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Developing a Data Quality Strategy

Originally published January 18, 2007

A data quality strategy describes a framework and a roadmap to address the challenges and achieve the benefits of improved information quality. There is a natural conflict associated with the development of many information quality initiatives. Most applications and business operations are dependent on data quality, yet while data quality cannot necessarily be mandated across administrative boundaries, the expected benefits of improved information value can only be achieved when all participants willingly contribute to successful data quality management.

Frequently, defined data quality frameworks focus mostly on defining dimensions of data quality, but seldom do they encompass the management, technical, and operational infrastructure that must be in place to support the conformance to expectations. When assembling your data quality strategy, it is worthwhile to apply industry best practices and combine those with quality disciplines from other industrial domains (e.g., manufacturing, software development or service industries). Ultimately, the data quality practices and processes should be relevant within your organization, and the approach to building the program should follow the patterns for other successful organizational programs.

It is a formidable challenge to establish the appropriate level of data quality to meet the needs of the diversity of participants, regulatory bodies, policy makers, and information clients when coupled with the different technologies and practices already in place. To address these, a data quality strategy requires governance, policies, practices, technology and operational solutions that are all-encompassing yet present themselves to all participants as pragmatic and practical.

When assembling a data quality strategy, it is necessary to identify the key success objectives for the program, evaluate the variables by which success is measured, establish information quality expectations, develop the governance model for overseeing success, and develop protocols for ensuring that policies and procedures for maintaining high quality data are followed by the participants across the enterprise. Information follows a “lifecycle” (e.g., create, distribute, access, update, retire), so it is necessary that the data quality framework provide protocols for measuring the quality of information at the various stages of that life cycle.

A data quality framework defines data quality management objectives that are consistent with the key success objectives and the enterprise expectations for quality information, either through integration as services to be integrated across an enterprise information architecture, or through the collaborative implementation of data governance policies and procedures. Performance associated with data quality expectations can be tied to a data quality maturity model. This maturity model establishes levels of performance, and specifies the fundamental best practices needed to achieve each level of performance.

Also included in your data quality framework should be a model for data governance that outlines various data quality roles for the participants in the enterprise community. This data governance model will provide an organizational structure and the policies and procedures to be followed by the community to ensure high quality data. The governance model defines data ownership and stewardship, and describes accountability for the remediation of data quality issues across the various enterprise information systems. If necessary, the governance model will also define procedures for the data quality certification of participants as well as ongoing auditing of data quality.

To achieve assurance of high quality data, the framework should provide for the identification, documentation and validation of data quality expectations. These expectations can be transformed into data quality rules and metrics used to assess the business impact of poor data quality, develop performance models to gauge severity of data quality issues, track data quality events and issues, and provide ongoing data quality measurement, monitoring and reporting of conformance with customer expectations.

To encourage coordination with the efforts to ensure data quality, there is value in educating participants in ways to integrate data quality as an integral component of the system development life cycle. The development of a component model for data quality services will expose the appropriate topics to be the subject of training material to facilitate data quality integration.

So in summary, your data quality strategy should:

  • Provide a framework of data quality concepts.
  • Specify a data governance model to manage the oversight of data quality, incorporating data ownership, stewardship, and accountability for community-wide data quality.
  • Formalize approaches for identifying, documenting, and validating data quality expectations.
  • Provide practices to evaluate the business impacts of poor data quality and to develop performance models for issue management and prioritization.
  • Integrate methods and processes for data quality event tracking, data quality monitoring and measurement, and reporting of conformance with customer expectations.
  • Formulate a component service model for data quality services that integrates within the enterprise/community interoperability model. 

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