[Sponsored Article] In this article I present my views on some challenges faced by the HKUST Business School in this era of big data, how it has been addressed so far, and some further points to consider. These are based on my experience as a statistician in the Department of Information Systems, Business Statistics & Operation Management (ISOM) at the HKUST Business School for 20 years, and as the new Director of our Master’s of Science in Business Analytics. Statistics and Data Science Data science is the term often ascribed to the emerging field concerned with the analysis and management of (big) data. While naturally arising in many fields, the technical aspects generally involve statistics, computer science (machine learning/artificial intelligence) and a considerable amount of data engineering with a view towards automation. However, to some who are unfamiliar with the various rumblings over domain and terminology, the admittedly simplified description sounds quite a bit like statistics. The key word of separation in the description is not “big” but rather management that leads to the conclusion that, while statisticians may be concerned with all aspects of data science, they (like all experimental scientists) generally want access to data, and are not directly involved with the engineering or computer science associated with database management. While it is pertinent to mention, this article is not meant to be a prolonged discussion about differences between statistics and data science. For that I would recommend an article 1 by the eminent Stanford statistician David DONOHO, who, among many other accolades, is the 2013 recipient of the Shaw Prize in the Mathematical Sciences. It is also noteworthy that departments at Yale and UPenn Wharton Statistics, among other places, have changed their names from Statistics to Statistics and Data Science. This article is also not about the role of Statistics in a business school, as that thankfully has been addressed by Professor Gareth JAMES 2 , of the USC Marshall Business School. Nonetheless, I will add that my statistics colleagues in ISOM have published in the top A-listed finance, economics, information systems and operations management journals. Big data occurs in many scientific investigations which may generally be outside the scope of interest of business schools. One can also conjure up myriads of examples of pre-information age big data, for instance data points corresponding to all the atoms on the earth. Below I present an anecdotal view of one of the primary, and now widely recognized, sources of big data that is of interest to corporate entities. Within the subtext, are my musings about how new tech quickly becomes commonplace or outdated technology. Intro to WWW, Internet, Big Data It was 1994, while in my friend’s, Patrice BERTAIL, office as a visiting scholar at the INRA 3 near Paris, that I wandered over to his desktop computer and first saw the World Wide Web. I recall exclaiming, “What is that?” I did not think much of it at the time. I was a regular user of emails by then, and welcomed the changes of size of floppy disks, for data storage, from 8 inches, to 5.25 inches, to finally 3.5 inches. DVDs had not quite arrived. MTV was still a primary source for new music. Apple was a moribund company, and needless to say, there were no smartphones, WhatsApp/WeChat, Netflix, Amazon/Alibaba, Facebook, Instagram. Twitter, LinkedIn, Google, Tencent, etc. I still used Normal Distribution tables, and regularly checked out stacks of books from the library. Despite the remarkable advances of that time, emails were not quite the same as phone calls, and social media, as we know it, was yet to emerge. We are well aware in 2021 that Sir Tim BERNERS-LEE’s invention of the web enabled billions of people to interactively connect with each other, academic institutions, libraries, scientific resources, social media and various corporate entities. Data generated from activity related to the web and smartphones is a large contributor to what we now refer to as “big data”. However, the web does not reflect the entirety of data available on the internet, indeed the web is not the internet itself, and there are also less accessible massive data stores housed by banks, governments and many other entities. Besides the companies mentioned above, and certainly other digital marketing or e-commerce enterprises, a growing number of other types of companies have recognized that there is potential value in data, and are eager to invest resources to capitalize on this. While a company such as Google or Alibaba is perhaps much more mature and prepared in this sense, they are all seeking talented individuals and the utilization of automated processes to help them achieve added value. Such talents are often referred to generically as data scientists, and more specifically as data analysts or data engineers. Business School Research The commercialization of big data, how it arises, and how it is used in various enterprises, issues of ethics, data privacy and compliance, employee training and the future of employment, fraud detection and risk management, and indeed adding to the discipline of Data/Business Analytics, are matters addressed by business schools worldwide. Within HKUST, despite big data creating new sources for commerce, it is still business, and many of the issues mentioned, such as data privacy, are readily tackled by our more seasoned forward-thinking faculty, based on applications of robust fundamentals. Nonetheless, given the scope of the impact of big data, the HKUST Business School has aggressively pursued new faculty hires from the best schools with appropriate new data analytic and machine learning skillsets. This also reflects an ongoing trend in institutions worldwide where machine learning talents are hired in various academic departments. It may not be surprising to know that many are housed in our multi-disciplined department of ISOM, and obviously Marketing. One can find such faculty in all departments including Accounting and Management. Furthermore, the