Context is Key to Measuring Data Quality
By Elliot King
Perhaps the most obvious criteria by which to measure data quality is
accuracy. Does the customer in your customer database actually reside at the
associated address? Is the email address actually correct? It’s not hard to
imagine a customer record filled with inaccurate data.
The issue of completeness is related to the issue of accuracy. All the
information you have may be accurate but you may not have all the information
you need, particularly the information you need to be able to link records
efficiently. If all you have is a customer’s name, obviously that will not be
good enough to serve as the foundation for a direct marketing campaign. (The
flip side of the completeness equation is important as well. Capturing a lot of
superfluous information can be just as problematic as missing information.)
Data can also be measured according to its consistency. For example, are
customer accounts activated and deactivated appropriately? It doesn’t make much
business sense to send a subscription solicitation to somebody who already
subscribes to a magazine. But it happens.
Other significant criteria by which the quality of data can be assessed are
timeliness and the ability to audit it. Does the data enable people to generate
reports according to their deadlines? Do your customer service representatives
have the most up-to-date pictures of your customers’ latest interactions with
your organization? Finally, can data be tracked back to the transactions that
There are other dimensions along which data quality can be assessed. Are records
duplicated? Are records captured according to the specified rules?
But the components of data quality are just that–components. Data quality itself
is holistic. It allows the processes in which it is used to function efficiently
and cost effectively or it doesn’t.