In this guide, our data architects demystify Enterprise Architecture, from its advantages to who needs it and even some examples of how we’ve used it in the past, allowing you to clearly judge if it’s the approach for you.

What Is Enterprise Architecture?

Enterprise Architecture focuses on the ‘big picture’. How things align to the overall strategic goals of your business, rather than focusing on the individual elements. The other side of the coin that focuses on specific project delivery would be Solution Architecture. Enterprise Architecture and Solution Architecture go hand in hand and efforts in foundational Enterprise Architecture work will pay dividends in ensuring that individual solutions are aligned to the bigger picture and deliver on your business’ goals.

What Does an Enterprise Architect Do?

An enterprise architect looks at your business in its entirety, including your existing data and processes, workers, customers and customer base, product and service offerings, and, above all, your short and long-term goals.

The architect then defines a set of capabilities, standards and roadmaps aligned to your business goals, including all the data solutions you’ll need to implement. This could involve a new or optimised data platform, AI tools, system synchronisation, etc. The result? A much smoother path from where your company is to where it wants to be with data whilst also considering the people and process changes required along the way.

Who Benefits From Enterprise Data Architecture?

If you have a complex business landscape with a number of moving parts, adopting an enterprise architecture approach can ensure that each part continues to work in harmony. This means that large businesses will almost always require enterprise architecture.

Mid-sized businesses often underestimate their need for an enterprise approach. Still, if your company has a lot of staff or multiple departments, enterprise architecture is likely the approach you’ll need to keep everything and everyone coordinated.

Even smaller businesses can benefit from aspects of enterprise architecture, the key is choosing the parts that help you achieve your goals in a strategic manner without becoming encumbered by a complex framework.

Pros and Cons of Enterprise Architecture

When you opt for an Enterprise Architecture approach, you ensure that your deliverables align with your overall business strategy, avoid conflicting technologies, and define aligned patterns and guiderails.

If your business is experiencing multiple changes at once, Enterprise Architecture can synchronise these changes so that they work in complete harmony. Whilst Enterprise Architecture can get you a long way on the journey, when it comes to considering implementation, Solution Architecture takes over the baton. A successful Enterprise Architecture phase will ensure that the Solutions Architect has everything they need to design a solution that conforms to wider enterprise standards as well as making sure that there is alignment to overarching business goals.

Common Concerns with Enterprise Data Architecture

Enterprise Architecture is a large-scale undertaking that touches every corner of your business, so it’s natural to have concerns, which we’re happy to unpack.

You may want to solve an issue straight away with a single tool, but Enterprise Architecture is the missing step required to understand what tool you need and how it will integrate into your existing landscape. To quote our Principal Data Architect, Matt Peckham: “Buying a “thing” over a capability may seem easier, but it’s far less effective.”

You may be concerned about the length of time Enterprise Architecture takes and desire an agile delivery approach, where parts of a solution are delivered in chunks. At the same time, upfront thinking and planning is still needed. As Peckham states: “Don’t think of Enterprise Architecture as a blocker but as an enabler for ensuring your end business value is delivered”.

Businesses also struggle to implement Enterprise Architecture when they are very operationally focused and react to operational demand. Moving to proactivity is a significant cultural challenge for businesses that operate this way. Concurrently, understanding longer term plans, as achieved with Enterprise Architecture, helps with reacting to the immediate by allowing you to learn from challenges and implement processes for future issues.

How Does Enterprise Architecture Respond to a Quickly Changing Tech Market?

As new technologies like AI constantly emerge, it’s understandable to wonder how Enterprise Architecture will adapt. Well, fear not – Enterprise Architecture holds strong against changing tech tides. 

In the words of Principal Data Architect Matt Strong, “Tech may change, but the approach to defining what technology you need doesn’t”.

How Has Oakland Successfully Used an Enterprise Architecture Framework?

At Oakland, we have utilised Enterprise Architecture to revolutionise many of our client’s approaches to their data. Sometimes this has leaned on existing frameworks such as The Open Group Architecture Framework (TOGAF) whilst on other occasions we have employed a more light-weight and pragmatic approach. 

Elvie needed to expand its initial data platform by adding product data. However, we knew this would be limited in value without understanding the big picture of how the business would use the data in question. 

To correct this, we used an Enterprise Data Model to distil the business into main points of activity, allowing us to make sense of the data within the business’s structure. This resulted in a data platform that seamlessly aligned with what Elvie needed, maximising the benefit of the product data.

In our work with Yorkshire Water, we dealt with a complex multi year, multi discipline, multi partner business transformation. Through a data strategy workshop we were able to understand their needs and overall business goal. We initially began with a quick win strategy to tackle their bio resource modelling before taking an Enterprise Architecture approach to align the new platforms with planned and preexisting platforms. This cohesion gave Yorkshire Water a more joined-up, single view of their customers. 

Why Choose Oakland’s Enterprise Architecture Services?

As a use-case-driven data agency, Oakland is committed to minimising the barriers to Enterprise Architecture. We begin with the basics, delivering only what you need without unnecessary additions. 

Furthermore, our Enterprise Architects get stuck right in—no planning away in an ivory tower for us! We embed ourselves right into your team, working as closely with you as possible to truly get to know not only your wants and needs but also your pain points. Through this approach, we craft an Enterprise Architecture Framework that fits your business like a glove.

Interested in how an Enterprise Data Architecture Framework can benefit your business? Contact Oakland today and we’ll be happy to arrange a consultation.

