HKUST Business School Magazine

Biz@HKUST Biz@HKUST 36 37 // Cover // Insight Big Business Big data is important for business, and the HKUST Business School is taking note of developments in business analytics and reacting accordingly. Chair Professor Lancelot F. JAMES Department of ISOM 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 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. 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 Corporate partnership and their sharing offer a holistic view for business analytics students