That was really my first immersion into the deep end of storytelling because you have to take them from the universe estimation methodology into the measurement methods that you or approaches that you've used and convince them that this is actually what the market looks like, and this is a number of table tops in the country, this is how much gum Gypsy fruit they're selling. And so, over time, I became conversant with how the framing of those stories needs to look like, but even more importantly, at the back of my mind, I always know that no matter how good the analysis is, for as long as the story you're telling is not good enough, and you risk losing everything that you've worked so hard for. And that sets the stage for the next 30 or so minutes of this keynote. Inherently, we are all storytellers, even beings. They say that you're alive, we live in a network of stories, and that there is no stronger connection between people than in storytelling. One of the writers that you'd like, who's a brilliant sapiens even says that stories and gossip are the reason we are where we are. So at the end of the day, in whatever field you're working with, storytelling becomes the connection between everything very technical, and that actually being impactful or making sense, and driving the value that you envision it to drive. That's the first point storytelling is important. So let's park. The second one is data science is simple incomes of how you think of the framework within which you do data analysis and data science at large, because you told collective data. You can do surveys through writing as well, code, or whatever, and then, you know, analyze it and then present it, right, very simple, but it's hard. All of us who've worked in this industry who are getting into it who will be studying it know that it's hard because it needs you to understand the business problem and the context is it a product that you're trying to build so on data to collect how does that product look like work with different stakeholders, and if it's KPIs, then this vary across the organization. What are the most important metrics for X score, the executive community that is what are the most important metrics for the operational teams the functional businesses, then you have to go and get that data. And that could be through surveys or it could be through writing and SQL or even getting different spreadsheets we have a lot of companies that still have spreadsheets as their databases or a mixture of all that. Then you get into the process of cleaning and exploring when you do that time and time again. The cliche within the industry is that 80% of the work of data science and data analysis is actually doing that. And there's a lot of truth to that is a lot of truth to that.  How do you present that is it through an open source tool is it through a PowerPoint like this is it through a BI platform when you deploy and and I'm obviously trivializing it, even by saying that because of how much work, the amount of work. The insurmountable amount of work that goes into that, but all that does not really matter unless you can get the output or the result or all that toiling into being useful or valuable or being impactful depending on what you're looking at, if it's a business scenario, operational scenario, even in not for profit organization then impact is very, very important. Is it a product or are you measuring the impact of that product? So you could do all that spend days, weeks, months, even a year building something and because of how you package it, then it turns up not becoming successful. That's a significant percentage of beta science projects fail. Let's have a look at, let's start getting into some of the reasons why some of these solutions end up not becoming useful. They're not entirely useless, so I will stay away from using that word, but they're not very useful. And that is what we all want to be, we want our work to be useful. The first gap that is related to storytelling is you being able to not ask, we being unable to ask and answer the right questions. And you could be asking whatever questions you ask and answering them very well, but for as long as they are the wrong questions, then it really does not matter. There is an art and a skill to this and I will go into the depth of it. Socratic methods and so many other ways of you being able to frame your questions. Now if you're not able to ask the right questions, then it means that a couple of things happen. One is that you end up using an approach that could be methodologically correct, but is answering the wrong question. Then you get into this rabbit hole or loop that you do not really want to be in where you have to answer particular questions spend so much time doing that. I mean then having to start all over again when you realize oh snap that was the wrong question. Right. I found myself in such scenarios and I think to some extent, I'm guessing a lot of us have done that, as you perfect our art or asking the right questions. The main thing here is one of the things that works is, you know, do not shy away from asking as much as it's hardly possible prior to starting out whatever you're doing. So if this is something that is led by particular functions or particular people in the organization, then you want as they are making that request or giving you these ideas about what they want. Is it a model, is it adaptable, is it a report, then you want to ask as many questions as possible, and keep going back to them at that initial phase. If it's you who's driving, they saw each an innovation, for instance, that you think would be useful to the business. Then over and above doing the things that you should be doing such as, you know, rolling out a pilot or a proof of concept, you want to ensure that you are getting as much feedback as possible from the stakeholders and the people who will actually make your new innovation, your product, whatever you're doing successfully. What metrics should you be looking at, are they exhaustive or you calculate those metrics, how do you define success because you could be defining success very differently from how they're defining it. And then listen, I do not think I need to speak a lot about that. By now, all of us should know the importance of listening as data analysts because of how imperative it is to storytelling. The other thing that I found to be very useful is, besides being from very early on, especially for analytics projects on whether you want to use either the hypothetical approach or the exploratory approach. Both of them have their pros and their pros. So you have to be very clear on have a lot of clarity on what you're looking for and what you can stand, what you can afford to lose depending on the methodology that you use. So when you go hypothetical, it means that you're limiting yourself from answering a lot of the other questions that exploration would give you answers to. And when you're doing exploration, one of the things that you lose is the component of especially time because you end up looking at the data slicing, analyzing it, analyzing it in so many different ways because you don't have a question that you're asking. It's good for different projects where no one knows anything.  I don't have a hypothesis about how our customers are behaving. I don't have a hypothesis about how things are changing over time around our different products. And then on the hypothesis side, you could say, okay, there's been a reduction in metric X because of why that is my hypothesis, can you get it to the written prove it. And so when you're having this early engagement, you must be very sure that even that hypothesis is very well defined and aligned with the different stakeholders. So that's one, I'm asking the wrong questions. And one is weak stakeholder buying which is slightly related. There's guaranteed failure no matter how good your work is, if you do not have the right stakeholder support. We always say that for instance for innovation project find a sponsor, find someone who is higher up in the organization if you're not. Who's an ex-co in the leadership team, a functional leader who's very attached to whatever you're doing to drive that for you. Functional and executive leadership must have a significant level of belief in the purpose and the value of what you're doing. And the only way you get there is by sort of creating that picture of how the results would look like. Do not again spring things up on them. Do not just, you know, appear and send an email for instance and say, oh, we found this and this and this and this is what we're doing about it. Without any context whatsoever. If it's an innovation if it's, you