Big Data in Pharma: Data Types, Sources, and Applications

STUDY OVERVIEW

The rapid evolution of Big Data is generating greater insights for both medical and commercial decisions within the biopharmaceutical industry. The inherent costs and challenges that come with harnessing large sets of information in varied formats have caused the pharmaceutical sector to embrace Big Data analytics slower than other industries.

Identifying the most useful types of data and sources of data is an important initial step to realizing the huge potential that Big Data holds for pharma. Just as important is understanding what types of Big Data studies are best for key medical and commercial decisions like drug development decisions and pricing & reimbursement strategy.

Best Practices, LLC undertook this study to probe current trends and best practices in utilizing Big Data. The study offers key benchmarks around what types of data organizations are using most frequently as well as the data sources they find most useful. Also, the research examines which types of Big Data studies organizations are performing to inform their medical and commercial decisions.

KEY TOPICS

• Executive Summary
• Big Data Team Overview and Key Study Insights
• Quantitative Key Findings
• Qualitative Key Findings
• Defining Big Data
• Data Types and Sources
• Applications

• What types of data are you using for listed (12) project types?
• Value of listed types of transactional data sources
• Value of listed types of reported/survey data sources
• Value of listed types of online data sources
• Value of listed types of scientific/clinical data sources
• Value of listed types of machine-generated data
• Value of listed types of data producers
• Percentage of Big Data projects that fall into retrospective vs. predictive studies
• Ranking of Big Data project application types by amount of use
• Percentage of Big Data projects used to support listed medical decisions
• Percentage of Big Data projects used to support listed commercial decisions
• Types of Big Data projects that support decisions about targets for drug development
• Types of Big Data projects that support decisions about customer targeting and positioning
• Types of Big Data projects that support decisions about allocation of resources for in-line products
• Types of Big Data projects that support decisions about pricing and reimbursement strategy
  
  

• No Types of Online or Machine-Generated Data Sources were Identified by Most as Highly Valuable for Big Data Projects: None of the Online and Machine-Generated data types were identified by a majority of study participants as highly valuable for Big Data studies. The highest rating went to online patient and physician communities, which about 20% of participants said were highly valuable data sources. If pharma can find a comfort zone with the regulatory and compliance issues that surround online data, it would seem the wealth of online information would be a powerful tool in Big Data studies, especially those around customers.
• Commercial Payers and Government Agencies Seen as Most Valuable Data Producers for Big Data Studies: A majority of participants identified commercial payers and government agencies like CMS and the VA as the data producers that have the highest impact on Big Data initiatives.

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