 Now, we'll get started. My name is Shannon Kemp, and I'm the Executive Editor for Data Diversity. If you have questions throughout the presentation, please enter them in the Q&A section in the bottom right-hand corner of your screen. If I'm not fluent in Portuguese, I will turn it over immediately to our esteemed speaker, Mario Faria, to introduce himself and start the presentation. Mario, hello. Thank you very much, Shannon. Good afternoon to all of you. This is an honor to be here at the Seattle Seminary for the people of Brazil, explaining a little bit about how to create and manage a data organization. If you have any questions, what would you like to do in Portuguese or English? The questions in the Q&A section are all answered during this seminar at the end. I don't think I can answer them, so I'll send them by e-mail to each of you, okay? So, okay, Shannon, you generally started doing the presentation, so I'm going to introduce myself. Who am I? I'm, according to MIT, one of the first chief data officers and head of data scientists in the world. For those who don't know this, I'm a Google Mario Faria chief data officer. And for the last few years, I've been working with TI, with strategic reconstruction, with the development of financial services, various types of products, and I also have a lot of experience in the area of private equity, which is a private capital fund to buy companies at its beginning. And for some time, I've been working with data warehousing and then I started working with this new concept of data management, data science, and analytics that emerged. And one of the great points was the contract, which was very useful to me, with my knowledge in the field of capital and value management. And for this reason, the agent and also a lot of people from CRM, digital market, I'm always invited to talk about these issues, both here in the United States, where I live today, in Europe and in several countries in Latin America, including Brazil, where I even lived, for example. There are several articles and today, I'm working on two fronts. I'm a member of the Bill Mesa Gates Foundation, I work on the data part, and I'm a member of MIT, which is a data science initiative, and it's going to have a very cool event in Cambridge, which is near Boston in July, where I'll be participating in the event, being part of the entire committee that is selecting the lectures and returning to the panel. So, let's go there. Before we go down, first I want to explain how you create a data organization with success, giving some points that can help you, depending on the degree of maturity that your organization is in. And one important point to mention, if you don't have Big Data, you can't work with the basic data part, and that's what I'm going to be showing you at the moment. My mission is part of the work that I've done, that I've always done, the task that I'm doing here, which is to make this data community develop increasingly with quality, being economically viable, and above all, in this mission that I have, it involves a lot of evangelization, apart from separating the tribe and the tribe, I'm sure that the main concepts are being used in a clear way, and above all, that the new functions that are emerging in this world, nowadays, of management, can bring new jobs. And then, in the case of Brazil specifically, we have an important part in hand today of doing a super-differentiated job. We can go back to a conference from San Diego, where there is diversity, which is the organization that is hosting this seminar that I'm doing now. After that, in the United States and Canada, we were the ones who had more participants in this conference already in San Diego. The conference was held once a year, and having the opportunity to work with the United States and Canada is a very proud thing. So, that's why I invite you to join me in the conference. Mario, since you're in Brazil trying to be interested in data, why don't we go back to the Brazilian market? And I'm a consultant. And as a consultant, whenever I approach my clients, the companies that are helping me to solve some problems, I like to say three things. And with the United States, I want to say three things about the United States. And today, how is it going? The complication that we are going through and, as a good consultant, always with the credit card, is to bring the solution. And what is the next step? You will be able to convince your boss at this moment to invest money in this opportunity that exists in front of doing an excellent management of data and Big Data. So, let's talk about the current situation. How is it going in this world related to Big Data nowadays? Because there is a lot of information about computers being used in the environment and a lot of information. Each decade has exploded. And there is a lot of information. First, the storage system started to get cheaper. Second, there was a population of computer devices. Third, with the advent of cell phones, which nowadays cell phones are not as far as the devices that you interact with with a series of applications, you access social networks, you listen to music. And all of this generated a huge range of data, a volume of data and part of these data are not structured. They are not in the model that we call TALA. This brings a peculiarity that is very interesting. Who will be working with this volume of data? Saber, which is a system in the 1960s when it was launched, they did around 84,000 actions per day. They were directed to enterprises. Saber did 100,000 actions per second. So, it is possible that all of these applications are generated to make this feel like a business and feel like a business is. How do I make more money? How do I reduce costs? This is where all of the data management comes into play. And then, the world of today is going through a very big change and these changes are being made because I call the four apocalypses. Computational, mobile computer and these are the studies of this information. In big data, you cannot combine these four elements. It is very complicated to make a big data project if you do not think of an infrastructure that is in the cloud. It is very complicated to think of a system that will allow you to have something that is considered