many hundreds of people have come out. When the police started to use the tear gas. Hey everybody, I've made this unlearning course, partly in frustration with the rush, I call it the rush to a solution, where everyone's like, okay, we know the problems, let's get together and work on the solutions now. And that's an admirable impulse, but when the solution mentality carries with it, the problems in hidden form that are not questioned. When the assumptions that are behind our solution sets and our solution pathways themselves embody the problem. Then we end up creating another version of what we already have. So if you are sensing that there's something wrong here with recycling the same solutions and want to go to a deeper level, then a little bit of unlearning may be necessary. So this course is my offering toward that enterprise, starting on the kind of level of issues, the way that things are politically formulated that draw us into fruitless conflict that leaves the underlying causes of things unexamined. Then moving to the interpersonal level, the judgments we carry that invite us into a false diagnosis of problems, both political and interpersonal. Then going down to the inner level, the personal level where the same patterns, the same knowledge structures operate, and then expanding back out again to the global level, to the level of, and this one is about unlearning despair. So those are essentially four units, each of which consists of about three videos that were filmed by Jonathan Hiller in December of 2017, and it was a beautiful gift-based collaboration. He just did that as from his own generosity. And then Katie Grant came across this material and was like, hey, there's actually a coherent course perhaps within this material. So she selected some of the segments and put them together. And so what I'm gonna add to it is following each set, each session, I'm going to add to it an invitation into a specific practice of unlearning, which you might call a fast. So there are gonna be four fasts that I will offer over the course of this course. Where they will take you is, I hope, to a place of knowing a little less than you know at the beginning. But that place of knowing less is the fertile ground from which other knowledge can grow. My hope then is that this other knowledge that comes into the cleared ground that the unlearning produces will help you become more effective as a change agent on whatever level you are working. Yeah, I went on a news fast by which, I mean, I didn't look at the news or read the news or anything for about half a year. Probably because I wanted to write my book, but also because I felt like it was kind of poisoning my mind because all of the news was inviting me into hating somebody, into outrage and indignation and kind of one adrenaline jolt after another, one fix after another. And so I wanted to kind of, yeah, like go on a fast or quit cold turkey for a while and see what happened. So everything that's happened in the last year, I've only heard about it secondhand, whatever Donald Trump said and did or the Me Too thing or anything like that. I just kind of got wind of it when people read me something or said, hey, so and so said this. And having stepped back a little bit from the fray of the media, from the cyclone of news, I got a little bit of, yeah, I got a bit more perspective on it. And it's kind of disturbing. And now I'm kind of stepping back in, but just seeing the level of hate and the level of war thinking and the level of polarization and the dehumanization of the enemy of the other side. So I've written this book, it's on, I'm not gonna tell you what it's on because I'll tell you why I won't tell you right now what it's on, not because it's any big secret, but because it is one of those issues that triggers the us versus them mentality. And I find that it doesn't matter what issue I'm speaking about. If I say something that's a little bit unorthodox that makes it hard to peg, okay, hon, what are you saying here? I haven't heard that before. So the first impulse that comes up is, well, what side are you on? In a war, the most important question is what side are you on? That's what you have to figure out first, are you a friend or an enemy? Are you on team good, i.e., my team? Or are you on team evil, the other team? And if you don't easily fit into one of the two sides, then you're the object of suspicion and hostility. And in fact, I find that in a war situation, the enemy is not as detested as the pacifist because the enemy at least validates your own identity. Like, who am I? We build identities from the stories that surround us, and the story of us versus them tells us who we are. I'm an American, I'm on the side of freedom, and they are the Russians, or they are the terrorists, or they are somebody else. So I know who I am through having an enemy. At least that's how it works in a war mentality, which is pervasive in our culture. So if you question that categorization of self and other, if you say, yeah, maybe the whole premises of the war are faulty, and we should question the things that are driving us to fight somebody, then you're questioning someone's identity. And yeah, so anyway, I'm seeing, this is something that's really been up for me as I've kind of started re-engaging with the news, and just seeing, and it's this weird, there's like this disconnect between what I'm seeing on the media and what I'm seeing on social media too, not just the news media, this more and more suspicion and hostility and hatred and us versus them and polarization. But on the other hand, in actual daily life, maybe it's my imagination, but I think people are getting friendlier. I think people are getting more polite. I think people are getting more human, more empathic. I notice it at TSA. The TSA officials are friendlier than they used to be.  