know, new metrics that you're rolling out then you're thinking of rolling out and testing, then you must think of it as a product manager for instance. So, one of the things here that I've done, I've seen a lot is new learning from the product managers within organizations and also thinking of itself as an analytics product manager where you create roadmaps. For instance, for the work that you're doing and help them and sorry ask them for help, because a lot of them are willing to help you and in the very few cases when you don't have to build something together then whatever you've learned from them is applicable into your different projects, but you don't have that clear roadmap at what point do you inform stakeholder A stakeholder B a function leader, at what point are you pushing out these different metrics at what point do you want to test that out with a group of users if it's a product and so on and so forth. But that ensures the stakeholder buying. If not then you're guaranteed to fail. The last one that I think a lot about is the fact that we end up creating a lot of unnecessary complexity and complications and those two tend to be quite different. Now, the structures and behavior of different phenomena is already complex as it is, especially in this day of interconnectedness and a lot of software driving different things and different decisions. So, as an analyst as a data scientist, it's not in your best interest whatsoever to increase the level of complexity and the level of complication within any particular project that you're working on especially at that latter stage of communicating and storytelling and driving impact on the other downstream. And there are different ways you can do this. I mean, first of all, a lot of the problems you really need to be honest here. They have a certain degree of complexity and complication but they tend to be mostly either known or knowable, meaning that the course and effect are perceivable, they're understood, they can be understood, and if not directly perceivable then the separation is across time and space. That's one. And the different techniques of reducing that complexity from a philosophical point of view, when you spend complex systems, you know, you could use homogenization techniques, abstraction, transformation, and so many other things that ensure that as you proceed, even if the techniques you're using are complex, then complex within that system because that's what they need to be, then they're not adding another layer of complexity, they're rather breaking that structure or that behavior pattern down. That's one. And downstream when you're communicating, make sure that you make it as simple as possible and focus on the rules that matter. That's one too. We have a tendency of using data science techniques as hammers and every single problem that we have is a nail especially, you know, for instance, deep learning. It's amazing for different reasons. But in a lot of problems, many of the problems that we are working on, then we do not need to make them to use such advanced techniques, just because we know them, we have the tools and we do not need to, you know, build a GPT3 on your next sales prediction or a GPT3 on whatever question that you've been asked by the head of operations, right? So don't make it complex by using these complex methods or complicated methods or that are hard for people to understand, interpret, and so on. Again, make sure of predictability. When we do not do that, then it makes it hard for us to go back and tweak things, change different things, and that's why, for instance, I is beautiful for this. And there are a lot of resources that enable you to make sure of predictability within the projects. Always remember that the major success is not how accurate your model is, not how sleek your dashboard is. It's on the business and operation sites. So if they're not able to, the users, the end users are not able to make decisions and get insights. If your product is not able to meet certain metrics that the organization has put, then you're not successful with respect to how sleek, how elegant the underlying model or underlying analysis is. Okay, so we laid down all those different problems and there are many. Those are just but a few that I've encountered and the few that I think have been possible for sources of friction and the performance of different projects that I've carried out. And we can also brainstorm on a few at the end of this. So with that background, I want to share two ideas around how we ensure that we use storytelling or we can ensure that storytelling is beneficial to us and actually is a net positive and not a net negative in terms of making our projects successful, successful, valuable. The first one is understanding the audience and that's quite simple, but there are a couple of things that I want to share today. The first thing is why do we communicate. Communication has different reasons, three of them that have been shown to be very important across organizations are, you know, direction providing direction. An example of this could be a that we need to make these additions to this dashboard or, you know, to this model to do this and this and this and this is maybe you as a leader or a manager, talking to the people who are uniquely in that direction. And the communication needs to have all the clarity around why are we doing that. Why is it important. And even sometimes go as far as saying, okay, I have an idea about how we should do that or, you know, here are some ideas, rough ideas that I'm thinking about, can you go research more, and then come to come back to me, and then we can make those adjustments that's one to its inform. This is when you're relaying information about different findings, different insights and stopping at that. So it's telling, you know, your sales people or your head of sales, your commercial director, that we are at risk of using 8% of our customers in the next month in the region, because of the increasing levels of out of stock in the different shops. You know, that's an example, you informed them after doing some analysis that gives you these insights. And then the last one is to pursue it where you're now going beyond just performing and telling them and actually having a point of view yourself in terms of what should be done and using different persuasion on the techniques to make to bring to make them actually do something about and not just see things in a particular way, but actually take steps towards doing something about that insight. So we've noticed in this problem, we should send personalized message to customers in the MITRE and offer 5% discounts to reduce the risk to 3% from 8% and this is an analysis that has gone into me making that recommendation. And that needs to be very, very clear. Now, the preface to that is that you must be able to understand your audience and be have clarity on what communication needs to go out then is it direction, is it informative, informational, is it persuasion. All audiences that you might be communicating to mostly similar in that one. All our minds as human beings as I started out by saying, our story processes and not logic processes meaning that we function better. We consume information better as stories and not as logic. That's one to we respond most strongly to stories that have a sort of pattern to them. They have a general structure. The hero, the hero story, for instance, when it starts out with a conflict, and that conflict leads into some sort of tension, and then within that tension, their characters who are either on the good or the bad side as a hero, and then the hero transforms. If you look at all the movies across time. That's sort of the structure that they follow because research has shown that that is a structure that human, the human mind, mostly responds strongly to. How do you tailor in your communication history telling in the same way you don't need to reinvent and just fitting in that particular structure. That's one. Only all audiences that you talk to all people will be similar in that they want to participate in stories based on a couple of things one of them is engagement and engagement here is not just about people paying attention. It's about them having some emotional investment that comes with that attention. So it's not just people paying attention you want as well to evoke something out of them. That's what engagement is. It's been shown around storytelling that's one too. You want to transport them that they want to be transported. So they want to be inserted or must into the story. And this is not hard in a business context, especially when you know what incentives. The difference