Speaker 1: Thank you all of you for being here today. It's such an honor to be here. And the idea today is to go through a trip all together. We're going to travel through the past, present, and mainly the future of data. It's true that data has always made part of history. And although it's said today that data is the new oil, data was used from very, very far away in the past. In the ancient Egypt, for example, we didn't talk about data, but we talked about statistics. And in the year 2600 before Christ, when they were building pyramids, they were using statistics. So yeah, it seems to be something really brand new, but it's not that it. And then also, many governments used data for the last 150 years. And in Spain, for example, we have the census campaign. First one, it was in the year 1856. And from the year 1900, we've been having census every 10 years without any interruption. And we're using this data, we're using the statistics for many purposes, but for example, to decide when and where to build schools or hospitals based on the age and also on the growth of population in each cities. The censuses are also used for other purposes that we all know, for example, to decide taxation. Based on the number of inhabitants and also the wealth of each city. So this is about the past. And if we move on into the present, we'll see that nowadays, we talk about a lot of concepts connected to data, some concepts difficult to define. And here today, the idea is to make something light for you. But for example, you've all heard about data warehouses, those electronic systems where we store information in a secure and reliable way. For example, also in business, you said the business and we talk about business intelligence is the way we use data to take good decisions in a business. And it helps us to make our companies grow. And then we have data mining and big data analytics, where we look for patterns, we look for correlation between different events and different variables in order to take also some decisions or try to conclude something. Then we have predictive analytics that tries to answer to this question about what will likely happen. So we gather all this data and we try to predict the future. For example, we have the World Cup now, and we can gather all the information and try to decide which company is going to win this competition. And we also, if we go inside ourselves, in human beings, we also have cognitive analytics that tries to predict somehow human behavior and how you as students and how citizens will behave in the future based on the patterns of their heritage, their parents, and also their behaviors nowadays. And then we have something that goes even far away, which is the augmented analytics that uses the natural language processing and also some machine learning in order to extract intelligence in a way that is quicker and simpler in order to go the extra mile. So these are seven concepts that we're using right now and that some years ago we've never heard about it. So nowadays, the present uses of data. Data's everywhere, okay? And it's not just about business, because if you see finance, for example, we have portfolio data. And what does this mean? Well, it's the use of data, of information, in order to decide where and when to invest. Sometimes it goes well, sometimes it doesn't, because data somehow, even if it's used to predict future events, it's not that easy to see what's going to happen in the future. And I'll explain you why later on. Then we have also people. I was HR manager before, and we start using people analytics, HR analytics. What's this for? Mainly, it is to try to improve the talent within our companies, try to get the most out of our labor force. And it's something really brand new, because HR departments did not have access to data. But now, with the new softwares being used by companies, it is possible. And it's something good, it's something positive. And then, obviously, we have sales, analyzing the customer journey of a client, and deciding in its touch points where to use those valuable data, those information that you got from the patterns of your clients. And this is when, after all, you're going to buy more this Black Friday, because all companies gather all information from your patterns, and they will attack you with those advertisements that you will see with those products that you really need. But there are other uses of data nowadays. We can see medicine, for example, to see some patterns of some hereditary diseases. And also, in the pharmaceutical sector, to try to predict which is the best drug to use for each disease, and how will a patient react to those medicines. Then we have sports. Again, as I told the World Cup that is going on. But you know, all athletes nowadays, they have something here that gathers all those information of the kilometers they run, whether they went left or right. And when we're going to the World Cup final, if it goes to penalty kickoff, each goalkeeper knows if that player is going to shoot right, left, up and down. Because all these teams gather all this information. It's easy even to predict that a country will win a competition or not. But you never know, because uncertainty is always there. And for example, other uses of data, we have mobility, and it's something good to create better cities, because with all those travels that we make, walking, because we have mobile phones that are connected to GPS, or public transportations, then town halls, they may create these heat maps, and they know exactly where people are. So they can actually decide if buses are going one way or the other, in order to live better within the city. And also in the logistics, if you have to deliver a lot of packages, business intelligence and data analytics will create you the best route in order to do it in a more efficient way. We talked about the past, we went through the present, but we're here to talk about the future. This TEDx, it's about the future of business, and this talk is about the future of data. So what will the future of data be like in the future? This is a really hard question to answer. And I would say that the future of data will be determined by the five Vs of data. First one, volume. It's the amount of data that we're using. More and more and more data. Second V, the value. And the value is related to the more operations that are driven through data. We've seen before that more and more operations that didn't use data