For our first Spotlight interview, Nicola, our Head of Marketing, sat down with Rajan Chavda to discuss how Microsoft Fabric is paving the way for analytics and AI adoption, the shift from infrastructure management to plug-and-play analytics, and why the single source of truth remains the enterprise’s main goal.

Joining the Dots – The Architect’s Role

Nicola: Rajan, you’ve been with Oakland for six months, focusing on the Microsoft stack. How do you define the value of a Solutions Architect?

Rajan: There are many interpretations of the role, but for me, it’s about joining the dots. We have experts dedicated to data strategy and governance, and engineers who handle the doing. The architect bridges the gap between business requirements and technical implementation.

Whether a client is struggling with disparate manual Excel documents or complex legacy systems, my job is to conduct a technology assessment, comparing vendors like Databricks, Snowflake, and Fabric, to ensure the chosen tools and approach meet their specific needs.

Deconstructing Microsoft Fabric

Nicola: Microsoft Fabric is being positioned as a significant evolution. What makes it a fundamental shift from traditional cloud platforms?

Rajan: Fabric is fundamentally a unified analytics platform. With other approaches such as Azure, you had to provision individual networks and components manually. Fabric moves toward a Software as a Service (SaaS) model. You buy capacity (think of it like a Netflix subscription) and get the entire suite – data engineering, data science, and Power BI – in one package. This plug-and-play availability allows us to focus on enriching data and building compelling visualisations rather than managing infrastructure.

Book our Microsoft Fabric workshop

Complementing the Existing Estate

Nicola: For an enterprise with an established platform like Databricks or Snowflake, where does Fabric fit?

Rajan: It’s rarely a rip-and-replace conversation, as Fabric is a complementary estate. While Databricks is exceptionally scalable for back-end engineering, it isn’t always as accessible for business engagement. Fabric can unlock this with Power BI and agentic capabilities, making it excellent for the serving layer. By putting them together, we help clients extract richer insights.

Solving the Fragmentation Problem

Nicola: What are the recurring challenges you’re seeing on the ground?

Rajan: Nine times out of ten, clients struggle to derive value because their data is siloed. I recently worked with a client who had 30 different systems, none of which were in sync with each other.

If you ask five people for the gross profit, you’ll get five different answers because they’re using different logic or disparate systems. Our goal is to build a single source of truth with unified logic. We create certified, Gold Standard products so that when someone asks a question, they know exactly which report to trust.

Before we wrap up, tell us something we wouldn’t expect about you…

Rajan: I’m an Avios collector with a goal to become an Avios Millionaire. I’ve only actually flown British Airways once, but I’m constantly finding ways to boost my points through strategy.

Is your data estate ready for a unified approach? Whether you’re looking to optimise your current Azure stack or evaluate a transition to Microsoft Fabric, our architects can help you bridge the gap.

Book our Microsoft Fabric Workshop

Rebuilding a data platform from the ground up with Microsoft Fabric

Our client is one of the UK’s leading transport organisations. Facing increasing business challenges, it needed to take full control of its data.

Read the full case study

Building a Data-Driven Professional Services Firm: A Data Strategy Journey

A leading UK accountancy and advisory firm on an exciting growth journey, expanding both organically and through acquisitions, needed the data capability to scale with it.

Read the full case study

The post Rajan Chavda Discusses MS Fabric 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.

The panel included:

• Andy Crossley, Oakland’s Chief Technology Officer (and our man on the ground at Microsoft Ignite 2025)
• Rajan Chavda, Oakland’s Microsoft Fabric expert
• Chris Hill, Oakland’s Purview and Microsoft Partnership Lead
• MLG (Mike), Oakland’s Principal AI Engineer

In typical Oakland style, nothing in here is theory or hype. It’s a grounded reflection on what Microsoft announced, what actually matters for organisations, and how these changes affect real data teams today.

Prefer to watch the full session? No problem – view the recording below!

What it Felt Like to Be at Microsoft Ignite 2025

Andy opened the session by sharing his experience of being on the ground in San Francisco. The scale was huge and the pace frenetic. Instead of a single moment where Microsoft revealed something dramatic, it became clear that innovation now moves so fast that Ignite is more of a checkpoint than a product launch.

What Andy heard most people talking about was the concept of the Frontier Firm. It’s Microsoft’s attempt to describe the kind of organisation that thrives in an AI driven world. Whether people loved the term or not (which is the subject of some debate), it dominated the conversations across the conference. The idea is not that you become a futuristic organisation overnight. Instead, every organisation will move at its own pace, building its own version of what Microsoft describes.

Understanding the Frontier Firm

What Microsoft Means

Microsoft’s model proposes three stages:

1. Humans supported by AI and Copilots
2. Humans and agents working together
3. Humans overseeing many agents that run key operations

The focus is not on shiny robots or science fiction. It’s on how work gets done and how AI can start taking on meaningful chunks of operational activity so people can focus on higher value work.

What this Means in Practice

The Governance Challenge

Chris highlighted that Microsoft is predicting more than one billion three hundred million agents by 2028. That scale raises the same problems we have always had in data. If you do not control access, permissions, and visibility, you quickly end up with an uncontrollable mess.

The Everyday Work Challenge

Rajan looked at the practical side. For him, the most important question is how AI reduces repetitive or low value tasks. The point is not to create agents for the sake of it. It is to support the real day to day work that teams do.

The Technical Reality

MLG spoke from his experience building agentic systems. Agents are powerful, but they can also be unpredictable. What stood out for him at Ignite was the improved tooling across Microsoft Foundry and the new Agent 365 ecosystem. These offer developers better ways to build, test and monitor agents so they can behave consistently inside real organisations.

The Cultural Reality

Andy added that organisations need confidence and clarity. Most people understand simple Copilot features. Most can imagine advanced automation. The challenge is everything in between. Organisations need a roadmap that is ambitious but also realistic.

Adoption Barriers and Opportunities

A question during the live event asked about the biggest challenges to adoption.

The group agreed that organisations need all three of the following:

1. Technical Readiness

You must have a clean, well governed and discoverable data estate.

