Data Governance Automation: Making the Most of Data and Analytics Technologies
Data governance is an evolving discipline that must adapt to rapid changes in data management. The scope of governance encompasses processes, policies, and practices related to data protection, data utility, data value, and data ethics. Despite the term “data governance” the reality is that we don’t govern data. We govern the things that people do when working with data. Policy setting, decision making, and guidance are all critical aspects of data governance that are necessary to manage data security, regulatory compliance, data privacy, data quality, data risk, and much more. In the rapidly changing world of big data, self-service, and data science, data governance programs and teams must be aware, agile, and adaptive.
Data governance is an imperative in today’s data-driven economy, but it is also a struggle for many organizations. Many of the data governance challenges can be alleviated by taking full advantage of the features and functions that are built into data management and analytics technologies. The technologies that are used to capture, refine, store, distribute, and analyze data provide data governance capabilities that are perhaps the most under-utilized features throughout the technology stack.
Modern data management must respond to increasing demands for regulatory compliance, data security, protection of privacy-sensitive data and personally identifying information, and high-quality trusted data that is the foundation for analytics and data science. As the pace of change in data, regulations, and requirements accelerates, data governance must respond quickly. Manual governance is too slow, too rigid, and too costly.