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

STUDY OVERVIEW
BENCHMARK CLASS
STUDY SNAPSHOT
KEY FINDINGS
VIEW TOC AND LIST OF EXHIBITS

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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
Big Data Staffing and Budget Levels
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

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

ID: PSM-316A

Pages: 11

Published: Pre-2018

Delivery Format: Online

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