform your knowledge management with customised artificial intelligence.

What is a Knowledge Management Strategy?

A knowledge management strategy is a plan designed to create, share, organise, and implement documentation and information. The goal? For knowledge to continually benefit processes, innovation and performance.

Your strategy should enable access to legacy information and organize new data. It should let staff who need information find it immediately so they can make faster, better-informed decisions. And it should stop siloing and enable communication and collaboration between teams.

A powerful knowledge management strategy, produced in line with an organisation’s goals and ethos, should ultimately create a knowledge-driven culture. One where knowledge is inherent to the way the business operates, helping drive and improve every aspect of it.

What is the Purpose and Importance of Knowledge Management Strategy?

A knowledge management strategy’s purpose is to enable the effective use of information and documentation within an organisation. 

Many businesses struggle with this. A 2022 International Data Corporation (IDC) survey of large businesses found that 33% experienced challenges with data siloing and collaboration, and 37% found external use of knowledge limited, manual, or time-consuming. 32% felt that data wasn’t in a usable format, and 32% said that existing tools were difficult to use.

Following this, a 2023 IDC survey found that 82% of organisations had siloed data, leading to nearly a quarter not trusting it. All this leads to employees, on average, losing 20 hours per month on poor knowledge management tools. Another survey found staff lost a massive 2 hours each working day searching for the right information. 

Ultimately, this has resulted in Fortune 500 companies losing around $31.5 billion each year from knowledge siloing. In the US, this equates to around $44.8 million lost per year on average per organisation. 

The challenges are vast, but solving knowledge management with a clear strategy can unlock a wealth of important benefits:

• Improved decision-making, time-to-market, innovation, and better processes and practices (39% of respondents in IDC’s 2022 survey).
• Better customer support through lower service volumes and quicker resolution times (35%).
• Greater satisfaction and engagement amongst employees, customers, and partners (35%).
• Improved employee performance through collaboration, learning and productivity (35%)
• Improved revenue, reduced expenses and greater profits (31%).
• In the survey, 0% of businesses experienced no benefits at all.

What are the Key Components and Methods of a Knowledge Management Strategy?

All effective knowledge management strategies should include certain key components, crucial in driving both immediate and long-lasting benefits.

1. Motivate knowledge sharing

Strategies should include methods to motivate members of the organisation. Help them understand the benefits and what they need to do to manifest them. Incentives and rewards, training managers on required behaviours and how to encourage them, and setting clear goals are all valuable.

2. Aid knowledge networking

To ensure your strategy sticks, incorporate aspects that enable knowledge networking. In practice, this means approaches that connect staff so they can effectively share their expertise and the information held within their own discipline. Communities, sharing spaces, and opportunities for discussion all play their part. 

3. Create and store knowledge

The lynchpin of your strategy is the creation, collection, storage, and access of knowledge. The use of generative AI can be enormously helpful here. Rather than relying on off-the-shelf platforms which might not entirely suit your organisation, a custom AI can have bespoke features and connections. Once it’s up and running, it will then automate the administration and delivery of knowledge from databases into processes and, finally, to end users and back. 

4. Analyse and apply knowledge

Knowledge, old and new, needs to be analysed through an automated system which categorises, summarises, and lets it be applied effectively. Once again, AI can play a critical part, learning and adapting based on previous analyses and categorisation and honing the ways data is applied through a process of constant review.

5. Codify knowledge

Data needs to be able to be searched easily and quickly, which can only come from a process of codification. In the process, similar pieces of knowledge are grouped together based on the ways in which they are used. These categories can then be used to aid knowledge retrieval through the system.

6. Spread knowledge

End users need to know their organisation’s knowledge is at their fingertips. This might involve creating a chatbot-style user interface that connects to your knowledge management system, alongside regular reminders to use it, the ways it’s being used, and the results it’s driving.

7. Demand knowledge

Your knowledge management pipeline should never be allowed to dry up. If it does, your users will experience diminishing value and end up using it less frequently. To stimulate demand, your strategy needs to bring sources of knowledge into your system. What data do you want to collect, how can it be collected, and how can it be connected to your end user?

8. Act on knowledge

Knowledge has no value unless it’s acted upon. Consider the ways in which team members can be encouraged and given the autonomy to put knowledge into practice through their workflows, processes, procedures and practices. 

9. Improve your strategy further

Businesses change over time, and so do the technologies their processes rely on. Knowledge management is no different. How your organisation generates and uses knowledge will change. Just as generative AI has revolutionised the discipline over the past few years, so will new innovations. Reappraise your strategy regularly – every six months, say – incorporating feedback and innovation to keep it razor sharp.

At Oakland, our expertise in generative AI lets us aid all of the above factors. Using approaches like intelligent agents, we can automate the outputs of your strategy so it not only manages knowledge and puts it to immediate use but continues to learn and adapt to your specific nature and needs. The result is a custom AI solution.

Learn how to enable knowledge management through generative AI.

How to Develop Your Knowledge Management Strategy

Once you have understood the components and methods your knowledge management strategy needs to include, you can start developing it.

