Cubegle
Data • AI • Cloud
Case Study

Global Data Platform for Operations Analytics

A unified data platform powering operations, finance and leadership reporting across multiple regions and business units — with automated pipelines and governed analytics.

Overview

A fast-growing operations-focused organisation needed a centralised data platform. Data lived in multiple databases, flat files and APIs. Stakeholders relied on manual reports and inconsistent KPIs.

Problem

  • Fragmented data across several production systems and regions.
  • No single source of truth for financial & operational reporting.
  • Heavy manual effort to prepare weekly and monthly reports.
  • Performance issues on ad-hoc queries and dashboards.

Objectives

  • Build a central, cloud-based data platform.
  • Automate ingestion from all key systems.
  • Standardise KPIs and metrics across teams.
  • Enable fast, governed self-service analytics.
Global Data Platform Architecture
Snapshot

Domain: Multi-region operations & finance
Scope: Data platform, BI, governance
Duration: Multi-phase engagement

Solution

Cubegle designed and implemented a modern data platform with a cloud data warehouse, automated ETL/ELT pipelines, and a semantic layer for BI & analytics.

Key Workstreams

  • Data modelling (fact/dimension schema for finance, operations, and product).
  • Automated ingestion from multiple transactional databases and APIs.
  • Centralised data quality rules and validation checks.
  • Standard KPI definitions (revenue, cost, utilisation, efficiency KPIs).
  • Power BI / analytics semantic layer with access control.

Architecture (High Level)

The platform used a three-layered design:

  • Ingestion Layer: ETL/ELT pipelines from DBs, APIs, files.
  • Core DWH Layer: Cleaned, conformed models in a cloud warehouse.
  • Semantic & BI Layer: Curated datasets for dashboards & ad-hoc queries.
Tech Stack

Cloud DWH (e.g. Azure SQL / Redshift / Snowflake), ETL (Pentaho / ADF / Python), Power BI / BI tool, Git-based version control, scheduler/orchestrator.

Impact

  • Leadership and operations teams aligned on one set of metrics.
  • Automated refreshes reduced manual reporting effort significantly.
  • Performance improvements on dashboards and ad-hoc analysis.
  • Foundation for future AI/ML and advanced analytics.
Where This Applies

This blueprint is ideal if you have multiple teams, products, or regions and need a governed, centralised data platform with reliable reporting.