The post What Does Enterprise Architecture Do? appeared first on Oakland.

As generative  AI continues to advance at pace, more businesses are adopting it into their data management and day-to-day operations. Picking off-the-shelf tools for your AI is simple and straightforward. Still, these ready-made generative systems don’t work seamlessly with your business, thanks to their one-size-fits-all approach. An Intelligent Agent, on the other hand, creates a new and fresh spin on generative AI, functioning as a virtual worker and performing the exact tasks your company requires. 

In this guide, we’ll uncover all the benefits of an Intelligent Agent approach to your AI data strategy. Need more context? Explore our AI guide for a broader look at how we use artificial intelligence to drive insights from your data. 

What is an Intelligent Agent in AI?

Intelligent agents are a natural evolution of the LLMs we are currently seeing enter the market. They are layered on top of large language models (such as OpenAI’s GPT model) and provided with specific instructions. At the core of an agent is the instruction to create a plan in response to a given problem – and then execute that plan. The artificial agents are also provided with powerful tools and a memory store to allow them to actually DO things for your company. These tools can be as simple as the ability to respond in plain text – but can be as complex as writing results back to a database, alerting employees through text, or creating graphical executive summaries. 

Memory stores are also crucial to the work of an agent. They can be short term stores for use immediately, or a significantly larger body of corporate knowledge to be drawn on. 

By leveraging these resources, artificial agents function effectively as artificial workers in your organisation.

What Makes Intelligent Agents Special?

There are a few key traits that make Intelligent Agents so incredibly useful, including:

• Autonomous Decision Making: Intelligent Agents are programmed to make decisions about information without needing direct human intervention.
• Goal Orientation: Each Intelligent Agent is designed to achieve a specific goal, resulting in highly targeted and deliberate outputs.
• Adaptability: Intelligent Agents can reconfigure themselves continually to environmental changes, whereas more basic AI would require expensive and time-consuming retraining to handle similar changes.
• Interactivity: Intelligent Agents work with their environment. 

What is a Knowledge-Based Agent in Artificial Intelligence?

Knowledge-based agents are a type of Intelligent AI Agent with a knowledge base inference engine that applies logical rules to an existing knowledge base. For example, a knowledge based agent can read large quantities of documentation that your employees simply wouldn’t have time to work through in order to understand and respond to specific requests.

This makes a Knowledge-Based Agent highly adaptable to your company – an AI that’s truly ready for your specific needs. If knowledge management is a key issue for your company, discover how we’ve used Knowledge-Based Agents to automatically pull data for clients like Network Rail. 

What are some Examples of Uses for Intelligent AI Agents?

Intelligent Agents are so customisable they can be used in a variety of applications to make your workers’ lives easier and drive ROI. 

Project Analyst

Free up your human project analysts from repetitive analytic tasks using an Intelligent Agent with capabilities to interpret free text project documentation, interrogate supporting data and summarise insights. 

Asset Historian

An Intelligent Agent can work as an Asset Historian by reading and supporting asset records, interrogating supporting data from many different datasets and providing a single comprehensive view of the asset.  It can also answer questions about the asset from staff.

Resource Allocation

Assign tasks effectively to the right employees within your company using an Intelligent Agent. For instance, you could feed CVs into the AI, with which it could then build and populate a skills matrix used to optimise task allocation.

Data Science Co-Bot

Take some of the weight off of your data science team with an Intelligent Agent that can create and execute code to perform ad hoc data preparation, mastering routine task management.

Alert Triage

Read and filter telemetry alerts for your control engineers with an Intelligent Agent using control room policies, asset history and alarm description analysis.

Why Develop an AI Intelligent Agent with Oakland?

Oakland’s Intelligent Agent Framework allows for swift and simple creation and customisation of your generative AI tools so that it fits naturally into your workflow. 

It works with your existing data and systems, preventing you from having to take time-consuming detours. Plus, we can use proprietary managed services, open-source technologies or both for a high degree of flexibility and control in your customisation. 

We also prioritise your security so no data will ever leave your business or be pushed to public-facing AI models. Our years of experience working with security teams mean you can trust us. And we are also proudly compliant with industry standards, including CIS and ISO 27001. 

With forty years of experience, Oakland knows company pain points around data and how decisions are made, and we use that knowledge when creating the ecosystem of virtual agents that we can deploy into your business. 

Explore Oakland’s AI service offering to create an Intelligent Agent that will transform your data management, and contact us to learn more about what we can do for your data. 

The post Why We Recommend the Intelligent Agent Approach to Generative AI appeared first on Oakland.

But what exactly are the benefits of using Generative AI, and Intelligent Agents, in your business? How do you make sure you’re getting the best bang for your buck on Generative AI solutions? And how do you calculate the ROI of using Generative Artificial Intelligence effectively? 

In this blog, we’ll answer all of your burning AI ROI questions. To learn more about Generative AI , and Intelligent Agents, and their uses, explore our AI guide.

Why is ROI in Generative AI Important?

In today’s business environment you need your Gen AI to do more than provide a soundbite or provide entertainment value; you need it to deliver.  You have pressure to provide enhanced customer experiences, improve productivity, comply with regulatory demands, and drive revenue growth, and that’s before breakfast!

Which is why your senior executives want to see ROI, not R&D bills. So, if you want to unleash the power of AI, you need solutions that can do more. Generic tools or under-prepared models quickly wither in a complex business environment. At the other extreme, nobody will wait five years for a mega transformation to get the business ‘ready for Generative AI’. 