1. Perform a knowledge audit

The best place to start strategy development is a thorough audit of your current approaches to knowledge management and the information held within your business. This includes:

• Processes and tools
• Information silos
• Knowledge gaps
• Size and makeup of knowledge base(s)
• Sources of explicit knowledge (documentation etc.)
• Sources of implicit knowledge (company processes, culture, workstreams)
• Sources of tacit knowledge (staff experience, expertise, and soft skills)
• Impact of operational inefficiencies due to poor knowledge management.

Analyse this data to clearly understand where you are and, after factoring in your objectives, where you need to be.

2. Define the value proposition

To ensure buy-in, particularly from management, you need to define the value of improved knowledge management, including benefits such as increased productivity, collaboration, innovation, and profit. 

Tailor the sources of value to your organisation, its challenges and objectives, then quantify them and show the likely return on investment.

3. Define your knowledge management objectives

Understand the objectives of your knowledge management strategy. Use the findings of your audit and the greatest sources of value to tailor short, medium and long-term goals which can help formulate your strategy and guide the wider initiative.

4. Specify your knowledge management metrics

To understand progress and the accomplishment of objectives, decide what metrics you will hold yourself accountable to. There are four main types of metrics to consider:

• Activity: Number of contributions, rate of user engagement, search trends, login frequency.
• Performance: Number of interactions, content retrieval time.
• Maturity: Average content age.
• Impact: Volume and quality of feedback, business outcomes.

5. Create your strategy

Put your insights into action and craft your strategy. As well as formulating how it will all work, remember to apply an adequate budget to ensure completion and assign the work to an expert or team so it isn’t slowed down by day-to-day activity. Finally, accurately timeline the formulation and implementation of the strategy to further ensure its success.

5 Key Knowledge Management Strategy Best Practices

On top of making sure your strategy contains and is developed the right way, there are some other smart best practices you can adopt to improve its efficacy:

• Run training sessions: Especially important if you haven’t had a strategy and process in place before, training sessions for users and administrators will prevent any bottlenecks in aptitude from preventing your strategy from landing.
• Create a learning environment: Supporting continual training and development, an online learning environment filled with helpful how-tos and guides will prove a helpful resource for new and existing employees.
• Create forums: Promote a culture of knowledge sharing by creating online and in-person avenues for users to discuss approaches, best practices, and results.
• Regular promotion by senior management: If your senior leaders share their excitement about the initiative, their passion will impress upon your teams. Leading by example will help guarantee the system doesn’t fall by the wayside.
• Create a dedicated team: By having a long-term team create the strategy, ensure it’s implemented, and analyse the results to improve it further, you can make knowledge a key part of your organisation.

Examples of Knowledge Management Strategies

In our experts’ work with Network Rail, we helped this large and complex organisation create an AI-driven knowledge management system which put past project learnings into practice. 

The result? Faster, less expensive, and more valuable rail projects and infrastructure for the UK. 

Your knowledge management strategy is your organisation’s ticket to improved performance and collaboration. Get in touch with your experts today or explore our AI consulting services to learn how we can help you unleash the value of knowledge.

The post Knowledge Management Strategy: Purpose, Practice, Methods  appeared first on Oakland.

Knowledge management is more than just storing documents; it’s about ensuring people have access to the right information at the right time. Whether it’s capturing the expertise of your employees, streamlining processes, or fostering innovation, knowledge management is key to staying competitive and efficient.

In this guide, we’ll walk you through what knowledge management is, why it’s important, and how to make it work for your organisation. From boosting collaboration to solving complex business challenges, knowledge management can be a game-changer.

What is Knowledge Management?

Knowledge management is the process of organising and distributing information and expertise within an organisation. It involves the explicit knowledge (documented information), implicit knowledge (behavioural patterns), and tacit knowledge (personal experiences and know-how) of employees and stakeholders.

A well-implemented knowledge management strategy ensures valuable knowledge is easily accessible by the right people at the right time. Organisations leveraging technologies like AI can significantly enhance knowledge management processes by using intelligent agents and custom AI solutions to automate knowledge capture and distribution. 

For more insights into advanced solutions, visit our AI custom solutions page or explore the advantages of intelligent agents.

Key components of knowledge management include:

• Knowledge Creation: Employees, teams, and departments constantly generate new knowledge. Knowledge management ensures this knowledge is documented and shared.
• Knowledge Sharing: Knowledge management encourages open communication and collaboration across teams, allowing employees to access relevant information easily.
• Knowledge Application: Captured knowledge should be applied to improve business processes, decision-making, and innovation.
• Knowledge Retention: Knowledge retention strategies ensure that critical information is preserved when employees leave the company.

Knowledge Management also helps organisations become more resilient by promoting continuous learning and adapting to changes more effectively. A robust knowledge management system ensures that organisations can build on past successes and lessons learned.

What is the Purpose of Knowledge Management?

Knowledge management ensures that critical information is accessible and usable by employees, leading to better decision-making and increased efficiency. It also promotes collaboration by making knowledge available across teams and departments, reducing redundancy and improving operational workflows. 

Additionally, knowledge management is key in retaining organisational knowledge, ensuring valuable insights and expertise aren’t lost, particularly when employees move on.

