Corporate leaders are bombarded with calls to explore the benefits of Data Science, Advanced Analytics, and AI projects. The articles they read and the sales materials they see describe potential gains while admitting risks and cautioning that properly prepared data is key to success. However, these pitches rarely use the technical terminology embedded in the DMBOK and other frameworks. Instead, they use business school terminology familiar to decision-makers, mixed with emerging terminology designed to evoke the benefits of new technologies, processes, and platforms. 

All of these projects depend upon guides who understand what "well-prepared data" means, what activities will be required, where points of risk exist and how to control them, and which data/technical teams need to be involved. Maybe you are qualified. Maybe you've been serving in this role.

But will they know? Would project managers think of you when they're looking for expertise in data industrialization, curation, interoperability, observability, explainability, or linkage assessments?

This session:

• Outlines emerging data specialties
• Explains new buzzwords and skill sets, and
• Maps them to skills, abilities, and terminology that you may already know by another name.

Speaker: Gwen Thomas

As the primary author of the DGI framework and guidance materials published at DataGovernance.com, Gwen has influenced hundreds of data programs around the globe. She spent 20 years working in IT shops and providing data management and data governance consulting services, before spending 10 years as an in-house data strategist for the World Bank Group's private sector arm. Much of her time there focused on translating between executives, program leaders, data governance teams, lawyers, architects, modelers, policy writers, auditors, and data quality teams. While she is a technician, her special focus is on helping non-technical teams become stronger advocates for their own needs.