Embed Size (px)
Citation preview
EXECUTIVE EDUCATION
ROTTERDAM SCHOOL OF MANAGEMENTERASMUS UNIVERSITY
LEADERSHIP CHALLENGES WITH BIG DATATurning data into businessEIGHT-DAY PROGRAMME
BUSINESS WAS USUAL
“Exploiting big data requires fundamental rethinking of how we do business.”
Prof. Eric van Heck
LEADERSHIP CHALLENGES WITH BIG DATA
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 data-
driven 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 INFORMATIONDates: Please see our website for the programme dates.
Length: 8 days
Fees: The fee for individual participants for the eight-day pro-
gramme 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 num-
ber 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 participant organisation
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
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
LEADERSHIP CHALLENGES WITH BIG DATA
LEARNING OBJECTIVESThe 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 applica-
tions
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.
WhO IS IT FOR?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.
EXCLUSIVITYTo 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.
BLOCk 1: INTRODUCTION AND PREPARATION SESSION
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:
CORE PROGRAMME
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-the-
art update, and each group’s case study
results will be presented to them.
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.
Programme design
Marketing analytics
Business operations
Supply chainmanagement
Transport mobility
HR & learning analytics
Governance
• integrate datasolutions in theorganisationand businessnetwork
• achievestandardisation
• manage datasecurity anddata privacy
•
ExploitationMethodologies
• new analyticalprocessingmethods, such assmart algo -rithms, datamining, socialmedia mining,predictivemodeling new visualisa -tion methods
•s
Opportunities
• identify dataopportunities
• formulatebusinessproblems
• use externaland internalcompany data
• collect mobile,web andoperational data
m•
• support decision making
• optimise businessoperations
• develop newbusiness modelsbased on data-driven insights
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 5Module 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 6Module 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 7Module 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 company-
specific applications of big data as a pitch to company executives Group discussion
EveningClosing dinner
BLOCk 3: COME BACk SESSION
DAY 8Module 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.
BLOCk 1: INTRODUCTION AND PREPARATION SESSION
DAY 1Module 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
EveningWelcome 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 3Module 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 4Module 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
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, market-
ing 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 concen-
trates 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 sys-
tems 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.
FACULTY
Faculty members of RSM and ESE combine impeccable academic credentials with a thorough knowledge ofbusiness practice. Selected for their ability and experience in executive teaching, they will draw ontheir 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.
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
PA ConsultingGroup
Rotterdam School of Management,
Erasmus University (RSM)
is a top-tier European business school and ranked
among the top three for research. RSM provides ground-
breaking research and education furthering excellence
in all aspects of management. RSM’s primary focus is on
developing business leaders with international careers
who carry their innovative mindset into a sustainable
future thanks to a first-class range of bachelor, master,
MBA, PhD and executive programmes.
Rotterdam School of Management,Erasmus UniversityExecutive Education
Bayle (J) Building
Burgemeester Oudlaan 50
3062 PA Rotterdam
The Netherlands
Tel. +31 10 408 8633
Email [email protected]
WWW.RSM.NL/OPEN
15
07
. w
ww
.pan
art.
nl
© 2015 Rotterdam School of Management, Erasmus University. The information in this publication is correct as of July 2015, but RSM reserves the right to make changes affecting policies, fees, curricula, or any other matter announced in this publication without further notice. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form by any means, electronic, mechanical, photocopying, recording or otherwise without written permission from RSM.
Accredited by
Follow us on:
Twitter @RSMexed
LinkedIn RSM Executive Education
Erasmus Center for Data Science and Business Analytics
is a joint initiative of various research groups within
Erasmus School of Economics (ESE) and Rotterdam
School of Management, Erasmus University (RSM).
The Center supports organisations in turning data
into business solutions. The center helps companies
to extract business value from their data. It also
presents a platform for knowledge exchange,
access to expertise, student interns, and offers
research-based solutions and innovations.
www.erim.eur.nl/dsba
Erasmus Centre for Data Science and Business Analytics
Mandeville (T) Building
Burgemeester Oudlaan 50
3062 PA Rotterdam
The Netherlands
Tel. +31 10 408 8816
Email [email protected]
