Module 1: Defining the Core Question

Turn a vague topic into a decision-driving question in 10 minutes.

1
Quick Intro
2
Guided Practice
3
Applied Task
4
Quick Refinement
1

Quick Intro

~2 min
WHAT

A core question turns a vague topic ("Cycling in Munich") into a single decision-driving question with a specific subject, time, place, and criterion.

WHY

Decision-makers act on questions, not topics. Without a sharp question, your data story describes but doesn't decide anything.

HOW IT FITS

This is the foundation for the whole story — you can't pick the right data, chart, audience, narrative, or interaction until you know what question you're answering.

EXAMPLE

Vague: "Cycling in Munich." → Sharp: "Should Munich expand its cycling network by 40 km by 2028 to lift cycling's mode share by 3 percentage points?"

1

See the difference

30 sec
Topic = what data is about. Question = what decision it supports.
Topic (vague)
"Public transport usage in Germany"
Question (actionable)
"Should we invest in suburban rail frequency to recover lost ridership?"
Which one tells a decision-maker what to do? The question does.
2

Spot the question

30 sec
A good question includes: who decides, what action, and what outcome.
Click the better question:
A
"CO2 emissions from transport"
B
"Should the city expand bike lanes to cut commuter CO2 by 15%?"
3

Learn the template

30 sec
Use this formula to convert any topic into a question.
Question Template
"Should [decision-maker] do [action] to achieve [outcome]?"i
Example
"Should the transport ministry invest €2B in rail electrification to cut freight emissions 30% by 2030?"
Memorized? You'll use this in 30 seconds.
Continue to Module 2 →
2

Guided Practice

~3 min
1

Fill in the blanks

1 min
Practice by completing this real mobility scenario.
🚌
Scenario: Bus route optimization
A city's evening bus routes have 40% empty seats. The transport authority wants to either cut routes or shift to on-demand service.
Complete the question
"Should switch to to ?"
2

Improve a weak question

1 min
Most questions are too vague. Add specifics to make them actionable.
Weak question
"How is cycling changing in Germany?"
↓ Make it specific ↓
Write an improved version:
3

Quick check: 3 elements

30 sec
Every good question has these 3 elements. Check yours.
Does your question include:
Tip: If you can't check all 3, add the missing element.
3

Applied Task

~3 min
1

Pick your mobility problem

30 sec
Choose a real problem you care about. You'll build your data story around it.
Select one:
🚗 Congestion
Traffic jams, commute times, road capacity
🌱 Emissions
CO2, air quality, climate targets
🚌 Public Transit
Ridership, frequency, accessibility
🚲 Cycling
Bike lanes, safety, modal shift
2

Write your core question

2 min
Use the template. Include decision-maker, action, and outcome.
Your question formula
"Should [decision-maker] do [action] to achieve [outcome]?"
Write your core question:
0 characters
3

Self-check your question

30 sec
Verify your question passes all 3 checks.
Final check:
4

Quick Refinement

~2 min
1

Review your question

30 sec
Read your question aloud. Does it sound like something a decision-maker would ask?
Your question
(Your question will appear here)
Test: Would a busy mayor spend 5 minutes reading data about this question?
2

Make one improvement

1 min
Add a number, timeframe, or comparison to sharpen your question.
Sharpening examples
• Add a number: "...to increase ridership by 20%"
• Add a timeframe: "...by 2027"
• Add a comparison: "...compared to pre-pandemic levels"
Refine your question (optional):
3

Commit your question

30 sec
Your core question is the foundation of your data story. Commit it now.
Your final core question: