An additional data packaging stage prepares a report detailing the credit application that is forwarded to an external data consumer, which may be the same data agent that originally supplied the process with data. Consider, however, if the applicant were to share a name with someone with a bad credit history. If so, this poorly informed decision would subsequently affect the owner of the original application in the form of a denied mortgage or strategic business loan. The assumption is that if no data quality problems are present, your data flows will operate smoothly. You can reasonably rely on decisions based on valid data, and an operation system will run smoothly as long as no invalid data items gum up the works.
Issues arise, however, when the information chains involve low quality data.
Conversely, improvements in data quality can reduce costs, increase revenues, streamline and enrich decision making, and improve customer satisfaction. We can divide these impacts into soft impacts, which clearly have an effect on productivity but are hard to measure, and hard impacts, which have effects you can estimate and measure. When calculating the total economic benefit of improved data quality vs. the economic detriment of poor data quality, you can quantify each impact directly against your company's bottom line. You can accurately measure the economic impact of poor data quality in each of these categories. Although some impacts are difficult to tie to precise dollar amounts, you can at least estimate their monetary value at orders of magnitude.