This session covers the use and output of Gartner’s AI-Ready Data Toolkit, which includes practices for both structured and unstructured data. The process develops metrics that “stack” as you progress from POCs to multicontext data use, operationalization and production support. The session also explains how to customize the toolkit with your own thresholds and readiness analysis.
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Speaker
Mark Beyer
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Mark A. Beyer is a Research Vice President and Distinguished Analyst in the ITL Data and Analytics group. He covers broad data architecture solutions including data management and its intersection with use cases and data governance issues. Mark just published research on how data management practices intersect with LLMs and other GenAI models. His most recent areas of client issues and research include "active metadata", data fabric, data mesh, and distributed data governance. Previous work included adapting best practices for data management (cloud, premises & Hybrid). With a combined direct experience as a power user, application developer, implementation project manager and data architect, he focuses on practical choices when possible and pragmatic delivery when choices are limited. His more aggressive research includes Gartner Maverick contributions and data integration disruptions while keeping healthy skepticism.
Bio from: gartner-data-analytics-apac-2025
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D&A leaders struggle to prioritize and justify data management spend, especially amid cloud-driven cost unpredictability. Value stream analysis links data production to direct and indirect business outcomes, driving quantifiable benefits. This session will link core research on cost management and FinOps with a means of using active metadata to measure value resulting in holistic cost optimization.
Most metadata in 2025 will remain passive in approaches with stats, reports, schema and business-developed glossary terms. Yet, organizations must grow their maturity in metadata management. We start with traditional metadata techniques — passive. With AI undergoing confidence issues and the demand to reduce risk, grow AI confidence and provide data assurance, active metadata becomes key.
This session covers the use and output of Gartner’s AI-Ready Data Toolkit, which includes practices for both structured and unstructured data. The process develops metrics that “stack” as you progress from POCs to multicontext data use, operationalization and production support. The session also explains how to customize the toolkit with your own thresholds and readiness analysis.
Models have become a commodity and the true differentiator lies in your data. This session will showcase seven real case examples from Uber, Rechat, J.P. Morgan, ChatDOC, Arize AI, Qodo, and Unstructure that span the entire AI-delivery life cycle, demonstrating how to transform your data into AI-ready assets to unlock its full value.
Most metadata in 2024 will remain passive in approaches with stats, reports, schema and business-developed glossary terms. Most organizations must grow their maturity in metadata management. We start with traditional metadata techniques — passive. With AI undergoing confidence issues and the demand to reduce risk, grow AI confidence, and provide data assurance, active metadata becomes key.
Most metadata in 2024 will remain passive in approaches with stats, reports, schema and business-developed glossary terms. Most organizations must grow their maturity in metadata management. We start with traditional metadata techniques — passive. With AI undergoing confidence issues and the demand to reduce risk, grow AI confidence, and provide data assurance, active metadata becomes key.
AI-ready data is a key pillar for enabling AI-related ambitions. But how should you do this, and what are others doing? This session explores what we mean by AI-ready, what clients and vendors are doing, and what Gartner recommends you do.