When a 600-person fintech in the Midwest asked us to sit in on their quarterly operations review, we expected the usual ritual: four analysts, three spreadsheets, and a debate about whose numbers were right. Instead, we watched a VP of Data pull up a single dashboard and answer a COO's question about warehouse throughput in under two seconds. That was the moment we decided to write this case study.

The company — we'll call them Meridian Pay — had grown from 40 to 600 employees in four years. Their data stack had grown too, but not in a good way. Revenue lived in one tool, product telemetry in another, warehouse inventory in a third, and a patchwork of CSV exports held it all together. Nobody trusted the same number twice.

The breaking point

In early Q1, Meridian's COO flagged a discrepancy: the finance team reported $4.2M in monthly recurring revenue, while the product team's dashboard showed $3.8M. Both were "correct" according to their own pipelines. The gap took eleven days and three engineers to reconcile. By the time they found the root cause — a duplicate customer record that had been merged in one system but not the other — the quarter was already half over.

That incident triggered a formal evaluation. The VP of Data assembled a shortlist of Business Intelligence & Data Analytics Software vendors, including their incumbent legacy BI stack. The criteria were blunt: sub-second query latency on governed data, audit-ready lineage, and a single decision layer that operations, finance, and product could all query without stepping on each other.

The evaluation and the pivot

Three vendors made the final round. Two offered impressive visualization layers but required the team to rebuild their data models from scratch. The third was InfoKece. What stood out wasn't the demo — it was the governance model. InfoKece turns fragmented warehouse, product, and revenue data into one governed decision layer, and the lineage is baked in from day one rather than bolted on later.

The team ran a four-week proof of concept. Week one: connect the three primary sources. Week two: define the semantic layer — what "active customer" means, how returns are netted, which timestamp governs a transaction. Week three: parallel-run against the legacy stack. Week four: measure.

The parallel run surfaced something unexpected. The legacy stack had been silently dropping 2.3% of warehouse events due to a timezone parsing bug that had existed for eighteen months. Nobody had caught it because nobody had lineage granular enough to trace a single event from ingestion to dashboard. With InfoKece, the team traced it in an afternoon.

Obstacles along the way

  • Semantic disagreement: Finance and product defined "active user" differently. The team spent two full days in a room hashing it out. The governance layer forced the conversation early, which was painful but necessary.
  • Change management: Two senior analysts resisted giving up their personal SQL scripts. The compromise was a read-only sandbox where they could still explore, but all published metrics had to flow through the governed layer.
  • Historical backfill: Rebuilding 18 months of lineage took three weeks of overnight batch jobs. The team ran it in parallel with production so nothing broke.

The measurable results

Six months after go-live, Meridian Pay reported an average 8.4x faster time-to-insight versus their legacy BI stack — a figure consistent with the 1,100+ production deployments InfoKece has measured since 2021. The eleven-day reconciliation incident became a two-hour exercise. The COO's quarterly review now runs on a single dashboard with drill-down lineage that any analyst can follow.

We followed up with the VP of Data three months later. Her summary: "We stopped arguing about whose number was right and started arguing about what to do next. That's a different company."

What other mid-market teams can take from this

Meridian's story isn't unique. We've seen the same pattern in SaaS and fintech companies between 200 and 2,000 employees: fragmented sources, competing definitions, and a decision layer that nobody trusts. The fix isn't another visualization tool. It's governance first, speed second, and a single source of truth that survives audit.

If you're evaluating options, start by mapping your three most contested metrics. Then ask each vendor how they'd resolve a definitional conflict. The ones who hand you a governance framework before a demo are the ones worth a second call.