ROTTERDAM SCHOOL OF MANAGEMENT
ERASMUS UNIVERSITY
EXECUTIVE EDUCATION
Prof. Eric van Heck
“Exploiting big data requires
fundamental rethinking of
how we do business.”
BUSINESS WAS USUAL
LEADERSHIP CHALLENGES
WITH BIG DATA
Turning data into business
EIGHT-DAY PROGRAMME
LEADERSHIP CHALLENGES WI
Organisations increasingly use big data to reinvent
their business. Big data brings disruption to
industries and organisations and can revolutionise
the way we do business. Big data also influences
customer relationships, redefines how firms develop
new products and services, changes how operations
are organised and managed, improves demand
and supply networks, and provides the basis for
new business models. New technologies for data
collection, analysis and prediction create huge
opportunities, but also ethical, legal, technical and
business risks.
The eight-day programme Leadership Challenges with Big Data
supports organisations in their transformation towards a datadriven company. It connects professionals in technical- and
methodology-oriented data science with professionals engaged in
business analytics, and links them to business’ best practices with
senior executive involvement. This programme is developed and
organised by the Erasmus Centre for Data Science and Business
Analytics.
PRACTICAL INFORMATION
Dates: Please see our website for the programme dates.
Length: 8 days
Fees: The fee for individual participants for the eight-day programme is ¤ 6,450. This includes course materials, access
to the e-learning platform, lunches, two dinners and the
social activities. Discount rates apply, depending on the number of participants per organisation. The table below shows
the cost for signing up as a company team, depending on the
number of participants following the programme. The costs
for teams with more than five participants equals team cost
of five participants plus ¤ 5,500 per additional participant.
Number of Cost per
Cost for partner
participants participantorganisation
1
¤ 6,450
¤ 6,450
2
¤ 6,250
¤ 12,500
3
¤ 5,950
¤ 17,850
4
¤ 5,750
¤ 23,000
5
¤ 5,500
¤ 27,500
Location:
Rotterdam School of Management,
Erasmus University
Language: English
Certificate: Rotterdam School of Management,
Erasmus University
Admission: Please see our website www.rsm.nl/lcbd.
Information:Dr Marcel van Oosterhout,
Senior Project manager
T +31 (0)10 408 8816
M +31 (0)6 48632174
Emoosterhout@rsm.nl
After successful completion of the programme you will become a
member of the alumni network of Rotterdam School of Management,
Erasmus University.
www.rsm.nl/lcbd
ITH BIG DATA
LEARNING OBJECTIVES
Who is it for?
The learning objectives of the programme are:
to provide high-potential professionals engaged in data

science or business analytics with academically sound, new
ways to apply big data technologies and methods to design
and implement innovative and winning business applications
to improve the business skills of technically focused data

scientists by exploring business thinking, business case
creation, and investigating problems from a business angle
to improve the technical skill set of business analysts as

they acquire new knowledge and understanding of data
science methodologies and techniques
to provide a cross-industry learning platform for these

professionals to learn from experiences in other, relevant
industries
to broaden data scientists’ and business analysts’ under
standing about privacy and security in order to provide solid
data-driven business applications
to engage participants and their senior executives and

supervisors as a basis for implementation of the developed
business applications.
The programme brings together professionals from various
companies and industries:
professionals engaged in data science, who are technically or

methodology oriented
professionals engaged in business analytics, who work with

business models and applications
senior executives and supervisors of participants, as sponsors

