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» Products & Services » » Market Research, Analytics and Forecasting » Analytics

Big Data in Pharma: Current & Future Trends for Big Data Utilization Across Medical Affairs Functions

ID: PSM-316


Features:

13 Info Graphics

28 Data Graphics

500+ Metrics

3 Narratives


Pages: 52


Published: 2015


Delivery Format: Shipped


 

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  • STUDY OVERVIEW
  • BENCHMARK CLASS
  • STUDY SNAPSHOT
  • KEY FINDINGS
  • VIEW TOC AND LIST OF EXHIBITS
The biopharmaceutical industry has seen an explosion in the availability of big data that can generate valuable insights for the Medical Affairs function. However, the inherent costs, difficulty in measuring ROI of Big Data, uneven senior management buy-in and other challenges that come with harnessing large sets of information in varied formats have caused the biopharmaceutical sector to embrace Big Data analytics slower than other industries.

Recognizing the huge potential that big data holds for key medical decisions, organizations are taking steps to develop greater big data capabilities. Best Practices, LLC undertook this study to probe current & future trends and best practices for Big Data utilization across Medical Affairs functions.


Industries Profiled:
Pharmaceutical; Biotech; Consumer Products; Diagnostic; Health Care; Chemical


Companies Profiled:
AstraZeneca; Novartis; Janssen; Esteve; Lundbeck; Bayer Healthcare; Merck; Genentech; Pfizer; Boehringer Ingelheim; Gilead Sciences


Study Snapshot

Topics addressed in the study include:
  • Types of Big Data Projects Used to Support Medical, Commercial and HEOR Decisions
  • Big Data Capabilities and Governance
  • Types and Value of Data Used for Big Data Projects
  • Data Producers, Dissemination & Requestors
  • Value Rating of Partnerships on Big Data Projects
  • Policies and Procedures Governing Big Data Activities

Key Findings

Post-Launch and Customer Segmentation Studies Most Common Big Data Projects: Medical segment participants said post-launch studies around product use and health outcomes were the most common types of Big Data projects they conducted.
Payers & Data Aggregators Cited as Best Partners by Majority of participants: A majority of participants said that partnerships with payers and data aggregators were highly impactful. Health systems were also seen as valuable partners by each of the three segments.

Table of Contents

I.Executive Summary  pp. 3-8

    • Research Overview  pp. 4
    • 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