to be the input of social networks. So, these four dimensions are very connected to what is the world today. And in this new world the person who is the consumer or who is the customer is often confused. We do not see if our partner can be the enemy of some years. If you look at some companies, and then you see BMW, BMW manufactures cars. And a week later, they start to manufacture some short-term films, 10 to 15 minutes, attracting super famous actors to make films. BMW starts to compete with who makes, with who generates content. So in this super new world that we are seeing, those relationships become very difficult, it becomes very difficult for you to faithfulize your client. And for all of this, that data makes sense. So, in my opinion, the greatness of companies, be it from the states, be it from Europe, be it from Brazil, they are super confused, they are super confused with what to do. How can I make this make sense for my business? Is it possible to be present here for a few years? Will I be able to make this change? And in the event of San Diego, I think it's a great opportunity to meet a person who is Aguento Thomas, who is the president of a data governance institute. And Aguento, one of the reps, he talks to me, and I thought it was very cool, because I have Aguento, I can put it in my presentation, I thought it was very cool, it was great. And now, Aguento, the following is the following. The forces, the power of R&D 2021 will influence so much, the ability that a company has to have its strategic assets of information so that it can bring this to business. So, in general, the new oil of this economy today is part of data. When we talk about data, we can't think of data as just a computer. Data is also about the people who use and generate these data, the technology that is involved on top of all the process of treatment of these data, the use of these data, the trend that is below this, the analysis models and statistics and mathematics created on top of these data, all part of communication to make the main areas of the business involved, what to do, when to do, how to do, and above all, data is to be done with which the possibility of taking decisions for the companies and that it generates actions on top of the information that was made imaginable and treated. And there begins a concept that is what is called Data-Driven Culture, which is the culture that is derived on top of a data management. And companies like Facebook, Google, are companies that do very cool work and the work model that they use with data, which is called Data Points, by other companies, by other models. And this makes it generate a tremendous amount of breakages to a number of companies. And I will talk about this a little bit. Big Data, what is Big Data? Big Data is the possibility of working with large volume of information and the information being generated at a very high speed. And if you use the traditional processes that are then used, you will not be able to extract the appropriate data. So Big Data, you talk about it three times, which is the speed, the volume, and the reality of these data that you are talking about. Because this is for me that I consider Big Data. And what happens when we talk about Data-Driven Culture, which is a culture generated on top of data, it even starts to change the way we develop applications. What do you do? Define the strategy, what will it be? What are the objectives that you want to achieve? You start to think about the applications, the applications you will deliver to the network, computers, and then you think about the information that is present. And it is important that this paradigm that lasted for almost 40 years begins to be broken now. And I start with the strategy, in the objectives, but the third point, I think about the information that is present, that I need to work on, and then, yes, I need to use the network structure, the computer structure, and from there I will use which of these applications, which are the systems that I need to be creating on top of that. So this part of data, which is called Data-Driven Culture, on top of that it has changed within the companies and in the applications of the network. But Mario, what is the difference between Business Agents and Big Data? So I will talk a little bit with you about this. Let's talk about the following. In the late 1990s, some of the books were called Competing Analytics, which for me is one of the most clear books that have been written until today, how to use the concept of analytics to make a decision. But when you start to use data, models, quantitative and statistical, and you start not to understand what happened, but to understand what can happen with a set of capabilities. So in the late 1990s, there were degrees of intelligence that were part of extracting a report, you can go to the office and do searches that are not standardized, and you can do for-casts and you can apply predictive models and manage the business, and that will take some time to do it. It is part of the process of a cultural experimentation in your company. And the point is, the companies that go through these degrees of intelligence are able to have a competitive advantage compared to their competitors. And so, as an Accenture, as a McKinsey, they look a lot in terms of the world of different sectors, insurance, products, and how they use analytics to take action. And they realized that what they do is better. They have more value, they have better performance, and they have better performance. That is a critical point. And then the following, if you look at the four types of analytics that exist, you will understand what happened. Second, is that you can, through the data and models, say why a certain fact happened. And then, from there, you can look at it and say the following, what may happen in the future with the potential of probability. And the last that I am most advanced and I say that very few companies do this today in a well-made way, is the time to do what will happen, which is the analytics and the main actions of the business. And the main features of a Big Data project, a traditional BI project that we have done since the 90s, is the project. In a Big Data project, if you look at the data, you think of an