of big data use cases.
The programme is relevant for companies in finance and
insurance; consulting; transport; consumer products and
services; media; information and communication technology;
energy; and retailers. Professionals who are active in non-profit
organisations and governments, for example if you are working
on smart city concepts, will also benefit from this programme.
EXCLUSIVITY
To guarantee an open collaborative atmosphere during
the programme, where ideas can be shared and discussed
freely among the participants, the programme is open to
teams coming from different industries. Acceptance into the
programme is based on the date of registration confirmation
by the participating organisation and the size of its team. If
several companies from one industry want to participate, they
can reserve places in a next series. Consultancy firms have no
exclusivity rights into the programme, it is open for various
consultancy firms to participate.
Programme design
Block 1: introduction
and preparation session
Block 2:
Block 1 consists of 1.5 day preparation
sessions, focusing on terminology,
leadership challenges and readiness of
companies, including their enterprise
architecture and digitised platform,
case studies from other companies,
and short presentations of participating
companies. Every company will bring in
at least one case study, which will be the
red wire for team participants to work
on during the programme and apply the
learned concepts to their own context.
Block 2 is a five-day programme, during
which participants explore the data-driven
company, discusses technologies for
analysis, prediction and visualisation,
business case considerations, change
management, and implementation. On
the last day of block 2, senior executives
from the companies receive a state-of-theart update, and each group’s case study
results will be presented to them.
core programme
Block 3:
Come back session
Block 3 is a one-day session to review the
latest developments and evaluate how
the company has progressed with their
own big data applications. It is set up
in blended learning format. During and
after the course there will be access to
a blended learning system with content
from Erasmus University Rotterdam and
other universities, including self-tests,
and relevant short courses and tutorials
to ensure a common, basic and individual
capability set.
Marketing analytics
Business operations
Supply chain
management
Transport mobility
HR & learning analytics
Opportunities
Methodologies
Exploitation
Governance
• identify data
opportunities
• formulate
business
problems
• use external
and internal
company data
• collect mobile,
web and
operational data
• new analytical
processing
methods, such as
smart algorithms, data
mining, social
media mining,
predictive
modeling
m
• new visualisation methods
• support decision
making
• optimise
business
operations
• develop new
business models
based on datadriven insights
• integrate data
solutions in the
organisation
and business
network
• achieve
standardisation
• manage data
ssecurity and
data privacy
Block 1:
Introduction and preparation session
Day 1
Module 1: Introduction to Leadership Challenges of
Big Data
Welcome participants
Leadership in the digital age
Challenges for data-driven companies
Best business practices: success factors, and impact on
business, industry and society
Introduction to company cases and assignments, learning format
Evening
Welcome dinner
Day 2
Module 2: Enterprise architecture for data-driven
companies
Agile companies and their enterprise architecture and
digitised platform
Closing the loop: sensing, storing, analysing, responding,
learning
Agility scorecard
Mind the gap: business analysts and data scientists
Module 3: Maturity of data-driven companies
Big data-savvy companies: maturity and capabilities
Successful big data projects: no time to waste
New ways of working with big data
Preparation company assignment and big data applications.
Company applications of big data will be focused on business
to consumer, or business to business to consumer. Business to
business can be included in case of linkages to the back office
or warehouse. Senior executives will be linked as mentors.
Presenting preliminary ideas about applications of big data
Block 2: Core programme
Day 3
Module 4: Current technologies for data analysis
Overview of key technologies and methods
In depth presentation of advanced technologies
Module 5: Exploring and visualising big data
How to summarise and explore big data
Data visualisation technologies
Day 4
Module 6: Advanced technologies for data analysis
In depth presentation of advanced cutting-edge technologies by
faculty members and representatives of selected participating
companies, such as chief data scientists
Work on company assignment
Module 7: Presenting and selling big data results
How to read data science results
How to present results to business
Speak the language of CEOs
Work on company assignment
Day 5
Module 8: Company assignments: discussion using data
analysis and visualisation tools
Present and discuss company assignments
Share initial ideas about business cases
Module 9: Business models and the value of information
Business models and the value of information
Stakeholder analysis
Investment portfolio and review
Work on company assignment using a business case template
Day 6
Module 10: Ethical, legal and privacy challenges
Ethical challenges when using big data
Privacy and security considerations and legislation
Module 11: Implementation – changing your company into
a data-driven company
Culture and change management
Implementation approaches
Management game: data-driven transformation
Day 7
Module 12: Recap leadership challenge with big data
Briefing and overview of programme for participants and their
senior executives and supervisors
Lessons learned from practice
Module 13: Presentations and final discussions
Participants present the results of the group work for the companyspecific applications of big data as a pitch to company executives
Group discussion
Evening
Closing dinner
Block 3: Come back session
Day 8
