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From Data to Insight: Why Meaning Must Be Made ExplicitThere’s a dangerous assumption inside most BI solutions: if we show the numbers clearly enough, the insight will land. It won’t.From data to insight only happens when meaning is made explicit.

The invisible gap in most dashboards

Think about the typical BI workflow.

• Collect the data
• Clean the data
• Model the data
• Visualise the data

And then we stop. We assume the insight is now “in there” somewhere, waiting to be absorbed by whoever looks at the report.

But insight doesn’t automatically emerge from a bar chart. What actually happens is this:

• People see patterns
• They interpret those patterns differently
• They fill gaps with assumptions
• They argue about what it means

And suddenly the conversation shifts from decision-making to interpretation.

The data was correct.
The visual was clear.
But the insight never landed.

Insight is not information

This is the key distinction.

Information answers: What is happening?

Insight answers: Why does it matter?

Those are not the same thing.

You can show that churn has increased by 2%. You can show that revenue dipped in Q3. You can show that customer acquisition costs are rising.

None of those are insights on their own. They are observations.

Insight only exists when someone can articulate:

• Why this change matters
• What it implies
• What should happen next

Until that happens, you have information, not insight.

Why this matters more than ever

As we’ve already discussed in this series, we’re not short of data,  we’re drowning in it. We are not swimming anymore!

In a high-volume environment, the ability to extract insight becomes more important than the ability to produce visuals.

Because when information increases, ambiguity increases with it, unless someone deliberately reduces it.

If insight isn’t made explicit, people will invent it. And when different people invent different interpretations, you get friction instead of forward motion.

Stories are the bridge

This is where storytelling comes in.

Not storytelling as theatre.
Not storytelling as spin.

Storytelling as structure.

A story connects numbers to the real world. It frames what we’re seeing, highlights what’s important, and explains the implications.

For example:

“Revenue declined by 3%” is information.

“Revenue declined by 3%, primarily driven by a drop in mid-market renewals following the pricing change in June, which puts our annual target at risk unless retention improves”   that’s insight.

The second version doesn’t dumb anything down. It makes the meaning explicit.

It removes the need for interpretation for the audience.

This is not about simplifying the data

There’s a common objection at this point:

“Surely people should draw their own conclusions?”

Sometimes, yes. But if your role is decision-support, your responsibility is clarity.

Being intentional about meaning isn’t manipulation. It’s discipline.

It means asking:

• What is the core takeaway here?
• What assumption needs to be removed?
• What decision does this support?

If you don’t answer those questions, the dashboard leaves too much open. And open interpretation in business environments often leads to stalled decisions.

In Power BI, structure matters more than visuals

This is the part that most people underestimate. Insight doesn’t come from choosing the “right” chart type alone.

It comes from:

• structure
• layout
• sequencing
• titles
• annotations
• narrative flow

A Power BI page with five technically perfect visuals can still fail if it doesn’t guide the viewer through a clear story.

• What are we looking at?
• Why does this matter?
• What changed?
• What should we do?

If that flow isn’t obvious, insight isn’t landing.

The cost of leaving meaning implicit

When dashboards leave meaning implicit, three things happen:

1. Meetings get longer
2. Interpretations fragment
3. Decisions slow down

Because the audience is doing analytical work that should have been done before the report was published. That’s not empowerment. That’s inefficiency.

The role of analytics is not to present options endlessly. It is to reduce uncertainty. And reduction requires making meaning explicit.

The shift we work on inside the Data Accelerator

Inside the Data Accelerator, one of the core exercises we run is simple:

Take a dashboard and force the team to write, in plain language:

• What is the key insight?
• Why does it matter?
• What is the implication?

If that statement is difficult to produce, the dashboard isn’t finished.

We don’t start by changing visuals.

We start by clarifying the meaning.

Because insight isn’t something the viewer extracts.

It’s something the analyst must articulate.

A simple test

Look at one of your key charts and ask:

If I removed the title and labels, could two different stakeholders interpret this differently?

If the answer is yes, then the insight hasn’t been made explicit.