To succeed, you need the right mindset. That means building Generative AI solutions that put your business first and work with your complexity otherwise you won’t get money to do more.

Calculating the Return on Investment of your Generative AI solutions is about so much more than simply judging whether your Generative AI is worth the money you’re spending on it.

Having a clear view of your Generative AI’s ROI is one of the best ways to assess its effectiveness. It allows you to identify areas of improvement, which lets us customise your Generative AI further to suit your needs. 

A detailed view of your Generative AI’s Return on Investment is also the best way to demonstrate the value of your Artificial Intelligence initiative to stakeholders. Failing to demonstrate ROI can often hold back Generative AI projects, with early ROI proof being crucial to building buy-in and momentum for lasting change. 

Is Generative AI Return on Investment Always Monetary?

You might think of ROI as straightforward money in vs money out, but it’s far more complex than that. On top of straightforward revenue generation, the ROI of your Generative AI can be measured in:

• Enhanced operations
• Time saved
• Cost savings
• Improved decision making
• Growth
• Productivity
• Customer satisfaction
• Quality improvement.

Some of these factors, such as customer satisfaction, may be less tangible to measure. However, measuring customer retention, personalised recommendations or satisfaction survey results can all aid this. Quality improvement is also hard to quantify but can be shown through error reduction, improved accuracy or improvements in product performance. 

Ways in Which Generative Artificial Intelligence Drives Return on Investment

We’ve covered the types of ROI that AI can offer, but how does using Generative Artificial Intelligence produce these benefits for your business?

• Data analysis and insights improve your decision-making and allow you to adapt more swiftly to market changes. 
• Automating routine tasks through Generative AI frees up time for your human workers to focus on more complex tasks.
• Using Generative AI to augment your knowledge management by undertaking tasks you could never afford to resource (see our Network Rail case study).
• Generative AI can analyse customer behaviour to personalise their experiences, driving greater conversions. 
• Predictive analytics help you stay ahead of the curve and adapt to make new trends quickly work for you.
• Generative AI can use data from your workers’ CVs to create a skills matrix, allowing it to assign tasks to the most skilled person for the job. This means work is completed more efficiently.
• Using Generative AI for tasks reduces human error, preventing you from paying out of pocket for costly mistakes. 

This is just the tip of the iceberg. There are many use-cases for Generative AI. We always suggest finding a well-known problem that the business is struggling with and using this as a jumping off point. 

How to Calculate AI Return on Investment

It’s always easier to measure the ROI of your Generative AI if you know the specific goals you’re reaching for or particular areas you’re looking to improve. 

These can be quantitative, such as savings or revenue increase or qualitative, like customer satisfaction. On top of looking at internal ROI improvement in these areas, you can also benchmark them against your competitors and industry standards. 

To fully understand the progress your Generative AI is making in terms of ROI, we recommend breaking implementation down into stages to view ROI over time. This gives a detailed view, plus it allows you to see where you may have gone wrong if ROI dips at any point.

Lastly, while driving immediate ROI is significant, it’s crucial also to consider the potential for future growth and innovation.

How to Minimise Negative ROI of Generative AI

As a company, you want to protect your finances, so even if your AI consultancy has a stellar reputation, it’s not uncommon to still have reservations about spending large sums on AI platforms and solutions right away.

At Oakland, we have 40 years of making change stick. We understand large complex organisations and all of their quirks. With us, you can use smaller pilot projects or proof of concept to assess your AI’s impact on ROI before launching full-scale. That way, you can invest with confidence.

When creating your pilot projects, finding the right use cases or problems to solve is critical for driving ROI later on, as understanding value drivers of AI solutions and identifying how you will measure that early will help you set up robust value tracking from the start. Our experts at Oakland can help you with this vital step with our complementary use case workshops. 

Discover Oakland’s AI service offering today, or explore our blog to learn more about the amazing things we can do with your data. Any questions? Please contact us. 

The post How to Drive ROI with Generative AI 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.

The Building Safety Act 2022 has been widely discussed from a legal or liability lens, so let’s spend some time looking at it from a Data perspective.

The main requirements and changes related to Data :

The enablers

While there are specific elements related to Fire Safety and Higher Risk buildings, the Building Safety Act 2022 is a comprehensive review of the regulation, with building control authorities, regulations, and framework for all new buildings.

As such, there is alignment with current best practices that are supporting compliance with the Act.

• BIM is a major enabler. Providing processes with standards, it is a robust foundation framework with increasing adoption both from the industry as well as the technology solution providers. Some concepts, such as Golden Thread requirements, will be easier to implement if you already have in place BIM practices (requirements and models) at Project, Assets, and Organisation levels
• TQM for construction and continuous improvement activities is an additional enabler. If you have such an initiative in place, you are already collecting, combining, and using information across procurement, BIM, waste, safety, design, and construction management. TQM initiatives are an enabler to connect information together all along the building lifecycle. We can’t miss this opportunity to reference our Chairman John Oakland’s seminal book on Total Quality Management.

What else is needed?

While you may have design, schedule, planning, or quality tools and solutions in place, there is a new crucial need to manage your data proactively. This is quite a new practice and a crucial one for success.

