Sometimes our analytical dashboards do not fulfil their role because they are too complicated.  They have been designed to solve many different problems, but as a result, we are faced with information chaos. Users of these tools are lost.  Imagine driving a car where the dashboard is overcrowded with indicators to the extent that you can't see the speedometer. This would make driving complicated because the most critical information, your speed, is lost among less essential details. Similarly, when creating analytical dashboards for marketing, finance, or supply chain purposes, we often fall into the trap of including too much information. This results in "too much of everything" dashboards where key data can be overlooked or hard to find. To build an effective dashboard, sometimes it's not about what to add, but what not to show ! And to know what to show and what not, we need to be able to understand : 👩🏻 👨🏼the user, his business goal and process, 📊 data analysis best practices (actionable insights, storytelling, visualisation , …), ⚙️ digital tools development best practices (automation, scalability, data modeling, code, …).