Data is raw material. Visuals are presentation. Insight is interpretation. And interpretation doesn’t happen by accident.

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In the next post, we’ll explore how structure, beginning, middle, and end, turns isolated insights into decision-ready stories.

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

Nah, dalam edisi kedua ini saya ingin membahas salah satu pemain muda yang berposisi sebagai pemain sayap.

Ia memiliki beberapa kemampuan yang menarik perhatian saya di dalam atribut permainannya.Salah satunya adalah eksplosivitas dalam kemampuan ball-carrying.

Nama pemainnya adalah Zaid Imam Nawawi.

Siapa dia dan bagaimana gaya permainannya? Mari simak pembahasan selengkapnya di bawah ini.

Mengenal Sekilas Tentang Zaid Imam Nawawi

Movement and Positioning to Receive

Musim ini, Nusantara United FC sempat dilatih oleh 2 Pelatih Kepala yaitu Apridiawan dan Ismed Sofyan.

Coach Apridiawan menggunakan pola formasi dasar 4-2-3-1 sedangkan Coach Ismed Sofyan menggunakan 4-4-2.

Bersama dua pelatih tersebut, Zaid Imam Nawawi akan ditempatkan di posisi Winger kanan. Namun, dalam match melawan Batavia FC ( Desember 2025) Zaid sempat berotasi menjadi Winger kiri oleh Coach Apridiawan selama pertandingan berlangsung.

Ketika Nusantara United sedang memprogresikan serangan maka Zaid akan sering kali beroperasi di koridor sebelah kanan serangan, baik itu di Wide Area maupun Half-space.

Zaid menunjukkan keaktifannya dalam mencari ruang di antara lini tengah dan lini belakang tim lawan. Ia juga akan sering mencari celah untuk melakukan pergerakan ke ruang yang ada di belakang garis pertahanan lawan.

Sebelum menerima bola, Zaid akan memperagakan open body shape secara konsisten sehingga dirinya mudah untuk membuat aksi lanjutan ketika ia sudah menerima bola.

Ia menunjukkan First Touch yang baik ketika menerima bola dengan kedua kakinya. Zaid juga mampu melakukan gerakan Turn (memutar badan) yang baik ketika dirinya menerima bola.

Dribbling and Carrying

Dalam memprogresikan bola, Zaid memiliki keberanian untuk bermain di bawah tekanan maupun ruang-ruang sempit. Ia akan mengandalkan eksplosivitas dan kecepatannya dalam membawa bola. Kemampuannya dalam melakukan akselerasi jarak jauh-menengah terlihat sangat baik ketika membawa bola di sisi sayap.

Zaid akan menggunakan kemampuan dribbling 1v1 untuk melewati lawannya. Pergerakan dribbling-nya ini cenderung untuk masuk ke tengah (inside). Ia akan memanfaatkan beberapagerakan skill dari kedua kakinya serta menggunakan body feint untuk melewati lawannya.

Meski begitu, kecermatannya dalam membaca situasi untuk memprogresikan bola melalui dribbling 1v1 masih harus ditingkatkan lagi.

Terkadang ia akan memaksakan dirinya untuk melewati lawan melalui aksi individunya padahal lawannya tersebut sedang dalam situasi unggul jumlah (1v2, 1v3) sehingga beberapa kali Zaid cukup mudah kehilangan penguasaan bola.

Terlebih jika Defender yang dihadapi oleh Zaid memiliki fisik dan tinggi badan yang menjulang maka Zaid akan cukup kesulitan dalam menghadapinya mengingat ia hanya memiliki tinggi sekitar 165 cm.

Meski begitu, ia tidak takut untuk berbenturan badan maupun menghadapi lawan yang punya fisik lebih kuat darinya.

Creating and Converting Chances

Pada situasi Attacking, Zaid akan sering beroperasi di sisi kanan lapangan. Beberapa penetrasi sering ia lakukan dengan pergerakan Inside melalui dribbling 1v1 di sisi kelebaran maupun half-space kanan.

Ia juga menunjukkan kemahirannya dalam memainkan Combination Play di sisi sayap bersama rekan-rekannya.

