Overview

What data experts are saying about Metadata-First What the world needs now is as much enlightenment about metadata that it can get - that's why you should take a look at Metadata-First. - Bill Inmon, author Shiller lays out the full range: from vision, to underlying principles, to actionable first steps. - Tom Redman, author A very insightful book... Metadata-centric is a key idea as we rethink our enterprise information ecosystems. Metadata should not be a documentary afterthought, but a key driver of business system functionality. - Dave McComb, President, Semantic Arts Metadata-First: The Future of Data is a practical, executive-level guide to solving one of the most expensive and persistent problems in modern business: unreliable, ungoverned, and unscalable data. Despite massive investments in data platforms, warehouses, lakes, and analytics teams, most organizations still struggle with the same issues: bad data driving bad decisions, ballooning costs, brittle pipelines, compliance exposure, and stalled AI initiatives. Leaders assume their data is ""good enough,"" only to discover too late that it isn't. The consequences are real: missed opportunities, higher risk, and competitors who move faster. Metadata-First offers a fundamentally different approach. Instead of treating metadata as documentation that inevitably becomes outdated, this book shows how to make metadata the operating system of your data ecosystem: the active, authoritative source that drives ingestion, transformation, quality, lineage, governance, and even business rules. The result? A data environment that is cleaner, faster, safer, and dramatically cheaper to operate. Drawing on decades of experience at organizations such as Bridgewater Associates, Goldman Sachs, Bank of America, and leading asset managers, Larry Shiller introduces a proven, scalable architecture built on non-contentful code: code that doesn't embed business logic but instead reads its instructions from metadata. This shift eliminates the ""hairball"" of fragile pipelines and duplicated logic that plagues most enterprises today. You'll learn: Why leaders consistently underestimate data risk and how Metadata-First exposes hidden weaknesses before they become costly failures How to reduce change risk and accelerate delivery, with most changes happening in metadata rather than code How to lower the total cost of data by simplifying architecture, reducing custom code, and making teams dramatically more productive How to achieve true AI readiness, with clean, well-described, lineage-rich data that LLMs and machine learning systems can trust How to structure teams and governance to support a Metadata-First operating model How to implement Metadata-First principles through conventions, guardrails, and a practical architecture that works across industries How real organizations have used Metadata-First to cut multi-year data initiatives down to months and sometimes weeks This book is written for: Board members and C-suite executives who need trustworthy data for high-stakes decisions and AI investments Data leaders and managers responsible for governance, risk, data strategy, and operational efficiency Architects and engineers who want a principled, scalable approach to building metadata-driven systems Anyone who suspects their data ecosystem is holding the business back and wants a clear, actionable path forward If your organization is struggling with data quality, slow delivery, rising costs, or stalled AI initiatives, Metadata-First shows you why and what to do about it. It offers a practical, battle-tested blueprint for transforming your data operatio

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