 The impact of current technology trends and information management seems like that's a topic you all like. Gobi, do you want to introduce yourself please? Yeah, look about yourself and what you think of it. Okay, good morning everyone. Did I hear good morning? Can you say good morning? Good morning. We're doing a little bit of energy here and I know that the coffee is coming but unfortunately only in 45 minutes. So we'll have to wait for that. But I'll try to make this 45 minutes at least entertaining a little bit. The topic of my presentation is, you know, sitting back there is an interesting thing. You have to see all of the participants here. And what I was doing in fact I was, you know, this observation, research, analytics and so on. So I was counting how many people are using in fact some, most of them in fact were using the smartphones, the iPad, there are some laptops and so on. It's over two thirds of us having electronic device open in front of you. Some of you were taking pictures of it. Some of you were preparing presentations. Some of them were on LinkedIn, I noticed that. Some of you were just chatting. But this is what the presentation is all about. What is the impact of this information technology on our life, on our work and especially because this is a topic on the information management. It's popular to ask questions so I'm going to ask a question again. How many of you are on LinkedIn? Can you raise the phone? Okay, fairly good number. So instead of introducing myself, I would invite you to visit me on the LinkedIn and find all the information there. The other thing, yes, I do come from the agency. I'm head of nuclear information section which does have, and I will have a presentation tomorrow. So I'll talk more about INIS and the rest. But currently it covers library, the IEA library, INIS International Nuclear Information System and a small group of IT specialists. So this thing is working. We love technology, right? This is the short overview of the things that I will try to cover today, in the next 45 minutes. You can go slowly to it and see it. But generally speaking, I'll just make a brief overview of one slide of the way I see this currently stand. Some of those IT big changes and the progress made. Information management, which I'll be very short because Anatoly did a nice job explaining already most of it. I will try to look at the information as an asset. Talk a little bit about that. And of course the main thing is some of the review of those IT trends. And they are the ones you can see listed here. And this will bring us to the end when it's the question of IEM challenges. IEM standing for information management. If you're looking since this is training school, if you're looking for some learning outcomes, I believe that at the end of this you'll be able to recognize some of those IT impacts, although you are familiar already with most of them. You'll be able to define the IEM, appreciate the information as an asset, and understand relevant IT trends and of course identify some of those challenges. So this is a very busy slide and my purpose is not to make you confused. It is just to maybe show how the world is kind of organized today of all these elements which are working together or maybe not working. We have 7.5 billion people, most of them living in cities. We have half of them already on the internet. And you have another 2.7 or 8 million active social, and I just said two-thirds of the people were using or were on some kind of social media already here. We have a great number of smartphones being used. I'm just going to try to see it a little better here so that I don't have to turn there. And these are just some of the statistics but they are amazing statistics because when you look at it you know that most of the 7.8 billion people and half of them are on the internet are using this. This is all of the tremendous change which we as information producers, information users, we have to take into consideration. Some of the things like famous Moore's law that the number of transistors in a dense integrated circuit has doubled approximately every 18 months. Some people are doubting even though they're saying it's every 16 months. The computer processing power from 1956 to 1915 increased one trillion times. And I totally mentioned this 3,000 times bigger density and the capacity for storage. And this is another one of one trillion fold. I mean it's sometimes very difficult even to understand the size of that. 75 to 2008 it's a very, very interesting number. We have one billion pieces sold and we said wow that's a big number. At the same time in 2013 alone we had one billion cell phones sold. I mean this is something that as managers and users and people working in any industry should take into consideration. I won't go through all of that. There's no time for it but you can, I believe the presentations are available on my site so you can have a look at it. I'd just like to go to the last two red squares in fact that says 90% of the old data in the world has been generated over the last two years. Does it mean that probably within the next one year we'll have another 90% generated? So this is tremendous amount and I will have a slide about this just to show how big is this big data really. When we are talking about 2.5 exabytes of data that are produced every day and we've just mentioned how much data we are producing, how we can organize, how can we store it, use it and so on. We have to understand that you know every day we are producing 250,000 biggest libraries in the world, Library of Congress. That's something that is sometimes even hard to understand. So if we have to generate all this and put some kind of a framework