Turning Data into Decisions: Building a "Single Source of Truth" for a Scaling Fintech Leader
At a Glance
The Challenge
Client context and business challenge
The client, a fast-growing digital lending platform serving SMEs, was facing a "data paradox": they had vast amounts of information but lacked the ability to generate actionable insights. Data was trapped in functional silos—marketing used one tool, the loan management system used another, and finance relied on a labyrinth of manual Excel spreadsheets. This fragmentation meant that the leadership team was often making critical strategic decisions based on data that was two weeks old and frequently contradictory.
As the business prepared for a major Series B funding round, the lack of data governance became a significant hurdle. Inconsistent definitions of core metrics, such as "Customer Acquisition Cost" and "Default Risk," led to internal friction and reporting delays. They needed a robust data strategy that could modernize their technical infrastructure while building a culture of evidence-based decision-making. Stravence was engaged to design a scalable BI architecture that would serve as the backbone for their next phase of regional expansion.
Our Approach
Strategic methodology and execution
Data Maturity Assessment: Audited the existing data landscape to identify technical debt, security gaps, and data quality issues
KPI Taxonomy Design: Facilitated cross-departmental workshops to standardize definitions for 50+ business-critical metrics
Data Governance Framework: Established clear ownership, access controls, and quality standards to ensure "one version of the truth"
Architecture Modernization: Designed a modern "Cloud Data Warehouse" architecture capable of ingesting real-time streams and batch data
Dashboard Ideation & Prototype: Developed intuitive, persona-based mockups for Executive, Risk, and Operational teams before full-scale build
Data Literacy Training: Empowered 100+ employees to move beyond basic reporting to self-service analytics
The Solution
Implementation details and technology stack
The Process
We implemented the Stravence Insight-to-Action Lifecycle, transitioning the client from manual data extraction to an automated ELT (Extract, Load, Transform) process. This ensured that data was cleaned and modeled into a star schema for optimized query performance.
The Tech Stack
Fivetran: For automated data ingestion from CRM, ERP, and Marketing platforms.
Snowflake: As the centralized Cloud Data Warehouse for high-performance storage and compute.
dbt (data build tool): For modular and version-controlled data transformation and modeling.
Power BI: To deliver interactive, real-time executive dashboards and deep-dive analytical reports.
Results & ROI
Measurable outcomes and business impact
Reporting Velocity
The finance team, which previously spent 10 days every month consolidating reports, now has access to real-time financial statements at the click of a button
Risk Mitigation
By integrating live credit-bureau data with internal loan performance metrics, the risk team identified high-risk clusters early, preventing an estimated $300k in potential defaults
Operational Clarity
Departmental "data arguments" were eliminated, as the standardized KPI framework ensured every leader was looking at the same numbers during weekly performance reviews
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