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.
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OVERALL APPROACH TO DATA QUALITY ROI
CONTENTS The Importance of Data Quality 1 The Importance of Data Quality 3 Steps to Data quality ROI Data quality is an elusive subject that can defy measurement and yet be 13 S tep 1: System Inventory critical enough to derail any single IT project, strategic initiative, or even a 13 Step 2: Data Quality company as a whole. The data layer of an organization is a critical component Rule Determination because it is so easy to ignore the quality of that data or to make overly 16 S tep 3: Data Profiling optimistic assumptions about its efficacy. Having data quality as a focus 17 S tep 4: Data Quality Scoring 19 S tep 5: Measure Impact of is a business philosophy that aligns strategy, business culture, company Various Levels of Data Quality information, and technology in order to manage data to the benefit of the 11 S tep 6: Data Quality Improvement enterprise. Put simply, it is a competitive strategy. Just as our markets 13 Summary today expect operational excellence, rich product features, everyday low 14 Appendix: Industry Focus prices, high product quality, and short time-to-market, one day they will also 14 Retail 16 Financial expect data quality. In the meantime, each company has the opportunity to 16 H ealthcare differentiate itself through the quality of its data. Leading companies are now 17 M anufacturing defining what the marketplace data quality expectation will be. 17 Telecommunications18 About the Author A parallel trend to data quality improvement in the marketplace is the 19 About Business Objects comeback of return-on-investment (ROI) measurement systems for technology-based initiatives. No longer is it acceptable to "throw money at problems," target soft measures, or lack accountability for results with technology spending. The approach of targeting ROI is even viable for efforts to improve the quality of data in a company and many executives are demanding payback for quality initiatives.It is important to note that many benefits accrue from improving the data quality of an organization. Many of these benefits are intangible or unreasonable to measure. Benefits such as improved speed to solutions, a single version of the truth, improved customer satisfaction, improved morale, an enhanced corporate image, and consistency between systems accumulate, but an organization must selectively choose which benefits to perform further analysis on and convert to hard dollars. ROI must be measured on hard dollars.
Author: William McKnight, Senior Vice President of Information Management, Conversion Services InternationalMeasuring data quality ROI requires a program approach to data quality. Data quality improvement is not just another technology to implement. We must change our way of doing business to fully exploit data. Investments in the technologies as well as in organizational changes are necessary to reap the full rewards. Data quality is right in the "sweet spot" of modern business objectives that recognize that whatever business a company is in, it is also in the business of data. Those companies with more data, cleaner data, accessible data, and the means to use that data will come out ahead.But how can your organization begin and justify data quality improvements? The cleansing process and maintenance costs money and may require dedicated staff. Management may not release funds dedicated to data quality improvement until such time as the cleansing improves the reliability and accuracy of key business processes like trending, analysis, or billing for product sales. The success of data quality can?and must?be measured based on its contribution to the improvement of such objectives.However, until now, there has not been a methodology to articulate and improve data quality ROI. You can't improve what you can't measure. So, we need a means for measuring the quality of our data warehouse. Abstracting quality into a set of agreed data rules and measuring the occurrences of quality violations provides the measurement in the methodology, which was developed in conjunction with IT best practices in successful efforts with data quality. This paper from Business Objects, an SAP company, examines an overall approach to data quality ROI.
Business Objects. Overall Approach to Data Quality ROI Steps to Data Quality ROI
Step 1: System Inventory In an environment that has not focused on it... [download for more]
