 I know that these will be spent running techniques in 4 years. So I'm going to learn all that in this book. I'm not a country guy, I work in a country for a long time. And a quick introduction, I started my career a couple of years ago. And spent about 9 years in the schools. Then spent 5 years in the industry. Then spent a lot of time here in the industry. And then over the last 10 years. Am I a country guy? Because more books is better. Yeah? Yeah. So then post that with us 10 years I've been a start of a company called Ensa Secre. I'm working on a company called Ensa. And we work with companies like Data. And we raise that data for marketing. That's really a quick background for me. What I'm going to do today is about 30 minutes. So I'm going to go very fast. I actually enjoy going very fast. It will stop me when I'm going too fast. So I'm going to cover this. I spent about 5 minutes just giving a context to the spectacle which I have seen later. Which is really marketing and business. And after that I can go through stories about data. That's the idea. That's what I'm going to do. I also don't want us to be on very speed. So I'm very happy to be talking with you. Taking questions or interacting. So it's not always late. I think it's early online. Yeah, so quickly the title, the reason I... And this was all of you who have heard of Ensa Secre's first. Right? It's an old saying. Often it gets loose. And when the data go in and start with this company. Even the word analytics is not so common. I think today probably it will be data, data size, analytics. Even Chalala might know these words. But that I don't know if it will work or not. And the common sentiment used to be that instinctive drives. And instinctive what the driver does is wrong. Data was not necessarily so well recognized. I think things have changed. And to that extent it has much more recognition. And the new age companies. Which is the Publix, Amazon, Amazon, all of them. Have made that the core of the business. And because of which I think others can see companies as well. But I think even then most companies find it difficult to absorb. With the age, different types of people. So I've been there with these companies. And I've seen outside companies make a different product. So I know that that's the problem. The data science team becomes a central unit. Which does work but they don't get as accepted hardware. Or business people feel that they know exactly what they know. And they don't have to come around. In a way it's not easy for people to navigate. And to build the data of the industry. So that's the way we are working. So starting with the perspective around build. Most companies, if you look at the financial recovery. Even the furniture will be in fixed assets. But customer data is not there. So our companies don't really value the data as they have said. And I think there's a really big problem. Accounting standards continue to try to change that. But interestingly, what are the best ways to make that change? In fact, for things like Amazon and others. I've proven that to create a mode. Which is a value for consumers. For competitors. By creating a data-led strategy. It can be very powerful. That's the goal. And I think for all of you. I think the importance is to keep understanding. How can you bring that value of data. In any distance. Because it's not that you value data as a philosophy. But if I'm a bank. And I give a loan. And I value the data. I'm using the data to give a loan. If I'm a DTH company. We work for the DTH company. When I sell on the phone. I call it. You are value-added. If I'm a DTH level. Working to a source. You are value-added. I think the data. The value of data is understood from that perspective. And I think the point I'm making is that. Whether you are data scientist. Or a data scientist. Or a philosophy person. I think with a large beat. Challenging and adopting data. This is not a technical problem. It's not like earlier. We've been building this generation. And now they're doing machine learning. And so they won't be adopted better. It is really a social problem we have to do. Extend the data. And have to talk about issues. Which are linked to data. And that's where the gap takes to. Right? Because I haven't touched on this data. What takes to happen is that. The guys and ladies who are dynamic with data. Who are great. And go geeks. Some of them. They have great personalities too. But when they're together. Every content is important to have a great personality. Because very often. They start building their own brand. Very key. I love my job. That's good enough. And just now. If you want to do your job. You have to be able to do the job. So that is an important message for the young people. And it's also a huge culture shift. At a personal level. For most people who are technical. And also a company level. To accept. And someone like. Who might articulate. I don't take a personal language. But they can do the data. This is a challenge for me. Quickly I want to introduce. A topic. A word called. Data. How many of you work with data. What's. You want to. You want to. Right. Now this is what. Data. This is. Daily. These companies. All of them. Who are really. Data. What's. They are hungry for data. They absorb data. They want data. They believe on data. And a bit. The. Disk around. Look around. But this. This company. Are. Taking. The. I don't see. I see. I see. Right. Right. But. You. Could. Be. Welcome. You can see in the retail world, again in my days in retailing and in today my current clients who are retailers, they look on to see who your physical retailers are, right? Because online businesses is just coming in and all of our customers, they do 80% of our research specialty online and they meet my client organization more than half, right? So the traditional company lines of who's my competitor has changed to retail, right? And it has changed with the new players who are coming in for data range and the total companies who are full age companies, not new