Case Study

Enterprise Data Platform Modernization

A presales case study showing how a fragmented reporting environment was reframed into a cloud data platform opportunity with a clearer business case and implementation path.

Industry Financial Services
Use Case Data Platform Modernization
Tools Azure, Databricks, Spark, Python

Client Context

A financial services organization was operating across fragmented legacy data systems, with reporting teams and business units relying on inconsistent and slow-moving data.

Business Problem

The customer needed to reduce reporting delays, improve data consistency, and establish a scalable analytics foundation without creating another disconnected platform.

My Role

I led discovery, translated reporting and governance pain points into architecture requirements, and partnered with sales to position a modern cloud data platform in business terms.

Outcome

The solution narrative reduced ambiguity around the target architecture, improved stakeholder alignment, and supported a deal path centered on faster reporting, stronger governance, and a credible foundation for future AI use cases.

Situation

The customer had multiple data silos, inconsistent reporting logic across teams, and slow turnaround for business-critical reporting. What appeared to be a technical modernization problem was also a trust and decision-making problem: leadership could not rely on the same numbers across functions.

Approach

I structured the engagement around four questions:

  1. Which reporting and governance pain points were most commercially significant?
  2. What architecture would reduce duplication without overcomplicating the migration path?
  3. How should the solution be explained differently to executives, analysts, and technical evaluators?
  4. What near-term value could justify the platform investment?

The discovery work focused not just on tools, but on operating friction: reporting latency, reconciliation effort, governance gaps, and future readiness for advanced analytics.

Solution Narrative

The proposed solution centered on a cloud-native data platform with clearer ingestion patterns, governed transformation layers, and a reporting environment designed for consistency and scale.

Rather than positioning it as infrastructure replacement alone, I framed it as a business improvement initiative with three linked outcomes:

  • faster and more reliable reporting
  • stronger cross-functional trust in shared data
  • a practical foundation for advanced analytics and AI adoption

Presales Relevance

This case study demonstrates the ability to:

  • translate fragmented technical symptoms into a business-priority architecture case
  • run discovery that surfaces both operational and strategic drivers
  • position modernization in terms executives can support
  • connect platform design to adoption credibility and commercial value