You ever get that sinking feeling?

The one where a stakeholder looks at your work, nods politely… and you never hear about it again.

You delivered what they asked for, but you missed what they needed.

The difference between the analyst who stays stuck and the analyst who gets invited into the room isn’t SQL or Python.

It’s curiosity.

And today, I’m giving you a 6-move system to weaponise it.

Here’s what’s inside:

• The Curiosity Flywheel: 6 moves to solve any data mystery.

• The story of how a 3-hour analysis beat a multi week project.

• A timely but tough reader question.

Give me 7 minutes. I’ll help you think like the person your execs want to promote.

Let’s go.

Let me tell you a quick story from a -long- time ago.

A department in an FMCG business I was working in was under a bit of pressure, dispatch delays were stacking up, and no one could really pinpoint why. There was talk of spinning up a project- consultants, process maps, probably multi-week timelines.

I had access to the ops data—scans, dispatch logs, shift rosters—so I started digging.

No one asked me to. I was just curious.

Within two or three of hours, I spotted an issue. A specific batch of items was being delayed by about 90 minutes every day. Same time. Same warehouse zone.

The root cause? Some prioritisation logic was still favouring an earlier product line that had been deprioritised months earlier—so high-priority items got bumped to the back of the queue without anyone noticing. Like a ghost in the machine.

I shared the finding. The team fixed it in 48 hours. Dispatch times improved.

Meanwhile, the formal project never needed to get off the ground.

Here’s the takeaway:
The data was always there.
The difference was curiosity.

This story illustrates a skill that is far rarer than it should be: data curiosity. 

It’s the habit of questioning, exploring and connecting dots beyond what a request specifies.  It’s what turns an analyst from a report writer into a strategic asset. 

Why Curiosity Matters in Analytics

I found a survey of more than 3000 employees which found that 92% of respondents credit curious people with bringing new ideas and improvements to the table, yet only 24% feel curious at work . 

I would suggest that gap between potential and delivered value is .. huge. 

Curiosity has always been at the heart of science.  The scientific method kind of revolves around it. In business, it’s now the difference between delivering a report and delivering a revelation. 

Curiosity:

• Uncovers hidden opportunities – Outliers and anomalies often point to overlooked segments, process bottlenecks or product issues.  A curious analyst treats them as clues instead of discarding them.

• Challenges assumptions – When a metric looks positive, a curious analyst asks whether it’s truly signal or if it masks a deeper problem.  This reduces the risk of confirmation bias.

• Drives innovation – Exploratory analysis can lead to new product ideas, targeted campaigns or cost‑saving initiatives that no one requested because no one knew they existed.

• Builds trust – Stakeholders begin to rely on analysts who consistently bring fresh, evidence‑based insights rather than rehashing existing dashboards.

Yet curiosity often gets squeezed out by time pressure, fear of being wrong and lack of business context.  The result?? Analysts who deliver what they were asked for but not what the business really needs. 

How does that help you? What is the ask?

The good news is that curiosity is not just a trait; it’s a muscle. 

Like any skill, it can be developed intentionally.  The system below provides a way for engaging with any new dataset and problem, whether you’re analysing marketing results, operational data or customer behaviour.

The Six‑Step Curiosity Flywheel

I tried to make this as simple and intuitive as possible.

(Its a flywheel because its meant to go round and round!)

This system combines lessons from our earlier newsletters (asking “What decision are you trying to make?” , crafting clear headlines , questioning requests and telling compelling stories ) with techniques to turn raw data into insight.  You can use it to quickly orient yourself with any dataset and extract value without getting lost in the weeds.

A quick initial tip - do this in a notebook. Somewhere you can track your work, your questions, your discoveried.

1. Define the Decision and Context

Before you open a dataset, clarify why you’re looking at it.  What business decision will this analysis inform?  What is at stake?  Who is the audience?  As our Strategic Operator archetype teaches, always ask the stakeholder, “What business decision are you trying to make?” .  If there’s no decision, determine whether the analysis is necessary.