Types of Knowledge in Knowledge Management

Knowledge management consists of three types: tacit, explicit, and implicit knowledge. 

• Tacit knowledge, learned through experience, is more difficult to document but vital for effective problem-solving and innovation. 
• Explicit knowledge includes all documented information, such as guidelines, reports, and manuals. 
• Implicit knowledge, while not always documented, can be inferred from patterns of behaviour within the organisation.

Organisations increasingly use AI-driven tools, such as intelligent agents, to automate and manage these different types of knowledge. Intelligent agents can assist in gathering, categorising, and delivering information to employees in real time, making the knowledge management process much more efficient.

What Are the Applications and Use Cases of Knowledge Management?

Knowledge management is highly versatile and can be applied across many industries and use cases to improve operational efficiency and decision-making.

Transportation and Infrastructure

In transportation and infrastructure, organisations like Network Rail use knowledge management to manage operational data and enhance safety. By integrating Generative AI, Network Rail improved its decision-making and operational efficiency, ensuring that critical knowledge is always accessible.

Healthcare

In healthcare, knowledge management systems help hospitals manage patient data, streamline administrative tasks, and facilitate the sharing of best practices among medical professionals. Knowledge management improves patient care and department collaboration by providing easy access to accurate records.

Legal

In legal services, law firms rely on Knowledge Management to organise case law, legal precedents, and research. This lets lawyers access crucial information quickly, making their decision-making processes more informed. Effective knowledge management also reduces time spent on legal research, freeing up valuable resources.

Manufacturing

In manufacturing, knowledge management is essential for ensuring production consistency and quality. By centralising knowledge of processes and product designs, it helps maintain standards across teams and locations. Knowledge management systems also enable better collaboration between departments like engineering, design, and production, driving innovation.

Financial services

In financial services, knowledge management aids in managing customer data and ensuring compliance with regulatory requirements. Financial institutions use knowledge management to streamline customer service and enhance risk management by providing fast access to critical information and past case insights.

Education

Educational institutions benefit from knowledge management through streamlined access to learning materials and research. This enables both students and educators to engage in continuous learning. It also facilitates collaboration between researchers by making academic resources easily accessible.

HR

Human Resources (HR) departments use knowledge management to store policies, handbooks, and training materials, streamlining communication and improving employee onboarding.

Retail

Retail businesses use knowledge management to improve customer service, optimise supply chains, and better understand consumer preferences. Knowledge management systems centralise product information and past customer interactions, allowing retailers to deliver a more personalised shopping experience.

Technology 

In tech, knowledge management helps manage technical documentation and project data. Tech companies use knowledge management to improve customer support and streamline product development by making essential knowledge accessible to teams and speeding up problem-solving and innovation.

With the rise of generative AI, knowledge management is more powerful than ever. AI lets organisations process and analyse vast amounts of unstructured data, providing deeper insights and automating decision-making. It also enhances collaboration by suggesting relevant content or experts based on ongoing projects, making teams more efficient and agile.

For more details, read our blog on Generative AI use cases.

The Importance and Benefits of Knowledge Management

Knowledge management can address critical business challenges and provide significant benefits. Here’s a table outlining common problems and how the practice can offer solutions:

What Should a Knowledge Management Strategy Include?

A well-rounded knowledge management strategy should begin with an audit of the organisation’s existing knowledge assets, followed by identifying gaps where knowledge capture and sharing can be improved. 

It’s important to define processes for capturing, sharing, and applying knowledge across all departments. Using the right technology is also key. AI-driven tools can help automate routine knowledge management tasks, making it easier to share knowledge across teams and departments.

For example, custom AI solutions can be tailored to your specific needs, ensuring that your knowledge management strategy is aligned with your business goals.

What Should a Knowledge Management Process Include?

A knowledge management process typically includes:

• Knowledge Creation: Generating new knowledge from projects, experiences, and research.
• Knowledge Storage: Organising knowledge in a structured, accessible system for easy retrieval.
• Knowledge Sharing: Promoting a culture of collaboration where knowledge is openly shared and applied to business processes.
• Knowledge Application: Using knowledge to solve problems, enhance processes, and drive innovation.

These stages are critical to maintaining a dynamic knowledge management system, which can evolve with the business. 

What is a Knowledge Management System?

A knowledge management system is a single technology or group of technologies that support the storage, retrieval, and sharing of knowledge. It includes databases, content management systems, and collaboration tools. 

Modern knowledge management solutions often leverage AI to automate knowledge categorisation and provide users with relevant information when needed.

What Information is Captured by Knowledge Management?

A knowledge management system captures a broad spectrum of data, including documents such as policies and manuals, processes like standard operating procedures, and employee expertise. Capturing this information ensures that valuable knowledge is shared across the organisation, improving collaboration and decision-making.

What are the Challenges of Knowledge Management?

Knowledge Management comes with its own set of challenges, such as:

• Data Overload: Managing vast amounts of information can be overwhelming, making it hard to find relevant knowledge.
• Capturing Tacit Knowledge: It can be difficult to formalise and document the personal experiences and insights of employees.
• User Adoption: Encouraging employees to actively use the knowledge management system can be a hurdle, particularly if the system is seen as complex or time-consuming.

