Traditionally, this process consists of little more than recounting anecdotes, hazy feelings about the difficulty of an implementation, or tales of customer dissatisfaction. This capability is all the more important when you consider that the evidence of such an impact may be difficult to correlate. Common issues that could imply poor data quality include frequent system failures and service interruptions, a drop in productivity vs. volume, high employee turnover, high ratio of new business to continued business, increased customer service requirements, or customer attrition. With a concrete means of doing so, you can indeed determine the extent to which bad information affects your bottom line and then take steps to address the problem. But first, it's important to know the main building blocks for constructing an economic model of data quality.
Most organizations use their data in operational as well as decision-making processing.
This information in turn feeds into additional operational or decision-support processes. The first step in understanding the effect of low data quality is to peer inside these black boxes and identify the steps through which information flows. Then, you can divide your system into a set of processing stages connected by directed information channels. I recommend that you create information channels of the directed variety because bidirectional communication may occur between any two points, and you want to be able to differentiate data flowing in one direction vs. another. For example, any organization in the business of extending credit will have a process that, based on a set of inputs associated with an applicant, will determine whether the credit request is approved.