The organisation capabilities

There is a risk of subjectivity in some of the expectations, for example, ‘relevant’ and ‘proportionate,’ especially when considering a much longer period of time of 30 years. Our data capability approach is designed to proactively manage and control data for ‘the right information at the right time for the right person:

• Data Quality: Is your information valid, relevant, and with the right level of quality?
• Data Catalogue: Do you have standards or definitions for your data, for example, when it comes to ‘competence’ or ‘forms’?
• Data Lineage: Do you know where your data is coming from, how it is modified or approved through which processes and systems? Can it be simplified?
• Master Data: Do you know how many times your data is replicated, copied and where are the golden records held?
• Data Governance: Do you have key business owners to drive the data requirements?

Those capabilities set the foundation for your data assets.

The data use cases

Information is not just managed to drive processes, activities, and completed definitions. Information and data are becoming the source of risk analysis, quality management, reporting, and process improvement.

To make it happen, we encourage companies to identify a few impactful use cases beyond the regulatory need. Those use cases will provide tangible benefits such as cost savings, waste reduction, or work optimisation.

The data use cases will make Data visible, and that will impact culture, literacy and support the Building Safety Act.

The data platform

We often have the question, do we need a data platform? The short answer is yes, as the only alternative will be to enter Excel Hell for a very long time!

Yet, we believe that the question should be, ‘What data platform is suited for my need.’ There are versatile platforms and systems, but without nailing down your needs, you will waste your investment and create red tape practices.

Based on the use case: reporting, MI, data quality monitoring, alerts, IoT, and real-time management or models, we can identify the best approach and platform to make your data visible and in the hands of the people who need it.

Your data culture and literacy

Finally, data culture and literacy are critical in your journey. Training and understanding the context of your data is the main enabler for a successful data journey. The objective is not to drive everything through data, but to understand everyone’s roles in data collection, management, and usage. Culture, literacy, and training are at the heart of trust, good behaviours, and relevant prioritisation when it comes to data cleansing.

How Oakland can help

First, at Oakland, we have already supported construction companies with tangible use cases:

• Data Platforms
• IoT and models
• Reporting needs and data improvement
• Data Quality assessment

In our engagements, we are careful to make sure that our activities are aligned with where you are now, your priorities and challenges, and your transformation approach for the future.

If you are struggling with any aspect of the Building Safety Act then please get in touch by emailing hello@theoaklandgroup.co.uk

Frank Brugnot is a Principal Consultant at Oakland

The post The Building Safety Act 2022 – How do you manage your data? appeared first on Oakland.

They talk about the benefits of having access to a team of experts, the value of a data governance maturity assessment, and the importance of culture in establishing effective data governance.

So Terri, what always goes through my mind is why would you choose to work with a data consultancy versus doing the work in-house?

Great question, Andrew, so the biggest advantage of working with a data consultancy like Oakland is the wealth of experience you gain. When working in-house, you may only have a small team, or I’ve worked in organisations where I have been the only data governance person relied upon to establish governance, making things challenging, to say the least. On the other hand, bringing in a consultancy means you have access to an entire team’s expertise, gained from many different projects, which means you’re not just bringing in one person, but a whole team of knowledgeable professionals. For instance, bringing in an expert in data architecture can be hugely beneficial, when looking at how you’d like to use your data to drive data analytics and machine learning further down the line. A consultancy can share use cases and client testimonials to demonstrate where they have helped deliver effective data governance for organisations that look a lot like yours.

But it’s not just about the quantity of experience, it’s also about the fresh perspective a consultancy can bring. When you’re working in a role, it’s easy to become blinkered and miss the bigger picture. By bringing in a consultancy, you get a new perspective on the organisation as a whole and can identify gaps and areas for improvement that might otherwise have gone unnoticed.

I’ve also found that a consultancy team can engage with the rest of the organisation differently than an in-house team might. They aren’t bogged down with day-to-day operations, so they can focus on the bigger picture and collaborate with various teams to develop and implement effective data management strategies helping you to drive better data-driven decision-making.

Thanks, Terri, there are some really great insights there. So, what are some of the pain points people need to look out  for when considering hiring a consultancy?

It can be really hard knowing where to start with data governance. It’s not all about data governance frameworks! I always find that looking for recurring data problems, such as a lack of trust in the data, data quality issues, or a lack of data sharing across different departments, is a good place to start. Very often governance is driven by regulatory compliance rather than being seen as adding business value to your enterprise data organisation.

Here at Oakland, we generally start with a data maturity assessment, which helps you to identify the right place to start. By doing this, you can get a clear picture of your current state, what needs to be improved, and what steps you need to take next.

To ensure that your organisation is making the most of its valuable data, there are important factors to consider that the data maturity assessment can help with, such as data literacy, education, and building a culture that supports good governance processes. Data governance is not a destination but a journey that requires constant revaluations and improvement as people and data evolve. As organisations create more and more data, it is necessary to keep checking the maturity levels of their data governance and literacy and continuously engage with your stakeholders so that they understand that data is an asset that adds value to the organisation.

The biggest shift I have seen in the past few years is that the chief data officer role has driven a step change in helping organisations with their risk management and recognising what is ‘right data’ and how that data can be used to improve operational issues. The data maturity assessment really helps to determine where you are on that journey and where improvements are needed.

One of the things I’ve come across repeatedly is how you get people to understand its importance. In different organisations, different roles may be more or less engaged with data governance. That is why it is so important to identify the right stakeholders. This is where a data consultancy can be really helpful with influencing your decision makers and change management, building roadmaps, and bringing their experience of working with different organisations to help you get senior leadership on board with the process, which can be half the battle. This is especially important because data governance is a foundational building activity, and it’s crucial to have buy-in from all levels of the organisation otherwise, it is very difficult to make a success of your data governance programme.

