4.3 Closing Remarks

4.3 Closing Remarks

If you want high data quality you must have highly accurate data. To get that you need to be proactive. You need a dedicated, focused group.

You need to focus on data accuracy. This means you need an organization that is dedicated to improving data accuracy. You also need trained staff members who consider the skills required to achieve and maintain data accuracy as career-building skills.

You need to use technology heavily. Achieving high levels of data accuracy requires looking at data and acting on what you see. You need to do a lot of data profiling. You need to have experienced staff members who can sniff out data issues.

You need to treat information about your data as of equal or greater importance than the data itself. You must install and maintain a legitimate metadata repository and use it effectively.

You need to educate other corporate employees in the importance of data and in what they can do to improve the accuracy. This includes the following elements.

  • Business users of data need to be sensitized to quality issues.

  • Business analysts must become experts on data quality concepts and play an active role in data quality projects.

  • Developers need to be taught best practices for database and application design to ensure improved data accuracy.

  • Data administrators need to be taught the importance of accuracy and how they can help improve it.

  • All employees who generate data need to be educated on the importance of data accuracy and be given regular feedback on the quality of data they generate.

  • The executive team needs to understand the value of improved data accuracy and the impact it has on improved information quality.

You need to make quality assurance a part of all data projects. Data quality assurance activities need to be planned along with all of the other activities of the information systems department. Assisting a new project in achieving its data quality goals is of equal or higher value than conducting assessment projects in isolation. The more integrated data quality assurance is with the entire information system function, the more value is realized. And finally, everyone needs to work well together to accomplish the quality goals of the corporation.