Define
Fix the six dimensions, completeness, accuracy, consistency, timeliness, validity, uniqueness, per critical field.
Done when: Each critical field has a measurable definition, not an adjective.
Data Quality & Observability Framework (DQF)
Data Quality & Observability Framework
Live monitors and clear escalation that treat a bad number like an outage.
The business problem
Bad data is found by the CEO, not by the pipeline.
Errors surface in board packs and model outputs long after they entered, and trust drops each time.
What you hear inside the organisation
Why is yesterday's figure missing?
We check the numbers in Excel before sending.
The model was trained on duplicated records.
Nobody knew the source had changed.
Who it serves and what changes
Your teams, before and after the Data Quality & Observability Framework
Client teams and what they need
Data engineering
Alerts before users notice
Data stewards
Quality rules they own
Analytics consumers
A visible trust score
AI teams
Clean training and input data
The outcome you get
- Quality rules on the data that matters most
- Freshness, volume and schema monitored
- Incidents routed to a named owner
- Trust scores visible to every user
A worked example
A bank whose board pack had a wrong figure
The problem we saw
A source change silently doubled a customer count. The CEO spotted it in the board meeting.
How the framework solved it
We put quality rules on critical data, monitored freshness, volume and structure, and routed incidents to a named owner.
The business result
Problems are caught in the pipeline, owners fix them before reports go out, and leaders trust the numbers again.
Illustrative example based on common client situations.
In short
What is the Data Quality & Observability Framework?
Data Quality & Observability Framework (DQF): Live monitors and clear escalation that treat a bad number like an outage.
Quality programmes fail when quality is an opinion.
- How it moves
- Each step closes with a check before the next begins
- Owned by
- Run by data engineering with the domain steward; incident severity signed by the data governance lead.
- You keep
- Quality specification, Monitor and alert set, Incident severity ladder
How it works
How the Data Quality & Observability Framework works
Quality that is only measured downstream is decoration. The framework is judged by how many defects were stopped at source.
Measure
Implement the tests in the pipeline and record a score history from day one.
Done when: Scores are produced automatically and stored, not calculated for a meeting.
Observe
Add freshness, volume, schema-drift and distribution monitors above the field-level tests.
Done when: A silent pipeline raises an alert; absence of data is itself an event.
Respond
Grade incidents by decision impact and run the matching containment path.
Done when: Severity one means the affected report or model is withdrawn, not annotated.
Prevent
Push every recurring defect back to its source system or its data contract.
Done when: A defect that recurs twice has a source-side change request raised.
Is it right for you?
Is the Data Quality & Observability Framework right for you?
A strong fit if
- Errors keep reaching reports or models
- Teams validate data by hand
- You are feeding data into AI
Probably not yet if
- Data volumes are small and checked at source
- No pipelines exist yet
Free assessment
Discuss the Data Quality & Observability Framework with our team
A working session with our senior team. We review what fits your situation, what does not, and where to start.
A focused conversation
We listen to your situation and goals.
Fit review
Which parts of this framework apply to you, and which do not.
A written view
Clear next steps, whether or not you work with us.
Go deeper
Applying the Data Quality & Observability Framework in practice
Open only the part you need: how it starts, what your team keeps, maturity levels, sector rules and the evidence a reviewer will ask for.
Quality programmes fail when quality is an opinion. DQF defines each dimension, the measurement method, the threshold and who is paged when it breaks, so a wrong number has an owner and a clock.
Version
v1.0
Last reviewed
August 2026
Family
Data & Analytics Operating System
How this was developed: Built from delivery practice on Saudi and international engagements, then reconciled clause by clause against the published standards below. Revised when a standard is updated or a gate proves unworkable in the field.
Quality specification
Dimension, method, threshold and owner per critical field.
Monitor and alert set
Freshness, volume, schema and distribution monitors with routing.
Incident severity ladder
Grades tied to decision impact, with the containment action for each.
Banking & finance
Regulatory reporting fields are severity one by default; withdrawal beats late correction.
- Who reviews it
- SAMA, plus PDPL and NCA controls
- Systems in scope
- Core banking, cards, KYC and AML systems, credit bureau feeds, the general ledger
- Where it lands first
- KYC and AML review, credit decisioning support, complaint and dispute handling
1
Define
- Quality specification
- Critical field register
2
Measure
- Test suite
- Score history
3
Observe
- Monitor set
- Alert routing
4
Respond
- Severity ladder
- Incident log
- Withdrawal notice
5
Prevent
- Root cause register
- Source change requests
Quality that is only measured downstream is decoration. The framework is judged by how many defects were stopped at source.
