Data Governance & Quality Services

Governed data platforms, automated quality frameworks, and enterprise data catalogues — the foundation that makes every dashboard accurate, every AI model reliable, and every regulatory audit defensible.

Bad data does not fail loudly. It fails silently — in decisions, in models, and in audits.

Poor data quality costs the average enterprise $12.9M per year. But the hidden costs — wrong decisions, failed AI models, and failed audits — are larger still.

Wrong decisions

Finance and operations report different revenue numbers. Executives make strategic decisions on data they cannot
fully trust.

AI model failure

ML models trained on ungoverned data inherit its errors. A model trained on dirty features produces confidently wrong predictions.

Regulatory exposure

HIPAA, GDPR, and financial regulators expect documented data lineage and access controls. Ungoverned data is undocumentable data.

Data governance and quality are not compliance exercises. They are the infrastructure that makes every downstream investment — analytics, AI, reporting — worth making.

Governance defines who owns data and what it means. Quality ensures it is accurate, complete, and reliable.

Data Governance

Governance is the organisational and technical framework that defines ownership, accountability, and control across your entire data estate. It answers: who is responsible, what policies apply, and how compliance is demonstrated.

Data ownership and stewardship — defined owners per domain, accountable for accuracy and fitness for use

Data catalogue — searchable inventory of every data asset, its definition, lineage, and certified status

Access controls — role-based permissions, column-level security, and data access request workflows

Data policies — retention, classification, sharing, privacy, and regulatory compliance rules

Lineage tracking — end-to-end audit trails from source system to dashboard, model, or report

Data Quality

Data quality is the operational measurement and enforcement of data standards. It answers: is this data correct right now, in this pipeline, for this use case — and will it alert when it is not.

Quality dimensions — completeness, accuracy, consistency, timeliness, validity, and uniqueness measured continuously

Automated quality checks — dbt tests, Great Expectations, and Soda assertions embedded in every pipeline stage

Quality dashboards — real-time visibility into data quality scores, failed checks, and trend over time

Anomaly detection on data — statistical profiling that detects distribution shifts in source data before they reach models

Quality SLAs — defined and enforced freshness, completeness, and accuracy standards per dataset and consumer

Most ML projects deliver predictions. We deliver systems that act on them.

An ML Vendor Delivers

A CaliberFocus ML System Delivers

Enterprise catalogue, automated quality engineering, and regulatory compliance architecture.

Enterprise Data Catalogue & Lineage

Know what data you have, what it means, and where it came from

Most data quality problems start with data nobody can find, nobody owns, and nobody fully understands. An enterprise data catalogue changes that — giving every analyst, engineer, and decision-maker a single, searchable, trusted reference for every data asset in the organisation, with full lineage from source to consumption.

Data catalogue implementation — Microsoft Purview, Alation, Collibra, or OpenMetadata deployment with business glossary and asset discovery
Business glossary design — standardised definitions for every business term: revenue, customer, patient, claim, and KPI across all departments
Automated metadata harvesting — scanners that discover and document tables, columns, reports, and pipelines without manual entry
End-to-end data lineage — visualised audit trails from raw source data through transformations to dashboards, models, and regulatory reports
Data ownership assignment — domain stewardship model with named owners, certified datasets, and escalation paths for quality issues
Impact analysis — understand which dashboards, models, and reports are affected before making any upstream data change

Automated Data Quality Engineering

Quality enforced in pipelines — not discovered in meetings

Data quality cannot be audited into existence after the fact. It must be engineered into every pipeline — with automated tests, profiling, anomaly detection, and alerting that catches quality failures before they reach the dashboards, models, and systems that depend on clean data.