integration project, you look at the whole data and you have value. In a Big Data project, what happens next? You can make some decisions, you can make a transformation in a much faster way using new tools that have emerged in the market that give you more flexibility without going through all those traditional stages of the project. And then, what do you think of analytics? You think of a high volume of information about a large volume of information about real-time access through our sources of access to these data, such as logs, data on cell phones and so on. We are talking about a transformation process that is complete within your organization. Whether it is born, when computers started to enter the company, it was a very big cultural shock because the computer, it was a way of describing it. It was a completely different way of describing a machine. And that's why now it is a process of learning to link with this culture of data and it has an effect on that. One of the most important is the marketing area of the organizations. For example, of the concept of digital marketing, each time it is more than the marketing professional to know statistics that he has knowledge about mathematical models and, above all, optional research for the use of results in his marketing campaign. So, analytics, above all, has been a huge factor for a number of companies. This is in the following aspect. It may be that you have a lot of comments but you know that you know how to use analytics that you know how to use everything that exists to compete with you. And these companies consider a rapid growth rate and consider what the company should be doing with you. And a very important point for me is this. When we talk about analytics, above all, we are putting the client in the center of decision making. What do we do when we are doing analytics? Because we analyze a chain of values, we analyze behaviors, we analyze business operations to see where they can be optimized, when they are part of decision making and marketing, when they are defining prices for the campaigns that consumers have and that they give the price on the selling point, on the social media or do it for monitoring and prevention that can happen. All of this in the background is putting the decision making in the center. For the first time, I see that this is really the fact. The companies start to look in a very, very way, using a microscope to see all the values of their customers. And then, when we talk about predictive analytics, what can you do if you do not want the data to be given? Your data has to be very well treated. Otherwise, you will not be able to do anything on top of predictive analytics. So, if you want to do what your analytical part works first, you have to choose your data very well and almost are the sources of food. Second, define the clear models that you will be using for the data and that you will be able to make a possible decision making. Above all, within your company and a culture that has to come from above, you will be able to make the data that are spread be effectively used by all the areas of business. So, for me, the big data that I have to translate into two different areas is the human behavior because we have the ability to use a very effective, fast and economical in human behavior. So, for the big data, which I think is a term but the flexes will be tremendous for us. With this, we will start to look forward to the same thing, the center of monitoring of infrastructure and the biggest companies that are already seeing what is happening in the company are sometimes in a standardized structure. The big data is very powerful and what I see is the same way that Google, Facebook and other companies have a lot of monitoring on top of their data. We will start to see a lot of data in various sectors because there is a tremendous value. This data is a file that will generate a competitive advantage of your business. So, I talked about the current situation. So, what is the complication of this? Now that I said that it is very nice, very beautiful, what would be the complication in the company? First of all, of course, there is a lot of zoom in all this. Nobody knows how to start, how to do it, what are the levels of responsibility regarding this matter. Who are the owners of the data? Who is the owner of the data in the organization? The CEO, the biggest in format, he thinks he is the owner of the organization. Many of the bank's bankers, the DBA, think he is the owner of the data in the organization. Many times it is the guy from the market who has all the information of the customers, how to develop the social networks or a result of a campaign, I think he is the owner. So, I will start to define in a very clear way who is the owner of the data in the organization. And the phrase that I said in a congress that I had in MIT, where one of the people of the army and the American government speaks very well, they are using very well all this part of data, exactly given to the US, has been done by the government's companies and through a very strong action of the Obama government. And of course, he is the secretary of the army, he is a high figure, translating this into our presidential context, who is the owner of the data is the president at the end of the day, because he is responsible for this data. So, not a guy from TI, not a guy from a business area, much the opposite, he is responsible for the data. The world that works with data actually is the following, the manager is being the maintainer of this information of a very, very common person, he is not the owner of this. But, the data is not available to experts, it is fragmented. So, the data that is in the S&P, the time, I will put it on the screen, it is in the hard drive of the person who is in the S&P, at that moment it is being budgeted, is a series of data. There are many places where the data is present, that no one is able to do it by hand, no one is able to do it by hand. I see this very much in the area of TI, which sometimes is a service for humanity, that there is adequate access, and many times the area of business that generates your data and are not using it in a corporate context. And, there is a lot of data and it happens the same, you don't have a data