around it we have tremendous PC power. We have the tremendous impact of mobiles. We have definitely impact of our own jobs, of all these changes. We have the information which is changing its concepts from, and I was just mentioning here, the data, records management, maybe knowledge management. But there is this concept which we have to look into it as something which is very, very dynamic. There was mention also already of expert systems, rule-based and so on. That was the beginning of artificial intelligence. So yes, there will be a tremendous impact of this on our lives. Robots which are almost everywhere, although we don't even realize sometimes that they are there. And of course this big data. In a summary, for the IT progress we are talking about what? We are talking about four or five major things which happen. From this development, and we saw those trillions of times or thousands of times of this place and so on, we have boundary pushing innovation which means that we know what was the standard yesterday, definitely is not today and will be completely changed tomorrow. This is something that you as young specialists will have to take into consideration while you are doing and organizing your own work or the work for others. This also means that we have a constant change. As soon as something becomes a procedure or a process and so on, the worst thing to do is probably to stop and say, okay, fine, we have finished it. It's exactly the same moment when you finish something to say, okay, fine, let's look at it critically and see what can be changed, what can be done there. This is all done in a very fast pace. And the last element here which I'm saying the social technology gap, I'd like to explain this, we have reached amazing technology development. But many scientists around the world, philosophers, thinkers and so on, they say that the social aspect, our human existence, haven't kind of matched that technological development. Where is the proof? The proof, for example, is today's discussion, literally today's discussion about the impact of artificial intelligence. We have the greatest minds in the world saying, don't ever touch that, don't do that, because that will be the end of humanity. And then you have some other people, leading politicians around the world, to say like, who masters AI will master the world. So these are just some of those social technology gaps that we will all be faced with. Just very briefly because Anatoly did an excellent job with this one. If you're talking about information management, you have to say, okay, fine, what is information? And there are zeroes of definitions and misunderstandings and so on. The simplest one problem is to say that information is data presented in a form that is meaningful to a recipient. So this is probably the only thing that we could generally agree on. We have the information management is the collection and processing of information from one or more sources and the distribution of that information to one or more audiences. So here, important to understand is that yes, you collect, you process, and what you do, you disseminate. So these three elements are the basic ones. And then you can see here on the right side, you have this more or less famous wheel with the steps or phases that you know, information management can go through. So I will just skip that and go. But it's also interesting too, this is not one of my most favorite pictures about information management. And this, you know, the famous pyramid from data knowledge, information knowledge. And I even hate to say wisdom, but yes, it is there. But what's important here is in fact this something in between data information that data has to be put in a context to become information. And then if you have the information and you have some meaning or add meaning to it, you might be talking about knowledge and I'll skip the last part. This is in a way somewhat sad part of this presentation. When you're looking for example of the current status of management, particularly of the bodies or units that are doing that, because we do have the libraries, information knowledge centers are disappearing. And I do have a number of presentations, some of them you can find it on the LinkedIn, talking about disappearance of libraries. We are all saying, wow, libraries are great. And when it comes to the question of budgeting and sourcing and so on, that's the first place to cut. And probably the next one is the archives and so on. We have also a tremendous amount of decrease in information management stuff, professionalism, and decrease in fact of their professional work. What do I mean here? How many of you have gone to a library or information center and somewhere you have found a volunteer? I'm not against volunteers, but there is this tremendous trend of putting people who are not really qualified doing this work, which literally diminishes the work of the professionals. There is the problem of budget, the price of external content is increasing, it's increasing over 10% a year. So no wonder that some of the journals, for example, in nuclear science or nuclear technology, they go up to 20,000 euros a year, one single subscription. We do have a problem with the cost of new systems and applications because the cost is also high. Everyone is talking about intellectual property protection and it's a very, very difficult thing to do because we have first of all to change something in our mind, the way we think, the way we consider information, we should consider it as an asset, right? I'll talk about it. So it's not everything free. We cannot get requests everywhere left and right to get the information. So this is the protection side. The other side is open