Tim Davis discusses key pain points and solutions to problems many enterprises face with data in silos, poor-quality data that cannot always be trusted, and managing and making large volumes of data available to derive more accurate insights and machine learning models. Tim shares a guidebook to addressing these problems, which details a fundamental change in approaches to data warehousing, data lakes, and data science. He also explores how enterprises are dealing with data distributed across business units and in scattered ungoverned shadow IT and how they are putting models into production in a scalable and transparent way.
When customers are intentional about building the “AI stack,” they get better results, faster. Tim concludes with a discussion on what the ideal stack looks like, key considerations, and common pitfalls to avoid.
This session is sponsored by IBM.
Tim Davis is the executive director of the Lab Services Technical Architecture Group for IBM Software Group focused on Information Management (IM), InfoSphere, and Information On Demand (IOD) worldwide. In this position, Tim directs customer-facing enterprise technical architecture engagements and IM product-solution deployments for customers worldwide. It is Tim’s mission to drive rapid product adoption through the advancement of industry-leading architectures and deployment roadmaps in master data management (MDM), enterprise data warehousing (EDW), ERP/SAP, high-performance computing, and enterprise content management. Tim is also one of the leading content providers for all IBM IM-InfoSphere-IOD curriculums, best practices, and methodologies and the founder of the IBM Center of Excellence for Data Integration. He recently led the development and launch of IBM’s Information Grid, MDM Server Rapid Deployment, and IBM’s SAP deployment accelerators. Tim joined IBM via Ascential Software, acquired by IBM in 2005.
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