Thinking like a data analyst didn’t come easily for me. I'd spend...
Tom Mitchell@tommitchelldata
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Nov 11, 2025
~2 min read
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Thinking like a data analyst didn’t come easily for me.
I'd spend hours staring at datasets, unsure what questions to ask.
I knew I had to change if I wanted to have a successful career in data.
Luckily, I found a way through.
Here's what I did in 4 steps:
I'd spend hours staring at datasets, unsure what questions to ask.
I knew I had to change if I wanted to have a successful career in data.
Luckily, I found a way through.
Here's what I did in 4 steps:
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Being analytical isn’t only about numbers.
It’s about:
> Learning to pursue the right questions + answers.
> Developing healthy scepticism and curiosity.
> Understanding the data available and how to stitch it together to get closer to the truth.
It’s about:
> Learning to pursue the right questions + answers.
> Developing healthy scepticism and curiosity.
> Understanding the data available and how to stitch it together to get closer to the truth.
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The benefits of developing an analytical mindset:
It helps you ask the questions nobody else is.
You'll make more informed decisions.
Tasks that would’ve felt big before can be broken down into manageable chunks.
It helps you ask the questions nobody else is.
You'll make more informed decisions.
Tasks that would’ve felt big before can be broken down into manageable chunks.
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So how can you sharpen your analytical thinking - even if you're not a "numbers person"?
1. Patterns & eye-catching outliers
See the norm, what sticks out, what needs a second look.
A simple chart is very effective at revealing this.
Picking abnormalities to investigate can often lead to many value-adding questions.
Once you have your question, dig deeper.
1. Patterns & eye-catching outliers
See the norm, what sticks out, what needs a second look.
A simple chart is very effective at revealing this.
Picking abnormalities to investigate can often lead to many value-adding questions.
Once you have your question, dig deeper.
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2. Ask “why?” more.
When things don’t look right, it’s important to get to the bottom of why.
I like to use the 1930s framework by Sakichi Toyoda (Founder of Toyota): Five Whys.
It can often direct you quickly to the root cause of a problem.
Rather than a complicated problem-solving process first, give this a try.
Start by defining the problem, then ask “Why?” 5 times.
At each level document the answers.
That will give you a full picture before planning any steps to resolve.
When things don’t look right, it’s important to get to the bottom of why.
I like to use the 1930s framework by Sakichi Toyoda (Founder of Toyota): Five Whys.
It can often direct you quickly to the root cause of a problem.
Rather than a complicated problem-solving process first, give this a try.
Start by defining the problem, then ask “Why?” 5 times.
At each level document the answers.
That will give you a full picture before planning any steps to resolve.
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3. Poke holes in your own logic.
Consider why you might be wrong.
Run it by those around you and those in the know.
We are often surrounded by a wealth of experience that can uncover flaws we can’t see.
Consider why you might be wrong.
Run it by those around you and those in the know.
We are often surrounded by a wealth of experience that can uncover flaws we can’t see.
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4. Put it all together and plan what's next.
Create a broad view of everything uncovered so far.
When you notice anything unexpected that catches your eye, ask yourself:
Why is this happening?
What's the impact?
What should we do about it (if anything)?
You’re looking for clues to inform your assertions and recommendations.
Create a broad view of everything uncovered so far.
When you notice anything unexpected that catches your eye, ask yourself:
Why is this happening?
What's the impact?
What should we do about it (if anything)?
You’re looking for clues to inform your assertions and recommendations.
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Finally, work out the next step.
Start at the result you want, and work backwards.
That will help identify the role data should play in the solution.
Start at the result you want, and work backwards.
That will help identify the role data should play in the solution.
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And there you have it, the 4 steps I took to start thinking more like a Data Analyst.
TL;DR:
- Visualise data first to reveal points of interest
- Ask "Why?" more
- Poke holes in your logic
- Plan next steps
TL;DR:
- Visualise data first to reveal points of interest
- Ask "Why?" more
- Poke holes in your logic
- Plan next steps
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I hope you've found this thread helpful.
If you did, I ask for 2 small favours:
1. Follow me @imtommitchell for more like this daily.
2. Share the first post to help someone else.
If you did, I ask for 2 small favours:
1. Follow me @imtommitchell for more like this daily.
2. Share the first post to help someone else.
Tom Mitchell@tommitchelldata
Thinking like a data analyst didn’t come easily for me.
I'd spend hours staring at datasets, unsure what questions to ask.
I knew I had to change if I wanted to have a successful career in data.
Luckily, I found a way through.
Here's what I did in 4 steps:
I'd spend hours staring at datasets, unsure what questions to ask.
I knew I had to change if I wanted to have a successful career in data.
Luckily, I found a way through.
Here's what I did in 4 steps:
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I share everything I know about building high-paying data skills from my 8+ years in the industry.
Subscribe for free here:
thedatadose.com