The step that is easy to skip.
Part 1 started with a simple principle: Analytics exists to help the business make better decisions and improve measurable outcomes.
Most Analytics teams already know this.
Yet many analyses still stop one step too early.
They explain what happened.
Sometimes they explain why.
Then they stop.
The most valuable question comes next: “What should we do?”
That’s the “so what?” and the difference between reporting and recommendation.
A good analysis helps people understand the business. A great analysis helps them decide what to do next.
What decision-ready analysis looks like
Every important analysis should answer five questions before it leaves your desk.
1. What happened?
State the facts clearly.
What changed? By how much? For whom? Relative to what baseline?
The audience shouldn’t need to interpret ten charts to understand the headline.
2. What does it mean?
Interpret the evidence.
Separate what the data shows from what you believe is the most likely explanation. Be explicit about whether you’re identifying a causal relationship, a likely driver, or simply a correlation.
Acknowledgements: The ideas and writing are mine, but they’ve been shaped by current and former teammates who challenged my thinking and helped evolve how we built and ran the Analytics team. Special thanks to my Chief of Staff, Anita Chan, for brainstorming, editing, and AI wizardry on the images, and to ChatGPT and Claude for serving as editorial critics.
I was really looking forward to the next post after last week’s post and it certainly did not disappoint. Thanks for this!
That is the ideal vision that normally data analysts are seen to create values. To recommend actions, this requires an understanding of business domain which might not be shared across teams. So with a common structured of a data team - centralized one, how do you think this org chart create a bottleneck for data analysts to give recommendations?