In this step:

• Gather background: How was the data collected?  What processes generate it?  Are there known biases?  Knowing the back story helps you avoid false conclusions.

• Identify constraints: Deadlines, resources and existing assumptions.  Write down your initial expectations (you’ll test them later).

• Map the data to business questions: Link columns and metrics to specific parts of the decision.  If you find gaps, note them for follow‑up.

2. Audit the Data

Curiosity starts with trust in the data.  A superficial glance can hide missing values, duplicates or mis‑labelled fields.  Perform a quick audit:

• Check completeness: Are there missing or null values?  Are all expected categories present?

• Validate accuracy: Spot-check against source systems or manual records where possible.

• Understand granularity: Are timestamps aligned?  How often is the data updated?

• Review definitions: Ensure you’re clear on how metrics are calculated (e.g., what does “active user” mean?).  Misaligned definitions lead to miscommunication.

This step connects to our Trusted Translator role—strip out noise and clarify definitions.  Document assumptions and ensure all stakeholders share a glossary.

3. Explore and Observe

Now the fun begins.  This step is about pattern recognition and anomaly spotting, not conclusions.  Use descriptive statistics and visualisations to get a feel for the data:

• Plot distributions: Histograms, box plots or density plots can show skewness, outliers and typical ranges.

• Break down by segments: Compare metrics across cohorts (e.g., user types, channels, time periods).  Ask, “What surprises me here?”

• Identify outliers: List the top and bottom performers in key metrics.  Don’t label them as errors yet; they’re your first clues.

• Look for change points: Are there sudden shifts in a trend?  Mark them for deeper investigation.

Throughout this step, keep a Curiosity Log.  Note every question that pops up, no matter how trivial.  Questions fuel further analysis.

4. Generate and Test Hypotheses

With patterns and anomalies in mind, begin to hypothesise.  For each observation, ask:

• What could explain this?  List possible causes, both internal (price changes, product releases) and external (seasonality, marketing campaigns, competitor moves).

• How would I prove or disprove each explanation?  Define the data needed to validate your hypotheses.  This might include additional fields, external data or qualitative insights.

• What would I expect to see if my hypothesis were wrong?  This helps counter confirmation bias.

Next, test your hypotheses through segmentation, statistical tests or qualitative research.  Talk to colleagues in other departments—sales, marketing, operations—and gather context.  Often the best clues come from outside the data itself.  As our Internal Consultant archetype reminds us, proactive cross‑functional conversations reveal hidden pain points .

5. Connect to Business Impact

Curiosity without impact is just interesting trivia.  Relate your findings back to the initial decision:

• Have a guess at the potential business value: How much revenue, cost savings or customer satisfaction is at stake?  You don’t need perfect precision; ballpark figures will do.

• Prioritise insights: Rank findings by expected impact and ease of implementation.  Focus on “high impact, low effort” wins first.

• Craft a clear narrative: Use the 3 tier story—a concise headline, context and implication—to show your discovery .  Practice your 30‑second impact story.

• Anticipate objections: Think through possible counter‑arguments and be prepared to respond with curiosity (“That’s interesting; what are you seeing that I might have missed?” ). This is actually harder than it seems but its great practice - leaders think this way.

6. Iterate and Document

Curiosity is not a one‑and‑done activity.  Capture what you’ve learned and refine your approach:

• Document your analysis: Summarise your findings, methods and open questions.  Keep this in an accessible place (e.g., an impact log) .

• Share results early: Don’t wait for a polished final report.  Share intermediate insights with stakeholders to get feedback and validate direction.  This reduces the risk of spending weeks chasing the wrong rabbit hole.

• Reflect: What surprised you?  Did any assumptions prove wrong?  How can you improve your process next time?

