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Guide to managing a data quality assurance program

Last updated:December 2014

Editor's note

Companies in various industries often suffer from a common problem: poor data quality. Despite an increased focus on data quality management efforts, often spurred on by regulatory requirements, many organizations continue to struggle with low data quality levels. For example, more than one-third of 210 IT and business professionals surveyed by consultancy The Information Difference Ltd. in early 2013 rated the quality of the data in their systems as "average" or below. It could have been worse: More than 40% said their companies hadn't set up enterprise-level data quality assurance programs.

A practical, comprehensive and well-managed data quality strategy can eliminate scattershot efforts in different business units and help ensure that business users throughout an organization have access to consistent and accurate information. But to do so, a program must address the root causes of data inconsistencies, fix errors through data cleansing and unite separate data quality initiatives. Implementing such a program calls for heavy amounts of collaboration between IT teams and senior business executives, as well as the involvement of business users.

This guide compiles a variety of articles that offer insight and advice on planning, instituting and managing an effective data quality process. The content featured here examines the roles and responsibilities required in data quality programs, ways to secure and maintain business buy-in for data quality investments, best practices for data quality management, and the potential benefits of combining data quality work with master data management and data governance programs. Tips and examples from organizations that have successfully worked to improve data quality are also included.

1Implementing and managing data quality improvement efforts

Once you've developed an enterprise data quality strategy, the next challenge is implementing it and sustaining the effort. Keeping a data quality program on track isn't easy, especially in a fast-paced business environment that generates and collects large amounts of data. The articles in this section offer advice on best practices and proven tactics for improving data quality in an organization.

2Working to resolve real-world data quality issues

Real-world examples often offer the best advice -- and expose mistakes to avoid. The articles in this section look at how companies from different industries have grappled with data quality problems and what they learned in the process. Their projects highlight the need for collaboration between IT and the business to achieve and maintain data quality improvements.

3Data quality definitions

Build up or refresh your knowledge of key data quality terminology with this glossary of definitions.

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