Entity

Data Quality Score

The measured assessment of data quality for critical data domains — containing completeness, accuracy, timeliness, and consistency metrics with thresholds that trigger remediation when quality degrades.

Last updated: February 2026Data current as of: February 2026

Why This Object Matters for AI

AI cannot trust its inputs without data quality monitoring; without it, model predictions degrade silently as upstream data quality erodes.

Technology & Data Management Capacity Profile

Typical CMC levels for technology & data management in Financial Services organizations.

Formality
L2
Capture
L2
Structure
L2
Accessibility
L2
Maintenance
L2
Integration
L2

CMC Dimension Scenarios

What each CMC level looks like specifically for Data Quality Score. Baseline level is highlighted.

L0

Data privacy requests arrive via email and are handled ad-hoc; there's no structured process for tracking GDPR DSARs or CCPA requests across customer databases.

None — AI has no Data Privacy Request records to reason about for regulatory compliance reporting.

Create a basic Data Privacy Request register logging each request type (access, deletion, portability), requestor identity, and submission date.

L1

A shared spreadsheet logs Data Privacy Requests with request type and submission date, but there's no linkage to which customer data systems were searched or what data was provided to the requestor.

Can list Data Privacy Request counts by type but cannot trace which financial services systems were queried or verify completeness of responses for regulatory audit.

Organize Data Privacy Requests with structured fields — request type, requestor verification status, affected data systems, response status — aligned with GDPR and CCPA requirements.

L2Current Baseline

Data Privacy Requests follow a standard intake form with request type, requestor identity verification, and affected customer accounts, but linkage to which databases were searched remains manual documentation.

Can track Data Privacy Request status and response timelines but cannot programmatically verify which trading, payment, and CRM systems were included in the search.

Link Data Privacy Requests to the CMDB and data classification registry so each request maps to specific databases queried and data elements provided.

L3

Each Data Privacy Request maps to affected CMDB data systems, data classification tags (PII, financial data), search results per system, and response actions taken with timestamps for GDPR/CCPA compliance tracking.

Can trace Data Privacy Request fulfillment across customer data systems, verify response completeness against data inventories, and generate regulatory compliance reports.

Enforce a validated Data Privacy Request schema with mandatory fields, automated linkage to data lineage graphs, and machine-readable response formats for audit trail verification.

L4

A validated Data Privacy Request schema enforces mandatory fields, links to data lineage graphs showing customer data flows across trading and banking systems, and produces machine-readable audit trails for regulatory review.

Can auto-verify Data Privacy Request completeness by checking searched systems against data lineage maps and flag gaps where customer PII may reside but was not included in the response.

Deploy automated Data Privacy Request fulfillment that queries customer data systems via APIs, aggregates results, applies legal hold checks, and generates response packages with zero manual data gathering.

L5

Automated Data Privacy Request fulfillment queries customer data across all financial services systems via APIs, aggregates PII and financial records, applies legal hold and retention policy checks, and generates compliant response packages without manual intervention.

Can autonomously fulfill Data Privacy Requests from intake through response delivery, with complete audit trails for GDPR Article 15 access requests and CCPA deletion obligations.

Ceiling of the CMC framework for this dimension.

Capabilities That Depend on Data Quality Score

Other Objects in Technology & Data Management

Related business objects in the same function area.

IT Asset Inventory

Entity

The comprehensive registry of all IT assets — servers, workstations, network devices, cloud instances, and software with specifications, patch levels, owners, and the relationships that form the configuration management database.

Security Threat Intelligence

Entity

The curated collection of threat indicators and attack patterns — containing IOCs, CVEs, threat actor profiles, and the risk contextualization that helps security teams prioritize responses.

IT Service Ticket

Entity

The transactional record for each IT incident or service request — containing issue description, affected system, priority, resolution steps, and the time-to-resolution metrics that drive service level performance.

Data Catalog Entry

Entity

The metadata record for each data asset — containing data definitions, lineage, ownership, classification, usage statistics, and the access controls that govern who can see and use each dataset.

Software License Record

Entity

The managed inventory of software entitlements — containing license types, quantities, deployment counts, renewal dates, and the compliance position showing over- or under-deployment.

Code Repository

Entity

The version-controlled collection of source code and configurations — containing code files, commit history, branch structure, pull request reviews, and the quality metrics that track code health.

Privacy Data Inventory

Entity

The catalog of personal and sensitive data across systems — containing data categories, storage locations, retention periods, processing purposes, and the data subject rights fulfillment status.

Disaster Recovery Plan

Entity

The documented recovery procedures for each critical system — containing recovery time objectives, recovery point objectives, failover procedures, test results, and the dependencies that determine recovery sequence.

Patch Deployment Decision

Decision

The recurring judgment point where IT operations evaluates which patches to deploy — weighing vulnerability severity, exploit availability, system criticality, and change window constraints to prioritize patching efforts.

What Can Your Organization Deploy?

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