Since 2011, Bacancy Technology has delivered data engineering projects for organizations across healthcare, insurance, banking, and finance industries. Over the past few years, data platforms like Databricks have become a regular part of that work as more clients move away from legacy platforms and invest in analytics and AI.
During these Databricks engagements, we have seen most of our discussions circle around data governance. Not because Databricks falls short of its capabilities, but because once the platform starts bringing together data from different systems and different teams, the questions change. It’s no longer just about pipelines or performance. It’s about who owns the data, who can rely on it, and who decides how it’s used.
This article brings together the Databricks data governance practices we recommend most often based on our experience delivering enterprise data projects across regulated industries.
Top 7 Best Practices Bacancy Technology Recommends for Databricks Data Governance
Here are the seven key best practices recommended by Bacancy Technology for clients aiming for Databricks data governance. Each of these best practices is derived from experience delivering Databricks consulting and implementation across banking, finance, insurance, and healthcare clients.
1. Establish Data Ownership at the Start of Your Databricks Project
One of the first governance decisions we help clients make has nothing to do with the Databricks platform itself. It’s deciding who owns the data.
This usually becomes visible once more business teams start using the platform. The data is available, dashboards are running, and new use cases keep getting added. Then someone questions a number in a report or requests a change to a shared dataset, and it becomes clear that ownership was never formally defined.
We have seen this with many of our insurance clients where claims, policy, and customer data were shared across underwriting, fraud, finance, and reporting teams. Everyone depended on the same datasets, but ownership wasn’t always clear.
Today, before we define any data catalogs or access policies, we recommend identifying business owners for the datasets that are key for the business. Databricks data governance becomes much easier when accountability is established before the platform grows.
2. Define Governance Boundaries Before Onboarding More Teams
One lesson we at Bacancy Technology have gained from Databricks implementations is that governance decisions are much easier to make before the platform starts serving multiple teams. We’ve had multiple experiences where changing catalog structures, data domains, or access models later meant revisiting reports, pipelines, and permissions that were already being used across the business.
Here is a very recent example. A US based banking client initially implemented Databricks for regulatory reporting before expanding the platform to risk and customer analytics. As additional teams came on board, the same customer data started appearing in multiple catalogs with different business definitions. Aligning those datasets and updating access policies after reports were already in use took more effort than if those governance boundaries had been defined during the initial rollout.
We’ve taken that lesson into every Databricks project since. Even if the first rollout involves only one or two business teams, we encourage clients to define governance boundaries with future expansion in mind. It has made onboarding new teams much simpler and avoided governance decisions that become far more difficult to revisit later.
3. Build Role-Based Access Around Business Functions, Not Individual Users
Role-based access control is one of the most important parts of Databricks data governance, but we’ve found that many organizations don’t fully benefit from it because they continue managing permissions one request at a time.
Across our healthcare, insurance, banking, and financial services projects, we’ve seen Databricks environments quickly grow beyond the original implementation team. Data engineers, analysts, data scientists, compliance teams, fraud analysts, and business users all begin working on the same platform, but they rarely need the same level of access to data.
Instead of assigning permissions directly to individual users, we recommend defining business roles first and mapping those roles to Unity Catalog groups. For example, an underwriting team may need read access to curated policy datasets, while fraud teams require additional access to claims data. Data engineering teams may manage ingestion pipelines without requiring access to business-facing Gold datasets, and data scientists may need controlled access to feature engineering environments without exposing sensitive production data.
This approach has made governance significantly easier for our clients as their Databricks environments expanded. New users are added to existing business roles instead of receiving custom permissions, access reviews become much simpler, and security teams can enforce consistent policies across hundreds of users without continuously redesigning access rules.
4. Classify Sensitive Data from the Start
Data classification became a much bigger discussion in our Databricks projects once clients started expanding beyond a few reporting use cases.
Healthcare organizations wanted patient data to support research without exposing unnecessary PHI. Insurance clients wanted claims and policy data to be available for fraud analytics while keeping personally identifiable information protected. Banking teams faced similar discussions around customer and transaction data as more analytics and AI use cases were introduced.
