The initial whistle sounds and the ball rolls into a random football stadium. At the same time that the referee has given the order to start the match, a single and lonely drop of rain falls on a grandstand that houses thousands of fans. Absorbed in what happens on the pitch, they completely ignore that the storm that looms over the stadium is not like any other. In a matter of minutes, the first drop has become a slight sparkle; After half an hour of play, the storm begins to be noticed more and the fans begin to take shelter. In the 50th minute of play no one is left in the stands or on the field because the stadium has had to be evacuated before the danger of flooding.
The data analytics and that strange rain that has caused the evacuation of the stadium in less than an hour share a common characteristic: the evolution of both phenomena is exponential. "First one drop falls, then two, then four, and so on. In the 47th minute, the pitch would be only slightly flooded and the surprising thing about this is that a few minutes later the stadium would have to be evacuated because otherwise people would drown, "explained Flor de Esteban, managing partner of Deloitte Digital, during the Deloitte Tech Talks event, organized by Deloitte in collaboration with CincoDías.
"With the data we are in that phase in which puddles have already formed, there is maturity, but soon and at a speed that we can not feel, we will be flooded, many things will be transformed," continues De Esteban. The expert talks about how in the future the relationship between human beings and machines will go beyond language and will pass to a stage of "total sensorization" in which the way of communicating with the environment will radically change.
The old new method
The technique that will make these advances possible, data analytics, is something as simple as gathering information, analyzing it and, subsequently, acting on it. Although a priori it does not seem something new or too complex, the development of new technologies, such as artificial intelligence or machine learning, is allowing to collect, analyze and use information in ways that until a few years ago were only possible in science fiction . "All the huge amount of data and all the technology that can digest that in real time is what today allows through a relatively simple algorithm to receive the relevant information at the right time," says De Esteban.
Juan José Casado, data analitics & AI director of Repsol, believes that big data and artificial intelligence are literally transforming everything.
"In an industrial environment like ours, in the entire value chain, machine learning and algorithms can help us optimize processes achieving greater energy efficiency," he says. Putting more concrete examples, the executive says that thanks to data analytics it is possible to develop "predictive maintenance" (repair structures or components before they fail) to ensure that the assets are always working correctly.
On the other hand, through deep learning techniques, Repsol predicts in advance the quality with which some of its products will be manufactured. "In the case of polyolefins, which is the material used to pack liquids, we are able to predict 15 minutes before the conditions in which the product will come out, which, when it falls from the estimated quality, the panelist can make the pertinent adjustments to correct the errors ", Married account.
Óscar Caballero, CDO of Orange, also agrees with the idea that thanks to data analytics companies have achieved the ability to predict the future. Orange has developed what they call "proactive customer service," which is nothing more than solving customer problems even before they realize they have them. According to Caballero, Orange knows at every moment if a customer has stopped running calls or the speed at which he surfs the Internet. "If we detect problems with the Wi-Fi connection and do not need to send any technician to the client's house, we fix it ourselves," he says.
On the commercial side, predictive tools based on data analytics allow Orange to set the optimal price of any device to maximize sales. "Not only is the price that competitors will place on an iPhone next month, but individually the probability is calculated that each of our customers will buy it and at what price it would be," Caballero reveals.
The impact of these technologies goes beyond the business world. According to Casado, "they are changing the whole society, our generation has the unique opportunity to make a better world thanks to these technologies."
As a potential transformer, data analytics and the technologies associated with it also have their own shadows. With a worrying record of abuses by some companies in the head, many users are wary of giving up their data. The CDO of Orange explains that consumers also benefit when they lend their data to companies because thanks to them they obtain offers and services more adapted to their wishes and needs. "The problem comes when the data that users give for a specific purpose are used for a different thing," says Caballero.
"Soon we will see ethical committees of artificial intelligence, there are many things unresolved, when computers learn to learn, with what criteria will they do it?" De Esteban questions. "If an algorithm decides for me the products that are most relevant to me, in reality it is also limiting what allows me to see, who is the machine to decide what interests me and what does not?", Reflects.