We don't really believe in these rituals that we're doing, but you've got to do your job and I've got to play my part and submit to this humiliation ritual that you have to go through in order to board an airplane so that everybody feels safe. But we don't really believe in it, it seems. It's like this kind of camaraderie of yeah, yeah, yeah, we're all stuck in this game, but we don't really believe in it anymore. And what do we believe in?  Like this, I can't say that this is the new ethos, but these ideas are infiltrating and leading people, I think, to just pause for a moment sometimes before letting loose with condemnation. Yeah, there's a danger though in saying, oh, I'm just an observer, I'm gonna sit back and not take sides because that can also be a recipe for inaction. And I'm not talking about that. I'm talking about rejecting the prescriptions for action that both sides might offer when they say the issue is about this, which side are you on? No matter which side you pick, you're still agreeing that the issue is about this and letting, if there's a deeper cause that both sides are ignoring and deeper agreements that both sides share that actually generate the problem to begin with, then no matter which side you pick, you're still part of the problem. So for example, in this country, in fact, a lot of countries, there's a big immigration debate. Which side are you on? Are you on the side that would like to let these people in and welcome them into this country to escape the hardships and dangers of where they came from? Or are you on the side that wants to keep them out because they're a different skin color or something like that? Which side are you on? Well, neither side is asking the important questions, such as why are all these people wanting to immigrate in the first place? What makes life in their home country so unlivable that they would risk everything, leave their families behind, leave their traditions behind, leave their language behind for a completely uncertain future, sometimes at the hands of human smugglers. Like, what would it take for you to do that? Why, what has made their home unlivable? And when you start asking those questions, you reach uncomfortable territory for a couple of reasons. One being that there's no longer an easy solution. The easy solution is, you know, build a wall, keep them out. The easy solution depends on identifying one cause, a single cause that's outside of yourself, something you can fight, like a germ that you can kill or a weed that you can pull up. But when you ask these deeper questions, you come to answers like the global economic system, neoliberal capitalism, military imperialism, that are not, they're so all-encompassing that there's no easy solution. There's nothing to fight because you're part of it too. And that's the second uncomfortable thing is that you're part of it too, I'm part of it too. Our daily lives, our routines, our way of life is deeply implicated in all of these, imperialism, capitalism, et cetera, et cetera. So we don't know what to do and it involves ourselves. Like you can't push it out onto another. So that's just like one example how the invitation to engage in the issue is a trap, trapping you into perpetuating the issue because all of that contention, all that froth and push, that whole war is a distraction from the deeper issues. And no matter which side wins, the issue won't go away, the problem won't go away.  I want to be useful to the evolution of this world. But I'm very wary of the existing solution sets, the existing actions that are offered to us because they're so often part of the problem in ways that we are unconscious of. Yeah, so I have kind of a new motto that's helped me clear the fog in the political realm these days, which is that in any polarized debate, the most important thing is in what neither side is saying and what both sides unconsciously agree on. So one of these polarized debates comes up in the area of climate and sustainability and stuff. And it's like, is it possible to transition to a carbon neutral energy source? Can we have what they call a sustainable economy, a sustainable society? Can we have enough solar panels and wind turbines and biofuels, et cetera, et cetera, to continue running our civilization? I think that is the wrong question or it is one of these debates that obscures the real question. So the question essentially of can we sustain, like what sustainability mean? What are we sustaining? Can we sustain essentially what they're asking is can we sustain the world as we know it? The assumption there being that it's worth sustaining. But do we really want to sustain this? Do we want to sustain like in this country, a world where the of spreading subdivisions and McMansions and traffic and automobile culture and the homogenization of what had been local cultures and the whole setup of modern society, do we want to sustain that? If we say maybe not, then we can look at the energy crisis and the climate crisis through a different lens. We can say, this is an opportunity to stop sustaining it. Sustaining it. This is an initiation, a potential initiation into a different kind of society where we question those things that we've taken for granted as progress and we receive alternatives. We create alternatives. We undo some of what we have already had. We look at the values underneath it and we ask questions like, well, what do we really want? What does a beautiful life look like? That's the