By David Loshin

There are two dominating questions that I am asked over and over again when people are in process to create a program for data quality management.

The first is “how can you develop a business justification for a data quality program?” and the second is “how do we get started?” We are currently working on a task with one customer who seems to be ready to commit to instituting a data quality management program, yet they remain somewhat resistant because of the absence of an answer to the first question and confused about planning because of the absence of an answer to the second.

Let me clarify the scenario somewhat: this is a large organization that has over the years empowered their user community with an atypical degree of data freedom. At the same time, they have a widely distributed management structure for information technology development. The result is, as you can imagine, some controlled chaos.

There are data validation routines here and there for extracting data (from one or more sources) and loading the data into a target system. But these routines are completely distinct and non-standardized, to the point where even in the few places where anyone actually looking at the validation scores would be challenged to make sense of them in attempting to assess data quality and usability.

Luckily, there is a new initiative for considering enterprise-level data services, and data quality has emerged as one of the potential foundations of this service strategy. In my upcoming post, we will look at some aspects of the business justification to be used in socializing the value proposition of data quality improvement.





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