Data Analytics


Life Sciences

The pharmaceutical industry has been experiencing a host of challenges that are impacting its operating environment including recurring patent cliff, increasing adoption of generics, rising research and development (R&D) investments with very little patent window to break-even, tighter regulatory controls, increased regulatory compliance requirements across different geographies, and rising compliance cost. The industry is evolving owing to the following key business imperatives:

  • Fundamental changes in the science of medicine, moving from treatment to cause, leveraging next-generation sequencing, large clinical data analysis to bring new diagnostics and treatments to patients leading to a massive, chemistry, biology and clinical data analysis.
  • Shift to value-based medicine requiring strong real world evidence for regulators, payers and providers to drive better patient outcomes.
  • Need for expansion to new markets with dynamic price controls leading to localization of scientific evidence as well as innovative pricing and marketing strategies.

These challenges and imperatives also offer immense opportunities to create better solutions that can help in addressing the following key areas of focus for the pharmaceutical industry:

  • Reduced cycle-times for product development and clinical trials improving time to market
  • Enhanced reporting for changing regulatory compliance needs
  • Enhanced risk management to increase predictability of product success
  • Enhanced patient outcomes

Infosys Point of View

Pharmaceutical and life sciences organizations rely greatly on data to drive decisions on product planning and positioning. Historically, this data has been largely fragmented and distributed across various data sources such as hospital data records, physician notes, clinical trials systems, sales and marketing databases, claims data, research data, and more. With advances in technology, pharmaceutical and life sciences organizations are now able to tap into different types of data – machine data from servers, social data from Facebook and Twitter, clickstream data from websites, voice logs from call centers, communication data from e-mails, data generated from sensors and IoT(e.g. wearable devices), and more.

  • “Boundaryless” information
  • Data from all the traditional and new-age sources need to be integrated in a seamless way in a “boundaryless” data platform to build advanced data analytics solutions. Such solutions can be leveraged to generate powerful insights to enable informed and insight-driven decisions. Effectively leveraging data harnessed from multiple structured and unstructured sources through “boundaryless” information platform and building data science driven predictive and prescriptive analytics will enable life sciences organizations to effectively respond to challenges and convert them into opportunities to drive competitive advantage.

  • Analytics
  • Analytics enables data exploration, analysis, and data science driven predictive and prescriptive analytics solutions which help in responding to the following key trends in the pharmaceutical industry:

    • Drug discovery analytics – Enables scientists to source scientific findings and insights from external labs or internal knowledge to jump start discovery which will in turn help reduce cycle time for product development aiding faster go-to-market
    • Reduce cycle-times for clinical trials – Through better insights driven by improved accuracy of analytics
    • Supply disruptions predictive analytics – Building predictive models using a combination of internal and external data would help reduce unforeseen shortages in availability of drugs impacting customer service levels and lost sales revenues
    • Product failure analytics – Via root cause analysis and predictive analysis of product failures (vendor data)
    • Risk analytics – For evaluation of potential risks posed by elemental impurities in a formulated drug product
    • Real-time medical device analytics and visualizations – Leveraging Interconnecting data from implanted devices and personal care devices
    • Digital channel analytics  / social analytics – To more fully understand customer perceptions about their products which helps in proactively fixing product issues or managing communication better
    • Enhance reporting systems – To meet the changing regulatory compliance needs more effectively
    • Visualization – Renew focus on understanding the underlying business data and generating analytical insights using latest business intelligence (BI) visualization tools

Pharmaceutical organizations can leverage big data and analytics in a big way to drive insightful decisions on all aspects of their business from product planning, design, manufacturing to clinical trials to enhance collaboration in the ecosystem, information sharing, process efficiency, cost optimization and drive competitive advantage. At Infosys, we focus on delivering business value through insights and predictive models as well as driving efficiencies in data investments through consolidation, integration, enhancing, enriching and monetization of data.

Success Stories

Generated savings of US$1.5 million per year for a leading European pharmaceutical giant with enterprise data warehouse (EDW) implementation and data governance initiatives.

Implemented customer analytics program for a global pharmaceutical company which helped the company to achieve targeted sales and increase cash flow.

Implemented supply disruption prediction analytics to predict the items that go into “sudden shortages” for a leading pharmaceuticals distributor.

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