age companies, who are not data age, do to try and enable themselves in this new data world age is I think the concept that I am talking about. And quickly touching on this, in the earlier days with the 70s, brands were now important to the brand, you will go with better brand, right? Those brands will get with it, for 70s, retail is a big deal now. So because of that, now, if you were to transform retail into a bargain, with a brand-new customer and so on. I think now, it has moved from an age of brands, to age of retailers, to the age of customers. And so therefore, we as customers, have far more cloud than we ever imagined. It's cloud, it's decentralized, it's not aggregated, but it's cloud, and therefore it's become the age of customer from the perspective of absorbing the customer. And the only part I believe of this slide for you, is that, what I mentioned earlier, that companies, therefore, won't go down the line. They won't be able to do so, or they won't be able to build new machinery technologies to be able to rearrange the data they have. But for them to bring it to their business operations, it's not easy, it's not trivial, and it's a struggle to do that. All of you who are part of the industry in some way, need to know that and play a role in that. So sometimes, you know, I, in my days, for example, the only thing that I would do is to take, that those years, early 2000s, when the data centers were not processed around there, right, it's become very popular now. But I have to ensure that the junior most person in the team would present their product in a meeting. So that they would see, get a flow of what this very senior person is going to do that day with this, right. How would he adopt that, right? And I think that would help them for you to go through to work with data, seek out some knowledge, that is. Because, finally, if companies don't change their mind, don't change their culture, they won't be able to have as much knowledge as they did back then. And that's important for all of us, right. And then go through with that, and the other thing should be able to talk about is that it's not only data. If it's about the company's vision, it's about how you use it, you raise that data for a customer experience. Finally, it must be able to fill out the point of connection. That's it. Yeah. So how many of you think that analytics should be historical? Why do you feel like this? If we want to then express data, or express what problem we are trying to solve then it is a story. How it is more understandable to the people you are trying to explain that it is more clear and we can understand about your value. Anyway, for example, some people have done, I'm sure, some of you have experienced a story there. Yeah. That's very important. And I'll give you also an anecdote, right. So, a few years back, you were working for this VTH company and you were analyzing inbound organizational records, right. Somebody had said that given the details of this the number of people calling and therefore look at that data and look at the evidence for us and also those callings, right. So, how many of you know of a technique like CHEH, C-H-A-I-D-CHEH or CAR? There are these techniques which are classification techniques which allow you to segment a bunch of customers or any other data points and put distinctly different justice and it's in a tree form. So, the standard for the parent-child initiative of the structure, right. And so, when you start by saying that 100% of people call from there, maybe one tree, one tree branch to talk about people who are new subscribers. The other people run the series of course. Then they would say, if you were subscribers, the other people don't have access people who are older or older or more than 6 years and so on. Are you guys in the picture? What is the... Now, if you imagine all of your techniques are real in this room, correct? All of your techniques, right. So, despite that, CHEH may not be a very big case. Now, in this particular case, my king of data scientists did this work, analyzed the possible records, they had some lovely findings and they had charts of that CHEH alone. And they put it into a presentation, right. The next task was to meet them in the same room. Now, tell me, how many of you think this would be a complex idea that you could absorb as much as you could at least, how many of you think so? Yeah, it would be complex. A lot of data has to be directed into the little complex, right. So, one of my ideas, I have a wonderful idea and he told us, only a phenomenal paper and I just tell you what the story was. He started in... He posted on the paper a slice of bar, this is fantastic work. And he was... When he consulted the important meeting editors there. So, the meeting started and I went over to the analyst and the analyst said that sir, I'm going to explain to you a technique we use as a mega scientist in a very different way first. So, they would give me five minutes without thinking on a business. First, a minute, just a second. Right? If you've got a machine or a man, if you've built a gallery or something, you're going to pass it and you're going to find it. He said, sir, I remember Titanic. So, he put up a graphic and it said that I analyzed the people who died in the Titanic. How did you know about the Titanic? So, I analyzed the data set people who died in the Titanic desk. Right? And I developed the following out of it. So, he said, what did you do? So, I said, he analyzed it and he said that I found some simple goals that if you were a man, the chance of your dying in the Titanic for hire if you were a man and you were a man, the chance of your life is lower than you're a man in the sink. If you were a man who was married with children, the chance was even lower. If you were a woman and single, the chance was a little higher but lower than a man. If you were a woman, married with children, the chance was lower and so on. And he put that word, Priya. Is this logic that when the war was ending, people were obviously