Building a single view of data for a retail group in Peru
A Peruvian retail group consolidates its data and analytics so decisions run on one trusted source rather than scattered reports.
The Situation
A retail group in Peru operated across channels and locations, but its data lived in fragmented systems that produced conflicting pictures of performance. Point-of-sale systems, e-commerce platforms and customer service tools operated in complete silos, so leadership could not measure true customer lifetime value or net contribution margins, and every downstream analysis inherited the same uncertainty. In a multi-channel environment where each stream reports a different number, the executives were effectively operating blind — deciding on intuition against the very data that should have guided them. The challenge was to consolidate that information into a single, trustworthy platform so the business could decide and act from one source of truth.
The Insight
A retail group's decisions are only as good as the single picture they are built on: when data is fragmented, every downstream analysis silently inherits the same confusion, and the cost is paid in poor allocations rather than in any single visible failure. The constraint was not a lack of data — the business was generating abundant telemetry across channels — but that it was unconsolidated, so no one could trust any one number. The economic logic is that a single source of truth is a force multiplier: it converts raw, scattered records into a lens through which customer value and margin can finally be seen, turning the data architecture from a cost center into an active commercial asset.
Diagnosis
Read through CORE™, the constraint was Capture: fragmentation at the data layer meant no single, reliable picture existed, so every downstream analysis inherited the same uncertainty.
CORE™ Maturity Diagnosis
Scale 1–7. Highlighted = the real constraint this diagnosis identified.
Framework applied: core-framework
The Strategy
The plan was to consolidate before optimizing, and the pairing of a Data Platform with Business Intelligence & Analytics was the right solution because it establishes one trusted layer first, then lets analysis run on top of it. The sequence was deliberate: unify the raw operational telemetry into a single lakehouse and streaming pipeline, then layer centralized BI models on top — so that every decision, model and trigger downstream operates on the same consolidated view rather than on conflicting reports.
Execution
The intervention built a data platform to consolidate scattered sources and layered business intelligence on top of it, giving the group one trusted view from which to analyze and decide. The concrete work engineered an enterprise data lakehouse and streaming pipeline that unifies raw operational telemetry into centralized Power BI and Looker Studio models with sub-15 minute freshness, enabling predictive purchase propensity models and automated replenishment triggers.
The Investment
The engagement ran as a 6-month program focused on building the consolidated layer rather than adding more sources. Its nature was a foundational data investment: turning scattered operational records into an institutional single source of truth, with value realized through the automated interventions and analytical clarity the consolidated layer unlocks.
The Results
The Analytics™ stack deployment (Single Source of Truth) resolved the severe data fragmentation that blinded executive decision-making across branch and digital channels. Under the CORE™ Capture diagnostic, point-of-sale systems, e-commerce platforms, and customer service tools operated in complete silos, preventing leadership from measuring true customer lifetime value or net contribution margins. Evox engineered an enterprise data lakehouse and streaming pipeline that unifies raw operational telemetry into centralized Power BI and Looker Studio models with sub-15 minute freshness. Analytical query latency decreased by 90%, enabling commercial teams to deploy automated predictive churn models and real-time replenishment triggers. Over 6 months, these automated interventions originated $1.85M in incremental attributed revenue. By converting unorganized raw records into an institutional single source of truth, Evox eliminated executive blindness and turned the company's data architecture into an active commercial asset.
| Indicator | Result | Detail |
|---|---|---|
| Analytical Query Latency | -90% | P95 analytical query runtime reduced from 34.8 seconds to 0.4 seconds on consolidated data model |
| Unified Customer Profile Resolution | 99.4% | Consolidated omni-channel purchase and behavioral records across physical branches and digital channels |
| Incremental Revenue from BI Triggers | $1.85M | Attributed revenue generated via predictive purchase propensity models and automated replenishment triggers |
| Data Pipeline Freshness SLA | 99.98% | Eliminated daily batch synchronization failures, ensuring sub-15 minute data freshness across ERP and analytics |
Analytical Query Latency
Unified Customer Profile Resolution
Incremental Revenue from BI Triggers
Data Pipeline Freshness SLA
Analytical Query Latency
Unified Customer Profile Resolution
Incremental Revenue from BI Triggers
Data Pipeline Freshness SLA
The Exact Mechanism
Consolidating fragmented data into one source of truth cut analytical query latency by 90% (from 34.8s to 0.4s), hit 99.4% customer profile resolution and originated $1.85M in incremental revenue over 6 months.
Transferable Lessons
- Decisions are only as good as the single picture they are built on — fragmented data inherits the confusion.
- A business already generating abundant data is often constrained by consolidation, not by a lack of signals.
- A single source of truth converts a data architecture from a cost center into a commercial asset.
- Consolidate first, then optimize — triggers and models only work on top of a trusted layer.
Discussion Questions
- How much conflicting data can an executive team tolerate before the confusion costs more than a consolidation project?
- Which comes first in a multi-channel business — unifying the data or acting on the channels themselves?
- What signal reveals that fragmentation, not demand, is the true constraint on growth?