2. Cultural Readiness

People must understand when and how to use agents and what good looks like.

3. Governance Readiness

You cannot deploy agents at scale without policies, controls and monitoring.

As Andy said, you would not hire a thousand people without onboarding and training. The same applies to AI agents.

MS Ignite Announcements by Product

The team reviewed the major changes across Fabric, Foundry, Purview and Microsoft AI services.

Microsoft Fabric: The Intelligence Layer Takes Shape

Rajan took the audience through the biggest changes in Fabric.

Fabric IQ

Fabric IQ introduces a new layer of intelligence across the platform. Ontology maps let you define the real business relationships behind your data. This makes insights richer and gives agents more meaningful context to work with.

Operations Agents

For Rajan, Operations Agents are the standout feature. They allow organisations to close the loop between insight and action. Instead of dashboards hoping someone does something, Fabric can now monitor data, suggest actions, and trigger workflows. This is surfaced directly in tools like Teams.

Interoperability with Databricks and Others

One of the most encouraging themes was Microsoft’s shift toward open integration. Fabric will work more closely with Databricks, Snowflake, and SAP. OneLake-backed compute for Databricks is planned for future releases. Andy noticed how often interoperability came up at Ignite. Microsoft now accepts that real organisations run mixed estates.

Purview and Agent 365: Governance Grows Up

Chris explained why Purview had a smaller public presence at Ignite. The reason is that governance is being repositioned within the new Agent 365 framework.

Agent 365 Brings Five Key Capabilities

1. A registry of every agent in your organisation

2. Central access control using Entra ID

3. Visibility into what agents do

4. An understanding of how agents interact across departments

5. Security anchored in Purview

This directly addresses the problem of agent sprawl. As Chris put it plainly, AI governance is not optional. It is essential.

Microsoft Foundry: A Mature Space for AI Developers

MLG walked through how Foundry has evolved into a unified space for building, evaluating and managing agentic systems.

Foundry IQ

This gives developers the ability to benchmark agents, measure accuracy and check factual grounding. Without testing, there is no trust. Foundry IQ is Microsoft’s answer.

Agent Lifecycle Management

Foundry now supports versioning, AB testing and controlled retirement. If one agent outperforms another, you can replace it safely and consistently. This is vital if organisations want agents to work at scale.

What Does All this Mean for Organisations?

From the discussion, four clear messages emerged:

1. Strategy First

Technology should support your business strategy. Without clarity on what you are trying to achieve, AI becomes a distraction.

2. Readiness Matters

Adoption will not work if your data estate, governance or culture is not prepared. Just because the capabilities exist does not mean you are ready to use them.

3. Trust Must Be Earned

People will only trust agent driven actions if results are reliable, transparent and grounded in fact.

4. Governance will Decide Success

Agent 365 and Purview will be central to safe adoption. Governance gives you the confidence to scale without losing control.

Final Thoughts from the Oakland Team

Ignite 2025 didn’t feel like a tech expo where one big announcement stole the show. Instead, it marked a point where Microsoft began stitching together years of change into a more coherent story.

The frontier firm is not a single destination. It is a direction of travel. Organisations will move at different speeds and in different ways. What matters is strong data foundations, clear governance and a realistic roadmap.

Oakland’s focus remains the same. We help organisations build the maturity they need to use AI responsibly and effectively. With the right support, AI and agents can streamline operations, free teams from repetitive tasks and unlock new value in the business.

FAQs about Microsoft Ignite

What is Microsoft Ignite?

Microsoft Ignite (MS Ignite) is the annual event where Microsoft showcases the trends, innovations, and future vision for their productivity, cloud, and security technologies. The event takes place over several days and is packed with keynotes from 2,000+ speakers, 800+ interactive sessions, networking opportunities, and more. It’s attended by over 20,000 people in-person, with a further 200,000 joining digitally.

Who should attend MS Ignite?

Developers, IT and data engineers, cloud architects, business leaders (including startup founders) will all find the event informative and inspiring. In the words of Microsoft themselves: ‘Microsoft Ignite 2025 isn’t “just another conference.” It’s a unique gathering that brings together technology leaders, tech professionals, developers, founders, and Microsoft partners for four days of immersive learning and groundbreaking announcements.’

When is Microsoft Ignite 2026?

The next event will be held at the Moscone Center in San Francisco between 17th and 20th November 2026.

The post Microsoft Ignite 2025: The Debrief from Oakland’s LinkedIn Live appeared first on Oakland.

It’s the place where the whole data industry comes together to find inspiration, share challenges (making AI work – anyone?), and see the latest tech solutions from the world’s premier data solutions providers. 

Every September, thousands of students, data leaders, practitioners, and innovators descend on London’s Olympia for Big Data London. It’s two days of finding out what others are up to, hearing about the latest tech innovations and, let’s be honest, a bit of eye-rolling about the latest hype cycle. 

For us at Oakland Everything Data, it’s more than just another industry event. Big Data London is where we meet our clients, past, present, and future. It’s where the conversations that matter in data and AI happen. And yes, we’ll admit it, stocking up on socks and T-shirts is a nice bonus, too. 

But this year, Big Data London feels bigger, bolder, and more important than ever. With AI hype at fever pitch, data leaders are under pressure to deliver real results – and quickly. That’s why we’re so excited about what’s coming up this September. 

Why Big Data London Matters 

What makes BDL special is its ability to bring the entire data community under one roof. From startups to the biggest global enterprises, everyone is here to share what’s working, what isn’t, and what’s next. It’s a unique opportunity to see cutting-edge solutions, compare experiences, and walk away with a roadmap for delivering value back in the real world. 

And while the exhibition floor is buzzing with activity, it’s the conference theatres that steal the show. Packed to the rafters year after year, these sessions offer hundreds of deep dives into everything from governance and data quality to AI ethics and platform modernisation. You need to get in line early to bag a seat, but it’s worth it.

Big Data London 2025 Speakers 

This year’s agenda is stacked with over 300 speakers, including data engineers from Netflix, analytics leaders from Google, and strategists from across industries. And of course, Oakland. For the first time since our partnership was announced, Softcat will be there too. 