discussing things like women and children who were the first to die. Then, obviously, the problem the people who died in the Titanic was shaping up like, is this common sense? It's understandable. Part should be explained, right? And once the MDR is created, which is a violinist, he doesn't know how to relate to my business. So, the next chart was 100 people go out there and this is how they speak up. Right? What do you guys think? Is there a nice view of the Titanic in Canada? A different way, right? Now, what are they trying to do? When you do something like this, visual is small. The point I would like to make here is what I constantly need to do is to look at the data and explain it using the narrative. Often, we don't we don't powerfully think through a narrative when we look at a narrative. I mean, after we understand the narrative, the narrative is small. But we don't spend much time talking about the narrative. So, once you do that, the explanation is far and easy. And then you make images or visuals. Right? Like the Titanic sinking and all that. So, it's a combination of insight. So, when you link all of these things together, the combination is inside. And moment in the inside about data, there is a readiness to take a decision. And that should be the structure of having to take a decision. And then, is this concept here? Is there any question that I have to ask in the video? Do I want to discuss something? I can do that. Is this concept here so far? Right? The point is that if we have data, right? If we use the narrative of the data, if we use the format, see the data. If we use the format in the narrative, then it will be clear. There will be an understanding of what the data is. Right? And then we use visuals around the data. What happens is that exciting news, sorry, if you start to use visuals the narrative of the data will be clear. So, the solution is to really understand the data. And then we use the narrative of the data and the visuals together. And therefore, we are going to take a decision. And then we start to engage to start saying, okay, then I take a decision with the data. Right? And this journey is not the skin and not lack of believe within that document. They are very largely socially free to justify so-and-so. And that's why we are talking about data. And I want to move a little bit and say that compare this to the worst gentleman. It's amazing that today all of us are talking far more data than you should because the media is very, very popular. And why is that? People will say in the chat journalists realize they teach an etiquette and they tell you you wouldn't start to clean up. But they bring an example of some of you mentioning and I don't know how many of you have seen this in your time and you have seen this. Some of you have seen an interesting thing this was a simulator which asked people before and used the data economic data of the value to respect the people I say and his friends see and help them making the budget they believe that it should be made. And very simple very ingenious all the underlying data was there but looked at the way it assembled and then to start things like I'm not that far away in half. That's my overriding narrative. I'm going to tell a little on the landscape I'm not that far away from here and to play with that kind of comment something. Tell me how many companies how many of the companies built an app So this is an example of which I thought of the other way, the other problem is that very often when we do a lot of data science work, the beauty here is that the effort we have taken to do this work should be justification for people to take that decision, right, which is how I got very popular, now it's your job to buy a data set, versus saying that my job is to sell this, right, and which is when the story that he started to work out, you'll see I will sell this data inside the data to date. And also what's something going on is that further down is the people who are younger. They can't come and take orders, right, if I put it in higher order for a couple of years back, my God, that's what we want to see, see the things that they like, and if you put it somewhere, if I ask for elections, you can tell you can't do open maps there, right. Today, a few days back I was reading a movie, the open maps now gives you when you look for a restaurant, I'm sure I'm not right, but I read about it. If you look for a restaurant, it will give you an average weight of time. So if I'm looking for a Chinese food in Banyarapur, and there are six Chinese restaurants, I can cook a separate weight of time amongst the group that's across this world as well, right. So the young people now, all of you and many others, are becoming data scientists by behavior and they give their life. So when you do this, you come to your work, you would say that, why do I know about it, but it's the first thing you decide, why can't I have enough of that, right. And then that's moving, that's changing, that's something you created with that. Okay, I just want to ask for a minute, how do you find that lens? So then I can move with you fast. Any questions? Yeah, so you can take the example of New York Times, that's what you're thinking of doing. Now, is there a problem in the 90s that you're staying on? That's the individualization of what I have, it's something popular. What's the reason for that? That you want the people, and it's by the food, the data, or by the foods, it is not easy to consume, right, as the science is doing, right. So how do you set up these kinds of people? Or are you increasingly speaking to people about it? Or is there something that we need to have in order to define such kind of a problem? Simply, simply, it's very important. Most times people are much more interested in what we have, as they are taking the distance that they want to take, and they're worried that the data is actually a central object to set up these things, right. Now, the point is that I can't see it in the other point. Now, the question is that