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Report Summary: Analytics Excellence: Current & Future Trends for Big Data Utilization in Pharma

ID: PSM-310A

Features:

39 Info Graphics

42 Data Graphics

600+ Metrics

20 Narratives

Pages: 17

Published: Pre-2021

Delivery Format: Online

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

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The last decade has seen an explosion in the availability of data that can deliver valuable insights for the pharmaceutical sector– from Electronic Medical Records and clinical trial data to medical claims and patient behavior data. All these information types are part of the large and complex data sets that make up what’s called Big Data.

The inherent costs and 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. But the pharma industry recognizes the huge potential that Big Data holds for providing critical insights that could have a major impact on clinical programs as well as commercial activities. Thus, many organizations are taking steps to develop Big Data capabilities.
Best Practices, LLC undertook this study to probe current trends and best practices in utilizing Big Data in both medical and commercial settings in the pharmaceutical sector.

Industries Profiled:
Pharmaceutical; Health Care; Consumer Products; Biotech; Chemical; Medical Device; Manufacturing; Diagnostic; Biopharmaceutical; Clinical Research; Laboratories

Companies Profiled:
Merck; Merck Serono; Novartis; Boehringer Ingelheim; Genentech; Teva Pharmaceutical Industries Ltd; Pfizer; Baxter Healthcare; GlaxoSmithKline ; Bayer; Gilead Sciences; Janssen; Lundbeck; Sanofi; Esteve; Daiichi Sankyo; Purdue Pharma; AstraZeneca

Study Snapshot

Topics addressed in the study include:

The types of Big Data projects and data being used
The types of data being used for specific medical and commercial Big Data projects
The data types and sources that are most valuable
How companies are organizing their Big Data efforts
How much staffing and budgetary resources companies are investing in Big Data
What are the most useful partners for Big Data projects
Policies and Procedures Governing Big Data Activities

Key Findings

Data Management Cost Trends: Data management - data acquisition and infrastructure - represents about 60% of participants’ Big Data non-labor budget. It also is expected to make up most of the future cost growth for companies as they expand their data capabilities and utilization of Big Data. The average Big Data non-labor budget for the benchmark was just over $14 million.

Data Types Used: Only 5 of� 36 types of Transactional, Reported, Online, Scientific and Machine-Generated data were rated highly valuable by a majority of study participants. They were: Claims; EHR (Electronic Health Records); Health Outcomes (provider/payer reported); Real World Studies; and Registries. No types of Online or Machine-Generated data were rated highly valuable by a majority.

Table of Contents

Executive Summary

Big Data Team Overview and Key Study Insights
Quantitative Key Findings
Qualitative Key Findings
Defining Big Data
Structure
Governance and Capabilities
Partnerships
Budgets and Staff
Data Types and Sources
Applications
Communicating Results
Performance

List of Charts & Exhibits

Do you have a centralized/dedicated group (Big Data team or function) to support Big Data projects?

When do you expect your organization to establish a Big Data team or function?
Large versus small pharma presence of centralized/dedicated Big Data team or function
US versus global pharma presence of centralized/dedicated Big Data team or function
Policies and procedures in place to govern Big Data activities
Regions where you have Big Data capabilities and Big Data governance resides
North American functions that utilize and/or lead Big Data projects
Internal vs. external staffing for list of nine data capabilities
Will your organization's data capabilities increase over the next 2 years and will resources be internally or externally increased?
Which partners are most impactful/valuable on Big Data projects?
Approximate current budget range for Big Data spending categories
Approximate number of FTEs providing/managing Big Data initiatives, projects
Approximate number of Big Data FTEs by company size (revenue)
What types of data are you using for listed (12) project types?
Value of listed types of transactional data sources
Value of listed types of reported/survey data sources
Value of listed types of online data sources
Value of listed types of scientific/clinical data sources
Value of listed types of machine-generated data
Value of listed types of data producers
Percentage of Big Data projects that fall into retrospective vs. predictive studies
Ranking of Big Data project application types by amount of use
Percentage of Big Data projects used to support listed medical decisions
Percentage of Big Data projects used to support listed commercial decisions
Types of Big Data projects that support decisions about targets for drug development
Types of Big Data projects that support decisions about customer targeting and positioning
Types of Big Data projects that support decisions about allocation of resources for in-line products
Types of Big Data projects that support decisions about pricing and reimbursement strategy
The most impacted target audiences for data analysis
Most common ways for disseminating real world data analysis
Indicate level of maturity within your organization for these data capabilities