the code and have how do you ensure that they find themselves inserted at different points of the story so it speaks to them. That is closely related to relevance, where obviously there's a close relationship between the story and the audience. And then for its influence. So, at the end of the day, it should have a certain level of influence on, you know, shifting a belief shifting the level of knowledge shifting a behavior. And that that is what would make an audience participate in a story and actually respond to it. Now, those are the similarities, whether you're in an organization, or you are there with your friends, when you're storytelling, those are for the four main things that lead people to participate in different stories. Now, those are the similarities. In a business context, audiences are also quite dissimilar in some ways one is, they could either be internal or external meaning, meaning one, one is, you always have to understand. It sounds quite obvious that you have to understand whether you are within a business to business framework, meaning that are you an agency, if you may, or a consultant or are you an internal analytics team, where your customer is actually people in the organization. Now, once you understand that dichotomy, then it means that you're able to answer a couple of questions that directly contributing to how you package your story.  What is the culture? If you're within an analytics team within an organization, then you're part of that culture, and you understand how the organization works, you know how you do your things, you know how to communicate, you know who to talk to. Whereas if you're a consultant or you are an agency, working an agency, then you must be able to master yourself into a different culture compared to your culture. So I've been in scenarios where for instance I'm working in a tech company that is very casual, very high energy, very innovative, you know, move quickly and break things. That's sort of the ethos, but then you're working with a bank for instance where that is not their culture at all. So meaning that how you do things, how you package them, how you communicate, it's sort of still in a slightly conservative way, how you dress when you go into meetings, how you package your presentations for instance is very, very different.   What is a frequency that helps you determine as well things such as the tools that you will be using for your story telling. Are you going to use a deck where you just get a dashboard. Let's go how that dashboard looks like if it's top leadership, functional leadership, how does that look like, which brings me to the next part of that which is are they are your audience at the bulk of your audience strategic or operational. So, and here are a couple of questions that you should always ask yourself is, what are they using this data and these insights for. Are they using them to make organization wide decisions or are they using them to make operational interventions on different things that informs how you package your story. Are they organizational or is it functional so meaning that are you just talking to the commercial team or the upstream or the customer experience team or are you talking to someone who oversees the entire organization. Are they more concerned about the forest or the trees, they care about the big picture. And they want to be masked and for the story to measure around the big picture or they want the final details. Again, this is based on whether they're strategic or operational, how much time do they have. They're very, very clear on that whether you're making a presentation or building a dashboard, however, how much time do they have at their disposal, depending on the level they are, they are in the organization. And of course you have to ask yourself about the frequency and the tools for that you used to do that. The other question you have to ask yourself regarding the audience, as we aim to understand them is, are they technical or not technical, meaning that one, how much technical information is actually useful to them, especially for those who are building models for instance and even dashboards to some extent and different data analysis presentations. Do you care, do they really care about model parameters, features, or do they really care about what are you predicting or what are the business outputs and you know stop that.  What do they know already, do they have the context. So if you're talking to your peers for instance, and an example of this is, if you're talking to, if you're an agency or a consultant and you're presenting the data analysis presentation, so presenting this to your customers, your business customers, how much do they know already. Are they, are you talking to the head of a department that is very technical so the head of data on the other side or on the head of marketing, for instance, they have different skill sets and they have different things that they care about. So any work we've done previously, that informs for instance, or if you're talking to technical people, how much for lack of our citations or literature review you have to show them that this methodology has been used here and here and here, and this is why we chose it. And then breaking that down into this is how we implemented it. This is the method, this is the approach, or if they're not technical, then this is how you use this output. This is how you use this product, and that's how you use this dashboard. Without caring about all the work that went behind, you know, combining the different tables or whatever sort of computations did on the back end, if it was data that was collected from the field. That is something that you have to always consider. That's the first idea. So take a note of all, of that, of that, those different ways of breaking down the audience and why it is important, such an important input to how you tell your story. That's the first idea. The second idea is, once you've established that how do you reduce the cognitive load. Human beings, by and large again, they like stories because it's actually hard, easier to process the logic. And so you always want to play into that, make it easy for your audience to process whatever you're telling them or even not process at all, because the story is very well, well structured and plays into, into what is inherent to them. Now, stories are obviously more impactful and powerful than statistics, because, like I said, they take less effort of the brain to contextualize a lot of complex issues. Okay, now I know you're asking yourself, we are at Saturday, Saturday, and I've not talked about our at least from a technical point of view, and this is where it comes in. Obviously, this is a commended tidy approach of doing data science. So I'm not going to focus on everything else. We have the entire day to do that. I'll just focus on the last 10, which is, you know, communication. And I'm going to sort of go through a use case of how we can reduce complexity within one of the things I like doing a lot, which is sports analytics. Therefore, those who are either fans of rugby or have worked around much, then, you know, work with me, if not, then I'm just focused on the visualizations and not the data itself or the ideas. All right. So, when one of the things that we do when looking at the statistics for instance or whatever statistics you're looking at around sports. It's how much high intensity distance is covered by different positions within the team. And the underlying idea is the foundational concept to them. Different players have different skills, different levels of fitness, and they have different responsibilities on the pitch. So we have what you call your backs and your forwards. So if you throw that data of high intensity distance into a gg plot, then you don't, this is, you know, sort of what you get in the basics. So there are a couple of things that happen here, one is, and then I hope that if this was my life conference, maybe a physical conference and go around asking a few questions, but right now I'll just go ahead and share my thoughts. Sorry, Commander, I'm sorry to interrupt, but we are pressed for time, so we request you to be finalizing. Yes, finalizing. Okay, thanks. Thank you, Maggie. And so you notice that there's a lot of color. That's one color is distracting or brains. Obviously, not very good at attending to color, especially when it's a lot of colors. So one of the things that you know it's a we do is, okay, let's start moving a bit on the background for instance, and, you know, add some of the basics. What are we looking at? What's the title or the subtitle? What are we actually measuring here? And as opposed