center and governance program, you know how to have data and you will not know what is the real information. The next step of data is complex processes that involve different technologies in various areas of maturity. One of the most crass that I see between data projects are managed by TI and they are business projects, not TI projects. And all of this, if you look at analytics, you don't have a common analysis, quantitative analysis. So, this is the big problem when you start with company data. And a very serious problem that I see is the following, data is a data concept. It is used in the memory disk. But data is a super abstract concept. Try to explain this concept of data to those who don't have computer data. It is very difficult. Data management of the data is captured, analyzed, analyzed, all parts of the installation and the data is maintained. And for each of these four main stages, there are seven sub-stage stages that involve this. The process of the data generation cycle is extremely complex in this whole context. Okay? And let's take a moment here and talk about Big Data technology. Well, RADU, PIMEP and DUS are the only Big Data technology. So, let's take a look at this chart here. The lower part, the lower part in the middle is where the data is being treated. There used to be a form of perforated cartons. These perforated cartons were the concept of archives, which were more in computers. From the terminal moment, the concept of tables, tables was the concept of relationship, the concept of communication. So, the lower part began to develop a series of other ways of working with DUS. The RADU PIMEP and DUS is one of the ways of working with Big Data technology. Not the only one. Today, you can do Big Data with a kind of Big Data technology. The main market is the RADU PIMEP and DUS. It's the same because RADU PIMEP and DUS have done a very strong job of creating new tools, but it's not the only one. The same way, my view is the same. You will remember when Linux emerged as an operational system a few years ago. It questioned how to use Windows, or how to use Windows, and so on. So, Linux entered as a category of operational systems. With the RADU PIMEP system, it entered as a technology for you to make information projects. However, today, there is a series of investments that have been made in this area. And what is next? The RADU PIMEP and DUS have increased a lot. Because every day there are more companies that are making investments on this subject. You see that a number of new languages have emerged on top of the RADU PIMEP and DUS technology. The RADU PIMEP and DUS with JLTP, was created to create a new ecosystem for you to work with information. And this ecosystem is part of a software-free community. And, in the same way, it has been questioned if I should make an investment in MIM, Oracle and Microsoft, or should I go to a RADU PIMEP technology that has to do with this technology. What I can say is that the great engineers, like Teradata, Microsoft, all of them are led by this great media production that has invested a lot in having their own RADU PIMEP and DUS. All of them. But it brings a problem that I, instead of buying a great IBM server, where IBM has a great margin of profit, which is a kind of solution or a solution that allows you to tackle the same problems based on a RADU PIMEP server. So, what should I invest in? And as a consultant, I say, it depends on the problem. And, I'm going to show you the confusion that is today in this world of technology. You can see that the main companies are thinking about the context of the cycle of life of the organization, which is based on data, analysis, and actions. The main players of the technology are the clients, as well as start-ups that have appeared in the last few years and are having a great success and map-reduce. When I look at this slide here, I see something, I would say that the theme of the way it is does not survive in the next few years. Some of these companies will be some of them will be bought by major companies and some of these companies will disappear throughout the story. This market is not as big as this one. The context is the same with data banks, operational systems, computers. Throughout this time we realize that all the changes, all the technological rupture that occurs, what happens? A hundred companies arise, so we will begin to live in a national and big-data market. Reduce for me is the following. These are fantastic technologies when the output ratio with the number of input registers you have is very large. This should generate much more output than a hundred. It is perfect to deal with this. Another advantage is that from the moment I can generate an initial program and a series of sub-problems where these sub-problems can be executed in a way that happens in the supermarket sometimes when you make purchases. So in the supermarket you buy fruits, you buy lettuce, you buy vegetables, you buy cleaning products, and so on. For example, if I go to the supermarket and there in the supermarket I take the list of lettuce for a server to execute. The list of the company's products I give to other servers to execute. The list of vegetables I give to another server. And the final response will come from the moment that each of these servers manages to execute your problem for those who asked for the request. And for me, MAPReduce has the best possibility and what I see in a big-data data architecture is a big-data architecture with a traditional model of data architecture that we live in. So, you take a bath in the baby and you throw the baby out and you throw the baby together. So it's not for you to throw all that you did outside. No, very on the contrary, the big-data architecture will coexist with the traditional model that exists today and create a the best in MAPReduce. Where the big-data architecture makes transformations in a very fast way where you want the best integration of data to be part of a traditional architecture. So, the big-data project will not have any value that is used in an analytical way and will give predictive models on top of these data. And these predictive models can make me a big-data architecture and I can do what they call clear extensions of what I have to execute from these