Our very own Andy Crossley, CTO of Oakland, will be joining forces with Alex Pearce, Chief Microsoft Strategist at Softcat, for a no-holds-barred session on why AI isn’t working for everyone and what you can do about it. Their talk, “AI: It’s Going Back to the Future,” will cut through the hype and get real about the foundations every organisation needs to succeed with AI. 

Expect honesty, practical lessons, and plenty of myth-busting. If you’ve ever struggled to show ROI on your AI investments, this is the session for you. 

Where and When is Big Data London 2025? 

Dates: 24–25th September 2025 

Venue: Olympia London, West Kensington 

Nearest Tube: Kensington (Olympia) is right next door, with West Kensington and Barons Court close. 

It’s easy to get to, and once you’re inside, everything is under one roof – whether you want to explore the exhibition floor, catch a keynote, or grab a coffee and talk shop. 

Who Should Attend Big Data London? 

One of the things we love most about BDL is its accessibility. It’s not just for hardcore techies or analysts – it’s for anyone with a stake in data. 

• CDOs and CIOs looking to build data strategies and prove business impact 
• Heads of Data and Analytics navigating the complexities of AI adoption 
• Finance leaders (CFOs) tasked with unlocking ROI from major tech investments 
• Practitioners and engineers eager to hear from peers and sharpen their skills 
• Students and early-career professionals looking for inspiration and opportunities 

Wherever you are on your data journey, you’ll find something valuable here. 

Where to Find Oakland and Softcat 

We’ll be exhibiting at Stand 60, located right next to one of the main theatres for maximum visibility. Pop by, say hello, and grab one of our legendary Oakland guides. Whether you’re wrestling with AI, data governance, or platform engineering, our team will be on hand to talk through your challenges. 

Oakland & Softcat on Stage 

Mark your calendars!

Session: AI: It’s Going Back to the Future 

When: Wednesday, 24th September, 15:20 – 15:50 

Where: Gen AI, App Intelligence & AI Agents Theatre 

Here’s a flavour of what Andy and Alex will cover: 

• Why so many AI projects fail to deliver real value 
• The critical data foundations every business needs to succeed 
• Real-world lessons from organisations finding AI harder than expected 

The good news? You’ll walk away with practical steps to start unlocking real value from your AI investments. 

Big Data London 2025 Theatres 

Big Data London is nothing if not comprehensive. With 13 dedicated theatres, you’ll have your pick of sessions to match your interests. Themes include: 

• Gen AI & AI Agents
• AI, Data Science & MLOps
• Analytics, Visualisation & Storytelling
• Data & AI Governance
• Data & AI Strategy
• Data Architecture Modernisation
• Data Engineering
• Data for Good
• Data Products & Data Fabric
• DataOps & Observability
• Fast Data
• Integrated Planning Analytics & Sustainability
• Teams & Culture

Big Names at Big Data London 

BDL always attracts an impressive roster, and 2025 is no exception. Expect speakers from organisations as varied as: 

• Magnum Ice Cream 
• The Ministry of Defence 
• Specsavers 
• Netflix
• Microsoft 

It’s not often you see such a diverse range of industries represented in one place proof, if you needed it, that data really is everyone’s business. 

And then, of course, there’s the headline speaker. This year, Professor Brian Cox looks at the small subject of: Is the universe the ultimate processor of information? Expect a mind-blowing session that blends quantum mechanics, information theory, and the very fabric of reality. 

How To Get Tickets for Big Data London 2025 

The best part? Big Data London is free to attend. You just need to register in advance. Tickets are available online until 19th September, so don’t leave it to the last minute. 

Why We Can’t Wait for Big Data London 2025 

For us at Oakland Everything Data, Big Data London is like being kids in a sweet shop. It’s not just about the content (though the content is world-class) – it’s about the energy of 15,000+ attendees, the buzz of the exhibition floor, bumping into old friends, and the chance to meet and connect with so many incredible vendors. 

It’s also about walking away with ideas – lots of them. From practical ways to scale AI responsibly to swapping war stories with fellow data leaders, we always leave BDL buzzing with inspiration and armed with new thinking to bring back to our clients. 

Final Word 

If you care about data and if you care about getting value from AI you can’t afford to miss Big Data London 2025. 

Join us at Olympia London this September. Come to our session, swing by our stand, and let’s talk about how to move from hype to value in AI and beyond. We’ll see you there!

The post Why We’re Counting Down to Big Data London 2025 appeared first on Oakland.

As ever, Richard brought his no-nonsense home truths to the conversation, which you can catch up on below. AI and business topics they dived into include: 

• What it really means to “liberate data” in the age of AI
• Why hype-driven adoption can lead to more headaches than headlines
• How organisations can get back to basics to unlock genuine business value from AI

True to form, his trademark mix of practical insight, a dash of scepticism, and the occasional hammer analogy kept the discussion around AI for business grounded in real-world experience.

“Let’s stop looking for things to hit with the AI hammer – and start with what actually adds value.”

Richard Corderoy, CEO of Oakland Everything Data

No More AI Fairytales

If you’ve been to any conference lately, you’ve probably heard the AI hype machine in full swing. 

• “It’s the future!” 
• “It’ll change everything!”
• “If you’re not doing it, you’re already behind!”

Richard says, “Honestly, I think we’ve lost our heads a bit. Everyone started with, ‘I have a hammer, what can I hit?’ and, funnily enough, that causes a lot of mess and not much actual value.”

For Oakland Everything Data, we believe the question isn’t “Should we do AI?”. It’s “Why do we need it, and what are we trying to achieve?”.

Value First – Start at the End

Oakland’s mantra is simple: start with value. We’re so passionate about value that it is one of our five principles for creating lasting data impact. 

“None of this matters until someone makes a better decision, a customer gets a better service, or a process actually changes for the better,” Richard explains. “So let’s start at the end –  what do you want to achieve – and work backwards from there.”

That means AI isn’t the starting point. It’s a tool, one of many, to help solve a business problem. And in Oakland’s experience, most of the time the real blockers aren’t technological at all. They’re about messy processes, unclear ownership, or bad governance.

