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Analytics Excellence: Current & Future Trends
for Big Data Utilization in Pharma
Best Practices, LLC Strategic Benchmarking Research Conducted
2
Table of Contents
I. Executive Summary pp. 4-19
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. 9-13
Qualitative Key Findings pp. 14-19
II. Defining Big Data pp. 20-26
III. Structure pp. 27-34
IV. Governance and Capabilities pp. 35-43
V. Partnerships pp. 44-50
VI. Budgets and Staff pp. 51-56
VII. Data Types and Sources pp. 57-69
VIII. Applications pp. 70-80
IX. Communicating Results pp. 81-84
X. Performance pp. 85-94
XI. About Best Practices, LLC pp. 95
Best Practices, LLC, conducted a customized study to better understand the growing influence of Big
Data in the biopharmaceutical sector and how it can impact medical, HEOR, and commercial
operations in the U.S.
Best Practices, LLC engaged 22 leaders
from 18 pharmaceutical companies
through a benchmarking survey. There
were multiple responses from 3
companies but they each represent a
different functional area (medical,
commercial, or HEOR).
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:
Eighteen Companies Participated in the Benchmark Study
22 analytics, marketing and HEOR leaders from 18 different companies participated in this study.
Participants were recruited because of their presumed investment in Big Data analytics. Three companies
had multiple responses - each representing a different functional area (medical, commercial, or HEOR).
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Value of Establishing a Big Data Team or Function
The following are reasons that study participants cited for establishing Big Data capabilities.
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KEY QUALITATIVE FINDINGS
The biggest benefit is that it finally makes various departments within your company focused
on the questions they're trying to answer with data. Pharma has a lot of trouble trying to
develop questions that can be empirically answered. They don’t do that very well. It turns, in
my opinion, it turns into a lot of just comparison -- because they have a clinical trial
mentality -- and that's not what this is.
“The biggest benefit is that it finally makes various departments within your company focused on the questions they're trying to answer with data. Pharma has a lot of trouble trying to develop questions that can be empirically answered. They don’t do that very well. It turns into a lot of just comparison -- because they have a clinical trial mentality -- and that's not what this is.” – Director, HEOR
“A big advantage is you are positioned, especially in the U.S., to accommodate the changing healthcare landscape. So by using big data you are more likely to be able to address questions that emerging partners have like ACOs, who are going to vertically integrate systems, as well as to position yourself for patient information.” – Senior Director, Managed Care
“So it is going to inform this business - it's going to inform how many, when and what. It’s not going to inform why people make the decisions - which is always going to be in the qualitative market research arena - but it’s certainly going to help you narrow down where you want to focus your qualitative research. As we move forward over the years, I think we’re going to see a lot less binary quantitative research and more use of this type of data to get those types of answers.” - Associate Director, Global Market Research
Help Focus
on Critical
Questions
Attract
Partners
Market
Research
Aid
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Most Have Centralized/Dedicated Big Data Team or Function
A majority of study participants said they have a centralized/dedicated team or function to support
Big Data projects.
N=19
Q: Do you have a centralized/ dedicated group of individuals to support Big Data projects (i.e. Big Data team or function)?
Yes 53%
No 47%
Dedicated Big Data Team (Total Benchmark Class)
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Benefits of a Centralized Approach to Big Data Team
What do you think are the most significant benefits of a centralized Big Data team approach?
“One is really sharing learnings
amongst everyone. I think
there’s a feeling that with one
team you can be more objective
in reflecting what’s the interest of
the organization versus what’s
the interest of your specific
division. I mean we’re part of
marketing now, for instance. So
that can be a fine line at times
when we say, hey, that program
did not return great ROI.”
--Associate Director
Shared
Goal
One
Information
Source For
Global
“If you're global, it's
probably more
valuable for your core
affiliates because
you're centralized in
supplying information
for them and so they
don’t have to go look
for it on their own.”
--Sr. Director,
Managed Care
Proactive
Strategy
Approach “With a centralized
team we can plan
strategically and
proactively on
product strategy
using real world data,
not just waiting
patiently for the
question to come up
and then start to run
analysis.” – Senior
Scientific Analyst
“I think [it’s important]
from a signaling
perspective for talent
development and
also recruiting.
Because if there is a
centralized data
function, the
company is invested”
--Associate Director
Talent
Development
& Recruitment
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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Rules about what analysis commercial functions can perform using…
Rules about what analysis medical functions can perform with Big Data
Rules on Big Data insights-sharing between commercial and medical
Rules governing disclosure of findings to public
Rules on proactive vs. reactive use of insights from Big Data
Rules on disclosure from regulatory perspective
Rules on publishing
Policies establishing clear ownership for various data types across the …
Policies and procedures for accessing data (e.g., who can see what)
Policies governing protecting identification/ de-identification of patient…
Policies governing clear ownership of IP generated through a…
Policies regarding review/ approval of research protocols
Prevalence of Data Governance Policies (Total Benchmark Class)
N=16
Q: Which of the following policies and procedures are in place at your company to govern Big Data activities?
Most Participants Have Range of Policies Governing Big Data Use
At least half of the study participants use all 12 of the listed policies governing Big Data use. More
than 70 % have policies related to disclosure in place for Big Data activities. These range from
policies protecting the identity of patients to rules around publishing study results.
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0.000%
0.020%
0.040%
0.060%
0.080%
0.100%
0.120%
0.140%
Current Future
Non-labor Budget*
Data acquisition Data infrastructure Data analytics Data dissemination Other categories
Data Management is Lion’s Share of Big Data Budget
For participants, data management (data acquisition and infrastructure) represents about 70% of their
Big Data non-labor budget and most of future cost growth.