science, the open access and all this open movement which are requesting from people, we want that. So what is happening is that on the one side, there is a very strong commercialization of all aspects of information managed on the other one is that we would like to have it open. Why? Because we believe that the social aspect which I was talking about which is not well addressed needs better access to this information to resolve it. So we do have a number of open access initiatives. You have a number of... Even in my presentation tomorrow, I will talk a little about information resources and I will mention some of those open access journals or open access repositories and so on which helps in fact overcome the problem of access to information. We have, of course, amazing competition which comes in the form of Amazon, Google and some other ones. I like this notion that everything is already on the web. INIS is one of those systems that has over 4 million records there and 1.7 million are full texts and people always say, but why do we need you? Why do we spend money on you? Because it's already on the web. There is one missing question there or should I say answer? The answer is someone had to put it there and most likely those were people in INIS who did it. So we did analysis of that and we found out that a great number of our own records are uniquely available only from us. That's also added value to information. Talking about information as an organizational asset and I love this cartoon, this is one of those in a book which was devoted in fact to this. I took it there which is a copyright breach by the way. It's the question, we all say what information is very important, it's valuable, it's asset and so on but do we really know what we are talking about? This particular asset, if we have to forget about information and if we have to read just the bottom part and you say the asset cost millions and nobody can tell where the asset sits, its quantity or where it came from, there is more than what a human or regular human brain can process. Many claim that they own the asset, the question of accountability and responsibility. It is not recognizable on a balance sheet and everybody will say what are you talking about? Where are we talking about information? That is one of the things which there is a little bit of a moment in accounting business that information should be put on a balance sheet as an asset but it's really far away from at least a number of years away from really doing that. One of the last parts there is like when they say that there is no measurement, no management and that is very, very important so because yes we have four point million records, four some million records in the innings, we have hundreds of thousands of documents and books in the library, so what? If we don't measure the impact, if we don't say that we have monthly over two million visits to our web page or searches, maybe that is the only thing that you can say if you find it is valuable because it's measurable. So that's something important for you to think about every time you and you're most of your engineers when you think about something, well I have to measure it, I know the dimensions, I have to know the values and so on so this is probably one of the things that needs to be done. So here we go, information is an organizational asset. First of all, when we are talking about assets we are talking accounting. In a balance sheet asset, that's their definition, anything which is tangible or intangible that organization owns or controls and that holds value or can produce economic benefit. This is very important to understand that you know in balance sheets and so on the asset needs to produce some benefit. If you are talking about information asset that's an identifiable collection of data or information stored in any manner, any type of storage device or form and recognizes having value for the purpose of enabling an organization to perform its business functions thereby satisfying and recognizing its business requirements. It's a long definition but the point is yes if there is a value for the organization and that value has to be measured that's most likely information asset. So every time we are dealing with something you know there's always this beautiful thing of I would love to keep it, I would love to have it. So let's store it. The question is what is the purpose of that? What should be done with that at the end? Is this going really to make some kind of benefit for the organization? It doesn't have always to be in dollars. It should be in dollars but it doesn't have to be always money value. It has to be the value maybe of improving the processes, making better decisions and so on. Here you have the characteristics, I won't go through them because you can probably or you have probably already read them while I was talking but yes, they are very important. They are mostly, we would call them general ones like for example the relevancy, sometimes very difficult to define. Timeliness, very important because no one wants to see statistics which are five years old and make a decision based on them. So there's a number of these prerequisites which if properly implemented, organized and managed would make the information as an asset for organization. The reason I put this block here on the right side where I talk about two theories of information and that is I believe an important thing to an important difference to make. We have a little bit traditional way of thinking and thinking of information as some kind of a tool for control. So what we want is information is used to alert us to a deviation from our plan. So we have a plan, we make one year plan, we have meetings