if you are able to show and have like this, you can send the data, much better for the own decision. Then you're changing the nature of the data completely. And that's a good point to say, the first two points, if you want to say that fast connection, next stage is the main theme for this topic. You know, we're good. So the directive that you must send it to them, I should be able to do this today, and that's the change that you should do. So I'm just going to move, you know, if you want to take one example, please do this, but let's take detail in the comment. And let's say you're a, you're going to be the manager, and you want to take some decisions around sales and you're going to be able to speak, you're going to be able to make sales. So, you know, what I'm saying is that it is a very catch-up business. So if you look at that catch-up, you can say, you know, I was within five, three hours, what's going on. So you understand that catch-up will be here, or what's going on in the catch-up, and then I can have options for both. That's a test. So let's say you take the example, and you put in the basic data for this, for this, you go to this source, and you sort it from, you're applying, you do some slides from here. So more, more here directly, because we're coming out with the planning meeting, we do some, okay, what should I, what should I do, and then we said, okay, let's look at this, let's look at analyzing this, okay, let's say, let's look at analyzing the school data on the base source, pink code information, and then see if there's some friends, or some director, or something like that, and then it's like, okay, so when you start changing the thinking that way, then even if you look at the source source, and you're sent to the database, so then you look at it and you see, okay, there is, okay, but when you do this, it will be very nice, okay, but the idea here also, in the event chart, it shows both of the codes, and how, and how they are actually moving, right, and if I look at the code, it's moving very better than others, where there seems to be, for some reason, a better, better sales fashion, and I look at it, I look at the data, and actually it goes where, and I look at it, and it looks better, and what's happening in the database for shopping, right, so then you forget the ring of import where you have the stackings, because there's a ring of import where I have the steamers, so they need shoppers coming down, and so that way it shows you the key to the problem, which is the ratio of the workers to us, so then you say, and look at understanding different segments, these segments will give you the technique like chair, or other segmentation techniques to cluster these things, so above the record is that I am changing the work directed from saving the work of the people to please stop in that city, and when I'm doing that, I want to move to a better place, to say, each store, what's happening, and therefore how many customers will change as the next stage changes, right, and so therefore all of this allows you to now check in the data completely from looking at your names tables to a question first of what's happening with the import, and then next question you say what's happening on this end of the import, and therefore going on from here, we want to say that there's something called a much more complex, much more identity, and therefore our communication has to be very clear, right, so that's something clear, now if you see the example again was the narrative was created and resumed for created and it brought these together and they gave it to you, and the output we wanted from that was to say that there are three important parts, especially the action art, and you wanted to sketch the art, right, is that clear, and that's how it goes, now another example would be that you take the art to another detail store, they want to examine their brands, my friend, it's a brand store selling my new brands, and their buyers are taking on analyze who said who, these buyers had a certain understanding of segmentation of their brands, so it means that I your type of product isn't there, the buyers is, and their type of brand is of the segment, with the most aspect, the less aspect of the business, and then store it into the business, and then when you say how you started your brands, but what if you change the narrative to say that I think your brand seems to be different, right, maybe there are questions that are correct, if you have a brand which you are calling a high fashion, but everything is full decision, so now if you provide under data completely differently, keeping this narrative in mind, or drawing the narrative out of the data, now we take that, what we found was actually made by two, because we saw that the one brands has had a high price tag, medium price tag, and low price tag, because earlier we found that they didn't have a low price tag, so you separate data, you have to find several 16 brands, but actually that isn't great, right, so to finish this, you are meeting with this narrative that there are some specific brands, so this means to match the offer of the brand, right, again, given narrative, given distribution, you change the focus of how they might be interested, so similarly, as we explained, again on two stores, in Magler, Bloor, Twinkle, and when you compare them, you also compare them again, so you look at this, why are the stores in one market, not one, and you try to understand that, and in the meantime, you set okay, there are some gaps in this data, and the one that is added was that, can you get a point in the narrative about how the brand goes to a lot of things where the store is just for reasons, so we looked at that, we found that actually, and this is too big for another procedure, but actually the way you observe it is, we actually map, we do map data with location start points and store keys, so and the reasons are Google map finding it, and we said you know what, some stores can do that, which is actually the most significant