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Data storytelling isn’t complicated. Great Power BI report structure starts with one simple idea: every good report should have a beginning, a middle, and an end.

Storytelling in data is simpler than people think

At this point in the series, people often assume that “data storytelling” means something complicated.

Something creative.
Something fluffy.
Something subjective.

It doesn’t. It starts with something very simple. Every good story has three parts:

• A beginning
• A middle
• An end

We instinctively understand this structure in films, books, and conversations. If someone starts a story halfway through, we feel lost. If they never finish it, we feel frustrated.

And yet the moment we open Power BI, we abandon that structure entirely. Most reports are all middle, with little if any beginning or end.

The problem: reports with no beginning

Open most dashboards and what do you see?

• Metrics
• Charts
• Comparisons

Straight away. No context. No framing. No explanation of why this page exists or what decision it supports.

It’s as if someone walked into a cinema, skipped the opening scenes, and pressed play in the middle of the film. The audience is immediately working harder than they should be.

They’re asking:

• What am I looking at?
• What timeframe is this?
• What problem are we trying to solve?
• Why does this matter now?

If your report forces the audience to orient themselves before they can think, you’ve already created friction. The beginning of a report should answer one simple question:

Why should I care?

The middle: where most reports live

The middle is where analysis happens. This is where you explore:

• What’s happening
• What’s driving it
• where the patterns are
• What’s surprising

And this is where most dashboards stop. They present the data. They present the breakdowns. They present the trends. And then they leave the room. No conclusion. No implication. No direction.

It’s the analytical equivalent of someone explaining a problem in detail and then walking away mid-sentence. Technically correct. Structurally incomplete.

The missing ending

Here’s the uncomfortable truth, If your report doesn’t have an ending, it isn’t finished. No matter how accurate the data is. The ending is where you make the implication clear.

It answers:

• So what?
• What does this mean?
• What should we do?

Without an ending, dashboards create discussion instead of decisions. And discussion is not the goal. The goal is clarity.

Power BI report structure should follow stories

A good Power BI report should function like this:

The beginning

Set context. Define the scope. Explain the decision.

Answer: Why should I care?

The middle

Explore the drivers. Surface the patterns. Highlight what matters.

Answer: What’s happening and why?

The end

State the implication. Reduce ambiguity. Point toward action.

Answer: What do we do next?

That structure isn’t creative writing.

It’s cognitive alignment.

Let’s try it on my football report

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Why we abandon structure in analytics

There’s a reason most dashboards are all middle.

• We’re trained to build models.
• We’re trained to calculate measures.
• We’re trained to visualise data.

We’re rarely trained to structure thinking. So dashboards become containers for metrics rather than vehicles for decisions. They’re built as analytical canvases instead of narrative flows. And that’s why they feel dense. Not because the data is wrong. Because the structure is missing.

The spreadsheet mindset

Spreadsheets don’t have beginnings or endings. They have rows and columns. They’re designed for exploration, not persuasion. When we treat Power BI like an interactive spreadsheet, we get exploration-heavy dashboards that rely on the audience to assemble meaning. But business stakeholders don’t need more exploration. They need clarity. That requires structure.

Structure reduces cognitive load

When a report follows a beginning–middle–end structure:

• The audience knows where to start.
• They understand what matters.
• They aren’t left wondering what the conclusion is.

Structure removes interpretation work. It guides attention. It makes insight land. Without structure, even good visuals feel fragmented. With structure, even simple visuals feel powerful.

A practical test

I’ll use this story telling framework with my FPL dashboard in the next post. But try this on one of yours. Open one of your key reports and ask:

• Where is the beginning?
• What page establishes context?
• Where does the report clearly end?
• Is the implication explicit?

If the report simply stops after analysis, it’s unfinished.

If it doesn’t answer “what now?”, it’s incomplete. Storytelling in analytics isn’t about creativity. It’s about finishing the thought.

In the next post, we’ll use this framework and apply it to my FPL report. And in subsequent posts we’ll explore how the “hero’s journey” reframes your role as the report author — and why you’re not the hero in this story.

Related: Decision-Driven Analytics in Practice: A Fantasy Football Example

Start the series: Dashboards Don’t Drive Decisions (And That’s the Real Analytics Problem)

At this point in the series, we’ve established three uncomfortable truths:

• Dashboards don’t drive decisions.
• Data, charts, and insight are not the same thing.
• Data overload is making decision-making harder, not easier.

So now we need to reset the conversation. Because if dashboards aren’t the answer, and more data isn’t the answer, then what is analytics actually for?

