Highly commended in EMEIA-wide analytics competition
Defined as global best practice for sell-side deal analytics
New analysis turnaround reduced from days to hours
Automated reconciliation eliminated duelling numbers
Built a live, self-serve data visualisation platform on Tableau for a sell-side due diligence process involving an international car maintenance provider. The platform unified ERP data from multiple legal entities into a reconciled star schema, giving the deal team, leadership, and prospective buyers interactive access to commercial performance with every figure tied to audited financials.
The Problem
When a business goes to market, the deal team and prospective buyers need to understand commercial performance quickly, clearly, and defensibly. For this international car maintenance provider, the raw material sat in ERP systems spread across multiple legal entities, geographies, and product hierarchies. Field definitions varied between entities, product categorisations were inconsistent, and revenue recognition rules differed. The finance team could produce audited totals, but breaking those down into the granular, interactive views buyers expect was a different challenge entirely.
The Solution
A live data visualisation platform was built on Tableau, underpinned by a purpose-built data layer that transformed raw ERP extracts into a curated, reconciled foundation. The platform gave the deal team, senior leadership, and prospective buyers interactive access to revenue, margin, customer, and product performance across every region, site, and hierarchy level.
The work had two distinct halves. First, a data engineering effort to extract, clean, standardise, and model the ERP data into a star schema with agreed definitions. Revenue rules, cost allocation, product hierarchies, and customer groupings were documented and signed off by the finance team before any dashboard was built. This step alone eliminated the largest source of confusion in prior deal processes. Automated reconciliation checks ran against audited finance totals, catching data quality issues early and giving the deal team an unambiguous answer to the question every buyer asks: "Do these numbers tie to the accounts?"
Second, a visualisation layer of Tableau dashboards structured around three modes of use: regional and site-level performance views with drill-downs; product and customer analysis showing margin contribution, concentration, and trends; and investment narrative story views that functioned like an interactive slide deck, letting the deal team present the equity story live with on-the-fly drill-downs. Adding new analytical views mid-process required only a new dashboard pointing at the existing model, not a fresh data engineering cycle.
Results and Impact
| Metric | Outcome |
|---|---|
| Industry recognition | Highly commended in an EMEIA-wide analytics competition |
| Practice adoption | Elements defined as global best practice for sell-side engagements |
| Ad-hoc request reduction | Self-serve drill-downs removed the majority of analyst bottleneck queries |
| Reconciliation confidence | Automated tie-out to audited financials eliminated "duelling numbers" |
| Time to new analysis | Reduced from days to hours by building on the existing semantic layer |
| Stakeholder reach | Deal team, leadership, and buyers all used the same platform with the same numbers |
The platform did more than serve a single transaction. The data model and dashboard templates became reusable assets, compressing timelines and reducing the risk of early-stage data errors on subsequent engagements.
Key Takeaways
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Speed matters, but trust matters more. In a deal environment, a fast answer that cannot be defended is worse than a slow one. Automated reconciliation to audited totals and locked-down definitions prevented the "duelling numbers" problem that plagues many sell-side processes.
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Self-serve drill-downs change the dynamic. When partners and buyers can explore the data live, the conversation shifts from "can you pull this cut for me?" to "here is what I found, let us discuss," saving time and building confidence in the platform.
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The semantic layer is the real asset. Dashboards are visible, but the curated data model underneath is what makes them trustworthy and reusable. Investing in standardised definitions, a clean star schema, and automated quality checks paid off well beyond the original transaction.