Turning data into business. Exploiting big data requires fundamental rethinking of how we do business.

Transcription

1 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

2 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. Location: Rotterdam School of Management, Erasmus University Language: English Certificate: Rotterdam School of Management, Erasmus University Admission: Please see our website Information: Dr Marcel van Oosterhout, Senior Project manager T M E [email protected] Number of Cost per Cost for partner participants participant organisation 1 6,450 6, ,250 12, ,950 17, ,750 23, ,500 27,500 After successful completion of the programme you will become a member of the alumni network of Rotterdam School of Management, Erasmus University.

3 TH BIG DATA LEARNING OBJECTIVES 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 understanding 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 work 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 professionals from different industries. Acceptance into the programme is based on the date of registration confirmation by the participating organisation and the size of its participating 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: core programme Block 3: Come back 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 is a five-day programme, during which participants explore the data-driven company, 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 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.

4 Marketing analytics Business operations Supply chain management Transport mobility HR & learning analytics Opportunities identify data opportunities formulate business problems use external and internal company data collect mobile, web and operational data Methodologies new analytical processing methods, such as smart algo - rithms, data mining, social media mining, predictive modeling new visualisa - tion methods Exploitation support decisionmaking optimise business operations develop new business models based on datadriven insights Governance integrate data solutions in the organisation and business network achieve standardisation manage data security and data privacy

5 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 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 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 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.

6 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.

7 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 Consulting Group

8 Rotterdam School of Management, Erasmus University (RSM) is a top-tier European business school and ranked among the top three for research. RSM provides groundbreaking 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 University Executive Education Bayle (J) Building Burgemeester Oudlaan PA Rotterdam The Netherlands Tel [email protected] 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. Erasmus Centre for Data Science and Business Analytics Mandeville (T) Building Burgemeester Oudlaan PA Rotterdam The Netherlands Tel [email protected] Follow us on: LinkedIn RSM Executive Education Accredited by 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.