Garbage in, garbage out. Those words of wisdom have been with us for as long as computers have played integral roles in business operations and, for all I know, even longer. 


It doesn’t take a rocket scientist to figure out that if the data used to solve a problem or perform a task is flawed, the results are going to be substandard as well.


Consequently, it would seem obvious that companies would realize that it would be in their interest to invest to insure that their data is of high quality. Except they don’t. The resistance to launching a comprehensive and systematic data quality and data improvement effort comes from many directions. 


First, the definition of “high quality data” can differ. Seemingly low quality data may be seen as “good enough,” for specific uses. Secondly, identifying the source of data flaws may not be easy. Third, data quality improvement programs can be complex and require a multifaceted approach to a problem, which can be hard to define. And finally, data quality programs are not necessarily cheap. 


As strange as it may seem, you need to make a business case to invest in data quality. In theory it is not that hard, though in practice it may be something of a challenge. The first step is to identify the real impact poor data quality has on an organization. 


Do the appropriate people have access to accurate information when needed?  If not, what is the result?  Does a shipment go out late? Is an order incorrect?  Is work flow slowed?  Does work have to be redone? These real issues can be quantified. 


They may result in lost revenue; they could cost the company money; and they can waste staff time.


The second step is to identify the root cause of the faulty data.  Does a form on your Web site for capturing names and addresses have substandard safeguard mechanisms? Do you have a sufficient process for verifying data from third-party sources? Are your employees properly trained? This is a cliché but people are human so when we input data, mistakes happen.


The third step in developing the business case for data quality is to determine how much it will cost to fix the problem.This step can be a little tricky because quality is a tricky issue and requires some expertise. And management may shy away from a full-scale assault on the problem.


So building a business case for data quality turns the usual case-building process on its head. 


You don’t do a cost/benefit analysis. You start with the benefits and then compare them to the costs, a benefit/cost analysis so to speak. But the bottom line is that garbage in cannot be good for most businesses.