Using AI-driven tools like intelligent agents can help overcome these challenges by automating knowledge capture and retrieval. Learn more about AI in our guide.

Guide to Artificial Intelligence.

How Can Knowledge Management Be Improved in an Organisation?

Improving knowledge management starts with fostering a culture that encourages collaboration and knowledge sharing. This involves creating an environment where employees feel comfortable sharing insights, ideas, and experiences and where knowledge is treated as a valuable resource for everyone to access. Leadership support is crucial here, setting the tone for open communication and cross-team collaboration.

Next, organisations can significantly enhance their knowledge management efforts by implementing advanced technologies such as AI and machine learning. These tools can automate many aspects of the knowledge management process, from categorising and indexing knowledge to retrieving relevant information quickly and accurately. AI-driven tools can even suggest useful content or experts based on the context of a task, making the entire knowledge-sharing process more intuitive and efficient.

Additionally, providing employees with proper training is key to knowledge management’s success. It’s not enough to have the right tools, employees must understand how to use them effectively. Regular training sessions can ensure that employees are comfortable navigating the knowledge management system, contributing knowledge, and using the tools to retrieve information. Building digital literacy and knowledge management skills will empower teams to fully leverage the system and make knowledge management a seamless part of everyday work.

Knowledge Management FAQs

What are the 4 Cs of knowledge management?

The 4 Cs in knowledge management are Content, Collaboration, Context, and Connection. These elements form the foundation of an effective knowledge management strategy:

• Content: The actual knowledge or information being managed.
• Collaboration: Sharing knowledge across teams to foster cooperation.
• Context: Ensuring that the knowledge is relevant and applied in the right situations.
• Connection: Linking knowledge to the individuals or teams who need it most.

What are the 5 Ps of knowledge management?

The 5 Ps include People, Processes, Products, Platforms, and Performance. These factors are integral to developing a successful knowledge management strategy. 

People are the most important asset in any knowledge management system, and processes ensure that knowledge is captured and shared effectively. Products and platforms refer to the tools and systems used for knowledge management, while performance measures how effectively knowledge is applied to improve the organisation.

How does knowledge management differ from information management?

While both knowledge management and information management deal with data and content, knowledge management goes beyond mere information storage. 

• Knowledge management focuses on how knowledge, especially tacit, experience-based knowledge, can drive innovation, improve decision-making, and solve problems. 
• Information management is more about cataloguing and storing data without necessarily focusing on its application.

What role does AI play in knowledge management?

Artificial intelligence is becoming increasingly important in knowledge management by automating processes and improving efficiency. AI can:

• Automate knowledge categorisation and retrieval: AI-driven systems can automatically organise vast amounts of information, ensuring that it’s easily accessible when needed.
• Predict knowledge needs: Intelligent agents can learn from user interactions to anticipate what knowledge is needed in real time.
• Streamline workflows: AI tools can integrate with existing systems to make knowledge sharing seamless across different platforms.
• Improve decision-making: AI can analyse data and present relevant insights to enhance decision-making processes.

How do I start building a knowledge management strategy?

Building an effective knowledge management strategy begins with an audit of your organisation’s current knowledge assets. This means identifying where knowledge resides, whether in documents, databases, or within employees’ expertise and understanding how it’s currently shared and accessed.

Once you have a clear understanding of your knowledge landscape, the next step is to define specific goals for your knowledge management system. These goals could range from improving collaboration between teams to streamlining decision-making or ensuring knowledge retention when employees leave. Setting measurable objectives will help guide the development of your strategy and ensure it aligns with your organisation’s broader business goals.

Choosing the right technology is critical to making your knowledge management strategy work. Tools like AI, machine learning, or a comprehensive Knowledge Management System (knowledge management) can automate the process of capturing, categorising, and distributing knowledge, making it easier for employees to access the information they need when they need it.

Lastly, it’s essential to foster a knowledge-sharing culture within your organisation. Encourage employees to actively contribute to and use the knowledge management system, and provide training to ensure everyone understands the value of sharing knowledge. By creating an environment where knowledge flows freely, your knowledge management strategy will have a much better chance of long-term success.

How Oakland Can Transform Your Knowledge Management

At Oakland, we specialise in providing cutting-edge knowledge management solutions tailored to your business needs. We combine advanced Artificial Intelligence (AI) technologies with deep industry expertise to help you capture, manage, and apply knowledge effectively. 

Here’s how we can support your knowledge management strategy:

AI-Driven Knowledge Management Systems

We develop custom AI solutions, including intelligent agents, to automate knowledge categorisation, retrieval, and dissemination, ensuring your employees have access to the information they need at the right time.

Knowledge Retention Solutions

Our tools help retain critical institutional knowledge, particularly tacit knowledge, so that it remains within the organisation, even when employees leave.

Optimisation of Knowledge Processes

We offer consulting services to help optimise your knowledge management processes, from knowledge creation to application.

Continuous Improvement

We stay ahead of industry trends to ensure your knowledge management system evolves alongside your business, incorporating the latest technologies like Generative AI.

If you are looking to improve your business’s performance, knowledge management could be the solution for you. To learn more about how we can help you build a smarter, more efficient knowledge management strategy, explore our AI custom solutions and services or contact us directly for a consultation.

