Ten departments. One version of the truth.
Finance tracked revenue. Sales tracked pipeline. Product tracked usage. Support tracked issues. Every department held a piece of the customer — and none of them could see the whole. Leadership was making decisions while simultaneously arguing about whether the numbers they were looking at meant the same thing.
10+
Teams on one layer
Contributing and consuming from the same source
↓
Churn rate
Usage-based early warning enabled timely intervention
↑
Marketing ROI clarity
Campaign → purchase → retention lineage preserved
A large enterprise software company had data in every department and visibility in none of them. A customer churned. Finance saw the revenue loss. Sales saw the contract end. Support saw three unresolved tickets from six weeks earlier. Product saw usage drop off ninety days before the cancellation. Nobody saw all four together — and so nobody acted on the signal that was there the whole time. Beyond the visibility problem was a measurement problem: KPI definitions varied by team, geographic hierarchies were defined differently, and every cross-functional meeting began with a negotiation over which version of the numbers was right.
The insight
“Before you can build a data culture, you have to build data infrastructure. Not a dashboard. A principled, maintained layer that defines what the data means, how it connects, and who can see what — at a cadence that makes it operationally useful.”
A multidimensional data layer connecting ten-plus departments at a daily cadence — a single source of truth that the silos fed into, not a reporting layer on top of them
A complete customer journey map from marketing through retention, with defined cardinality at every touchpoint — campaign to purchase to usage to support to cancellation attempt, all tied to the same customer record
An org-wide KPI taxonomy — one definition per metric, owned, versioned, and accessible to anyone in the organisation, eliminating the competing definitions that had made cross-functional decisions unreliable
Marketing spend became attributable end-to-end for the first time. Product usage data surfaced as a churn early-warning signal — customers who reduced usage significantly were disproportionately likely to cancel, and that pattern became actionable before cancellation was initiated. Data became a culture, not a department.
“Most organisations have a data problem that looks like a technology problem. It isn't. It's an architecture problem — who owns which data, how it connects, what it means, and at what cadence it needs to be fresh. Solve the architecture and the technology becomes straightforward.”
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