IBM Information Governance

Information is the lifeblood of any organization and if the quality of that information isn’t properly governed, it could be detrimental to the business. Poor quality can lead to higher costs, inability to properly market, compliance risks and an overall lack of trust.

Data Quality Governance is a discipline to measure, improve, and certify the quality and integrity of an organization’s data. Data Quality includes data understanding, standardization, matching, cleansing and monitoring. Organizations need to ensure high data quality and put measurements in place to keep the quality high over time.

Focus Areas

There are three main areas organizations should focus on for improving and maintaining data quality:

• Understand & Define: Understand the data and define standards to promote consistency in how data is used and captured/stored.
• Develop & Test: Create standardized database structures for storing master data. Focus on reliable ways to create good realistic, privatized test data.
• Cleanse & Manage Continuously: Make use of the defined quality standards and create the rules necessary to ensure the data adheres to them over time. Rules and guidelines should be implemented in a way that automates the maintenance of high-quality data and ensures re-use of business logic.

Next Steps

• Understand enterprise data
• Build enterprise data quality models for your industry
• Integrate data and govern quality throughout the life cycle
• Build high quality applications with secure test data management
• Focusing on data quality will help ensure successful outcomes for Master Data Management (MDM) and Data Warehousing projects.