Why AI Projects Stumble

When asked what’s holding big firms back, Richard doesn’t hesitate: “Data complexity. Even mid-sized organisations are drowning in it. They’ve tried a few tech fixes, maybe moved to the cloud, but haven’t got the full value they expected. Then AI comes along with all this promise – and panic sets in.”

He sees it often: the fear of being left behind meets uncertainty about where to start. Add pressure to spend wisely, and you’ve got a recipe for half-baked experiments that never make it to production.

Agentic AI – The “Smart Intern” Approach

One of Richard’s favourite concepts is Agentic AI – not a giant all-knowing brain, but a network of small, specialised AI “agents” that each do one job well.

“Think of them as smart interns. You wouldn’t hire 50 people to read and categorise all your product descriptions, but you can get a couple of agents to do that. They’re not free but they’re a lot cheaper than a human team.”

Richard Corderoy, CEO of Oakland Everything Data

The trick? String these agents together so they can collaborate much like people in a process and let them handle repetitive, time-heavy work so humans can focus on higher-value thinking.

A Real-World Example

Richard shares a client project he’s particularly proud of. A large sales organisation needed to recategorise tens of thousands of products into new business-defined categories.

“The old way? Interns Googling products, reading PDFs, updating records… and then doing it all again 50,000 times. Painful.”

Instead, Oakland built a team of AI agents:

• One read and extracted existing product data.
• Another searched supplier websites.
• Another analysed documents.
• A “coordinator” agent pulled the inputs together and made the final categorisation.

The result? Accuracy jumped to 98%, manual effort dropped drastically, and the process now runs automatically for every new product.

“It’s not about perfection. If a human team had a 5–10% error rate, we’d accept it. So why demand 100% from a system? Set sensible tolerances and move on.”

Richard Corderoy, CEO of Oakland Everything Data

Governance Without Red Tape

Richard is clear: governance matters, but it can’t become a three-year paperwork exercise.

“We’ve all seen those rooms with shelves of process binders no one looks at. That’s governance done wrong. The right level of governance depends on what you’re governing and the risk involved. And you can improve governance while delivering value, you don’t have to wait years before you start.”

Use our blog to understand more about data governance in the age of AI.

Ethics – Don’t Hold AI to a Higher Standard than People

When the conversation turns to AI ethics, Richard challenges a common double standard: “We say we don’t want bias in AI, but every human decision-maker has bias shaped by where they’re from, their education, their experience. AI shouldn’t get a free pass, but let’s not pretend bias is new.”

Oakland’s approach is to help clients define what “ethical” means for them, pick use cases with manageable risk, and design governance around that.

Richard’s Advice for Businesses Feeling Overwhelmed

1. Stop talking about AI as if it’s one thing. 

Decide what type of AI you’re talking about and why it matters to you.

2. List your biggest problems without a tech person in the room. 

Then figure out which ones could be solved with AI.

3. Accept “good enough” in the right contexts.

 Perfection is a luxury, value comes from speed and integration.

4. Build in small steps. 

Prove value early, then scale.

“It’s like electricity – when it first came along, the lightbulbs were rubbish, but the potential was huge. We didn’t throw it out, we found the right applications. AI’s the same. It’s not magic – it’s just another tool. Let’s use it properly.”

Richard Corderoy, CEO of Oakland Everything Data

“It’s like electricity – when it first came along, the lightbulbs were rubbish, but the potential was huge. We didn’t throw it out, we found the right applications. AI’s the same. It’s not magic – it’s just another tool. Let’s use it properly.”

Richard Corderoy, CEO of Oakland Everything Data

For all of Richard’s wisdom, please watch or listen to the full webinar. And to understand how we can support practical AI for your business, please contact our friendly team.

The post Practical AI for Business: Cutting Through the Hype with Richard Corderoy appeared first on Oakland.

Data is core to modern, effective operating models that are helping leaders to increase efficiencies, optimise resources, and drive down costs – all while maximising profit. So, what do you need to do to incorporate data into your target operating model, and does it need one of its own? 

Read on for what you need to know about target operating models, and why data is integral to their success.

What is a Target Operating Model?

A target operating model – or ‘TOM’ as we like to say – is the blueprint of how your organisation runs. It includes the overall capabilities of the business, from people and processes to assets and technology, and is used to make sure operations and processes are in line with the vision of the leadership team. 

If you’re wanting to drive meaningful, long-lasting change, a target operating model is the place to start.

Data Target Operating Model Explained

As the name suggests, a data target operating model focuses on delivering everything data, from your overriding data strategy to ongoing data management . It’s just as aligned with your overall business TOM and vision, but the elements within are more targeted towards data. 

“A data target operating model is how an organisation’s data capabilities are structured,  managed and sustained to support delivery of the data strategy, which should align with delivering your business outcomes and needs.”

Craig Lambert, Senior Consultant at Oakland

Dive deeper into the importance of data to your business in our blog: Why is data important for business?

What Should a Data Target Operating Model Include?

Your data TOM involves the different systems and frameworks you have in place to aid data-driven decision-making. When developing a data operating model, we structure it using the following framework:

• Strategy and purpose

Sets the organisational vision and mission.

• Leadership and talent

Translates the strategy and makes it meaningful to different business units, as well as demonstrating the business’s ways of working.

• Organisational structure, roles and responsibilities

The size and structure of the team, and the different roles and associated responsibilities of each colleague. The policies regarding secure data management and how issues are raised and resolved are also included in this area.

• Culture and ways of working

Embedding data as a core business asset requires the right skills and mindsets. Your data TOM is the perfect place to set out what these are and how leaders/managers can empower employees to confidently own and manage data. 

• Technology, tools, and data

Typically includes the technologies that can be scaled easily to future-proof longer-term growth or automate tasks to increase efficiency. It should include all tools used for data integration, storage, and analysis, too, so the different data components are reflected in the wider business TOM.

• Processes and feedback loops

To deliver the data operating model effectively, well-defined processes that connect technology solutions are key. Here is where you can explain how insight over data issues should be shared with other areas of the business to make for more effective functions. 

