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The Complementary Relationship of Data Governance and Management
Data governance and data management, often intertwined and confused, are foundational to the effective use of data in decision-making. Data governance lays down the policies and procedures, while data management enacts these to use data effectively. They form the backbone of responsible and effective data use in organizations, enabling informed decision-making and strategic business planning.
Companies are recognizing the value of data governance and management. For instance, organizations have often invested in educating and involving their entire senior executive leadership team in data governance. This has led to a shift from manual management to machine-automated processes. The shift to automation for metadata management and data governance has been crucial as manual processes were found to be insufficient for highly regulated actions like data rights fulfillment.
The implications of this shift are profound. With the adoption of automated data governance and management, companies are not only improving operational efficiency but also spurring real new insights and innovation within the company. This has led to the elimination of millions of dollars in cost from their data ecosystems and enabled digital and analytics use cases worth millions or even billions of dollars.
Looking ahead, the future of data governance and management is promising. The market for data governance is expected to grow to over 5.2 billion dollars by 2026. Cutting-edge technologies like AI/ML and blockchain are being used to automate and enhance some aspects of data management. Furthermore, data governance must evolve to meet the opportunities and risks inherent in generative AI tools and capabilities.
Data governance and data management are complementary disciplines that are crucial for the effective use of data.