To be effective, data governance cannot operate in isolation. It is important that everyone who handles data in your organisation understands how their actions impact other areas of the business. I’ve seen organisations who have set up a central data governance function without giving it the authority or connectively to ensure the data is managed well throughout the organisation. This results in the data governance team constantly fixing problems after the fact instead of addressing the root causes and preventing issues from happening in the first place.

We talk a lot about data ownership, can you tell me why you think it is so important?

Ownership is a key concept in data governance, and a strong ownership model is really important. This involves articulating the roles and responsibilities of the people who will help drive your data agenda across the whole organisation, not just in siloed teams. A consultancy can help build the skills, behaviours, and capabilities to create data stewardship roles which support data governance within your organisation.

What other services can a consultancy help with Terri?

Data quality assessments are also important, as is understanding the materiality of poor data and how data governance integrates with other aspects of your organisation such as data analytics, data glossaries, and data dictionaries.

It’s also essential to measure the value and return on investment of data governance. This requires benchmarking and understanding the current state of your organisation, which a consultancy can certainly help with. They will have worked with similar challenges and will be able to help you to build the foundations for strong data governance by helping you to create a culture that values its data.

So Terri how can Oakland help?

Oakland have a team of incredibly knowledgeable experts who are able to support with both strategic and implementation of data governance. Cross functional teams that work together on projects, including governance, platform architecture, target operating models, and data strategy. They can focus on the end to end data journey with a team of technically excellent people who are passionate about their area of expertise and have a long track record of delivering successful projects and engaging clients. The Oakland website is a great resource with lots of helpful content including blog posts, working guides and case studies. We run regular webinars and podcasts and the Oakland LinkedIn page is the best place to see what we have coming up.

Andrew Sharp is a Principal Data Governance Consultant at The Oakland Group

The post What are the benefits of working with a data governance consultancy? appeared first on Oakland.

In the face of unprecedented temperatures and prolonged drought, a problem that has plagued companies across the industry for years is coming to a head. The halcyon days of plentiful supply masking fundamental flaws in water networks look to be over. Regulators will likely take a dim view of anything but plans to reduce leakage over the next AMP significantly.

There will be no shortage of solutions to this complex challenge underpinned by networks that are under-maintained, the underlying infrastructure that is aging, and countless pressure variants that cascade through unmanaged parts of the network. On the engineering side, innovative solutions are available in the market now, and early-stage research is proving promising in other Active Leakage Control Efficiency in the Sustainable Economic Level of Leakage Calculation (ukwir.org).

On the technology side, solutions abound, and countless case studies enthusiastically tout the effectiveness of this logger, model, or digital twin at creating sustainable reductions in leakage. Unfortunately, the fact remains that leakage is still a problem and no one company seems to be on the fast track to fixing it. We do not portend to have solutions to the underlying issues (unless we are ready to press pause on our lives for a few years while we dig everything up and fix the core problems). However, our experience has led us to a few observations that may be of use to companies looking to make a dent in their leakage over the next AMP:

• There are no silver bullets. Digital twins first came into the lexicon in the early 2000s but has only really become technically possible since the mid 2010s.   These give the promise of bridging the IT/OT divide, modell all kinds of operational scenarios to maximise efficiency. Unfortunately, many dreams of a digital twin utopia have been quashed due to beleaguered data quality challenges, data gaps, and operational teams’ poor uptake. Despite advances in processing power and the models that underpin digital twins, “small data” challenges and culture still create headwinds for their success. Rather than buying into the latest sales pitch for a silver bullet technology solution, think about the long-term capability you are building to respond to the problem.
• Build a capability to drive response.  If you don’t have it already, build a leakage team. This is not just a team to focus on reactive response; it should be a partnership across multiple disciplines in the organisation: operations, capital planning, regulatory response, OT, IT, innovation, and procurement. Initially, this team may take a “skunk works” view of the world with many solutions to the problem. Still, it should evolve into a capability designed to support reactive, proactive, and predictive correction of leaks. This requires that your organisation be driven by both process and data (with an underlying operational excellence framework) and can functionally form and disband teams reasonably agilely.
• Data must be brought together.  Any leakage operation in your organisation will require that many data sources be integrated. This would include base nightline calculations across DMAs, elevation data, logger information, work history, asset data, etc. If this data does not exist in a central place, where it is joined up and conformed for consumption by individuals or systems, any dream of a digital twin or leakage prediction is likely lost. Don’t underestimate the challenge of bringing this data together and improving its quality, both of which are major efforts in and of themselves.
• PoCs are hard to scale.  Innovation funding is great for identifying early-stage technologies that will likely have a practical application.  Unfortunately, many innovation projects are assumed to be able to scale without much effort, which is far from the truth.  Innovation projects tend to take the form of a proof of concept.  In a proof of concept, there should be a bounded scope of something trying to be “proven” and a defined period of time to prove things out.  After that timeframe, the concept is deemed to have been proven or not and can then be reorganised to scale and pilot in the operation. Doing it the right way takes time, which many companies want to short circuit, so they take a PoC and ask it to work on a more extensive data set or for a larger scope. Inevitably, this results in disaster, as the PoC was never built to scale and cannot support a full operation.
• Take a hybrid approach.  As with our initial point, a lack of silver bullets means you will need to try multiple approaches to solving the leakage problem. This may involve a blend of technology, capability, and engineering solutions that position your organisation, initially, to better respond to leaks reactively.  Then you may add more capabilities that improve your ability to proactively target leakage hot spots and put crews on the ground before a slow burner becomes a major incident. Finally, once you have built up strong capabilities in both reactive and proactive response, you should then jump into predictive leakage monitoring.  Too often, companies try to become masters of the crystal ball rather than build up a set of solutions that will address a wide range of leakage types and incidents.

