In Conversation with Mary Ellen Gordon

In Conversation with Mary Ellen Gordon

In our much-anticipated 7th installment of “In Conversation with Sage Authors”, we are privileged to engage with the distinguished Mary Ellen Gordon, the author behind the groundbreaking book “Business Analytics: Combining Data, Analysis, and Judgment to Inform Decisions“.

Prepare to embark on a journey that not only delves into Mary Ellen’s illustrious background but also uncovers the driving force that inspired her to craft this indispensable textbook tailored for students in their Business Analytics courses.

Join us as we glean insights directly from the author herself on how “Business Analytics” is poised to reshape the academic and professional landscape for these students, equipping them with vital skills for their future careers.

But that’s not all! As a special treat, we’ve posed the question to Mary Ellen: What are her thoughts on the influence of Artificial Intelligence in the dynamic field of Business Analytics?

Without further ado, let’s dive in!

Can you tell us about yourself and your background?

I’ve always worked at the intersection of business, data, and technology, and have done that both within academic institutions and within organisations. My undergraduate, Masters, and Ph.D. were all in marketing, but with a heavy data focus. As part of my Masters degree I did the first survey of US IVF centres for the pharmaceutical company I was working for at the time. The results had an effect on both my company’s strategy and the practice of IVF at the time, and it really made me see how powerful good data can be in informing decisions. It also motivated me to get a PhD to further develop my analytical skills. After getting my PhD and working as an academic for a period of time I subsequently started a market research company that focussed on Web surveys when those were in their infancy and then on research in and about virtual worlds when those were in their early stages of development. After that, I worked in Silicon Valley using research and analytics to provide insights into how people were using apps and devices such as smartphones and tablets. I subsequently went back to academia where I did research on things such as marketing technology and recommendation systems and taught classes in data science and business analytics, among other things. I also teach professional development courses to help working professionals improve the use of data and the communication of analytical results in their organisations.

What inspired you to write “Business Analytics”?

I hadn’t been able to find a book that covered all of the topics I wanted to cover in the business analytics class that I was teaching (nor in the professional development courses I teach). Most of the business analytics textbooks really just seemed to be statistics textbooks rebranded and with mainly business-related examples. Obviously, a business analytics course needs to discuss statistics, but I couldn’t find books that also did justice to the broader business analytics context. What I mean by that are things such as the common use cases for analytics within organisations, but also things such as ensuring analytics are being implemented in a way that’s legal, ethical, and culturally sensitive. Many of the books were also not really current in the sense that they focussed on using samples to project to populations, and while that’s still often relevant, many current business analytics use cases, such as app and Web analytics or analysis of transaction data, involve population-level data rather than samples. Since I hadn’t been able to find a book that included everything I wanted, I had been using a book, but also creating a lot of my own content to supplement it. The book I wrote evolved from the content that I had already started creating and refining teaching my own business analytics and professional development classes.

What sets this textbook apart from others in the same field?

I think it’s that broader focus on use-cases, contextual factors, and population-level data. It also gives greater attention to how analytical results are communicated than other business analytics texts I’ve seen do. Those differences all probably stem from my experience working in analytics-related roles. Based on that, I know that for data to have an impact it needs to be relevant to the specific needs of the organisation at that moment and it also needs to be communicated in a way that is understandable and accessible to the people who will need to use it and not just accurate. I also know that people using data in ways that are not legal, ethical, or culturally sensitive has resulted in problems for many organisations and their stakeholders, so even before students touch any data I want them to be thinking about those risks.

Would you be able to share with us how this textbook can help a student on his/her business analytics course?

To be honest, I’m more concerned about how the textbook can help students in their careers and their lives more broadly than in the particular course in which it was assigned. I often find that students have retained very little from quantitative courses they have taken in the past – even if they did well in them. They seem to have memorised things temporarily or replicated what a tutor or instructor did, but never really internalise the core concepts. My intention is for this book to give students a more intuitive understanding of analytics that they will carry with them after the course is over. For students who are only taking a single analytics course, I hope that means they will know when analytics would be useful for making decisions and ask good questions of people presenting them with analytical results. For students going on to do an analytics-related degree, I hope that means they won’t lose the ‘forest’ of using analytics to solve real problems when they become immersed in the ‘trees’ of more sophisticated tools and techniques.

Can you share any stories about how your book has made a positive impact on the readers, students, or instructors?

The New Zealand Police contacted me last year looking for students to do data visualisation work on a project they were undertaking and I recommended those who had done the best on an assignment that was similar to the exercises in Chapter 5 of the book and drew on content in that chapter. The Police hired two of those students on temporary contracts, and both of those people now have permanent analytical roles with the Police. I heard from one of them recently saying they still discuss the course often, so I hope the practical approach described previously has helped contribute to them getting their careers in analytics off to a great start.

In light of the increasing prevalence of Artificial Intelligence, how do you anticipate its influence on the landscape of business analytics?

Already there is a fairly wide gap between super-specialists in AI and data science and everyone else. I believe the growing prevalence of AI will exacerbate that, which is unfortunate. People who are super-specialists in AI are probably not also experts in the types of problems and opportunities facing an organisation or in legal, ethical, and cultural issues. And those with expertise in those other areas may either feel intimidated by AI or have unrealistic ideas about what it can and can’t do. That creates an opportunity for people who are skilled in business analytics to act as translators between those two groups since they tend to be somewhat closer to the data than subject matter experts and somewhat closer to an organisation’s strategy, objectives, and day-to-day problems than people focussed exclusively on AI. Beyond that, while AI can do a lot more than it could even a year ago and its capabilities are likely to continue growing rapidly, there are still a lot of tasks within business analytics that are likely to be more efficient or effective to do manually for at least the next decade or so. During that transition period, there will also be a need for people with analytical skills to have input on things such as what tasks would benefit from AI, what data to use to train AI systems, what legal, ethical, and cultural issues must be considered before implementing AI systems, and how best to communicate analytical results derived using AI.

Like what you read? Don’t forget to explore our other Sage Authors’ Interview articles:

In Conversation with Virginia Braun and Victoria Clarke

In Conversation with Stewart Clegg

In Conversation with Claire Cartwright

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