Data debt is the build-up of data-related problems over time including quality, categorization, and security issues. It is typically caused by a lack of reliable and repeatable data governance and management processes. The end results are that data is harder to find, employee resources are wasted, data costs increase, and legal and regulatory liability expands.
Here are some information governance-related tips for reducing the footprint of 7 widely recognized data debt categories:
Dark Data, or data that hasn't been properly cataloged or analyzed. Organizations can reduce this type of data debt by implementing appropriate data cataloging and analytics solutions.
Unsecured Data, such as data that hasn't been classified, scanned for privacy and other controls, is not adequately encrypted or has other major security (or security policy issues). This kind of data can be reduced by implementing (and educating personnel about) improved encryption, access controls, and data classification policies.
Duplicate Data, or data that derives from or is spread out among multiple primary sources. This type of data can be reduced by centralizing data within a unified system and creating robust and relevant data governance policies that allow personnel to identify and delete duplicate records.
Dirty Data, or data that has verified (and sometimes unknown) data quality issues. This type of data debt can be reduced by using data quality tools and quality audits that improve the accuracy of the data used.
Murky Data, or data that is not well documented or understood and that requires the interpretation of subject experts in order to be properly used. This type of data can be reduced by creating data catalogs and defining data dictionaries so that teams can understand and use it more effectively.
Dysfunctional Data, or data that is created because the organization is using the wrong data management tools. This type of data can be reduced by implementing better data management tools, such as image tagging and metadata extraction.
Masterless Data, or data that is siloed across multiple regions or that is not properly connected to enterprise master data sources. One solution to reducing this type of data is to establish a master data management system that links customer data from various sources.
Resolving (or at least improving) data debt issues requires organizations to combine technical solutions, budgeting, legal guidance, and organizational changes, all of which should align with specific business objectives and data management strategies.
As a result, like all information governance improvements, it is critical to establish a cross-functional team.
Let's all work together to reduce data debt so that we can find (and use) the right information, in the right place, and at the right time!