data. So, the big-data will be connected to a clear business strategy and it will not bring value. So, you don't define very well what is the problem not in a technical way when you start looking at a big-data project as what you want to extract from these data. And this is a experimental project that you don't know where it will take you. So, the company's recommendation was to define a few people who are not involved in the day-to-day business and put these people together with the big-data. They put them together with the business area. They don't leave these people together in the IT area because they don't see any big-data initiative. So, let's go. And then, the next point. We often start the project with technology. Guys, if you just want technology and not change even if you really want a change in analytics, you have to start to rethink the process, rethink the mentality and invest in the training of the business. And then, with the application of technology, you will be able to change your result. But the point is the following. Few companies use the company's consulting until you understand that if you leave a vision of a data management you can deal with the information as a strategic asset. Few people know how to do this well. We are still in the process. So, as a tremendous opportunity to give you a quantum leap in your career, since you know how to do it in a very clear way. And then, I talked about the situation and now I'm going to talk about the solution to all of this. When I said that data management is an abstract concept, first, do you think that people can interact with this object? What I'm showing you is a factory. Maybe some people are in this seminar here and have never placed their feet on a factory. But if I talk about what a factory is, there is already a factory in a factory that works. If there is any material, there are people working there, there are robots, and there are products that are produced. This is a factory. You may think as if you are eating human body food in front of us, but do you think it is a real object that you can explain to all these things with a chain of value related to data management? How where data is captured? Where data is created? What is the whole process of processing, of collecting, of storage of these data? And how are these data distributed in artifacts where the areas of business are accessed where their main customers access these data? This is what I call the chain of value. If you don't understand the chain of value of data in this organization, don't forget, there is no project with any big data. If you think about the chain of value in an organization, a man who is working for me, he created in the mid-1930s a model of production based on systems. He created computers for military use. So I thought, let's think about the chain of value in a company, but there are things like a system where there is material, where there is material distribution, there is material treatment area, and there is distribution of this finished product. And then you connect all the projects and you connect to your model of the chain of value of data. If you apply a formal methodology of treatment of this, there are big chances that you will have a more systemic view and the data processor. And the best thing is quality. And the quality of data above all, is not the quality of the data or the IITI I think exists. And without the perception of a financial area for a supply chain for your final client. And with the quality of data we have to think how much data was created, what is the completeness and accuracy of this data. And what I say to you is that the data never ends. And in this concept I say, if your data has quality you have the credibility of your industry. In the United States, in the hardware and this part of credit sound is widely used. For you to do anything, to have a credit card, a phone, a cell phone, to buy a house, a car, and everything else. In each 4 consumers of the year there was a problem that the regulatory organs of the United States began to work with companies of Birode Crédito to try to improve this concept. So there is a series of marketing programs today in the market, I won't even talk about them. The point is, if you want to bring data with the seriousness of the organization implement some of these programs with the quality of data such as sexing, women, and so on, you will have a chance to have a better result. And this is the following, to realize that they must have leadership specifically about the data in the organization because it is a function of ITI and it is not a function of the business to think about it. And this is the following, being created the data teams that look at a series of dimensions about the value chain of the strategy, analytics, architecture, governance, quality, acquisition, operation, politics, security, such that each of these dimensions has been finalized by groups and this with a very critical look of the business. A new figure in the market that we call ITI, data officer or head of analytics or lead data scientists who are responsible for dealing with each of these dimensions and the executive reporting for the company's advice and this composition today that can bring the best result of the organization. So this chief data officer, chief analysis officer, in fact, is an executive, the mission is to think about the model of data that is related to the president as to a CEO and the way to think about the data as a strategic asset that will help the development of your strategy. Just to make the following, the head of the chief data officer has caused a lot of concern in the ITI in the information managers and I say why it should not cause this concern. First, the chief data officer will reduce the process of looking at the data looking for information and above all, make sure that these processes are operationalized in the vision of architecture to bring value to the organization. The head of the chief data officer will look at this process as a whole. And why do I say that ITI, the information managers, are not the ones who are going to make the transition to the ITI. The technology helps a lot but the data is the point that makes a conversation between ITI and the business. So the chief data