Assessing Data Product Readiness for AI Agents
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Assessing Data Product Readiness for AI Agents
What You'll Learn In This Masterclass
Understand what makes data ready for AI agents, and why most current data products fail when put to the test.
Data Product for AI Agents: Think of it as a polished storefront (not a messy warehouse). Data is clean, documented, reusable, and built for autonomous consumption—not just dashboards or reports. It fuels AI agents that plan, reason, act, and interact like humans.
The Catch: Most data products today are human-oriented (built for dashboards, not decisions). AI agents break when data is stale, undocumented, or inconsistent. Real challenge? Maturing your data to be agent-compatible, then agent-native.
Common Blockers:
- Data is locked behind tribal knowledge or batch pipelines.
- Schemas change silently, breaking agents.
- Data products lack real-time access or rich metadata.
- No observability leads to agents failing silently, and no one knows why.
Sandipan lays out a clear maturity model for AI-ready data:
- Human-Oriented: Batch access, minimal schema enforcement, no context. Works for people, but not agents.
- Agent-Compatible: Basic APIs, some schema validation, starting to think agent needs.
- Agent-Optimized: Real-time, robust contracts, rich metadata, better observability.
- Agent-Native: Fully adaptive data products with guaranteed schemas, automated governance, designed for autonomous systems.
AI agents are fast, autonomous, and fragile. They depend on data that is:
- Accessible: Real-time, low-latency, API-driven—not batch exports.
- Resilient + Predictable: No silent schema changes; reliable contracts.
- Self-describing: Metadata is oxygen. Without context, agents fail.
- Observable: So you know what’s working, and what’s not.
- Error-tolerant: Clear, machine-readable error handling to support recovery or retries.
Your data strategy isn’t about adding pipelines. It’s about progressing through 4 phases:
- Foundation: Define ownership, context needs, and access design.
- Standardization: Consistent schemas, governance, contracts.
- Optimization: Enable real-time, low-latency integration, robust observability.
- Innovation: Deliver agent-native data that adapts and drives business impact autonomously.
And yes this means transforming not just tech, but teams. From ad hoc data work to dedicated product owners, AI ops, and federated domain responsibility.
Stop counting pipelines. Measure what matters:
- Technical: Data quality index, API latency, freshness, schema stability.
- Agent: Adoption rate, query volumes, success/error rates, integration effort.
- Business: Faster decisions, cost savings, new revenue, user satisfaction.
The shift: Measure success by how well your agents perform, and not how many data jobs you ran.
- Build data products like products: Clear ownership, APIs, contracts, and not ad hoc exports.
- Treat metadata as oxygen: If agents don’t know what data means, they’ll fail.
- Focus on observability early: You can’t fix what you can’t see.
- Evolve the team structure: Without the right people, even the cleanest data won’t help.
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