this and that and we would like to have information coming telling us okay fine, this is working or this is not working. This is a little bit of a traditional way of looking at information and yes it works and we should probably have that one but what is much more important is to have this different view of information when we are looking at information as a learning tool as something that will help us adapt. And what do I mean by that? It's used today's, these are turbulent times. We have fast changing world, we have requests for persistent change, the uncertainty in that change. We need to make decisions quickly. We have to go on the different changes with HR changes or financial, there is pressure there and so on. So the information in that sense should be used in fact to make proper decision in proper time. So it is a little bit of difference, you can see it's like the previous one which is controlling and saying okay fine, this is the way it was. This is more saying okay fine, this is what we have, maybe the suggesting way is so on. So if you are thinking about the role of information it is preferable that if we make this little switch to seeing information in this decision-making process for something for future. My last slide about organizational assets and I really like this because it's a very simplified model. It takes three basic elements. It looks at information where the value is already realized. That's when you have the information and all the elements of the information are currently available and are being used. So we have for example a set of databases and so on, it's all set there, everything is fine and that value is being realized. The next step in fact is that it is based on expected capabilities and plans. In other words we are planning what we could do with this. It's not like we are just doing it, we are using it, information source is fine, but it's the thinking of what can we use it for? Is there something else that we can do with that? So this is the question of, and they put it nicely there, realized value and probable value. And the management specialist would look at it and say you are talking here with a very blatant, in fact, performance gap. So when you have this performance gap analysis, this is what they are looking at. This is a realized value and this is the probable value that we can identify through this and come up with. What's very important in fact is this potential value. If you apply the data to all relevant business processes, what will be the future outcome of all that? And that's so-called this vision gap. And by the way, this is not what I invented, this is by Gartner, which I'm not marketing Gartner here. I'm just saying that they do have amazing information resources regarding trends and impact on industries and so on. This is one of those slides that you probably will have a headache just looking at it, so don't look at it. Just listen. What I try to do here is, you know, it's like there are different ways of analyzing trends. So I said, okay, look at the most organizational consulting companies, groups that have the biggest impact on the way organizations manage them. So I took Gartner, Forbes, Forrester, Delight and Accenture, and I only made the, each one has one of the reports and you have the reference there. It's only Delight that has two of them and I'll mention it why. So if you look at some of them, you will see that they are in fact similarities. And I will go on the next slide when I talk about what is important for us. But yes, you can see that, you know, many of them are talking about augmented reality, the virtual reality, Internet of Things, big data and so on. The reason I put this as an emphasis is because this is probably the only one which is talking directly about the human impact or the human capital because they call it, I think, human capital trends. And that's Delight they did. And this is a very, very interesting read when you look at it. It will impact the organization of the future, that we have the talent acquisition problems, that we have the performance management, that there is digital HR, diversity and inclusion, careers and learning, and this is exactly what we are doing here, career development and so on. You do have the references here, so if you ever want to visit, you can find them on the web and go and read the details. When I analyzed all the ones that we just had so many of them listed, that's the reason you don't pay attention for that, I kind of picked up the most important ones. And the most important ones are these, in my opinion, these six. So we are talking about AI and machine learning. AI stands for artificial intelligence, virtual and augmented reality, Internet of Things, digital platforms, big data and analytics. Time permitting, I will probably go through each one of them because I do have slides. I might go through some of them a little faster, but you still have the reference there. And I believe it was already mentioned at the beginning of the school that all of us working at the agency are open for communication, so if you do have later on any questions, problems, or you would like something else, please contact us. And there will be, at least my email is there and I think that it was done for everyone else. So when we were looking, yes, why did I select them? I selected them because I believe those are the main disruptors of the industry and the processes we are doing. Because some of them, yes, there will be changes, but I believe that these will be the changes which are, which could be categorized as, in fact, major disruptors. So artificial intelligence. What is IA? Those are the systems that can think and act rationally like humans. There is a huge debate