Financial Services

Saving 600 Person-Hours Every Week Through Data Infrastructure Modernization

Impact Snapshot

IMPACT SNAPSHOT

We made a visible and measurable impact to our client’s business.

600 person-hours saved every week by reducing manual reporting effort

SCALE OF TRANSFORMATION

500+ tables migrated
14 data sources integrated
30 business-critical KPIs enabled across key business processes

The engagement reduced the effort spent preparing reports while creating end-to-end visibility across the customer lifecycle and a centralized foundation for analytics and future AI applications.

Challenge

INDUSTRY OVERVIEW

Lending and financial services businesses generate data across multiple stages of the customer lifecycle, including lead generation, loan origination, servicing, partner operations, disbursement and collections.

As these businesses scale, data often remains distributed across operational systems built for different purposes. This makes it harder to create a consistent view of performance, slows reporting and increases the amount of manual effort required to prepare information for business teams.

A modern data foundation must therefore do more than consolidate information. It needs to support repeatable data processing, governed access, timely reporting and future analytical use cases without increasing operational complexity.

THE PROBLEM

A leading education-focused NBFC was operating with LMS, LOS and other application data spread across multiple systems.

The existing architecture did not provide a centralized environment capable of bringing these datasets together for end-to-end analytics. In particular, LOS data was not available within a common data lake or data warehouse, creating integration challenges and limiting visibility across the journey from lead generation and disbursement to debt collection.

The existing architecture also had limitations around the migration of unstructured LOS data and needed to accommodate future data growth, automation, real-time analytics and AI/ML requirements.

Without a centralized data foundation, reporting remained effort-intensive and the business lacked a consistent view across important customer and operational processes.

OUR ROLE

Ganit was brought in to modernize the organization’s data infrastructure and establish a centralized architecture connecting LMS, LOS and other enterprise data sources.

The mandate was to reduce data silos, automate recurring reporting workflows, improve visibility across the customer lifecycle and create a data foundation that could support both current BI requirements and future Applied AI initiatives.

Our approach

METHODOLOGY

The engagement was structured around three core questions.

How can data from multiple operational systems be brought into one consistent analytical environment?
The first priority was to consolidate data that was distributed across LMS, LOS, partner systems and other internal and external sources.

How can that data be converted into information that business teams can use without depending on manual reporting?
The next step was to create structured datasets and business KPIs that could directly support reporting and decision-making.

How can the architecture continue supporting the business as data volumes and analytical requirements grow?
The platform therefore had to support repeatable processing, orchestration, governance and additional BI or AI use cases without requiring the core infrastructure to be rebuilt.

SOLUTION STRUCTURE

We built an integrated data system to enable this at scale.

Layer 1: Centralized Data Foundation
Data from LMS, LOS, partner databases and other internal and external systems was consolidated into a centralized data lake and warehouse environment.

More than 500 tables from 14 data sources were migrated, reducing fragmentation and creating a common analytical foundation for the organization.

Amazon S3 supported data storage, while Amazon EMR and AWS Glue were used to process and transform information. Amazon Athena and Amazon Redshift supported analytical querying and structured business consumption.

Layer 2: Business Analytics and Decision Support
The centralized environment was structured around 30 business-critical KPIs covering key processes including:

Partner onboarding
Partner journey
Lead to disbursement
Portfolio assessment

This allowed business teams to move from fragmented reporting towards a more consistent view of operational and customer performance. BI capabilities were connected to the centralized data environment so users could access business information without repeatedly compiling data from multiple systems.

Layer 3: Data Pipelines and Orchestration
Automated pipelines were established to ingest, process and curate incoming data.

AWS Glue, Amazon EMR and supporting orchestration components were used to create repeatable processing workflows, while access controls and security components governed how data was consumed.

The cloud environment was also simplified into a single AWS account, reducing infrastructure complexity while improving governance and operational control.

ENABLING CONSUMPTION

The platform was designed around how business teams would ultimately consume the information.

Rather than stopping at data migration, the engagement connected centralized datasets with BI and reporting workflows. Business-critical KPIs were organized around operational processes such as partner onboarding, lead conversion, disbursement and portfolio assessment, making the resulting data directly relevant to day-to-day decision-making.

Automating reporting workflows also reduced the effort previously required to prepare and reconcile information manually.

The resulting architecture created a reusable foundation for additional reporting, analytics and Applied AI use cases without requiring teams to reconstruct the underlying data layer for every new requirement.

A valuable difference

OUR IMPACT

The modernization shifted reporting from a fragmented, effort-intensive process towards a centralized and automated data environment.

The most significant measurable outcome was the elimination of approximately 600 person-hours of manual effort every week, allowing business teams to spend more time interpreting information and making decisions rather than preparing reports.

At the same time, migrating 500+ tables across 14 data sources created a broader view of enterprise information, while 30 business-critical KPIs improved visibility across important stages of the lending and customer lifecycle.

The organization also gained a simpler cloud environment and a centralized data foundation capable of supporting future BI, analytics and AI requirements.