What do you teach? 

I’m currently teaching data analytics and tech across programs in the School of Human Sciences & Technology (HST), from data insights and visualization to blockchain, change management, and digital transformation. Regardless of the class, though, I teach my students that it is important to challenge the status quo and think differently – and that technology can help them do that.

Where is your hometown?

I’m originally from Madrid although I consider myself from Asturias.  All my family is from there and I have been going back since I was young as a way to take time to reflect and recharge. In fact, I’m going now more than ever to work on a personal side project in which drones will be used to track wildlife in remote areas.

Tell us a little bit about your background.

I’m a mining engineer specialized in energy, with a PhD in thermoeconomics. Since my very first job, I’ve worked in business transformation (now called digital transformation), for example I worked on the introduction of a new agent in a highly regulated sector in Spain, the international expansion and integration of a Spanish utility, and the restructuring of the downstream and corporate units of an oil and gas company in the UK. I’ve also worked in Brussels as part of a supranational policymaking body, in Brazil with a biofuels company, and went to the “Future Energy” Expo 2017 in Kazakhstan. All my roles, including my PhD, have required a deep understanding of different technologies, data analytics, and business. This understanding led me to invent a new electricity generation process, write a book, and start a consulting practice. And now, in addition to my responsibilities as Vice Dean of Data Science & Technology at HST, I have my own startup that helps at-risk women in Eastern Europe rebuild their lives.

What gets you excited these days in the world of tech?

Technology is capable of changing people’s mindsets and fostering collaboration. And I think the best is still to come.  Although we currently talk about technology in isolation, the day will come when our discourse will focus on the delta of collaboration that all technologies, when correctly orchestrated, can bring – to our lives, to our professions, to businesses. That will be the time in which human sciences meets technology and the real re-evolution will have begun.

Let’s get this out of the way: There tends to be a concern that robots are going to take away jobs. Is there any truth in that?

Thanks to technology, such as robotics, we are at the dawn of a significant change in how we live, interact with one another, and work. It is true that some jobs will disappear but it is also true that new ones will appear. For whatever reason, when we think about technology, we sway towards the negative.  Thus, the focus tends to be on the lost jobs rather than imagining the opportunities that will come. Human beings are not always comfortable with change.

Yet, adaptation is to this century what knowledge was to the last century. So I think that in the mid-to-long run, in considering the new scenario with new technologies, the net effect will be positive. The opportunity to create new jobs and the ability to upskill ourselves has never been more promising.

How can students – and job seekers in general – prepare for jobs that haven’t even been imagined yet? 

In order to be ready for these new jobs, students should work on their ability to adapt, nurture their curiosity, and always be willing to learn.  These are continued efforts.  I myself try to focus on them every day.

Education helps. After four years in a bachelor program, and then perhaps one or two more in a master’s, students will have a foundation that helps them to keep learning, the hard skills that structure thinking, and the soft skills that expand knowledge and employability.

As in any field of study, passion is important.  To delve into something that is of no interest to you is difficult indeed, so it is important to “do what you love and love what you do.”

Many companies claim to use big data, but how can an organization get the most out of its data?

I was recently working with executives who believed themselves to be working at a very customer-centric company.  Yet they had seven different departments contacting the client throughout the customer journey, procuring different – and essential – bits of information. While trying to streamline the process, I noticed that there was no clear agreement on where the responsibilities of each department started and ended, and this meant that customers remained in limbo for months. Meanwhile the company’s competitors closed similar deals within days. The company and the competitors had more or less the same amount of data, and the same tools and specialized team. The problem for the company was twofold: there was no true conversion from big to smart data and the organization structure inhibited the company from adapting to its evolving needs.  In short, it’s not enough to gather data, nor use data.  It’s the whole package of how data is handled within an organization from start to finish.

What role does intuition play in big data and analytics?

Intuition (and creativity by extent) has a crucial role in big data and analytics. To start with, when massive amounts of data are available and resources are limited, decisions must be made, business problems defined, and hypotheses developed and tested. Intuition comes into play during the data analytics cycle, for example when tackling missing data points, choosing between analytical models, or determining how best to visualize the data.

As an anecdote, during my student years, I had a professor who asked us to come up with analytical models that provided accurate predictive capacity but no meaningful conclusions. The exercise helped me understand the importance of context and when to rely on my intuition in analysis. Intuition is what brings meaning to tech and data.

Where is AI really happening and working well?

Artificial Intelligence represents a broad field of knowledge that includes machine learning, in which large data sets are supplied to the computer so that it can recognize patterns when new data is provided and then accurately execute a given task (think personal assistants, plagiarism checks, traffic jam predictors, recommendation engines, etc.) Within machine learning (yes, a subset within a subset), deep learning is quickly advancing thanks to both mass amounts of data furnished by the “digital world” and the reduced cost of computing power.  Ironically, when AI works well, it ceases in being considered as AI. For example, the use of autopilots is one of the earliest applications of AI technology (1914), which we now take as commonplace, particularly in the automotive and aviation industries.

Looking ahead, what really excites me is how we will be able to pull together different fields within artificial intelligence.  There are companies already combining AI’s knowledge representation and reasoning with its statistical learning side to effectively reduce the field’s main drawback of the need for large data sets and to improve the way in which facts and inferences are generated.

Can you recommend a few books?

The books that I would highly recommend are those that have had a deep influence on my life: The Stranger by Camus, Humboldt’s Gift by Saul Bellow, the Essays of Michel de Montaigne, Brave New World by Huxley, Songs of Innocence and of Experience by Blake.

One of my projects is to finish writing the book I started during my last stay in London. It focuses on the new wave of business transformations from a human-centered viewpoint, in an attempt to help others avoid the pitfalls that I have personally experienced. The working title is We collaborate, we communicate and we innovate.  Available in bookstores soon!

Professor Borja González del Regueral was interviewed and photographed by Kerry Parke in Madrid.