with artificial intelligence. This is probably one of the simplest definitions at least to understand that, you know, yes, there is this human element in there. There is a very complex, they are very complex for development and maintenance and also for deployment. And you will find them everywhere. It's not just, you know, that we are talking about high-tech industries. You will find them in artificial intelligence, for example, in washing your own car. Yes, there is some, there are sensors there, there are robotic arms, there are this and that and they know that, okay, fine. So, yes, that's one of those simple ways of using it, but it's one of the ways. They do combine different technologies and techniques. Some of them I believe are mentioned here, like deep learning, neural networks, NLP and so on. They move beyond traditional rule-based algorithms because that was the beginning when Anatoli mentioned the expert system and some of the rule-based, that was the beginning of artificial intelligence. They have moved away from that to create systems that understand learning. They can learn by themselves, they can predict, they can adapt and potentially operate autonomously. And that last part, in fact, is the one that bothers many philosophers because, well, what about if they turn against the humanity? But that's a different topic and different discussion. They are built into physical devices, so sometimes you don't even know that they are there. If you see a robot here, you'll probably figure out, okay, there is AI somewhere inside. They are built into cars, they are built into consumer electronics, security apps and services, like, for example, virtual personal assistants, smart advisors, voice recognition, vision, translation, finance and so on. Anyone knows how many sensors are in a simple car? Just a number. 10. They are over 100 already in a very simple car. They are already over 100 sensors, which in fact are somewhere connected to AI. So this is just to show that, you know, and by the way, as soon as you see some kind of warning lamp or something, going on you can certainly find this in some of the sensors that telling me something or whatever it is. So this is a very specific use and you have them in any type of industry. Starting for God forbid in aviation, when you have on the aircraft and you have some red light there, which could be light threatening to any other one like nuclear power plants. And even if you're talking in administration, which is far away from really using that, you can have those dashboards and so on where you can have that, and Alina is looking here at me and she's like, well, what we can have is that, you know, that we are under the budget, you know, we are spending too much money and she's flashing the red light that we have to do something about it. So the last thing is also interesting that AI becomes a new user interface. This is somewhat difficult to understand, but what it tells us is that, you know, we don't have any more the static interface to any type of device. We might have something in between, which will tell us, okay, fine, this is important for you, this is not important for you. So it becomes a completely different way of accessing tools and information and such. Virtual augmented reality, a little bit of definition, we are, and I like this one, we are, takes us out of reality and brings us to some other place. While the augmented reality takes our current state of reality and adds something to it. So it's a little bit of thinking to do, but it's probably easier if we compare them. So the best one that I could come up with is this virtual versus augmented when you're talking about the scuba diving, where you have the real emergence, you have the appearance of that, you're part of that environment, and then you go to the aquarium and you see something. Virtual reality can bring us to a construction site, for example, where we can walk in any direction and see every single detail. Augmented reality is helpful for a client who can't visualize something. The idea is that a designer, for example, an architect and a homeowner who sits around the table and look at the same 3D model table instead of the famous 2D plans and makes decisions. Human mind is not able to tell the difference. That's a very dangerous thing here, but that is the fact that the human mind, unfortunately, is not able to make the difference between computer-generated images and the real world. This is being used in abuse probably in some of those games like a Pokemon Go, when you go and see people and they're doing something and you're looking and there's nothing there. Yes, this is the type of the impact it might have on some completely different aspects of our life. Yes, they're being used in military, medical science, nuclear science, manufacturing, real estate, and so on. Internet of Things. It's a system of interrelated computing devices, be it mechanical or digital. The objects could be also animals or people that are provided with a unique identifier and the ability to transfer data over a network without requiring human-to-human or human-to-computer interactions. Simply, what do we need for that? Okay, we need the unique identifier, the IP. We need some kind of Wi-Fi connection, a Wi-Fi connection. We do need sensors. Here we go again. Okay, and we need electronic circuits which will collect the data and transfer it somewhere and probably process it. So as you can see, those elements are very simple. But once you put them all together, their impact is tremendous. So a thing can be goods. It could be objects, machines, appliances. They could be buildings. They could be animals. They could be people where things become very tricky. They could