action, right, and so therefore, then what is what is the reason that the type of code is very, very, very big. Now, for some reason, you have to be open to the analysis, you run a large data survey, so in this case, this analysis is related to why we product together, so, co-purchase, so in today's, what is the, what is the nature of the product that you buy this together. Now, this is why in 205, 6 rows you have a 7-year rule here, but imagine if you have a 5,000 rows, you will bring this up and so therefore, what part of the university will be the 5,000 rows before the time is charged, the public side will be 8 minutes before the day gets equal, and the color will be giving to how long the student is involved, and suddenly that app, the So, keep on doing this, how do you do that? You go out on the side. So, once you fix it, you can keep playing. And you also have time. You need a lot of extra data out there. I want to give you an example. Let's take an example of... This is the most interesting data out there. The New York City, for example, has several knowledge and complaints about it. Now, we can take an example. Many others have extra data, and you can have value within the giga so that you can have more access to it. So, you can have more access to it. But, sure, I think in this example, if you want to take that as a level, how can error game do something negative? So, to do this, what you can do is that if error game... or how many of you can do error game, can you do it, right? So, you're going to end the round. You're going to, let's say, Singapore for an holiday, and you're going to end the place. And this actually happened to me. I took an error game, and took a lovely place, but I couldn't do this in the last days. It was perfect. It's just like a bin show on an error game. It was like a building and an instruction list. So, I took a lot of noise. Now, if error game doesn't produce this earlier data, it's done the noise data. And then I looked at the properties that are looking at it. If they could match their data up with the index of this code, it was noisy, right? Suddenly it changes my my directive about how I live by as a customer, how will I live? Right? And so this is an example of forcing data to actually be changed with the order to be modified. And bringing an element customer data that it exists earlier. And there's many such data which I play for you. Right? So, okay, we have the last section. How many of you recognize this code? I'm just going to let you be right there. Not so many of you. Not so many. So, how many of you recognize the name of this code? How many of you know the name of this code? You have to make videos with it. So, I can't tell you which way you talk about how that story between data is too short, because that's as bad. Visualization is crying at the heart of my own work, too. I teach you how to laugh. And I know having the data is not enough. I have to show it in ways that people both can see and understand. Now, I'm going to try something I've never done before, animating the data in real space with a bit of technical assistance from the crew. So, here we go. First, the next is Brad. Life expectancy from 25 years to 75 years. And down here, an accessible web income per person. Phenomenon, 4,000 and 40,000 dollars. So, down here is more and safe. And up here is rich and healthy. And we'll be showing the world 200 years ago in 18 tech. Here come all the countries. Europe, France, Asia, America, the East, South, Saudi Arabia and the Americas, Europe, and the size of the country, but also the size of the population. And in 18 tech, it was pretty crowded out there, wasn't it? All countries were safe. And more life expectancy were below 40 in all countries. And only the UK and the Netherlands were slightly better off. And not much. And now, why start the world? Industrial revolution makes countries in Europe and elsewhere move away from the rest. But the colonized countries in Asia and Africa are stuck out there. And eventually, the western countries get happier and healthier. And now, we still don't to show the impact of the First World War on the Spanish people back then. We love the Americans. And now, I speak out through the 1920s and the 1930s. And in spite of the great depression, western countries fall short towards greater world back then. Japan and some others try to follow a more substantial state of mind after the tragedies of the Second World War. And I don't admit to look at the world in 1948. 1948 was a great war reservoir. Sweden took the metal and took it out of the wind. And I was born of the differences between the countries of the world was wider than ever. The United States was in the front. Japan was catching up. Brazil was way behind. Iran was getting the little richer from oil. But still at short times. And the Asian governments China, India, Pakistan, Bangladesh and Indonesia they were still poor and safe down here. But look what is going to happen. In my lifetime former colonies gave me independence and then finally they started to get healthier and healthier and healthier in the 1970s. Then countries in Asia back in the back started to catch up in the western countries. They became the emerging economies. Some in Africa falls. Some in Africa falls. So I can say the war and all is by a journey. And now we can see the world today in the most up to date the experts. So in this event when you start learning a story it takes the data into a very new world. It just would have come to that. Two points. Really data science, data analytics is not a technique. And it is how do you explain and how do you test your life cycle and so they will go to the left-hand right-hand. It's also not just a theoretical problem but a theoretical problem. So I think the audience of the word is so effective that this is a big problem. Other old age companies can talk about it. How do you link that data and understand what is the business? That's the truth. The word and the war and I just love that. That's the thought I have.