Target Operating Model Case Study

One of the best examples of the importance of a target operating model is our work for a major UK media organisation. While creating the customer data platform that delivered growth and competitive advantage, we also developed a TOM. The organisation’s headcount and skillset was growing fast, so an operating model became critical to success.

Overall, the TOM we developed has delivered: 

• Increased control and governance across the delivery cycle 
• More streamlined and insightful execution 
• Better reporting, KPIs and performance tracking 
• Higher clarity over the functional split and handoffs between teams 
• Identification of areas needing investigation

For the full details of our work with this media organisation, please read: Creating a customer data platform for long-term innovation.

Harness Data to Deliver Real Business Impact

You’ll know that data holds the key to unlocking more efficiencies and informed decision-making within your organisation. But there’s a seismic difference between how far you’ll get with average data and the opportunities you’ll discover with great data. 

To deliver sustainable, long lasting value from your data and ensure desired returns on investment in your data capabilities and products, you’ll need a target operating model that’ll safeguard your business for years to come. 

Please contact our friendly team to find out how we can support data transformation within your business.

We’ve also got plenty of resources for you to use to improve the integrity of your data TOM and overarching data strategy.

The post Why Data is Integral to Your Target Operating Model appeared first on Oakland.

At Oakland, anything and everything data is at the core of the data and AI consultancy services we provide. When it comes to data engineering, Databricks is a central block in how we build advanced data platforms to provide actionable data insights for clients. 

In this article, we dive into the details of building a data platform with Databricks, including:

• The role of Databricks in data engineering
• Why Databricks and ETL (extract, transform, load) are a match made in data heaven
• An overview of Azure Databricks
• The pros and cons of Databricks
• Use cases of Databricks

Let’s start.

The Role of Databricks in Data Engineering

Databricks plays a central role in modern data engineering by providing a scalable, high-performance platform. Built on Apache Spark (which is around ten times faster than traditional SQL databases), it enables teams to ingest, transform, and process vast volumes of structured and unstructured data efficiently. 

With features like Delta Lake for reliable data storage, and Photon for accelerated SQL performance, Databricks powers robust ETL (extract, transform, load) pipelines, real-time processing, and advanced analytics. Its unified workspace supports collaboration across data engineers, analysts, and data scientists, making it a key component of enterprise data platforms.

Databricks and ETL: A Match Made in Data Heaven

As a cloud-based platform, Databricks lends itself to ETL workflows – in fact, several of its tools and features have been specially designed with ETL pipelines in mind. So if slicker data extraction, transformation, and loading is important to your data activities, a platform engineered using Databricks could be a perfect fit. 

Some of the benefits (and the features that enable them) are listed below:

Easier ETL development: 

Thanks to Databricks Lakeflow Declarative Pipelines (previously Delta Live Tables), the operational complexities of ETL processes are automated. You define what should happen, not how, reducing boilerplate code and enabling ETL in SQL or PySpark, speeding up development cycles and reducing operational overhead. ETL can be written in Spark or SQL, too, for extra flexibility.

Streamlined workflows

ETL tasks, analytics, and machine learning pipelines are all orchestrated in Databricks Lakeflow Jobs (previously known as Databricks Workflows).

More focus on data quality

Thanks to features like Lakeflow Declarative Pipelines and automated data quality (DQ) testing, Databricks reduces the need for engineers to manage pipeline infrastructure or check DQ, freeing up time to deliver high-quality data.

What is Azure Databricks?

Given its power, it was only a matter of time before Microsoft jumped on the Databricks capability. In 2017, they became a first-party provider of Databricks’s cloud-base platform, integrating it with its own Azure cloud services. The result? Azure Databricks, the open analytics platform that allows you to build, deploy, share, and maintain enterprise-grade data, analytics, and AI solutions at scale. 

Naturally, as a Microsoft Partner awarded the Analytics on Microsoft Azure specialisation, we were super excited about the integration! Azure Databricks is another building block in our data engineering toolkit, allowing us to engineer data platforms at enterprise scale. Not to mention the immense potential it’s opening up for our customers and their data assets.

Our blog, ‘How to create a secure Azure data platform’, looks at Azure services in more detail.

“Our collaboration with Microsoft builds on our momentum as a leading cloud platform for Apache Spark-based analytics. The ability to provide our Unified Analytics Platform to all Microsoft Azure users in such an integrated fashion is invaluable to end users looking to simplify big data and AI.” 

Ali Ghodsi, Co-Founder and CEO of Databricks

Databricks Use Cases

With this in mind, let’s cut to three of our recent use cases using Databricks as part of our advanced data engineering service.

1. Building a sustainable, long-term data platform for Network Rail

Data platform engineering is an investment, so you need to make sure the technology is set up for future success. Databricks enables an open approach, reducing the complex nature of being ‘locked in’ that comes from using a more traditional platform vendor. Something our client, Network Rail, knew all too well.

Like many other large organisations with legacy data platforms, Network Rail was struggling to access data, which made extending the capabilities of their datasets difficult. Using Databricks, we built an open data platform architecture for the rail services provider, which has:

• Automated manual processes, freeing up valuable time and resources to spend elsewhere
• Reduced lead times by merging two loosely data pipelines into one
• Enabled smarter decision-making, thanks to the deployment of advanced analytics and ML tools

Yet that’s just the start – Click here to read the full case study.

2. Informing sales strategies for a leading provider of IT infrastructure

Sales team struggling to extract data from multiple sources? We recognise the challenge, and it’s one we helped a leading provider of IT infrastructure overcome. 

After we designed the IT service firm’s new data analytics platform, we leveraged the Databricks stack to build a machine learning and data science model. Their sales team now have access to far richer insights, driving better margins for the overall business. These insights include:

• A customer’s tendency to buy certain product categories 
• A highlighting system of the products that customers are likely to buy
• Product penetration and available spend information, so staff can quickly spot where to focus time and energy

3. Driving an ROI increase of £150m+ for Yorkshire Water

As part of an overall data transformation programme, we developed a new data platform for Yorkshire Water. Databricks was primed to be the enterprise data architecture for the utilities company and was pivotal to the design and build of their new, strategic data platform. 