dbt test frameworks — not-null, uniqueness, referential integrity, accepted values, and custom business rule tests on every transformation
Great Expectations and Soda pipelines — statistical expectation suites and quality assertions embedded as pipeline gates
Data profiling automation — column-level statistics, distribution monitoring, and cardinality tracking across all datasets on every load
Anomaly detection on source data — ML-based detection of unexpected shifts in row counts, null rates, value distributions, and date ranges
Quality dashboards and SLA monitoring — real-time quality scores per dataset, trend over time, and consumer impact mapping
Quality incident management — automated alerting, root cause classification, and stakeholder notification workflows on quality failures

Regulatory Data Compliance Architecture

Data governance your regulators and auditors can review and approve

HIPAA, GDPR, CCPA, BCBS 239, and emerging AI regulations all require documented data lineage, access controls, retention policies, and privacy frameworks. We design the governance architectures that satisfy these requirements — built into the platform, not assembled in a spreadsheet the week before an audit.

HIPAA data governance — PHI classification, access control architecture, audit logging, and BAA-aligned data handling frameworks
GDPR and CCPA compliance — data inventory, processing records, consent management, right-to-erasure workflows, and cross-border transfer documentation
BCBS 239 data lineage — critical data element identification, lineage documentation, and data quality reporting for regulatory submissions
Data classification frameworks — sensitivity labelling (public, internal, confidential, restricted) applied automatically via cataloguing and policy engines
Retention and purge automation — policy-driven data lifecycle management with automated archival, anonymisation, and deletion workflows
Audit readiness packages — governance documentation, access review reports, lineage diagrams, and quality evidence ready for regulatory examination

From policy and standards through to monitoring and audit readiness. Built in, not bolted on.

Governance Layer

What Gets Built Here

Policy & Standards Layer

Data governance policies, classification standards, ownership model, and quality SLA definitions. The documented organisational commitments that every technical control enforces.

Catalogue & Discovery Layer

Data catalogue, business glossary, automated metadata harvesting, and asset search. Every analyst and engineer can find, understand, and trust data without asking an engineer.

Quality Engineering Layer

Automated quality checks embedded in every pipeline — dbt tests, profiling, anomaly detection, and SLA monitoring. Quality enforced continuously, not audited occasionally.

Lineage & Audit Layer

End-to-end data lineage from source to consumption, impact analysis, change tracking, and audit trails. Every data decision traceable, every regulatory question answerable.

Access & Privacy Layer

Role-based access controls, column-level security, PII masking, consent management, and retention enforcement. Data shared with the right people — and only the right people.

Monitoring & Reporting Layer

Real-time governance dashboards, quality score trending, compliance posture reporting, and audit readiness packages. Governance that is visible to leadership, not just technical teams.

Every regulated industry has specific governance requirements. We know them all.

Healthcare & Life Sciences

HIPAA data governance — PHI classification, audit logging, minimum necessary access controls, and BAA-aligned data handling across EHR, billing, and analytics systems

Clinical data quality — automated completeness and accuracy validation on clinical data feeds before they reach coding AI, risk models, and quality reporting

RCM data lineage — end-to-end audit trails from claim submission through adjudication to payment posting for payer dispute and compliance documentation

FDA data governance for life sciences — 21 CFR Part 11-compliant data governance for clinical trial data, pharmacovigilance, and regulatory submissions

Financial Services

BCBS 239 compliance — critical data element identification, lineage documentation, and data quality reporting frameworks for regulatory examination readiness

GDPR and CCPA governance — data inventory, processing records, consent management, and right-to-erasure workflows for customer and transaction data

Model risk data governance — SR 11-7-aligned data lineage and quality documentation for all data inputs feeding regulatory credit and risk models

Financial data quality — automated reconciliation checks, balance validation, and completeness monitoring across core banking, trading, and reporting data

Enterprise & Multi-Cloud

Enterprise data catalogue rollout — organisation-wide data asset discovery, ownership assignment, and business glossary across multi-cloud data estates

Data mesh governance — federated governance framework with domain-level ownership, inter-domain contracts, and central quality and policy enforcement

