Governance takes on increased significance in a data-driven world. Democratized users working with self-service visualization and analytics need to know which data sets are appropriate. Data scientists who are building and operationalizing predictive models and machine learning algorithms must follow governance rules and policies, especially for data privacy. Organizations need to ensure that data curation processes for data quality, validity, and authenticity also address governance. Governance today must function across hybrid, on-premises, and multicloud data landscapes.
Just as governance is critical to building internal users’ trust in the data, it also assures customers, consumers, partners, and regulators that your organization is on top of how people and algorithms consume, analyze, and share data. Across the world, data governance regulations affect not only data privacy but also how product manufacturers and supply chains collaborate. Organizations risk financial penalties and damage to their reputations if they do not pay attention to governance.
TDWI research finds strong interest in applying a variety of technology solutions to governance such as data catalogs, glossaries, data access authentication services, and data management and integration systems. People processes and change management also need modernization to handle today’s governance challenges. This talk will discuss recent TDWI research and provide best practices strategies.
Topics this talk will address include:
- Meeting the challenge of governance in a hybrid, multicloud world
- Strategies for achieving a balance between governance and self-service user agility
- Principles for establishing and keeping data trust – among users and in the public eye
- How governance requirements affect analytics model development and operationalization