Interactive Scorecard

Is Your Data
AI-Ready?

Dashboards tolerate bad data because human intuition acts as a safety net. Autonomous agents do not have human intuition. If you feed bad data to an agent, it scales the error at machine speed.

Use this 10-point scorecard to evaluate your data architecture. Check the boxes that your enterprise currently satisfies.

Data Readiness Score 0 / 10

Unprepared

🔗 I. Accessibility & Integration

1. API-First Accessibility

Data is exposed via secure, documented APIs rather than locked in proprietary SaaS silos, flat files, or static reports.

2. Centralised Discovery

There is a unified data catalog or fabric where AI agents can programmatically discover available datasets, endpoints, and schemas without human intervention.

📈 II. Quality & Semantics

3. The Semantic Layer

Business logic (e.g., the exact definition of "net revenue") is codified in a centralised semantic layer, ensuring agents do not have to guess or hallucinate SQL calculations.

4. Master Data Management (MDM)

Core entities (Customers, Products, Employees) have a single, golden record. Agents will not execute conflicting actions based on misaligned CRM and ERP data.

5. Automated Quality Breakers

Data pipelines feature automated circuit breakers that halt the flow of corrupted or anomalous data before it reaches the models powering your agents.

🕵 III. Governance & Security

6. Programmatic PII Masking

Sensitive data and Personally Identifiable Information are automatically tokenised or redacted at the pipeline level before being exposed to an LLM context window.

7. Row/Column Level Access

Access controls are highly granular. When an agent queries a database, it only retrieves the specific rows the triggering user is explicitly authorised to see.

8. Versioning and Lineage

Every dataset has trackable lineage. Compliance teams can prove exactly which data points and pipeline versions informed a specific automated decision.

♻ IV. The Data Flywheel

9. Systemic Feedback Loops

Workflows are designed so that when a human corrects an agent's mistake, that correction is automatically captured as training data to improve the underlying model.

10. Real-Time Ingestion

The architecture supports event-driven or near real-time data ingestion, ensuring agents act on current realities rather than yesterday's batch jobs.

You cannot scale on bad data.

Stop investing in AI models until you have fixed the data architecture beneath them. Book a briefing to align your team on the requirements for a production-ready AI Control Plane.