invitation that crisis in general can offer. Whereas the refusal of the invitation would be to say, let's do everything we can to prevent that crisis from affecting anything else. So that would be the world in the climate conversation. That would be to install giant carbon-sucking machines in every city to enable the continued combustion of fossil fuels, et cetera, et cetera, et cetera, the continued development of unspoiled places on earth. I've written a book on climate change, which I hesitate to say because as soon as I say it's on climate change, people, they may not, they may be too polite to say this to me face to face, but they're thinking, I'm not gonna read that. I already know what it's gonna say. It's hopeless, it's urgent, I've got to do this, I feel paralyzed anyway, I don't know what to do. Like I don't wanna read another book on that. So that's why I'm a little bit shy about saying that I've written a book on climate change. It's called Climate a New Story. And one of the things that it questions is sustainability. Do we wanna sustain this? And what would an alternative look like? So when I was growing up, I was told that it's important to read the news so that one can stay informed and be an informed citizen. But I think that many people, if you think about it, you recognize that that's not the real reason that you read the news or watch the news. Because if you miss the news, if you don't read it for a few days or a few weeks, it's not like you become unable to function in society. I mentioned in the material for this session that I went on a news fast. I've done this a few times in my life, sometimes for a few days or weeks, sometimes for months at a time. And I never actually suffered from lack of information in any appreciable way, nor did I become less effective as a participant in civil society. So there's some other reason why we are drawn to watch the news or read the news. It can feel kind of like an addiction. And if you take my invitation here, which I'll extend right now, the invitation is to go on a news fast for the duration of this course, however long you spend on this course. So it could be a few weeks to go on a news fast and just stop looking at the sites that you normally use to read about the news as it is presented to us. This is a profound deprogramming because for one thing, the very choice of what gets into the news and what isn't, what is newsworthy and what is not, that reinforces a certain worldview. It says, here's what is important and here's what isn't important. And by implication, here's how the world works. Here's what you should care about. Here's what is going to change things. That choice of what to put in the news and it's not a conspiracy, it's more of an unconscious conspiracy, if you will, a collective unconscious choice that reflects society's values and the metaphysics or the causality that society takes for granted. So by pulling back from the news, you pull back from a constant reinforcement of that belief in causality, in that belief of here's what's important. So pulling back from the news will help you in unlearning some of these deeper programs. You also might find, when you're honest with yourself, that one reason that you read or watch the news is for reassurance. Reassurance that I'm right. I'm doing this right. I think the right thing. I believe the right thing. I know things. Most people, and this is the echo chamber effect, most people read news from a source that they pretty much agree with that shares their outrage and their indignation about some things, shares their celebration about other things. So it helps maintain a social identity, excuse me, in a time, excuse me again, it helps maintain a social identity in a time of a loss of identity that comes from a breakdown in our overarching stories and ways of making meaning of the world and of our own lives. So there's an addictive quality then, and you may have noticed this, an addictive quality to watching the news because it pretends to meet a need that ultimately it fails to meet. The need for identity, the need to belong. It doesn't actually make you belong. It's no substitute for deep intimate connections to place and community and nature, but it at least takes the edge off the hunger that comes from that unmet need. So you might find, if you are like a serious news addict, you might find yourself going through withdrawal symptoms and those withdrawal symptoms, they come up most strongly in the empty times, the empty spaces that you would otherwise be devoted to devoting to watching the news. So the invitation then is to hold through those episodes of withdrawal and just to take note of what it's like to be free of the news, free of the reinforcement of certain patterns of perception and behavior and thought that the news induces in you. And then also with that distance, you might get a glimpse of what else, the other thing that I talked about in the session, the what's hidden beneath the polarized discourse? What's hidden beneath this side and that side? And so here's a spectrum of opinion. Here's one poll, here's another poll and you might agree with this, you might agree with that, you might be somewhere in between, but what about being off that spectrum completely? What's being hidden underneath the surface of the debate? As you pull back, you might gain more and more awareness and it might seem, wow, it's a tempest in a teapot.  And everyone is intoxicated by the issues, leaving the real issues untouched. And so you might get more clarity into what the real issues are as you withdraw from the hypnosis of the news. So that's the invitation, a news fast. So yeah, thank you.