In total, ROI from the overall business transformation has exceeded £150m.

What are the Pros and Cons of Databricks?

It’s fair to say our Databricks and Azure Databricks use-cases and results speak for themselves. However, it’s important to weigh up the pros and cons of any data architecture to make sure you’re choosing the best fit for your business needs. We’ve outlined some of the major pros and cons of Databricks below to give you a better understanding of whether it’s right for you or not.

Data Engineering Advice

Of course, for more advice on Databricks and data engineering, or to speak to us about your needs for a data platform, please get in touch with our friendly team. That’s what makes us Oakland, everything data.

The post The Role of Databricks Architecture in Data Engineering appeared first on Oakland.

At Oakland, we know that running a data quality check can seem daunting, especially within an organisation that handles an abundance of data. It can also sound expensive. So, do you need to invest in a shiny new tool to carry out the exercise, or is legacy software enough?

Read on to find out and understand the impact of regular data quality monitoring along the way.

Why is Data Quality Important?

Data quality is a key part of successful data management. It makes sure the data you’re inputting, analysing, and reporting on is accurate, complete, consistent, valid, and reliable. 

If you’re handling poor quality data, then you open up space for a host of negative impacts to be felt across the business. For instance, outdated customer data can lead to upselling opportunities being missed. Or, incomplete data that causes employees frustration and impacts both their satisfaction and productivity. Check out our blog for more information on the costs of poor quality data.

Not to mention if you want to use AI to improve your business processes and efficiency you have to get the basics right, which starts with the quality of your data.

The Impact of High Quality Data

The higher the quality of your data, the higher the overall quality, accuracy, and reliability of the decisions you – and your people – are using it to make. The more you invest in your data, the more opportunity you have to add value to both your people and processes, resulting in increased:

• Confidence
• Productivity
• Profitability 

Are Traditional Data Quality Checks Outdated?

It’s no secret organisations are handling bigger, more varied volumes of data than ever. And it’s only going to grow. Traditionally, data quality checks were a manual task, meaning they were prone to human error. They also ate into a vast amount of time, reducing efficiency and risking inaccurate or low quality data being detected too late.

Today, there are plenty of new and evolving technologies out there to support organisations in upholding data quality. Think artificial intelligence, which reduces the need for manual intervention (and the risk of human error), or machine learning algorithms, which can quickly spot anomalies and patterns in data. 

Learn how we can help you leverage AI to unlock more value from your data: Artificial Intelligence.

Do You Need to Invest in New Technology?

If you’re just starting out on your data quality journey, expensive technology and tooling isn’t necessarily the answer, and in many cases, it can be a costly distraction. We often see organisations with low data maturity jump straight into the tech, hoping they’ll solve deep-rooted issues. But without a clear understanding of what you’re trying to improve and why technology alone won’t deliver meaningful change.

For organisations that have a clear strategy for managing and improving data quality, the right technology can absolutely accelerate progress. They can bring speed, automation, and scalability to what you’re already doing well. But choosing the right tool is key, and with so many options out there, it’s easy to pick something that doesn’t quite fit.

There’s no silver bullet. Data quality tooling should be chosen based on the specific challenges you’re trying to address. It sounds obvious, but we’ve seen too many teams invest in tech that, six months in, isn’t meeting their needs.

It’s amazing how many organisations manage their data quality through Excel, so you don’t always need an all-singing, all-dancing data quality tool to get an overview of the state of your data quality. 

So, how do you get started? Here’s the advice from our data quality consultancy specialists.

How to Ensure Data Quality Without New Tools

1. Perform a data profiling exercise

First things first, you need to make sure you understand your current data quality. A data profiling exercise is the best place to start. This will give you an overview of information about your data, from the most popular values to if there are any duplicate values, and how many. This usually highlights anything that looks out of the ordinary.

To run out a data profile, you need to choose a small subset of data to investigate. For instance, customer contact details (addresses, emails, and phone numbers) As we mentioned you can use simple Excel to help with this, but if you are looking for more heavy lifting then  Databricks, or Microsoft Purview can be good options to help carry out the profiling and analyse the results. Then, you can start to ask questions to better understand the quality of the data, such as:

• What are the most common values and value distributions in key fields?
• What percentage of fields are null, zero, or default values?
• Are there outliers or anomalies we weren’t expecting?
• How many formats/structures are present in supposedly standard fields?

Enhance your decision-making even more with our Data Analytics consultancy services!

2. Assess data by developing business rules for each field

Now, you can actively assess your data by developing business rules around each data field. Make sure you assign each rule to data quality dimensions, such as:

• Completeness
• Accuracy
• Validity
• Timeliness
• Consistency
• Uniqueness

As you carry out your data assessment, you’ll begin to spot data that’s below an approved threshold or doesn’t follow the business rules you’ve set. With this knowledge, you can then carry out a root cause analysis to find out the cause of the issue. From here, you can put together improvement options and a long-term remediation plan. You may need to look at shorter-term fixes, too, depending on the types of issues you find in your data.

Here’s an idea of what your data quality improvements could involve:

• Ensuring values are updated in suitable timescales.
• Providing training, supporting documents, a business glossary or data dictionary to those who enter the data itself.
• Developing regular scorecards that track quality KPIs (completeness, accuracy, timeliness) for high-impact datasets.
• Ensuring that key reference data (like country codes, product categories, or customer types) are aligned and centrally managed.
• Setting up automated alerts or dashboards to flag anomalies, missing values, or outliers in real time.

• Assigning responsibility for data quality to specific individuals or teams to ensure accountability and continuous improvement.

3. Put regular data quality monitoring in place

With the bulk of data profiling done, you can put regular monitoring in place. Not only will this ensure you’re proactively keeping track of your data quality, but it enables you to set alerts when data quality drops or there are improvements that need investigating.

Power BI is one of the best ways to do this without a new or fancy data quality management tool. Developed by Microsoft, Power BI is a software product that helps you visualise data. Primarily, it’s focused on business intelligence – hence the name ‘Power BI’!

