Big Data in Pharma: Current & Future Trends for Big Data Utilization Across Medical, Commercial and HEOR Functions

However, the inherent costs, uneven senior management buy-in, difficulty in measuring ROI of Big Data and other 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.

As the pharmaceutical industry strives to develop big data capabilities, they are wrestling with questions such as:

• Which big data projects are valuable?
• What data capabilities do we need?
• Should analytics be centralized or decentralized?
• What is the appropriate staffing and budget levels?
• What governance system or policies should be in place?
• And what type of organizations are best to partner with on projects?

Study Snapshot

Best Practices, LLC engaged 22 leaders from 18 pharmaceutical companies through a benchmarking survey. 

Research analysts also conducted seven deep-dive executive interviews with selected benchmark  participants

Key Findings

Most Have Centralized or Dedicated Big Data Team or Function: Half of the study participants for both the medical and HEOR segments said they have a centralized/dedicated group (Big Data team or function) to support Big Data projects. For the commercial segment, 40% said they have a centralized/dedicated Big Data group.
Majority of Each Segment Rate Internal Groups as Most Impactful Targets for Data Analysis: Internal Commercial, Medical and Development functions were rated by a majority of medical, commercial and HEOR respondents as the most impactful targets for data analysis presentations. A majority of each segment also said these internal groups were also the most frequent requestors of presentations.

Table of Contents

Executive Summary

• Big Data Team Overview and Key Study Insights
• Quantitative Key Findings
• Defining Big Data
• Data Types / Sources by Medical, Commercial and HEOR Segments
• Data Producers, Dissemination & Requestors by Medical, Commercial and HEOR Segments
• Centralization
• Governance and Leadership

• Predictive Modeling Use Case
• Classification Trees and Random Forests
• Classification Trees in Pharma
• Predictive Biological Modeling (PBM)
• Impact of Transactional Data - Medical, Commercial and HEOR
• Impact of Reported/Survey Data - Medical, Commercial and HEOR
• Impact of Online Data - Medical, Commercial and HEOR
• Impact of Scientific/Clinical/Medical Data - Medical, Commercial and HEOR
• Impact of Machine-Generated Data - Medical, Commercial and HEOR
• Impact of Data Producers - Medical, Commercial and HEOR
• Impact of Data Dissemination Channels - Medical, Commercial and HEOR
• Impact on Data Dissemination Targets - Medical, Commercial and HEOR
• 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 these medical decisions
• Preference and Popularity of various Study Types