> tpl_aim_009
Data Quality Plan, Rules Register and Scorecard
Comprehensive data quality engineering standard defining the 6 core dimensions (Completeness, Uniqueness, Timeliness, Validity, Accuracy, Consistency), automated assertion rules, and threshold scorecards.
Automated data quality testing specification defining Great Expectations assertions, anomaly alerting thresholds, and business impact scorecards across the 6 DAMA dimensions.
Important Tech Document Template & Operational Notice
TinyCTO.tv Tech Document Template Notice: This template is a general educational and operational starting point. It is not legal, tax, accounting, investment, procurement, regulatory, security or certification advice. Requirements vary by jurisdiction, organization, contract and risk. Review and adapt it with qualified professionals before relying on it.
Problem Solved
Silent data corruption, duplicate keys, null values in mandatory fields, and delayed pipelines pollute data warehouses unnoticed, causing executives to make erroneous business decisions based on flawed reports.
When to Use
- •Establishing automated data quality assertions in dbt or Great Expectations test suites
- •Measuring and publishing monthly data quality scorecards for executive data governance
- •Configuring pipeline circuit breakers that halt downstream ingestion when error rates spike
When NOT to Use
- •For general software application unit tests that do not evaluate data pipelines
- •For network latency performance monitoring with zero dataset evaluation
5 Template Sections & Structural Outline
Completeness, Uniqueness, Timeliness, Validity, Accuracy, and Consistency mathematical definitions.
Great Expectations / Soda test assertions: not_null, unique, accepted_values, relationships, and volume thresholds.
Row count anomaly detection, statistical distribution drift (Z-score), and pipeline auto-pause logic.
Weighted overall data health index, domain scorecard breakdown, and SLA pass-rate tracking.
Severity levels (P1 to P4), root cause analysis protocol, owner escalation paths, and resolution timers.
Completion Instructions
Independent Review Checklist
- All mandatory sections completed
- No secrets or passwords included
- Executive sponsor sign-off obtained
Data Quality Plan, Rules Register and Scorecard - Worked Case Study
Fictional Entity: OmniHealth Claims Ingestion & Analytics Pipeline
Real-world production case study demonstrating complete operational adoption for OmniHealth Claims Ingestion & Analytics Pipeline.
- •Configured 120 automated Great Expectations rules across all 6 DAMA dimensions
- •Prevented $2.1M in erroneous insurance payouts by triggering automated pipeline circuit breakers
- •Achieved 99.4% overall data quality index score across 18 enterprise warehouse marts
Frequently Asked Questions
What are the DAMA 6 Dimensions of Data Quality?
The 6 core dimensions are: Completeness (no missing values), Uniqueness (no duplicate records), Timeliness (data freshness), Validity (conformance to syntax/type), Accuracy (reflects real-world truth), and Consistency (matches across systems).
How does a data pipeline circuit breaker protect downstream consumers?
When automated test assertions detect a high error rate (e.g. 10% null values in customer_id), the circuit breaker aborts the staging-to-production swap, preserving the previous day's valid data.
What open-source tools integrate best with this Data Quality Plan?
Great Expectations, Soda Core, dbt-expectations, and Elementary provide seamless programmatic execution of the rules defined in this specification.
Download Tech Document Pack
Auth RequiredDownload all blank templates, worked scenarios, and verification manifests in a single verified archive.
Authoritative Sources
- DAMA 6 Dimensions of Data Quality FrameworkDAMA UK • OFFICIAL REQUIREMENT
- ISO 8000 Data Quality StandardsISO • OFFICIAL REQUIREMENT