It’s clear that nobody wants bad data, yet it is a costly reality that is often ignored.
Here are some tips to help you jump-start your own initiatives.
Understand the bus iness context
Successful data quality initiatives are driven by the requirements of business initiatives.
By starting the IT-business conversation, you can confirm the business context and learn which data is needed.
Discover and profile data
Data discovery provides insight into whether you have the data you need, and data profiling examines the structure, relationship and content of existing data sources to create an accurate picture of the state of the data. This helps in planning the best ways to correct or reconcile information assets to answer the business questions at hand. Data discovery and profiling technology can be deployed in-house, or provided by a professional services organization.
Monitor data qua lity
You need oversight to ensure that data quality efforts aren’t degraded by the creeping return of errors, such as the introduction of incorrect or nonstandard data. Active data monitoring in profiling reports and scorecards, for example, could generate email or system alerts when certain conditions are met, such as a high percentage of exceptions or nonstandard data. Another, more dynamic, approach entails applying methods used to cleanse and enhance data in real time as it enters and moves through the enterprise.
Implement a data qua lity methodology
The methodology should include the processes and technologies used to create and maintain the quality standards specified by the business rules. It should include discovery/profiling and monitoring as mentioned above, as well as processes for accessing and modifying data from diverse sources; correcting, standardizing and validating data; and enhancing existing data by incorporating external information.