to looking at minimum group, what is a group? Now we're looking at position. Let's even go on step further and clean it back up even a bit more. Let's remove all the color, leave it at gray, and just leaving the outliers. We are able to see, okay, we're looking at the high intensity distance where the distance covered is where high intensity defined as the distance covered at more than 18 kilometers per hour. There doesn't seem to be, there seems to be a lot of homogeneity around the forwards, but there are three employers on the box, one is extremely good, the other one, and there are two who are not recovering a lot of high intensity distance. We can even go a step further and add some annotations to those outliers. So the first one is number 13, and that's how much distance they covered. The other one is number 11, and number 14, and that's how much distance they covered, which is significantly lower than number 13. Because the person you're talking to is the coach, or even the players themselves who are not very technical, then it's very easy for them to consume this and say that if our expectation for use number 1114 is to cover this much in high intensity distance because it has a correlation to winning or it has, it's a significant driver of us winning or losing matches, especially on offense, then, you know, you're not doing very well, right? I mean for other parts of the organization, if it's the management team, then this is what I need to see as well. And then let's keep it as various possible and, you know, avoid color as we tell the story because that's distracting. Only use color when you need to categorize something that needs to stand out because that's where the human eye will always go. The other idea is around, we always, we have a certain, I don't know, we have an edge every now and then to use tables to present information. And there's not much that you can infer from this, whether you're technical or not. If you may be technical, you know how to read tables at a little level, maybe if you're an accountant, you spend all that time looking at spreadsheets and you can sort of tease out some patterns from this. And so cognitively, this is a lot of load for anyone who's processing that. You could just, you know, for instance, do this and keep everything at gray and say, okay, what are we trying to show here? We're trying to sort of show the correlation for every single position between the heart rate of reliability and the high intensity distance covered. Ideally, those two should have a positive correlation. When people do a lot of high intensity distance, you do not want the heart rate of reliability is a key measure of performance, cardiovascular performance, to go down in any way. So, how about we do that, for instance, and I've spoken a lot about this idea in terms of how we use color, size, orientation, the shapes, the density, the hue. In terms of letting and making presentations and visualizations work for us in storytelling, but in the interest of time, I believe that. So, as you wind up a recap is one story telling always remember is an inherent human inclination make every single communication presentation, a story, optimize on not just the data but the insights and then at least those must always go hand in hand. You must always begin by understanding the context where the context is a combination of the audience and the expectations and the relevance and how you bring them into the story. You must always reduce your community, the constant load that you're putting on your audience by, by ensuring that you're cleaning up your visualizations as much as possible and using all these different techniques that optimize on the use of hue intensity, etc. A rule of thumb for me is only start gray and then build up from there. And then the last one is, that's all, the use of the use of pie charts as much as possible. Yeah. You can meet outside and debate but thank you very much. Thank you. Thank you for the insightful talk commander. So I'll allow you one minute to answer two questions and then close this. So, one question is, have you found yourself in a position where you need to educate your audience to get to the point where you can engage with them over your results. Any tips. Yes, any tips. One is if the audience is maybe fairly non technical. And then you can spend a bit of time educating them FAQs or perfectly send some periods especially before presentation, if that's what you want to do and tell them that this is what we'll be talking about during this meeting. And here is the background information or he is a preview of the presentation. I'm happy to answer any questions between on the meeting so that by the time we start in this discussion, we are all on the same page. Yeah, those are some of the things that are formed to work. If you eat a dashboard for instance that has a fairly detailed or potentially complex data set or or visualizations then make sure that you have notes that accompany that. And it is very clear that this is the idea we're trying to communicate. And this is how we arrived at that particular idea. Okay. Thank you for that. And the last question is what is one thing you do differently if you were given a chance to start the data science journey all over again. My mind has been a very boring route to get assigned because it was mostly academic and I've had the lack of going from school studying statistics and computational intelligence into industry and all those have all that has come together quite well. I'd say that the one thing I do do is sort of back maybe undergrad I'd have spent a bit more time programming, not just for now, but trying to do so. And, and, and that's something that I always tell to, you know, other students who come to me for advice and the people I mentor. Maybe you have a lot of time on your hands in school, try different things and develop your capacity to learn different tools, different techniques, and different ideas as much as possible from very early on. Okay. Thanks for that. So, so that's it. Just a confirmation Exco means executive committee right. Yes, it does. Okay, okay, okay, okay. So that's it. Thank you so much commander and next we encourage you to stretch for five minutes after which we will have our first two workshops, which have actually started and they're running in parallel. And this is our introduction to data management in our as well by Christopher Maronga as well as introduction to package development by Balugone Steven. So please refer to the email that was sent to us today for the respective zoom links. So enjoy the sessions. Thank you so much.


from Austin, Texas. It's theCUBE, covering DockerCon 2017. Brought to you by Docker and support from its ecosystem partners. Welcome back. Hi, I'm Stu Miniman joined with Jim Copilis. You're watching theCUBE's SiliconANGLE Media's production of DockerCon 2017. We're the worldwide leader in live enterprise tech coverage and we can't finish any DockerCon without having Jerry chain on. So Jerry, partner with Greylock, always a pleasure to interview you. We've had you on the Amazon shows, lot Docker, other ecosystem shows. So great to see you. Stu, Jim, hey, thanks for having me as always. It's great to be here. All right, so first of all, I mean, you invested back in the dot cloud days. Could you imagine when you were meeting with Solomon and those guys and everything, that we'd be here with 5,500 people as to where they go. What's your take on the growth? Every year just blows my mind. Both in open source community, developers, ecosystem partners, and more recently the past year and a half, the enterprise customers that take Docker seriously and replatform the applications on Docker amazes me. I think I did the investment in 2013 and there's a few hundred thousand downloads of Docker. Now there's billions and billions of containers being pulled. When I talked to CIOs, I do frequently through like Docker, containers, what is this thing, pants? And then three and a half, four years later, I can't have a conversation without a 4 to 500 CIO without talking about their Docker container strategy. By the way, I hear if you do send back a belt or something that's broken to the Docker people, they'll fix it for you and maybe send you some whale stickers. It's like the old school Nordstroms where they'll take any return, like this urban store, the four tires will return Nordstroms, return some pants to be fine. We work on container strategy, but we're also your repair shop for men's apparel. So it's always interesting to look at. Integration fabric is what they work at. Yeah, exactly, it's good to date. Brilliant. The maturation of technology, of ecosystem, of