Some customer data is missing

How often does this come up as a problem to solve? It may happen more frequently than you think.

Having clean, comprehensive, and consistent data is paramount to the most appropriate customer engagement and interaction. If your business is also an advocate and heavy user of automation, machine learning and artificial intelligence then your technical teams will tell you that the results of their efforts are commensurate not only with their efforts but also with the quality of the data that they are working with.

Without the best possible customer data, your staff and systems are exposed to a partial picture which can result in bad decisions, model bias and skewed results.

The US National Library of Medicine and National Institute of Health (PMC) journal contains an article from May 2013 describing three types of potential data deficiency in any given data set. While the focus in this case by the author, Hyun Kang would be on suitability for studies, this basis is useful for considering customer master data in general.

The three types are Missing Completely at Random (MCAR), Missing at Random (MAR), and MNAR (Missing Not at Random). Each with its own cause and potential solutions.

We’ll look at this through the lens of a customer master data management system. Read more at Pretectum.com