Data governance and data quality have traditionally been separate disciplines. However, at a recent data governance conference, a keynote speaker made a point that resonated with many attendees: talk about data governance as a way to achieve data quality.
An organization might start looking for a data quality tool because analytics and BI projects are affected by poor data quality. Or, a regulation such as CCAR, Solvency II, BCBS 239 may be pushing the business to get a grip on the correctness, completeness, and accuracy of data.
However, buying a data quality tool first is like booking a surgery before being diagnosed. Organizations have thousands and thousands of different data elements. Which ones should they focus on? Which ones could be left out of scope? Which ones are making the greatest impact on the business and should be managed first?
Two approaches to answer the above questions are:
In both cases, once the areas of focus have been detected and prioritized, data governance creates a collaborative framework for managing and defining policies, business rules, and assets to provide the necessary level of data quality control.
Data owners can define key systems and processes involved. At the same time, the business can state what standards the data should adhere to when it moves through the systems. This is where policies, requirements, and business rules are created and agreed on.
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