Data Quality ROI
By Elliot King
most of all, did you realize the benefits you anticipated? Calculating the ROI of an investment should tell you all that.
But ROI is also the bane of many IT professionals’ existence as well. Many of
the benefits technology produce are intangible. Assigning a monetary value to
those benefits can seem arbitrary, at best, and fictional at worst. Moreover,
calculating ROI is hard work and not the kind of work many technical people like
to do (and the financial people often just don’t seem to understand the
challenges of collecting the necessary metrics.)
Fortunately, data quality professionals can build ROI models that are credible
and reusable. In the simplest iteration, the process of calculating an expected
return on investment consists of five steps. First, select a target
application–data quality programs do not necessarily have to be corporate-wide.
What data must be used to execute function X? Next, determine the quality of the
existing data. Third, determine what would have to be done to raise the quality
to a specified level and how much would that cost? Fourth, anticipate what the
benefits of having improving the data quality would be. Finally, measure the
actual benefits realized.
Direct marketing is one of the most straight-forward areas to determine ROI for
data quality. What data do you need to execute your direct marketing campaign?
Such as names, addresses, email addresses, etc. Then investigate how accurate
your contact database is and what would you have to do to improve it to a
desired level? Next, calculate the anticipated benefits of the improved data–how
many more orders would you receive and how much would the cost of returns be
reduced. After you complete the marketing campaign, analyze if your projections
While improved data quality can lead to soft returns such as improved
decision-making and better operational efficiency, in many cases tangible
metrics are available to determine at least a minimum return on investment.