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Fictional client sample

Data Readiness Scorecard

Assessment of data foundations required to support production AI use cases.

Meridian Financial Services • February 2026 • Confidential

Executive Readout

Overall Data Readiness

Composite Score

58/100

Moderate readiness with governance and quality bottlenecks.

"You have data, but governance and access constraints suppress AI value."

01 Dimension Scores

Data Foundation Breakdown

Six dimensions scored for readiness to support AI pilots and scaling.

Data Availability

75

Good

Comprehensive customer, transaction, and operational data across core banking, CRM, and wealth platforms.

Data Quality

45

Needs Work

Inconsistent standards and duplicate records, with missing fields in wealth profiles affecting model reliability.

Data Governance

30

Critical

No catalog or metadata ownership model. Quality checks are manual and fragmented by team.

Security & Privacy

65

Moderate

Strong baseline controls (SOC 2, encryption, role-based access) but no AI-specific privacy framework.

Data Accessibility

50

Needs Work

Core APIs exist, but access approvals are slow and cross-departmental visibility remains limited.

Lineage & Observability

40

Critical

Traceability is weak, with no automated lineage tracking for schema or pipeline changes.

02 Priority Blockers

Top 3 AI Data Constraints

Critical blocker

No Data Catalog or Metadata Layer

Teams do not know what data exists or who owns it. Data scientists spend disproportionate time discovering sources.

Impact: Adds 4-6 weeks to most AI initiatives.

Critical blocker

Cross-System Definition Drift

Core banking, CRM, and wealth teams define customer identity differently, introducing model inconsistency.

Impact: Raises model error rates and weakens decision confidence.

Critical blocker

Siloed Access Controls

Critical personalization use cases are blocked because marketing cannot access relevant sales and transaction data.

Impact: Blocks 3 of 4 shortlisted use cases.

03 Quick Wins

30-60 Day Remediation Actions

Launch a searchable data catalog

Enable self-service discovery of assets, ownership, and quality status using a managed catalog platform.

Effort: LowImpact: High

Implement baseline quality rules

Start with high-value fields: required values, duplicate detection, and format validation across CRM + lending.

Effort: LowImpact: Medium

Create a cross-functional access policy

Define legal/compliance-approved data sharing with masking, logging, and explicit model usage boundaries.

Effort: MediumImpact: High

Full Scorecard Continues

This sample highlights the opening sections. The full scorecard contains system-level diagnostics, remediation ownership, and implementation costing.

  • Detailed architecture and lineage diagnostics
  • System-by-system quality issue inventory
  • Remediation roadmap with owners and target dates
  • Data tooling shortlist and selection rationale
  • Cost model for governance and platform uplift
  • Operating model for sustainable data stewardship