These are by no means an exhaustive list of things to do, but a view from individuals who has been engaged deeply in the industry but has also had the perspective of other heavy industries trying to tackle similar problems. What do you think?  Have we gone completely out of touch with the reality on the ground?  Or have we tapped into a nerve that can help push for some sustainable change in this space? Let’s chat!

Jeff Gilley is a Senior Principal at The Oakland Group

The post How to solve the leakage problem appeared first on Oakland.

From an extensive systematic literature review we were able to put forward a full but rather clunky draft working concept definition of Quality 4.0, together with ten emerging principles. These were then cross-examined using an online survey and interviews with practitioners and experts/advisers, together with a focus group discussion with some members of the European Organisation for Quality. The lines of enquiry were focused on addressing:

• the extent to which organisations have a clear vision and strategy, which includes Q 4.0;
• levels of agreement to the proposed working definition;
• opinions on the level of importance/usefulness of each of the 10 emerging principles, and whether each of them is necessary, and together are sufficient;
• the extent to which knowledge of Q 4.0 is developed throughout organisations;

The analysis also captured the invited qualitative comments made by participants about the proposed definition and principles.

The overwhelming feedback from the online survey was one of positive agreement with the draft definition. For example, of the responses to the question, “Does the definition reflect the concept of Quality 4.0?” 66 per cent agreed or strongly agreed, 27 per cent partially agreed and only 7% (3 respondents) disagreed or strongly disagreed.

This is all referred to in the QW article, of course, and it enabled us to generate a revised user friendly definition: “Quality 4.0 is the leveraging of technology with people to improve the quality of an organisation, its products, its services and the outcomes it creates.”

We were also able to amalgamate and reduce the ten emerging principles to develop a revised set of Eight Quality 4.0 Core Principles, published in the QW article and covering the following areas:

Co-creation of value;

Cybernetics;

Transparency and collaboration;

Cyber-physical systems;

Mutual trust;

Rapid adaptive learning;

Data value;

Technology and combined intelligence;

And for each Core Principle we provided an illustrative example, in the clear and colourful Q 4.0 Infographic presented as a ‘centre-fold’ in the issue of Quality World.

Other key findings from the online survey are as follows:

• The adoption of Q 4.0 is still in its infancy in respondents’ organisations but, where organisations are not adopting Q 4.0 principles and practices, there is an intent to do so.
• There is a need for quality professionals to collaborate with fellow “driving forces,” but some are not being consulted, highlighting the threat that the Q 4.0 agenda could be driven by other disciplines.
• There is a need for a new vocabulary in order to enable quality professionals to collaborate with other disciplines that are involved in Q 4.0.
• Cost reduction is not a primary pressure for these changes.
• It is important to align a Q 4.0 intent with the overall corporate strategic plan – a key element.

The CQI recognises that Q 4.0 is a gamechanger for the profession and this properly structured, wide-ranging and systematic research creates the need for quality professionals to engage with our research outcomes to begin a personal development plan.

We are now in engaged in Stage two of the research, seeking to identify what are or will be the key issues for top managers leading implementation of Q 4.0, around the five core elements of the CQI competency framework: Leadership, Governance, Assurance, Improvement and Context.  This will primarily cover practices – the how, where and who –  so we will be able to understand how organisations are implementing Quality 4.0, or planning to do so, what are the costs/benefits, how are those benefits measured, and what are the implications for senior executive teams and the quality profession.

To access the research and info-graphic the CQI has set up a Quality 4.0 hub that can be accessed here…

https://www.quality.org/quality-4-point-0

Professor John Oakland

The post What is Quality 4.0? appeared first on Oakland.

Last week, we explored the engineering architecture required for a modern Project Analytics capability. This week, we look at how to create a roadmap from your Project Analytics pilot into a production-ready solution that is fit for the future. 

Introducing the Phases of Project Analytics Deployment 

Phase 1: Data Architecture Conception / Proof of Viability 

We covered most of this phase in last week’s article that unpacked the topic of data engineering for Project Analytics.  

Key milestones in phase 1 are to determine the availability of the data you require, and the viability of the architecture you have in mind for your Project Analytics initiative. 

Coming out of this phase should be a roadmap of how your solution will be built and an ‘illustrator’ that supports any internal business case activity. 

Phase 2: Building the Core Platform 

During this phase, you will begin to leverage your earlier data engineering exploration to increase the level of data ‘ingestion’ into your Project Analytics platform. The goal here is to build a live demonstrator that possesses enough functionality to deliver value to the business. 

For example, in phase 2 you’re looking to demonstrate: 

Live connectivity to core systems:  

You’re looking to demonstrate that your analytics platform can pull in operational data, in an acceptable timeframe, and translate this live data into some form of insight 

Data organisation/productionisation:  

As discussed earlier in this series, a lot of major projects reporting in the past has required substantial manual cleansing and manipulation of the data before it’s deemed ‘fit for purpose’ enough to hand over to senior management.  

