Data Strategy & Business Intelligence / South Asia (Emerging Digital Economy)
Turning Data into Decisions: Building a single source of truth for a scaling fintech leader
A digital lending platform needed reliable, real-time business intelligence before a major funding and expansion phase.
At a glance
The measurable readout.
In time spent on manual monthly reporting
Across Finance, Risk, and Sales departments
In loan portfolio performance through real-time risk monitoring
Client context and business challenge
The 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.
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.
Strategic methodology and execution
Our Approach
- 01Data Maturity Assessment: Audited the existing data landscape to identify technical debt, security gaps, and data quality issues.
- 02KPI Taxonomy Design: Facilitated cross-departmental workshops to standardize definitions for 50+ business-critical metrics.
- 03Data Governance Framework: Established clear ownership, access controls, and quality standards to ensure one version of the truth.
- 04Architecture Modernization: Designed a modern cloud data warehouse architecture capable of ingesting real-time streams and batch data.
- 05Dashboard Ideation & Prototype: Developed persona-based mockups for Executive, Risk, and Operational teams before full-scale build.
- 06Data Literacy Training: Empowered 100+ employees to move beyond basic reporting to self-service analytics.
Implementation details and technology stack
The Solution
The process
We implemented the Stravence Insight-to-Action Lifecycle, transitioning the client from manual data extraction to an automated ELT process.
This ensured data was cleaned and modeled into a star schema for optimized query performance.
The tech stack
Fivetran
Used for automated data ingestion from CRM, ERP, and marketing platforms.
Snowflake
Centralized cloud data warehouse for high-performance storage and compute.
dbt
Used for modular and version-controlled data transformation and modeling.
Power BI
Delivered interactive, real-time executive dashboards and deep-dive analytical reports.
Measurable outcomes and business impact
Results & ROI
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 because the standardized KPI framework ensured every leader was looking at the same numbers during weekly performance reviews.