Golnya ke gawang Batavia FC diawali dengan kombinasi permainan satu sentuhannya dengan membentuk Third Man Run dan diakhiri melalui gerakan Inside dengan melakukan Shooting menggunakan kaki kiri.

Kamu bisa menonton proses terjadinya gol Zaid Imam dalam link video yang saya bagikan di bawah ini:

Namun, secara keseluruhan kemampuan Shooting dari Zaid masih perlu ditingkatkan lagi terutama terkait akurasi tendangannya yang masih belum sepenuhnya konsisten.

Selain itu, biasanya ia akan mengkreasikan peluang dari sisi sayap melalui Crossing maupun Through Passes kepada rekan penyerang setimnya di final third. Namun, beberapa Crossing dan umpan terobosannya masih terlihat belum maksimal terkait jangkauan umpannya.

Zaid tidak ragu untuk menggunakan kedua kakinya dalam mendistribusikan bola maupun mengkreasikan peluang. Kapasitas dari penggunaan kaki kanan maupun kaki kirinya terlihat sama baiknya walaupun secara frekuensi terlihat bahwa kaki kanannya akan sering ia gunakan daripada kaki kirinya.

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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Everything changes and everything stays the same.

I’ve found myself repeating that phrase more than once over the last couple of weeks, partly because it’s true, and partly because it explains a pattern I’m seeing again and again across organisations.

Most of the time, we talk about technology as if it moves in a straight line: from old to new, from on-premises to cloud, from siloed to shared, from manual to automated. But in the real world, it’s rarely a one-way journey. Strategy is cyclical. Priorities shift. Economic and political pressures change the conversation. And suddenly, decisions that felt “settled” are back on the table.

A conversation that took me back 22 years

I recently had a call with a UK local authority. I always have a soft spot for local government because that’s where I started my career in the early 2000s. In my first few years, I worked across both Caerphilly and Torfaen, and those years shaped how I think about public service, accountability, and the reality of delivering technology with limited budgets and high expectations.

When I left local authority employment at the tail end of 2004, there was a major push for shared services. The idea was simple: neighbouring authorities would combine back-office functions, IT, HR, finance and so on to reduce duplication, save money, and provide more consistent services.

I remember discussions about setting up shared service organisations across multiple councils. I kept in touch with people after I left, and I know those changes did happen.

Fast forward 22 years,  which makes me feel slightly ancient writing it down, and I’m speaking to a different local authority about SQL Server training. The reason? They’d been operating within shared services, and the DBA function was largely a contractor team. A decision had now been made to dismantle that shared model and bring services back in-house within each authority.

Full circle.

Everything changes and everything stays the same.

On-premises reporting is back in the conversation

That local authority conversation wasn’t the only “cycle” moment. Since the tail end of last year, I’ve had several separate conversations with clients about Power BI training. That’s not unusual, we deliver a lot of Power BI training, but what is unusual is the direction of travel.

Historically, most organisations wanted training aligned to the cloud-based Power BI Service, the PL-300 style path: semantic models, publishing to the Service, governance, sharing, workspaces, and deployment considerations.

And until very recently, if you’d asked me how many clients we have ever had that have been actively using Power BI Report Server on-premises, I’d have said “one” without hesitation. They were using it back in 2018 when they still had a strict “no-cloud” policy. It was a transitional step until internal policy changed and cloud adoption became possible.

But in the last few months, I’ve seen a noticeable increase in interest in on-premises Power BI Report Server, not as a short-term workaround, but as a deliberate choice.

Is this about SQL Server 2025… or something bigger?

Some of this renewed interest may be linked to the reporting changes announced with SQL Server 2025, particularly the consolidation of on-premises reporting into the Power BI Report Server model. I recently wrote about those changes because, candidly, I’d almost forgotten the announcement myself, and I know plenty of organisations still rely heavily on SSRS today.

However, based on the conversations I’m having, I’m not convinced product announcements alone explain the shift. In several chats, the motivation feels broader, and sometimes unspoken.

