 Organization probably to have, you know, three functional usability labs for us. One in New York City, one in Pune and one in Chennai. So we have a dedicated team of user researchers, usability analysts, visual designers, UI developers, usage analytics folks, everybody. So basically, right, I'll start with, you know, a topic here is big data killing customer experience, right? Every movie or every book or anything that we, you know, kind of read has a three act play. What is a three act play? Right? If we take up a fairy tale, right, there is this Cinderella story, we, they introduce the character, right? There is a poor girl who's facing so many issues in a, you know, home and then suddenly she gets a boon and then she loses it. And then finally she, you know, kind of, you know, realizes her dream and lives happily ever after, right? That's a three act play. I'm trying to follow a kind of a three act play for my presentation here. And I'm going to tell you two stories, one in the beginning and one at the end and probably set the stage for some of the big data, you know, you know, preliminaries or some of the, you know, difficulties that we handle due to big data, right? So that being said, I'll start right away with the big data story, right? Now, we are a company that respect, you know, information security a lot. So I'm trying to mask names, but in reality we are talking about our company here, right? So the idea here is I'm a part of a financial organization. Inautics is, you know, customers are all, you know, financial folks, right? Financial services, brokers, traders, activity, that is kind of bothering all his customers or users. In our case, the user is the customer because, you know, we make applications which are used by brokers and traders who are internal as well as external customers, right? So this person wants to find out which is the most monotonous and repetitive activity that is going on in this application, which is the most used application or the portal and then try to make their lives better, right? That is the kind of exercise that is interested upon him. Now, what he does is he obviously goes to his set of, you know, experts. In this case, whenever we talk about data or intelligence, we speak about business intelligence teams, right? So obviously he goes to his set of, you know, business intelligence folks and then he puts the same question across to them, okay? What is bothering my users? What is bothering my customers, right? And how can I help them? And please find the most repetitive and monotonous activity in this application so that we can make their lives better, right? These business intelligence guys, they go around, they start fishing information, they collect data, and then, you know, they organize data, you know, they kind of, you know, put in some visualizations. They come back with a, we would have seen in movies, you know, these in the streets, right? These guys bring bars along with them, and then they start hitting the drum and the bar starts dancing, right? Alan Cooper has written a book called, you know, inmates are running the asylum. It's a very interesting book. It talks about how electronics is designed by people who don't actually know the customers, right? He talks about dancing barware, right? The metaphor here is a dancing bar, which is very entertaining to see, right? Of course, it's interesting. People sit around and watch it, you know, people who went for, you know, go for grocery shopping, people who go for, you know, important errands. They sit there and watch the bar dancing in the streets. As soon as the show is over, they remember, oh God, I forgot everything. They run back to their errands. It has served no purpose for them, right? So it's called a dancing barware, apparently. That's what we see here. It's a card diagram, but apparently, none of the data underlying this is revealed, right? So he asks, what the hell is happening here? Can you tell me what's happening here? Right, that's the obvious question that the product manager asks, because he has no intel. Nothing, you know, which can reveal what's going on. Now we go to the, you know, general setup of what big data is. I'm hoping that all of us know about big data, right? So generally speaking, you know, can anybody give me an example of big data? Anybody? Google search, awesome. So search engines, big data. You just put in a, you know, a small keyword and it, you know, gives you 17 pages of results. Big data, right? Social media, every day, every day in time, every minute, you know, we use Facebook and it keeps on updating so much information, right? So a lot of big data and, you know, Twitter, LinkedIn, you know, so many social media applications. We are pumping in data into the digital world and apparently, you know, it's getting crowded and crowded and crowded, right? So that is big data. A little bit of history, right? How it came into being. In 1786, there was a person by name William Playfair who came up with this big first dashboard on a copper plate. He explained, you know, what are the exports and imports that happened in and out of England, which was the very first dashboard. It was very interesting to see and it was very, very clear to see. But then what happened during the due course of 200 years from 1786 to mid-1980s was that they invented what is called as the executive information systems. That, again, is a very interesting piece of information, right? So this executive information system is nothing but for executives, they are giving some sort of financial data. And Steve and Fugh, he's a very interesting author, he researches data dashboards and everything, right? He says that even executives can read the executive information dashboards, right? Even executives is a term which is kind of demeaning because he mentions that executives are basically dumb and they have very less attention span. As they move up the ladder, they become dumber. That's what we say. Attention span is very low. Whenever we go to our manager and start talking beyond two minutes, they say, okay, okay. Just tell me what's the bottom line, right? That's what is their attention span. So we need to understand their mindset and this executive information system was done with a very noble purpose. To make sure that people understand what is going on with what is happening in the company, right? So that kind of transformed and data warehousing came into being, business intelligence came into being. Walmart started the