Big Data technology (BD) is prompting a global revolution in our society, just as the steam engine did in its time, or electricity. This is essentially because it can be applied across many fields where intelligent automation wasn’t employed before. In this interview, Carlos Morrás, director of the Master’s in Big Data at the Comillas Pontifical University (ICAI) speaks about the impact of Big Data and Artificial Intelligence (AI) on organizations and the labor market, and the challenges they present.
How have Big Data(BD) and Artificial Intelligence (AI) transformed the business model?
The impact of BD and AI is notable in three ways. The first, and most visible, is that there are many more businesses based on data. We have moved from a few businesses, generally locally based, not excessively large, such as telephone books, debt collectors, or mailing lists, to thousands of enormous, global companies (Google, Facebook, etc.) based on personal data, whose turnover is almost the same as Spain’s GDP, or businesses based on company data, such as hotel or flight search engines, apps, etc.
The second way is through personalization. Due to the abundant data available on each person, the business model changes from a classic segmentation of clusters, to individual personalization in all sectors, thereby generating new business opportunities.
The third way is by radically optimizing processes. This includes improving existing processes, such as automating processes that used to be manual, as happens with bots (an apheresis of robot, they are computer programs that automate repetitive tasks through the Internet; such tasks would be impossible or very tedious for humans to perform), or by opening bank accounts using facial recognition.
What competitive advantage does BD provide?
The advantage is huge: it is the difference between having to go out of business, or being hugely profitable. In a business or a transaction, knowing what is happening, why it is happening and how to make it happen, is everything.
How does it affect the client’s experience of marketing and sales?
Really, the customer experience is key. Purchasing as a pleasurable experience rather than simply acquiring products is the natural evolution of markets with ever increasing standards of living. It is no longer about buying a good product at a good price, instead it is about enjoying the process, even being able to brag about the process. It is like the difference between just feeding yourself, or going to a haute cuisine restaurant. This trend means that the customer comes first, and the analytical capabilities of BD really help, and in fact, have revolutionized this. It is only possible to turn the enormous volume of data needed to analyze all mouse activity in an online store, or where and how an app (application) is used, into knowledge for improving the customer experience, by using BD. It is also starting to be used in physical stores, analyzing customers’ movements around the store, their favorite routes, hot and cold zones, whether or not they look at the price, if they compare it with the online price, the duration of their visit…
“It is only possible to turn the enormous volume of data needed to analyze all mouse activity in an online store, or where and how an app (application) is used, into knowledge for improving the customer experience, by using BD.”
So it has shifted from providing a descriptive analysis to a predictive and prescriptive analysis?
Yes, that’s the natural evolution of BD and analytics: increasingly sophisticated and complex analyses, from the simple descriptive analysis that is computationally inexpensive and can always be done, to first predictive, and then prescriptive. These types of analyses require much more data, more computing capacity and increasingly more sophisticated techniques, but they provide a much more relevant, valuable and sometimes, surprising, analysis. It means shifting from describing a situation, to proposing what needs to be done to ensure that what I want to happen probably does happen. It is a radical change.
By using Big Data the probabilities become more precise and detailed. Does this create more confidence or caution regarding the results obtained?
Initially the quantity and detail of the probabilities that can be obtained can be alarming, due to the intrusive, powerful aspect they can represent, but with practice, their reliability turns the concern into confidence and, sometimes, also blind confidence, if no critical sense is applied. It can also cause confusion between high probability and determinism: the truth is that almost nothing is 100% probable. There is always some probability against it, and when it is relating to people, it is fantastic to know that nothing is 100% certain; anything can change for better or worse.
Are there ethics for Big Data?
Of course. BD is a very powerful technology that raises multiple ethical challenges. Just because the technology can do it, doesn’t mean that it is ethical or that we should do it, or that we should even be working with it, legal or not. I’m not only talking about the dilemma for example, of who an automated car should try to save in the event of an accident, the passengers or the pedestrians. There are also certain aspects around using knowledge that you might have through BD and its relationship with privacy and discrimination. Do I refuse to give health insurance to a person because their probability of having cancer or another illness is high? Do I avoid offering credit to a particular nationality, race or religion because statistically they are problematic? As with all technological advances, it is not about placing limits, but avoiding misuse. It has to be used in the service of humankind, freedom, development and well-being.
How does the architecture of BD translate into a business environment?
Using BD for concept or laboratory testing is not the same as using it in a business. Implementing BD means the company embarking on a digital transformation that requires leadership and job roles with specific expert training. Processes and business models need to be transformed and there need to be data engineers, data scientists, data architects, business analysts, etc.
“BD is a very powerful technology that raises multiple ethical challenges. Just because the technology can do it, doesn’t mean that it is ethical or that we should do it”
Is it possible to have a balance between data, technology and talent?
Talent always comes, although there might be a significant imbalance in the short term. The most deterministic and mechanical tasks will be automated by BD and artificial intelligence (AI), but these areas also demand an enormous volume of professionals, which is always increasing. The challenge is to train these professionals, with the talent and abilities to lead the future, so that they help society and do not get left behind. For their part, data and technology go hand in hand: the volume of data and the capacity to analyze it increases with improvements in technology. Both factors have been balanced for some time.
How will AI transform the labor market?
The labor market is continuously transforming to adapt to the changing reality. Each time there is a technological advance, the labor market readjusts, as it has done since the invention of the steam engine. In the same way that when cars appeared, drivers and mechanics increased and stables and horse-drawn coaches decreased, the same is happening with AI. Some jobs have already appeared, such as Data Scientist and Data Engineer, and other new ones will be developed with names that still aren’t defined, that might be things like AI Configurer, Robot Pool Supervisor, or Robot Maintenance. In addition, the future labor market will have more opportunities for positions with more personal relationships and human interaction; roles that are more creative and include more improvisation; roles that offer more intellectual added value, and of course, those that are related to technology.
Carlos Morrás Ruiz-Falcó
Professor Carlos Morras gained a BSc in Telecommunications Engineering at the Polytechnic University of Madrid and a Master’s in Business Administration and Economics from IESE- University of Navarra. In 2000 he started teaching post-graduate courses at the University of Deusto (Bilbao) and various courses at the CEU San Pablo University, where he taught strategy, business vision, CRM and customer management. In 2017 he started teaching at the Comillas Pontifical University ICAI, coordinating the Big Data master’s thesis and teaching courses on Big Data and data governance, and business cases for the Master’s inn Big Data Technology and Advanced Analytics, which he also directs.
