How a Diversified Indian NBFC Built a Unified Data Lake to Integrate Business Lines

Learn how a flagship financial services company of a large Indian industrial group partnered with Nihilent to build a modern Azure‑based data lake and warehouse, breaking down silos across lending businesses and enabling a single, analytics‑ready view of customers and performance.

Service

Data & Analytics, Cloud Data Platforms, and Modern Data Warehouse

Vertical

BFSI (Retail, Commercial and Institutional Lending)

Region

India (Multi‑Business, Multi‑Location Operations)

Tech Stack

Azure Data Lake, Azure Databricks, Azure SQL Data Warehouse, Azure Cloud Services

How a Diversified Indian NBFC Built a Unified Data Lake to Integrate Business Lines

Client Overview

The client is a Mumbai‑headquartered non‑banking financial services company and the flagship financial services arm of a large Indian conglomerate, serving retail, SME and institutional customers through multiple lending and advisory businesses. Its lines of business span consumer loans, commercial finance, wealth services and card distribution, supported by a nationwide presence and a growing digital footprint.

Rapid expansion in products and business lines meant that operational and customer data was distributed across multiple systems and storage locations, often duplicated and inconsistent. Leadership recognized that to support growth with sharper risk, collections and marketing decisions, the organization needed a single, trusted view of customers and performance, underpinned by a scalable cloud data platform.

Business Challenges

As the group expanded into new products and segments, it faced several data and analytics challenges that limited its ability to take timely, insight‑led decisions.

Siloed Data Across Business Lines

Data residing in separate systems and distributed storage created duplication, reconciliation effort and inconsistent views of business performance.

No Single Source of Truth

The lack of a unified data backbone meant there was no single source of truth or single view of the customer for reporting and analytical needs.

Limited Proactive Monitoring and Insights

Fragmented data restricted proactive monitoring of portfolios and reduced the ability to use existing data for holistic, cross‑business decision‑making.

Need For Scalable Cloud‑Native Platform

With growing data volumes and use cases, the organization required a modern cloud platform that could support advanced analytics, data science and future growth.

How Nihilent Addressed This

Nihilent adopted a consulting‑led approach, starting with discovery and stakeholder alignment before designing and implementing a cloud‑native data lake and warehouse on Azure. The focus was on demonstrating value early, while creating a robust foundation for long‑term analytics and data science initiatives.

Consulting‑Led Discovery and Roadmap

Nihilent conducted detailed data discovery and application landscape assessments and used joint workshops to show how existing data could support more holistic, cross‑business decisions for key stakeholders and teams.

Proofs of Concept and Hackathons on Azure

During solution design, Nihilent ran proofs of concept and hackathons to demonstrate the value and relevance of Azure cloud for the client’s requirements, benchmarking it against alternative approaches.

Cloud Data Lake and Analytics Roadmap

Nihilent partnered with the client to define an analytics and data science roadmap covering both operational excellence and growth‑oriented use cases, anchored on a cloud data lake built on Azure with Databricks.

Integrated Data Lake and Warehouse for Advanced Analytics

A modern data warehouse was implemented using Azure Data Lake to capture and store structured and unstructured data in any format, complemented by Azure SQL Data Warehouse for enterprise reporting and Databricks for advanced analytics.

Customer Insights and Sentiment Pilots

Nihilent also executed technology pilots around customer sentiment and insight generation, proving out new analytical capabilities on top of the consolidated data platform.

Key Results & Outcomes

Modern, Scalable Data Lake & Warehouse

A modern data warehouse using Azure Data Lake now captures and stores diverse data types, providing the flexibility and scalability needed for evolving data storage and analytics demands.

Unified View for Cross-Sell & Up-Sell

The Azure‑based data lake and Databricks layer provide a consolidated view across business lines, enabling better up‑sell and cross‑sell opportunities throughout the customer base.

Enterprise-Wide Reporting & Advanced Analytics

A SQL data warehouse, complemented by Databricks, supports organization‑wide reporting and advanced analytics for risk, marketing, operations and customer insight.

Scale Proven with Multi-Terabyte Data

The solution manages around 7 TB of data with daily incremental loads of roughly 25 GB, demonstrating the platform’s ability to handle growing data volumes.

Broad Adoption Across the Business

Over 1 million customers, 15 data marts, 150‑plus reports and 50 dashboards are now supported on the platform, indicating strong adoption across business and functional teams.

Key Takeaways

For a fast‑growing Indian NBFC, integrating data across business lines was essential to move from fragmented reporting to truly insight‑led decision‑making. By combining a consulting‑driven discovery phase, value‑focused proofs of concept and a modern Azure‑based data lake and warehouse, Nihilent helped the organization build a single, scalable data backbone that serves both operational and growth agendas.

With a unified view of data, robust reporting and advanced analytics capabilities now in place, the client is better positioned to understand customers, manage risk and identify new opportunities - while continuing to expand its portfolio and digital footprint on a cloud‑ready foundation.

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