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You Are Not the Hero of the Report (And That Changes Everything). In analytics, the audience is the hero, not the data. Once you understand that, the way you design Power BI reports changes completely.

The subtle mistake most dashboards make

There’s a subtle mistake sitting quietly inside most analytics work.

• It’s not a modelling issue
• It’s not a visual design issue
• It’s not even a data quality issue

It’s an identity issue. Most analytics is built as if the data is the hero. And that’s the problem.

The data-centric trap

If you’ve worked in analytics for any length of time, you’ve seen this pattern. A dashboard is presented with quiet pride:

“Behold the data.”

• Multiple charts
• Complex relationships
• Drill-through pages
• Impressive DAX measures.

Technically excellent. Structurally dense. And yet something doesn’t land. The executive leans forward and asks:

“So what does this mean?”

The manager asks:

“What should we do?”

The room becomes interpretative instead of decisive. Not because the data is wrong. Because the report was built as a showcase rather than a guide.

The hero’s journey (business edition)

In classic storytelling, every narrative has a hero.

• The hero faces uncertainty
• The hero encounters obstacles
• The hero needs clarity

And along the way, the hero meets a guide. In business analytics, we often cast the wrong character in the starring role.

We treat:

• The dataset as the hero
• The dashboard as the hero
• The analyst as the hero

But none of those are the hero. The audience is the hero in analytics.

• The executive deciding strategy.
• The manager reallocating budget.
• The team choosing priorities.

They are standing at the edge of uncertainty. And they are looking for clarity.

When the data becomes the hero

When the data is positioned as the hero, the focus shifts subtly:

• We emphasise completeness over clarity
• We demonstrate sophistication over simplicity
• We showcase what the model can do

The dashboard becomes a performance. While that may impress technically-minded audiences, it rarely accelerates decisions. Because the audience is left to translate complexity into action themselves. And that is not their job.

Story-centric analytics

Now flip the perspective. In a story-centric approach, the data still exists. It still matters. But it plays a supporting role. The hero is the decision-maker. The report exists to guide them from:

Uncertainty → Understanding → Action

Instead of asking:

“What can we show?”

You ask:

“What does the hero need to know?”

The guide vs the hero: what great stories teach us

Think about some of the most recognisable stories in modern culture.

In Toy Story, Woody isn’t the all-powerful hero, he’s the guide who helps Buzz Lightyear understand who he really is and how to navigate Andy’s world. Buzz may have the arc, but Woody provides the grounding.

In Wreck-It Ralph, one of my favourite films ever. Ralph believes he needs a medal to be a hero, but it’s characters like Vanellope who guide him toward understanding that identity isn’t defined by a scoreboard dashboard.

In Harry Potter, Harry is clearly the hero, but without Dumbledore, Hagrid, and even Hermione guiding him, he never survives the journey. The story works because the guides remove confusion and illuminate the path. “Happiness can be found, even in the darkest of times, if one only remembers to turn on the light”

In The Lord of the Rings, Frodo carries the burden, but it’s Gandalf and later Sam who act as guides. They don’t take the ring. They help the hero endure the journey.

In each case, the guide isn’t there to show off power or complexity. The guide exists to help the hero succeed. That’s your role in analytics.

The executive is Frodo.
The manager is Harry.
The team is Ralph.

You are Gandalf. You shall not pass! How cool are you?

You are the guide

Once you accept that the audience is the hero, your role becomes clear. You are the guide. Guides do not show off. Guides do not overwhelm. Guides remove obstacles. Guides reduce confusion. Your job is not to demonstrate technical skill. Your job is to help someone succeed in making a decision.

What guides actually do

A good guide:

• Anticipates questions before they’re asked
• Removes unnecessary paths
• Signals risk clearly
• Points toward the right route

Applied to analytics, that means:

• Pre-answering obvious objections
• Highlighting what matters most
• De-emphasising what doesn’t
• Structuring information intentionally

It also means being analytically opinionated. This matters more than that. This pattern is meaningful. This metric is noise.

