Overall Approach to Data Quality ROI is a worthy data quality whitepaper freely available from William@williammcknight.com.
William McKnight
The author of the whitepaper is William McKnight from US Analytics. William focuses on delivering business value and solving business problems utilizing proven, streamlined approaches in data warehousing, master data management and business intelligence, all with a focus on data quality and scalable architectures. William has more than 20 years of information management experience, nearly half of which was gained in IT leadership positions, dealing firsthand with the challenging issues his clients now face. His IT and consulting teams have won best practice competitions for their implementations. In 11 years of consulting, he has been a part of 150 client programs worldwide, has over 300 articles, whitepapers and tips in publication and is a frequent international speaker. William and his team provide clients with action plans, architectures, complete programs, vendor-neutral tool selection and right-fit resources. Additionally, William has an excellent blog on the B-eye-Network and a new course Introduction to Master Data Management coming soon to eLearningCurve.
Whitepaper Excerpts
Excerpts from Overall Approach to Data Quality ROI:
- “Data quality is an elusive subject that can defy measurement and yet be critical enough to derail any single IT project, strategic initiative, or even a company as a whole.”
- “Having data quality as a focus is a business philosophy that aligns strategy, business culture, company information, and technology in order to manage data to the benefit of the enterprise. Put simply, it is a competitive strategy.”
- Six key steps to help you realize tangible ROI on your data quality initiative:
- System Profiling – survey and prioritize your company systems according to their use of and need for quality data.
- Data Quality Rule Determination – data quality can be defined as a lack of intolerable defects.
- Data Profiling – usually no one can articulate how clean or dirty corporate data is. Without this measurement of cleanliness, the effectiveness of activities that are aimed at improving data quality cannot be measured.
- Data Quality Scoring – scoring is a relative measure of conformance to rules. System scores are an aggregate of the rule scores for that system and the overall score is a prorated aggregation of the system scores.
- Measure Impact of Various Levels of Data Quality – ROI is about accumulating all returns and investments from a project’s build, maintenance, and associated business and IT activities through to the ultimate desired results – all while considering the possible outcomes and their likelihood.
- Data Quality Improvement – it is much more costly to fix data quality errors in downstream systems than it is at the point of origin.
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