be also, believe it or not, plants and soil. And Samara was true. So for example, a person with a heart monitor implant in this world of IoT can become probably the happiest person in the world because that thing worked and informed his doctor. There is a danger and so on. It could be a farm animal with a biochip transporter saying, okay, transponder, saying that, okay, find that specific cow needs to be moved or something and it's far away. So there are different types of uses. Most of them should be okay. Some of them are questionable. I already mentioned these cars and so on. They could be connected and learn about food, monitoring supplies, search, location, managing cities, control use of electricity, game, immersion and so on. So we are here in Italy. There was a very interesting example of, I believe it was in Bologna, it was the supermarket chain that introduced this type of technology to help people make choices of food they're buying. So yes, you can just come to something and suddenly on the screen you will get everything. This is the origin from there. This is good for that or not good for that. And it was a tremendous difference between once you go out and say, okay, the price for this is 10 euros. Okay, fine, maybe I buy, maybe I don't. Because this involves the actual consumer much deeper than just having a thing. And then, yes, you can even ask questions. You can use your search and so on. So as you can see, the possibilities are really, really great. What's important here is that, and that is the most important part of that, that we are moving from a standard way. We are moving from people to computer creating and capturing data. And that is, because we have things now communicating and generating tremendous amount of data putting it somewhere, if they're putting it and storing and so on. We have, of course, amazing complexity. There is a problem with the policy, which is in this, with the privacy, sorry, because in this particular case it almost does not exist. And they're also saying that it could be a weapon of mass disruption. There was a joke about this, you know, mass disruption of these things. You know, if you hook someone like your spouse with that and you know that the spouse is not really at the place where it's supposed to be. So yes, it could be a very massive disruption in that particular case. But there are many, many things. The digital platform, I just go briefly through that. Because digital platforms are, it's a technology-enabled business model, in fact, which is being changed, that creates value by facilitating exchanges between two or more independent groups. Network affects the value increase as more members participate. Big definition, right? Hard to understand. But look at the bottom there. Those are the examples of those digital platforms. So for example, in advertising, you have Google, you have Baidu, you have Tencent, redirect. In social, you have those platforms like Facebook, Twitter, Instagram, LinkedIn. In commerce, the most famous, probably Amazon and Alibaba. In application stores, there's Apple Store, there's Google Play. Those are the examples of digital platforms. What are the benefits? The benefits are simply put, revenue brings together end users and producers that can transact easily with each other. You go to Alibaba, you made a query, you have zillions of those sellers who would offer you something you can choose, you can build, you can do all kinds of things, you can do the same thing with any of those. So there is a much bigger possibility to generate revenue. In fact, saving on one side and revenue probably on the producer's side. It's also the question of reducing cost. Sometimes it's on demand, or production on demand, or print on demand. It depends what you want. So this is one of the ways that, you know, if you have the systems well connected. There is a collaboration through different technological devices or softwares, APIs and so on. There is a part ability, because that's mostly based on the cloud and other technologies which provide that. And there is a question of protection, which is a questionable one. It's intellectual property, which needs to be protected even through digital platforms because there is an invisible change that some process, that some transaction goes through, but at every step it needs to be protected. This is one of my favorite slides probably here. We already talked about how big is big. So if we assume that one bite is a grain of rice, what is a kilobyte? A kilobyte is just one cup of rice, right? And then we are talking about some of those very old computers. We are talking about hobbyists. We are talking about some little small stuff. What is a megabyte? If you take that same rice, the grain of rice, the megabyte would be eight bags of rice. Okay? That's the gigabyte. We are already talking about three semi-trucks, and you probably know it's a semi-trucks. It's the big trucks, you know, eight-wheelers or twelve-wheelers, whatever they call them. You know, the big, big ones. So it's three of them for one gigabyte. And there we are already talking about probably a desktop. If you are looking at terabyte and most of us have already storage devices either at home or in office, you know, which is one terabyte. And it's nothing big, right? One terabyte is two container ships. And that container ship is not one container. It's thousands of containers on that ship. And it's two times two. These examples are from the U.S., so they are talking about what's the petabyte? It can blanket Manhattan. Major part of New York. Huge part. So if you put everything there next to each other, those grains, that's the petabyte. And that's what we are talking about today about internet. That's