monetization, I feel like you talked about the growth of the containers. We've seen the ecosystem has gone through some fits and spurts and changes over the last couple of years. I think we really well received this week and then there's kind of the money maturation and how they mature that. What do you see? How does open source fit into your investment strategy and any commentary on Docker and beyond? Yeah, it's interesting. We're thinking about this on the flight over here today. Open source today is very different than open source five years ago, 10 years ago, it was 15. So what Red Hat did 20 years ago, very different than what Zen tried to do 10 years ago when I was at VMware, very different than what Docker's doing today. And it's different in a couple of ways. I think the way you monetize is different because you have cloud and cloud changes things. The ecosystem's very different because all of a sudden the developers and contributors are not just kind of your misfits and rebels working on the weekends. There are Fortune 100, Fortune 50 companies, their jobs are how dedicated they do this. And then the business models of the developers, each system, how you work with them is very different. So before you had maybe one or two models to make money at open source in one or two ways to develop a community. And we did that at Red Hat with Graylock was lucky to have the investors in years ago, it was at VMware, our cloud founder when we built that, we had a model in mind and we had spring source as well. And I think we're seeing with Docker the past three or four years, if they're really pioneering a way to kind of bring open source and community ecosystem into the next 10, 20 years. So I think it's one to watch. I think some is probably as good as anybody understanding what developers need. Yeah, so a little broader, what's your thoughts on developers today?  There's two big developer shows this week. You've got F8 and you've got Docker gone. Two very different communities. Right, it's kind of funny, it's kind of the census, do you consider yourself a developer? So if I write a line of JavaScript for my developer, my key sense is yes, right? If like developer is from JavaScript to Swift to Docker to kernel hacking, it's all great. But if you look at those two conferences, you have F8 going on right now and the announcers there around augmented reality and messaging and they try and be a platform, but they're doing many the same things. They have a distribution platform, be it Messenger or Facebook and they're open sourcing technologies around the camera, the lens, the filters to have developers A, go through the channel, B, add apps or widgets and it's really beyond my belly copper head of these filters. But Docker today announced a couple great projects, Moby and Linux kit, much in the same way as trying to give tools to the ecosystem developers to build what they want. I think what you've learned is if you give developers the building blocks or legos as they call it today, they're going to build some awesome structures. Yeah, I mean, Jim, we've talked about coming in here, it's like the role of how data science fits into the developers and there's developers such a broad term as to what we have here. One of the core things I have is that the data scientist is the nucleus of the next generation developer because much of the IP that's being built into applications now is statistical models, machine learning and so forth, driving recommendation engines. But much of that development is being containerized, using new tool kits and so forth, but it needs to be more containerized so you can deploy statistical predictive models, machine learning, deep learning throughout a distributed ecosystem of hybrid cloud to perform various functions. You have to be, right now there's, in most companies, there's a data engineer, there's a data scientist and the two typically work hand in hand. One man does a Hadoop cluster and the other one does the modeling. Does the modeling, so one speaks in R and Python and works in a Jupyter notebook, the other person runs on Hadoop or Database or Redis and the two need to work together and so what you're seeing now, and obviously we're investors in Cloudera, that's another great office source company, what you're seeing now is either A, a set of tools and technologies to either blend the two together in some cases, either enable engineers to be more like data scientists or enable data scientists to be more engineers or also see a bunch of technology tools that say, no, two different roles, I'm going to create tools purpose built for the data scientists and create tools purpose built for the power of data engineer and I think there's space for both, right? So to the extent that you have applications running from like news feed or ads to like predicting my self-driving cars to make a left turn, you're going to need tools that kind of are used by both types of populations. Well, Cloudera I think has now a collaboration environment because of data science. IBM has something very similar with what they're doing so it's a team that has specialties such as coders, such as data modelers and data engineers, point well taken. Cloudera has made a major entrance into that space of collaborative development of these rich stacks of IP essentially that include deterministic program code but also probabilistic models in a deepening stack, yeah. So I think you've seen Cloudera definitely follow that path from Hadoop and low level file system like ACFS to these high level tools for your data scientists that's becoming a platform for machine learning for these next generation applications. And I think you see Docker in the infrastructure analogy doing low level tools like Project Moby and Linux Kit to like high level services around Docker data center, right? And so you can either have the basic tools for your low level developer or for the system admin or administrator who wants to operate and run the cloud, you have tools for him or her too. Yeah. It's interesting, you look at some of these projects and some of the maturation in PivotGC, we talked in DotCloud, went over to Docker. I see a bunch of open stack companies that are now Kubernetes companies. I see companies that were big data, oh, I'm an AI or ML company. So it's always like, is it, it's usually not the tool, it's the wave. It's, what's the driver? Is data the driver of our next wave there? Is it the application? Is it some combination of the two? Those are the two that I usually look at, follow the data, follow the application. Yeah, I would say it's data driving, it's really data application making you, it's data in the application making use of the data. Algorithms, I think, is a component, right? They're important, but they're a component. So what you're seeing now is to be on the right side of history, data's outstripping compute and storage, right? So the amount of videos and central data that we're generating from our phones or cars or homes, that data's outstripping most of the other charts and compute, networking, whatever. So that's definitely kind of a rising tide or a wave, as Stu was saying. Now, how do we extract data or value from this data? And historically, because you didn't have infrastructure in that cloud or compute capacity to kind of make use of this data, it was kind of stranded. And so what you've seen in a generation of technologies like Hadoop or Big Data or cloud technologies, like what Docker can do to distribute your application across a cloud, that's actually enabling you to now build applications to get value out of this data. And that value can be something like forecasting your sales this quarter. It could be about figure which shade of brown belt you should wear with your pants, right? Going back to your clothing analogy. But it could be like, hey, let me build a model around how this car, this drone should drive or fly itself. And so you combine kind of a vast amount of data in your nearly kind of infinite resource of compute with these kind of machine learning or AI techniques and machine learning is one AI technique, but all these other AI techniques, you can build another generation of applications, this new kind of intelligent application to power everything from your home, your car, your watch, or your enterprise app. That's wonderful. Much of the seed change is that less and less actual coding or programming is being done. It needs to be done because more of the application logic is being distilled directly from the data in the form of machine learning. There's automated machine learning tools that are coming up. Google has been a major investor as it stays with automated machine learning. I would say