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. 

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 

1. 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.

2. TYPES OF ANALYTICS

2.2 Data Scientists – who are they? How to recruit them? How to manage them? 

3. The ROLE AND RESPONSABILITIES of the Chief Data Officer.

4. MANAGERIAL IMPLICATIONS from Data Technologies Interview with Data Analyst.

5. The CAO and The CDO OUTSORCING the Chief Data Officer and Analytics Functions.

6. MANAGEMENT ISSUES

Data Governance, 6.2 Ethical issues, 6.3 Security and privacy 6.4 Implications of the Internet of Things.

7. RESPONSABILITIES of the IT Professional (and the data analyst!)

8. CLOSING

Course review, presentation preparation time and in team consultations 8.2 Team Presentations.

• 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