In phase 2, you’ll start letting go of those manual processes and start relying on the data engineering platform you created in Phase 1 to better organise and manipulate the data in a more automated fashion. You may still be some way off a fully operational, signed off solution, but your team should be putting in the right processes to ensure a more predictable result from the inbound operational data. 

Blocker removal: At this phase of the journey, it’s not uncommon to start experiencing some blockers to your progress, these typically fall into the following categories:  

• Organisational blockers 
• Process blockers 
• Data blockers 
• IT blockers 

Building out a Project Analytics capability requires a great deal of change; you’ll need to accept this and plan appropriately. For example, this may be the first time your organisation gets to agree on simple classifications and definitions of some key data terms. You’ll soon realise the importance of building a data governance strategy to set up data stewardship and accountabilities within major projects. The quality of your analytics will be greatly improved if each department comes to an agreement on the most critical data within the business. 

Quite often, you’ll begin to shine a light on longstanding data quality issues that need addressing as you build out your analytics capability. This is to be expected, so don’t shy away from making organisational, process, data and IT recommendations that improve the final outcome of your analytics reporting requirements. 

Phase 3: Production Build 

In phase 2, you’ve been mostly building out a ‘beta’ version of the core platform, but in phase 3, you’re going to start extending your analytics platform to support production capabilities. 

For example, you’ll be introducing different environments (e.g. Development/QA/Production) to coordinate regular cycles of production-ready software releases. 

One challenge at this phase will be managing the wave of inflated expectations. What you’ve created by this point will be far more advanced than previous project reports so you’ll need to ensure you have a communications plan and clear roadmap for engaging the users and stakeholders.  

You’ll also need to make it clear in those communications that, due to increased automation, there may be the occasional poor quality result coming through into the final analysis. This is to be expected as you scale up your production operation. The key is to eliminate the root-cause of any data defects, and follow up with some transparent discussions around what the users and stakeholders can expect from the data as it transitions through these early ‘growing pains’ of your production process. 

Quite often, occasional reporting issues are not always highlighting technical issues, but required changes in culture. With the added emphasis on data automation, your project staff will need to get accustomed to improving their data entry and project data quality. 

Other data challenges will become noticeable as your production environment ingests more data, particularly as you ‘widen the net’ for inbound data sources. A common issue will be the ‘single version of the truth’ problem that invariably arises when different applications, or even different departments, hold conflicting records. For example, it’s common for different systems to have differing project baseline data, for a variety of reasons.  

At the end of phase 3, you would expect your production environment to be an ‘enterprise grade’ system, meeting whatever relevant IT, security, data protection and data governance policies and controls your organisation may impose. 

Finally, phase 3 is where you will typically have enough resource and stability in your platform to start exploring any use cases for Artificial Intelligence and Machine Learning. 

Phase 4: Transition Back to Business 

At this phase, you’ll be looking to create a Business as Usual (BAU) scenario with your Project Analytics capability. Don’t underestimate the effort required to fully train and orientate the business on their obligations for taking over the solution.  

Phase 4 is where you’ll be in a good position to integrate with other large data initiatives, if they exist. By this point you’ve already created a robust data platform so you can expose your data to these bigger programs as just another source of quality data, but be sure to apply data quality controls/tests for the data you supply. Likewise, you want to be checking for data quality on any inbound data you receive from other data initiatives. 

During this phase, you can also start to realise the bigger benefits of Project Analytics by ‘democratising’ your data to project staff in need of good quality data and reporting insights. This project analysis ‘LEGO® set’ creates the building blocks of a self-service business intelligence capability, complete with a clean set of unified project data. 

Finally, project staff can swap their spreadsheets and hours of manual data wrangling, for a trusted and fully operational project analytics environment. 

Next Steps 

In the final article of this series, we’ll recap on all the steps you will have travelled so far, and outline how all of the typical Project Analytics use cases will come together, and who will benefit the most. 

If you have any questions about any of the techniques we have discussed in this series, feel free to get in touch for more information. 

The post The Four Phases of a Project Analytics Roadmap appeared first on Oakland.

Getting to grips with the reality of project data 

If you’re focused on delivering Project Analytics, sooner or later, you will have to address both the data availability and project processes. 

When it comes to data, we’re all familiar with the concept of ‘rubbish-in, rubbish-out’.  

In an ideal world, you would have a single repository of clean data, but within the project world, this ‘single version of the truth’ remains elusive for a variety of reasons:  

• Lack of consistent technology, data standards, and processes within organisations 
• Fragmented supply chains across suppliers and partners 
• Different requirements between customers and stakeholders 
• Lack of cross-referencing between systems means they can’t be joined-up 

Issues like these create a culture where there is a begrudging acceptance that the data will need to be manually joined up before it can be analysed. 

Once you start manipulating the data, you soon notice how bad the data quality is, which requires even more manual effort to get it cleaned before it can be joined up ready for analytical processing. 

Building your Single View of Project Data Analytics 

When building a single view of the data, you don’t always need to move all your data into one system, but it does need to be feasible to link up data (via cross-referenced identifiers) from across multiple systems. 

