By Elliot King
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 were accurate.
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.
Developing metrics to determine the return on investment is both a boon and a bane for IT professionals. A credible return on investment projection is invaluable for guiding the deployment of technology resources. And an after-the-fact calculation of actual ROI is essential for continual improvement. Did you meet your project goals within budget, and
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 were accurate.
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.
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