AI Readiness
What Is Trusted Data for AI and How Do You Get It?
Trusted data for AI has checked quality, defined meaning, traceable lineage, an owner and governed access. Here is how to get it, one use case at a time.
October 30, 2026Articles & tutorials
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Data Catalog
How Can a Data Catalog Support Self-Service Analytics? 5 Jobs
A data catalog supports self-service analytics by helping users find, understand, trust and safely access data inside the BI tools they already use.
October 30, 2026
Data Governance
Dresner AI, Data and Analytics Governance Market Study 2026
What the Dresner AI, Data and Analytics Governance Market Study 2026 covers: scope, use cases, the 77% trust finding, method and vendor ratings.
October 30, 2026
Data Governance
How to Build the ROI Case for a Data Governance Investment
Build the ROI case for a data governance investment line by line: a copyable template of cost and benefit lines, how to measure each, and who owns each number.
October 30, 2026
Ai Agent
Genie One MCP: How Agents Reach Unity Catalog Data Over MCP
How the Genie One MCP server lets agents query Unity Catalog data: tools, connection URL, OAuth, Unity Gateway grants and the other managed MCP servers.
October 28, 2026
Ai Agent
Unity Catalog Business Glossary and Domains: GA or Preview?
Checked 28 Oct 2026: Unity Catalog domains and the Discover page are GA, the business glossary ships as Pages in Beta, and Genie Ontology is in Public Preview.
October 28, 2026
Ai Agent
How Does Purview Govern Microsoft Fabric and Power BI?
Purview governs Microsoft Fabric and Power BI with sensitivity labels, DLP, audit, insider risk and Data Map scans. What each control covers, and its limits.
October 28, 2026
Data Catalog
Open Source vs Commercial Data Catalog: Total Cost of Ownership
Open source vs commercial data catalog total cost of ownership: 10 options scored on license, infrastructure, people and switching, plus a TCO worksheet.
October 28, 2026
Data Governance
How to Fix Data Quality Problems at the Source with Governance
How to fix data quality problems at the source with governance: name an owner where data is made, write a data contract, enforce rules at the point of entry.
October 28, 2026
Ai Agent
Agentic Software Factory: Specs, Agents, and Enterprise Context
Learn how an agentic software factory works, where humans approve, and why data products need business context alongside specifications and code.
October 27, 2026
Ai Agent
Google Agentic Data Cloud vs Context Layer: Who Owns the Meaning?
Compare Google Agentic Data Cloud with a neutral context layer: current feature status, business meaning, agent access, portability, and coexistence.
October 27, 2026
Ai Agent
How Do AI Agents Use an Ontology? One Request Traced Step by Step
Trace how AI agents use an ontology at runtime to resolve terms, plan queries, apply policy context, coordinate handoffs, and explain each answer.
October 27, 2026
Ai Agent
Why a Neutral Ontology Layer Matters: Own Your Business Meaning
Learn why a neutral ontology layer keeps business meaning owned, portable, and consistent across every AI agent, model, and platform you run.
October 27, 2026
Ai Agent
Why AI Grounding Is Driving Ontology Demand for Enterprise Agents
Learn why AI grounding put ontologies back on the enterprise roadmap, what ungrounded agents get wrong, and why a first ontology draft is now cheaper to build.
October 27, 2026
Ai Agent
Ontology and Context Layer: How They Fit in Your AI Architecture
See where the ontology sits inside a context layer and how binding, approval, versioning, and delivery keep enterprise meaning current for AI.
October 27, 2026
Ai Agent
Ontology Vendor Evaluation: 30 Questions to Expose Manual Work
Use these 30 ontology vendor evaluation questions to expose manual modeling, schema-change work, review hours, and hidden services costs before signing.
October 27, 2026
Ai Agent
Ontology vs Taxonomy: What Is the Difference?
Ontology vs taxonomy: a taxonomy sorts terms into a broader-narrower hierarchy; an ontology adds typed relationships and rules a machine can reason over.
October 27, 2026
Ai Agent
How to Create Databricks Metric Views: YAML, SQL and UI Steps
How to create Databricks metric views: the permissions you need, the Catalog Explorer steps, CREATE VIEW WITH METRICS YAML syntax and MEASURE() queries.
October 27, 2026
Ai Agent
Semantic Layer vs Data Model: How Do They Differ?
A data model defines how data is stored. A semantic layer defines what it means. See how they differ side by side, how they stack, and why AI agents need both.
October 27, 2026
Data Catalog
Can a Data Catalog Sit Atop Existing Catalogs Without Replacing?
Yes, a data catalog can sit on top of existing catalogs. See what a catalog of catalogs owns, what Unity Catalog, Horizon and Glue keep, and how to add one.
October 27, 2026
Data Governance
How to Scale Data Governance Across Business Domains
How to scale data governance across business domains: the operating model, central and domain roles, a rollout sequence, automation, and the metrics to track.
October 27, 2026
Ai Agent
How to Start a Context Layer Pilot or Proof of Value
Start a context layer pilot or proof of value in 6 to 10 weeks: pick one AI use case, scope the context, set a pass mark, and test an agent on real questions.
October 21, 2026
Ai Agent
Where does a context layer fit with Agentforce and Data Cloud?
A context layer sits under Agentforce and beside Data Cloud (Data 360). See the stack, what each layer owns, and when CRM-only context stops being enough.
October 21, 2026
Ai Agent
How to Manage KPI Definitions and a Glossary in a Data Catalog
How to manage KPI definitions and a business glossary in a data catalog: the fields each term needs, column-level links, owners, approvals, and reviews.
October 21, 2026
