ROI for Data Quality
Published: January 1, 2003
Published in TDAN.com January 2003
Every organization has a problem with data quality – there is no doubt to that. The issue that many organizations are currently grappling with is not the existence of data quality issues, but rather how critical those problems are to the business. Even in companies that recognize the importance of data quality within the enterprise, there is hesitancy to attack these problems. Having spoken to a number of information practitioners, it appears to me that the reason for this is not a misunderstanding of the scope or size of the problem, but rather this is due to two fundamental problems:
An approach that we have had some success with in dealing with both of these issues revolves around a limited assessment of data quality that helps yield some metrics on the size of the data quality problem, help determine which problems are the most business-critical, and help determine the initial steps that need to be taken to address the problem.
The DQ ROI Problem
One of the most frustrating issues associated with data quality improvement is not knowing how bad data really affects the organization. In some companies, the methods to address poor data quality may incorporate interim data corrections, nominal customer service adjustments, multiple copies of non-standard data, or other “topical” solutions, all of which incur some “correction” costs to the company but probably do not address the source of the problem. In fact, though, it is possible that despite the existence of data that is not compliant with knowledge worker expectations, the costs associated with fixing the problems overwhelm the aforementioned correction costs.
This is where the concept of the data quality Return On Investment (ROI) assessment comes in. The goal is to provide some set of metrics that highlight the more critical data quality issues, and tie those issues to actual business problems, which can either be related to increased costs or with lost opportunities. Calculating the scope of the actual costs of those business problems and then comparing those costs with what it will take to improve data quality provides that elusive ROI model.
This ROI model can be used to address both of the above-mentioned roadblocks. By providing clearly defined metrics and their actual measurements, and tying them to actual business problems, this ROI model builds the argument for senior-management support of data quality improvement initiatives. And by highlighting the most critical data issues, this model provides a starting point for the improvement process.
Costs Associated with Poor Data Quality
We can divide the costs associated with poor data quality into the soft costs, which are clearly evident but yet hard to measure, and the hard impacts, whose effects can be estimated and measured. Ultimately, the level of data quality rolls up to the company’s bottom line – allowing low levels of data quality to remain will lower profits, while improving data quality should increase profits.
Hard costs are those whose effects can be estimated and/or measured. These include:
Soft costs are those that are evident, clearly have an effect on productivity, yet are difficult to measure. These include:
These costs can be manifested as one or more of these specific impact classifications:
Value Associated with Improved Data Quality
Improved data quality can add to the company’s bottom line, either through optimization in operational systems or by improving the value of knowledge generated through a business intelligence process. The following kinds of improvements are typical as the result of improved information quality:
Creating The Value Proposition Through DQ Assessment
One might think that a complete system assessment is required to provide the comprehensive ROI for improved data quality, but typically a constrained assessment is both effective at isolating significant problems that can be directly related to increased costs as well as providing insight into the direction an improvement process should take. We have had success in small-scale analyses that focus on one particular data set. The process is as follows:
Building the ROI model for data quality is a valuable business process that requires a small investment in time and energy yet provides valuable documentation of the scope and costs associated with poor data quality. Performing a limited assessment of data quality yields important metrics on the size of the data quality problem, helps highlight those problems that are the most business-critical, helps determine the initial steps that need to be taken to address the problem. Most importantly, the ROI model provides an irrefutable argument to convince senior managers of the importance of information compliance.
Recent articles by David Loshin
David Loshin - David is the President of Knowledge Integrity, Inc., a consulting and development company focusing on customized information management solutions including information quality solutions consulting, information quality training and business rules solutions. Loshin is the author of The Practitioner's Guide to Data Quality Improvement, Master Data Management, Enterprise Knowledge Management: The Data Quality Approach and Business Intelligence: The Savvy Manager's Guide. He is a frequent speaker on maximizing the value of information. David can be reached at firstname.lastname@example.org or at (301) 754-6350.
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