Data Cleansing with intelligent identification


In many cases an inductive method of data cleansing is the way to go. With the right tools and expertise you can inspect, transform and cleanse entities in a database and reach high levels of data quality without the need to use external reference data. In some cases, however, only working with the internal data and inductively identifying and fixing data patterns is not sufficient. Let’s take a practical example: a bank needs to report on a particular segment of its clients to German bank supervisor BaFin – the Federal Financial Supervisory Authority aka Bundesanstalt für Finanzdienstleistungsaufsicht. The bank apparently has done its homework and has created a central database containing all entities needed for the compliance check. Moreover, the bank has worked out a rather complex set of rules how data must be processed and corrected. One of the most important anchor points in this specific framework is the separation between B2C and B2B entities and for the latter the exact identification of the correct legal form. But what if you cannot trust this identification? Continue reading ‘Data Cleansing with intelligent identification’