Data Governance Consulting That Makes Your Data Trusted, Traceable, and Fit for AI
MinervaDB's data governance consulting practice designs and operates the controls that make enterprise data usable with confidence: a governance operating model with named owners, data quality engineered as tested code with SLOs, master data and identity resolution that produce one golden record per customer, product, and supplier, catalog and lineage that answer the auditor's question, and access and privacy controls enforced at the database. All of it built on the database-grade engineering that runs our clients' transactional and analytical platforms.
Data Governance Consulting Built on Database-Grade Engineering
Governance that lives in a policy document changes nothing. Governance that lives in the schema, the pipeline tests, the access policy, and the on-call rota changes everything. We build the second kind.
Data governance at MinervaDB is the discipline of making every important dataset owned, defined, measured, and protected, and of proving it continuously. Each governed dataset has a named owner, a definition in the catalog, quality checks that run at every hop with thresholds agreed with its consumers, lineage back to the source system, and access rules enforced where the data lives. Our data governance consulting engineers those five things, then operates them until your team is ready to own them.
The practice sits beneath our data engineering, decision intelligence, and enterprise generative AI work, because none of them survives bad data. Most governance programmes fail on the same three points: controls that are documented but not executed, quality rules that are checked once at a quarterly review rather than on every load, and master data projects that stall on matching rules nobody can explain. Our engineers have operated PostgreSQL, MySQL, SQL Server, Oracle, MongoDB, ClickHouse, and the cloud warehouses for two decades, so controls are implemented at the storage and pipeline layer where they cannot be bypassed.
Data Governance Consulting Services, End to End
Six control domains, delivered as a bounded programme, embedded engineering, or 24×7 managed data quality operations.
Governance Operating Model & Policy
Data governance consulting starts with the operating model: data domains, owners, and stewards mapped to the systems they actually control; a decision-rights matrix for schema changes, new consumers, and retention; policies for classification, retention, residency, and sharing that are short enough to be followed; and a governance forum with a cadence tied to release cycles rather than to the calendar.
Data Quality Engineering
The core of our data governance consulting: quality rules written as tested, versioned code (dbt tests, Great Expectations, Soda, or native database constraints and assertions) executed at ingestion, transformation, and serving, with thresholds and severities agreed per dataset, results logged to ClickHouse, and breaches routed to an engineer as incidents rather than to a dashboard nobody reads.
Master Data & Customer 360
Data governance consulting for the entities that matter most: identity resolution across CRM, billing, e-commerce, support, and marketing systems with deterministic and probabilistic matching, explainable survivorship rules, a golden-record store on PostgreSQL with full history, and consent flags carried with every merge. Product, supplier, and location masters engineered the same way.
Metadata, Catalog & Lineage
Data governance consulting is only as good as its metadata: technical and business metadata captured automatically from the warehouse, orchestrator, and BI layer into DataHub, OpenMetadata, Amundsen, Alation, Collibra, or Unity Catalog; column-level lineage through OpenLineage; and definitions maintained in the same repository as the transformations they describe, so the catalog stays true.
Access, Privacy & Compliance
Data governance consulting for protection: role-based and attribute-based access, row-level security, column masking and tokenisation implemented in PostgreSQL, SQL Server, ClickHouse, Snowflake, BigQuery, and Databricks; audit logging of every read of sensitive data; and retention and deletion workflows that satisfy GDPR, India DPDP, HIPAA, SOX, PCI DSS, and SOC 2. Coordinated with our database security services.
Data Observability & SLOs
Data governance consulting does not end at go-live: freshness, volume, schema, and distribution monitoring on every governed dataset, exported into the Prometheus and Grafana stack that already monitors your databases; data SLOs with error budgets; and monthly reviews with dataset owners on quality trends, incident history, and the cost of the controls themselves.
How Data Governance Consulting Puts Controls Where They Execute
Every control has a place in the platform where it runs automatically. If a control only runs when someone remembers, it is not a control.
Ownership is assigned to a system, not a slide. In our data governance consulting method, an owner who cannot approve a schema change or a new consumer is a name on a page. We map domains to the databases, topics, and models they control and give owners the decision rights and the tooling to exercise them.
Quality is tested code. Rules live next to the transformations in version control, run in CI before a change ships and on every production load after it, and produce results that are queryable in ClickHouse. A failed rule at the agreed severity opens an incident with the same response targets as a failed pipeline.
Access is enforced at the engine. Row-level security, column masking, and audit logging are implemented in the database and warehouse, not in the BI tool, so a notebook, an API, and a dashboard all see the same governed view.
