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Home / Case Studies / Enabling Data Driven Decision-making for a Leading Financial Enterprise
Enabling Data Driven Decision-making for a Leading Financial Enterprise
Case Studies
BFSI

Enabling Data Driven Decision-making for a Leading Financial Enterprise

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On This Page
  • At a Glance
  • Services
  • Intelligence
  • Industry
  • Problem
  • Strategy
  • Transformation Journey
  • Business Outcomes
  • 400% Growth
  • 0 Wait-Time
  • 75% Improvement
  • 85% Reduction
  • 100% Data Availability
  • 5X Productivity
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At a Glance

When this leading financial enterprise struggled to scale due to poor data storage, management, and analytics, TVS Next partnered with them to rebuild their data landscape, set up advanced analytics, and helped them grow 400%.

Services

Intelligence

  • Data Pipeline
  • Data Orchestration
  • Enterprise Data Lake
  • Cloud Data Warehouse
  • Self-Serving Reports
  • Data Governance and Management
  • RPA
  • Batch Processing

Industry

Financial Services

Problem

The data landscape of this prominent financial enterprise was on-premise, siloed, and deficient in good data governance and management. They did not have a data pipeline/ETL to onboard new systems and missed out on many functionalities required to advance the business.

Multiple business units required batch and real-time data analysis on different systems and in various formats. But their existing data warehouse was unable to support these requests.

Their analytics team used Excel for basic reporting and analytics. When the business units requested reports, they had to wait between a few weeks and months. These long wait times and the inability to self-serve reports affected timely decision-making.

The lender was also missing many upselling and cross-selling opportunities due to these issues. The financial enterprise partnered with TVS Next to execute a rapid digital transformation to expedite its growth.

Strategy

TVS Next’s Intelligence advisory team put together a team of cloud architects and technology experts and conducted extensive accelerated discovery workshops with the C-Suite. We determined their objectives, as-is system state, technology drivers, business goals, and transformation scope.

We leveraged our Cloud Data and Analytics Platform Modernization (CDAPM) framework to create a comprehensive migration and modernization roadmap.

We swiftly executed a proof of concept (POC) on the recommended target-state architecture in the Azure platform. Through this, we demonstrated that the target platform addressed the client’s key business KPIs.

Transformation Journey

The POC validated TVS Next’s agility, expertise, and understanding of the enterprise’s needs. Their leaders agreed to a shared long-term vision. This facilitated change management and empowered the business units to embark confidently on their cloud-first journey.

We began migration and modernization with unanimous agreement from the enterprise’s board.

Our team brought together data in various forms from ten different departments and multiple external sources onto a single cloud data warehouse. We set up automated data pipelines and ensured end-to-end functionality through automated testing.

We changed the reporting module from an Excel-based one to real-time visualization on Tableau and implemented hyper-automation across business processes using Automation Anywhere.

By introducing batch processing, automated monitoring and alerting, and self-service reporting, we ensured that the business units were self-sufficient and could swiftly take action without dependencies.

The TVS Next team also defined data standards and policies to ensure good data governance and management. We have an ongoing relationship with the enterprise, providing infrastructure management and technical support.

Business Outcomes

As a result of TVS Next’s partnership in modernizing data, the financial enterprise received the following benefits:

400% Growth

Modernization pivoted the enterprise to accelerate their growth.

0 Wait-Time

Business users now self-serve their reporting requirements.

75% Improvement

In data access and retrieval speeds.

85% Reduction

In the time required to produce reports.

100% Data Availability

Resulted in accurate analytics to make business decisions.

5X Productivity

Due to automation cutting down repetitive tasks.

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