What we’ve learned is that these decisions are much easier when datasets are classified before they’re published through Unity Catalog. Once teams begin building dashboards, machine learning models, or AI applications on top of shared datasets, changing masking policies, access rules, or approval processes becomes considerably more disruptive.
Today, we encourage clients to classify sensitive datasets as they’re brought into Databricks rather than after new business use cases emerge. That single decision influences everything that follows, from RBAC and dynamic masking to audit policies and AI governance.
5. Maintain Visibility Through Audit Logging and Monitoring
In healthcare, banking, and insurance projects, audit requirements often become more detailed as Databricks adoption expands. Teams are not only concerned about who has access to data, but also whether they can trace how that data was accessed and used.
We have seen this become important when multiple teams work with the same sensitive datasets. For example, a healthcare organization may have analysts, operations teams, and research groups using patient-related datasets, while a financial institution may have different teams accessing customer and transaction data for reporting, risk, and analytics.
In these environments, we recommend setting up audit visibility early through Databricks audit logs and monitoring capabilities. Access history, permission changes, and user activity should be available when security or compliance teams need to review a dataset or investigate an unexpected access pattern.
For regulated organizations, this visibility becomes especially valuable as the platform grows. It helps teams answer compliance questions without relying on manual tracking or reconstructing access history after the fact.
6. Separate Workloads Based on Data Sensitivity and Usage
As organizations expand their Databricks adoption, a single platform often starts supporting multiple types of workloads. Development, analytics, machine learning, and production reporting may all operate within the same environment.
Across our healthcare and financial services projects, we have seen the need for clearer separation between workloads become more important as more teams start experimenting with new use cases. The access requirements for a data scientist testing a model are different from those for a production reporting environment handling regulated information.
We recommend defining workload isolation strategies based on data sensitivity, user groups, and business requirements. Workspace separation, compute controls, and appropriate permission models help organizations maintain security without limiting teams from building new solutions.
If your Databricks platform is expected to support analytics, machine learning, and AI alongside regulated data, you should hire Databricks developers with experience designing workload isolation strategies. Determining where workspace boundaries should exist, which workloads can safely share infrastructure, and where governance controls need to differ often requires implementation experience that’s difficult to gain from a single deployment.
7. Extend Governance to AI Workloads Early
In our recent Databricks projects, we’ve spent just as much time discussing AI governance as building AI workloads themselves.
Across healthcare, insurance, banking, and financial services, many organizations are building machine learning models, retrieval pipelines, and generative AI applications on top of governed enterprise data. That naturally raises a different set of governance decisions. Not every governed dataset is suitable for AI, and not every team building AI should have the same level of access to sensitive business data.
We’ve worked with clients who needed to determine which datasets could be used for model training, how personally identifiable information should be handled before data entered AI workflows, and who should approve the use of governed datasets for AI initiatives. These are governance decisions that are much easier to address before AI development begins than after models have already been built.
Our recommendation is to treat AI governance as an extension of your Databricks data governance strategy. Ownership, data classification, lineage, access controls, and approval processes should evolve alongside AI workloads so that new initiatives can move forward without creating additional governance challenges.
Final Thoughts
Databricks gives organizations a powerful foundation for analytics, machine learning, and AI. Building the data architecture on the platform is only one part of the work. Keeping data trusted, governed, and manageable as more teams begin using it is what determines whether that foundation continues to support the business over time.
Working across healthcare, insurance, banking, and financial services has shown us that every organization approaches governance differently, but the fundamentals remain consistent. Clear ownership, practical governance processes, trusted data, and business involvement make a measurable difference long after the platform goes live.
These recommendations reflect the approaches we’ve found most effective across our Databricks engagements, and they continue to shape how we design governed data platforms for our clients today.
Author Bio
Chandresh Patel is a CEO, Agile coach, and founder of Bacancy Technology. His truly entrepreneurial spirit, skillful expertise, and extensive knowledge in Agile software development services have helped the organization to achieve new heights of success. Chandresh is leading the organization into global markets systematically, innovatively, and collaboratively to fulfill custom software development needs and provide optimum quality.