Some organisations are reassessing cloud dependency. Others are responding to data sovereignty requirements, governance concerns, budget controls, or procurement constraints. In some cases, it’s about cost predictability. In others, it’s about control, simplicity, and the comfort of having reporting entirely within the boundary of the corporate network.

And in a few conversations with certain clients, I’ve simply sensed a cultural recalibration: a pause and a question being asked  “Do we need or want all of this in the cloud?”

To be clear: I’m not suggesting for an instant that the cloud story is reversing wholesale. It isn’t. Azure adoption remains strong. Microsoft Fabric momentum is real. AI workloads are undeniably cloud-led. But at the same time, I’m seeing more organisations pursuing optionality: hybrid models, on-premises reporting capability, and architectures that feel internally governed.

The real constant is decision-making

Which brings me back to the phrase. Everything changes and everything stays the same.

Twenty years ago, local authorities were centralising into shared services. Today, some are decentralising again. Over the last decade, many organisations moved reporting to the cloud. Now, more are reconsidering where the balance should sit.

Products evolve. Licensing models shift. Platforms merge. But the underlying questions remain remarkably consistent:

• Where should control sit?
• How do we manage cost and predictability?
• What risk profile are we comfortable with?
• What skills do we need in-house?
• What reporting experience do users actually need day-to-day?

As a consultant and training provider, I find this fascinating,  and it’s a useful reminder that technology decisions are rarely purely technical. They’re organisational, political, financial, and cultural.

Is your organisation moving back from the cloud?

So I’m curious:

Are you seeing similar shifts? Are you doubling down on cloud-first strategies, or introducing more on-premises or hybrid components again? Is Power BI Report Server on-premises back on your roadmap?

If you’re exploring on-premises reporting, need to upskill your team, or want an honest view of the trade-offs, we can help.

If you want tailored training (Power BI Service or Power BI Report Server) or practical guidance on modernising your reporting stack, get in touch via gethynellis.com.

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FAQ

What is Power BI Report Server?

Power BI Report Server is Microsoft’s on-premises reporting platform for hosting Power BI reports (and paginated reports) within your own infrastructure, rather than in the Power BI Service.

Why are organisations reconsidering on-premises reporting?

Common drivers include governance and data sovereignty requirements, cost predictability, procurement constraints, reduced cloud dependency, or a preference for keeping reporting fully within the corporate network boundary.

Is on-premises reporting replacing the cloud?

In most cases, no. Many organisations are aiming for hybrid capability — using cloud where it fits, while keeping specific reporting workloads on-premises for control, compliance, or operational reasons.

Data Overload Is Killing Decision-Making (And More Dashboards Won’t Fix It)We’re not short of data. We’re drowning in it.Every year, organisations collect more information than the year before. More systems. More integrations. More telemetry. More tracking. More metrics.

And the default response? Build another dashboard. Add another page. Track another KPI. Create another breakdown.

The assumption is simple: more visibility equals better decisions.

But that assumption is collapsing under its own weight.

The real constraint isn’t data. It’s attention.

Here’s the uncomfortable truth.

Data has scaled exponentially. Human attention hasn’t.

Executives still have the same number of hours in the day. Managers still have the same cognitive limits. Teams still operate under time pressure, competing priorities, and constant distraction.

When everything is measured, nothing feels important. People do what you measure them by when you measure everything that priority is lost. When every metric is highlighted, none of them stand out.

And when dashboards present ten signals at once, the brain quietly gives up.

What data overload actually does

There’s a belief in analytics that more information reduces uncertainty. In reality, after a certain point, it increases it. When people are confronted with too many metrics:

• They hesitate.
• They defer.
• They look for confirmation of what they already believe.

Data overload doesn’t create clarity. It creates cognitive friction. And friction leads to avoidance.

That’s why so many dashboard conversations end with:

• “We need to dig deeper.”
• “Let’s break this down further.”
• “Can we see this by…?”

Exploration becomes a substitute for decision-making.

The illusion of sophistication

Dense dashboards often look impressive.

• Multiple charts
• Rich interactivity
• Filters everywhere
• Granularity on demand

Technically, they’re sophisticated. Practically, they’re exhausting.

