Data Platform Transformation for wellness products company based in the US
Overview.
Customer partnered with Coforge on a data platform transformation journey to shift from a highly complex, monolithic, enterprise-wide data platform that is slow and costly to adapt and maintain to a dynamic Data-Platform-as-a-Service (DPaaS) approach that is flexible and easy to adapt and enables simplified data sharing across the enterprise via a data mesh architecture approach.
The customer faced several key challenges, including a fragmented data landscape across multiple vendors and systems, lack of a centralized data platform for ingesting and analyzing data, inefficient data operations with a large number of disparate data products, difficulty in setting up the required data infrastructure for launching new products, lack of standardization in their data architecture, complexity in migrating all global data products to a new DPaaS platform, and the need to design and implement shared services and capabilities to ensure optimal performance of the new data platform.
Solution
The Coforge solution approach is described below:.
Leveraged AI to drive insights on Customer experience. Build, deploy and train AI Models for processing Customer feedback from Sprinklr API.
Leveraged Snowflake for efficient and scalable data storage, management, and analytics, ensuring high performance and data integrity throughout the migration and integration processes.
Data Platform as a Service - Re-architecture of Data platform across Core, Function and Market (Geo) nodes. Built an ingestion framework to bring data from various Functions and Markets into the platform.
Consolidated data operations and support across a fragmented vendor and diverse technology landscape. Managing ~45 data products in production catering to 5 business functions
Partnering to build a data platform for the launch of Smoke Free Product (SFP) IQOS in US Market in May 2024 supporting Pre-Launch, Launch & Post Launch phases.
Support client to finalize the future state standard Node Architecture.
Migrate all Global Data Products (GDP) to the DPaaS Core Node architecture in a managed services model.
Design and implement suite of shared services and capabilities essential for optimal Node performance – covering DG, Data Observability, XOps, CI/CD, etc.
The Impact
20%
Higher improved product design
30%
Higher efficiency
30%
Operations costs reduction
20% Higher Efficiency through improved product design.
30% Higher Efficiency by Repeatability & Standardization
30% Reduction on Operating costs
25% Higher revenue via new product launches
Alignment with core architecture and enhanced end-to-end data capabilities
