Data Quality Council: Distributed Responsibility, Collective Reward

By Lynn K. Oser

We have all entered the world of Big Data, seeing the value of bringing together volumes of internal and external information to paint pictures of our markets, our clients, and our businesses. Effectiveness of the information making up the Big Data universe is dependent upon quality data that is managed over time. The key to the management of internal data is shared responsibility. Formation of a Data Quality Council distributes both ownership and understanding of potential uses of the data.

Often when installing and configuring a new system to manage information, a small project team of business owners and information technology implementers leads the way. The team focuses on the immediate needs of the business owner group, rarely considering the value of the information as part of the Big Data picture within the organization. The team makes decisions regarding fielding, pick-lists, and data imports without necessarily considering integration with other systems or of the possible uses of the data beyond their original intent. This results in siloed systems that often require interpretation from an expert in the business unit that owns the data.

Creation of a Data Quality Council, comprised of business unit subject matter experts, brings order to data and positions it for use by the business as a whole. Representatives gain a voice in the collection, use, and re-use of data within systems and can identify integrations that would otherwise be missed. They also have the opportunity to take ownership of data, taking responsibility for data quality and harmonization of data across systems.

Shared ownership of data via establishment of a Data Quality Council enables a collective understanding of the organizations major data repositories and sets the stage for expanded use of Big Data in all aspects of business.

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