Case Study
Accelerated Data Integration using Agilisium’s customized SnapLogic Migration Framework Reduces Effort by 20%

The Challenge

  • The existing suite of Data Integration platform on Informatica had high cost of ownership, archaic user experience, traditional outlook and complex offerings
  • Disparate, disconnected and duplicate data across multiple sources impacted customer experience
“Agilisium architected, designed and delivered an elastically scalable Cloud-based Analytics-ready Big Data solution with AWS S3 Data Lake as the single source of truth”
The client is one of the world’s leading biotechnology company, with presence in 100+ markets globally, was looking for ways to maximize impact of their sales & marketing efforts.

The lack of a single source of truth, quality data and ad hoc manual reporting processes undermined top management’s visibility of integrated insights on sales, sales rep interactions, marketing reach, brand performance, market share, and territory management. Understandably, the client wanted to align information that has hitherto been in silos, to gain a 360-degree product movement view, to optimize sales planning and gain competitive edge.

The Challenge

  • With continued investments in Big Data Analytics technologies that support its business decisions, the client did not have the bandwidth to support day-to-day maintenance of the systems and limited time to focus on strategic initiatives
  • The Big Data Analytics environment is in the process of constant redesign and migration of jobs. The support team must address these changes while stabilizing existing processes and improving performance

Our Solution

  • Agilisium built a centralized and Elastic SnapLogic Data Integration platform that Ingested data from 10+ applications into AWS Redshift cloud data warehouse
  • A home-grown migration framework called ‘ASAP’ – Accelerated SnapLogic Adoption Programme is built and articulated based on Agilisium’s hands-on experience in driving digital transformation engagements for various customers with focus on data integration technology transformation
  • Cluster based data processing platform which is fall tolerant and enables continuity
  • 300 individual integration pipelines have been deployed across the enterprise by lean team of 4 members and plan to implement 1000 integration pipelines by end of 2018

How We Worked Together

Post a maturity assessment, Agilisium devised and enacted strong governance frameworks at executive, program and operational levels with scheduled check point review meetings, to close current gaps identified in scope management.

Scrum calls twice daily ensured that application owners were apprised about progress made at operational level. While monthly milestone review meetings aligned priorities at program level, the quarterly executive steering committee meeting clearly set the engagement priorities at executive level.

To ensure a smooth transition of new solutions, a 1-week workshop and demo on S3, Redshift and Tableau were given to the client’s business and technical teams.

Results and Benefits
  • Reduced Total Cost of ownership
  • Deployment of the framework ensures faster deployment thereby helping customer achieve faster time to market and with up to 20% reduction in overall labour costs for implementation
  • Self-service integration and analytics that increased user productivity
  • Comprehensive data and analytics (SnapLogic + AWS + Tableau) which improved decision-making
  • Uncovered opportunities that improved business operations
  • Able to integrate systems, technologies, and applications in cloud platform (AWS)
  • Able to gain real-time insights and increased visibility into sales, marketing and Master data
  • Provides real-time data to business users and customers
  • The solution shall process 100 + Million records in a single pipeline
  • SnapLogic is 100% hosted on AWS platform
  • Client chose Amazon Web Services as their preferred partner to lead their big data initiative. With SnapLogic, we created data lake within Amazon Redshift, and ingested data from multiple sources