Outlier detection
How are potential “false alarms” excluded here and in other areas of application for Data Science? This is where the fundamental topic “outlier detection” comes to the fore. It underscores what is important for every Data Scientist: “understanding” the data. After all, the mere collection of data does not generate any added value itself. Rather, clear recommendations for action must be derived from this. In this context, the mentioned outliers are important. Put simply, these are data points that do not meet expectations. In order to “detect” them, Körber Digital experts measure the distances between two of these data points and classify outliers. As a result, a complex data cleansing process is necessary before one can say with certainty whether there is really a technical problem – an “anomaly” – or just a random measurement error. “With the help of secured anomalies, we can identify defective components, for example, or discover negative effects caused by retrofitting a machine,” says Dr. David Breyel, Data Scientist at Körber Digital. “The possibilities are very extensive.”