St John’s University New York, United States of America
Dr. Geoffrey Dick currently teacher at St John's University in New York City. First appointed full Professor of Information Systems in 2009, he has taught in a number of universities in the United States and aroudn the world. He has a particular interest in online education and its future, including how it might be affected by the Covid-19 pandemic.
Geoff has taught and researched Information Systems for over 30 years. His research (around 100 publications) is mainly in the areas of telecommunting (his PhD) and on-line education - he is the recipient of the ICIS prize for best paper in education and was awarded the 2009 Emerald Management Review Citation of Excellence for one of the best papers published worldwide in the top 400 business journals.
He has been a visiting fellow at UC Davis, the University of Malaya, the TEC de Monterrey in Mexico, University of Agder in Norway, and has taught in the prestigious programs of the ESAN Summer School in Lima, Peru, the CETYS International Summer Program in Ensenada, the International Summer Programme in UAS Nysa and at ITAM in Mexico City.
COURSE DESCRIPTION
This course has the objective of preparing students for the advent of big data and analytics as a management resource in their organizations, against a background of the current wave of emerging technologies. Using current academic and practice-based readings and case studies, the course will examine the key issues in the establishment, utilization and maintenance of the necessary analytical tool framework and resources. While based primarily on academic articles and practice-based papers, which students will read and present as a summary to class, they will also be invited to identify potential big data sources that might be relevant to their current or expected organizations, and design an analytical implementation program to take advantage of the opportunities it provides, while identifying relevant problem areas. Students, working in teams, will develop and present a practical, business analytics proposal as part of the course.
WHO IS IT AIMED AT?
This course is designed for all students interested in the use of analytics to solve business problems and take advantage of opportunities. The advent of high-speed processing power, communications links and cheap storage have led to the use of data as an information asset – in particular of “big data”. Organizations using analytics as a form of business intelligence are outperforming their competitors who are not. However, problems and pitfalls abound. The course will look at ways to take advantage of this emerging technology for competitive advantage – indeed the survival of the organization. It will also look at ways to overcome some of the obstacles and impediments to successful implementation. Students participating in this course are expected to come from a wide range of specializations at their home universities – analytics is everywhere! They will identify analytics as a competitive strategy for use in modern organizations
CONTENT OF THE COURSE
INTRODUCTION
1.2 Introduction to Business Analytics and Lessons from Industry so far. 1.3 Big Data – industry Use, Discussion on readings, preliminary thoughts on managing the function.
TYPES OF ANALYTICS
2.2 Data Scientists – who are they? How to recruit them? How to manage them?
The ROLE AND RESPONSABILITIES of the Chief Data Officer.
MANAGERIAL IMPLICATIONS from Data Technologies Interview with Data Analyst.
The CAO and The CDO OUTSORCING the Chief Data Officer and Analytics Functions.
MANAGEMENT ISSUES
Data Governance, 6.2 Ethical issues, 6.3 Security and privacy 6.4 Implications of the Internet of Things.
RESPONSABILITIES of the IT Professional (and the data analyst!)
CLOSING
Course review, presentation preparation time and in team consultations 8.2 Team Presentations.
METHOD OF VERIFICATION OF THE LEARNING OUTCOMES
20% Class attendance and active participation
40% Student paper presentations and group projects
40% Final project
SPECIAL PREREQUISITES
This course will be taught in English. It requires students to have proficiency in the English language allowing them to read and comprehend the required readings, write reports and compile presentations, understand the lectures presented, interact successfully with the instructors and fellow classmates, and engage effectively in class discussions and presentations.
SUGGESTED LITERATURE
Tableau “Six trends in Retail Analytics” 2017
Davenport et al “How Big Data is Different”, Sloan 2012
Davenport “Analytics 3.0”; HBR 2013
Short and Todd, “What’s Your Data Worth?” Sloan 2017
“The Big Data Talent Gap”
Harris and Mehrotra “Getting Value From Your Data Scientists”; Sloan 2014
Power “Data science - supporting decision-making” DSS 2016
Redman “Are You Ready for a Chief Data Officer?” HBR 2013
Davenport and Redman “Great Data Teams” 2021
Marchand and Peppard “Why IT fumbles Analytics”; HBR 2013
Kearney “Big Data and the Creative Destruction of Today’s Business Models” Columbia 2022
IBM “Insights for the New Chief Data Officer”, 2014
O’Regan “Chief analytics officer: The ultimate big data job?” Computerworld 2014
Bednarz “Major League Baseball” Network 2021
Maras “IoT Security and Privacy”; Security and Privacy 2015
Arias et al “IoT Wearables Privacy and Security”; IEEE 2015
Andriole “Optimizing Operational and Strategic IT” IEEE 2015
“You are now remotely controlled” NYT 2021
“Spyware Wars” NYT 2022