utility of big data within fintech is showcased in the Business School’s RGC-funded theme-based research project, “Contributing to the Development of Hong Kong into a Global Fintech Hub”, under the leadership of Dean TAM Kar Yan. As is the general case when new subfields that employ new methodologies and viewpoints emerge, there are challenges, in terms of competition for more talents, retention and promotion. MSBA Business School Education In reaction to the commercialization of big data, the Business School, like many academic institutions, has introduced various courses and professional programs. Most of our masters of science programs have significant elements of analytics within their curriculum. Here I will focus on our Master’s of Science in Business Analytics (MSBA), which was, until recently, under the directorship of Professor Mike K.P. SO. The MSBA is a one-year technically intensive interdisciplinary program which is housed in our department of ISOM. ISOM faculty members deliver the core curriculum, and the majority of electives. There are also electives offered by other departments. The program is designed so that graduates leave with much more than the typical technical skills to execute data analytics, but also with developed data-analytic thinking, business acumen and communication skills to play front office roles in the process of leveraging data for adding value to their firm or clients. A director’s task, especially in a dynamic field such as business analytics, is to always look for ways to add value for students and prospective employers. Furthermore, it is important to understand the issues that these parties may encounter. One of my sources for a general view of the field has been the internet. After spending time on LinkedIn, blogs and in other formats, one sees for example, problems with ambiguous job titles. More alarming is the frequent post lamenting mismatches between expected deliverables and employee capabilities. I am also quite conscious of the growing role, and inevitability, of automation and AI within analytics. A Holistic View of Business Analytics at HKUST I have taken a holistic view of the Business School departments as all having potential relevance to the added value of students in Business Analytics without changing the usual core competencies and valued electives. Naturally this cannot be done without the cooperation of departments, individual professors, and the MSc office. I will discuss some details and acknowledge some individuals who have shown enthusiastic support for the program in various ways. This year, responding to the need in the industry, new electives will be introduced. Professor Chris DORAN and Professor Joon Nak CHOI from Management, will teach management consulting courses with some tailor-made adjustments for the MSBA, and Professor Song LIN and Professor Coral PUIG-GARRIGO, of Marketing, will teach Marketing Analytics related courses for the program. Industry-based projects will be directed by some of these faculty as well as Professor Kris Baoqian PAN, the Associate Director of MSBA. In general, there is a welcome sharing of electives across the various MSc programs, now headed by Salad KWOK. Professor Veronique LAFON-VINAIS, of Finance, and Executive Director, Career Development & Corporate Outreach SBM, has helped me with public relations, and my search for Business Analytics-specific corporate partners. As an important start, with the help of the Kellogg-HKUST EMBA Office (KH-EMBA), the program has been fortunate to establish a formal collaborative agreement with Sprint Milestone, which is a data management advisory firm specializing in data warehouse, automation, analytics and AI solutions for companies. Its founders, Akihito KATAYAMA and Khai San BAN, are alumni of the KH-EMBA, with extensive analytics experience in the banking sector prior to starting their company. Their willingness to graciously, and frequently, share their expertise is invaluable for understanding how to prepare students for the future of business analytics. “Exactly what I was looking for!” While there are many developments envisioned specifically for the MSBA program, including a professional business analytics advisory board, I believe that the business school itself can treat Business Analytics in a more holistic fashion. There is currently the recently established Center for Business and Social Analytics (CBSA), under the directorship of Professor HUI Kai-Lung. However, as a vehicle to expand the MSBA and other MSc program capabilities, there is room for a complementary entity for the taught professional postgraduate programs, which can serve as a nexus for fostering Business Analytics-specific corporate partnerships to help provide a practical training ground for MSc students, and perhaps expanded to include MPhil/PhD students who may wish to pursue a career in industry. The Business School’s positioning in Hong Kong and its strengths, for instance in Logistic and Supply Chain Analytics, as in our MSc program in Global Operations, suggest that training could also target individuals in Belt and Road nations 4 and likewise train local students for a similar purpose. These considerations will serve to make the university an enduring go-to place for students, and potential employers, for an elite Business Analytics education which develops value driven data-analytic thinking for a cybernetic 5 future. 1. David Donoho (2017) 50 Years of Data Science, Journal of Computational and Graphical Statistics, 26:4, 745-766 2. James G.M. (2018). Statistics within business in the era of big data. Statistics and Probability Letters (2018), Elsevier, vol. 136(C), pages 155-159. 3. Institut National de la Recherche Agronomique in Ivry Sur Seine, France. 4. One may find the 2019 report by the British Chamber of Commerce in China on “Education on the Belt and Road” to be of interest. BritCham Report - Education on the Belt and Road - British Chamber of Commerce in China | Beijing 5. Thanks to Prof. Joon Nak Choi (JC) for the terminology within this context and discussions related to this.