data warehouse revolution and we are at a point where we are the slaves of big data and big data visualizations, right? So hopefully it comes back, okay? So we are moving towards this big data visualization trends. One of the examples that I showed you was the card diagram, right? Let's wait for a second here, right? I just wanted to show you this, right? We are all moving towards this big data visualization, the bubble charts, you know, the card diagrams which I showed you, you know, the circle packing. If you look at this, so much information has been packed into tiny spaces, right? And people expect us to find what is going on here, right? So that is kind of, you know, it's kind of insulting to me as a UX designer or a customer experience analyst, right? So basically we'll come to that part, right? The cribbing part comes later. But now let's talk about, you know, one interesting thing that I read in Gartner, Gartner's research, right? They say that by 2015 we'll be needing 4.4 million data scientists. The word scientist means that they do research, but in reality here they speak about people who can just take data from multiple sources and make it usable, transform them into data. I mean, usable form of, you know, information. That's what they call scientists. It's not about analyzing data, it's so much about just making it into packets, you know, messages, you know, usable formats and using them. So it's a staggering number and we are not even invited to play their game, right? So we need 4.4 million data scientists, but not 4.4 million UX analysts, right? The data is big, but there is no one to tame it. And one other thing is it states that only 1% of the world data is being analyzed currently. So imagine all that junk that is lying around in the digital world, nothing is happening, right? It just stays there. You know, people create random websites, they throw in a lot of information. Let me share this stupid video. If I don't have any time to kill, I just upload a video on Facebook, right? The junk keeps adding on and on and on. Right, one other important thing is the revolution of data I was seeing brought about by Walmart. Walmart, as you know, is one of the top 10, you know, most valuable companies of the world. It spins money like anything. And it says the tagline says our prices are always low. You know how they came up with this strategy? They are the ones who invented data warehousing, right? In 1992, they started 2 TB of data, and today they are handling 2.5 petabytes of data, right? I'm sure you all know the categorizations of this, you know, gigabytes, you know, terabytes, petabytes, exabytes, right? Zeta bytes, Yota bytes, God knows what will come later, right? So if it keeps on increasing, and you know, the US Library of Congress, they preserve history, they keep on collecting more and more and more information every day. And then you know, where can they store all this, right? We cannot see it, but we need to store it somewhere, right? How can we reduce spaces and increase the storage? That's what they're always about. And 571 new websites are being created every minute of the day, right? So with all that, you see this? We cannot crib that so big data is being, you know, thrown around everywhere. It's us who are creating this big data, right? As I already told you, we throw this stuff around into the digital space, and by 2020, look at how much data we are going to throw into that space, right? So it's really bewildering, right? Guessing time, right? Anybody, can anybody tell me what kind of chart this is? Any guesses? Okay, anybody else? Obviously it's called a node map or a node chart, right? Because, see, there is a point to this. I'll come to it, okay? It's called a node chart. It's used for, you know, relationship mappings. Obviously there is a central core and, you know, it keeps on going around and around and around. It gets collected, you know? It's a huge, huge, you know, sectors. And then, you know, it is obviously made for, you know, showing affinities, relationships, so on and so forth, right? Any guesses on what this chart is? It looks like a floor plan, obviously. But the name is not very intuitive as it looks, right? It's called a tree map. God knows why it is a tree map. There should be some sort of an explanation for why it is called a tree map. And obviously, right? A New York Times published, you know, article on Obama's budget planning using this tree map, right? So we ought to look into that and find out what's going on in the US. So, yeah, those are some sort of examples. This is even bewildering. I didn't want to, you know, even mention the name of this. This is called a heat map. You know? I don't know what's happening in this area, right? So technology only has ways to, you know, convert data into visualizations one on one. It doesn't simplify it. So it has only so much intelligence, right? There is no simplification of big data. There is only one on one mapping, right? Now, this is an interesting part, right? We spoke about how data is picked, okay? There is this data warehousing. You know, data mining happens, and then there is the business intelligence team which picks it up, you know? It pulls from various databases, creates data visualization charts, et cetera, et cetera, pushes reports to executives and managers, right? So this is kind of small, but I'll explain what is happening here, right? There are various data sources in an organization from which data is sent, and it's pulled up into a big data ball, right? From there, what they do is, they use, you know, big data platforms like Hadoop, you know, MapReduce is a technique. I mean, Google's MapReduce. You know, they kind of, you know, transform this data into usable information, and then what they do is, they push it to the next team, which is the business intelligence team, right? They create beautiful charts, which we saw previously, and then they push it out, right? Now the executives and managers need to make informed decisions based on what is being thrown out. Now, is there something missing? Obviously there is a UX conference, so UX is missing, right? But how or why? Why should we introduce UX into this process, right? I would say that a visualization expert should be a part of this cycle, right? What is a visualization expert? Probably some companies have already used a visualization expert as a kind of, you know, job role. I'm not sure about that. We just want to introduce a new breed of UX folks called the