For many organisations, the ‘big corporate’ approach is often to invest in a single analytics tool and implement a ‘big bang’ initiative that builds out the analytics capability in one push. In theory, this is great when it works, but from our experience, these ‘big bang’ approaches are often: 

• Expensive and slow to deliver, hindering tactical exploitation of the here and now 
• Difficult to pin down requirements before project kick-off 
• Prone to a high failure rate
• Impacted by an ‘imagination gap‘ between the project analytics strategists and those who understand the data at a granular level 

We recommend a more incremental (Agile) approach so that you can get stakeholders excited and moving quickly whilst de-risking the project by only delivering what you need at each phase. 

From a technical perspective, we’ve found the Agile approach to be flexible enough to align with the changing landscape of the business. By building incrementally, you get to deliver immediate value whilst contributing to any longer-term ‘supertanker‘ data and technology programs coming down the line. 

To create an Agile approach in this way requires the type of data team we introduced in the second article of this series, combined with the data engineering strategy you’re going to learn in the rest of this article. 

Designing your data engineering platform 

Firstly, you’re going to need to know your data, a topic we covered in last week’s article. As discussed, you need to pay close attention to your data processes and any discovered issues. 

It helps if you are focused on discovering and resolving smaller problems initially to demonstrate that Project Analytics is achievable and delivers value, so be selective with your battles.  

You will need to weigh up the benefits against the risks and consistently illustrate the benefit of Project Analytics, even if stakeholders are not explicitly asking for proof. 

Starting from the right point 

Knowing the maturity of your systems and associated data processes is vital for understanding where to start your Project Analytics journey. 

You can use the following grid to help establish your starting point based on your system and data process maturity. Elon Musk may have made the goal of ‘moon shots’ popular – but we find that measured improvements from an agreed starting point make more sense : 

Designing your data architecture for the future 

Selecting an architecture is not straightforward because you’ll likely have to choose between:  

1. Adopting a ‘big corporate’ Business Intelligence/Data Analytics platform 

1. Software vendors who claim their project tool ‘has all the analytics you’ll ever need’ (but is likely to be lacking in key areas) 

1. Build a flexible data architecture and analytics platform that allows you to ingest data from across internal and external systems, using a suite of tools (right tool for the right job) 

Our preferred option is the third approach – creating a flexible architecture. It doesn’t lock you into a particular project analytics/software vendor, and it also allows you to build incrementally. 

Whichever option you go for, you must have the relevant expertise on your project to make the right choices. 

Sample architecture for building out a Project Analytics platform 

The following diagram is a standard architectural blueprint based on a Microsoft design for ingesting, processing, and presenting your Project Analytics data: 

Because it’s a combination of Microsoft and open source technologies, none of it requires proprietary project software, so you’re not tied into any specialist vendors.  

Being Microsoft, this architecture also integrates with all the popular data formats, including the most common of all, Microsoft Excel. 

We find it offers a flexible and relatively straightforward Project Analytics architecture because: 

• It’s future-proof – Microsoft present zero risks of becoming defunct 
• IT and technical teams rarely object to it and are comfortable with maintaining the different elements
• If you’re using Microsoft already, it will fit right into your corporate IT strategy and support structures 
• Expertise is widely available and easy to find 

Please note: Similar architectures are also available for Amazon Web Services (AWS) and Google Cloud if your organisation does not wish to build on Microsoft architecture. Contact us for details of alternative approaches. 

The key thing to remember is that these architectures are easy to understand conceptually, but you can get lost if it’s the first time your project team have built this type of architectural design. 

Data architectures like this are no different from any other major projects discipline. If you don’t have the knowledge in-house, you should first seek the right level of external expertise, then build up your internal capabilities over time. 

For those new to this type of architecture, here is a simplified overview of each component:  

• Azure Data Factory: A cloud-based data integration service that allows you to build data pipelines and workflows that ingest, process, and export data from a vast amount of systems and databases. 
• Azure Data Lake: A cloud-based repository for housing your Project Analytics information in an accessible, secure and flexible format. 
• Azure Data Bricks: An analytical tool that allows you to accomplish anything from simple reporting and presentation, all the way through to machine learning analytics. 
• Azure Synapse Analytics/Analysis Services: Think of these as providing a scalable repository for housing cleansed and prepared data ready for the business to analyse, along with the necessary services to let you govern, deploy, test, and deliver the final analytics data. 
• Power BI: The extremely popular suite of business intelligence tools for providing reports and detailed analyses securely across the organisation 

Considerations for purchasing a separate Project Analytics solution 

Some organisations will undoubtedly opt to go down a separate path and procure a different product or tool to deliver their Project Analytics initiative. 

If so, be sure that the technology can deliver what the sales literature claims. 

You will be changing source/target systems over time, so how will your proposed analytical solution cope, and what are the risks of getting locked into a single vendor? 

Architectures such as the one above, and Amazon Web Services (AWS), promote ‘decoupling’ and open configurations that allow improved solutions to be ‘slotted in’ over time to enhance performance, reduce costs, and deliver a better overall customer experience. 

Despite the naysayers, it is hard to compete with the benefits provided by modern cloud solutions. 

The Project Analytics journey so far, and the next steps 

In this series, we started off by examining the evolution of Project Analytics, how it is benefitting the Major Projects sector, and some foundational principles for getting started. 

In the second article in this series, we examined the skills required to deliver effective Project Analytics and the steps required when building a case for smarter Project Analytics. 

This article explained how to demonstrate the viability of your data analytics platform and explored what’s required to showcase some early benefits. 

In the fourth article, you will learn how to extend a Project Analytics pilot into a production-ready solution that is fit for the future. 

The post How to implement a data engineering strategy for your project analytics initiative appeared first on Oakland.