Today we've got the MS Fun Run where my sister, myself and a few other friends are running. It's important that everyone exercises and does these type of things for a good cause. I think sport is a real leveler. In all sports you can have a really high moment and then all of a sudden you could be out for a golden duck the next day. It brings you back to earth and that's what I enjoy about sport and I think that's what the Australian public love as well. I was born in India and then moved straight away to America and then Kenya and then Australia. I was adopted when I was three weeks old and my parents had an older girl so they wanted another child and I think one of the nurses said, I know you're looking for a boy but there's this little girl and mum said she fell in love with me straight away.  My father's Indian, born in Bombay.  So we came here to get my citizenship and I think the story goes, us three girls kind of said, no I think we want to stay here, this is the place to live. Sport is a massive part of Australian culture, whether you're going to the beach and doing things or out in the park running around. So it allowed me to I think fit in and feel a lot more comfortable in school whereas I know other people who weren't good at sport and who immigrated to Australia found it difficult. My father first introduced me to the game and said, I've joined you in the local boys team and I'm like, oh no. And then the year that I finished school I then made the New South Wales Open's women's team. 2005 was my first World Cup experience. It was over in South Africa. We played India in the final and I was fortunate enough to be involved in a really big partnership. Pretty special moment to be part of that team and to walk away with the trophy. When I first walk out of course there's a certain level of nerves but once I might out there and start my routine really quick it is about who's got the smartest brain, who can keep their nerves under control, who can execute the skills under pressure. When I started attending uni my mother was diagnosed with secondary cancer. My days consisted of going to uni, taking mum to the hospital, going to training. It was difficult to see my mother deteriorate. I gave a bit of a whack sometimes when I wasn't in a great mood. So I could almost forget what was going on back at home and come to cricket and just focus on becoming a better cricketer. My father's been a mentor from way to go and I have a lot of similar traits to him even though I'm adopted. Anything that I did with my father was competitive and that's where I got it from and let's just say I don't do things for fun, I do things so that I win.  My nephew now is at an age where you can have decent conversations with him and we're such a small family that having another personality is exciting and being around a little kid makes you realise that life isn't too bad sometimes it puts things into perspective. I'm a city girl through and through and in this household we tend to cook up a big meal and enjoy a glass of wine. Okay, ready set go. Especially if we've got friends over we'll sit around and play a board game because most of my friends have got that competitive spirit as well. Well I love Sydney, I love the harbour, I love what is on offer here in Sydney and I'm a busy girl through and through so I'll always settle here. But the main thing is the fact that I went out and did the Australian things I went out and played cricket, went to the beach and I'm not a strong swimmer but I enjoyed the beach and I made sure that I had friends that weren't Indian or that I was embracing every part of Australia and as long as you do that of course celebrate your culture and the diversity that you bring to this country that you taste what's Australian as well.


Hello friends, I am Sanjay Gupta. In this video, I am going to demonstrate you how you can calculate some of two 1D arrays using pointers. Before starting, you can note my information, you can follow or subscribe my YouTube channel through the URL youtube.com slash sanjaygupta underscore tech school. You can download my programming app Techimus which is available on Google Play. Now I am going to implement a program in C programming which will demonstrate you how you can calculate some of two 1D arrays using pointer. So first I am including a header file that is stdi.h then I am implementing main function inside main function. I am going to declare three 1D arrays then three pointers P1, P2 and P3 and a variable I. So here you can see three 1D arrays ABC and three pointers P1, P2, P3 are declared and a variable I is declared for counting the rotations of loop. Now with the help of printf, I am going to display the message enter 10 elements of first array. Now with the help of for loop, I am going to receive those 10 elements. This for loop will repeat scanf statement and all the elements of first array will be stored inside A. Now to read elements of second 1D array, I am using again the printf statement which will display enter 10 elements of second array. Then for loop will be same and scanf will receive all 10 elements and those elements will be stored inside B. So this way I have received elements of A and B 1D arrays. Now I have to perform the addition of these two arrays with the help of pointer. So I am assigning addresses like this. Here you can see I have assigned base address of A array into P1, B array into P2 and C array into P3. So here you have to remember that name of array provides base address of array. So this way base addresses of these three arrays will be assigned into three different pointers. Now I can easily access the elements which are stored inside these arrays with the help of pointer. So for that purpose I am using this notation. Inside for loop I am going to apply addition operation. So for that purpose I am writing asterisk P3 equals to asterisk P1 plus asterisk P2. So this way elements of A array will be added into B will be added with the B array and the results will be assigned into C array. To move these pointers to further locations I am incrementing