Despite the close relationship that exists between both facets, ethics and regulation remain at an early stage in which they have not yet been directly related in this field. "With the new data regulation, algorithm developers incorporated privacy when programming. The same thing is going to have to be done with ethics, taking it into account in the design phase, all of us who work in this must have in our heads the guidelines that come from the ethics committees, "Caballero said.
Effects on employment
The advancement of data analytics and its associated technologies has a strong impact on the way of understanding the labor market. The experts distinguished between two clearly differentiated effects. On the one hand, technology serves to enhance the decision-making capacity of workers while on the other there is the undeniable risk that some professions will be completely replaced by technological advances.
According to the data analitics & AI director of Repsol, these technologies are "for and to help people in their day to day". "We help our employees to do their job better, previously supported by intuition, now they have one more tool to make better decisions," he says.
Data analytics can become a key to setting the company's contracting policy. Knowing the flow of calls that a call center will receive in the future allows Orange to reduce or expand its workforce of 10,000 telemarketers depending on the needs of the moment. "The agents are people and you have to hire them, if the margin of error of the predictions is 10%, or the company has hired 1,000 more workers or there are 1,000 calls from customers waiting, which gives a nefarious service", explains Óscar Caballero.
Referring to those same call center tele-operators, Flor de Esteban highlights that as technology advances, the question will no longer be to estimate how many hiring will be needed depending on the moment, but that these jobs run the risk of ending up being replaced by machines. "As there are going to be robots that are going to be 24 hours ready to serve the customers and they will know how to solve everything that the users who call request, we will take a leap and then those workers will no longer be needed, that is the difficulty of the matter "He says.
While some jobs will be replaced, the experts talk about how others will gain relevance and the great importance of training to adapt to these changes.
"Everything that has to do with the data and the profession of data scientist is supposed to be the most sexy of our century, but you also have to break a myth, what people need is education beyond the background they have," says Juan José Casado.
"The teams are totally multidisciplinary, anyone with the curiosity that they have to have in mind for the data and to see what they can provide, with the right training, can take advantage of these technologies," he concludes.
A two-speed revolution
While there are managers who understand that this issue is a strategic move, according to Flor de Esteban, 90% of companies still remain outside of this paradigm shift.
Spanish companies, at the forefront
"The managers in Spain have learned in a very short time that the transformation or lead or suffer, which is a matter of speed," says De Esteban. In his opinion, many of the Spanish companies are taking the lead in this process of digital and analytical transformation, which is a source of pride because "in Spain we adopted these ideas much faster than in other places," he argues. An example of this good position that some of the companies in Spain have is in the case of the team led by Óscar Caballero. The Orange CDO has been responsible for the creation of data analytics teams in different countries in Europe and at the time of the event, was preparing a training on data analytics and AI for the entire workforce. Orange Spain.
Abundance of talent, lack of profiles
From the experience gained from having collaborated with international companies for their development in this field, the managing partner of Deloitte Digital praises the teams of data scientist and data engineering that exist in Spain, "they are the best that can be found , we export ways of working that we create here in other parts of the world, "he says. On the contrary, despite the abundance of talent that exists in Spain, according to experts, there is a problem of scarcity of profiles. "We have to encourage our students and universities to form more STEM profiles," advises De Esteban.
The keys for a company to face the change
In the opinion of Óscar Caballero, how to organize is the key issue when facing the transformation of a company: it can become the difference between success or investing a lot of money and leaving the initiative half done. "In the first place, we must have a lot of focus on centralizing to reorganize all the data well, to have government of the data at the company level and to recruit and train the workers," he reveals. "Ethical and privacy guidelines should be prepared with very homogenous formulas and then, as the working method matures, it can be extended throughout the company."
(tagsToTranslate) analytical (t) analytic (t) data (t) light (t) shadow (t) next (t) next (t) revolution (t) industrial (t) progress (t) exponential (t) phase (t) t) early (t) early (t) analytical (t) allow (t) take (t) better (t) decision (t) increase (t) efficiency (t) level (t) unsuspected (t) ethics (t) protection (t) follow (t) be (t) two (t) subject (t) pending