Here’s the shift. Analytics exists to create clarity, not complexity.

The reporting trap

Most organisations treat analytics as a reporting function.

• Track everything
• Measure everything
• Make everything visible

The assumption is that transparency equals progress. That if we just expose enough data, the right decisions will follow.

But reporting and decision-making are not the same activity.

Reporting answers the question:

What has happened?

Analytics decision support answers a much more demanding one:

What should we do next?

When analytics stops at reporting, it often becomes descriptive rather than directional. It shows performance but avoids interpretation. It presents numbers but doesn’t prioritise meaning.

And that’s how you end up with dashboards that are technically accurate but strategically unhelpful.

Analytics is about making sense of complexity

Modern organisations are complex by default.

• Multiple systems.
• Multiple teams.
• Multiple objectives.
• Conflicting incentives.

Analytics should help navigate that complexity.

It should identify which signals matter. Which patterns are meaningful? Which changes require attention. It is not about tracking everything that can be measured. It is about selecting what should influence behaviour. As I have said many times people do what you measure them by. When analytics tries to represent the full complexity of the organisation without filtering it, it mirrors chaos instead of reducing it.

Good analytics simplifies without oversimplifying.

Clarity is the real outcome

Clarity is not a soft concept. It is a practical, observable outcome. Clarity means someone can look at a report and understand:

• What’s happening
• Why it’s happening
• What decision is required

If any of those are missing, clarity hasn’t been achieved.

A dashboard that increases confusion, sparks debate over interpretation, or requires verbal explanation every time it’s used is not creating clarity. It’s outsourcing thinking. And when thinking is outsourced to already busy stakeholders, decisions slow down.

Complexity is easy. Clarity is hard.

It is much easier to build a complex dashboard than a clear one. Complex dashboards feel safe. They show your working. They demonstrate thoroughness. They reduce the risk of being accused of omission. Clear dashboards require judgement.

They require you to decide:

• Which metrics truly matter for this decision?
• What can be removed?
• What conclusion is the data pointing toward?

That level of intentionality can feel uncomfortable. But it’s exactly what separates reporting from analytics decision support.

The mindset shift that changes everything

Here is the critical distinction: Analytics is not a reporting function. It is a decision-support function.

That single shift changes how you design everything. If analytics is reporting, your success metric becomes coverage and accuracy. If analytics is decision-support, your success metric becomes clarity and action.

You start asking different questions:

• What decision is this report helping to unblock?
• Who owns that decision?
• What would change if we had clarity?

And once those questions are clear, the design naturally follows.

You stop adding charts “just in case”. You stop tracking metrics that don’t influence behaviour. You start structuring reports with beginnings, middles, and ends.

When analytics does its job properly

You know analytics is working when:

• Meetings get shorter
• Conversations move quickly from “What does this mean?” to “Here’s what we’re doing.”
• Disagreements reduce because interpretation is aligned.
• Confidence increases, even when the news isn’t good.

That’s the real test. Not how many dashboards exist (or how interactive they are), but whether they help someone decide.

Why this is difficult in practice

Most analytics teams are trained technically, not structurally. They learn modelling, DAX, visualisation techniques, performance tuning. What they’re rarely taught is how to design analytics around decisions. They’re rewarded for being right. Not for being useful. So dashboards optimise for completeness instead of clarity. This is not a tooling issue. It’s a framing issue. And until analytics is positioned as decision-support inside the organisation, the same problems will keep resurfacing — no matter how advanced the platform.

This is the shift inside the Accelerator

One of the core reframes inside the Data Accelerator is resetting the purpose of analytics. We work with teams to:

• Define the decision before touching the data
• Identify the 3–5 signals that genuinely matter
• Structure reports around clarity
• Explicitly state implications

When that shift happens, analytics stops being a passive layer of information and becomes an active part of decision-making. Not louder. Not denser. Clearer.

A simple test

Look at your most important dashboard and ask:

If this report disappeared tomorrow, what decision would be harder to make?

If the answer is “none”, you’re reporting. If the answer is clear and specific, you’re supporting decisions. Analytics exists to create clarity. Everything else is noise.

If you would like to discuss analytics decision support in your business, feel free to book a call or reach out and connect with us on Linkedin

In the previous post, I made a deliberately provocative claim: Dashboards don’t drive decisions.If you haven’t read that yet, it’s worth starting there, because this post explains why that problem keeps showing up,  even in organisations with good data, good tools, and capable analysts.The short answer is this: We keep confusing data, charts, and insight as if they’re the same thing. They’re not.

