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Dan Wolfson, ACM DE

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Co-Founder - Pragmatic Data Research Ltd

A proven CTO, Architect, and Development Director, Dan has combined technology development, team development and operations to build and deliver numerous ground-breaking products and technologies. As a CTO and Distinguished Engineer, Dan has served as a trusted advisor to customers on data architecture, information and governance strategies across many industries.

Dan's last project before retiring from IBM was to lead the collaboration between IBM Research and Development teams to shape, implement and operate the IBM Environmental Intelligence Suite to provide customers insights on how Weather, Climate and Geospatial analytics can enhance their sustainability, operations and planning. Previously, Dan was the CTO for the IBM InfoSphere Brand covering data integration, governance, master, reference and metadata.

Dan is a founder of Pragmatic Data Research Ltd. - a consultancy specializing in accelerating digital transformations through innovative data architectures and governance. He is an active member and maintainer of the Egeria Open Source project.

Dan has over 40 years of experience in research and commercial distributed computing . A co-author of numerous papers and books, Dan is also credited with over 50 patents worldwide. Appointed an IBM Distinguished Engineer in 2003, the Association for Computing Machinery recognized Dan in 2010 as an ACM Distinguished Engineer and is currently an IEEE Computer Society Distinguished Visitor.

Bio from: Big Data LDN 2024

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A Pragmatic Approach to Building Collaborative, Open Governance for Your Data and AI Ecosystem

2024-09-19 · Big Data LDN 2024
Face To Face
with Dan Wolfson, ACM DE , Mandy Chessell CBE FREng
AI/ML Linux

Your world is filled with an ever-changing landscape of tools that create and use metadata. Each tool is useful, but independent, unable to share and link what it knows to information from other tools. The result is a disconnected story throughout your data and AI operations, making it hard to know where data came from, how it can and should be processed; leading to uncertainty in the trustworthiness of your AI results.

Using open source software from the Linux Foundation, (including Egeria, Open Lineage, Unity Catalog) we will share a simple approach to incrementally link and govern these tools to create end-to-end lineage, provenance and information sharing along your tool chains.