Module 14: The latest state-of-the-art developments
Review the latest state-of-the-art developments in big data
applications and business models
Evaluate participants’ progress with their big data applications
Module 15: Company-specific data applications
Reflection on company-specific applications of big data
Presentation of results of company-specific big data applications
and in class discussion. These can include initial prototypes,
results of analytics studies, business case calculations, videos,
and should address the topics presented during the course work.
faculty
Faculty members of RSM and ESE combine impeccable academic credentials with a thorough knowledge of
business practice. Selected for their ability and experience in executive teaching, they will draw on
their research and knowledge to deliver a unique learning experience.
Dr Jan van Dalen
is associate professor of statistics in the Department of Technology and Operations
Management at RSM. He has a background in econometrics and obtained his PhD in
quantitative modelling of wholesaling. His main research interests are in quantitative
analysis of information, logistics, trade and organisational processes. Jan has been involved
in various research programmes, such as monitoring trade and traffic flows with CBS,
trade lane risk assessment in Cassandra, cross-chain collaboration in 4C4More/Dinalog.
He is the co-founder of the recently established Erasmus Centre for Data Science and
Business Analytics and co-director of E-Urban, and leads the Urban Big Data knowledge lab
in collaboration with the City of Rotterdam. In addition to research, he has extensive teaching experience in applied
statistics, forecasting and big data in bachelor, master and executive teaching programmes.
Prof. Dennis Fok
is an endowed professor of applied econometrics at the Econometric Institute, Erasmus
School of Economics (ESE). He specialises in developing models to describe, understand,
and predict decisions made by consumers. In his research, he often collaborates with
industry. Among his technical interests are modelling unobserved heterogeneity, marketing econometrics, and Bayesian statistics. Dennis’ research has been published widely in
peer-reviewed academic journals. He is also associate director of the Erasmus Research
Institute of Management (ERIM), and has supervised many econometrics master students
with business-related data questions.
Prof. Eric van Heck
is a professor of information management and markets at RSM. His research concentrates on the role and impact of advanced information systems and technologies helping
to solve complex societal and business challenges. He is working on sustainable ways of
working, multi-agent systems for smart energy grids, mobile banking platform ecosystems
for financial inclusion, and sustainable maritime logistical systems. Research is carried
out with innovative companies and universities in Brazil, China, Europe, Indonesia, and
the USA.
Prof. Wolf Ketter
is a professor of next generation information systems at RSM. He is the founder and director
of the Learning Agents Research Group at Erasmus University Rotterdam. The goal of this
group is to research, develop and apply autonomous and mixed initiative intelligent agent systems to support decision-making in the area of business networks, electronic markets, energy
grids and supply chain management. His is also the founder and director of the Erasmus
Centre for Future Energy Business which enables robust, intelligent, efficient and sustainable
energy networks of the future. Wolf leads Power TAC, a new TAC competition on energy retail
markets. Since 2011 he has served as the chair of the IEEE Task Force on Energy Markets.
Dr Marcel van Oosterhout
is senior project manager in RSM’s Department of Technology and Operations Management.
He is responsible for business development and project management, and a member of
the daily board. Marcel is also business director for the Erasmus Centre for Future Energy
Business. He received his PhD from RSM in 2010, for which he researched business agility
and IT in service organisations. A spin-off company called Agility Factor is currently under
development. His key research areas include IT applications in global supply chains and
freight transport; future energy business; organisational agility and the role of IT; and new
ways of working.
Prof. Peter Vervest
is a professor of business telecommunications at RSM. His key research areas include
decision science; network technologies and applications; business networks; competitive
strategy; and change management.
COLLABORATIVE PARTNERSHIPS
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Rotterdam School of Management,
Erasmus University (RSM)
Erasmus Center for Data Science and
Business Analytics
is a top-tier European business school and ranked
is a joint initiative of various research groups within
among the top three for research. RSM provides ground-
Erasmus School of Economics (ESE) and Rotterdam
breaking research and education furthering excellence
School of Management, Erasmus University (RSM).
in all aspects of management. RSM’s primary focus is on
The Center supports organisations in turning data
developing business leaders with international careers
into business solutions. The center helps companies
who carry their innovative mindset into a sustainable
to extract business value from their data. It also
future thanks to a first-class range of bachelor, master,
presents a platform for knowledge exchange,
MBA, PhD and executive programmes.
access to expertise, student interns, and offers
research-based solutions and innovations.
www.erim.eur.nl/dsba
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Erasmus Centre for Data Science and
Business Analytics
3062 PA Rotterdam
Mandeville (T) Building
The Netherlands
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Tel. +31 10 408 8633
3062 PA Rotterdam
Email openprogrammes@rsm.nl
The Netherlands
Bayle (J) Building
WWW.RSM.NL/OPEN
Tel. +31 10 408 8816
Email moosterhout@rsm.nl
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Executive Education