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

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

ID: PSM-310


Features:

39 Info Graphics

42 Data Graphics

600+ Metrics

20 Narratives


Pages: 95


Published: 2014


Delivery Format: Shipped


 

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  • 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 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; Biotech; Chemical; Medical Device; Manufacturing; Consumer Products; 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