visualization experts, now that we are all moving into big data. If they need 4.4 million data scientists, we need 4.4 million visualization experts, at least half of it, right? We should be responsible for creating order, not chaos. Right, apparently my team thought that this is a very, you know, attractive visualization expert. And so apart from the attraction that, you know, this emanates, what are the other qualities of a fine visualization expert, you know? The person should have a user experience, best practice knowledge, and then obviously a bit of domain knowledge, and then a bit of knowledge on the big data technologies, and then a bit of knowledge in the data visualization methods, right? A bit of is a very tricky term, right? I would give 100% importance to both these things, right? On the left side, domain knowledge and user experience best practices. Now when that is clear, the person would know what kind of, you know, objectives are needed by the company, what is the information that executives are looking for, you know, what are managers looking for, that will be clear if that is there. Now these are just guidelines, data visualization methods are guidelines. You can take a book, open it, you can find data visualization methods, simple ones. And then big data technologies, once we start conversing with these business analysts, you know, we just need to get acquainted with these things. So this is a typical role, blended role that a visualization expert needs to have, and every company which handles big data should have a visualization expert. That is something which I wanted to propose. And just a little bit of information, right? I'm not going to, you know, kill you with these bullets, but let me just give you two instances here, right? One is, John Maida is a graphic designer, and he is an alma mater of, you know, MIT. He came up with a book called Loss of Simplicity, where he wants to create order out of chaos. If there's a big room, there's a, you know, huge chunk of people there, how do we simplify? Even in, you know, perceiving them, how we can, you know, come up with simplicity, right? There are tech talks with John Maida, very interesting. So the key focus here is simplicity. That is very, very important. And then there should be business focus, right? By business focus, what I mean is that, you know, they keep on talking about data requirements, data requirements, but nobody talks to them, asks them to tell stories, right? They need to dream big. Walmart should say that, okay, I'm dreaming big. I should be the number one company. And then they should say, how do you want to become a big company, right? Make them tell stories. That is where we get business focus, right? And we cannot keep on, you know, dwelling on data. Data focus is a part of it, but we should also be focusing on business focus and user focus, right? And then finally, right, there is this very interesting phrase going on, design for volume, velocity, value and variety. If people have read, you know, big data papers, that's a very important thing that they say. But I should, we should say, right? We should design for scalability, priority, minimalism and usability, right? Most of these terms are very, very obvious terms, scalability, right? So what happens if data keeps on increasing? How would we design for that? We should keep that in mind, right? Priority. What happens if, you know, they say, okay, today our idea is to go to, you know, Latin American markets. Tomorrow the priorities may change. They should, they would say, you know, I want to concentrate on European markets. Priorities change. Minimalism, you know, keep the screen simple. Card diagrams wouldn't work, right? And then finally usability. So these, I think, should be the best practices. And then there is a simple example, right? How we, you know, perceive things. You know, we just look at things as a whole and then, you know, divide them into bits. You know, there are so many chairs. Changes to there are 50 chairs. Okay, there are red chairs, yellow chairs. So perception of how it is processed is we look at it as a whole and then, you know, bit by bit we digest the information and finally there is an objective. I need to go sit somewhere. We go and sit in an empty chair, right? That's how the perception happens. That is the same way in which we have to approach the big data UX, right? We have to dream big, as I said. Walmart dream, dream big. They dissected it into small elements. I want to concentrate on these markets and then they defined the pattern saying, okay, for this market I'm going to concentrate on one, two and three. Here, one, two, three and four. So those kind of patterns are formed and finally designed for decisions and then they win. But then the problem is we should never be convinced with our solution. We should keep on improvising, right? That is something which is key here. That brings us to the second part of the story, which is the successful story after we follow the big data UX cycle, right? There is Jane Doe, obviously a woman, I mean a woman wins, a man loses in our story, right? So Jane Doe comes in, puts the same request and this time there is a team which has visualization experts too. Now they come up with the dashboard, which is simpler. Though it's big data, it is simpler. There are so many users. This is the most time spent on a page. The most clicked button is buy and sell, which is clicked to a 20, 35 times. You want to look at it as a number, look at it. You want to look at it as a graph, look at it. And these are the top five screens that the users use. Simpler, right? Can anybody say that this is confusing? I don't think so, it's easy, right? It's easy to understand because it gives you insights, not just big data, insights, right? So what is the difference that we have between Jane Doe and Jane Doe, right? So yeah, we can get to the questions part. One minute, please. I'll just complete this. Bear with me, sorry about that. Right, so the difference here is Jane Doe uses key data and Jane Doe uses big data, right? This is decision-centric that lacks insights. Insights are what is going to propel your solution. This is user-centric, that is technology-centric. That, with that moral of the story in mind, I say that, you know, with your intervention, big data dashboards can reach a higher level of maturity. We don't need to get to a place where it is dark, can get to a better place, right? That's the idea, so I guess I'm on time.