application logic from the insights, right? So in my mind, application logic, application is reflecting business process, right? Hire to fire or to cash. You still need to program the business logic. Data in itself or AI in itself without that context, without that business process is meaningless, right? Just having a model around, you know, a gym or a stew and it doesn't matter unless you're trying to buy something. So Google pioneered kind of machine learning in a workflow perfectly. You're searching for something. They knew who you were based upon in history and they search through the right ads. They say, oh, you really want to buy a car. You want to buy a house. So in the workflow or in the application logic of a search, they use ML to serve you kind of time information. So if now you're in an enterprise, you're looking at help desk tickets, be it ITSM like ServiceNow or support tickets like Zendesk supporting B2C support tickets. That's a workflow. There's application logic to take information on a user or a grumpy customer and then do things like all Mac respond to a help ticket, reset your password, provision a server. And so I think when you have AI or have applications using this data in the context of the business process, that's magic, right? And I think what we're seeing is some core technologies like TensorFlow out there that they're super compelling but we're seeing a generation of developers and founders take that technology, apply to a problem, could be like I said, HR, CRM, ITSM or to a vertical construction, finance, healthcare. Streaming media analytics is a core area where that's coming in. There's a ton of data. Just understand what you're watching, what you want to see. And so you apply things to vertical like healthcare or apply technology to a problem space like media analytics and you have kind of a wonderful application and hopefully a great company. Right. Jerry, we've talked a lot at the cloud shows about how do the startups maintain relevant and get involved when there's all of these platforms. We've talked about Google cut ties. Amazon of course is eating the entire world and everything. Microsoft is making a lot of moves here. How do companies, what do you look for? Has your investment strategy changed at all in the last couple of years? It is, it's don't, think about this a lot in terms of business models and defensibility. And the question goes, what are the sustainable modes you can build around your business as a startup anymore? You see, feel like a kind of scale and ecosystems, network effects, those were historically big defensive modes for like Windows and operating system. Or now those apply to like Facebook's platform, Apple's platform or AWS. They have scale and they have network effects for the ecosystem. So now your startup is saying, okay, how can either I A, overcome those modes? Or B, how can I develop my own IP or my own motor around myself that I can actually sustain and thrive in this generation? I think it's got to be, you got to play a different game. Like as a startup, you're not going to try to outscale Google or Microsoft, right? Leave that to Amazon and there's three or four players. But you can get scale within a domain. So either a problem space like autonomous vehicles, security is a great one. Or a vertical like construction or healthcare. And you can find, you redefine the market that you can kind of dominate, you can build your own motor around that IP. Yeah, it's interesting. Did you hear Adrian Cockroft who, you know, went from the VC's on the IP and now over at AWS. And he's like, well, rather than go start up that business, come build that next thing at Amazon. And we'll do it there.  Oh, I think Adrian, probably his motivation to get more developers on Amazon now. But I was saying like most of our companies not all by a lot of them start on Amazon. Some start on Azure, some start on Google. Some start with their own data centers. I think what they believe is they'll get started on these clouds, but I don't believe this. You know, we talked about this before. It's not a one cloud rules all world, right? I think there'll be three or four, if not more clouds and every different geography from Europe to Asia to Russia to US will have different clouds, different players. So I think it's fine to get started on Amazon and be a two pizza team with your two pizza team. But over time, you know, I see these applications being cross cloud. And that's where some like Docker comes into play, right? Docker let's it be cross cloud, better than any other technology out there. Yeah. You know, on some level, I mean, actually the mode could be, well, increasingly is the training data that drives the refinement of your AI like Tesla is a perfect example that the self driving capabilities that they built into the vehicle, they have now a few years worth of rich test data, training data, I should say, that is a core mode in terms of continuing refinement of those algorithms. So that gives you a sort of an example of some startup might come along with some very specialized application that just takes the consumer world by storm and then they build up some deep well of training data in some very specialized areas that becomes their core asset that their next competitor down the pipe doesn't have. It has to be a set of data that's unique or proprietary. You're not going to basically out train your model on cat photos than Google, right? So it has to be a combination either proprietary data or a combination of data sources that you can stick together. So it's not just one data source. I believe you have to combine multiple data sources together. Yes. So, Jerry, sitting over Jim's shoulder is VMware's booth. Oh yeah. I haven't talked about VMware at all this week. You worked at VMware. I've worked with VMware since pretty early days. What advice would you give VMware in kind of the containerized cloud future? How should they be doing more to be part of more conversations? Well, I think it's amazing that they have a presence here in the size of scale because the past couple of years they've really done a lot to embrace containers in Docker. So I think that's first and foremost. They've done a couple of great moves lately. Embracing Amazon last year with VMware on Amazon was a big move. Abracing containers with some of their cloud native technologies I think is an aggressive move too. So I think they're moving in the right direction, right? So I think what they need to understand is are they going to revolutionize themselves? Are they going to push these new technologies aggressively? Or are they going to keep hanging on to some of their old businesses? And for any company of their size and scale, they have multiple motivations, but I think they're making the right steps, right? So five years ago or four years ago, I don't think they would have taken this DockerCon seriously. I don't think they were exibbers of the first DockerCon, right? But the past call it 24 months, they've done some amazing moves. So I would say it makes me smile to see them take these great steps forward. Jerry, want to give you the last word. Any cool companies we should be looking at or things that are exciting you without giving away trade secrets? I can't broadcast the companies I want because everyone else is going to choose because there's investments. I don't know, I think I'm going to enjoy spending time actually less with the companies here but a lot with the developers and the customers, right? Because I think by the time that they have a booth here, everybody knows the company exists and it is probably too far along maybe for me to invest, maybe not. But I think talking to developers kind of here, what are their friction points? I think when you hear enough friction either in this ecosystem or another ecosystem or at AWS or VMworld, then there's something there that's got to scratch. Yeah, I was talking to some of the people working the booths and they just said the quality of the attendees here and you just learn something with every single person you talk to and there's only a few shows that say that. I mean, Amazon re-invents one and the quality of attendees always real good, this one and a few others. I think people who come here by definition are all learners, both the companies and the individuals and you've got to want to surround yourself with learners, people who are open, honest, knows learning. All right, well, Jerry, I think that's a perfect note to end it on. We are always learners here in helping help our audience try to understand these technologies. So Jerry Chan, always a pleasure and we'll be back with a wrap up here of day one, DockerCon 2017, you're watching theCUBE.


