Deciding with data without being an analyst
Leadership does not need to code, but it does need to ask the right questions about a figure, a dashboard or an AI recommendation. What to know, the most common mistakes and a 30-day plan that sets an example.
The 5 most common mistakes
The questions leadership asks shape how the rest of the company works with data.
Asking for more data instead of better questions
More reports and dashboards are requested, but nobody has defined which decision they should support.
How to avoid itStart with the decision: what do I need to know, and which figure would tell me?
Accepting a figure without asking where it comes from
A decision is made on a number without knowing how it was calculated or when it was updated.
How to avoid itThree questions: where does it come from, how is it calculated and when was it updated?
Going with the anecdote
One striking case outweighs the trend across hundreds.
How to avoid itAsk for the aggregate figure and its trend before generalising.
Dashboards with too many indicators
Thirty indicators on one screen and none leads to an action.
How to avoid itFew indicators, each with a target, an owner and what to do if it drifts.
Trusting or distrusting AI on instinct
Either everything AI proposes is accepted, or everything is rejected.
How to avoid itAsk what data it uses, how accurate it is and who reviews its outputs.
Sound familiar?
Measure your team’s level with the free self-assessment: 20 statements scored by dimension.
Download the self-assessment →What each profile should know
The levels come from the data literacy framework: basic, use, analysis and management. Not everyone needs to reach the same one.
| Profile | What they should be able to do | Level |
|---|---|---|
| Leadership team | Ask the right questions about a figure and decide which indicators are reviewed | Use / Management |
| Heads of department | Read their area’s dashboards, spot variances and explain their figures | Use |
| Middle managers | Use data in the team’s daily work and lead by example | Use |
| Programme sponsor | Drive the training and measure whether decision-making changes | Management |
Your data training, in three steps
Short training using the team’s real data. Each step links to the guide and the template.
Start with the leadership team
Take the data literacy assessment as a leadership team and share the result: it is the best way to set an example.
Change the meetings
Ask for every figure to come with its source, its comparison and the proposed action, and cut the leadership dashboard to a few indicators.
Launch the programme
Approve a 90-day role-based plan for the rest of the company, with an owner and measurement before and after.
Frequently asked questions
Why should leadership be trained?
Because the questions leadership asks shape how everyone else works. If the board asks for sources and context, teams learn to provide them.
How much time does it take?
Little: two or three short sessions using the company’s real data and, above all, changing how figures are presented in meetings.
How do I know the programme works?
Measure the level before and after with the same assessment and look for concrete changes: fewer arguments about which figure is right, faster decisions and fewer reports nobody uses.
What does the AI Act say about training?
Article 4 deals with AI literacy for staff who use AI systems. The sensible approach is to train each profile according to how they use AI and keep a record of it.
What level of data literacy does your team have?
Free five-minute assessment: your score in the four dimensions and what to work on first.