You can use Power BI to create dashboards that record and track your data quality, with fast access to results. The Microsoft software is also great for presenting your findings to colleagues or stakeholders, such as data stewards and project managers or your executive board.

For insight on how you can get executive buy-in for data-driven excellence, read: How to deliver a successful data strategy presentation.

Remember, Communication is Key

As we’ve set out above, you don’t need a new or state-of-the-art tool to run a data quality exercise and return insightful, actionable findings. However, there’s one thing you do need: good communication.

The better your people’s understanding of the requirements of the data your organisation holds, the easier it is for them to play their part in improving data quality activities, like data entry. Make sure you have your business rules approved and then communicated across the business, so everyone’s working with the same data quality ideals in mind. 

We also recommend you share any improvements that come from data quality reviews and assessments, so your people understand the tangible difference it’s making.

Data Quality Consulting

For more support on how you can improve data quality within your organisation, please contact our friendly team. And if you’re interested in more insight from our data quality consultants, read our blog: Why invest in data quality?

The post Can You Improve Data Quality Without New Tools? appeared first on Oakland.

At the heart of data management is data ownership – i.e. who’s responsible for data, and what this involves. It goes without saying that the better your people’s understanding of the importance of data ownership and their roles in it, the better the overall data management of an organisation. 

We know getting people to take ownership of data can be a hurdle in itself. So, we’ve pulled together advice on how you can implement a data management plan that promotes ownership of data and returns powerful results for both your people and processes.

Barriers to Data Ownership

Traditionally, anything and everything ‘data’ has fallen under the responsibility of an organisation’s IT department. They’ve held the keys to data access and management, but it’s fast becoming outdated in a business world where users need to retrieve and analyse data, fast. 

Data is at the heart of more target operating models than ever, so democratising it needs to be a priority for organisations wishing to remain competitive in a rapidly evolving landscape. On paper, it seems relatively simple. But the reality is a challenge. People who have had little, or not even any, data responsibilities may feel apprehensive about owning data, or unprepared for these new accountabilities. 

Need support with your data strategy? Find out how we can help!

What is a target operating model?

A target operating model is the ideal operating state of an organisation. You can think of the target operating model as an organisation’s blueprint for carrying out its strategy to run efficiently and meet targets. People, systems, and data are all fundamental to target operating models.

Read our blog, Target operating model: Delivering your business strategy, for more details.

Why Data Ownership is Important

On average, poor data quality costs organisations $13 billion a year (according to 2020 research by Gartner). The quality of data is directly linked to your data management system and ownership, because it’s a result of how people enter, process, and use it. So not only can data ownership empower users to better understand data, but it holds influence over decisions, not to mention the bottom line.

Dive into the impact of poor data quality further in our blog: What are the costs of poor quality data?

Overcoming Data Management Challenges

The first challenge you have to tackle is understanding your organisation’s strategic goals and pain points. Once you understand your organisation’s ambitions you can start to look at people and processes. Data ownership doesn’t happen overnight – it’s an ongoing activity that you’ll need to continue refining for the best results. When data owners understand how their data contributes to key business outcomes such as customer satisfaction, operational efficiency, or regulatory compliance they are far more likely to prioritise accuracy, timeliness and completeness. 

We’ve broken down three areas to look at to take the first step to better data ownership for your people and processes:

1. Executive buy-in
2. Policies and documentation
3. Training and communication 

1. Get executive buy-in

When executives or leadership are bought into data ownership, you’re immediately proving its worth and importance to your people. Plus, you have someone to allocate the resources (people, time, money etc) necessary to prioritise and solve problems. Where data ownership is practiced organisation-wide, it naturally becomes intrinsic to how people work. With executive buy-in, people are more likely to buy into the importance of data ownership themselves, and feel driven to make time for the responsibilities it involves. 

Whether you’re wanting to make someone a data owner, delegate, steward, or custodian, make sure you’ve got executive buy-in first – and watch as your people’s confidence in their data management role grows. Our blog has advice on how you can talk about everything data to your board of execs: How to deliver a successful data strategy presentation to the board.

2. Nurture confidence with data ownership policies and documents

On this note, confidence comes when your people are clear about what’s being asked of them, and why. Having the right policies and documents in place (as well as related training and support) are the basics of defining roles and responsibilities so that data ownership activities are understood and carried out. 

We also recommend you add details of data ownership roles and responsibilities to employee records, like objectives, and performance reviews, so they can be referred back to as and when needed. Remember to update these as ownership responsibilities evolve, too!

3. Invest in ongoing data ownership training

To make sure your people have the necessary knowledge and skills to confidently carry out their ownership role, providing the right training is imperative. Alongside standard courses or training, speak with your new data owners to understand how else you can support them with the additional responsibilities. Communication is key to building a culture where everyone understands:

• Why data is important for business
• How to use data effectively
• Their individual role in looking after/owning data
• Who the data owners are

Better Data Ownership for Better Results

The clearer you are with your data owners about what they’re responsible for, and why, the better your data quality – and in turn, data insights, will be.

For advice on how to tailor a data management plan that empowers people to take ownership of data, please contact our friendly team.

And for support with building the data strategy to take your business to the next level, check out our guide: How to write your data strategy.

The post How Does Data Management Help People Take Ownership of Data? appeared first on Oakland.

• What is Microsoft Fabric?
• Why does it exist?
• Microsoft Fabric VS Power BI: Is there a need for both?
• Microsoft Fabric VS Databricks: How do they stack up?
• Why does Fabric promise to reshape the landscape of enterprise data platforms?

Below, we round up the insight Chris shared during the informative session – a must-read if you’re considering modernising your data stack. 

How Microsoft Fabric Empowers Radical Simplification

For organisations navigating increasingly complex data estates, Microsoft Fabric promises a radical simplification. 

“Fabric isn’t just a bundle of tools; it’s an integrated platform designed from the ground up to work seamlessly, making it far easier to extract value from data without the usual configuration headaches.”

– Chris Webb, Microsoft Fabric expert and Fabric Customer Advisory Team Member

But what spurred Microsoft to develop Fabric, and what problems did they set out to solve with another data platform?

