The rapid evolution of Big Data holds the potential of generating greater insights for key commercial decisions within the biopharmaceutical industry. Despite this, the biopharma industry is still only in nascent stages when it comes to big data utilization for informing critical commercial operations. The complexity of the healthcare ecosystem combined with the inherent costs and other difficulties associated with big data utilization pose a huge challenge to most companies in leveraging Big Data. Best Practices, LLC conducted a benchmarking study to review the best practices, winning strategies and current and future trends for big data utilization across the commercial function. The study, “Big Data in Pharma: Current & Future Trends for Big Data Utilization Across Commercial Function” provides benchmarks around the most valuable data types and sources for strategic commercial decisions; governance policies and leadership; and the most impactful data producers, dissemination channels and targets. Biopharmaceutical organizations can devise/revamp their big data strategies by analyzing the big data trends, insights and benchmarks revealed in the study. Read More at: http://www.best-in-class.com/bestp/domrep.nsf/products/big-data-in-pharma-current-future-trends-for-big-data-utilization-across-commercial-function?OpenDocument

1. 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 Function

2. 2
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

3. 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

4. Benchmark Class:
Twelve Companies Participated in the Commercial Segment
12 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.

5. Copyright © Best Practices, LLC
5
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

6. Copyright © Best Practices, LLC
6
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

7. 7
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Point of Sale (POS) Claims Electronic Health
Records (EHR) /
Electronic Medical
Records (EMR) /
HIE (Health
Information
Exchanges)
ePrescription /
pharmacy fulfillment
Wholesalers /
Group Purchasing
Organizations
(GPOs)
Government (e.g.,
cost data)
Credit card
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

8. 8
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. 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. 10
0%
10%
20%
30%
40%
50%
60%
North America EU Europe (nonEU) Asia
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.

11. Copyright © Best Practices, LLC
11
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.
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Chapel Hill, NC 27517
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About Best Practices, LLC