Planning Big Data Analytics Solution for the
Pharmaceutical and Life Sciences Industry

Gain the full potential of big data pharma analytics and be more data-driven in your decision making

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Over the last couple of years, the worldwide pharma sector has been in a slump, pushing for more investment in pharma big data analytics. With rigid government regulations, touch competition, stiff margins, and typical supply chains, pharma companies are being forced to innovate and adapt by investing in high-end tech pharma analytics solution to create a robust framework to make smart decisions and succeed with their strategies.

By deriving data from different sources and creating a fast and efficient data pipeline for top functions, pharma analytics gave several vital opportunities for pharma and life science industry to grab and capitalize upon. Major achievements by using big data analytics are drug discovery, streamlining clinical trial data, operational efficiency, and optimizing sales and marketing effectiveness.

Top challenges faced by the pharma and life sciences companies

Pharmaceutical organizations get huge insights to boost and augment drug development. Big data assists in decision making, befitting marketing, patient approach, and drug research. Healthcare industries aim to be more data-driven, agile, and tech-fetched in their decision making, improving patient experiences, reducing healthcare costs, and enhancing the quality of care. But, several technical, regulatory, and organizational challenges hinder them from leveraging the full potential of big data. The real-world data is unstructured. Moreover, it is available in different formats. It is challenging for pharma companies to manage such data in an acceptable format and following complexities.

  • To process data silos and embedding this data to gain cross-functional insights
  • Setting up a suitable foundation to turn big data into useful insights and smart data
  • Gathering and using unorganized medicine distribution and clinical data
  • Keeping current on industry and government regulations
  • Infrastructure complexity and setup
  • Disparate technology and unstructured clinical and medicine distribution
  • Complete insights on clinical trial data
  • Defining involvement rules and data privacy on customers’ data

Important application and benefits that are covered in pharma analytics

To gain maximum benefits, a company-oriented strategy is required to mobilize big data analytics. Using big data processes, data science, and strong data analytics in operations has become essential in increasing operational efficiencies, customer service levels, and cost-saving. Moreover, advanced analytics shows a real and significant benefit for pharma companies to collect required data and develop models for turning it into useful insights for quick data-driven decision making.

  • Accelerate drug discovery and development
  • Drive effective sales and marketing operations
  • Increase the efficiency of clinical trial data
  • Patient approach, personalize and create targeted medications
  • Reduce cost and increase drug utilization
  • Quality and compliance adherence (GxP and HIPAA)
  • Supply chain and distribution management

Modern data architecture to leverage near real-time analysis

Centralized data lakes with a purpose-built data warehouse that can support multiple use cases within the business unit such as:

  • Scalable data lake
  • Purpose-built data services
  • Seamless data movement
  • Unified governance
  • Cost effective performance


We offer the best industry resources for big data analytics solution for pharma industry. We know how to tackle complex issues through data analytics, low-code applications, insightful dashboard reporting, etc. We promise to offer decent resources with a robust concept in pharma analytics.

Connecting data across the enterprises

  • Reducing time to access cross-functional data
  • Increase the no of cross-functional data use cases
  • Optimize spending across duplicative data processing systems
  • Integrated platform with one version of truth utilized across several teams

Implementing effective data management

  • Reduce data reconciliation time due to quality errors
  • Increase user satisfaction regarding trust in data
  • Accelerated time-to-insights enabled by a highly scalable AWS platform

Enable data democratization

  • Improve Awareness on availability of Enterprise-wide data sources
  • Increase utilization of available data sources

Advance pharma analytics 360-degree view of data

  • Accelerate enablement of high-impact AI/ML use cases
  • Enable efficiencies by improving the re-usability of existing AI/ML assets
  • Improve Data Science team satisfaction
  • score Multiple users and multiple dashboards with self-serve analytics

Accelerating data findability and usability

  • Reduce time to insight
  • Improve team productivity by streamlining findability


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Gain the full potential of big data pharma analytics and be more data-driven in your decision making