Data quality is a complex animal to comprehend, far less tame - and it is an ongoing battle for the vast majority of organisations today, and has the potential to seriously undermine the ways in which data is used in Customer Management.

Taming of data quality as an issue requires a very clear articulation of the problem, and typically (due to resource constraints) a priorisitation of improvement activity - whether that be strategic fixes to broken systems or processes or tactical cleaning exercise. The Data Quality Framework is our means of articulating the problem at hand, and setting out the repair options. This framework looks across all data content (as defined overleaf), and scores ten weighted components of data quality to give one clear data quality index for the data being assessed.