Data Governance Services
Know what data you have, who can use it and whether you can trust it.
Stack we work in
Microsoft Fabric
dbt- Apache Airflow
Power BI- Terraform
- GitHub Actions
- Python
- Projects delivered
- 680+
- Clients served
- 310+
- Client retention
- 98%
- Certified engineers
- 20+
- Building since
- 2018
Figures as of Oct 2026
Trusted by
- Aralia
- Rosa Pistrina
- Lashbo
- Micromunchi
- Payroll Ninja
- POD
- Petdx
- Yokey
- Spin Up
- Starwear
- Udlins
- Vegpal
- Wander Wings
- Wish It
- Wisl
Signs your data needs governance
We put data governance in place on the platforms you already run.
Read the full overview
Our data governance services cover data catalogs and business glossaries, lineage from source to report, role-based and attribute-based access control, data quality testing, and classification of personal and sensitive data. We work with Microsoft Purview, Databricks Unity Catalog, Snowflake Horizon and Google Cloud Dataplex, add data quality checks with dbt tests, Great Expectations, Soda and Elementary, and capture lineage with OpenLineage. Where you use Collibra or Alation, we integrate them with your platform. Policies, ownership and stewardship are written down and built into pipelines, so governance supports your GDPR obligations without slowing delivery.
Governance problems rarely announce themselves. They show up as slow answers, mistrust and audit stress.
No one knows which table is right
Several versions of the same customer or revenue table exist, and each team trusts a different one.
Access granted by chat message
Permissions are given to individuals on request and are never reviewed or removed.
Personal data in unknown places
Copies of customer data sit in sandboxes, exports and spreadsheets that no one tracks.
Users find the data errors
Quality problems are spotted in dashboards by the business, not caught in pipelines.
Audit questions take weeks
Showing who accessed what, or where a figure came from, needs manual digging through logs and code.
Outcomes
What you get
- A searchable catalog where every important table has an owner and a description
- Lineage that shows which reports are affected when a source changes
- Access granted by role and policy, with sensitive columns masked
- Data quality tests that run with every pipeline and alert the owner
- Written policies and evidence that support GDPR and audit requests
What we deliver
What you receive from a governance engagement
Governance operating model
Roles for owners and stewards, decision rights and review cycles, written down and agreed.
Catalog and glossary
Important tables described, owned and searchable, with business terms linked to the data behind them.
Access and masking policies
Groups, roles, row filters and column masks defined as code and applied on your platform.
Classification and PII inventory
Sensitive data found, tagged and tied to the policies that protect it.
Data quality framework
Tests, thresholds, alerts and owners for your critical data, running in every pipeline.
Lineage and documentation
Lineage from source to report, with policies and runbooks your team can maintain.
Catalogs & lineage
- Data catalog and business glossary setup
- End-to-end lineage with Unity Catalog, Purview and OpenLineage
- Metadata and documentation with dbt docs
- Collibra and Alation integration
Built with
- dbt
- OpenLineage
- Collibra
- Alation
Access & privacy
- Role-based and attribute-based access control
- Data classification and PII discovery
- Column masking and row-level security
- GDPR support: retention, deletion and access logging
Quality & stewardship
- Data quality rules with dbt tests, Great Expectations and Soda
- Data observability and alerting with Elementary
- Data ownership, stewardship and governance policies
- Governance on Microsoft Purview, Unity Catalog, Snowflake Horizon and Dataplex
Built with
- Microsoft Purview
- Snowflake Horizon
- dbt
- Great Expectations
- Soda
- Elementary
- 01StoreWarehouse and lakehouse
- Databricks Unity Catalog
- Snowflake Horizon
- Microsoft Fabric
- 02TransformModels and tests
- dbt
- Great Expectations
- Python
- 03OrchestrateScheduling, CI/CD, catalog
- Apache Airflow
- Microsoft Purview
- Terraform
- 04ServeBI, semantic layer, activation
- Power BI
Why teams choose Equitysoft for data governance
On the tools you already run
We implement governance in Microsoft Purview, Unity Catalog, Snowflake Horizon or Dataplex before suggesting another product.
Controls as code
Access policies, masking rules and data quality tests live in version control, so they are reviewed and repeatable.
One domain at a time
We start with your most critical data and one domain, so governance shows value without slowing the whole team.
Your privacy team sets the rules
We implement the policies your privacy and legal teams agree, under NDA. 50+ engineers, 20+ of them certified on cloud and data platforms, since 2018.
How we work
Assess
We map your data, platforms, current access, existing policies and regulatory requirements, and find the biggest risks.
You receive Governance assessment and gap list
Prioritize
We start with the data that matters most, such as customer, finance or patient data, rather than trying to govern everything at once.
You receive Critical data and first domain agreed
Design
Owners, stewards, decision rights, access policies and sensitivity labels are agreed with your business and privacy teams.
