5 Best Practices for Data Quality Management

Here are five best practices for data governance and quality management that are being leveraged by companies that have successfully achieved -- and benefited from -- peak data quality in their enterprise.

Start tackling your data quality management problems by performing a complete analysis of the current state of your data. Information with errors, inconsistencies, duplicates or missing fields can often be difficult to identify and correct. That's because bad data can be buried deep within legacy systems, or is received from external sources such as third-party data providers, external applications and social media channels like Facebook and Twitter.

An independent analysis will provide the organization with an in-depth report that includes accurate and detailed statistics about the quality of the organization’s data. The business can then formulate or refine a data quality management strategy tailored to its unique organizational needs, and develop governance policies that address specific data management requirements.

Data is a strategic information asset, and the organization should treat it as such. Like any other corporate asset, the data contained within the organization's information systems has financial value. The value of the data increases and correlates to the number of people who are able to make use of it. Feeding inaccurate data into your data warehouse or mastering systems will not only make it difficult to obtain clear business insights and gather actionable information, it will also damage good data.

A virtual data quality firewall detects and blocks bad data at the point it enters the environment, acting to proactively prevent bad data from polluting enterprise information sources. A comprehensive data quality management solution that includes a data quality firewall will dynamically identify invalid or corrupt data as it is generated or as it flows in from external sources, based on pre-defined business rules.

Even with the best data governance policies in place, this alone is not enough to protect data. The sheer volume of data that flows through enterprise systems can make it particularly challenging to maintain peak data quality at all times. It simply isn't possible to manage quality record-by-record, or to attempt to govern every piece of data that is collected by an organization. The key to success is to identify and prioritize the type and volume of data that requires data governance.

Business intelligence (BI) solutions allow organizations to determine which data sets are most likely to be utilized and should be targeted for quality management and governance. Astute data management processes can then be used to collect that data -- for example, customer preferences or purchasing information -- and move it to a repository for cleansing and analysis as a high priority.

Advanced organizations realize business professionals need to take ownership of the data they are helping to create and feed into IT systems. This has prompted many companies to create a data governance role to manage data quality from end-to-end.

The data governance director is typically chosen from a business group, and is the primary focal point for all data related-needs within that group. Some organizations have multiple roles for data governance to represent different areas of the business. These data overseers take a leadership role in resolving data integrity issues, and act as liaisons with the IT group that manages the underlying information management infrastructure.

The primary objective for instituting a data governance board is to mitigate business risks that arise from highly data-driven decision-making processes and systems in the current business environment. These boards include business and IT users and are responsible for setting data policies and standards, ensuring that there is a mechanism for resolving data related issues, facilitating and enforcing data quality improvement efforts, and taking proactive measures to stop data-related problems before they occur.

Successful data governance starts with a solid, well-defined data management strategy, and relies upon the selection and implementation of a cutting edge data quality management solution. The key to effective data quality management is to create data integrity teams, comprised of a combination of IT staff and business users, with business users taking the lead and maintaining primary ownership for preserving the quality of any incoming data. While data integrity teams will drive the data quality management plan forward, it is also important to have a comprehensive data quality management solution in place. This will make the strategy more effective by enabling data governance professionals to profile, transform and standardize information.