1.
Richard McLean
@mcleanonline
Welcome to Elsevier
Who is Elsevier?

2.
Who is Elsevier?
You might know us as a
science and medical
publisher…

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39,000
+
Books
253
6
Journal
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Who is Elsevier?
You might know us as a
science and medical
publisher…
Who is Elsevier?
• … but we are a global
information
analytics business
specializing in science
and health!

6.
“Extinction is the rule.
Survival is the exception.”
Carl Sagan

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Extinction at work

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“It is not the strongest species
that survive, nor the most
intelligent, but the ones most
responsive to change.”
Charles Darwin

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Customer information needs
Early 20th Century Early 21st Century

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We help scientists make
new discoveries, doctors
and nurses improve the
lives of patients
We provide information-based analytics and decision
tools for professional and business customers.

11.
100+
Digital Products,
Tools & Platforms
2500+
Digital
Touchpoints,
Systems &
Services
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100+
Digital Products,
Tools & Platforms
2500+
Digital
Touchpoints,
Systems &
Services
~11m
Mendeley enables
over 11 million users
worldwide, to
organize, write,
collaborate and
promote their
research.
1b
1 billion articles
were downloaded
by researchers.
320+
The machine-learning
capability of Reaxys®
enables the chemistry of
drug discovery for over
320 pharma innovators,
140 chemical companies
and 470 universities..
25,000+
Elsevier products are
used at more than
25,000 academic and
government
institutions around the
world.
73m+
Scopus, the leading
abstract and citation
database, contains
over 73m records
across 24,000
journals, sourced from
more than 5,000
publishers.
16m
ScienceDirect has
16m monthly unique
visitors.
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Growing Data
Vast volumes of information
are hard to navigate and
organize, increasing the risk
of critical data being
overlooked.
• Research and healthcare
are more complex when
incorporating findings from
multiple disciplines.
• It’s tougher to ensure the
credibility of data.
• In the globalized academic
community, there is more
competition than ever for
jobs, students and funding.
Analytics & AI
New opportunities arise when
applying advanced AI/ML
techniques to big data.
Some examples:
• Detect trending topics per
domain
• Recommendation engines
• Entity extraction e.g. genes,
diseases, drug names, etc.
• Recognizing relationship
between entities
• Building a knowledge graph
across scientific disciplines

14.
Aalborg
Amsterdam
London
Philadelphia
Oxford
Chennai
Elsevier TechHubs: Centres Of Excellence

15.
How we work
Agile at Elsevier: 4 key ingredients
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Ingredient 1: Autonomous teams
Autonomy in:
• Process (XP,
scrum, Kanban,
etc.)
• Tools
• …
Allows for:
• Innovation
• Speed
Focus on:
• Outcomes, not
outputs
• Solving user
problems

17.
How to collaborate across lots of autonomous
teams?
Need for coordination and alignment
of:
• Strategy
• Goals
• Dependencies
• Architecture
• Delivery timelines
• UX/UI Design

18.
Ingredient 2:
OKRs for direction
• Aligning objectives helps
to set direction and focus
• Teams agree OKRs
quarterly
• Teams track and report
on their KRs as they
progress

19.
Ingredient 3:
Product management
Developing products
according to Silicon
Valley Product Group
Shared mindset & framework:
• Cross-functional squads
• Who does what
• Product discovery &
delivery
• Methods & best practices

20.
Discovery & Delivery… the Double Diamond

21.
Ingredient 4:
Technical skills
Continuous improvement:
• Tools
• Data
• Benchmarks
• Share best practice in CI/CD, testing &
other capabilities
• OKRs that combine What (customer
outcome) and How (faster, more
stable)

22.
Technical
skills
Product
management
Squad OKRs
4 key ingredients

23.
Thank you
Richard McLean
@mcleanonline