first P1 then P2 and then P3. So these pointers will be incremented to the next location which are available inside array. So initially P1, P2, P3 is containing base locations or base addresses. After adding base location P1, P2, P3 are incremented to move to the second location of the array. So this process will repeat 10 times so that all 10 elements of A and B arrays will be added and the result will be assigned into C array. But you can see addition is done with the help of pointers. Now after completion of addition operation I can print the result on console. So with this printf statement the message addition will be displayed. Now I am going to implement a for loop which will display the results of C array on console. At last I am writing return 0. So you can see that at the time of addition operation I have only used pointers. But reading and printing is done with the help of array or you can see variable names A, B and C. So friends this is the solution of the problem which I have mentioned in the first statement of this program. Calculate some of two 1D arrays using pointer. So to read values I have used A and B arrays then I have assigned addresses into pointers. Then addition is done with the help of pointers and then printing is done with the help of array. So now I am going to execute this code. I am entering elements of first 1D array. Now I am entering elements of second 1D array. You can see the output addition is 5. So first 1D array is containing 1 in all locations. Second is containing 4 at all locations. So addition is 5 and addition is printed with the help of C array. So this way I have demonstrated you how you can access the locations of array with the help of pointer and how you can update locations of array through pointer. So A and B are received through P1 and P2 pointers but C array is updated with the help of P3 pointer. So friends if you want to process any array with the help of their addresses then you can prefer pointers in the program. I hope you have understood how I have implemented this program with the help of pointers. If you want to watch more programming related videos you can follow or subscribe my YouTube channel through the URL youtube.com. You can download my programming app TechMS which is available on Google Play. Thank you for watching this video.


From Cambridge, Massachusetts, it's theCube, covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. Welcome back to Cambridge, Massachusetts, everybody. You're watching theCube leader in live tech coverage. We're here covering the MIT CDO conference, MIT CDO IQ day two, we're wrapping up Bob Parr. He's here, he's a partner and principal at KPMG, and he's joined by SriKar Prishna, who's the Managing Director of Data Science, AI, and Innovation at KPMG. Gents, welcome to theCube. Thank you. All right, so let's start with your roles. So Bob, where do you focus? My focus, within KPMG, we've got three main business lines, audit, tax, and advisory, and so I'm the advisory Chief Data Officer. So I'm more focused on how we use data competitively in the market, more the offense side of our focus. So how do we make sure that our teams have the data they need to deliver value as much as possible, working concert with the enterprise CDO, who's more focused on our infrastructure, our standards, and security, privacy, and those. So you're focused on making KPMG better, as opposed to a KPMG client, okay. I also have a second hat, and I also serve financial services CDOs as well, so. Okay, so you get dragged out by the sales guys. Exactly. And Sreekar, what's your role? Yeah, you know, I focus a lot on data science, artificial intelligence, and overall innovation. So I actually represent a center of excellence within KPMG that focuses on AI, machine learning, natural language processing. And I work with Bob's division to actually advance the data side of the story, because all AI needs data, and without data there is no algorithms. So we are focusing a lot on how do we use AI to make data better, think about data quality, think about data lineage, think about all of the problems that data has. How can we make it better using algorithms? And I focus a lot on that working with Bob. So Bob, focus on internal data, KPMG, No, it's customers and internal. I mean, we are a horizontal within the firm, so we help customers, we help internal, we focus a lot on the market. So Bob, you mentioned used data offensively, so 10, 12 years ago, data was a liability. You had to get rid of it, you keep it no longer than you had to, because you're going to get sued, so email archive came in and obviously things flipped after the big data took off, so what are you seeing in terms of that shift from the defensive use of data to the offensive use? What does that all mean? Yeah, and it's really, when you think about, and let me define sort of offense versus defense, so on the defense side, historically, that's where most of the CDOs have played. That's risk, regulatory, reporting, privacy, even litigation support, those types of activities today. And really, until about a year and a half ago, we really saw most CDOs still really anchored in that. I run a forum with a number of CDOs and financial services, every year we get them together and we ask them the same set of questions. This was the first year where they said that, you know what, my primary focus now is growth, it's bringing efficiency, it's trying to generate value on the offensive side. It's not like the regulatory work's going away, certainly in the face of some of the pending privacy regulation, but it's a sign that the volume of use cases as the investments in their digital transformations are starting to kick up, as well as the volumes of data that are available, the raw material that's available to them in terms of third party data, in terms of the just the general volumes that exist that are streaming into their organization. And the overall literacy in the business units are creating this massive demand, and so they're having to respond. They're having to rethink that. Is this because