- Classification applied at the column level and propagated through lineage to every derived table
- Quality thresholds and severities agreed per dataset with its consumers, versioned with the rules
- Golden records with full merge history and reversible unmerge, never destructive overwrites
- Catalog definitions maintained in the same repository as the SQL, reviewed in the same pull request
- Access policies expressed as code, promoted through environments, and tested like any other change
- Evidence for auditors generated from the platform, not assembled by hand before the audit
One Golden Record, Engineered on the Database
Master data management fails when matching is a black box and survivorship is a guess. We make both explicit, testable, and reversible.
Identity Resolution: Deterministic First, Probabilistic Where Justified
Our data governance consulting applies deterministic matching first: exact and normalised matches on verified identifiers resolve most records and are fully explainable. Probabilistic matching (Fellegi-Sunter models through Splink, or embedding similarity through pgvector for unstructured attributes) is applied only to the residue, with match thresholds tuned on a labelled sample and reviewed by stewards. Every match carries its rule and score, so a customer service agent can see why two records were merged and a steward can reverse it.
Golden-Record Store on PostgreSQL
In data governance consulting engagements the master store is a transactional database, and we engineer it as one: surrogate keys with crosswalks to every source identifier, temporal tables for full history, row-level security so regional teams see only their records, and CDC out to the warehouse and the operational systems that consume the master. Our PostgreSQL consulting practice sizes and operates it.
Quality Results and Audit Evidence on ClickHouse
Data governance consulting needs evidence. Every rule execution, every match decision, every access to sensitive data is logged to ClickHouse tables partitioned by day. Quality trends, control effectiveness, and audit evidence are then ordinary analytical queries, and the auditor's question about a specific record on a specific date is answered in seconds. Our ClickHouse consulting practice operates this layer.
Four Ways to Work With Our Data Governance Consulting Team
From a bounded assessment to fully managed data quality operations under a 24×7 SLA.
| Model | Best for | What you receive |
|---|---|---|
| Governance & Quality Assessment | Organisations preparing for AI, a regulatory audit, a migration, or a merger; teams that do not trust their own numbers | Dataset inventory with ownership gaps, measured quality baseline per dimension, access and privacy findings, catalog and lineage gap analysis, and a sequenced roadmap |
| Governance Programme Build | Standing up the operating model, catalog, quality framework, and access controls for a data platform or domain | Operating model and policies, catalog and lineage deployment, quality rules as code with SLOs, access policies as code, and runbooks |
| Master Data & Customer 360 Build | Fragmented customer, product, or supplier data across many systems; personalisation or AI blocked by identity | Matching and survivorship rules, golden-record store, steward workflow, CDC integration to consumers, and reconciliation reporting |
| Managed Data Quality Operations | Lean data teams that need 24×7 coverage for quality, access, and master data controls | SLO-backed monitoring across five quality dimensions, incident response under our severity matrix, steward support, and monthly governance reviews |
Data Quality Operated to the Same Standard as Production Databases
A quality rule nobody is paged for is a quality rule that will fail on the morning the regulator asks. We run governance controls with the operating discipline of our 24×7 Remote DBA practice.
Under our data governance consulting SLA, every governed dataset carries SLOs on five dimensions: accuracy (agreement with the system of record on sampled keys), completeness (reconciled row counts and mandatory-field population), freshness (age of the newest record against its target), consistency (cross-system agreement on shared entities), and uniqueness (duplicate rate after identity resolution). Error budgets are tracked per dataset and reviewed monthly with its owner.
Incidents follow the same severity model as our database support: a regulatory report built on a dataset that failed its completeness check is an S1 with a 15-minute response target; a marketing segment with a freshness breach is an S2. Every incident closes with a root-cause analysis and a preventive action, usually a new rule or a fix upstream. Quarterly, we rehearse the scenarios that matter: a subject access request, a deletion request across every copy, a schema change that silently breaks a masking policy.
- 24×7 monitoring of rule executions, freshness, schema drift, duplicate rate, and policy violations
- Quality trend dashboards per dataset and domain, reviewed monthly with owners and stewards
- Steward queue for match review, survivorship overrides, and definition changes
- Access recertification and audit-evidence packs generated from platform logs
- Retention and deletion jobs executed with verification before and validation after
- Patch and upgrade management for catalog, quality, and policy tooling
Data Governance Consulting Across Regulated and Data-Intensive Industries
Where the cost of a wrong number is a fine, a failed audit, or a customer lost, governance pays back first.
Banking, Payments & FinTech
Customer and counterparty masters, BCBS 239-style lineage and controls, PCI DSS-scoped tokenisation, and evidence packs for regulators. See data engineering in banking and FinTech.
Healthcare & Life Sciences
Patient and provider masters, HIPAA-class access and audit, consent management, and de-identification pipelines for research and AI.
Retail & Consumer Goods
Customer 360 across e-commerce, loyalty, and stores; product masters across channels and suppliers; consent-aware personalisation. See our retail data architecture practice.
