>$1M
saved through smart alerting
>$500K
saved via platform consolidation
5
TB/year processed through a single unified pipeline
Continuous data quality scoring across 4 dimensions organisation-wide
A unified observability platform replaced fragmented, reactive tooling for a global retailer, consolidating data ingestion, quality measurement, business intelligence, and real-time anomaly detection into a single pipeline processing 5 TB of data per year. The platform delivered over $1.5M in combined savings from smart alerting and platform consolidation while establishing continuous, quality-aware data monitoring across the entire organisation.
The Problem
A major international furniture and homeware retailer operates a complex, distributed data landscape spanning multiple regions, business units, and technology stacks. Data flows from dozens of sources (warehouses, stores, supply chain, finance, e-commerce) into analytics and reporting systems that drive daily decision-making. The existing observability tooling was fragmented across teams with no unified view of data health, entirely reactive (issues discovered when dashboards broke, not before), and expensive due to overlapping platform licences. Data could be incomplete, stale, or inconsistent and nobody would know until it caused a problem downstream.
The Solution
A unified Observability Platform was built from the ground up, replacing fragmented tooling with a single, end-to-end pipeline covering data ingestion, quality measurement, business intelligence, and real-time anomaly detection, processing 5 terabytes of data per year across the entire organisation.
The platform begins with a source-agnostic ingestion module that connects to any structured data source (BigQuery, Oracle, PostgreSQL, or others) through a consistent interface, eliminating bespoke connectors and making onboarding new sources fast and repeatable. All ingested data is transformed to conform to a standardised data model, ensuring consistent structure regardless of origin.
A Data Quality Model then continuously evaluates incoming data against four dimensions: completeness (are all expected fields present?), timeliness (is data arriving within freshness windows?), accuracy (are values within expected ranges?), and consistency (does data agree across sources and over time?). These scores are surfaced to users and feed directly into the service layer, so stakeholders understand the reliability of the data behind their dashboards and alerts. The service layer provides two core capabilities: business intelligence dashboards giving a quality-aware view of operational and strategic metrics, and real-time anomaly detection that identifies unusual patterns and alerts teams before issues escalate.
Results and Impact
| Metric | Value |
|---|---|
| Smart alerting savings | >$1,000,000 |
| Platform consolidation savings | >$500,000 |
| Data volume processed | 5 TB/year through a single unified pipeline |
| Quality dimensions tracked | 4 (completeness, timeliness, accuracy, consistency) |
| Rollout | Organisation-wide across all business units |
| Data sources supported | BigQuery, Oracle, PostgreSQL, and others |
Key Takeaways
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Data quality must be measured, not assumed. Without explicit scoring across completeness, timeliness, accuracy, and consistency, organisations are flying blind. Making quality visible transformed how teams consumed and trusted their data.
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Source-agnostic ingestion removes friction. A single module that connects to any data source makes onboarding fast and eliminates duplicated tooling, delivering immediate cost savings through platform consolidation.
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Quality-aware services build trust. When users can see the reliability of the data behind their dashboards and alerts, they make better decisions and trust the platform, which is the foundation for everything else.