Stop reporting numbers and start telling stories
How to get your stakeholders to PAY ATTENTION đ
Connecting data work to business decisions means transforming technical analysis into actionable insights that directly influence company strategy, drive efficiency, and improve profitability.
Data Scientists are rarely working in isolation. Most are embedded into cross-functional product teams, working closely alongside product managers, engineers, and designers. So the role isnât just about delivering technical analysis itâs about making that analysis useful to the people around you.
Which means the most valuable skill for the profession isnât technical analysis alone. Itâs how well you can communicate your findings; turning numbers into a story that actually influences decisions for teams.
The difference between delivering a finding and telling a storyâŠ
Imagine a data scientist standing before a room of executives. They present a flawless, accurate chart showing that company revenue fell by 12% in Q3. The analysis is mathematically perfect and shown with clean visualizations.
The audience agrees with the data. But then they move on to the next agenda item with no call to action or any decision made.
Why? Because a finding is not a story.
Exploratory Data Analysis (EDA) is brilliant at answering what happened. But that alone isnât enough, simply showing the relevant data doesnât showcase the underlying trends driving the results.
To move people, you must bridge the gap between exploration of data and trends driving the results. Thatâs where you ask the question EDA never asks: so what?
Maybe it was a pricing change that impacted demand. Maybe production costs rose or competition increased in the market. This is exactly what stakeholders actually need to know the underlying reasons driving the trend, the reasons they can act on.
Thatâs the difference storytelling brings: it adds the why. It gives people a reason to care and to act.
This means that you as the Data guru must understand the business youâre working in, not just the numbers and the code.
The Core framework to turn data into a decision:
Brent Dykesâ framework breaks the process of turning data into a decision into three questions: Who, What, and How.
Each one builds on the last; you canât pick the right question (What) without knowing your audience (Who), and you canât build real insight (How) without a sharp question to build it around.
WHO (Audience Profiling)
Before building any analysis, start with the purpose of your analysis and profiling the audience.
Ask yourself,
Who is the target audience?
What do they care about? What keeps them up at night?
What is the call to action for the audience reading the analysis?
WHAT (Choosing the Right Question)
At this stage, choose the question you want people to focus on. This is also the moment to address any outliers. All of this builds your insight.
A good central question is,
Unexpected
Disruptive or counterintuitive
Puzzling, with a story to unravel
HOW (Building the Insight)
Creating insight requires constant interaction among three key elements which forms a good data story:
Data: This is your foundation on which your analysis is built.
Narrative: Then there is the narrative which is your structural arc used to engage, guide, and persuade the audience. The way to structure it is by using below elements:
Visuals: This one is simply the charts or âvisualizationsâ that help the audience easily spot patterns, anomalies, and insights.
The Bottom Line
Statistics tell you what happened. Stories tell you what to do next.
We form stories out of data for a few key reasons:
To trigger a reaction from the audience
To persuade the audience to act
To share insight rooted in data surfacing unexpected or non-obvious information
To build knowledge and memory in the reader
Data scientists who understand the difference between the two tend to be the ones who influence decisions in a business.