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Best practices for data quality management

Quality data management is required by every company relying on huge volumes of data in their database. But there is a lot of struggle going on for managing a good quality of data throughout the organization. Most of the organizations do not recognize the consequences of inconsistent, inaccurate and incomplete data which is already present in their system. They use only a fraction of the enterprise information to gain the insight needed to make superior performance of the business.

A significant amount of revenue is lost of the data is of bad quality and this makes a compulsion in shifting data quality strategies from occasional cleaning to ongoing quality data management initiatives. Data quality management is an ongoing process and not just a onetime stuff and it has to be embraced by all the levels of the organization in order to maintain a consistent data quality throughout the organization. The bad information should be filtered by defining policies and enforcing them across the organization with proper approval procedure. With the growth of competition and modern methods of fast data collection, data quality management has become the business of everyone and it can never be ignored now. Let us discuss some of the best practices for managing data quality in organizations-

Perform a data quality assessment

Your data quality issues can be best tackled if you perform a complete assessment and analysis of your current organizational data. You need to analyze the current state of the data, erroneous information, duplicates, inconsistencies, missing fields and other data errors in the organizational data. This assessment is required because bad data is buried deep in the legacy systems and it is also received from external sources like third part providers of data, external apps, social media platforms like Facebook, Twitter etc. On the basis of the quality data assessment a data quality management plan should be set up in the organization and the data governance policies are set that address specific data management needs of the organization.

Building a firewall for data quality

Data quality can be maintained if a virtual data quality firewall is set up in the system which detects and blocks any kind of bad data which enters in the system. This is a proactive technique and dynamically detects and prevents bad data from entering into the system.

Appoint data quality stewards in your system for data quality management

The organizations which realize the importance of quality data management appoints business professionals who take the roles of data governance and represent different areas of it in the business. They take the responsibilities of solving the data quality issues in the organization and coordinates with the IT groups and other departments in maintaining a good quality of data in the system.

Data Governance boards

Creating a governance board for data quality is an important step in data quality management. These boards include IT and business users who are responsible for setting the data standards and policies. This ensures that there is a mechanism to resolve data related issues and proactive measures can be taken before the data quality issues occur.

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