Posted by Garry Moroney in:
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Determining the aggregated return on investment for a data quality management initiative is notoriously difficult. Typically a minimum or partial ROI can be estimated by reference to the impact of low quality data on one or two key projects or processes. For example in a CRM project data quality ROI can be tied to reductions in customer contact failures and increased sales due to high quality segmentation. But given that the same set of master data will be used more than once in most organizations (i.e. customer master data will also be used in the billing system, the supply chain system and so on) and will add value (or destroy value!) in all of these processes, basing your ROI calculations on a single system or process will always underestimate the true returns.
For an organization trying to estimate the total returns across the enterprise from a data quality initiative, there are two difficult questions that must be addressed:
• How valuable is this dataset to the enterprise - assuming 100% data quality?
• How does its value decrease as quality erodes?
While these questions might at first seem unanswerable, it is worth noting that these are not unusual questions for a business to ask. In fact businesses need to be able to answer questions of worth and depreciation for all their tangible assets - property, stock etc.
Unfortunately data is one of those intangible assets where normal valuation approaches like recorded cost or replacement value are ineffective. But there are other intangible assets such as IPR, work-in-progress, customer and partner relationships (good will) where significant research has been done to develop effective valuation methodologies. It just might be possible to leverage these methodologies to value your data. For example, the value of customer data is directly related to the value of the customers themselves and so "customer lifetime value" methodologies should be applicable in estimating the value of customer data and the extent to which this value varies with data quality.
Have any of you out there attempted to put a real value on your company data in this way? Perhaps you'd be willing to share your experiences with us.
For more information on building a business case for data quality and calculating potential return on investment see the Informatica white papers: Data Quality Profiling Calculating ROI for Data Migration and Data Integration Projects
and The Data Quality Business Case—Projecting Return on Investment.
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