The Need for Another Microsoft Data Platform

If you’re already invested in a secure Azure data platform, it may not be the first time you’ve asked this. Azure already boasts tools like Power BI, Azure Data Factory, Spark, and Synapse. However, as Chris noted, combining these services to build a functioning data platform has historically been complex and time-consuming. 

Interested in Azure? Find out how we delivered a Microsoft Azure platform to leverage market expansion and growth for Emerald Publishing.

What Fabric offers is a unified software-as-a-service (SaaS) platform where everything is pre-integrated. With Fabric, you don’t need to spend time wiring services together, managing multiple security layers, or stitching storage solutions across products. You simply turn on Fabric, and everything, from data ingestion and transformation to data warehousing, reporting, and governance, works together by default. 

At the heart of Fabric is OneLake, a single storage location where all workloads store data in the Delta format. This allows teams to move from ingestion to insight without needing to copy or reformat data, a significant leap forward in usability and performance. 

Read our blog to understand if it’s right for you:

Microsoft Fabric: Power BI with Superpowers

For many, Fabric’s appeal begins with familiarity. Chris, a Power BI specialist at heart, explained that Fabric ‘is effectively Power BI with superpowers’. It builds on Power BI’s ease of use and widespread adoption, empowering 30 million monthly users and over 375,000 paying customers with enterprise-grade tools for engineering, science, and real-time intelligence. 

In part, Microsoft Fabric was built because so many organisations already trust Power BI. Instead of talking about Microsoft Fabric VS Power BI, we’re talking about the two solutions learning from one another.

“We’re taking the Power BI way of working (focused on customer feedback and monthly innovation) and bringing it to the enterprise data platform as a whole.” 

– Chris Webb, Microsoft Fabric expert and Fabric Customer Advisory Team Member

Oakland’s Partnership with Microsoft

Did you know Oakland is a Microsoft Data and AI solutions partner? Our people are Microsoft-certified in Data and AI, Power Platform, Infrastructure and Security, as part of the Microsoft Partner Network.

Rapid Growth and Real-World Adoption of Fabric

Since its launch just 15 months ago, Fabric has seen explosive adoption. Over 19,000 customers are already using Fabric, and more than half of them are running three or more workloads beyond Power BI. What’s more, 70% of Fortune 500 companies are now Fabric users. 

Chris highlighted UK-based case studies to show real-world momentum:

• Iceland, the supermarket chain, is using Fabric for real-time transaction analysis.
• Centrica, a major energy company, and the London Stock Exchange Group are deploying Fabric to power their enterprise data strategies.

Who will be next?

What’s New from FabCon: Key Feature Announcements 

We couldn’t be in Las Vegas for FabCon, but Chris brought the pizazz to Leeds and touched on some of the most exciting product announcements and updates. 

OneLake Security advancements

Now, you can apply row-level and column-level security once in OneLake, and that single policy flows through all workloads. That’s whether users are querying data in Python notebooks, SQL, or viewing dashboards in Power BI. It’s a game-changer for data governance, significantly reducing complexity for enterprise teams managing sensitive data.

Copilot for Power BI sees accessibility expanded 

Previously limited to higher-capacity SKUs, Copilot for Power BI will now be available in all Fabric capacities starting from F2. This means organisations of any size can now use natural language to explore data, create reports, and uncover insights without writing a single line of code. 

Chris confirmed that by the end of this month (May 2025), the feature will be much more accessible, democratising analytics powered by artificial intelligence. 

Real-time intelligence gets a boost 

Real-time analytics has emerged as a breakout feature in Fabric. Chris described how organisations, like Porsche Racing, are ingesting high-frequency telemetry data from vehicles and analysing it in near real time. 

Recent updates to Event Streams and Event House now make it even easier to integrate and act on data from a range of sources, including new connectors, like MQTT and weather feeds. The ability to run SQL transformations in-stream was also highlighted as a major feature coming soon.

Materialised Views in Spark 

Chris demonstrated how Materialised Views allow Spark developers to build complex transformation pipelines: bronze to silver to gold layers without managing orchestration. Fabric:

• Auto-resolves dependencies
• Runs quality checks (read our article on why you should invest in data quality)
• Surfaces visual lineage to simplify debugging

These views appear as Delta tables and can be queried directly in Power BI or SQL, showcasing Fabric’s tightly integrated architecture. 

End-to-end developer experience 

Another demo showed a deeply integrated development workflow combining SQL, Python, user-defined functions, and new variable libraries. These libraries make it easy to define parameters that update automatically when moving from dev to test to production environments, streamlining deployment and reducing the risk of errors. 

The notebook experience in Fabric now rivals the flexibility of platforms like Databricks, while offering much tighter integration with Microsoft tools. 

Use our blog to understand Microsoft Fabric VS Databricks: Taming your data assets with Databricks.

Will Power BI be Forgotten?

With all these advancements in mind, there was a common concern among event attendees: Has Power BI been left behind as Fabric takes centre stage? 

No.

Power BI continues to evolve as a core part of Fabric, with major updates to visuals, slicers, and storage modes. One of the biggest innovations is DirectLake mode, which combines the speed of import with the freshness of direct query. No more waiting for scheduled refreshes. Good news all-around!

Microsoft Fabric is More than a Product – it’s a Strategy

Chris Webb’s presentation made one thing obvious: Microsoft is positioning Fabric not just as another data tool, but a fundamental rethinking of how enterprises manage, analyse, and act on their data. I.E., their overarching data strategy.

From real-time intelligence to AI-driven development, unified governance to Power BI integration, Fabric empowers businesses to do more with less complexity. 

Head to our guide for full details on how to write your data strategy.

Modernise your data stack with Oakland

If your organisation is looking to modernise its data stack, now might be the time to consider Microsoft Fabric. Engaging a Microsoft Fabric consultant, like our experts at Oakland, can help you accelerate your adoption, avoid common pitfalls, and make sure your data investments deliver value from your data assets. 

Get in touch to talk about all-things data stacks and Fabric, or book a free exploratory workshop with our data consultants.

The post How Microsoft Fabric is Reshaping the Enterprise Data Platform appeared first on Oakland.