Stage
Exit gate
Artefacts
1
Define
Fix the six dimensions, completeness, accuracy, consistency, timeliness, validity, uniqueness, per critical field.
Exit gate
Each critical field has a measurable definition, not an adjective.
Artefacts
- Quality specification
- Critical field register
2
Measure
Implement the tests in the pipeline and record a score history from day one.
Exit gate
Scores are produced automatically and stored, not calculated for a meeting.
Artefacts
- Test suite
- Score history
3
Observe
Add freshness, volume, schema-drift and distribution monitors above the field-level tests.
Exit gate
A silent pipeline raises an alert; absence of data is itself an event.
Artefacts
- Monitor set
- Alert routing
4
Respond
Grade incidents by decision impact and run the matching containment path.
Exit gate
Severity one means the affected report or model is withdrawn, not annotated.
Artefacts
- Severity ladder
- Incident log
- Withdrawal notice
5
Prevent
Push every recurring defect back to its source system or its data contract.
Exit gate
A defect that recurs twice has a source-side change request raised.
Artefacts
- Root cause register
- Source change requests
Standard
What this framework satisfies
DAMA / DCAM
Data quality knowledge area, measured rather than described.
NDMO
Data quality specification with evidence per critical data element.
NIST AI RMF
Measure function, validity and reliability inputs for model risk.
ISO/IEC 42001
Monitoring and measurement obligations evidenced by the score history.
Dashboards instead of tests
A quality dashboard nobody is paged by changes nothing. Route alerts to a named person.
Measuring every field
Thousands of checks produce noise. Start with the fields the decision actually depends on.
Fixing in the report layer
Patching the last mile hides the defect and guarantees it returns next month.
Built on
- 1
DAMA-DMBOK and EDM Council DCAM
Data management knowledge areas and capability assessment.
- 2
NDMO data management and personal data protection standards
National Data Management Office specifications and domains.
- 3
NIST AI Risk Management Framework 1.0 and Generative AI Profile
National Institute of Standards and Technology, Govern, Map, Measure, Manage.
- 4
ISO/IEC 42001: AI management systems
International Organization for Standardization, management system requirements for AI.
Citations describe the parts of each standard the framework answers. Bilytica is not an accreditation or certification body.
In Saudi Arabia
The data layer, as NDMO reads it
Most AI programmes in Saudi Arabia stall underneath the model. These frameworks work on that layer, against the control set the national office actually assesses.
AuthorityWhat it requiresWhat we buildWhat you keep
NDMO data management controls
Owners, policies and measurable maturity across every data management domain.
Baseline assessment, then control-by-control remediation with dates and owners.
Maturity baseline and a re-assessment on the same control set.
SDAIA, PDPL
Classification and retention applied to personal data wherever it is copied.
Classification enforced in the platform, so a copy inherits the rules of its source.
Lineage view showing classification travelling with the data.
ZATCA, for invoicing and transactional data
Invoice and transaction records reconcile to the filed position.
Reconciliation checks run as data quality rules, with exceptions routed to an owner.
Reconciliation report and an exception log with resolution times.
Where it runs
Registers, lineage and quality evidence live in your environment inside Saudi Arabia and stay yours.
Arabic, as it is spoken here
Business glossaries are maintained in Arabic and English together, with Arabic as the governing definition.
FAQ
Frequently Asked Questions
How many checks should we start with?
One domain, its critical fields only. A short monitored list beats a long unmonitored one.
Who owns a data incident?
The domain steward owns the incident; the source system owner owns the fix when the cause is upstream.
Does this need a specific observability tool?
No. The monitors can be implemented in the existing pipeline; the framework specifies what must be watched, not with what.
Where this connects
Rest of the library
DFFData Foundation FrameworkAIRMAI Readiness ModelVALUEAI Value FrameworkTRUSTAI Assurance FrameworkDELIVERAI Delivery FrameworkAGENTAgent Operations FrameworkSOVEREIGNSovereign AI FrameworkDMMData Management Maturity ModelDPAData Platform Architecture FrameworkDECIDEAnalytics & Decision FrameworkDGOVData Governance Operating ModelSDCFSaudi Data Compliance FrameworkAQSArabic Quality StandardKSA-READYIn-country Deployment Framework
Topic cluster
Explore the topic cluster
Curated links across our Saudi hubs, sector pages and long-form blog research.