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Products & Services Market Research, Analytics and Forecasting Analytics

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

ID: PSM-315


Features:

12 Info Graphics

28 Data Graphics

600+ Metrics

3 Narratives

20 Best Practices


Pages: 52


Published: 2015


Delivery Format: Shipped


 

License Options:
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Single User: Authorizes use by the person who places the order or for whom the order was placed.

Sitewide: Authorizes use of the report for a geographic site. All people at site can view the report for a year and copies can be printed.

Corporate: Authorizes use for the entire company for a year and copies can be printed. No limitations for usage inside the company.




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919-403-0251

  • STUDY OVERVIEW
  • BENCHMARK CLASS
  • STUDY SNAPSHOT
  • KEY FINDINGS
  • VIEW TOC AND LIST OF EXHIBITS
The last decade has seen an explosion in the availability of data that can deliver valuable insights for key commercial decisions within the biopharmaceutical industry - from Electronic Medical Records and medical claims to clinical trial data and patient behavior data. All these information types are part of the large and complex data sets called Big Data.


However, the biopharmaceutical industry has taken a long time to embrace big data due to the inherent costs and other challenges that come with harnessing large sets of information in varied formats.

Recognizing the huge potential that big data holds for strategic commercial decisions, biopharmaceutical companies are taking steps 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 commercial function.

The study offers benchmarks around the most useful data types and sources for key commercial decisions; governance policies and leadership; and the most impactful data producers, dissemination channels and targets.


Industries Profiled:
Pharmaceutical; Biotech; Chemical; Health Care; Biopharmaceutical; Clinical Research; Laboratories


Companies Profiled:
AstraZeneca; Genentech; Baxter BioScience; GlaxoSmithKline ; Boehringer Ingelheim; Daiichi Sankyo; Merck; Novartis; Pfizer; Purdue Pharma; Sanofi; Teva Pharmaceutical Industries Ltd

Study Snapshot

Best Practices, LLC engaged 12 leaders from 12 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: 40% of the study participants said they have a centralized/dedicated Big Data group.

  • Small Percent of Participants Expect Data Capabilities to Increase: Between 30-40% of participants said they expect their data capabilities to increase over the next two years.

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




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