they're getting a handle on the data, they're actually finding where it is, they're categorizing it, they're organizing it? That is still a challenge. I think it's better when you have a very narrow scope of critical data elements, going back to the structured data that we were talking about with the regulatory reporting. When you start to get into the offense, the generating value, getting into customer experience, really exploring that side of it, there's a ton of new muscle that has to be built. New muscle in terms of data quality, new muscle in terms of really more scalable, operating model. I think that's a big issue right now with CDOs, is we're used to that limited swath of CDEs and they've got a stewardship network that's very labor intensive, a lot of manual processes still, and they have some good basic technology, but it's a lot of it's rules-based. And when you think about how that constraints going to scale, when you have all of this demand, when you look at the customer experience, analytics that they want to do, when you look at just AI applied to things like operations, the demand and the focus there is going to start to create a fundamental shift. So Srikhar, one of the things that I've seen, and maybe it's just my small observation space, but I wonder if you could comment, is this seems like many CDOs are not directly involved in the AI initiatives of the organization. Clearly the chief digital officer is involved, but the CDOs are kind of in the background still, you're seeing that, and is that changing? That's a fantastic question, and I think this is where we are seeing some of the cutting edge change that is happening in the industry, and when Bob represented the idea that we can offensively look at data, this is what it is, that CDOs for a long time have become more reactive in their roles, and that is starting to come forefront now. So a lot of institutions we are working with are asking what's the next generation role of a CDO, and why are they in the background, and why are they not in the foreground? And this is when you become more offensive or proactive with data, and the digital officers are obviously focused on the transformation that has to happen, but the CDOs are their backbone in order to make the transformation real. And if the CDOs start to think about their data as an asset, data as a product, data as a service, the digital officers are right there, because those are the real, like the day-to-day that they are living, so CDO can really become from a back office to really become a business line. We've- Who do you see taking the reins in machine learning, in machine learning projects and the companies you work with? Who is driving that? Yeah, great question. So we are seeing different, I would put them in buckets, right? There is no one model fits all. We are seeing different generations within the companies. Some of the ones who are just testing out the market, they're still keeping it in their technology space, in their back office tech, IT, and forward IT, let me call that, where they are starting to experiment with this, but you see the mature organizations on the other end of the spectrum, they are integrating machine learning and AI right into the business line, because they want the CXOs having the technology right by their side so they can leverage AI and machine learning right for the business, right there. And that is where we are seeing some of the new models come out. I think the big shift from a CDO perspective is using AI to prep data for AI. That's fundamentally where the data science was distributed, some of that data science has to come back. So for data integration, for data quality. Yeah, for data prepping, because you've got all this data, third party and other, from customer streaming into the organization. And the work that you're doing around anomaly detection is it transcends developing the rules, doing the profiling, doing the rules, the very manual, the very labor intensive process. You got to get away from that. So you're talking about using it for this to be scalable. Algos and AI to figure out which algos to apply? To clean, to prepare the data, to see what algorithms we can use. So it's basically what we are calling AI for data rather than just data leading into AI. So it's, I mean, you know, we've developed a technology for one of our clients and it's a pretty large financial service. They were getting close to like a billion data points every day. And there was no way manually you could go through the same quality controls and all of those processes. So we automated it through algorithms. And these algorithms are learning the behavior of data as they flow into the organization. And they are able to proactively tell where problems are starting to emerge. And this is the new phase that we see in the industry. You cannot scale the traditional data governance using manual processes. We have to go to that next generation where AI, natural language processing, think about unstructured data, right? I mean, that is like 90% of the organization is unstructured data. And we have not talked about data quality. We have not talked about data governance for a lot of these sources of information. Now is the time, AI can do it. And I think that raised a great question as you look at unstructured and a lot of the data sources as you start to take more of an offensive stance will be unstructured. And the data quality, what it means to apply data quality isn't the profiling and the rules generation, the way you would with standard data. So the teams, the skills that CDOs have in their organizations have to change. You have to start to, and it's a great example where you guys are ingesting documents and there was handwriting all over the documents. Yeah, great example, Bob. Like, we would ask the client, like, is this document going to scan into the system so my algorithm can run? And they're like, yeah, yeah, everything is good. I mean, the data is there. But when you then start scanning it, you realize there's handwriting and the information is in the handwriting. So