Guide to managing a data quality assurance program
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Article
Business users, data virtualization tools have data quality roles
Consultant David Loshin says organizations should make business units responsible for applying data quality rules and use data virtualization software to help manage the process. Read Now
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Time to take data quality assurance efforts to a higher level
Consultant Andy Hayler says survey results show that data quality isn't getting better, despite efforts by companies. That needs to change, he adds. Read Now
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To make it work, engage everyone in the data quality process
Accurate data is vital in technology-driven business processes, making it imperative for organizations to involve business users in data quality initiatives. Read Now
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Improve BI success with comprehensive data quality strategy
Organizations once might have been able to get away with not tying data quality initiatives to their BI programs. That's not so likely now, consultant Lyndsay Wise says. Read Now
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.
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Article
Improve your info by evicting bad data's evil twin sisters
Consultant Wayne Eckerson says data quality problems can create a shaky foundation for business intelligence and analytics efforts if they aren't forcefully addressed. Read Now
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How to do data profiling -- and estimate how long it will take
Data profiling is a key element of data quality assurance. David Loshin details how it works and supplies a simple formula for calculating the time needed to profile a data set. Read Now
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Data quality assessments need solid structure, human touch
In a Q&A, author and data quality architect Laura Sebastian-Coleman offers practical advice for organizations that are working to improve their data quality levels. Read Now
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Getting started on data quality assessment: What it is, how it works
In a book excerpt, Sebastian-Coleman explains data quality assessment terminology and concepts and details a framework for measuring data quality levels. Read Now
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Integrate data quality into your master data management strategy
Companies should incorporate effective data quality management processes into MDM initiatives from the outset, according to consultant Anne Marie Smith. Read Now
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MDM, governance work in concert with enterprise data quality
Combining data quality, MDM and data governance programs can help ensure that data remains accurate and consistent, analysts and consultants say. Read Now
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.