• Plan the next step: If further analysis is warranted, outline it.  If not, move on to the next question.  Avoid analysis paralysis by recognising when you’ve reached diminishing returns .

Techniques to Fuel Your Curiosity

In addition to the system, here are practical techniques to spark curiosity:

• Ask better questions: Replace “What is the average?” with “What would I expect to see if our hypothesis were wrong?” and “How does this vary by segment?”.

• Follow outlier stories: Instead of dropping extremes, investigate them.  Digging into why a channel performs unusually well can reveal valuable lessons .

• Borrow a beginner’s mind: Share your visualisations with someone outside your team and note what they find interesting or confusing.  Fresh eyes reveal hidden questions.

• Use random sampling: Pick a random user or row and trace their journey.  This humanises the data and uncovers patterns aggregated metrics miss.

• Schedule curiosity sprints: Dedicate 30‑minute windows to exploring new datasets without deliverables.  Time‑box exploration to prevent deep dives from derailing other work .

Overcoming Common Curiosity Blockers

• Time pressure: Protect your calendar with recurring “curiosity sprints.”  Even 30 minutes a week makes a difference.  Present your manager with a plan that demonstrates the ROI of exploration.

• Fear of being wrong: Embrace vulnerability.  Our issue on The Courage to Be Wrong emphasises that admitting uncertainty builds trust; when challenged, respond with curiosity .

• Lack of business context: Act like an internal consultant.  Proactively ask stakeholders about their pain points and processes .  This context fuels better questions.

• Analysis paralysis: Use hypotheses and the Curiosity Log to stay focused.  Define “good enough” and stop when additional digging yields smaller returns .

Your Data Curiosity Challenge

This week, cultivate your curiosity by working through the six‑step system on a dataset you think you already know.  Follow this plan:

1. Define and Audit – Select a familiar dataset.  Write down the business decision it informs and note your assumptions.  Audit for completeness and accuracy.

2. Explore – Use visualisations and summary statistics to spot patterns and anomalies.  Log every question that arises.

3. Hypothesise – Choose one surprising pattern.  Brainstorm possible explanations.  Talk to a colleague in another department for context.

4. Test – Segment your data or gather additional information to test your top hypothesis.

5. Synthesize – Craft a 30‑second impact story and share it with a stakeholder.  Include the headline, context and implication.

6. Iterate – Document your findings in your impact log.  Note what worked and what didn’t, and plan one follow‑up question.

7. Reflect – Consider how curiosity changed your understanding.  Did you discover anything actionable?  How will you apply this to your next analysis?

Reply to this email with the most surprising question you asked this week.  I read every response and may feature your story in a future issue.

In a world where AI can spit out dashboards and code, the analysts who rise are those who constantly ask “why?,” who dig beyond the obvious and who connect data to decisions. 

By cultivating curiosity—using the six‑step system, protecting time to explore and communicating findings clearly—you turn analysis into impact. 

Keep questioning, keep exploring and keep climbing!!

Best,

Tom.

Reader Question

"Hello Tom,

Your articles have inspired me to contribute at a higher level.

With my current duties, it is difficult to find time. What is most respectful way to ask my manager for their support to dedicate small amount of time to developing these skills for our team's benefit?

Thank you,
Indira S."

This is a tough and probably a common situation, ive had similar from past coaching clients.

Forget asking for more time for now. Instead, use this "Stealth curiosity" play:

On one of your next projects, deliver exactly what was asked for. But add one extra slide or one extra paragraph.

Title it: "One Interesting Side-note."

In it, share one small, unexpected pattern you noticed. For example: 'Side-note: I noticed customers from this channel behave completely differently from all others. We might be missing an opportunity here.'

This takes you 10 extra minutes, not 10 extra hours. It doesn't require permission. But it plants a powerful idea with your manager that you think beyond just the brief.

Do this three or four times, and hopefully you've built a case that giving you more time to explore isn't a cost—it's an investment!!