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Emerging Key Trends in Data and Analytics in Pharma Industry

Key data and Analytics Trends are evolving rapidly as they are increasingly being driven by business needs. Rajeev Dadia looks at the technologies that are supporting the emerging and existing trends in data and analytics in the pharmaceutical industry.

Technologies evolve in direct proportion to the demand. In the pharmaceutical industry, the demand for better and more efficient ways of leveraging data has been growing exponentially. This demand is driving technology trends for achieving certain business outcomes:

  • Ability to get flexible, personalized, and real-time insights
  • Leveraging data to be prescriptive, not just predictive
  • Developing ways to harmonize data across platforms
  • Ability to predict or classify data where outcome is not known

Here are a few data and analytics trends that are working towards making these desired business outcomes come true:

  • Late Binding Data Lake
  • Fast Data/IoT and Edge Analytics
  • Virtualization and NoSQL
  • Deep Learning

Let’s look at the top reasons these trends are coming into focus:

1. Reusable, flexible, adaptable: It is becoming more and more necessary that the technology that is used to create a solution is reusable, reconfigurable, and flexible. Investing in a technological solution is a huge undertaking that requires investment and ties up resources. As business needs keep growing, the technology will need to evolve to keep up too. With organizations, deploying an end-to-end data solutions strategy, adaptability is a highly desirable feature for them. Data lake, Deep Learning, and NoSQL fit this bill perfectly.

2. Ability to integrate data sources: We already know that organizations are focusing on the three Vs – volume, velocity, and variety. While volume has been the focus for many pharmaceutical companies so far, as they deal with massive amounts of data, variety is increasingly becoming a sought-after aspect. Pharmaceutical companies have many vendors working with them, each with their own data platforms and systems. It is simply more efficient for them if a technology has the capacity to work with a wide variety of data sources. An analytics platform that can seamlessly draw insights from structured as well as unstructured data sources, is immensely valuable to any business.

3. Leverage AI: As the volume of data generated grows, the need to effectively employ machine learning and AI also becomes critical. A technology that works with new models and evolves with changing business needs, is an ideal choice for any pharmaceutical organization. Deep Learning is one such technology. It has the ability to “learn” models from a large data set, which can prove to be invaluable while working with unstructured data.

Stay tuned for more insights into each of these technologies as we dive deeper into each of them and discuss their merits. In the meantime, feel free to contact me at [email protected] for further dialogue.

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