The post What is Knowledge Management? appeared first on Oakland.

At Oakland, we pride ourselves on our extensive expertise in data and our approach to simplifying it. This ensures you can fully understand how your business can reap the benefits of having a strong data strategy.

In this comprehensive guide, we’ll teach you all about the power of data and the right way to approach it to do just that.

What is a Data Strategy?

A data strategy sets a vision and roadmap that explains how an organisation will use data and analytics to achieve its strategic objectives.

A well-defined data strategy aligns data and analytics activity with clear business aligned objectives. This involves outlining the changes, investments, and capabilities needed to make the data strategy effective.

Remember, data strategy is not about creating a different future for your organisation. At Oakland, we want to use data to realise the organisation’s vision and strategy. It’s all about using data to serve organisational goals and drive value.

The wider future of the organisation is (or should be) already imagined through the overall business strategy.

Why Do You Need a Data Strategy?

Simply put, it today’s market, organisations need a data strategy in order to propel them towards their business goals. Here are just a few of the key reasons having a data strategy is essential:

Opportunity Identification

 Many organisations sense missed opportunities with their data but struggle to pinpoint them. A structured data strategy helps uncover and prioritise these opportunities.

Vision Setting

A data strategy creates a clear future vision to unite data efforts and guide decision-making.

Alignment

It aligns priorities, resources, and roadmaps behind a clear vision, addressing disjointed data functions and activities.

ROI Realisation

Ensures investments in data yield expected benefits and helps secure necessary budget and resources.

Customer Satisfaction

Successful data implementation improves data availability, access, quality, and timeliness, addressing common frustrations.

Foundation Building

Links customer frustrations with root causes and provides solutions, fostering data governance.

Breaking Reactive Cycles

Helps data teams move from a reactive stance to a proactive, strategic approach.

If these points resonate with your organisation, it’s likely time to consider creating or updating your data strategy.

Finding the Right Approach to Your Data Strategy

At Oakland, we have an extensive collection of expertise in all things data; from strategy and governance to data  engineering, analytics and artificial intelligence. One thing we feel incredibly passionate about is simplifying data strategy by taking an easy, seamless approach that everyone in your organisation can understand. 

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?

A common mistake is to fall into the trap of creating a ‘strategy for data’ rather than using data to achieve organisational goals. Avoid thinking of data as separate from the wider organisation. Instead, re-imagine data as a means to meet organisational needs.

A Better Way

To avoid the ‘strategy for data’ pitfall, our data strategy consultants work collaboratively alongside your organisation to ensure your data strategy focuses on the following:

1. Organisational Outcomes: Understanding what outcomes your business needs and the role of data in achieving them.
2. Balanced View: Incorporating people, processes, and technology, rather than overemphasising tool and technology alone.
3. Compelling Narrative: Crafting a data strategy that tells a compelling story, making the technical aspects accessible and engaging for non-technical stakeholders.
4. Collaborative Creation: Developing the strategy through workshops, customer engagement, and stakeholder collaboration to ensure alignment and instill confidence.

Oakland’s Approach: What Should a Data Strategy Include?

At Oakland, we have created a foolproof 3-step approach to create a comprehensive data strategy, ensuring it covers the following key components:

1. Strategic Vision

Outline what you aim to achieve and the high-level changes needed. Key elements include:

• Purpose: The role of data in the organisation
• Scope: The scale and content of the strategy
• Future: Big-picture changes and benefits
• Objectives: Tangible goals aligned with business objectives
• Key Results: Metrics to measure success
• Capabilities: High-level capabilities to be built or enhanced

2. Case for Change

Present a compelling rationale for pursuing the data strategy. Include:

Current State: An unbiased assessment of current data and analytics capabilities

Voice of the Customer: Real-world data problems and aspirations from the customer community

Target State: Future data maturity goals tied to business needs

Business and Financial Rationale: Clear and agreed-upon assumptions on profitability and benefits

3. Detailed Data Strategy

Add detail to your strategic vision to make it implementable. Focus on:

• Future State Definition: Specific ways the future will differ from today
• Concept Designs: High-level views of future data and analytics operations
• Levers of Influence: How the strategy will be implemented and influence the organisation
• Key Gaps to Close: Documenting gaps and establishing accountability
  

4. Strategic Roadmap

Outline the phases and activities for implementing the strategy. This will include:

• 2-3 Year Roadmap: Ambitious yet flexible to account for shifts in organisational context
• Transitional States: Clear transitions to sequence the roadmap.
• Projects on a Page: High-level view of scope, activity, and resources for implementation
• Governance: Oversight, reporting structure, and governance plans

5. Target Operating Model (TOM)

Describe the people, processes, and technologies required for delivering value from data. Key components include:

• People: Roles, responsibilities, and skills needed
• Processes: Workflow and procedures for managing data
• Technology: Tools and platforms to support data initiatives
  

The 5-Step Phase: How to Create Your Data Strategy

So far, we’ve outlined what to include in your data strategy. Writing it is 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

Understand the current state and future goals of your business. Key questions include:

• What is the current maturity of your data capabilities?
• What are the experiences and needs of data customers?
• What are the organisation’s strategic goals, and how can data drive them?
• What is the vision for data and analytics?

