Everyone wants AI to work.
But before you can trust AI to act on your behalf, you must trust your data, where it came from, what it means, and how it’s being used.
That trust doesn’t come from more dashboards or bigger models. It comes from context: understanding a data asset’s journey, its definition, and its relationships.
This is why three disciplines once dismissed as “data governance overhead” are now the foundation of AI transformation:
- Data Lineage – visibility into where data comes from and how it changes
- Business Glossary – shared, standardized definitions
- Data Catalog – a connected, contextual view of the enterprise’s data ecosystem
Together, they form the context layer, the missing ingredient that turns raw data into intelligence and AI into something you can trust.
The Context Problem in AI
AI doesn’t just need data. AI needs context.
Without it, even the most advanced models misinterpret signals, misalign decisions, and erode trust.
Most enterprises see this firsthand:
- “Revenue” means one thing in Sales, another in Finance
- “Customer” is a person in one system, an account in another
- Models break because upstream data fields changed
- Dashboards conflict because teams define metrics differently
This isn’t an AI problem. It’s a context problem.
Industry leaders have been warning about this:
Gartner reinforces this. In its 2025 forecast, Gartner states that through 2026, organizations will abandon 60% of AI projects that aren’t supported by AI-ready data – meaning the failure point is the data foundation, not the model.
Gartner also predicts that by 2027, 80% of data and analytics governance initiatives will fail if they aren’t tied to clear business outcomes, highlighting how fragile governance remains without context.
The data bears this out globally.
McKinsey reports that while nearly 9 in 10 companies now use AI somewhere in the business, only 39% achieve real enterprise-level financial impact.
BCG finds that only 5% of companies realize strong, measurable value from AI, while nearly 60% see little or none.
Across all of this research, one message is consistent: AI fails when context is missing.
1. Data Lineage: The Map That Keeps AI Honest
Data lineage is the GPS of your data ecosystem.
It answers:
- Where did this data come from?
- What transformations were applied?
- What systems or pipelines touched it?
- What breaks downstream if this changes upstream?
In the AI era, lineage is indispensable for explainability, risk mitigation, and trust.
Databricks captured it perfectly:
A global financial services customer uncovered a 30% drop in model accuracy due to inconsistent upstream transformations across CRM, ERP, and warehouse pipelines. Mapping lineage revealed the inconsistencies, enabling them to enforce schema rules and rebuild model performance.
Outcome: Transparent pipelines, consistent predictions, faster troubleshooting.
2. Business Glossary: The Dictionary That Aligns Humans and Machines
Lineage shows where data comes from. A glossary defines what data means.
A robust business glossary ensures shared understanding across humans and machines:
- Active User = logged in within 30 days
- CLV = total expected customer revenue
- Closed Won = executed and invoiced deal
Without consistent definitions, you get conflicting dashboards, unreliable models, and mismatched KPIs.
Snowflake highlights this:
One global SaaS company standardized definitions for metrics like pipeline, conversion, and retention. Once glossary terms were linked to datasets in their catalog:
- Disputes over metrics dropped 60%
- Forecasting models aligned across regions
- AI insights saw significantly higher adoption
When meaning becomes consistent, intelligence becomes reliable.
3. Data Catalog: The Bridge That Connects It All
A modern data catalog is the front door to enterprise data knowledge.
It unifies:
- datasets
- lineage
- glossary terms
- business context
- ownership
- policies
- data quality
- usage patterns
With event-driven platforms like Workato, catalogs become self-maintaining systems.
When a new data asset is created or updated, workflows automatically:
- Update lineage
- Link glossary terms
- Notify data owners
- Validate schemas
- Refresh downstream analytics
- Trigger compliance rules
This turns the catalog from a static repository into a living map of your data ecosystem.
AI Needs Lineage, Glossary, and Catalog Working Together
When combined, these three disciplines form the intelligence layer that makes AI reliable:
| Discipline | Purpose | Value for AI |
| Data Lineage | Shows the data journey | Transparency & explainability |
| Business Glossary | Defines meaning | Semantic consistency |
| Data Catalog | Connects assets & context | Discoverability, governance & reuse |
McKinsey’s research shows organizations that operationalize metadata lineage, definitions, quality, usage, see 2–3× greater ROI on AI initiatives.
This is the foundation of AI governance: deploy AI with confidence because every insight, action, and prediction is backed by traceable, trustworthy data.
Kimball’s Timeless Insight: Meaning Before Metrics
Today, the same principle applies to AI pipelines, real-time decisioning systems, and autonomous agents.
Lineage, glossaries, and catalogs operationalize Kimball’s wisdom for a real-time, AI-driven enterprise.
How Workato Operationalizes Context
At Workato, we see lineage, glossaries, and catalogs as living systems, not documentation tasks.
With Workato Event Streams, AI@Work, and Automation HQ, enterprises can:
- Capture lineage from APIs, SaaS apps, and integrations
- Enrich events with glossary-defined metadata
- Sync glossary terms into catalogs automatically
- Trigger governance workflows when data contracts are violated
- Maintain real-time data quality and compliance
This creates a self-governing ecosystem, where every AI action is powered by trusted, contextual data.
The Future: Context as a Service
As AI agents proliferate, context will shift from static documentation to Context-as-a-Service delivered through APIs.
AI systems will query context before acting:
- “What does this field mean?”
- “Who owns this data?”
- “What’s the lineage?”
- “What policy applies?”
- “Is this the latest version?”
When AI understands context the way humans do, it stops guessing and starts reasoning like your business.
Conclusion: Clarity Is the Foundation of Intelligence
AI is only as trustworthy as the data beneath it. And trustworthy data doesn’t come from luck.
It comes from:
- Lineage → visibility
- Glossary → meaning
- Catalog → context & governance
Together, they create contextual intelligence, the foundation of reliable AI, analytics, and automation.
Before AI can act with intelligence, the enterprise must think with clarity.
And clarity starts with knowing your data, where it came from, what it means, and how it’s connected.