officer is responsible for all the governance and use of the data making a link between ITI and the business looking at the processes that are related to the data. And above all we have to think that any business, any company has three architectures. The architecture of the business that I want to talk about is the architecture of the business. The second is the architecture of the data that will be supported by my decision. What are the data and what are the doctors and what is the architecture that will be supporting everything. And for each one of these areas there is a group of responsible people. I don't put the business card to take account of the technology I don't put the technology card to take account of the data. They are completely different models of these three architectures. The function of the information architecture of the company is what the business needs or the requirements to make this bridge. It has to be working very closely with the ITI area. It has to be working very closely with the business areas. These are completely different views with this. And here comes the point. Why do I need to focus on some things such as practical management, processing integration, data development, support operation before thinking about analytics, mining, productivity. I'm talking about the Maslow pyramid, where the most basic cities are not attended, I can't think about things. I want to sell a car for those who don't have the condition to bring it to their family. It's the same thing here. If I don't have the most basic parts working today, then it has to be a joint process where I think about analytics and productivity, but I have to have the infrastructure of my business ready for that. And the Active Data Officer will make this connection with your team. It will think in all of this in a more broad way. It will have a fantastic link that was launched a month ago by Dr. Peter A. And with the help of the Active Data Officer, I will organize all of this through looking at engineering, looking at architecture, and looking at the process of delivering this information. So there is a very complex function of this vision process, looking at this data, looking at this information, understanding it, using it for my business. And what I do within the Active Data Officer team is to define a team responsible for the data science. They have to be connected to the strategy of what in that vision is the quality of management of MDM. So I think that nowadays I have a part of storage, I have a part of treatment and management, and I have a part called Data Science, which is a part of learning, analytics, reports, and so on. And I have the benefit of this business to have these three main components having people under the structure responsible for each of these blocks. Right? First of all, the function of the Active Data Officer is to bring money. It is not to write in the script, it is not to know about SQL, it is not to have technical discussions. It is the same way that the CIO which is the leader of Formatica, is not to be discussing there the code. The function of the Active Data Officer and the Active Information Officer has to be above all, a vision aimed at bringing money to the companies. Where technology and methodology will be part of this achievement. To talk more about this there is a very nice report that Gardner made and he talks about this. It is a report aimed at the company president. It is a report aimed at the company president because the invention of the Active Data Officer becomes the most important part of the business I have. And what the Active Data Officer and its team have to do above all is to look at Big Data and look at different sources and know the following how I will be able to better my business. How I will be able to supply chain my financial area. How I will be able to be faster taking decisions how I will be able to do for it. Above all, it is a team of customer service within the organization. So, if you want to see a Big Data success data project, you have to look at the following. How do I make money with money and costs or how do I make money because if you want to see a big data and have a lot of money you can approve any very easily. Above all, if you want to see big data you have to have data and you have to give it money that I will use and above all I have people. People who will implement, people who will be able to use for information in a very clear way. Let's think about the following. If there is a training where where everything starts but the data you generate information, you will do your most deep analytics. This will generate what? Intelligence of your business? This will generate a better performance for your organization. When you start these things with a clear view regarding your infrastructure of things that need to be mounted on top of everything there is no data area that doesn't have qualified people who don't have a working methodology that doesn't have a technology. So, if you want to have a working data area you have to pay for it and not for anyone. In the same way there are smaller companies that don't have a technology that needs all this with bigger suppliers. So, think about the following. How much the majority of your company is ready for this and how much I will be as a person who is watching this seminar and if you are interested in making this change. So, the work with data on top of everything is amazing. Try to read because this universe is not ready yet. There are many things that are in changes. This team has to be in a position for people to be excellent communicators because they will make a bridge between business and TI. Above all, you have to understand your companies. It's not easy for you to have professionals with all this level of qualification. Just one thing. Looking at the future more companies from 2015 that will be successful are not the biggest ones. They are the companies that will adapt to the future. They will be able to use analytics but they will be able to use analytics in a very clear way with their human capital and with their business. And the next thing that happens for those of you who work with data is to test your company. Do you think it will influence others? Or is it a company that always follows others