Phase 2: Define

Outline the data capabilities that align with your organisational vision by considering the following aspects:

• Future state design principles
• Strategic questions and trade-offs shaping the data strategy

Phase 3: Plan

Establish the processes, standards, technology, and organisational changes needed. This includes:

• Strategic roadmap and transition states
• Success factors and measurement framework
• Programme mobilisation and change management

Phase 4: Execute

Delivering the transformation via iteration, gathering lessons to adjust delivery. Activities 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. Ensuring you have the flexibiliy to meet changing demands and sustain the delivery of change. This focuses on:

• Continuous improvement aligned with key performance indicators.
• Transition and support for a sustainable future.
• Monitoring and assurance of delivery and benefits.

By following these guidelines, you can create a data strategy that effectively leverages data to successfully achieve your organisation’s strategic goals and objectives.

Don’t Just Take Our Word For It

At data experts, we know more than most that the proof is in the pudding. Visit our client case studies to learn more about the projects we’ve successfully aided.

Begin Your Data Journey Today: How Can Oakland Help? 

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

To take the first step towards leveraging your data strategy,  contact us today to chat with one of our friendly experts who can help you get started. 

Looking for more more tips and advice on all things data? Head to our dedicated knowledge hub, where you’ll find helpful guides and case studies, videos and more.  

The post How to Write a Data Strategy appeared first on Oakland.

Oakland, which has a proven track record in business improvement and transformation, has joined forces with Data Literacy Academy. This strategic partnership combines Oakland’s expertise in data strategy and technical implementation with the Data Literacy Academy’s specialised knowledge and resources in developing data literacy skills.

Why a Two-Way Street Approach to Data Literacy Matters

The partnership is built on the fundamental understanding that data literacy is a two-way street. It’s not just about data professionals understanding business needs; it’s equally important for business leaders and decision-makers to understand the potential and limitations of data. This two-way communication is essential for organisations to unlock the true value of their data and make informed decisions. Far too often, businesses work in siloed ways without communicating effectively, which can lead to missed opportunities.

Addressing the Evolving Landscape of Data and Business Leadership

The partnership acknowledges the evolving landscape of data roles and leadership. We are seeing a new wave of data leaders emerging, often with strong business acumen but limited technical expertise. This creates a crucial need to help these leaders bridge the gap between data capabilities and business strategy.

Everyone talks about the value gap of what people can achieve through data. Teams are particularly poor at defining or even trying to define ROI metrics. Back to the two-way street approach. Business people must share what problems or opportunities they are trying to create using data, and data people need to be able to translate the technology, what it delivers, and how it works.

Often, data literacy, or data training as most people call it, is seen along with data governance as a tick-box exercise. They don’t understand how to execute education in the right way to achieve the outcomes they are looking for. You don’t get endless chances to make people see the value in becoming data literate, so you have to get it right.

The Rise of AI and the Need for Support

The growth of AI is opening up the use of data to everyone. AI is probably powering half of the applications you use every day. It’s easy to think of AI as a chatbot or ChatGPT, but if you are tasked with how your business should use AI, that becomes tricky, and if you’re responsible for coming up with that solution, then it becomes even more challenging.

For AI to realise its full potential, it must move on from the chatbot. It must be process native. You need to design AI capabilities into your processes. It’s not just a technology solution. This involves a whole new level of data literacy!

There is a lot of groundwork to be done so businesses are truly ready for AI, and have the skillset needed to drive value from it. We believe this is an incredible opportunity; people shouldn’t be fearful. AI gives your data the opportunity to talk – just imagine what it can tell you. But you must be able to walk before you can run. You must be data literate before you can become AI literate.

Our Partnership’s Goals: Building Bridges and Fostering Growth

This collaboration aims to achieve two key objectives:

1. Embedding Data Literacy Early On: Unlike traditional IT projects, data initiatives often lack a focus on user training and adoption. The partnership aims to address this by highlighting the importance of data literacy and embedding it throughout the entire transformation journey, from initial planning to implementation and ongoing use.
2. Bridging the Imagination Gap and Empowering Growth: Through a clear understanding that people and technology have equal value. Our partnership aligns perfectly with that mission by equipping individuals with the data literacy skills needed to understand the “art of the possible” and make informed decisions that drive growth.

By bridging the gap between data and business understanding, this partnership empowers individuals and businesses to unlock the true potential of data. Ultimately, it’s about enabling freedom and growth through a deeper understanding of data and its potential applications.

What does our partnership look like?

Our customers will have the opportunity to get support on the technology side, as well as the people side. When training and data culture change is needed, Data Literacy Academy will set up an impactful change management programme to enable teams. Supported by Oakland through strategic planning and technical implementation.

All too many technology programmes end in disappointment.  With our combined expertise, our goal is that no business needs to experience failed technology projects again, because they had the professional resources to do it right the first time.

Mastering the 5 Stages of a Successful Data Project

We’re determined to turn the tide of lacklustre data projects, which is why we have created our new guide. A blueprint to mastering the 5 Stages of a Successful Data Project.