Well, time of the week again for some fishing tips on this week's segment. Ray and Mandy talk about safely transporting bait. Tonight we want to talk a little bit about invasive species. It's something that we're all hearing more and more about. I think it's important to know why we're doing what we're doing and some of the things that we can and can't do when we're transporting live bait.  It's holding larvae for different invasive species, different kinds of plants, parts in there. So just because you don't see it doesn't mean it's not there. So if you're transporting your bait you know from lake to lake or home to back and forth and using the water from the lake that you're previously on you're actually being part of the problem. If you're using minnows you can transport up to 12 dozen minnows in Minnesota without a commercial license. If you're transporting more than that you need to be a license bait dealer or a bait harvester. So if you buy minnows at a bait shop I would suggest strongly that you put those minnows in whatever container you have. I've got an aerator here that'll hold minnows, that'll hold a large number of minnows here. So I'll put the fresh water in here, I put the minnows in here, I close it up and I put this in the back of my vehicle before I get to the lake I'm going to so that if a conservation officer does stop me he knows that this came from somewhere so be a fresh water out of a tap or it could be out of you know from the bait shop tap or it could be from my own home if I'm a trapping for my personal use. It's important to realize then when you're done fishing that day you cannot take this home. You must take the bait out of here and if you're going to save the bait you need to have fresh water in the back of your vehicle where you can drain the water off from here or you can take it to your vehicle then you can take it to the vehicle and put those minnows in that new fresh water and don't drain the water that you had in here in the lake drain it up on shore somewhere. If you don't want to deal with the hassle of transporting your bait back into fresh water doing it properly or even worried about the legality of the situation and you want to dispose those minnows at the landing there is actually a proper way to do it. Don't just take your bait and throw it in the middle of the parking lot or walk off to the side and dump it in the grass. At most of the landings there's going to be an actual designated receptacle to put bait in. It's always a good idea to carry a plastic bag if it's a shopping bag or something put your bait inside that tie it up and put it into the receptacle. I'm Ray Gillbaugh with the Nissau Guides League and I'm Andy Eric the Bassin Biologist. If you've enjoyed this segment of Lakeland News please consider making a tax deductible contribution to Lakeland Public Television.


Wow, Jean, thanks so much for coming out to San Francisco for the open group event. And we've been talking to each other now for how long? Twelve years, I think. We met in 2004 at the ITS MEPH conference in Washington, D.C. Yeah. And it's funny how many of those themes about work intake, work prioritization, queuing, you know, how does IT show up in IT for IT? And I've been an admirer of your work for many, many years. In fact, your book, The Cobbers... Yeah. The Cobbers' Children, yeah. About architects, you know, in architecture. I think it was just a seminal book, and I'm so delighted to see so many of the concepts that you were proposing back then, now really being adopted and stew-wide in IT for IT. Yeah, oh, thank you. It's been an interesting journey, certainly. So what made, if you don't know what I'm asking, what made IT for IT, what caught your interest, and what made it seem so promising to you? What was the earliest indication for you? Well, they were clearly going down a similar route, and I'd actually had some indirect influence on it that I did not realize. It's a long story, but the people who were working on IT for IT were also aware of my work and had the book about how to use tools like value streams, value stream mapping in understanding digital delivery, but certainly when I looked at what they'd done and I looked at the value stream approach, I thought it was more compelling than what I had done.  I said, well, we've got an asset life cycle, a tech product life cycle, a service life cycle. And I still think there's merit in thinking about it that way. But when I saw what Lars Ronson and team had done with strategy portfolio, requirement to deploy, request to fulfill the technical record, I said, well, that's also an orthogonal and very compelling way of understanding the different domains of practice we have. And it makes sense and it's held up. I mean, I'm very critical. I look at stuff like that. I'm like, okay, can I disprove that? You know, because that's all it takes is one point of disproof. I've not yet seen the point of disproof on those value streams. One of the Zen paradoxes for me, if I can use that word, in doing IT for IT and also being very interested in tracking the agile and related DevOps progress over the years, has been the ideas of standards, frameworks, bodies of knowledge. They're not that well regarded in certain segments of the agile community. And, you know, I kind of just deal with that contradiction on a daily basis. And I look at, you know, things like skilled agile framework and some of the controversies that have swirled around it. And so I find it exciting. And it was totally, I guess, it's interesting to me that there's so many people from the ITIL and CSRS-Mantlin community as part of this effort. And, you know, what a better group of people to be able to say, all right, here's a larger world to operate in than sort of classical IT service management.  They came to the world of rational, unified process and all that. I guess I think they're a minority and I think the rest are, you know, as mentioned really today, it's like they have more than one job left to go, right? They have sometimes deep feelings about what do I need to do to stay relevant and they have a vague feeling that automation and DevOps and agile have something to do with it. Boy, any place where we can be much more explicit about your guidance, your stuff to learn, that's hugely valuable.   He was a CIO for HP, later HPE, a Fortune 20 company, and, you know, he said, you know, what we need is buoys, not boundaries, right? So it's kind of using the river channel metaphor. So the days of like mandatory practices, mandatory processes, mandatory tools, I think those days are over that it's really about an internal marketplace of ideas. But on the other hand, the goal is, how do you maximize developer productivity by giving them these very safe channels to go down and also give them permission to strap sounds outside the channel and, you know, even saying, you know, you understand the business objective better than anybody if that's what you need to do it. And that might be the next source of innovation that we have to integrate into our enterprise architecture. Yeah, no, that makes perfect sense. I think that architecture, it will not go away. You know, there's still a need for it. As you say, it's Chief Architects who are coming to the DevOps Enterprise Summit, which was one of the really surprising findings. I mean, I think there's actually hope here. Yeah, probably just to clarify this, I talk to these people that, I mean, so what are the, in my mind, the words that come to my mind when I think of Chief Architects? They're global thinkers, not local thinkers, right? They tend to be more abstract than concrete. And I actually tend to be global abstract. So these are, so no wonder they're seeing kind of a need for these problems that are absolutely intended problems. So I think you have to say, well, what is architecture trying to do and, you know, what are the value drivers? How do you quantify it better? I've been putting some thoughts together and I think it still is a challenge for the architecture community. You saw a little bit today on how I look at quantifying the value of just limiting the technology portfolio, which is only one small part of what architects do. But yet, you know, vendor diversity, platform diversity, that's a problem, that's a cost. One of the things you also have observed is that developers are compensated financially and in terms of the experience you give them. You can call a resume padding, and I used that term earlier today, but the thing is it's legitimate because at the end of the day, you kind of be able to hire top-tier talent if all you're running is cobalt and you have to pay an economic cost for making decisions like that. Yeah, I'd love that presentation from earlier today from the Simple Learn. The CEO of AT&T saying, you know, we need every engineer to spend five to ten hours a week learning new skills and I thought that was actually, you know, that seemed compelling on so many different reasons, right? You have to stay relevant, you have to accept that we are all lifelong learners. So yeah, I think many people would agree including the CEO of AT&T is like, if you want to stay in the game, that's the amount of time you need to invest. Well, Gene, as always, it's been a real pleasure, great to spend time with you and hopefully we'll do something like this again sooner than later. Absolutely, and again, I learned a lot today and thanks for having me and look forward to working with you here. Yeah, thank you.


