Analytics Governance Is Important
With increasing regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), enterprises face more external oversight of their data governance practices. The risk of significant fines associated with violating these and other regulations, coupled with enterprises’ internal compliance requirements, has brought more attention to data governance practices. A data governance strategy is only as good as its weakest link, however. An enterprise may believe it has everything buttoned down, but a single overlooked area can undermine its data governance efforts. All too often, that weak link is an enterprise’s analytics governance policies and processes.
Data governance is incomplete without analytics governance. Analytics often contain some of the most valuable data an enterprise works with, looks at and distributes. There should be equal concern around the distribution of analytic results, and yet our research shows that while 95% of enterprises believe data governance is important, only approximately one-half or less govern analytics. Data and analytics tasks are often handled by different teams, with data engineering tasks falling to IT or technical resources while analytics are typically performed by line-of-business analysts. How and by whom that data is accessed is typically controlled by IT. However, after insights are drawn from data, IT lacks visibility into where reports are being shared and with whom. Even if an organization goes to great pains to lock down access to its data with the appropriate restrictions and privileges, those efforts can be circumvented relatively easily with an export of sensitive data.