Score each dimension from 1 to 5 using observed metrics, not perceptions:

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How to Run a 30-Day Assessment

Week 1: Set Scope and Anchor Value

• Select 8–12 data products or use cases that drive revenue, reduce operating cost, or control risk
• Define business KPIs (e.g., days to onboard a merchant, fraud false positive rate, claims cycle time)
• Inventory policies, tools, owners, and data contracts to surface gaps
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Weeks 2–3: Gather Telemetry

• Extract quality metrics from data observability and pipeline logs
• Pull catalog coverage and active usage from the data catalog
• Inspect access policies, API uptime, and p95 latency from the platform
• Review audit reports and DSAR logs with privacy and security
• Validate findings via short interviews with stewards, platform owners, and business users

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Week 4: Align Decisions

• Score each dimension (1–5) using the measures above
• Quantify impact by linking gaps to cycle time, incident cost, regulatory exposure, or customer experience
• Agree a 90-day remediation plan with owners, budgets, and target metrics

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Examples:

• Lift stewardship coverage from 40% to 90% for critical elements
• Halve duplicate customer records
• Cut time-to-data from 20 to 10 days

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Risk to Watch

Scoring sessions drift into debate. Keep them evidence-led. Tie each score to a metric and threshold. Where metrics are missing, create an instrumentation task before debating maturity.

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Linking Scores to Business Value

Data readiness is not abstract:

• Quality lifts model accuracy and reduces rework
• Governance lowers failed releases and speeds root cause analysis
• Accessibility accelerates time-to-market for analytics and AI features
• Compliance de-risks audits and shortens regulator response times

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A CFO funds work that drops cost per incident, avoids penalties, and accelerates product launches. A CISO backs controls that demonstrably reduce high-risk findings in the next audit cycle.

The diagnostic is the start, not the program. Convert the four dimensions into an operating rhythm:

• Publish SLAs for data products and enforce on-call rotations for critical pipelines
• Use change control with schema diff checks to prevent breaking changes
• Tie catalog certification to freshness and test coverage, not opinion
• Review compliance measures monthly with privacy and security
• Rehearse incident response for data quality and privacy event

Architecture and Tooling Without Vendor Bias

Any modern stack can support a data readiness assessment and scorecard:

• Lakehouse or warehouse for storage and compute
• Pipelines with unit tests and embedded data quality checks
• A metadata catalog with APIs capturing lineage and certification
• Access control tied to identity
• Observability that tracks freshness, schema change, and reliability

Tooling matters less than telemetry you can trust, and teams accountable for thresholds and time to fix.

Governance and ROI

Operationalize governance. Policies create value only when enforced by controls, observed in logs, and owned by accountable people.

Explainability improves when lineage and data contracts exist and SLAs are met. Regulators and auditors respond to evidence, not intent.

Business value shows up when:

• Time-to-data
• Incident MTTR
• DSAR response time

...move on a known cadence.

Invest where the scorecard and the business case intersect. Retire work that doesn't change a number that matters.