Your data estate grew faster than your architecture
Most enterprise data landscapes weren't designed — they accumulated. A warehouse for finance. A lake for the data science team. Extracts feeding a reporting layer. Point-to-point pipelines built to solve a problem three years ago that nobody has revisited since.
Each addition was individually reasonable. Together they produce a landscape where the same question returns different answers depending on who asks it, where onboarding a new analyst takes weeks, and where nobody can say with confidence who has access to what.
This is where AI initiatives quietly fail. Not on the model, but on the data underneath it — inconsistent, ungoverned, and impossible to trace back to a source anyone trusts.
Governance isn't the brake on AI. It's the accelerator
Governance usually gets framed as the thing that slows delivery down — a review board, a checklist, a reason your request takes six weeks. That framing is what produces shadow data estates and duplicated extracts.
A lakehouse built on Unity Catalog inverts it. When access control, lineage, and audit are properties of the platform rather than processes bolted around it, you can open data up to more people, not fewer. Analysts self-serve because permissions are enforced automatically. Auditors get lineage on demand instead of a two-week reconstruction exercise. Data scientists move models to production because the governance question was answered before they started.
Fast and governed aren't a trade-off here. Ungoverned is what eventually makes you slow.
Key capabilities, integrated into a single platform
Databricks replaces the warehouse, the data lake, and everything you built to move data between them with one governed platform.
Ingest
Get everything in one placeBring in batch and streaming sources — ERP, CRM, operational databases, SaaS platforms, event streams, files — through managed connectors and Lakeflow pipelines. Land data once, in an open format, without building a bespoke integration for every source.
Structure
Make it reliableApply a medallion architecture so raw, cleansed, and business-ready data are clearly separated. Delta Lake brings transactional guarantees, schema enforcement, and versioned history, so a bad load is a rollback rather than an incident.
Govern
Make it trustworthyCentralize access control, lineage, and audit in Unity Catalog. Row- and column-level policies, data classification, and a single catalog spanning workspaces and clouds — mapped to your actual regulatory obligations, not a generic template.
Activate
Make it usefulPut governed data to work through AI/BI dashboards, SQL warehouses, and production ML. The same permission model follows the data into every dashboard and model, so opening access doesn't mean widening exposure.
Why VMG's approach works
Architecture-led, not tool-led
We start with your data landscape, your regulatory obligations, and your transformation roadmap — then design the platform around them. Databricks is a capable engine; the value comes from how it's architected against your specific constraints.
Registered Databricks partner
We're a registered Databricks partner with hands-on delivery experience across lakehouse architecture, Unity Catalog, and production data engineering.
Enterprise systems depth
Most data platform problems are really integration problems. Our consultants average 20+ years in enterprise systems — ERP, finance, logistics, HCM — so we understand what your source data actually means before we start modelling it.
Pragmatic delivery
Start with the highest-value use case and a foundation sized to support it. Prove value, then expand. We'd rather have one governed workload in production in eight weeks than a complete platform nobody's using in twelve months.