Data is not an end in itself. It is only valuable when it is clear what it measures, what it does not measure, and what decisions it can support.
In my work, the focus is therefore not on “more data,” but on reliable data. On clean measurement models, meaningful metrics, and a realistic understanding of uncertainty.
Many organizations fail not because of missing technology, but because data is misinterpreted, overestimated, or passed along uncritically. A central point is the distinction between observation and explanation: correlation is not causation. Dashboards do not replace thinking. And not every number is suitable as a control metric.
Anyone who wants to base decisions on data must understand how data is generated, what assumptions are embedded in it, and where its limits lie. The goal is not perfect reporting, but better decisions.