Why great analytics feels simple

There’s a reason the best reports often feel calm and straightforward. It’s not because the underlying problem was simple. It’s because someone did the hard thinking before the report was built.

• They clarified the decision
• They identified the critical signals
• They structured the narrative
• They removed the distraction

So the audience didn’t have to.

The ego trap in analytics

Analytics is a technical discipline. It rewards sophistication. And that can create subtle ego traps. If you’ve spent hours building a model, it’s tempting to show all of it. But the audience does not experience pride in your model. They experience uncertainty about their decision. The shift from hero to guide requires humility.

A simple test

Open one of your dashboards and ask:

• Who is the hero here?
• Does the report showcase the data?
• Or does it guide someone toward action?

If it feels like it’s helping someone move forward, you’ve made the shift.

Ready to become the guide?

If you want your Power BI reports to move from showcasing data to guiding decisions, that’s exactly what we focus on inside the Data Accelerator.

We help teams shift from reporting to decision-support — structuring analytics around clarity, confidence, and action.

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In the next post, we’ll get practical and look at how to start with the real question — the decision that’s currently blocked — and build everything around that.

Related: Power BI Report Structure: Beginning, Middle, End

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

Fantasy Football and Decision-Driven Analytics: A Practical Example. So far in this series, we’ve talked about dashboards not driving decisions, data overload, clarity, insight, and structure.Now let’s ground all of that in something concrete.Everyone loves Fantasy Football, right?Whether you play seriously or just for fun, the game forces you to make weekly decisions with incomplete information. And that makes it a perfect example of decision-driven analytics in practice.

From Data to Insight

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Earlier in this series, we discussed how insight only exists when meaning is made explicit.

Numbers don’t speak for themselves. Visuals don’t explain themselves. People create meaning.

In business dashboards, we often jump from raw data straight to visuals and assume the insight will land.

Fantasy Football exposes that flaw immediately.

You don’t just want to see player stats. You want to know:

• Who should I transfer in?
• Who should I captain?
• What gives me the highest chance of scoring more points this week?

That’s not an exploration exercise. It’s a decision.

Gameweek 25: One Question

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Let’s take this Gameweek 25 example.

The report asks one very simple question:

“What transfer should I make this week?”

Not ten questions.

Not “let’s explore everything.”

Not “let’s see what the data says.”

One decision.

Everything on this page exists to support that single choice.

• Projected points (ep_next)
• Average points
• Form
• Total points

Each metric has a purpose.

They aren’t there because they were available. They’re there because they influence the decision.

What Makes This Different?

This isn’t a neutral dashboard. It has intent.

The layout guides attention. The metrics are prioritised. The context is explicit. The narrative is implied:

• Here’s the player
• Here’s why they matter
• Here’s the evidence
• Here’s why this is the rational move

That’s what data-driven storytelling looks like in practice. It reduces uncertainty. It increases confidence. It makes the choice easier.

Why This Matters Beyond Fantasy Football

This might be a game. But the structure is exactly the same in business. Imagine replacing “Who should I transfer?” with:

• Which supplier should we renegotiate with?
• Which region deserves investment?
• Which product line should we discontinue?

The goal isn’t to show every possible metric. The goal is to design an artefact that helps someone make a decision now. Not next week. Not after three more breakdowns. Now.

Good Analytics Doesn’t Answer Everything

This is the uncomfortable part. Good analytics does not answer every possible question. It answers the right question well.

In this case:

What transfer should I make this week?

That constraint is powerful. It forces discipline. It forces prioritisation. It forces clarity. And that’s exactly what most business dashboards lack.

Decision-Driven Analytics in Practice

This is what I mean when I talk about moving from reporting to decision support.

The report isn’t there to show how clever the model is.

It’s there to reduce cognitive load and increase confidence.

That’s the difference between:

• A dashboard
• A decision tool

Fantasy Football just makes the stakes obvious. If you make the wrong transfer, you lose points. In business, the stakes are higher. But the principle is identical.

The Standard We Should Aim For

We should not be building dashboards that show everything. We should be building artefacts that help someone make a decision.

