New Paper: Understanding the Financial Value of Data Quality Improvement
I have just finished a paper sponsored by Informatica titled “Understanding the Financial Value of Data Quality Improvement,” which looks at bridging the communications gap between technologists and business people regarding the value of data quality improvements. Here is a summary:
As opposed to the technical aspects of data validation and cleansing, often the biggest challenge in beginning a data quality program is effectively communicating the business value of data quality improvement. But using a well-defined process for considering the different types of costs and risks of low-quality data not only provides a framework for putting data quality expectations into a business context, it also enables the definition of clear metrics linking data quality to business performance. For example, it is easy to speculate that data errors impede up-selling and cross-selling, but to really justify the need for a data quality improvement effort, a more comprehensive quantification of the number of sales impacted or of the total dollar amount for the missed opportunity can be much more effective at showing the value gap.
This article looks at different classifications of financial impacts and corresponding performance measures that enables a process for evaluating the relationship between acceptable performance and quality information. This article is targeted to analysts looking to connect high quality information and optimal business performance to make a quantifiable case for data quality improvement.