how big internet is. If you are talking about big data now, we are getting to something called exabyte. That one can cover, it's again a U.S. example, West Coast. So in other words, it can cover more than the whole of Europe. That's how big it is. Zetabyte. And I have an article written on Google somewhere about Zetabyte. How big it is. How many thousands of... A lot of color you can fit into it. Zetabyte can... And it fills the Pacific Ocean. That's big data. And then something that we can probably not understand. And there are people already talking about it. The size of big data. Zetabyte. That is literally an earth-sized rice ball. So you can imagine how much data it is. So there was a question there, you know, how do we manage that? We can probably go as far as the internet and manage some things. But if you are looking at the future, we do need new devices. We do need new software. We do need analytics tools and so on, which will help us manage all this. There is no solution at the moment that we can successfully... What everyone is doing, you know, when they say big data, okay, fine. They take a chunk of this. They call it the big data. For some of them, it is very big. And they try to manage it for their own purposes. If you are talking globally or if you are talking some specific or interesting things, it's so far difficult. There was already mentioning of this. I just won't spend time on that. There is the question of volume. Detained rest when you're talking about this one and those are those exabytes of data. There is variety, different performance, velocity, that's the question of quality, redundancy and so on. And, of course, I already mentioned this is the question of value. And this is known as... It's five of them, but it's known as four Vs. Because the last one, last V, which is value, was added later on. Now they are saying that there are even six, seven of them. But, you know, this is just like maybe getting overboard. So these are, in fact, five Vs of big data of information put in an, I would call, theoretical model. And I like also this data in motion, which is velocity, rate of creation, which is tremendous. How does the scientist cope with all this information which comes to him or her? Very, very difficult because it is impossible. We just went to a slide, you know how much data is created daily and so on. Analytics. And I took this definition from the Institute of Operational Research and Management Science, which says analytics is the scientific process of transforming data into insight for making better decisions. So as you can see, it's a bit of a different view and take on analytics itself. I'm mentioning here that the business analytics explores past performance to gain insight and drive business planning and there are different types, which could be a descriptive one, which that's the type of analytics that most of the organizations are using currently, and there could be also the predictive ones. Different applications are risked. Thank you, five minutes, yes. And the last one is that from here we have different technologies listed like data management, data mining, text mining, and Hadoop, which you probably already know about. And the opportunity is similar to the previous one when I mentioned there's a cost reduction, fast and better decision making, new products and services. We're coming very close to the end. This is the IAM challenges, information management challenges based on these technological changes or trends. I have listed four of them. These are the first two. This is from the aspect of an organization and from the technical aspect. From the organizational aspect, it's disappearing IAM units, available financial and human resources, business focus, very important, and competition with the big players. From the technical one, changing technical requirements, long-term preservation, and I've already talked about it, multitasking, rapid delivery, more difficult access to information, interconnectivity, interoperability, and affordability of top-of-the-line analytical tools. Because anyone who ever wanted to purchase some of those tools probably was shocked by the price it was given there. And finally, I have to skip something. Finally, the conclusions could be different once. I'm not making any conclusions. I'm leaving it up to you. I'm just listing here some of the elements for making conclusions. Those are the elements that we already have. We have big data. We have robots. We have sensors. Someone mentioned already nanotechnology there. Virtual reality, cyber-physical system, cloud computing, emerging technologies, AI, IoT, smart devices, real-time analytics. But when you look all that, just please remember one thing. Out of all this presentation, and these 45 minutes, if you remember one thing, that would be very valid. And that's we are entering the industrial revolution for zero. And if you want to maybe explore some of these trends, you can literally forget about whatever was written there and try to read a little bit about what is this industrial revolution for zero all about. And I end up with one little story. CEO of Mercedes, Mercedes-Benz, was recently in an interview asked, who are your biggest competitors? Any guesses? That was very close. Yeah. Because people were expecting it to be Toyota, it would be GM, or I don't know what. No. He said it's Tesla. It is Google. Why? Because he said in the car industry for years, what we have been doing, we are improving something which was not as the same. But we were improving it. These guys, they said forget about the past and they are doing something else, something completely new. It's disruptive. Those are my biggest competitors, he said. Thank you very much.