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Evaluating Business Impacts of Poor Data Quality
January 2011
David Loshin

Process errors will introduce risks to any business activity. Observing errors in a typical manufacturing process (taking raw input and generating a single output) allows one to institute quality controls that can identify (and therefore fix) imperfections early in the process. Data, however, is a “reusable resource” that is generated through numerous processes, with multiple feeds of raw data that are combined, processed, and fed out to multiple customers both inside and outside your organization. Because of data’s dynamic nature, in which it is created and used across many different operational and analytic applications, there are additional challenges in establishing ways to assess the risks related to data errors and failures to meet business user expectations.

Any business case for data quality management should identify the ways that poor data quality impedes business success. By establishing quantifiable measures that relate real gaps in value due to data errors, one can develop a “return on investment” business case for data quality improvement.

Anecdotes showing specific examples where flawed data has led to business problems help to motivate and raise awareness of data quality as an issue. However, developing a performance management framework that helps to identify, isolate, measure, and improve the value of data within the business contexts requires correlating business impacts with data failures and then characterizing the loss of value that is attributable to poor data quality. This in turn requires some exploration into assembling the business case such as reviewing the types of risks and costs relating to the use of information and then considering ways to specify data quality expectations within the context of those costs and risks.

This article describes some of the types of risks attributable to poor data quality as well as an approach to correlating business impacts to data flaws.