AI-Ready Data

Name: AI-Ready Data
SKU: c0bd53ac19030f701eac2db4b40f0e77
Price: 33.99 EUR
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Bol HOW TO BUILD THE SOLID DATA FOUNDATION EVERY SUCCESSFUL AI STRATEGY REQUIRES Every failed AI initiative shares a common root cause: the data was not ready. Data silos fragment critical information, quality decay introduces errors that propagate through models, and governance gaps leave organizations exposed to compliance risk. These are not peripheral concerns—they are the primary reasons AI projects underdeliver, and they demand a strategic response that begins long before the first model is trained. Andrew Madson draws on years of experience transforming data strategy at organizations like JPMorganChase, MassMutual, and Dremio to lay out a practical framework for building AI-ready data infrastructure. Covering traditional AI, large language models, and agentic AI, the book maps specific architectural decisions to measurable outcomes: faster model deployment, lower technical debt, and systems designed for regulatory compliance from the start. Whether you are a CDO confronting data quality challenges, a CTO evaluating AI infrastructure investments, or a data professional building the systems that AI depends on, AI-Ready Data offers a concrete, actionable path from fragmented data practices to a scalable, governed foundation designed to support AI at enterprise scale. Build scalable AI systems by fixing data silos, quality decay, and governance gaps AI-Ready Data provides a structured roadmap for organizations deploying traditional AI, large language models, and agentic AI systems. Written by Andrew Madson, who has held data strategy leadership roles at Fortune 100 companies including JPMorganChase and MassMutual, the book focuses on the foundational data challenges that undermine AI outcomes. It connects AI engineering, data strategy, and infrastructure planning into a unified approach for building production-grade AI systems. The book details how to identify and resolve data silos, quality decay, and governance gaps that create hidden costs and erode AI ROI. It covers modern data product architectures and compliance-ready systems designed to accelerate model deployment and reduce technical debt. Each chapter addresses specific operational pain points, from dirty data remediation to building scalable infrastructure that supports traditional ML pipelines, LLM integration, and agentic AI workflows. Readers will also find: Strategies for diagnosing and eliminating data quality decay across enterprise data pipelines before it undermines AI model performance Frameworks for building modern data products and architectures that reduce technical debt and accelerate model deployment cycles Governance models designed to close compliance gaps and create audit-ready systems for AI initiatives at enterprise scale Methods for breaking down organizational data silos that block cross-functional AI adoption in Fortune 100 environments Practical approaches to calculating and reducing the hidden costs of dirty data that erode AI return on investment AI-Ready Data serves business and technology leaders, including CIOs, CDOs, CTOs, and CISOs, as well as data professionals responsible for building and maintaining the data infrastructure behind AI initiatives. It delivers actionable frameworks for resolving data quality, governance, and architecture challenges that directly affect AI system performance.

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HOW TO BUILD THE SOLID DATA FOUNDATION EVERY SUCCESSFUL AI STRATEGY REQUIRES Every failed AI initiative shares a common root cause: the data was not ready. Data silos fragment critical information, quality decay introduces errors that propagate through models, and governance gaps leave organizations exposed to compliance risk. These are not peripheral concerns—they are the primary reasons AI projects underdeliver, and they demand a strategic response that begins long before the first model is trained. Andrew Madson draws on years of experience transforming data strategy at organizations like JPMorganChase, MassMutual, and Dremio to lay out a practical framework for building AI-ready data infrastructure. Covering traditional AI, large language models, and agentic AI, the book maps specific architectural decisions to measurable outcomes: faster model deployment, lower technical debt, and systems designed for regulatory compliance from the start. Whether you are a CDO confronting data quality challenges, a CTO evaluating AI infrastructure investments, or a data professional building the systems that AI depends on, AI-Ready Data offers a concrete, actionable path from fragmented data practices to a scalable, governed foundation designed to support AI at enterprise scale. Build scalable AI systems by fixing data silos, quality decay, and governance gaps AI-Ready Data provides a structured roadmap for organizations deploying traditional AI, large language models, and agentic AI systems. Written by Andrew Madson, who has held data strategy leadership roles at Fortune 100 companies including JPMorganChase and MassMutual, the book focuses on the foundational data challenges that undermine AI outcomes. It connects AI engineering, data strategy, and infrastructure planning into a unified approach for building production-grade AI systems. The book details how to identify and resolve data silos, quality decay, and governance gaps that create hidden costs and erode AI ROI. It covers modern data product architectures and compliance-ready systems designed to accelerate model deployment and reduce technical debt. Each chapter addresses specific operational pain points, from dirty data remediation to building scalable infrastructure that supports traditional ML pipelines, LLM integration, and agentic AI workflows. Readers will also find: Strategies for diagnosing and eliminating data quality decay across enterprise data pipelines before it undermines AI model performance Frameworks for building modern data products and architectures that reduce technical debt and accelerate model deployment cycles Governance models designed to close compliance gaps and create audit-ready systems for AI initiatives at enterprise scale Methods for breaking down organizational data silos that block cross-functional AI adoption in Fortune 100 environments Practical approaches to calculating and reducing the hidden costs of dirty data that erode AI return on investment AI-Ready Data serves business and technology leaders, including CIOs, CDOs, CTOs, and CISOs, as well as data professionals responsible for building and maintaining the data infrastructure behind AI initiatives. It delivers actionable frameworks for resolving data quality, governance, and architecture challenges that directly affect AI system performance.

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