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Big Data in Pharma:
Current & Future Trends for Big Data Utilization
Across Medical Affairs Functions
Best Practices, LLC
Strategic Benchmarking Research Study with Segmented
Responses by Medical Affairs Functions
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
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 11 pharmaceutical companies
through a benchmarking survey in
regards to Big Data usage in Medical
Affairs.
 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:
Eleven Companies Participated in the Benchmark Study
Best Practices, LLC engaged 12 leaders from 11 pharmaceutical companies through a benchmarking
survey in regards to Big Data usage in Medical Affairs.
Copyright © Best Practices, LLC
5
Quantitative Findings: Big Data Types and Utilization
Across the medical segment, the following key Big Data types, sources and utilization were observed
in this study.
Medical segment participants said post-launch studies around product use and
health outcomes were the most common types of Big Data projects they
conducted.
Post-Launch and
Customer Segmentation
Studies Most Common
Big Data Projects
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.
Only 5 of 36 Types of Data
Rated as Highly Valuable
Across The 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 in
Medical Affairs groups. 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 participants
6
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 (Medical)
Highly impactful Somewhat impactful Not impactful Not used
Transactional Data: Medical Segment Rates Claims, EMR Most Valuable
A majority of participants responding from a Medical perspective rated claims and Electronic
Medical Records (EMR) as highly impactful or valuable types of transactional data for Big Data
studies.
N=12
Q: How impactful (or valuable) has each of the following types of transactional data sources proven to be?
7
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Impact of Data Producers (Medical)
Highly impactful Somewhat impactful Not impactful Not used
Medical: Many Find Range of Data Producers Useful
Medical teams broadly find more value across data sources compared to analytics in other
functions. Government agencies and commercial payers are clear leaders, likely due to their
growing influence on formularies and the need to understand their value calculus.
N=12
Q: How impactful (valuable) is each of the following types of data producers?
8
Medical Leaders Describe Analytics As Centralized
Medical leaders were the most likely to describe their analytics efforts as having centralized or
dedicated personnel.
N=12
Q: Do you have a centralized/ dedicated group of individuals to support Big Data projects?
Yes
58%
No
42%
Dedicated Big Data Team (Medical)
9
0%
10%
20%
30%
40%
50%
60%
70%
North America EU Europe (nonEU) Asia
Big Data Capabilities and Governance by Region (Medical)
Regions with Big Data capabilities Region where governance resides
Medical Leaders See Smallest Governance Gaps
Our benchmark class reported that their Big Data capabilities and governance are generally based
in North America and the EU. This finding varies little between functions, although medical leaders
were more likely to report capabilities and governance residing in the EU than commercial leaders.
N=12
Q: Please indicate the regions below where your organization has Big Data capabilities, and where Big Data
governance resides.
10
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Partnership Value (Medical)
Highly impactful Somewhat impactful Not impactful Not used
Medical: Health Plans/Payers, Aggregators and Health
Systems Seen as Highly Impactful Partners
Medical leaders were most likely to rate partnerships as highly valuable across all varieties of
collaborations. Most popular were collaborations with payers and data aggregators.
N=12
Q: Which of the following partners are most impactful/ valuable on Big Data programs and projects?
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.
Best Practices, LLC
6350 Quadrangle Drive, Suite 200
Chapel Hill, NC 27517
www.best-in-class.com
About Best Practices, LLC

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Big Data Trends in Pharma for Utilization Across Medical Affairs

  • 1. Big Data in Pharma: Current & Future Trends for Big Data Utilization Across Medical Affairs Functions Best Practices, LLC Strategic Benchmarking Research Study with Segmented Responses by Medical Affairs Functions
  • 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 11 pharmaceutical companies through a benchmarking survey in regards to Big Data usage in Medical Affairs.  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: Eleven Companies Participated in the Benchmark Study Best Practices, LLC engaged 12 leaders from 11 pharmaceutical companies through a benchmarking survey in regards to Big Data usage in Medical Affairs.
  • 5. Copyright © Best Practices, LLC 5 Quantitative Findings: Big Data Types and Utilization Across the medical segment, the following key Big Data types, sources and utilization were observed in this study. Medical segment participants said post-launch studies around product use and health outcomes were the most common types of Big Data projects they conducted. Post-Launch and Customer Segmentation Studies Most Common Big Data Projects 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. Only 5 of 36 Types of Data Rated as Highly Valuable Across The 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 in Medical Affairs groups. 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 participants
  • 6. 6 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 (Medical) Highly impactful Somewhat impactful Not impactful Not used Transactional Data: Medical Segment Rates Claims, EMR Most Valuable A majority of participants responding from a Medical perspective rated claims and Electronic Medical Records (EMR) as highly impactful or valuable types of transactional data for Big Data studies. N=12 Q: How impactful (or valuable) has each of the following types of transactional data sources proven to be?
  • 7. 7 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Impact of Data Producers (Medical) Highly impactful Somewhat impactful Not impactful Not used Medical: Many Find Range of Data Producers Useful Medical teams broadly find more value across data sources compared to analytics in other functions. Government agencies and commercial payers are clear leaders, likely due to their growing influence on formularies and the need to understand their value calculus. N=12 Q: How impactful (valuable) is each of the following types of data producers?
  • 8. 8 Medical Leaders Describe Analytics As Centralized Medical leaders were the most likely to describe their analytics efforts as having centralized or dedicated personnel. N=12 Q: Do you have a centralized/ dedicated group of individuals to support Big Data projects? Yes 58% No 42% Dedicated Big Data Team (Medical)
  • 9. 9 0% 10% 20% 30% 40% 50% 60% 70% North America EU Europe (nonEU) Asia Big Data Capabilities and Governance by Region (Medical) Regions with Big Data capabilities Region where governance resides Medical Leaders See Smallest Governance Gaps Our benchmark class reported that their Big Data capabilities and governance are generally based in North America and the EU. This finding varies little between functions, although medical leaders were more likely to report capabilities and governance residing in the EU than commercial leaders. N=12 Q: Please indicate the regions below where your organization has Big Data capabilities, and where Big Data governance resides.
  • 10. 10 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Partnership Value (Medical) Highly impactful Somewhat impactful Not impactful Not used Medical: Health Plans/Payers, Aggregators and Health Systems Seen as Highly Impactful Partners Medical leaders were most likely to rate partnerships as highly valuable across all varieties of collaborations. Most popular were collaborations with payers and data aggregators. N=12 Q: Which of the following partners are most impactful/ valuable on Big Data programs and projects?
  • 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. Best Practices, LLC 6350 Quadrangle Drive, Suite 200 Chapel Hill, NC 27517 www.best-in-class.com About Best Practices, LLC