Multi-cloud data governance — unified governance policies, consistent access controls, and consolidated lineage across Azure, AWS, and GCP environments

ERP data quality — automated quality checks on Dynamics 365 and SAP data before it reaches analytics, AI models, and executive reporting

Manufacturing & Operations

IoT data quality — sensor data validation, outlier detection, and device health monitoring that prevents bad telemetry from reaching predictive maintenance models

Supply chain data governance — supplier data standards, master data management, and quality controls across ERP, WMS, and supplier portal integrations

Production data lineage — audit trails connecting raw materials, production parameters, and quality outcomes for regulatory and customer compliance reporting

Master data management — golden record creation and maintenance for product, supplier, customer, and asset master data across enterprise systems

40%

Reduction in time spent resolving data discrepancies

3×

Faster data discovery with an enterprise data catalogue

Zero

Surprises in regulatory audits with documented lineage and access controls

60%

Reduction in data quality incidents post-implementation

The complete enterprise governance and quality stack.

Domain	Tools & Platforms We Work With
Data Catalogues	Microsoft Purview · Alation · Collibra · OpenMetadata · Apache Atlas
Quality Frameworks	dbt Tests · Great Expectations · Soda · Monte Carlo · Bigeye
Data Lineage	OpenLineage · Marquez · Purview Lineage · dbt lineage · Atlan
Access & Privacy	Apache Ranger · Databricks Unity Catalog · Snowflake RBAC · BigQuery IAM · Immuta
Profiling & Monitoring	Great Expectations · Soda Cloud · Monte Carlo · dbt Exposures · Grafana
Policy Engines	Microsoft Purview Policies · OPA · Collibra Policy Manager · BigQuery Policy Tags

Data Engineering & Pipelines

Quality checks and governance controls embedded into every pipeline we build.

Cloud Data Platform & Architecture

Governance frameworks designed into the platform architecture from the first design decision.

AI Governance & Responsible AI

Data governance connects directly to AI governance — trusted data is the foundation of trustworthy AI.

Data for AI & Feature Engineering

ML-grade data quality and lineage that ensures AI training data is accurate, documented, and reproducible.

Most organisations discover data quality problems in board meetings, audit findings, or failed AI models. A data audit finds them first.

Industries we serve

We collaborate with global technology leaders to deliver secure and scalable growth-driven digital solutions. Our partnerships strengthen our ability to innovate, accelerate transformation, and drive measurable business impact for our clients.

Case Studies

We used generative AI to automate documentation, compliance checks, and medical coding. The solution improves accuracy, cuts manual effort, speeds turnaround, and ensures regulatory compliance in clinical use.

0 +

200 +

When patient data was summarized clearly, documentation felt less burdensome. With CaliberFocus, clinician satisfaction rose from 58% to 81% without changing how teams work.

Dr. Rebecca HallCMIO, Healthcare

Better documentation and fewer audit issues delivered real savings. With CaliberFocus, billing compliance improved to 98.6%, reducing risk while easing the burden on clinicians.

Sophia WilliamsCTO, Clinical care

We gained clear visibility into student performance. Engagement rose, scores improved, and administrative effort dropped by nearly 30 percent, giving educators time to teach.

Andrew MillerDirector, Education Technology

June 18, 2026

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CaliberFocus delivers AI and machine learning development services that combine deep machine learning and deep learning expertise with production-grade MLOps. As a trusted machine learning service provider, we help organizations move models from experimentation to scalable production, delivering measurable business impact, accuracy, and long-term value.

Data Governance & Quality

Data Your Entire Organisation Can Trust — Every Time.