all the algorithms break down. Now how do you- Yeah, this is tribal knowledge. Tribal knowledge, exactly, exactly. So that's what we are seeing. If we talk about the digital transformation in data, in the CDO organization, it is this idea that nothing is left unseen. Some algorithm or some technology has seen everything that is coming into the organization, has a pair of eyes on it. So it can tell you where the problems are. And this is what algorithms do. They scale beautifully. So the data quality approaches are evolving, sort of changing. So rather than a heavy, heavy emphasis on masking or deduplication and things like that you would traditionally think of, participating in the data quality. Not that that goes away, but it's got to evolve to use machine intelligence. So what kind of skill sets do people need to achieve that? Is it the same people, or do we need to retrain them or bring in new skills? Yeah, no, great question. And I can talk from the perspective of where AI is disrupting every industry now that we know, right? But when you look at what skills are required, all of AI, including natural language processing, machine learning, still require human in the loop. And that is the training that goes in there. And who are the people who have that knowledge? It is the business analysts. It's the data analysts who are the knowledge bearers. The C-suite and the CDOs, they are able to make decisions, but the day-to-day is still with the data analysts. Those SMEs. Those SMEs. So we have to upskill them to really start interacting with these new technologies where they are the leaders rather than just waiting for answers to come through. And when that happens, now, me as a data scientist, my job is easy because the SMEs are there. I deploy the technology. The SMEs train the algorithms on a regular basis. Then it is a fully fungible model which is evolving with the business. And no longer am I spending time re-architecting my rules and what are the masking capabilities I need to have, it is evolving as the business goes. Does that change the number one problem that you hear from data scientists which is the 80% of their time is spent on wrangling, cleaning data, 10, 15, 20% on your fun stuff? If you run into SMEs being concerned that they're going to be replaced by the machine they're training. I actually see them being really enabled now where they are spending 80% of the time doing boring job of looking at data. Now they're spending 90% of their time looking at the elements which are creative and which requires human intelligence to say, hey, this is different because of X, Y, and Z. So let's go out, it sounds like a lot of what machine learning is being used for now in your domain is to clean things up. It's plumbing, it's basic foundation work. So go out three years after all that work has been done and the data is clean, where are your clients talking about going next with machine learning? Bob, do you want to take a look at that? I mean, it's a whole, it varies by industry, obviously, but it covers the gamut from, and it's generally tied to what's driving their strategy. So if you look at a financial services organization as an example today, you're going to have really AI driving a lot of the behind the scenes on the customer experience. You know today with your credit card company it's behind the scenes doing fraud detection. You know, that's going to continue. So it's when you take the critical functions that we're more data, it makes better models, that's just going to explode. And I think they're really, you can look across all the functions from finance to marketing to operations. I mean, it's going to be pervasive across all of that. So if I may add on top of what Bob was saying, I think what our clients are asking is, how can I accelerate the decision making? Because at the end of the day, all our leaders are focused on making decisions and all of this data science is leading up to the decision. And today you see what you brought up, like 80% of the time is wasted in cleaning the data, so only 20% time was spent in real experimentation and analytics, so your decision making time was reduced to 20% of the effort that I put in the pipeline. What if now I can make it 80% of the time that I put in the pipeline, better decisions are going to come on the other end. So when I go into a meeting and I'm saying like, hey, can you show me what happened in this particular region or in this particular part of the country, previously it would have been like, oh, can you come back in two weeks? I will have the data ready and I will tell you the answer. But in two weeks, the business has ran away and the CDO or the C-suite doesn't require the same answer. But where we are headed is as the data quality improves, you can get to real time questions and decisions. So decision support, business intelligence. That's right, yes. Well, we're getting better as an industry. Used to be six months to build a cube, right? Now it's two weeks, still not good enough for moving too fast. As I was saying, data is plentiful, insights aren't. Yes. So in your view, will machine intelligence finally close that gap and get us closer to real time decision making? It will eventually, but there's so much that we need to, our industry needs to understand first and really ingrain. And today there is still a fundamental trust issues with AI. We've done a lot of work. Why, because it's black box or? Yeah, part of it, part of it. I think the research we've done and some of this is nine countries, 2,400 senior executives, and we ask them a lot of questions around their data and trust and analytics. And 92% of them came back with, they have some fundamental trust issues with their data and their analytics. And they feel like there's reputational risk, material reputational risk. This isn't getting one little number wrong on one of the reports. That's a more of a systemic issue. We also do a CEO study, and we've done this many years in a row. Going back to 2017, we started to ask them, okay, making a lot of companies are data driven. When it comes to- Or they say they're data driven. Well, they say they're data driven and that's the point. At the end of the day, they're making strategic decisions where you have an insight that's not intuitive. You trust your gut, or you go with the analytics.  