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

Across Commercial Function

Best Practices, LLC Strategic Benchmarking Research Study with Segmented Responses

by Commercial Functionwww.best-in-class.com

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Table of ContentsI. 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

Best Practices, LLC, conducted a customized study – with responses segmented by medical, commercial and HEOR functions - to better understand the growing influence of Big Data in the biopharmaceutical sector and how it impacts medical, HEOR, and commercial operations in the U.S.

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.

Research Goal

Research Methodology

Produce reliable industry metrics on current and future trends for Big Data utilization across medical, commercial and HEOR groups.

Topics Covered

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

Investigate data types, data partnerships, and staffing/budget levels companies are using as they move to a more analytically based approach to commercial, HEOR & medical decisions.

Research Overview

Research Project Objectives & Methodology

Benchmark Class:

Twelve Companies Participated in the Commercial Segment12 analytics, marketing and HEOR leaders from 12 different companies participated in this study. Participants were recruited because of their presumed investment in Big Data analytics.

Copyright © Best Practices, LLC5

Analytics Team

Big Data Function

Understanding Big Data Use in Medical, HEOR & Commercial Decision-Making

Big Data Questions• Medical• Commercial• HEOR

Big Data Info Types• Electronic Health

Records• Government Cost Data• Health Outcomes

Big Data Projects• Value Proposition

Development• Post-Launch Performance• Real World Studies

Big Data Decisions• Go/No Clinical Decisions• ID Market Opportunities• Developing Economic

Models/Assessments

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Quantitative Findings

Across the commercial segment, the following key Big Data findings were observed in this study:

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 of any segment.

Forty percent of the study participants said they have a centralized/dedicated Big Data group.

Between 30-40% of participants said they expect their data capabilities to increase over the next two years.

Only 5 of 36 Types of Data Rated as Highly Valuable

Most Have Centralized or Dedicated Big Data Team

or Function

Small Percent of Participants Expect Data Capabilities to Increase

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0%

20%

40%

60%

80%

100%

Impact of Transactional Data (Commercial)

Highly impactful Somewhat impactful Not impactful Not used

While a majority of commercial participants rated claims and Electronic Medical Records (EMR) as the most valuable types of transactional data for Big Data studies, 50 % also cited point of sale as highly impactful or valuable.

N=12

Q: How impactful (or valuable) has each of the following types of transactional data sources proven to be?

Transactional Data: Commercial Rates Claims, EMR Most Valuable

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Commercial Leaders May Face Barriers to Centralized Data

Commercial leaders were least likely to describe their analytics as centralized or dedicated. This data point may be driven by discomfort with analysts who address both marketing and clinical questions, something a centralized analytics function might try to do.

N=12

Q: Do you have a centralized/ dedicated group of individuals to support Big Data projects?

Yes; 40%

No; 60%

Dedicated Big Data Team (Commercial)

9

Commercial Sees Highest Uncertainty On Dedicated Role

Commercial leaders were most likely to not know the future structure of their analytics efforts, as well as least likely to have centralized/dedicated personnel already in place. This may be driven by heretofore strict separation of clinical and marketing functions.

N=12

Q: When do you expect your organization will establish a Big Data team?

Have It; 36%

Getting It By 2015; 27%

Getting It By 2017; 9%

Don't Know; 27%

Plans for Dedicated Big Data Team (Commercial)

10

North America EU Europe (nonEU) Asia Other0%

10%

20%

30%

40%

50%

60%

Big Data Capabilities and Governance by Region (Commercial)

Regions with Big Data capabilities Region where governance resides

Capabilities Outstrip Governance for Commercial

Commercial leaders reported marginally more often that there were Big Data capabilities in Asia than other functions. The gap between those with capabilities and those with governance is most pronounced among commercial leaders.

N=12

Q: Please indicate the regions below where your organization has Big Data capabilities, and where Big Data governance resides.

Copyright © Best Practices, LLC11

Best Practices, LLC is a research and consulting firm that conducts work based on the simple yet profound principle that organizations can chart a course to superior economic performance by studying the best business

practices, operating tactics, and winning strategies of world-class companies.

Best Practices, LLC6350 Quadrangle Drive, Suite 200

Chapel Hill, NC 27517www.best-in-class.com

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