From Data to Insight
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
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
0 Comments