Every organization collects and stores data. The quality
of that data is only as good as the edits in the applications that collect the
data. Poor quality data leads to many things including additional cost to your
corporation. In order to ensure data quality you need to understand why your
applications create bad data, and then take the necessary steps to improve the
quality of your data.
Identifying that your organization has data that is
inconsistent and inaccurate is the first step in improving the quality of your
data, but additional steps are needed to clean up that bad data. You also need
to define the business rules that your data must meet, and a measurement
mechanism by which you measure the quality of your data. Once you’ve identified
why your applications are collecting dirty data you can design, build, and
implement processes that will improve the quality of your data. After you have
implemented a data cleanup process, you need to reassess the quality of your
data. When these follow-on data quality assessments identify poor quality data,
then you need to repeat the analysis and process of cleaning your data, to
fine-tune the quality of your data.
This white paper discusses five steps you should take to
improve data quality. Each of the steps is described in detail, and helps
provide you with a better understanding of an approach to cleaning up poor
quality data. Help guide your organization’s data quality improvement efforts
by understanding these five steps to improve data quality.