before now are using data. Then we go to the variety, the different range of things that we can apply to when using data. Then, velocity, the speed. I mean, I've been talking here for five minutes, and in these five minutes, I'm pretty much sure that a huge amount of data was created. And if we want, if we access our mobile phones, we will have access to that data. This didn't happen 10 years ago, and this didn't happen 20 years ago. And the fastest it goes, the more volume of data we have been creating. And then something also really important nowadays that we talk about a lot of fake news is veracity, the fifth V of data. It's knowing about what is good, what is bad, what is true, what is false. And in a world full of data, it's not that easy to distinguish whether something is good or bad. So this is kind of the biggest challenges of data for the future. And it is kind of paradoxical, because we use data to predict the future, but on the other hand, the future of data is kind of unpredictable. And why is this? Well, specialists say that we are living in a Banny world. You know that Banny stands for brittle, anxious, nonlinear, and incomprehensible. And it's kind of an improvement of the past VUCA kind of view of the world, which was the volatile, uncertain, complex, and ambiguous. So we made an upgrade. And it's even more difficult to predict things. And my question is, what are the challenges? What are the barriers? What are the limits of data? And I'm afraid that the answer, it's us. It's you, people. And why do I think that? Well, I'll give you my personal view about this, and here I'll leave you with some statements. First of all, data, it's only valuable if you are the one who has access to it. If there's another company or another person that has access to that data, then this won't mean an advantage. So when you are the first one going to the market, you can create barriers on that market which will make you leader of that market. But then, in the future, with the speed and the democratizing of access to data, you will no longer be the leader anymore if you don't continue to do things better. You will have more competitors, and there will be no barriers. Then third, the time, the time between you gathering that information and the time you take a decision of what doing with that information will make the difference. Because first, data will be accessed by others, and second, if you take too much time to decide, that data won't be useful, that data won't be reliable anymore because of the speed, because of the velocity I told before. And fourth, companies are starting, and this is quite risky, they are starting to be not data-driven, but data-obsessed. You know that companies, sometimes they go, they put client first, client is always right, then they decide to be results-oriented, and now they say that they are data-driven. The thing here is that with the amount, the huge amount of data they have, they are not being data-driven, they are being data-obsessed, and they don't know what to do with data. And I think that it will be easier and best if you put people in the center, because if you put people in the center, people are the ones that are going to give a good service to the client, people are the ones that, knowing their results, will foster and try to grow and reach those numbers, and people are the only ones that can actually decide what to do with data. So that's why it's so important to put people in the center. And I think that in the future we will have two kinds of people, two types of people, and also two types of companies. The ones that really see in data the solution, and the ones that see in data a problem, because they don't know what to do with that. And same thing applies to governments. Having a lot of information may be dangerous if this information is gathered by a government that has some ideas that are not very democratic, because they can decide what to do with those informations. And we have an example, for example, of data in the past. Mr. Schindler had a list of 1,200 Jews, and he decided what to do. Save them from the Nazi. But he could have decided something completely different. So it's not about having or not information, it's what to do with the information, and that decision is made by a person. So we must be prepared, and you are quite in the barrier of being a data-native generation, because in the future our children, they will know exactly what to do with data, because they will be prepared to process all that information. Because we are not. Because we are in this life with a lot of information, with a lot of data, but we were not prepared to do so. That's why we have to go faster. And same thing with big corporations, they have, when you go, your mobile phone, you see those ads, you tend to buy, and you say, oh, this is fantastic, because I was just looking for that. But then you start thinking about it, and you start to be apprehensive, and you start to be afraid, and you start not accepting cookies, reject all cookies. Because then, obviously, you will realize that your mobile phone knows more about you than your best friend, your boyfriend, your girlfriend, your mother, even yourself. And this is quite frightening. And again, what do you think people's reaction will be to this amount of data, to this new reality? They will close themselves. And this is quite dangerous, because we will work in silos in the companies, because we won't have the ability and the interest of showing and sharing. And also, it will be dangerous, because if data goes in the hands of non-democratic governments, they will think about what to do with that. They will try to influence you. And we can go even beyond with cognitive analysis, and we can see these governments installing chips in your head, and making you decide what to do. Or if we are in kind of a reality where everything is recorded, and with the interaction we may have with cameras, they will see our patterns, and they will know exactly how we feel, and how to motivate us, and how to lead us to what they want to decide. So, main conclusion of this is that the only agent capable of changing the future, it's one, the individual. Because this will be the one, if well prepared, that can make with data something good, and not something dangerous. And to finish, last conclusion, is that no matter how much technology, science, and data evolve, the human being will definitely always, always be in control. Thank you very much.