N=18
Economies of Scale
“It is really cost saving
to the company to have
a centralized team …
we have multiple
products and a lot of
products can share the
same database.”
- Analytics Lead
Data Management
Q: What is the approximate current budget range for each of the following Big Data function spending categories?
*Weighted average based on
midpoints of spending buckets.
Spending set to
increase across
the board
The highest areas for spending
are also the weakest areas for
performance in data management
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Point of Sale (POS) Claims Electronic HealthRecords (EHR) /
Electronic MedicalRecords (EMR) /
HIE (HealthInformationExchanges)
ePrescription /pharmacy fulfillment
Wholesalers /Group Purchasing
Organizations(GPOs)
Government (e.g.,cost data)
Credit card
Impact of Transactional Data Sources (Total Benchmark Class)
Highly impactful Somewhat impactful Not impactful Not used
Transactional Data: Majority Say Claims, EMR Most Valuable
The only types of transactional data that a majority of study participants said were highly impactful
or valuable for Big Data Studies were claims (70%) and Electronic Medical Records (EMR – 53%).
N=19
Q: How impactful (or valuable) has each of the following types of transactional data sources proven to be?
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Partnerships Are Potentially Valuable Sources of Data
The pharma industry sees data aggregators, payers, and government agencies as key partners,
but still purchases 40% of its data. While partnerships offer another avenue for obtaining data,
often these arrangements still require payment for the data.
0% 20% 40% 60% 80% 100%
Health Information Exchanges(HIEs)
Consulting companies/ Analyticsoutsourcing companies (e.g.,…
Academic institutes
Contract Research Organizations(CROs)
Academic medical centers
Accountable Care Organizations(ACOs)
Medical Groups/ Health CareProviders (HCPs), Group…
Government groups (e.g., CMS,HHS, NIH)
Health Systems/ Hospitals/Integrated Health Networks…
Data aggregators
Health plans/payers
Highly impactful Somewhat impactful Not impactful Not used
N=18
Q: What percentage of your data comes from each of the following sources? Q: Which of following partners are most valuable for Big Data projects?
Purchased 44%
Partnerships 15%
Interally Generated
37%
Other 4%
Sources of Data
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Predictive Analytics an Untapped Game Changer
Despite relative confidence in analytics capabilities, a majority of study types are retrospective
-- based on econometric models that rely only on observations from the same period as the
dependent variable. Predictive models could give pharma a more accurate forward view.
“I work a lot on products, and we were
able to find an opportunity for one product
that didn’t even exist.
“We looked at different sets of patients
and we modeled algorithmically the
patients and put them in different settings,
mathematically. We were able to determine
that there was opportunity for a product
that didn't exist.
“I had the very good fortune to work with
the head of scientific affairs who actually
got what we were doing, and they
generated a product which turned out to
be a big seller for them. And it was unique
because nobody had ever used the data
to tell them where the opportunity was.”
— Director, HEOR
Predictive, 26%
Retrospective, 74%
Q: Please estimate the percentage of your Big Data projects and studies that fall into
each of the following two categories:
N=21
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Drug Development Decisions Being Influenced By Analytics
The percentage of techniques covered in the survey employed across a variety of questions is charted
below, showing the relative sophistication of the benchmark class.
Q: Percentage of Big Data projects currently used to support these medical decisions:
“There are a multitude of benefits. One is
you can get information earlier to make
decisions about what you're investing in.
So you're basically going to use these to
figure out what's the burden of information
you need to have to bring a product to
market. You're going to start pushing back
that cycle of waiting till Phase 3 for things
to fail and you're going to earlier on figure
it out.”
– Senior Director, Managed Care
N=21
Mo
re S
op
his
ticate
d
Le
ss S
op
his
ticate
d
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Targets for Drug DevelopmentDecisions
Other Drug Development/Submission Decisions
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Impact on Data Dissemination Targets (Total Benchmark Class)
Highly impactful Somewhat impactful Not impactful Not used
Data Targets: Internal Functions are impacted most by Data Analysis
A majority of study participants felt that internal company functions – specifically commercial,
medical and development – were the target audiences impacted most by data analysis from internal
analytics groups.
N=18
Q: Who are the most impactful target audiences for your data analysis?
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Governance(strategy &
policy)
System design:infrastructure,architecture &
analytics
Dataconnectivity
Dataidentification &
acquisition
Dataorganization &
storage
Data cleansing/validation
Datadissemination
Data analysis& visualization
Datainterpretation/
insights
Mature Somewhat Mature New Capability None/Don't Know
Pharma’s Self-Assessment of Big Data Capabilities is a
Combination of Highs and Lows Most partners say they can visualize, interpret, and disseminate data well, but lack a strategic
vision and the technical horsepower to grow.
Q: Please indicate the level of maturity within your organization for each of the following capability types:
N=17 Low = Strategy + Tech HP High=Analysis + Distribution
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Universal Accessibility and Unification of Data Is Beneficial
“I think all the data should be in one
place. So anyone who is interested
knows where to go to find out what’s
there. That's kind of the basic building
block.
Then the next level I think is where it’s
linkable, we should link it. So if you
know what you have, then you go,
‘Wait a minute, these two can link.’
If they’re in different places or if you
don’t know, if you can’t see them all,
then you don’t know which ones kind
of link. And part of the value of Big
Data is when you start linking data
sources.”
- Director, Customer Data
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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