A Familiar Scene
A global manufacturer greenlights a seven-figure analytics investment. Eighteen months later the Databricks workspace is live, the Tableau dashboards are polished, and the ingestion pipelines run on schedule.
And the VP of Supply Chain still pulls numbers from a shared drive every Monday morning.
This is not a technology failure. The pipelines work. The data is clean. The dashboards render. The problem is upstream of all of that — the system was designed by people who understand data platforms but not the decisions those platforms are supposed to support.
The Domain Gap
Building a PSI reporting layer for a medical device operation is not a generic BI problem. A finished-goods PSI model tracks fundamentally different signals than a raw-materials model. MEIO safety-stock targets only mean something if you understand how they feed back into planning decisions downstream. The difference between an inventory metric a regional planner checks at 7 AM and one a divisional VP reviews at quarter-end is not cosmetic — it changes the grain of the data, the refresh cadence, and the alert thresholds.
None of this is documented in a requirements spreadsheet. It lives in the heads of the operators who run the business. And if the analytics team never sits in those rooms, the dashboards they build will be technically correct and functionally useless.
Three Patterns That Predict Failure
1. The handoff model. A consulting firm interviews stakeholders for four weeks, writes a design spec, and passes it to an offshore implementation team. By the time the dashboards are delivered, the business has moved on. The spec was already stale when the first line of SQL was written.
2. The tool-first pitch. A vendor leads with platform capabilities — data pipeline architecture, Unity Catalog governance, real-time streaming — and works backward to find a business problem that fits. The result is technically impressive and strategically irrelevant.
3. The rotating team. Consultants cycle through on six-month assignments. Each rotation restarts the learning curve. Institutional knowledge walks out the door every time a badge is deactivated.
What Actually Works
The analytics projects I have seen succeed — the ones where adoption holds and the investment compounds — share a common structure:
The builder understands the decisions. Not just the data model. The actual operating decisions the data needs to support. Which planning meeting the metric appears in. What action a planner takes when a threshold is breached. Why a 3% variance in one SKU category triggers an escalation and a 10% variance in another does not.
The engagement is continuous. Domain expertise is not transferable via documentation. The person who built the first model needs to be in the room when the business outgrows it. Long-term relationships compound. Short-term engagements depreciate.
Implementation and strategy are the same person. The architect builds. The builder advises. There is no translation layer between the whiteboard and the code. When the VP of Supply Chain asks a question in a review, the person who answers it is the same person who wrote the query.
Closing the Gap
This is the thesis behind Summit Analytics — not that Databricks needs better implementation partners, but that enterprise supply chains need analytics operators who have spent years inside the business. People who know that the real deliverable is not a dashboard. It is a decision made faster, with more confidence, using data that was organized by someone who understood why it mattered.
If your organization has made the platform investment and is still waiting for the business value — that gap is where we work. Let's talk about what's getting in the way.