Data Health Checks: The Diagnostic Tool for AI-Ready Infrastructure

A data health check is essential before deploying generative AI. Organizations attempting generative AI without baseline data readiness are building on sand.  

From Reactive to Proactive 

Passive maintenance isn't enough as data scales across cloud platforms and business units. A formal data health check surfaces architectural gaps, governance misalignments, and AI-specific blockers before they become crises. 

A data health check is essential before deploying generative AI. Organizations attempting generative AI without baseline data readiness are building on sand. 

When to Conduct a Health Check 

An annual cadence provides baseline. Conduct additional assessments: 

• Before enterprise system implementations (Workday, Oracle, SAP) shift data sources 

• Following M&A or restructuring requiring realignment of ownership and definitions 

• Before major AI or generative AI deployments where model performance depends on data quality 

• When compute or storage costs spike unexpectedly 

• When trust in data erodes (low dashboard adoption, frequent disputes) 

• Before implementing RAG systems where source document quality determines output quality 

For organizations planning generative AI, a health check is non-negotiable. 

The Diagnostic Assessment 

A comprehensive health check evaluates: 

• Data freshness and SLA compliance – For generative AI, is training data current enough? 

• Schema and metadata consistency – For unstructured data, are taxonomies consistent? 

• Governance clarity – Is PII clearly marked to prevent sensitive information leakage? 

• Lineage visibility – Can you explain why a model produced specific outputs? 

• Quality metrics – For generative AI, are hallucination rates monitored? 

• Integration health – Can you process data at the scale models require? 

• Compliance alignment – Are controls in place preventing sensitive information in outputs? 

• AI-specific readiness – Is training data curated and trustworthy? Can RAG retrieve context in real-time? 
  
  

Key Diagnostic Questions 

• Are dashboards actively maintained and trusted? 

• Are infrastructure costs increasing without business growth? 

• Can you trace data lineage transparently? 

• Are data definitions consistent across departments? 

• Do you have an inventory of unstructured data? 

• Can you identify sensitive information to prevent model training on it? 

• Is your source document library cataloged and searchable? 

If multiple questions raise concerns, a health check is overdue. Generative AI questions reveal gaps requiring immediate attention.
 

The Competitive Imperative 

Organizations conducting health checks identify architectural misalignments before crises. They scope AI opportunities and execute without foundational delays. Those skipping this work face escalating costs and competitive disadvantage. 

For generative AI, the stakes are higher. Organizations that assess readiness before deployment will deploy faster, build more reliable models, avoid regulatory risk, and achieve faster ROI. Those rushing generative AI without preparation face expensive course corrections, disappointed stakeholders, and competitive disadvantage. 

Organizations whose data infrastructure hasn't been formally assessed should schedule a comprehensive health check immediately. If you're planning generative AI deployments, move this to urgent priority. 

The best time to prepare was yesterday. The next best time is now. 

How We Can Help

Generative AI hype makes it tempting to skip readiness and jump straight to deployment. That's how organizations waste millions.

We help you get AI-ready through:

• Data Health Checks: Assess your current state and identify quick wins + strategic priorities
• Data Architecture Design: Build modern cloud-native infrastructure (Snowflake, Databricks) that supports real-time AI workloads
• AI Application Development: Deploy production AI systems for Retail, Construction, Services, HR analytics, and more—only after your foundation is solid
• Ongoing Managed Services: Scale and optimize continuously so your team focuses on business impact, not infrastructure maintenance
  
  

Reach out to learn more!

Footnotes 

¹ Fivetran, "AI and Data Readiness Survey," May 2025. 

² Precisely, "2025 Planning Insights: The Rise of AI," 2024. 

³ Gartner Research, "Poor Data Quality Costs Organizations," cited by multiple industry sources. 

⁴ Qlik, "AI Survey," February 2025. 

⁵ Labovitz, G., & Chang, Y., "The 1-10-100 Rule," 1992. 

⁶ MIT Technology Review Insights & Snowflake, "Data Strategies for AI Leaders," 2024. 

⁷ Precisely, "Data Quality and Governance Research," September 2024. 

⁸ Actian, "The Governance Gap: Why 60% of AI Initiatives Fail" (citing Gartner), July 2025. 

⁹ Databricks/Economist Impact, "Unlocking Enterprise AI: Opportunities and Strategies," November 2024. 

¹⁰ MIT Technology Review Insights & Snowflake, "Data Strategies for AI Leaders," 2024. 

¹¹ Precisely, "Data Quality and Governance Research," September 2024. 

¹² Actian, "The Governance Gap: Why 60% of AI Initiatives Fail" (citing Gartner), July 2025. 

¹³ NVMD Insights, "Enterprise AI Data Infrastructure: The $3.1 Trillion Mistake," August 2025. 

¹⁴ Precisely & DBTA Research, "Data Governance Adoption Survey," 2024-2025.