So, okay, this is 2017. This industry's moving quickly. There's tons and tons of investment. Look at it in 2018. Go down? No, it went up. 78%. They haven't read Moneyball. So it's not an awareness issue. There is something more fundamentally wrong and you hit it on, part of it's black box. And part of it's the data quality and part of it's bias. And there's all of these things flowing around it. And so when we dug into that, we said, well, okay, if that exists, how are we going to help organizations get their arms around this issue and start digging into that trust issue? And really, the front part is exactly what we were talking about in terms of data quality. Both structured, more traditional approaches and unstructured, using the handwriting example and those types of techniques. But then you get into the models themselves and it's the critical things you gotta worry about is the lineage. So from an integrity perspective, where's the data coming from? Where are the sources? Where are the change controls on some of that? We need to look at explainability gained at the black box part where can you tell me the inferences, the decisions? Are those documented? And this is important for the SME, the human in the loop to get confidence in the algorithm as well as that executive group. So they understand there's a structured set of processes around that. And the money ball problem is actually pretty confined. It's pretty straightforward. I don't know, 32 teams are throwing minor leagues, but the data model is pretty consistent. True. The problem with organizations is that no data model is consistent in any organization. You mentioned risk, Bob. The other problem is organizational inertia. If they don't trust it. What does a P&L manager do when he or she wants to preserve their existing position? They attack the data. I don't believe that. Let me hear. Well, which is a fundamental point, which is culture. I mean, you can have all the data science and all the governance that you want, but if you don't work culture in parallel with all this, it's not going to stick. And that's, I think a lot of the leading organizations are starting to really dig into this. We hear a lot about literacy. We hear a lot about top-down support. What does that really mean? It means senior executives are placing bats around and linking demonstratively, linking the data and the role of data, data as an asset into their strategies and then messaging it out and being specific around the types of investments that are going to reinforce that business strategy. So that's absolutely critical. And then literacy, absolutely fundamental as well because it's not just the executives and the data scientists that have to get this. It's the guy-in-ops that you're trying to get to. They need to understand, not only tools, and it's less about the tools, but it's the techniques. So it's not the approaches being used are more transparent and that they're starting to also understand the issues of privacy and data usage rights. That's also something that we can't leave at the curb with all this innovation. It's also believing that there's an imperative. I mean, there's a lot of, for all the talk about digital transformation, and you hear it everywhere, everybody's trying to get digital right, but there's still a lot of complacency in the organization, in the lines of business, in operations. They say, hey, we're actually doing really well. We're in financial services or healthcare. It really hasn't been disrupted. Everybody says, oh, it's coming, it's coming. But there's still a lot of, well, I'll be retired by then, or hanging on to the past. Well, actually, it's also the fact that in the previous generation, if I had to go do a shopping, I would go into a shop, and if I wanted to buy an insurance product, I would call my insurance agent. But today, the new world, it's just a tap of my screen. I have to go from Amazon to some other app. And this is real. This is what is happening to all of our clients. Previously, they thought that customers bucketed them in different experience buckets. It's not anymore. It's real in front of them. So if you don't get into the digital transformation, a customer is not going to discount you by saying, oh, you are not Amazon, so I'm not going to expect that.  So you have to become real with digital. I was a little surprised that you said you see the next stage as being decision support rather than customer experience, because we hear that for CEOs, customer experience is top of mind right now. There are two differences, right? One is external facing is absolutely the customer. Internal facing, it's absolutely the decision making. Because that's how they are separating the internal versus the external. And most of the meetings that we go to, customer insights is the first place where analytics is starting, where data is being cleaned up, where questions are being asked about can I master my customer records? Can I do a good master of my vendor list? That is where they start, but all of that leads to good decision making to support the customers. So it's like that external towards the internal view. Well, and back to the offense versus defense and the shift, it absolutely is on the offense side. So it is with the customer. And that's a more direct link to the business strategy. So that's the area that's gained the money, the support, and people feel like they're making an impact with it there. When it's down here in some admin area, it's below the water line. And even though it's important and it flows up here, it doesn't get the visibility. So that's the big show. Guys, great conversation. Thanks for coming on. You've got to leave it there. Thank you for watching. We're right back with our next guest, Dave Vellante with Paul Gillan from MIT, CDOIQ, right back. You're watching theCUBE.