when they are using data? It depends on your company and your company being here with time when you think about it. And in this day and age of data if you want everything you have to notice first, you have to come from above. The people, the president the main thing is to embrace and support this cause. You can start from below from the moment that the people on top are willing to spend this money. Second, you don't have to invent the wheel. There is a series of methodologies that are already in place. So use and abuse. Look at the best examples and implement them in your organization. You don't have to invent something that already exists. You have to communicate clearly what you want to do when you are going to do it, how you are going to do it. The CTI has been the worst in communication and it has been today the target of the Chacota during the congress, the whole event, the whole discussion of business. People have to differentiate in this context. And the question of Quickwin Straga is something new, something important for the business. I'm going to take months and months to make the result. Always bring the result constantly. And above all, you want to help with success against the people who have more eyes than you can find. People who have a lot of money to spend the money to make this happen. So bring the most effort to work for this organization. And that's what you're going to do. But it's important to treat a businessman as a lover. It's very expensive. Think about it. And then I wanted to talk to the people about success. Always look at the stress and the impulse. Put your heart into what you're doing. So, that's what I wanted to talk to you about. Here are my contact information. It's going to be a honor to have you in my group. It's a group that I listen to. Big hit, digital brand, and everything else. Here are my contact information. And at this moment I'm going to speak in English for Shannon to answer some questions. Shannon, hi. I don't see any questions yet. But feel free to out there, just type the questions in the Q&A section in the bottom right-hand corner of your screen. We've had a few chats going on. Sure. I have one here for Marçal. I'm going to switch to Portuguese. Thank you, Marçal. Thank you for coming today. Thank you for the introduction. I have a question for Marçal. I don't know. Marçal, that's an excellent question. Let me tell you why this process helps in the business. When we decided to create a business the first thing we saw was how to base our business. When it comes to methodologies, we realized that it was more suitable for the model of work. When you start to implement a business method, you can decide to understand those ordinary words. You have to look at things that you've never seen before. Make sure you start to see some problems that you've never seen before. It was very important and we realized that it was very serious in our process that we were able to implement it. So, if you have a certification in 1901 or a DmBock certification the first thing is to make sure that nothing happens. You have to be very, very rigorous for what you do and you start to discover a number of projects that have a lot of problems inside of them. Maybe that was the main thing we had. I have a question from Gilberto. Does Gilberto do well? Does Gilberto give structure in the context? Does knowledge be in the document? Does knowledge be in the document? Gilberto, what happens when you start to compare structured data and not structured data? The structured data is called SG, the name of the mother and the name of the father. The structured data is not in the text file. For example, logs of networks, of access information of social networks. All of these are text files but you can't find them in the entity model. And the not structured data for me would be the data that is in video, sound or image. For me, this is not structured data. So if you find that your data is not structured or not structured you have to do several of these data that part of the figure of you have an excellent meta data where having a consistent meta data you can extract the highest value. The higher the information that you have in the meta data the easier you will be to work with your data not structured data, not structured data. But with this, it gives a huge work to do. You will have to determine your cut line. We have a question that he asked in English. Let me put it. How do you change the structure of data of Radoop and MapReduce? How do you change the structure of data of Radoop and MapReduce making it cheaper and less expensive? So I want to save Radoop and I want to save. There is a company here in the region of SEA a big global player and they had to make a huge upgrade of the IBM server. The upgrade of the IBM server was for analytics people. And this is a small pilot that came to Radoop and MapReduce to collect some information that existed in this IBM server and threw it there to make some tests. They could have the same type of response but the cost was ten times smaller than what IBM was charging for the upgrade of the server. Instead of making the upgrade of the server, they decided to change the structure of Radoop and MapReduce. So if you want to change the structure of Radoop within the organization, use these options in Microsoft, Amazon and many other places in Brazil. So make a pilot and see what can happen. Maybe with this you will realize that it is cheaper to use this information. I'm not here like in the 905 the last hour that we have. Yes, thank you so much. Though I couldn't understand most of what you were saying, the conversation was full of great information. I thank you so much for this and thank you to the fans for attending our first webinar in Portuguese. This was very exciting. Mario, if you wouldn't mind let everybody know my thank you and that I will get a copy of the recording and a copy of the slides out to everyone by every day Monday along with answers to the questions we didn't have time to get to today. Thank you very much. Let's see if we can do another webinar in Portuguese. We will use a link with what happened today with the slides for you to download. Thank you very much for the affection and attention. Thank you very much. Thanks everyone. Have a great day.