Consolidated Data, improved security with better resource utilization.

Client : Confidential

Industry : Pharmaceutical

Duration : 90 Days

When it comes to adapting cloud technology, life science and pharmaceuticals do not have the best of reputation and it is what it is, but you cannot deny its repercussions.

Imagine the frustrations of your pharma R&D team when they must deal with stagnant pipeline and declining success rates, work in a 10–15-year-old infrastructure that does not support modern research (advanced data modelling, analytics, etc.), is extremely slow and often faces downtime issues. The struggle is real. If only you allocated funds wisely. Better late than never.

Business Case

The amount of data generated, and the type of molecular research conducted has changed drastically over the last two decades. The client was facing the limitations of a traditional Oracle-based on-premises data management. The systems were slow, did not offer much flexibility and scale, high maintenance cost, multiple siloed data sets, and there was not much room for using AI, analytics.

They were looking for a partner to migrate their data to AWS with advanced security features, such as security groups and network access control lists, to enable inbound and outbound filtering.

The challenges the team faced were primarily technical except for a few compliance and clearance, which is expected of a pharma giant.  

  • Multiple data sources
  • The data was raw, and could not be moved directly to cloud or used for analysis
  • Data silos
  • Integrate data from multiple sources, clean, transform and de-duplicate it. Prepare the data for use of Data Analyst and Scientist in S3 buckets (S3 is your typical data bucket in AWS dataset)
  • AWS glue, which is a managed service that makes it easy to prepare and load your data for analytics. In addition, it is a serverless approach, you pay only for the time Glue is running.
  • AWS is flexible when it comes to tool usage and offers several connectors and APIs. We prepared the data in S3 put it into Redshift, which is a cloud data warehouse by Amazon through which Tableau can pull data. The client had an existing Tableau license and so it was the natural choice for Data Analytics Tool.
  • The entire infrastructure was built within an Amazon Virtual Private Cloud (VPC) setup that provides advanced security features, is easy to use and customizable. To provide data security, S3 buckets are not accessible from outside by default.
  • The team at i2e developed a scalable and serverless data source using AWS services, which resolved the data silos issue
  • Superior support for pharma data and analytics over existing infrastructure
  • Cloud offered pre-trained pharma machine learning models for R&D
  • 60% reduction in infrastructure cost
  • Consolidated Data, improved security with better resource utilization
  • Improved Performance
  • Pay only for cloud infrastructure
  • Easy to implement, Scalable, Flexible infrastructure
  • Happy R&D team

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