When a report demands that the audience:

• scan ten visuals,
• compare five dimensions,
• remember values from previous charts,
• and infer cause from correlation,

You’re asking them to do advanced analytical thinking on the fly. Most won’t. Not because they’re incapable. Because they’re busy.

When everything matters, nothing does

One of the quiet dangers of modern analytics is that we treat measurement as inherently good (People do what you measure them by because that is what is important). But measurement without prioritisation is noise.

If revenue is up, churn is slightly down, engagement is flat, costs are rising, and pipeline is volatile,  what matters most? Who cares?

If the dashboard doesn’t make that clear, the audience must choose. And different people will choose differently. That’s how you end up with debate instead of direction.

Data overload doesn’t just slow decisions, it fragments them.

Why do more dashboards make it worse?

When organisations sense confusion, they often respond by adding more analysis.

• A new page
• A deeper drill-through
• An extra KPI.

But adding more information to an overloaded environment is like adding more tabs to an already busy browser.

It doesn’t increase clarity. It increases switching costs. Without intentional reduction, dashboards evolve through addition, not refinement. They grow. They rarely improve.

Good analytics is about reduction

This is the part that makes people uncomfortable. Effective data storytelling isn’t about showing everything. It’s about choosing what not to show.

It’s about making deliberate decisions:

• What decision is this report supporting?
• Which metrics directly influence that decision?
• What can be removed without harming clarity?

Reduction isn’t dumbing down. It’s discipline. It’s acknowledging that the goal isn’t to display the richness of the data model, it’s to help someone act.

Focus is a design decision

Clarity doesn’t happen by accident. It’s created by:

• limiting the number of simultaneous messages,
• creating a visual hierarchy,
• sequencing information intentionally,
• and being explicit about what matters most.

This is why the earlier posts in this series matter.

Dashboards don’t drive decisions.
Data, charts, and insight aren’t the same thing.

And now this: Even correct charts won’t help if you overwhelm the human brain.

The Accelerator and the discipline of reduction

One of the core shifts inside the Data Accelerator is teaching teams to design for focus, not volume.

We work on:

• starting with the decision, not the dataset,
• identifying the 3–5 metrics that genuinely matter,
• structuring reports with intent,
• and reducing cognitive load before adding visual polish.

When teams adopt this mindset, something changes.

• Meetings get shorter
• Arguments reduce
• Decisions accelerate

Not because there’s less data, but because there’s less noise.

A simple overload test

Look at your main dashboard and ask:

• How many visuals are competing for attention?
• If I removed half of them, would the core decision still be supported?
• What is the single most important signal on this page?

If that answer isn’t obvious within five seconds, your audience is already overloaded.

Data isn’t the problem. Overexposure is. And until we design analytics around human limits instead of system capacity, more dashboards will continue to produce less clarity.

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In the next post, I’ll look at how the human brain actually processes visual information, and why understanding cognitive limits is the key to designing dashboards that work.

Read the previous post: Data, Charts, and Insight: Why Seeing the Numbers Isn’t Enough

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

Dashboards Don’t Drive Decisions (and that’s the real analytics problem)I have a new conference talk that made it debut at Tenerife Winter Sessions last weekend and is scheduled for another outing at the Birmingham Power BI Meetup Group #PBIBrum at the beginning of March. The talk is called “Data-Driven Storytelling with Power BI” and this is the most provocative statement in the presentation, and it’s intentional:Dashboards don’t drive decisions.If they did, most organisations would be operating perfectly by now.

They’re not short of dashboards. They’re not short of data. They’re not even short of beautifully designed Power BI reports. Yet the same conversations keep happening. The same issues resurface month after month. And the same decisions get deferred, diluted, or quietly ignored.

If that sounds familiar, it’s not because your data is wrong. It’s because your dashboards stop at information.

The familiar meeting that goes nowhere

You’ve probably seen this play out.

A dashboard goes up on the screen. People nod.
Someone asks a sensible question.
Someone else offers an interpretation.
A third person disagrees.

And then the meeting ends.

• No decision.
• No clear action.
• No change in direction.

