By Elliot King

Elliot King

In many organizations, when one side of the house starts talking about improving data quality, the other side of the house starts hearing one thing and one thing only–costs. They assume that initiating a data quality program is going to be a heavy lift financially, requiring consultants, investments in technology, training and more. And even if those

responsible for containing costs agree that improving data quality is important and can have a real impact on the bottom line, they often wonder if the impact will be big enough to justify the investment. Investing in data quality is seen as a choice and there may be more effective ways to invest limited resources.

But data quality improvement does not have to be high cost. Data quality rests
on people and processes as well as technology and by focusing attention on the
first two, companies can make significant progress in improving data quality
inexpensively.

Perhaps the easiest first step is to make somebody responsible for data
quality–appoint a data steward charged with monitoring data quality or at least
trying to determine how data quality could be monitored. Depending on the size
of the organization or the department within an organization, this does not have
to be a fulltime job; nor does the person initially have to be an expert. In the
beginning, data stewards can educate themselves about the quality issues.

The next easiest step is to have the data steward poll employees responsible for
entering data about where mistakes happen. Front line personnel represent a deep
repository of knowledge about which they are seldom asked. If a process or data
screen is broken leading to data entry errors, they will know.

Then, companies should focus their efforts on safeguarding the quality of their
most important data. Organizations do not have to do everything at once. Just
knowing what is most important is a critical step forward.

These ideas are not completely cost-free but they certainly are not expensive.
The key is starting somewhere, even if the first couple of steps are very small.


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