Duncan Boyne
The Power BI Kinda Guy
Norwich, United Kingdom
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With a background across ERP systems including Epicor ERP, Microsoft Dynamics 365, Infor Visual, and Sage 200, Duncan brings a deep understanding of how data flows through businesses. He focuses on reporting that reflects real operational processes, not just what looks good in a dashboard.
He works across Power BI, Dataverse, Power Apps, and Power Automate, with a particular interest in report design, semantic models, governance, and building systems that scale.
Outside of client work, Duncan runs the Norfolk Power Platform User Group and is the founder of the East of England Power Platform Summit, creating spaces for people at all levels to learn, connect, and grow.
Outside of tech, he’s a dad of two, usually being outnumbered at home by kids and a dog. When he does get a bit of time, it’s spent gaming, reading, getting lost in manga, or listening to music that most people would describe as “a bit aggressive.”
Area of Expertise
Topics
Design Before DAX: Wireframing Better Power BI Dashboards
“Can you build us a management dashboard?”
Most Power BI projects start exactly like this, with a vague request and the expectation that the developer will somehow turn it into something insightful, beautiful, and easy to use.
Too often we jump straight into Power BI and start dragging visuals onto a canvas. The result is dashboards that look cluttered, inconsistent, and difficult to scale.
This session takes a different approach: design first, build later.
Starting with a typical stakeholder request, we will walk through the process of designing a Power BI dashboard from scratch using wireframes. Together we’ll decide what belongs on the page, how information should be prioritised, and how layout, colour, and typography influence the way users read data.
Along the way we’ll explore questions that good report designers ask before opening Power BI:
Why are we using these colours?
Is the design accessible and readable for all users?
How will this layout scale when more pages or reports are added?
How do we turn stakeholder requests into a structured visual plan?
By the end of the session we will have produced a complete dashboard wireframe and a repeatable design approach that can be applied to any Power BI reporting project.
What You’ll Learn
How to translate stakeholder requests into structured dashboard wireframes
How to design layouts that prioritise clarity and usability
How colour, typography, and spacing affect data interpretation
How to design reports that scale across multiple dashboards and teams
Key Takeaways
A repeatable wireframing process for Power BI report design
Practical design principles you can apply before opening Power BI
Techniques for creating dashboards that are clearer, more accessible, and easier to scale
Dashboard in a Day? Nah. Let’s Do One in 45 Minutes. A live, end-to-end Power BI build
A live, end-to-end Power BI build
This is a demo-first session. No slide decks full of theory. No pre-built models. No “imagine if” examples.
The only slides you’ll see at the start are my face and a highlight reel of things I’m unreasonably proud of—purely for credibility, ego maintenance, and to prove I’ve broken Power BI in enough ways to know what actually matters. Then we switch straight into Power BI and build a real dashboard from scratch in 45 minutes.
We’ll start with raw, messy data and finish with something you’d genuinely feel comfortable putting in front of stakeholders.
Along the way, you’ll see:
How to decide what not to build (the fastest win in Power BI)
A practical approach to modelling without overengineering
The small number of measures that usually drive the most value
Layout and visual choices that prioritise clarity over decoration
How to keep momentum when time, scope, and attention are limited
This session is about decision-making under constraint—the same constraint most of us work under every day.
You’ll leave with a repeatable approach you can use when “Dashboard in a Day” simply isn’t realistic.
When Native Visuals Aren’t Enough: Advanced Visual Architecture in Power BI
Designing Production-Grade Custom Visuals in Power BI with Deneb
As Power BI practitioners mature, many eventually hit the same wall: native visuals are powerful, but they impose structural limits. Layering complex KPIs, building precise layouts, or controlling visual behaviour beyond the format pane often requires compromise.
This session explores how to move beyond those constraints by designing production-grade custom visuals using Deneb and the Vega-Lite grammar of graphics.
Rather than focusing on basic syntax, we will examine:
How to think in layers, marks, and encodings
When in-visual transformations outperform DAX measures
Structuring maintainable and reusable visual specifications
Handling dynamic scaling, conditional logic, and controlled interactivity
Avoiding common performance and maintainability pitfalls
Through real-world examples, we will rebuild complex reporting scenarios that native visuals struggle to represent cleanly — such as layered KPI cards, dynamic bullet charts, and composite visuals that would otherwise require multiple visuals stitched together.
This session is aimed at experienced report creators, data architects, and consultants who already understand Power BI modelling and DAX, and want to take control of the visual layer itself.
Attendees will leave with a clear framework for deciding when Deneb is appropriate, how to architect custom visuals responsibly, and how to elevate their reporting craft beyond default components.
Is Your Data Ready for AI or a Risk to Your Business?
AI is rapidly becoming part of modern business strategy. From copilots to predictive analytics, organisations are being told the same thing: adopt AI or fall behind.
But there is a critical question many businesses haven’t answered yet.
Is your data actually ready for AI?
In this session, we explore how Power BI can be used as a strategic lens to assess AI readiness across an organisation. Using a real Power BI report built with anonymised data, we will demonstrate how leaders can quickly evaluate the health of their data estate, from data quality and governance to platform maturity and operational risk.
Rather than focusing on building reports, this session shows how analytics can surface the issues that often sit beneath the surface: fragmented data sources, inconsistent standards, security gaps, and hidden operational dependencies.
For technology leaders, this provides something extremely valuable. A clear, visual understanding of where your organisation is prepared for AI and where the foundations still need work.
If your data is ready, you gain confidence that AI initiatives can move forward.
If it isn’t, you gain something even more useful: a clear roadmap of what needs fixing first.
Because the biggest risk in adopting AI isn’t the technology.
It’s the data you build it on.
AI isn’t your problem. Your data is.
Bad data doesn’t just cause a few awkward reports. It can cost real money. The question is simple… are you using AI to grow your business, or are you feeding it rubbish and hoping for the best?
Because that’s the bit people miss.
The blocker isn’t the tech. It’s what you’re giving it to work with. And fixing that starts with actually understanding where your data is breaking down.
None of this is new, by the way. Bad data has always been around. The difference now is the stakes. AI just amplifies whatever you feed it. Good or bad.
Think of it like this. AI is the building. Your data is the foundation.
If the foundation is weak, it doesn’t matter how impressive the building looks. It’s coming down.
This isn’t about adding AI as a shiny feature. It’s about whether your business can keep up.
The companies that get their data right will move faster, make better calls, and pull ahead. The ones that don’t… won’t. And this isn’t something you park for six months while you “explore a proof of concept.” By the time that’s done, you’re already behind.
Here’s a simple example.
Your sales report says you sold 1,000 products.
999 at £1. One at £100.
Looks fine, right?
Except it isn’t real. Someone missed a decimal point.
Now imagine building forecasts, pricing strategy, or AI models on top of that. You’re not just slightly wrong. You’re confidently wrong.
That’s where this session comes in.
We’re not just looking at it from a tech angle or a business angle. It’s both. Because that’s the only way this works in the real world. Between us, we’ve seen this play out in places like Bank of America, the NHS, McDonald's, and across UK policing. Different industries, same problem. Data quality quietly shaping big decisions.
What we’ll show you is how to actually see the problem.
Pull your data together. Finance spreadsheets, HR systems, platforms like Dynamics 365. Look at it as one picture, not a bunch of disconnected bits.
That’s where tools like Microsoft Power BI come in. Not as the end goal, but as a lens. A way to surface what’s really going on.
Because once you can see it, things get uncomfortable… but useful.
You start spotting outliers. Numbers that don’t make sense. Gaps. Inconsistencies. The kind of stuff that quietly breaks AI but never gets noticed.
And then the conversation changes.
It’s no longer “should we invest in AI?”
It becomes “are we actually ready for it?”
That’s a much better question.
This isn’t about leaving it with IT or a data team to figure out. It puts that visibility in front of the people making decisions. Clear enough to act on.
If your data is in a good place, great. You move forward with confidence.
If it isn’t, that’s fine too. At least now you know what to fix, and where to start.
Because AI doesn’t fail because the models are bad.
It fails because the data is.
Caught Between AI Ambition and Data Reality? Here’s the Way Through
Everyone’s talking about AI right now.
Boards are pushing for it. Leaders are being told to move fast or risk falling behind. So projects start. Budgets get signed off. Expectations go up.
But there’s something sitting underneath all of that that doesn’t get talked about enough.
The data.
Not because people don’t care about it… but because most of the time, they can’t actually see it.
That’s the gap.
The people making decisions about AI don’t have a clear view of the data those decisions depend on. And the people who do understand the data aren’t the ones setting direction.
So AI moves forward… while the biggest risk stays hidden.
And that risk is simple.
Bad data can cost you millions.
In some cases, a lot more.
The blocker isn’t AI.
It’s the data.
And the problem isn’t just that the data has issues… it’s that those issues aren’t visible to the people making decisions.
Bad data isn’t new. But with AI, the consequences are bigger than ever.
AI needs strong foundations. Build on weak data, and you’re building on sand. It might look fine at first, but it won’t hold.
This isn’t about adding AI as a feature.
It’s about business survival.
The organisations that get their data right will lead.
The ones that don’t will fall behind.
And this isn’t something you can afford to wait on. This is about decisions you’re making now, not six months from now after a proof of concept tells you what you could have seen earlier.
Take a simple example.
Your sales data shows 1,000 products sold.
999 at £1. One at £100.
Looks fine.
Except it isn’t real. Someone missed a decimal point.
Now imagine building forecasts, pricing, or AI models on top of that. You’re not just slightly wrong… you’re confidently wrong. And AI will scale that mistake.
In this session, we bring both sides together. The technical view and the business view. Because this problem sits right in the middle.
Between us, we’ve seen this play out across organisations like Bank of America, the NHS, McDonald's, and UK policing. Different industries, same pattern. Data quality quietly shaping decisions at scale.
What we focus on is making that visible.
Using Microsoft Power BI, we bring together data from across your organisation. Finance spreadsheets, HR systems, platforms like Dynamics 365. One view of what’s really going on.
From there, you can see it clearly.
Where things don’t line up.
Where risk sits.
Where opportunity is being missed.
We look at data quality, governance, platform maturity, and operational risk, but more importantly, what those things actually mean for the business.
A key part of this is exposing what’s usually hidden.
Simple techniques like spotting outliers and inconsistencies quickly bring problems to the surface. The kind that would otherwise stay buried, but have a real impact on AI outcomes.
This takes AI readiness out of a dark room and puts it in front of the people making decisions.
Clear. Visual. Hard to ignore.
So instead of guessing, leadership can see where the organisation really stands, what needs to improve, and when it actually makes sense to invest in AI.
Power BI isn’t the focus.
Getting your data into the right shape is.
Power BI is simply the most effective way to make that visible, understandable, and something you can act on straight away.
If your data is ready, you move forward with confidence.
If it isn’t, you leave with something just as valuable. A clear roadmap of what’s in the way, and what to fix first.
Because AI doesn’t fail because the models are bad.
It fails because the data is.
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