Big Data in Pharma: Current & Future Trends for Big Data Utilization Across the Health Outcomes Research Function

The biopharmaceutical industry has seen an explosion in the availability of big data that can generate valuable insights for key Health Outcomes Research decisions.

However, the biopharmaceutical industry has been slower than other industries in embracing big data analytics due to the inherent costs, difficulty in measuring ROI of Big Data, uneven senior management buy-in and other challenges associated with harnessing large sets of information in varied formats.

As the industry recognizes the huge potential that big data holds for crucial HEOR decisions, rapid steps are being taken to develop greater big data capabilities.

Best Practices, LLC undertook this study to probe current & future trends, winning strategies and best practices for Big Data utilization across the HEOR function. The study investigates the most useful data types and sources for key HEOR decisions; governance policies and leadership and the most impactful data producers, dissemination channels and targets.

Study Snapshot

Best Practices, LLC engaged 15 leaders from 13 pharmaceutical companies through a benchmarking survey to discuss how their companies approach Big Data utilization within HEOR. Executive interviews were also conducted with function leaders and others from Medical Affairs and Commercial.

Key Findings

Post-Launch and Customer Segmentation Studies Most Common Big Data Projects: Six out of ten benchmark participants used post-launch studies around health outcomes. The second most highly rated reported data type is patient reported outcomes data.

• Most Have Centralized or Dedicated Big Data Team or Function: Half of the study participants said they have a centralized/dedicated group (Big Data team or function) to support Big Data projects.

§ Universe of Learning pp. 5-6

§ Big Data Team Overview and Key Study Insights pp. 7-8

§ Quantitative Key Findings pp. 9-12

II. Defining Big Data pp. 13-20

III. Data Types and Sources pp. 21-26

IV. Data Producers, Dissemination & Requestors pp. 27-31

V. Centralization pp. 32-34

VI. Governance and Leadership pp. 35-51

VII. About Best Practices, LLC pp. 52

List of Charts & Exhibits

Big Data Use in Medical, HEOR & Commercial Decision-Making

• Predictive Modeling Use Case
• Classification Trees and Random Forests
• Classification Trees in Pharma
• Predictive Biological Modeling (PBM)
• Impact of Transactional Data
• Impact of Reported/Survey Data
• Impact of Online Data
• Impact of Scientific/Clinical/Medical Data
• Impact of Machine-Generated Data
• Impact of Data Producers
• Impact of Data Dissemination Channels
• Impact on Data Dissemination Targets
• Frequency of Data Requests by Source
• Do you have a centralized/ dedicated group of individuals to support Big Data projects?
• Plans for Dedicated Big Data Team
• Big Data Capabilities and Governance by Region
• Big Data Use, Leadership by Function
• Internalization by Capability
• Please indicate whether you expect your organization to increase its expertise (Big Data capabilities) and whether you expect to increase the capabilities internally (vs. outsourcing) over the next 24 months.
• Which of the following partners are most impactful/ valuable on Big Data programs and projects?
• Prevalence of Data Governance Policies
• Maturity of Capabilities
• Capabilities of U.S. Companies
• Capabilities of Global Companies
• Types of Big Data projects currently used to support medical decisions
• Types of Big Data projects currently used to support commercial decisions
• Preference and Popularity of various Study Types