Data Governance and Data Management in the Corporate AI Environment

The New Rules for Governing Enterprise AI 

A practical framework for building AI environments that are efficient, secure, focused, and aligned with the business.

Corporate AI introduces responsibilities that go far beyond traditional data governance. Organizations must protect confidential information, control which knowledge enters the AI environment, manage processing costs, preserve business context, and ensure AI interprets enterprise concepts consistently.

Without a disciplined approach, corporate LLMs can become expensive, unfocused, difficult to secure, and increasingly prone to unreliable or inconsistent responses.

In Data Governance and Data Management in the Corporate AI Environment, W.H. Inmon and Jamie Knowles explain how governance must evolve to address structured data, unstructured documents, external sources, business meaning, and AI interactions as part of one connected environment. The guide introduces practical concepts such as Textual ETL, the Context Catalog, the Enterprise Semantic Backbone, and the four pillars of corporate AI governance.

何を学ぶか

• Why corporate AI requires a different approach to data governance and data management
• How to reduce unnecessary LLM processing costs by filtering irrelevant and repetitive content
• How Textual ETL identifies, contextualizes, and prepares trusted information for corporate AI
• Why structured database information must be securely incorporated alongside documents and text
• How an Enterprise Semantic Backbone creates consistent business meaning across AI knowledge sources
• How metadata, security classifications, lineage, stewardship, and business rules support AI governance
• The four pillars of corporate AI governance: enterprise data, enterprise knowledge, enterprise meaning, and AI interaction
• How to measure governance success through lower hallucination risk, improved consistency, stronger security, and greater business adoption

Who Should Read This Guide?

• Data Architects
• Enterprise Architects
• Data Governance and Data Management Leaders
• Chief Data Officers
• AI and Machine Learning Teams
• Metadata and Knowledge Management Professionals
• Security, Compliance, and Risk Leaders
• Organizations Building Private or Corporate LLM Environments

Download the Guide

Learn how to govern not only the information used by corporate AI, but also the business meaning, policies, security, and context required for trustworthy reasoning.

Download the guide to discover a practical framework for creating corporate AI environments that are more focused, efficient, secure, explainable, and aligned with how your organization actually operates.