Saturday, April 6, 2019
1) What are the major EXTERNAL laws/regulations shaping data governance requirements this year?
Consumer privacy and data sovereignty are in the forefront of the news and the focus of regulatory compliance this year. GDPR, California Consumer Privacy Act, HIPAA, and others are driving internal business decisions related to technology use. Critically, the center point of many of these includes understanding the risk impact across the organization and including risk impact in your business decisions.
2) What are the major INTERNAL factors or requirements that require more vigilant or comprehensive data governance?
Internal factors driving – demanding – more vigilant and comprehensive data governance include respecting consumer and government data rights. Consumers are increasingly savvy and protective of their privacy in the backwash of Facebook and Google’s data analytics practices, and governments continue to require data sovereignty. Organizations with multinational locations must deal with these complexity factors when managing data across use cases and geographic locations. In each case, however, data should be aggregated to the extent possible, protected and monitored per applicable and changing requirements. This is a real challenge that requires proactive management.
3) Are organizations keeping up with these requirements?
Organizations struggle with the requirements. The diverse topology of new cloud-inclusive hybrid architectures and the velocity of new requirements have created a blind spot to understand what needs to be done. There is a lot of activity but it is not always the right activity. Keeping up is a challenge.
4) What tools or technologies are assisting with efforts to achieve more effective data governance? (Note to vendors: okay to discuss product categories, such as AI/machine learning, but we cannot mention specific product names, sorry.)
Three specific elements – visibility, alignment, control – work together to provide effective data governance. There are tools and technologies which provide visibility across portions of the enterprise, perform analytics, and offer some control. The ideal solution digs deep to detect every asset across the enterprise, performs predictive analytics, and provides necessary and often-repeated actions such as reporting, mitigation, and forensic detail. The differences are in scope, scale, manageability, and producing truly meaningful information. If you can’t produce meaningful and actionable information from a tool, that it’s time to rethink the value of that tool.
5) What types of changes are necessary to organizations, and the way people are managed, to achieve more effective data governance?
Organizations looking to understand the people part of the data governance problem over time have to have a firm grip on the content of their data and the body of relevant requirements from regulations, standards, best practices, and organizational policies that apply. Content and Requirements. These can change often. The most effective way to manage people is to communicate the importance of data content and regulatory requirements, including how to provide effective data governance over compliance and data risk.
Posted by CD at 8:14 AM