You receive Operating model, policies and classification scheme
Implement
We configure the catalog, roles, masking, row filters and lineage capture, with policies defined as code where the platform allows.
You receive Catalog, access policies and lineage on your platform
Test and monitor
Quality rules run in pipelines and results go to the data owner, not just the engineering team.
You receive Data quality tests and alerts
Embed and support
We train owners and stewards, set review cycles for access and quality, and extend governance to further domains.
You receive Steward training and runbooks
Tech stack
Technologies we use
Data platforms
- Microsoft Purview
- Databricks Unity Catalog
- Snowflake Horizon
- Microsoft Fabric
Pipelines and orchestration
- dbt
- Great Expectations
- Apache Airflow
BI and analytics
- Power BI
Languages
Cloud and DevOps
Other tools
Engagement models
How we can work together
Project-Based Delivery
A defined scope, timeline and deliverables, from design through go-live.
Fixed-Price Implementation Projects
An agreed price for a clearly scoped implementation, such as a migration or a new warehouse.
Contract and Subcontract Engineering
Our engineers work under your contract, or as a subcontractor to consulting firms and system integrators.
Dedicated Data and AI Engineers
Data engineers, data scientists and analysts who join your team and work in your tools and processes.
Remote Consulting and Development
Consultants and developers working remotely, with overlap in your working hours.
Architecture and Technical Advisory
Architecture reviews, platform choices and design guidance for your own team.
Production Support and Maintenance
Monitoring, incident handling and ongoing fixes for the pipelines, models and dashboards you run in production.
Proof of Concept and Assessments
A short, fixed-scope engagement to test an approach on your data, prove a model or plan a migration.
Questions, answered
With the data that carries the most risk or value, usually customer, finance or employee data, and with named owners for it. Governing one domain well and then extending it works better than a company-wide program that never reaches production.
Microsoft Purview, Databricks Unity Catalog, Snowflake Horizon, Google Cloud Dataplex and AWS Lake Formation for platform governance; dbt tests, Great Expectations, Soda and Elementary for data quality; OpenLineage for lineage; and Collibra or Alation where you already use them.
Not always. If most of your data sits on one platform, its own catalog (Unity Catalog, Purview, Horizon or Dataplex) is often enough. Enterprise catalogs help when data is spread across many platforms and business teams run stewardship. If you already have one, we connect your platform to it.
We combine the classification scanners in Purview, Snowflake, Dataplex and Unity Catalog with pattern rules for your own identifiers. Data owners review the results, and the resulting tags drive masking and access policies.
Rules for completeness, uniqueness, valid values, freshness and reconciliation run inside pipelines with dbt tests, Great Expectations or Soda. Critical failures stop the load; others raise a warning. Elementary or similar tools show results and trends to data owners.
We implement the technical controls GDPR relies on: classification, access control, masking, retention and deletion jobs, and access logs. Interpreting the regulation and signing off compliance stays with your data protection officer or legal adviser.
We use the lineage built into your platform where it exists, and OpenLineage events from Airflow, Spark and dbt to connect steps across tools, so you can trace a report figure back to its source.
It should not. Access is granted through groups and standard requests, policies and tests live in code next to the pipelines, and checks run automatically. Engineers get fewer ad hoc questions about where data came from.
Your data owners and stewards. We train them, document the policies and processes, and leave runbooks for access reviews and quality issues. Ongoing support is available if you want it.
An assessment takes a few weeks. Governance for a first domain usually follows within a few months, and further domains are added in waves. Availability of business owners to make decisions often sets the pace.
It depends on scope, mainly the number of platforms, data domains and policies involved. An assessment or a first domain can be fixed price, and ongoing work suits time and materials. We send a written estimate with its assumptions after a first call. As a guide, an assessment usually takes 2 to 3 weeks and costs $3,000 to $10,000, and a first production phase takes 6 to 10 weeks and costs $15,000 to $60,000 (USD).
More about our Data Governance
Working togetherHow we work with your team
Business owners from day one
Governance needs decisions from the people who own the data, so we agree owners before building anything.
Your privacy team sets the rules
We implement the controls; interpreting regulation stays with your data protection officer and legal team.
Controls as code
Access policies, masks and quality tests are versioned and reviewed like any other change.
One domain at a time
Each domain is governed end to end before the next starts, so value shows early.
Where data governance matters most
Financial services
Access reviews, audit trails and lineage from source systems to regulatory and management reports.
Healthcare
Classification and masking of patient data, with access limited by role, in line with HIPAA, GDPR and local privacy rules.
Retail and ecommerce
Customer consent, retention and deletion applied consistently across orders, marketing and CRM data.
SaaS
Tenant separation, customer data deletion on request and evidence for security questionnaires.
Talk to a data governance engineer
Tell us which data you need to control and the rules it has to follow.