Personas
Not everyone needs the same data & AI skills. A customer service agent reading a dashboard, a marketing manager designing an AI workflow and a director deciding where to invest all need something different. Our Data & AI Skills Framework splits the workforce into three personas, so a program can be aimed at real jobs instead of at everyone at once.
Behind the personas sits our Data & AI Skills Framework: 21 skill domains in four categories, with over 900 skills defined underneath them. That is what lets us assess where someone actually stands, aim a training track at the gap, and measure skill growth instead of attendance.
The framework also defines a fourth persona: the Data & AI Specialist — data analysts, data scientists, data engineers and ML engineers. Our programs today are built for the three personas above. Talk to us if you need specialist tracks as well.
At first I thought it was going to be very technical, but it became very easy to follow and actually made me realise I use AI more than I think.
Learner, AI Literacy Fundamentals — Data & AI User
I love the fact that I can work in a real version of the tool. Applying the training to cases that are relevant to us made all the difference.
Learner, Tableau Fundamentals — Data & AI Builder
What sets Data Booster apart for us is that the trainings feel purpose-built for Jaarbeurs from day one, because they are. The in-depth discovery phase ensured the content connected directly to the daily reality of our people.
Corine Bos, CTO Jaarbeurs — Data & AI Leader
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Through a skill assessment at the start of the program. Job titles are a starting point, not the answer — a marketing manager in one organization is a User, in another a Builder. The assessment places people against the framework and the results tell you who sits where.
People move. A User who starts building dashboards becomes a Builder, and Builders who move into leadership need the Leader skills. We aim a track at where someone actually works today, and reassess as the program runs.
No. Many organizations start with the User group, because it is the largest and the foundation for everything else. What we do advise is not to skip the leaders: teams read quickly whether data and AI are genuinely part of how decisions get made.
They are the fourth persona in our framework: the Data & AI Specialist. Our programs today are built for the three personas above, so specialist tracks are something to discuss rather than something off the shelf.
The method is the same — cohort-based, hands-on, exercises on your own tools and data. The content is not. Each persona gets its own track, aimed at the skill domains that matter for that group.
We measure skills before and after against the same framework, so growth is measured rather than self-reported. Across our programs that comes out at 21% average skill growth, with a 79% completion rate.
