TL;DR
- Context: I came across the Data Thinking method at a conference, then went to Berlin for the full training with its creator.
- Problem: SWPS University students had a UX hackathon coming up at the weekend. They knew Design Thinking but had no way to think about data.
- What I did: I ran an original, pro bono workshop for ~20 teams (40–60 students) using my own worksheets.
- Result: Participants rated the workshop very highly, and the students were prepared for the weekend hackathon.
Diagnosis
Design Thinking teaches you to design around user needs. Data Thinking teaches the same, but around data: what you already have, what’s missing and how to use it to solve a problem. I came across the method at a conference (a talk by Tizian Kronsbein), and then went to Berlin for his full training. When SWPS University was preparing a weekend UX hackathon, I ran a pro bono prep workshop for ~20 student teams. I didn’t use the ready-made template (the Data Innovation Board). I designed my own worksheets so that participants would go through the stages one by one and not see the whole process at once.
flowchart LR
A["Stakeholder groups<br/>and their tasks"] --> B["User<br/>needs"]
B --> C["Types of data<br/>sensors, APIs, systems"]
C --> D["AI/ML solutions<br/>segmentation, scoring, anomalies, prediction"]
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classDef accent fill:#27272a,stroke:#3f3f46,stroke-width:1px,color:#fafafa,rx:14,ry:14
class A,B,C neutral
class D accent
linkStyle default stroke:#a8a8b3,stroke-width:1.5px
What I did
- I designed the workshop scenario: students played the managers of a modern building (apartments, restaurants, offices, shops), and all teams worked in the same context.
- I built my own worksheets in four modules: stakeholder groups and their tasks, user needs, types of data (sensors, APIs, internal systems), AI/ML solutions (segmentation, scoring, anomaly detection, prediction).
- I explained the basics of HTTP and APIs. For many students it was their first contact with how to pull data automatically from external sources.
- I ran the workshop myself, pro bono, for ~20 teams of 2–3 students (~40–60 people), in summer 2023.
Results
- ~20 student teams (~40–60 people) went through the whole workshop.
- Participants rated the workshop very highly.
- The students were prepared for the weekend UX hackathon.
- I turned the method from Berlin into my own worksheets.
The problem
Why did the students need a workshop before the hackathon?
Because they knew Design Thinking but had no way to think about what data they had and how to use it. Without that, many teams would have gone into the hackathon straight away with an interface idea. They wouldn’t have stopped to ask what data the decision they were designing was even based on.
Why not use the ready-made Data Innovation Board template?
Because if I had shown the whole template at the start, participants would have jumped to the end of the process. I wanted them to discover the next questions gradually: first who’s involved and what they do, then what they need, and only then what data and which AI/ML solutions could help. My own worksheets gave me control over that pace.
My role / scope
- Sole facilitator, pro bono, while working full time (Product Manager at an e-commerce company).
- I designed the scenario, the worksheets and the whole flow of the workshop: from learning the method at its source to facilitating in the room.
Process
- Learning the method at its source: a conference, then the full training in Berlin with Tizian Kronsbein.
- Fitting it to the group: stages revealed one by one.
- A shared scenario (managing a building) for all teams, so approaches could be compared and facilitation was easier.
- Run once, in summer 2023, right before the weekend hackathon.
Key decisions & trade-offs
- My own worksheets. Designing them from scratch took me quite a lot of time. In return, I could reveal the stages one by one and match the pace to the length of the workshop. A ready-made template wouldn’t have allowed that.
| Approach | Trade-off | Decision |
|---|---|---|
| Ready-made Data Innovation Board | ready faster, whole process visible at once | rejected |
| My own worksheets | more work, control over the pace | chosen |
- A shared scenario for all teams. Teams couldn’t pick their own topic. But they all started from the same place, so it was easier to compare approaches and help the ones that got stuck.
- The workshop happened only once. I have to admit that with a full-time job and a pro bono format, I didn’t have the time to turn it into a series.
How it turned out
The workshop took place once, in summer 2023, and participants rated it very highly. Today, with vibe-coding tools around, I’d add a fifth module. In it, students would go from the worksheet with an AI/ML idea to a working prototype in Google AI Studio or a similar tool. They’d see how short the distance is from an idea to something you can click.
What I took away
- It’s better to reveal stages one by one. Participants then think about the step they’re on and don’t try to jump to the end.
- A shared scenario for all groups makes facilitation easier. It’s easier to help a team when you know the context they got stuck in.
- The method transfers to other groups. It works both in a company and with students. Only the scenario changes.
- In my view this format fits working with AI well. When you connect data sources with AI agents, building a tool, testing and monitoring can be largely automated. A UX designer can then lead the whole thing, from researching needs to rollout.