The dangerous assumption at the heart of analytics

There’s a deeply ingrained belief in analytics that goes largely unchallenged:

If we collect the right data and visualise it clearly enough, insight will emerge on its own.

It sounds reasonable. It’s also wrong.

Data plus charts does not equal insight. What it usually equals is more to look at.

When organisations struggle to make decisions, the response is often to add more dashboards, more visuals, more breakdowns. The hope is that clarity will eventually appear if we just keep refining the charts. But insight doesn’t magically appear when you put numbers into a bar chart.

What actually happens instead

What usually happens is cognitive overload. People look at a dashboard and they do see patterns:

• trends going up or down
• outliers that look worrying
• comparisons that seem interesting

But they don’t know:

• which patterns matter
• what’s driving them
• whether they’re signals or noise

So the brain does what it always does when meaning isn’t explicit, it fills the gaps.

Different people bring different assumptions, experiences, and incentives into the room. The same chart produces multiple interpretations. And suddenly the conversation isn’t about action anymore. It’s about debate.

Why “correct” charts still lead to bad outcomes

This is the part that frustrates analysts the most.

• The charts are technically correct.
• The measures are accurate.
• The data model is sound.

And yet… nothing happens.

That’s because correctness is not the same as usefulness. A chart can be accurate and still be ambiguous. It can show a trend without explaining its cause. It can highlight a change without indicating whether it’s good, bad, or expected.

When insight isn’t explicit, analytics quietly shifts responsibility onto the audience:

• You decide what this means
• You decide what matters
• You decide what to do next

That might feel neutral, but it’s actually abdication.

The insight gap no one talks about

There’s a gap in most analytics workflows that rarely gets named.

We go from:

• data collection
• to modelling
• to visualisation

And then we stop.

We assume insight lives somewhere inside the charts, waiting to be discovered by the viewer.

In reality, insight only exists when someone makes meaning explicit:

• This matters because…
• This is happening due to…
• This means we should…

Without that step, dashboards become pattern libraries rather than decision tools.

Why conversations end with questions, not conclusions

If analytics conversations in your organisation tend to end with:

• “We need to dig into this further”
• “Let’s take this away”
• “Can we get a breakdown by…?”

That’s not curiosity. It’s uncertainty. Those questions aren’t a sign of engagement,  they’re a sign that the report didn’t do enough thinking on behalf of the audience.

Exploration has its place. But when every dashboard invites exploration, and none of them land a conclusion, decision-making slows down dramatically.

This is how you end up with organisations that are “data-driven” in theory, but instinct-driven in practice.

Insight requires intent, not just visuals

The missing ingredient isn’t a better chart type. It’s intent.

Insight only appears when analytics is designed to answer a specific question for a specific decision-maker at a specific moment.

That means:

• deciding what the chart is for, not just what it shows
• choosing what to exclude as deliberately as what to include
• making the implication clear, even if it feels uncomfortable

This doesn’t mean removing nuance or hiding uncertainty. It means guiding interpretation instead of leaving it to chance.

Why does this keep happening

So why do organisations keep falling into this trap? Because most analytics teams are rewarded for:

• accuracy
• completeness
• technical sophistication

They are rarely rewarded for:

• clarity
• decisiveness
• influence on outcomes

As a result, dashboards optimise for being right rather than being useful.

Until that changes, we’ll keep producing analytics that looks impressive but struggles to change behaviour.

From charts to insight: the shift we work on in the Accelerator

This distinction,  between data, charts, and insight,  is one of the foundations of the Data Accelerator.

The Accelerator exists to help teams:

• Stop assuming insight will emerge on its own
• Design analytics around explicit decisions
• Reduce cognitive overload instead of adding to it
• Turn Power BI outputs into a shared understanding, not competing interpretations

When teams make this shift, the quality of conversations changes. Fewer questions are asked at the end of meetings — not because curiosity disappears, but because clarity increases.

A simple test for your dashboards

Here’s a quick way to spot the problem. Look at a chart and ask:

• What conclusion should everyone reach?
• What assumption does this remove?
• What decision does this support?

If those answers aren’t obvious, the chart isn’t finished yet.

Data is not insight. Charts are not understanding.

And until we stop treating them as interchangeable, dashboards will continue to fail at the one thing we expect them to do: help us decide.

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In the next post, I’ll look at how data overload makes this problem worse,  and why more dashboards often lead to less clarity, not more.

Read the previous post: Dashboards Don’t Drive Decisions (And That’s the Real Analytics Problem)