As enterprises scale AI initiatives, fragmented architectures, disconnected data environments, and inconsistent governance continue to slow progress.
Modern data fabrics are emerging as a powerful approach for delivering trusted, unified, and scalable data access across hybrid and multi-cloud environments. By combining real-time connectivity, governance, and automation, teams can improve data access and AI readiness.
DBTA recently held a webinar, Modern Data Fabrics: Delivering Trusted Data Access for AI, with Abhilash Mula, director, product management at Informatica from Salesforce and Preston Wood, chief security and strategy officer, DataBahn, who discussed how modern data fabrics are helping businesses build more connected, AI-ready data architectures.
According to Mula, the gap between AI ambition and execution is the market opportunity and it is wide open. However, data volume, complexity, and fragmentation are delaying AI projects. It’s not just a data problem; it’s a context issue.
Context is the accumulated understanding of your business, Mula said. Trusted enterprise context is the new currency of AI.
Informatica offers a data value chain for trusted enterprise context, Mula explained, with Salesforce Agentic Enterprise Architecture and Informatica’s Intelligent Data Management Cloud. These solutions offer broad support for the following use cases:
- Analytics and business intelligence
- Democratized trusted data access
- Operational excellence and agility
- AI and agentic transformation
- Single source of truth
- Legacy modernization
- Governance, regulation, and compliance
- M&A and tech consolidation
For decades we prepared data so people could read dashboards, said Wood. AI-ready data serves a world where the analyst is a machine and readiness is never universal. Data is ready for a specific use case.
Readiness is measured against what a specific AI system must do but the real question is never “is our data ready?” but “ready for what?”
According to Wood, good AI-ready data satisfies each of these:
- Accuracy
- Completeness
- Consistency
- Representativeness
- Lineage
- Governance
Every property so far concerns the data itself. But an AI can only reason over what it is given and a data point in isolation is often meaningless, Wood said.
Irrelevant context isn’t neutral. It dilutes the signal, eats the context window, drives up cost, and pulls a model toward confidently wrong answers.
Match context to the question with enough to fully inform it, little enough that nothing extraneous competes. That matching is exactly what retrieval (RAG) is for.
Quality and context both assume the data is honest, but a source can also be poisoned, and a few hundred malicious records can backdoor a model of any size. You wouldn’t pipe in untested water into your house; don’t pipe in untested data, Wood noted.
Readiness is enabled in the pipeline. A pipeline is a series of automated, repeatable steps that move data from its sources to where AI uses it with transforming and checking it at every stage.
DataBahn moves normalization, enrichment, and governance into the pipeline itself. Enterprise telemetry arrives already AIready, not landed raw and waiting to be cleaned, Wood said.
“AI-ready data is the difference between trustworthy results and confident nonsense. A well-built pipeline enables readiness on every run and defends it long after the first AI goes live,” Wood concluded.
For the full webinar, featuring a more in-depth discussion, Q&A, and more, you can view an archived version of the webinar here.