Data Quality Requires its Own Strategy

What's your data management strategy (DMS)?  Most organizations that have a data management or data governance program recognize that they need one. How about your data quality strategy (DQS) Surprisingly, a lot of organizations miss this need, or fail to define a DQS, and as a consequence fail to meet data quality needs.

A DMS defines your data management program; its vision, scope, objectives, how it will be structured and resourced (among other things), but it misses expectations and guidance related to data quality. That’s where the DQS comes in. It should define your strategy to achieve and maintain the quality levels necessary to ensure that the data under management will in fact meet the business needs.

The DQS is generally a separate item, or may be a part of your DMS if that’s what makes sense for your organization, but the key is that it needs to be a specific and deliberate topic. It should define such things as data domain priorities, criteria, definitions of quality dimensions (the EDM Council has a great set you may find helpful), the quality objectives for each quality dimension, rules for how data will be sampled, evaluated, measured and reported, and stakeholder roles. A DQS should also specify expectations related to control points across the data lineage or supply chain that are used as gates for ensuring that only quality data passes downstream. The content of the DQS should also be explicitly aligned and tied to business objectives so that as these change, we can recognize where we need to make changes to the quality strategy.

If data of quality is our objective (and it should be), then we must ensure that we give deliberate thought and guidance in advance to ensure that our data processing systems and methods are specifically designed to achieve the level of quality necessary for us to use and trust it. Skipping this step will ensure that the data will not meet your needs.

How is your data quality? Is it being managed according to your DQS?