Commercial Data
Pipeline, revenue, and retention numbers that survive a finance review.
Forecasts fail when CRM stages, billing actuals, and product usage cannot be joined on a shared grain. Revenue intelligence is a data-product problem before it is a dashboard problem.
Revenue meetings spend their time reconciling systems, not making decisions.
CRM pipeline, ERP bookings, billing, and product usage each tell a locally true story. Without a governed commercial model, leadership cannot see conversion, margin, or retention the same way twice.
Pipeline is a CRM artifact
Stage definitions and close dates do not match how revenue is recognized or collected.
Bookings vs. billings vs. revenue
Commercial teams and finance use the same words for different facts.
Retention is reconstructed monthly
Churn and expansion require a hero spreadsheet because product and billing are not modeled together.
Outcomes
A commercial semantic layer
Pipeline, bookings, revenue, margin, and retention defined once and reused everywhere.
Forecast inputs that can be audited
The data behind a forecast is lineage-visible, not assembled the night before.
Faster commercial reporting
Operating reviews use the same gold tables as executive reporting.
How the architecture works
01
Commercial sources
CRM, billing, ERP, and product usage mapped to a shared account and time grain.
02
Metric contracts
Stage, amount, date, and recognition rules documented as data products—not as dashboard filters.
03
Consumption
Databricks SQL, AI/BI, and (where appropriate) Genie on the same governed model.
What we implement
- Pipeline and bookings models
- Revenue and margin data products
- Retention and expansion metrics
- Forecasting datasets
- CRM-to-finance reconciliation design
- Executive commercial dashboards
Where this shows up
SaaS revenue operations
ARR, NRR, pipeline coverage, and cohort retention on a governed lakehouse model.
Multi-channel commercial reporting
Direct, partner, and digital channels rolled up without losing the source grain.
What should be measured
The business case is built on a baseline, not a promise. These are the numbers this solution is accountable to.
- Forecast accuracy vs. actuals
- Time spent reconciling commercial numbers
- Pipeline coverage confidence
- Retention and expansion visibility by cohort
The first sensible pilot
One commercial metric contract in production
Model pipeline-to-revenue for one segment with agreed stage, amount, and recognition rules — and run one forecast cycle on it.
Questions
Will this replace our CRM reports?
CRM operational reports can remain. The lakehouse model becomes the place pipeline is combined with billing and product facts. That is usually what leadership actually needs.
Do you forecast for us?
We build the trusted datasets a forecast requires. Statistical or ML forecasting is a later workload on that foundation—not a substitute for it.
Related
Databricks SQL & AI/BI
Building Trusted Executive Analytics on Databricks
Executives do not need more dashboards. They need a small number of numbers that survive contact with finance, operations, and the next question.
9 min
Start with the business case
Find the first data or AI opportunity worth proving.
We evaluate the business problem, systems, data, architecture, and economics behind it—then identify the smallest production engagement capable of proving whether the opportunity is real.
Business case first · Architecture-led · Production-focused