Improvement & Optimization
1.Treat data quality as a business problem. CIOs must not take ownership of the data cleansing initiative. Instead, they need to convince their C-level colleagues to place responsibility for data quality where it really belongs - in the business.
2.Raise the profile of data quality. In order to gain the enterprise-wide support and commitment that a data cleansing initiative requires, executives must understand the impact of the issue on the enterprise. Prepare and submit a business case to the executives at the C-level that quantifies the way in which poor-quality data is affecting the bottom line.
3.Establish a steering committee. Set up a governance process and steering committee with key decision makers from both business and IT to prioritize requests and issues, and to address governance conflicts.
4.Find a Data Steward. Appoint a Data Steward to champion the data quality program and to establish an enterprise-wide data management strategy. Ideally, this person should have a balanced mix of business and technical skills.
5.Make a sustained commitment to managing data quality. Data quality issues cannot be solved with a standalone project. Continuous monitoring is crucial for the effective use of data. The Data Steward must establish a formal and continuous data quality program to help the company move from a reactive to a proactive approach.
6.Implement technology to support the cleansing effort. Data integration solutions provide functionality that helps enterprises build consistent, accurate, and reliable data. These technologies include data quality applications that identify and remediate problems with enterprise data. A mix of pure-play, BI, and database vendors serve the data integration market. Table 1 provides a list of major vendors from each space:
Here is a document on Step by Step Guide to Data Quality Remediation.
https://docs.google.com/Doc?docid=0ARPZNlBxg3PSZGQ5Z2o2MmJfMGRxMms4OXYz&...