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Data Platform Modernization

Modernizing a Fragmented Data Platform for Enterprise Reporting

  • Case Studies
  • March 2025
  • 4 min read

A finance and operations organization relied on overnight extracts and manual spreadsheet consolidation to produce enterprise reporting. Prodatalabs delivered a governed cloud data platform with automated pipelines and a single reporting layer.

Business impact at a glance

Representative Outcomes

Indicative results from an engagement of this type. Figures are not attributed to a named client.

Reduction in report preparation effort
60%Reduction in report preparation effort
Faster month-end reporting cycle
35%Faster month-end reporting cycle
Governed source for enterprise metrics
SingleGoverned source for enterprise metrics

The Challenge

Reporting depended on extracts from six operational systems, reconciled manually each cycle. Definitions varied by team, and leadership had limited confidence in the numbers presented.

  • Overnight batch extracts with no lineage or auditability
  • Conflicting metric definitions across finance and operations
  • Manual reconciliation consuming several analyst days per cycle
  • No ability to support ad hoc analysis without engineering effort

Our Approach

  1. Step 01

    Assess and define

    Mapped source systems, data flows and reporting demand, then agreed a shared set of metric definitions with business owners.

  2. Step 02

    Design the platform

    Designed a layered cloud data architecture with ingestion, curated and semantic layers, plus governance and access controls.

  3. Step 03

    Build and migrate

    Implemented automated pipelines with validation and lineage, migrating reporting workloads incrementally to limit disruption.

  4. Step 04

    Enable and hand over

    Delivered documentation, runbooks and paired enablement so internal teams could extend the platform independently.

Solution Delivered

A governed data platform that ingests operational data on a scheduled and event-driven basis, applies validated transformations and serves a single reporting layer.

  • Automated ingestion pipelines with validation and alerting
  • Curated data models aligned to agreed business definitions
  • Role-based access control and full data lineage
  • Self-service reporting layer for finance and operations
  • Monitoring and operational runbooks for internal ownership

Technologies Used

  • Cloud data warehouse
  • Managed ETL/ELT
  • Infrastructure as code
  • BI and reporting

Business Outcomes

Faster reporting cycles

Manual consolidation was replaced by automated pipelines, shortening the month-end reporting cycle materially.

Higher confidence in numbers

Shared definitions and visible lineage removed recurring disputes over which figure was correct.

Capacity returned to analysis

Analysts moved from data preparation to interpretation and forecasting work.

A reusable foundation

New data domains are onboarded using established patterns rather than bespoke builds.

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