That means:

• Starting with the question
• Selecting only the signals that matter
• Structuring the page intentionally
• Making the implication obvious

If your dashboard disappeared tomorrow, would a specific decision become harder? If not, it’s reporting. If yes, it’s decision-driven analytics.

Where This Leads

This practical example is exactly how we approach analytics inside the Data Accelerator.

We don’t start with datasets. We start with decisions. And then we design everything backwards from there. Whether it’s Fantasy Football or forecasting revenue, the standard should be the same:

Not more dashboards. Better decisions.

Useful Links

Dashboards Don’t Drive Decisions

Data Overload Is Killing Decision-Making

Why Data Initiatives Stall as Organisations Grow

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Our 2026 Microsoft Data & Analytics Training Schedule Is Live

We’re excited to share our public training schedule for 2026, covering three of the most in-demand Microsoft data certifications:

• PL-300 – Power BI Data Analyst (3 days) – £995 per seat
• DP-600 – Implementing Analytics Solutions Using Microsoft Fabric (4 days) – £1,200 per seat
• DP-700 – Implementing Data Engineering Solutions Using Microsoft Fabric (4 days) – £1,200 per seat

Across 2026, we will run 24 instructor-led courses, split evenly between UK time and North American time. UK and US courses do not overlap and do not run in the same week, and we’ve planned around key UK bank holidays and US federal holidays.

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2026 Course Schedule (UK & North America)

All dates below are scheduled in separate weeks for UK vs North America delivery each month.

Month	UK (UK time)	North America (NA time)
January	PL-300 — Mon 12 Jan to Wed 14 Jan 2026	DP-700 — Mon 26 Jan to Thu 29 Jan 2026
February	DP-600 — Tue 10 Feb to Thu 12 Feb 2026	PL-300 — Tue 17 Feb to Thu 19 Feb 2026
March	PL-300 — Tue 10 Mar to Thu 12 Mar 2026	DP-700 — Tue 24 Mar to Fri 27 Mar 2026
April	DP-700 — Tue 7 Apr to Fri 10 Apr 2026	PL-300 — Mon 20 Apr to Wed 22 Apr 2026
May	PL-300 — Tue 12 May to Thu 14 May 2026	DP-600 — Tue 26 May to Fri 29 May 2026
June	DP-700 — Tue 9 Jun to Fri 12 Jun 2026	PL-300 — Tue 16 Jun to Thu 18 Jun 2026
July	PL-300 — Mon 6 Jul to Wed 8 Jul 2026	DP-700 — Tue 14 Jul to Fri 17 Jul 2026
August	DP-600 — Tue 18 Aug to Fri 21 Aug 2026	PL-300 — Tue 25 Aug to Thu 27 Aug 2026
September	PL-300 — Mon 7 Sep to Wed 9 Sep 2026	DP-600 — Tue 15 Sep to Fri 18 Sep 2026
October	DP-700 — Tue 6 Oct to Fri 9 Oct 2026	PL-300 — Tue 20 Oct to Thu 22 Oct 2026
November	PL-300 — Tue 10 Nov to Thu 12 Nov 2026	DP-700 — Mon 30 Nov to Thu 3 Dec 2026
December	DP-600 — Tue 8 Dec to Fri 11 Dec 2026	PL-300 — Tue 15 Dec to Thu 17 Dec 2026

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Who These Courses Are For

Our public courses are particularly well suited to:

• Data analysts and BI professionals
• Data engineers and analytics engineers
• Consultants and contractors
• Teams transitioning to Microsoft Fabric
• Organisations standardising on Power BI and the Microsoft data platform

We aim for an average of 10 attendees per session, keeping class sizes small enough for meaningful interaction, questions, and discussion.

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Reserve Your Seat

If you would like to reserve a seat, discuss group bookings, or explore private or tailored delivery, please get in touch.

👉 If you would like to reserve a seat, please get in touch and contact us.

Early expressions of interest help us confirm capacity and ensure you get the dates that work best for you and your team. If your prefer team training contact me directly and we can disucss what you need