The dashboard did its job, technically speaking. It showed the data. The numbers were accurate. The visuals were clear. But nothing happened next.

This is the uncomfortable truth: showing information is not the same as enabling a decision.

Why dashboards feel useful but change very little

Most dashboards are built with good intentions. They’re designed to be comprehensive, neutral, and flexible. They show performance from multiple angles. They let the audience explore and “draw their own conclusions”. That sounds sensible.

In reality, it’s where things break down.

Humans don’t make decisions from charts. They make decisions from understanding. And understanding doesn’t come from being presented with options and hoping meaning will emerge. It comes from context, narrative, and relevance to a specific choice that needs to be made.

When a dashboard presents ten charts of equal importance, it silently asks the audience to do the hardest work themselves:

• What matters most?
• What should I focus on?
• What does this mean for the decision I’m responsible for?

Different people answer those questions differently. That’s why dashboards often create debate rather than direction.

“Interesting” is not a success metric

One of the most dangerous words in analytics is interesting.

If someone looks at a dashboard and says, “That’s interesting,” what they usually mean is:

• I can see patterns
• I’m not sure what they imply
• I don’t yet know what to do

An interesting dashboard might get attention. It might even get praise. But if it leaves the audience thinking “Hmm…” instead of “Here’s what we need to do”, it has failed its most important job.

A successful analytics product doesn’t just inform. It reduces uncertainty at the moment a decision needs to be made.

The missing ingredient: decision intent

The real issue isn’t visualisation. Its intent.

Most dashboards are built by starting with the data:

• What tables do we have?
• What measures can we calculate?
• What breakdowns might be useful?

Decision-driven analytics starts somewhere else entirely:

• What decision is currently blocked?
• Who owns that decision?
• What would change if we had clarity?

When you start with the decision, everything else sharpens:

• Fewer metrics matter.
• Visuals become explanatory, not exploratory.
• The report develops a point of view.

This doesn’t mean manipulating the data or hiding nuance. It means accepting responsibility for guiding the audience, rather than outsourcing interpretation to them.

Why better dashboards aren’t the answer

When organisations realise their dashboards aren’t driving action, the usual response is to build more of them. Or rebuild them. Or redesign them.

But without a shift in thinking, you just end up with nicer dashboards that still don’t lead anywhere.

The constraint is not Power BI. It’s not DAX. It’s not modelling. The constraint is that most teams have never been taught how to design analytics around decisions, rather than around data structures.

From dashboards to decision tools

This is exactly the gap the Data Accelerator is designed to address.

The Accelerator isn’t about teaching people how to build more reports. It’s about changing how analytics work gets framed in the first place:

• starting with real business decisions
• designing reports with a clear beginning, middle, and end
• reducing cognitive load rather than adding visual complexity
• turning Power BI outputs into tools that actually influence behaviour

When teams adopt this approach, something interesting happens.

Meetings change. Conversations move faster. Decisions become clearer.

Not because the data is different, but because the analytics finally lead somewhere.

A simple test for your last dashboard

If you want to pressure-test your own work, try this:

• What decision was it designed to support?
• What action should follow from it?
• Would two different stakeholders reach the same conclusion?

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

Dashboards don’t drive decisions.

But decision-driven analytics does, when it’s designed that way on purpose.

Next in this series, I’ll dig into why “more data” often makes decision-making worse, and how to design analytics that work with the human brain rather than against it. If you to come and see me talk about this at the Birmingham Power BI User group on 4th March, you can signup here�

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)

With the release of Microsoft SQL Server 2025, one of the most important, and most missed changes, isn’t about performance, security, or AI features. It’s about reporting. And SQL Server Reporting Services in particular.For years, organisations have relied on SQL Server Reporting Services (SSRS) for on-premises operational reporting, invoices, statements, regulatory extracts, and those “must-print” pixel-perfect outputs.In SQL Server 2025, released at the end of last year, Microsoft has drawn a clear line in the sand: SSRS is no longer moving forward as a standalone product, and on-premises reporting is now consolidated under Power BI Report Server.This post explains what’s changed, what’s bundled (and what isn’t), and how to think about your next steps.

