By IT Brew Staff
less than 3 min read
Definition:
Because cutting-edge initiatives such as AI and data science require high-quality, accessible data, data governance has become an increasing priority for organizations of all sizes in recent years. With burgeoning amounts of structured and unstructured data to wrangle, organizations must also ensure their data governance aligns with compliance and regulations, avoids data siloing, and maintains necessary data quality for scalable AI and analytics projects.
The first step toward healthy governance is to catalog all the organization’s data; after that, stakeholders should evaluate that data for quality, including accuracy and completeness. Then they can then turn to classifying data, ensuring its security, auditing access, and ensuring teams can discover, share, and collaborate with it. In addition to auditing procedures, organizations can rely on data governance tools that automate data discovery and classification, ensure data meets industry regulations, set data protection rules, and more.
Depending on an organization’s needs, it can opt for a centralized data governance strategy, in which one team develops policies which are then pushed to various business units; a more decentralized approach empowers individual teams and data owners to make data governance decisions around specific projects. In a similar fashion, federated data governance allows teams to determine their data policies, with some degree of oversight from a centralized committee (such as a governance council or steering committee).
If executed correctly, data governance can speed up the time necessary to analyze data and launch new, data-dependent projects (such as analytics dashboards or AI chatbots), reduce data latency and errors while boosting data consistency, and ensure greater regulatory compliance, particularly in sensitive industries such as healthcare.