What ungoverned data costs your organisation

Wrong decisions

AI model failure

Regulatory exposure

Two disciplines one outcomes

Data governance and data quality — defined

Data Governance

Data ownership and stewardship — defined owners per domain, accountable for accuracy and fitness for use

Data catalogue — searchable inventory of every data asset, its definition, lineage, and certified status

Access controls — role-based permissions, column-level security, and data access request workflows

Data policies — retention, classification, sharing, privacy, and regulatory compliance rules

Lineage tracking — end-to-end audit trails from source system to dashboard, model, or report

Data Quality

Quality dimensions — completeness, accuracy, consistency, timeliness, validity, and uniqueness measured continuously

Automated quality checks — dbt tests, Great Expectations, and Soda assertions embedded in every pipeline stage

Quality dashboards — real-time visibility into data quality scores, failed checks, and trend over time

Anomaly detection on data — statistical profiling that detects distribution shifts in source data before they reach models

Quality SLAs — defined and enforced freshness, completeness, and accuracy standards per dataset and consumer

The Difference that Matters

From ML models to decision systems

An ML Vendor Delivers

A CaliberFocus ML System Delivers

What we built?

Three core governance & quality capabilities

Enterprise Data Catalogue & Lineage

Automated Data Quality Engineering

Regulatory Data Compliance Architecture

The Architecture

Enterprise data governance — six layers

Governance Layer

What Gets Built Here

Policy & Standards Layer

Catalogue & Discovery Layer

Quality Engineering Layer

Lineage & Audit Layer

Access & Privacy Layer

Monitoring & Reporting Layer

Where this works?

Data governance & quality in production — by industry

Healthcare & Life Sciences

HIPAA data governance — PHI classification, audit logging, minimum necessary access controls, and BAA-aligned data handling across EHR, billing, and analytics systems

Clinical data quality — automated completeness and accuracy validation on clinical data feeds before they reach coding AI, risk models, and quality reporting

RCM data lineage — end-to-end audit trails from claim submission through adjudication to payment posting for payer dispute and compliance documentation

FDA data governance for life sciences — 21 CFR Part 11-compliant data governance for clinical trial data, pharmacovigilance, and regulatory submissions

Financial Services

BCBS 239 compliance — critical data element identification, lineage documentation, and data quality reporting frameworks for regulatory examination readiness

GDPR and CCPA governance — data inventory, processing records, consent management, and right-to-erasure workflows for customer and transaction data

Model risk data governance — SR 11-7-aligned data lineage and quality documentation for all data inputs feeding regulatory credit and risk models

Financial data quality — automated reconciliation checks, balance validation, and completeness monitoring across core banking, trading, and reporting data

Enterprise & Multi-Cloud

Enterprise data catalogue rollout — organisation-wide data asset discovery, ownership assignment, and business glossary across multi-cloud data estates

Data mesh governance — federated governance framework with domain-level ownership, inter-domain contracts, and central quality and policy enforcement

Multi-cloud data governance — unified governance policies, consistent access controls, and consolidated lineage across Azure, AWS, and GCP environments

ERP data quality — automated quality checks on Dynamics 365 and SAP data before it reaches analytics, AI models, and executive reporting

Manufacturing & Operations

IoT data quality — sensor data validation, outlier detection, and device health monitoring that prevents bad telemetry from reaching predictive maintenance models

Supply chain data governance — supplier data standards, master data management, and quality controls across ERP, WMS, and supplier portal integrations

Production data lineage — audit trails connecting raw materials, production parameters, and quality outcomes for regulatory and customer compliance reporting

Master data management — golden record creation and maintenance for product, supplier, customer, and asset master data across enterprise systems

What you can expect?

Outcomes from governed data platforms

40%

3×

Zero

60%

Tools & platforms we work with

Why CaliberFocus?

What makes our governance approach different?

Governance Built In, Not Bolted On

Quality as Engineering Practice

Regulatory Depth Across Sectors

Operationally Practical

Connected Services

Governance connects every layer of the data & AI stack

Data Engineering & Pipelines

Cloud Data Platform & Architecture

AI Governance & Responsible AI

Data for AI & Feature Engineering

Enhancing
Clinical Care,
Fewer Readmits!