SaaS & Technology
Tenant and account masters, usage-data lineage for billing, residency controls, and SOC 2 evidence. See data engineering for SaaS.
Manufacturing & Supply Chain
Material, supplier, and asset masters across ERP, MES, and procurement; quality controls on sensor and inventory data that feed planning.
Insurance
Policyholder and claims masters, regulatory reporting lineage, and privacy controls on health and financial attributes across legacy and modern platforms.
Why Enterprises Choose MinervaDB for Data Governance Consulting
Data governance consulting that lands where it cannot be bypassed
Most data governance consulting stops at the policy and the catalog. Our engineers implement quality checks in the pipelines, access rules in the databases and warehouses, and master data on a transactional store they operate, so the controls run whether or not anyone is watching.
Vendor-neutral, measurement-driven
We sell no catalog, MDM, or quality licences and earn no referral fees. Every recommendation names the metric, log, or system table that justifies it, and we will tell you when a governance product is more than your estate needs and when dbt tests and database constraints will do.
Production posture from day one
Governance changes are treated as production changes. Merges are reversible, deletions carry confirmation gates with verification before and validation after, and every procedure states its blast radius and rollback path before it runs.
Knowledge transfer by default
Operating model, rules, matching logic, policies, and runbooks are documented and handed over. Your stewards and engineers should be able to run what we build; if they choose to have us keep operating it, that is a decision, not a dependency.
"A governance policy nobody can execute is a wish. A quality rule that runs on every load, pages an engineer, and leaves evidence behind is a control."
— The MinervaDB Data Governance TeamHow a Data Governance Consulting Engagement Runs
Discover
Dataset and system inventory, ownership and consumer mapping, measured quality baseline per dimension, access and privacy findings, and regulatory scope.
Design
Operating model, classification and policy set, quality framework with SLO targets, master data matching and survivorship design, catalog and lineage architecture, and a staged plan with rollback.
Build & Validate
Rules, policies, and matching delivered as code; reconciliation against systems of record; steward workflow rehearsed; audit-evidence generation tested before go-live.
Operate & Improve
SLO-governed data quality operations, monthly governance reviews, control tuning from incident history, and knowledge transfer until your team owns the programme.
Data Governance Consulting: Frequently Asked Questions
What does MinervaDB's data governance consulting include?
Governance operating model and policy, data quality engineering with SLOs, master data management and Customer 360, metadata, catalog and lineage, access, privacy and compliance controls, and data observability. Each can be delivered as a bounded assessment, a programme build, embedded engineering, or 24×7 managed data quality operations.
Do we need a data catalog or MDM product before we start?
No. Our data governance consulting starts with ownership, definitions, and quality rules as code on the platform you already run. Many organisations get most of the value from dbt tests, database constraints, OpenLineage, and a golden-record store on PostgreSQL before a catalog or MDM product is justified. When a product is warranted, we say so and recommend one without a commercial interest in the answer.
How do you measure data quality?
On five dimensions per dataset: accuracy against the system of record, completeness of rows and mandatory fields, freshness against a target, consistency across systems on shared entities, and uniqueness after identity resolution. Each has a threshold agreed with the dataset's consumers, runs on every load, and is logged to ClickHouse so trends and audit evidence are queryable.
How does master data management fit into data governance consulting?
In our data governance consulting, master data is governance applied to the entities that matter most: customers, products, suppliers, locations. Identity resolution, survivorship, and the golden-record store are where ownership, quality, lineage, and access controls all meet, which is why we engineer master data inside the same programme rather than as a separate tool project.
Which regulations and frameworks do you design for?
Our data governance consulting designs for GDPR, India DPDP, HIPAA, SOX, PCI DSS, SOC 2, and sector-specific expectations such as BCBS 239-style lineage for banks. Controls are implemented at the database, warehouse, and pipeline layer so evidence is generated from platform logs rather than assembled by hand before an audit.
Why does data governance matter for AI programmes?
Models and retrieval systems inherit every defect in the data they are built on, and regulators increasingly expect lineage and access controls on AI inputs. Our enterprise generative AI, MLOps, and decision intelligence practices all depend on the governance controls described here, which is why data governance consulting is usually where an AI programme should begin.
Let's Make Your Data Something You Can Trust
Talk to a MinervaDB principal consultant about the dataset, the audit, or the master data problem that is holding your programme back. The first conversation is always with an engineer, never a salesperson.
Schedule a Consultation → Download the MinervaDB Corporate Flyer (PDF) →Data Governance Resources from MinervaDB
- Data Engineering Consulting and Managed Pipelines
- Database Security Services
- Data Strategy and Analytics
- Fractional Chief Data Officer
- Decision Intelligence Consulting
- MLOps Consulting
- PostgreSQL Consulting
- Data governance (Wikipedia)
- OpenLineage documentation
- Splink probabilistic record linkage documentation