In the field of automation, there is a lot of talk about sensors, networking, and artificial intelligence. In practice, however, it is becoming increasingly clear that it is not the lack of technology that poses the real limitation, but rather the quality of the available data.
After all, data alone doesn’t improve a process. Only when they are consistent, complete, temporally reliable, and technically accurate can they serve as a reliable basis for decision-making. If these very qualities are missing, a familiar scenario quickly emerges: Sensors provide readings, systems collect information, dashboards fill up—and yet the process doesn’t improve.
This becomes particularly critical in situations where automation relies on stable signals. When measured values are not comparable, conditions are described inconsistently, or data from different sources do not align properly, it is not just the validity of the results that suffers. In that case, analyses, optimizations, and automated decisions also become unreliable.
So the real challenge often isn’t in generating even more data. It lies in making existing data technically usable. Those who underestimate the importance of data quality quickly end up creating digital transparency on paper without gaining true control over the process.
Automation therefore does not always reach its limits in terms of hardware or software first. More and more often, their potential is limited by data that is unclear, inconsistent, or not reliable enough. Data quality is therefore not a secondary IT issue, but rather a key technical prerequisite for stable and sustainable automation.
