For companies to create high-quality databases and maintain them at a high level, they must build the concept of data quality assurance into all of their data management practices. Many corporations are doing this today and many more will be doing so in the next few years. Some corporations approach this cautiously through a series of pilot projects, whereas some plunge in and institute a widespread program from the beginning.

The next three chapters cover the structure of a data quality program built around the concept of identifying inaccurate data and taking actions to improve accuracy. The assertion is that any effective data quality assurance program includes a strong component to deal with data inaccuracies. This means that those in the program will be looking at a lot of data.

The data-centric approach encompasses a methodology in which data is examined to produce facts about data inaccuracies. These facts are converted into issues. The issues are managed through a process of assessing business impacts that have already occurred or those that can potentially occur. Remedies are proposed, implemented, and monitored. You look at the data to find the issues, and you look at the data again after remedies have been implemented to see if those remedies worked.

Various ways of integrating the process with the rest of the data management team are explored. Each corporation must have a game plan on how they will initiate or get involved in projects and how they will interact with other departments. This can strongly determine the effectiveness of the effort.

Different ways of evaluating the business value of data quality assurance efforts are discussed. This is one of the most difficult topics to deal with in getting a program established.