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woodfrog
Data Foundations
Regulated and data-sensitive companiesUse case on Databricks, no client result claimed

Know who can see what, and where it came from.

Data is spreading across teams and tools, and nobody can say who has access to what or where a number came from. We put governance on the lakehouse itself, from the first table.

On DatabricksSchematic
Tables, files and models across teams
Databricks with Unity Catalog governance
Access, lineage and audit, by table and column
RegisteredDatabricks partner, in Pune
CertifiedDatabricks practitioners on the team
6 weeksMedian to a first production result
Week 1Fixed-fee audit
TypeUse case: what we build on Databricks, not a client story
ForCompanies with personal or sensitive data, an audit to pass, or several teams sharing one lakehouse
EngagementWeek 1 audit, then build, fixed scope, fixed fee
Runs onDatabricks with Unity Catalog, on your cloud
Before and after

From the old way to the new.

Before
  • Access granted by whoever asked loudest
  • No record of where a figure came from
  • Personal data copied into places it should not be
  • An audit means weeks of collecting screenshots
After
  • Access by role, at table and column level, written down
  • Lineage from the number back to the source
  • Personal data masked at the query, not copied around
  • An audit trail you can export in an afternoon
The problem

More data, more people, and no map of who sees what.

Governance is usually added after the lakehouse works, and by then access has grown by habit. People copy tables to avoid asking, personal data leaks into test areas, and nobody can answer the auditor's first question: who can see this, and why?

A phone wrapped in a chain and padlock
Access that is written down is access you can audit
What was broken
  • Access rules live in tickets and memory, not in the platform.
  • Nobody can trace a dashboard figure back to its source.
  • Personal data is copied into sandboxes and spreadsheets.
  • Different teams define the same field differently.
  • Audits are manual and slow.
You might be here if
  • Someone asks who can see a table and nobody knows.
  • A new starter is given access to everything for convenience.
  • You hold personal data and are unsure of your duties under the DPDP Act.
  • Sharing data with a partner feels risky.

Why it mattered. A governance gap is a risk that grows silently, and fixing it after an incident costs far more than building it in.

The approach

Put the rules in the platform, not in a document.

We start with the few decisions that matter: who needs which data, what is personal, and what must be provable. Then we encode them in Unity Catalog: roles, column rules, masking and lineage. The rule runs on every query, so it cannot be forgotten.

Where it is today
Access by request
Data copied for convenience
Manual audit evidence
woodfrog method
Classify the dataWhat is personal or sensitive
Encode access and maskingRoles and column rules
Capture lineage and auditAutomatically, on every query
What changes
Access by role, in the platform
Masking at the query
An audit trail on demand
Governance that runs by itself
Deliberately not doneA policy document nobody reads. The rules run in the platform, on every query.
Your data owners decide the rules. We build them and show they hold.schematic
The solution

Rules in the platform, enforced on every query.

What we build
  • A data classification: what is personal, sensitive or open, owned by named people.
  • Unity Catalog set up with catalogs, schemas and roles that match how your teams work.
  • Column-level access and masking for personal and sensitive fields, enforced on the query.
  • Lineage from source to dashboard, so any figure can be traced.
  • An audit log of who read what, ready to export.
  • A short runbook so your team can grant, review and revoke access safely.
It runs on Databricks with Unity Catalog in your own cloud. The rules are in the platform, so they apply to every tool that reads the data.
How it would runWeek 1 ends with the data profiled and the scope fixed. Operate is open-ended and drawn at one week only to fit.
The impact

What we would measure.

What we would measureNo client values yet
Tables with a named owner and classificationCounted
Personal fields masked at the queryShare of the total
Time to answer "who can see this?"Before and after
Time to produce audit evidenceBefore and after
Access reviews completed on timeEach quarter
Put governance on your lakehouse.A short call, or record a message. A person replies within one business day.
Contact usCertified Services Partner, Claude Partner Network