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Companies run on data. As data is truly a valued asset for an organization, it needs to be guarded closely – part of the reason we’re seeing regulations involving data. Establishing a data governance program can help with safeguards and meeting requirements for compliance, but it can also improve an organization’s decision making, ultimately leading to greater corporate performance.
So what exactly is data governance?
CIO.com put forth an excellent definition earlier this year: “Data governance defines roles, responsibilities, and processes for ensuring accountability for and ownership of data assets across the enterprise.”
While data governance is a systematic methodology for businesses to comply with external regulations such as GDPR, HIPAA, Sarbanes-Oxley, and future regulations, it can also establish a foundation and controls to strengthen internal decision-making for determining product costs, inventory, consumer demand, and more. While there are many factors to consider for building a data governance program, two of the most pressing items that should be top of mind are data quality and self-service analytics.
Data Quality
It’s advantageous to include efforts to ensure data quality is part of your data governance program. Trying to govern data that is old, corrupted or duplicated can become quite messy. Although the tools for managing quality and governance are generally different, data governance provides a framework for data quality.
Poor data quality exists for many reasons, such as having data spread out in department silos, different versions of the “same” data or information lacking in common name identifiers. Without data quality, organizations also face a real possibility of making faulty business decisions and having a sub-standard governance program.
Generally, the more data governance a company has, the stronger its data quality will be. For example, data governance might rule that the business expose its data across different levels in the company for evaluation by multiple users (assuming approved access). In addition, data governance might mandate that each line of business have local oversight within its unit.
When different data sources are combined, someone on the front-line has to be responsible for this data to ensure control, quality and security. This solution requires one go-to person within each functional business department who has some technical skills and understands this line of business, its department culture, and its users’ needs enough so that the designated person can oversee data quality. This can be a better solution than having IT do it because IT won’t have the business knowledge for particular business departments. This would be an additional duty rather than a full-time role.
Self-Service Analytics
Business users now demand…
