Adapting Data Governance to Tend the Changing Data Landscape
Emerging technologies are outpacing data governance at a rapid clip. Specifically, the rate of growth and development of emerging technologies in areas such as artificial intelligence (AI), the Internet of Things (IoT), and machine learning (ML) drastically exceeds the current speed and willingness of businesses to change their governance models to manage and protect their data and information assets. Unfortunately, the larger the delta becomes between the advancements in technology and the changes in governance, the greater the risks and losses for the business.
IoT, ML, and AI will undoubtedly play significant roles in nearly all industries—from retail to military missions—and everything in between. However, to truly realize the potential value of these capabilities, businesses must adapt their approaches to data governance. And, these changes need to start now, not at the buckling point, where advanced technologies render traditional governance ineffective and failure abounds.
Traditional data governance provides a strategic, rigorous framework designed to establish data standards, outline roles and responsibilities, and create policies and procedures for data management and use throughout the enterprise. In fact, traditional data governance is necessary to maximize productivity and efficiency in the use of core business data assets in transactional and data warehousing environments. With focus typically on trust, data quality, and overall protection of data, these conventional methods serve well for recognized data sources with known business value. But once unknown or unstructured data sources or sources with undetermined business value such as big data or IoT are introduced, traditional data governance models fall short. Add on the capabilities of AI and ML, and the shortcomings become even more evident. The rigid nature of conventional data governance policies and procedures limit the possibilities created by advanced data and analytics technologies by expecting them to conform to standards designed for legacy data platforms and infrastructure.
