ENVU Data Transformation Case Study with Cloudaeon
Challenges
Migration execution time was reduced by 70%+ across scanning, refactoring, and deployment phases.
Over 500 pipelines were transitioned through a structured and controlled migration program.
Outcome
Migration execution time reduced by 70%+ across scanning, refactoring, and deployment phases.
500+ pipelines migrated through a structured and controlled program.
Solution
Databricks Modernisation
Challenges
Solution
Technology Stack
Outcomes
As a newly independent organisation from Bayer, Envu required a fundamental redesign of how KPIs were defined, governed and consumed. At the time, performance reporting was fragmented across regional Excel files, manual to maintain, inconsistent by design and incapable of supporting real-time decision-making at scale. Collaboration with Cloudaeon proved to be a game-changer for Envu. Cloudaeon led their transition from spreadsheet-driven reporting to an automated Databricks lakehouse with Sigma consumption, establishing a single, real-time source of truth and an operating model built for scale.
This case study is a perfect example of how retiring spreadsheets as systems of record and re-engineering KPI foundations offer governed, real-time analytics at enterprise scale.
Client Problem
After its separation from Bayer, Envu had to operate itself operating as an independent enterprise. However, their reporting processes were still anchored in regional spreadsheets. What had once been “good enough” inside a larger parent organisation became a constraint overnight. Leadership needed faster and more reliable insights for every small business decision. They wanted to stop spending time updating the Excel sheets and focus on error-free operations. The requirement was very clear, they wanted to move from regional, manual reporting to a scalable analytics foundation to ensure real-time decisions without sacrificing on control.
Technical Pain Points
At ground level, the problems were structural:
Data fragmentation: KPI tracking lived in separate Excel files across regions. Each file told a slightly different story.
Manual update cycles: Copy-paste workflows and periodic refreshes introduced delays, errors and reconciliation overhead.
No real-time view: Leadership discussions were driven by lagging indicators rather than current operational reality.
Governance not fit for scale: Excel offered no durable model for access control, auditability, or growth in data volume.
Operational Impact
Teams which were highly capable ended up spending more time on curating spreadsheets instead of operating the business. Moreover, leadership took important decisions on stale and inconsistent data.
Root Cause Analysis
This was not a BI tooling gap. It was an architectural failure rooted in spreadsheet-centric system design.
Lack of standard data model: KPIs were defined independently by region. Definitions, filters and cut-off logic drifted over time, making global rollups unreliable.
Human-in-the-loop pipelines: Manual updates created non-deterministic data flows. Errors were inevitable and fixes were impossible to reproduce consistently.
Missing governance primitives: Excel provides no native RBAC, lineage or audit trails forcing compliance to rely on process rather than engineered controls.
Latency baked into the operating model: When updates are manual by design, “real-time” insight is structurally unattainable.
Excel wasn’t just the interface it had become the system of record. And this situation wasn’t helping anyone.
Solution Architecture
Cloudaeon engineered an end-to-end, automated KPI workflow using Databricks as the processing backbone and Sigma as the business-facing analytics layer.
Target architecture
Source layer: Regional KPI inputs, previously scattered across Excel files, are consolidated into a centralised ingestion layer.
Processing + storage (Databricks): Transformation pipelines land curated datasets into Delta Lake, enforcing schema and governed access.
Consumption layer (Sigma): Business users interact with live KPI dashboards through an Excel-like experience without extracts or shadow copies.
Bi-directional writeback: Controlled updates entered via dashboards are synchronised across regions and persisted consistently in the lakehouse.
Governance + security: Role-based access and compliance controls are embedded directly into the platform design.
How We Delivered
Our delivery followed a disciplined step-by-step engineering approach rather than a dashboard-first method:
Discovery & workflow decomposition: Regional KPI workflows were mapped end-to-end, withdata quality checks and KPI definitions normalised into a single reporting contract.
Migration to Databricks: Spreadsheet-driven tracking was replaced with Databricks-backed pipelines designed to handle structured and semi-structured inputs.
Pipeline automation: Manual refresh cycles were eliminated through automated ingestion and transformation, ensuring datasets remain current by default.
Curated data layer: Regional silos were consolidated into governed, reusable datasets with consistent transformation logic.
Sigma dashboard engineering: Real-time KPI dashboards were built for business usability while remaining directly connected to governed datasets.
Writeback controls: Bi-directional updates were enabled without compromising data integrity or governance.
Security & governance: RBAC and compliance controls were applied as first-class platform components.
Validation: KPI parity was validated against legacy spreadsheets to ensure leadership views algned with agreed operational definitions.
i-directional writeback: Controlled updates entered via dashboards were synchronised across regions and persisted consistently in the lakehouse.
Technology Stack
Databricks
Delta Lake
Sigma
Outcomes
Envu achieved 99% reduction in manual data processing, thereby eliminating spreadsheet maintenance overhead.
97% elimination of Excel-based KPI tracking that transitioned into a fully automated system.
95% reduction in FTE cost tied to manual reporting operations.
Real-time decision support through live, trusted dashboards.
There was a single KPI truth across regions, removing reconciliation debates entirely.
POD & Managed Ops Transition
Once the platform stabilised, delivery moved seamlessly from build to ownership without reintroducing manual side channels.
Solution: An automated KPI platform on Databricks with governed Sigma consumption.
POD: A dedicated POD owns KPI evolution, new-region onboarding, governance expansion and performance optimisation, ensuring continuity beyond initial delivery.
Managed Ops: SLA-backed monitoring, data quality checks and operational governance keep the platform reliable as usage and volume scale.
This model ensures the platform compounds value over time rather than degrading after go-live.
Conclusion
This case study shows that analytics modernisation succeeds when it is treated as a system redesign rather than a reporting upgrade. By retiring spreadsheets and re-engineering the KPI lifecycle on a governed Databricks lakehouse, Envu established a real-time, scalable foundation that supports confident decision-making as an independent enterprise.
Are you facing similar challenges? Interested in knowing how this could work for you? Talk to Databricks expert now!