David B. Keever
David B. Keever
Data Strategist & Data Management Authority at Leidos
David Keever is a Leidos Technical Fellow, Chief Engineer, Master Solution Architect, and Senior Program Manager. He develops and leads team-based, advanced technology programs in data strategy, analytics, and data science. He possesses in-depth knowledge and experience in designing and operating high-performance, data-centric technology systems to formulate policy, conduct studies and analysis, and improve operational efficiencies in human-systems ecosystems in transportation, energy, and security. His projects apply advanced data-centric technologies and algorithms to rapidly extract useful solutions from a diverse number of global, multi-media sensors and data sources. Through focused yet open collaboration, his team developed in 18 months a $200M award-winning project for data stream processing of multi-media data using microservices with embedded AI/ML modules for data quality, entity extraction, and petabyte data exploitation.
Currently, he supports the Leidos CEO’s effort in defining and establishing corporate technical core competencies and business strategies for Leido’s Data Science and Engineering practice. He manages an enterprise cadre of more than 600 senior Solution Architects, promoting design thinking, diversity, and innovation to solve many of our customer’s toughest challenges.
Executive
Track: Government
Data Products Using Data Mesh Principles
The volume and variety of data in organizations continues undiminished. Current architectures are not necessarily designed to keep pace with the speed, scalability, and security of data and data-centric analytics. This presentation describes the technical trends and motivation for data products, the principles of a data mesh architecture, and application of a multi-layered approach to building data products in an organizational setting using data mesh principles. As part of exploring industry technical trends and emerging advances in Artificial Intelligence-Machine Learning (AI-ML), this presentation also discusses implementation opportunities and challenges.