A quick recap: what SSRS used to be

SSRS has long been the default Microsoft option for on-premises, server-hosted reporting. It’s best known for paginated reports (RDL): highly formatted, page-based reports designed for printing, exporting to PDF, or distributing by email.

It has been the workhorse for operational reporting in countless SQL Server estates, and for good reason: it’s reliable, mature, and fits well into traditional IT governance.

However, Microsoft has confirmed that SSRS 2022 is the final release of SSRS, and that there is no SSRS “version” shipping with SQL Server 2025.

Reference:

Reporting Services consolidation FAQ (Microsoft Learn)

So what replaces SSRS in SQL Server 2025?

The consolidated on-premises reporting platform is now Power BI Report Server (often referred to informally as “Power BI Reporting Services”).

Power BI Report Server is an on-premises server product that supports:

• Paginated reports (RDL) — the same report type SSRS was built for
• Interactive Power BI reports (PBIX) hosted on-premises
• A modern web portal experience, security integration, and standard report management capabilities

In other words: rather than shipping and maintaining two separate on-premises products (SSRS for RDL and Power BI for interactive reporting), Microsoft has aligned the on-premises story around a single report server.

Reference:

SQL Server 2025 announcement (Microsoft Tech Community)

Is SSRS and Power BI Report Server “bundled into the same product”?

Not as two separate installs. The practical change is this:

• SSRS is not included with SQL Server 2025 as a new, updated SSRS release.
• Power BI Report Server is the consolidated on-premises reporting product going forward.
• Power BI Report Server supports both RDL (paginated) and PBIX (interactive) reports on-premises.

So if your question is: “Do I now have one on-premises reporting platform that covers both SSRS-style paginated reporting and Power BI-style interactive reporting?”  the answer is effectively yes, via Power BI Report Server.

If your question is: “Is SSRS still bundled as its own separate reporting feature in SQL Server 2025?” — the answer is no.

Why this matters for organisations running SSRS today

If you’re currently using SSRS heavily, you do not need to panic, but you do need a plan.

SSRS 2022 remains supported under its lifecycle, but Microsoft’s direction is clear: future on-premises reporting investment is centred on Power BI Report Server.

This matters because many estates still treat SSRS as a default dependency, embedded in operational workflows, tightly coupled with SQL Agent jobs, triggered exports, scheduled subscriptions, and business-critical PDF pipelines.

The good news is that RDL and paginated reports remains a first-class citizen in the on-premises world via Power BI Report Server,  you’re not being forced to redesign everything as dashboards overnight.

What should you do next?

Here’s a sensible, low-risk approach:

1. Catalogue your SSRS reports and classify them (operational/regulatory / management / ad-hoc).
2. Identify the “hard” ones: complex subscriptions, custom extensions, unusual authentication, or legacy dependencies.
3. Stand up Power BI Report Server in a test environment and validate a representative set of RDL reports.
4. Decide your target model: on-premises PBIRS, cloud Power BI, or a hybrid approach.

Reference:

Power BI Report Server overview (Power BI)

Conclusion

SQL Server 2025 marks a clear change in Microsoft’s reporting roadmap. SSRS as a standalone product isn’t moving forward in new SQL Server releases, and Power BI Report Server is now the consolidated on-premises reporting platform that supports both paginated (RDL) and interactive Power BI reporting.

If you’re responsible for an on-premises SQL Server estate, now is the time to understand the shift, assess your SSRS footprint, and plan your reporting future in a controlled way, before the change becomes urgent in a few years time.

Need help migrating from SSRS?

If you’re running SQL Server and relying on SSRS for operational reporting, now is the time to plan your next move.

We help organisations:

• Audit and rationalise SSRS estates
• Design and deploy Power BI Report Server environments
• Migrate reports safely with minimal disruption
• Modernise reporting architecture (on-premises, cloud, or hybrid)
• Improve performance, security, and governance

Whether you need a structured migration plan, hands-on technical support, or strategic guidance on your reporting roadmap,
we can help you move forward with confidence.

Book a Reporting Strategy Call

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