Download the guide, and you’ll learn:

• Insider tips to align your data and business strategies, avoiding the dreaded silo effect
• Actionable strategies to ensure your data projects are eagerly adopted across the board
• Real-life anecdotes to illustrate the dos and don’ts of data project management

The post Oakland Partners with Data Literacy Academy: Bridging the imagination gap appeared first on Oakland.

Whilst you might have identified areas of low maturity and capability gaps, you will now need to focus on defining and planning the required initiatives to drive the business forward, meet identified business needs and deliver long lasting capability. If you can’t do this, your maturity assessment becomes a wasted exercise. This is where a Data Strategy comes in.

Completing your data discovery

A word of caution: before you jump into the “Define” phase for a future data strategy, is your data discovery complete? From our experience, we see a lot of organisations focus purely on “in the moment” analysis of their current state, without providing key context or clarity on future vision and data capability needs for the organisation.

Contextless maturity scores are not very helpful to a non-technical audience. Have you understood the vision, aims and objectives of your organisation and the required data capability and maturity to meet these? Have you looked at future data capability and maturity needs and set realistic targets for where the organisation needs to be? Have you understood your use cases and capability needs to deliver these?

A data strategy should not be done in isolation. As we often say, it’s not a strategy for data, it’s data for strategy. Higher data maturity, and the required investment to deliver and sustain this, should always be outcome and value focused, driven by a real business need.

Defining your data strategy

Once you’ve completed your discovery work, we can start to define the strategic pillars of the data strategy. What are the 3-5 key changes or themes that define the strategy in response to the vision, objectives, and desired future state and maturity of the organisation.

Key initiatives are defined for each pillar, considering required changes across people, process, technology, and data. It’s at this stage we address desired outcomes and deliverables, the associated benefits and impact on the business and in shifting your data maturity, and the resourcing and enablers for delivery.

From here we begin to design our conceptual operating model, organisational structures, and capability deployment strategies and principles to successfully sustain the data strategy.

Planning for delivery

At this stage you should have a solid, evidence-based case for change, with clearly defined initiatives to deliver required data capability. However, without a pragmatic, actionable plan and business case to support investment, you’ll leave key stakeholder asking, “so what?”. This is where the crucial planning stage comes in.

This begins with your initiative road-mapping, driven by initiative profiling, prioritisation, understanding of key internal and external dependencies, risks, and transition points. Initiative KPIs and success measures should be documented at this stage, whilst setting out the programme and project governance structures.

The data strategy should then be grounded by a solid business case and supporting cost benefit analysis to justify required investment. It’s important that you align benefits to the business problems and needs identified during your data maturity assessment and complete discovery.

If you’re looking for help in delivering a data strategy or data maturity assessment yourself then please drop us a line craig.lambert@weareoakland.com, and why not download our free data strategy guide in the meantime https://weareoakland.com/data-strategy-guide

Craig Lambert is a Senior Consultant here at Oakland, focused on helping organisations with data and digital transformation strategy and implementation, data governance, business case development and target operating model design. Over the past 14 years, Craig has led major transformation programmes in both consulting and industry contexts.

The post Develop an actionable data strategy from a data maturity assessment appeared first on Oakland.

In launching the DMAG, Megan Lee Devlin, the CEO of the Central Digital and Data Office (CDDO), stated, “Permanent Secretaries, Chief Digital and Information Officers, and Chief Data Officers across government have endorsed this important tool and committed to using it in a core part of their organisation within the first year.”

The expectation from the Cabinet Office is for wide adoption across the public sector. However, the DMAG maturity framework is also freely available and potentially suitable for wider use.

What is Data Maturity Assessment?

A data maturity assessment evaluates an organisation’s data strategy, policies, and capabilities to determine its level of data maturity. It helps in understanding how well an organisation manages its data and identifies areas for improvement. The assessment is crucial for developing a robust data management strategy and enhancing overall data governance.

What is the DMAG Framework?

The DMAG framework is comprehensive for a data maturity assessment. This will build a picture of data maturity across a department or organisation, but it takes some getting to grips with!

In summary, there are 97 assessment rows across 10 topics of data maturity. Each row is also aligned to a cross-cutting ‘theme’. Each row has a detailed description of what would score between a Level 1 (Beginning) and Level 5 (Mastering).

As this is a qualitative framework, each row must be assessed and judged, rather than calculated. Overall, this builds a picture of data maturity across an organisation’s data ecosystem rather than providing a single ‘score’. This step is often a prerequisite to building a data management strategy or refining a roadmap.

When you’re attempting a comprehensive assessment like the DMAG, it really pays to break things down and take them one at a time.

What are the Key Benefits of Data Maturity Assessment?

Conducting a data maturity assessment offers several key benefits:

• Improved Data Management: Identifies strengths and weaknesses in data practices, leading to better data management strategies.
• Enhanced Decision-Making: Provides insights that help make informed decisions based on reliable data.
• Increased Efficiency: Streamlines data processes, reducing redundancy and improving operational efficiency.
• Regulatory Compliance: Ensures that data practices meet regulatory requirements, reducing the risk of penalties.
• Strategic Planning: Supports the development of a clear roadmap for data improvements aligned with organisational goals.
• Cultural Shift: Promotes a data-driven culture, improving data literacy and engagement across the organisation.

