The cost of low data quality

The Monetary Cost of poor Data Quality

Poor data quality can lead misguided decisions, as they are based on data that is not able to portray reality in a way that is sufficient for decision-making. Beyond those misguided decisions and their consequences, people at your company that are working with data may encounter duplicates, which creates the need for de-duplication, a basic necessity for FAIR principles adherence.

In our Guide to calculating the ROI for a Data Strategy, we calculated that an organization with 10.000 employees may save at least 15.6m $ annually! By implementing a data governing structure that increases data quality through de-duplicaiton for example. 

A Return on Investment (ROI) is often, but not always needed to make a business case for something, and Reference and Master Data (RMD) is no exception. There would be no need to develop an ROI analysis for RMD if it was mandated by regulators, but this is not usually the case, and executive management will need to be provided with an understanding of what it will cost to implement and run RMD and quantification of the benefits of this implementation. RMD will be just one of many budgetary priorities that executive management is asked to fund. The team proposing an RMD initiative may not understand, or even be aware of all of the other competing priorities. Likewise, the RMD team should not expect executive management to have an intuitive and detailed understanding of RMD. This means that not only must an ROI be developed for RMD, but the ROI must be presented in a way that can be consumed easily and effectively by executive management. Also, the ROI need to have the return on the investment in a reasonable timeframe.

In this report we show that the long-term gains from investments in RMD have a yearly ROI 16m$ for any organization with about 10 thousand employees. Today, there is general recognition that Reference Data and Master Data need to be managed well due to its enterprise-wide impact on data quality. Accordingly, RMD is a key part of the data strategies. The required efforts are, however, often under- estimated and we see a gap between recognition of the importance of RMD and the pace at which the maturity of RMD is increasing – often much too slow. While there are content and organizational reasons for the slow adoption, we additionally see a need to more explicitly calculate the ROI for RMD to unlock or re-allocate more resources. The main challenge here is to make long-term effects visible and recognizable in the short term of, say one year or so.

With ACCURIDS, we provide a data registry platform for managing distributed reference and master data across different parts of your organization. The registry is a light-weight approach to reaping benefits of RMD while keeping integration efforts and change in the existing data landscape low.