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.
1Data quality strategies-
Planning and developing a data quality assurance program
For many companies, data has become their most valuable asset. Used effectively, it can sharpen competitive edges and drive higher revenues and profits. But poor data quality often undermines business strategies. Identifying and fixing data quality problems isn't a job just for IT managers and staffers -- business executives and users also need to be involved to make the process work. These articles explore the strategic and organizational aspects of data quality assurance efforts.
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. Continue Reading
It's tempting to think that your organization's data quality is fine and there's no need to worry about it. But that kind of thinking can lead to business trouble for companies. Continue Reading
Consultant Andy Hayler says survey results show that data quality isn't getting better, despite efforts by companies. That needs to change, he adds. Continue Reading
Accurate data is vital in technology-driven business processes, making it imperative for organizations to involve business users in data quality initiatives. Continue Reading
Involving business users in data quality improvement efforts requires approval from corporate executives. To get it, connect accurate data to the bottom line. Continue Reading
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. Continue Reading
Get tips from David Loshin on how to define data quality rules and set reasonable milestones for addressing data quality problems in your organization. Continue Reading
Data quality improvement efforts should start with changes to internal processes to help minimize data errors -- then move on to the possible addition of data quality software. Continue Reading
2Data quality best practices-
Implementing 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.
Consultant Wayne Eckerson says data quality problems can create a shaky foundation for business intelligence and analytics efforts if they aren't forcefully addressed. Continue Reading
Consultant David Loshin offers a five-step program for developing a data quality plan that can help identify and fix data errors before they cause big business problems. Continue Reading
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. Continue Reading
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. Continue Reading
In a book excerpt, Sebastian-Coleman explains data quality assessment terminology and concepts and details a framework for measuring data quality levels. Continue Reading
Companies should incorporate effective data quality management processes into MDM initiatives from the outset, according to consultant Anne Marie Smith. Continue Reading
Combining data quality, MDM and data governance programs can help ensure that data remains accurate and consistent, analysts and consultants say. Continue Reading
Educating business users and upgrading internal business processes can help companies stamp out data quality issues without laying out a lot of money. Continue Reading
Working 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.
A team examining a telecom company's data found lots of discrepancies, forcing the company to implement stronger data quality procedures. Continue Reading
Before it could launch a new customer relationship management system, railroad operator CSX Transportation first had to throttle up its data quality levels. Continue Reading
A move to unify different elements of the British Army into a single force has ratcheted up the need to ensure that personnel data is accurate and up to date. Continue Reading
Fixing data quality issues enabled trucking company U.S. Xpress to save what it says were "millions" of dollars by reducing the amount of gas used while trucks are idling. Continue Reading
Faulty data about products can lead to costly mistakes and missed business opportunities, according to consultants and experienced IT professionals. Continue Reading
Data quality definitions
Build up or refresh your knowledge of key data quality terminology with this glossary of definitions.