From our experience, these are the six key steps to delivering the DMAG:

Understand the Framework

Get to grips with the intricacies of the DMAG and you’ll be able to articulate the assessment to set yourself up for success.

Focus Your Assessment

Shape the assessment to match your organisation’s function and objectives.

Balance and Target Your Assessment Methods.

Target how engagement should be made with specialist and non-specialist stakeholders.

Run the Assessment

Conduct the assessment thoroughly. Validate results with a knowledgeable party within the organisation.

Communicate the Results

Provide a clear ‘so what’ to accompany the results. 

Define Forthcoming Activities and Objectives 

Communicate the agreed commitments that will be undertaken to drive improvements.

Understanding the Framework

If you are responsible for delivering the DMAG, it is important to take the time to understand it. Doing this ahead of delivery will ultimately set you up for success. 

Then, you’ll be able to discuss it with the rest of your organisation, many of whom may not encounter maturity assessments in their day-to-day roles. Getting your team’s understanding is equally important so that they feel included throughout the process.

You’ll also understand which data practices assessed would be most interesting to view in detail from your organisation’s perspective, either from corporate priorities or operational challenges.

The GOV.UK site (Data Maturity Assessment for Government) provides great insights into how this has been created and the intended cross-government use.

Focus Your Assessment

As per the guidance, there is no need to hit all 97 DMAG rows. At least 40 rows are required for a full assessment.

To narrow this down, we advise that the rows included should be those aligned to either:

• The department or organisation’s objectives, sector, or environment you are working in.
• The assessment should be balanced to ensure good coverage for each topic and theme, which will add robustness to it.

So, if you’re working with particularly sensitive data, we suggest including most, if not all criteria from ‘Protecting your data’. 

In this example, you may wish to reduce the number of rows assessed from ‘Engaging with Others’.

Balance and Target Your Assessment Methods

The assessment should take into account specialist and non-specialist views. Surveys provide a great method for rapid assessment and canvassing opinions from a large group. Significant effort should be put into rewording a survey so non-data specialists can understand it while staying true to the DMAG.

Interviews and workshops can also be included. Typically, these will support the DMAG scoring and provide further insight into user issues and data needs across the organisation. Whilst this isn’t included in the assessment, it can be used when the results are published.

Understanding your department or organisation will inform you on how to deliver the assessment. For example, ensuring that workshops are safe for people to give honest feedback.

Regarding the assessment, it makes sense to plan out which assessment method should be used for each row. Building in multiple assessment methods for rows is ideal. However, this will increase the scope of your work!

Certain rows will suit different assessment methods. For example, with row number 62, ‘Acquiring existing data in the right way’, it would not be expected that an end user of an analytics platform (a data consumer) would be aware of the status of the data pipelines required to provide the data from the source system.

In this scenario, a targeted interview with a data engineer would likely be more appropriate. For other rows, such as 1. ‘Making data available for those who need it’, it is important to have a range of responses to balance whether user needs are met.

Run the Assessment

It is important to conduct the assessment row-by-row, record the evidence used and how the decision has been arrived at. Decisions should be made based on what is typical and usual. Throughout the evidence gathering, the extremes can often be drawn out. If there is contention, the DMAG guidelines advise defaulting to the lowest maturity level.

The results should then be further validated. This avoids any potential bias in the assessment and gives certainty to your current maturity level.

Communicate the Results

The results should be presented back to internal stakeholders. It will be important to reintroduce the DMAG at this stage, such that the audience understands how the results have been presented.

There is an important step, which is to define the ‘so what?’ around the results. 

Ask yourself:

• Are the results fit for the purpose of our organisation?
• Would improved results support us in delivering our objectives?
• Are there limitations faced by staff due to gaps in the data maturity?
• Are there opportunities to provide a better service with enhanced data capability?

When delivering the results, it’s vital to give context, and additional information or quotes that have been collected provide valuable insight. This builds the rich picture of data maturity and, ultimately what the case for change is as an organisation. If actions have been agreed as a result of the DMAG, these should be communicated alongside the results. If actions are yet to be agreed, commit to a timeframe to provide these. This supports engagement and takes stakeholders on the journey of improving the data maturity.

Define Future Activities and Objectives

At the time of writing, the DMAG is still within its first year of release and may be refined in years to come.

We suggest building the majority of your objectives at the Topic and Theme level, in case any future changes are made to individual rows.

If you want to achieve buy-in for your recommendations, it’s vital to clarify the business benefit gained through improved data maturity. Often, efficiencies of effort, avoidance of regulatory penalties or increased revenues are cited. 

There may be other cultural benefits, such as improving data literacy across the organisation, which are not as clearly tied to increased revenues or reduced costs, but these are still of great value to your organisation.

The next steps are to put your plans into action and conduct another assessment in another 12-18 months.

If you’re looking for help delivering a data maturity assessment for government (DMAG), please drop us a line at stuart.benzies@theoaklandgroup.co.uk.

The post How do you deliver a Data Maturity Assessment for Government (DMAG) appeared first on Oakland.