What's going on everybody? Got Kyle and Sean here with Layton Sports Cards. We are ripping two boxes for Allen V. It can be one box inception baseball and one box select WWE first off the line. Good luck, good luck. Start with an inception. See we got Mr. Allen. Then we got Steven after this, Michael and Aaron. It's like I said about 30 minutes on the queue if anybody wants to get an order in. We can get it going pretty quickly here. Let's do some inception. What do you want for Christmas? I don't know dude. Some WWE. I don't really. I don't like the hat bro. Don't do it. There's a green rookie Edward Julian. Edward Julian got a bow nailer rookie green. And Cole Reagan's on the refractor rookie. Base rookie. Gonna be O'Neill Cruz base. Yeah more hats. I mean a lot of hats to catch up with Sam. A lot of hats to catch up with Sam. Gunner Henderson rookie. Got CJ Abrams and Otto. It's gonna be Buddy Kennedy. That'll be $2.99 on the pink auto. For the D-backs and got a RPA as well. Jonah Bride, $2.149. For the A's on the three color patch. Layer worn men. Nice one there on the two autos. Extra one. And now we got a select WWE first off the line. Good luck Allen. Let's see what we got. I haven't ripped too many of these. I like them though man. Seen some fun stuff coming out of the select WWE. The E. I'm sure I'll get a few more hats for Christmas. Yeah. Here's a Carmella on the refractor. And our first auto is gonna be a Braun Breaker silver auto. On these signature selections. And holding up the NXT belt. Braun Breaker on these silver signatures. Otis, Shawn could take on Braun Breaker. Shawn could take on Braun. It's a Silver Giovanni Vinci. Gable Stevenson, Shawn Michaels. All HBK action. I have no chance of catching up to Sam. There's a Silver Dicup Braun Breaker. Another one. And Silver Jersey or Silver Relic is gonna be Rhea Ripley. Rhea Ripley on the Silver Relic. Got Hans Walker or Hank Walker. Yeah I know I'd have to buy probably more than that honestly. He's got a serious collection. Cora Jade on the light green to 75. Cora Jade. What do you mean? I don't know. And Becky Lynch.  Dad wouldn't mess with that thing.  Now this one I'd be like. Yeah no Fortnite. I think Forrest might be playing. Ooh that's a color wheel. Ooh. Okay I haven't seen one of these. That is super short printed here. There we go Allen. Got a Brutus beefcake on the tricolor. Brutus the barber. And color wheel is gonna be the ultimate warrior. That is clean right there. First color wheel I've seen out of here. Nice one there. The ultimate warrior. This guy was natural. All natural there. I'll get a mag on that one on the color wheel ultimate warrior. His sweet man first color wheel we've seen out of a select wrestling. Yeah you might have a chance with Forrest and Jake right now. They got a couple hours Shane. You might have a chance with them for a couple hours. How can you say he's not natural? And a Becky Lynch. That one is a zebra. Nice one there. That's Becky freaking Lynch. Nice one there on the zebra. It's a nice box so far. Got Rhonda Rousey, Apollo Cruz. These are my instincts on them. Yeah. Literally coming out of everything he wears. Yeah. There's a red Roxanne Perez to 175. Got a filler. Elton Prince. Oh I got you Shane. I got you Shane. Oh we are later tonight. There's a Goldberg Silver. Goldberg another on thatty. Yeah he's totally thatty.  Dude he's, I enjoyed watching him. Got a gold. I think that's Oscar. That is Bianca Belair. Bianca Belair on the gold 210. I think that's the Oscar that she's flipping. It's a 10 there 9 to 10. Nice one there on the gold Bianca. Nice. Got a Becky Lynch zebra on the Bianca gold. That'll work. Batista. Come on now this guy. No there's no juice. Come on man. No juice. Look at those veins dude. No juice. Good lord. All natural. Look at those veins man. AJ Styles Refractor and this is going to be a 25. It's going to be a Finley Auto. Finley on the ringside action on the tie dye. Finley. Finley ringside action to 25. Yeah you could certainly be right Rich. I have no idea. I'm just going by what I uh, I thought the black hair looked like her. Could certainly be Bailey. There's a blue Brock Lesnar. Cowboy Brock. Brock to 249. Got a Sensations Matt Riddle. And one more. It'll be our first offline exclusive. It is going to be AJ Styles. Nice one there. That is to 7 on the first of the line exclusive Mojo. You go Alan, nice box of a select WWE man. Sante Adonis and that'll do it with the uh, the blue meanie. The blue meanie was the last one. Yeah Rich we hit a color wheel. One of the first ones we've seen out here. The Ultimate Warrior. I haven't seen a color wheel out of there yet. Alrighty there Alan. Nice couple boxes there. Inception with the extra auto and then select. The Becky Lynch zebra. We'll get that right out to you my man. Thank you, thank you.


Okay, so it's Episode 3 right? We're going to the playoffs or no at casts of championship we're going to add this championship Don't tell me I lost my belt one to grow That's important to me Not what I thought in, I found it up. Okay I'm moving out soon.   I've been wondering how I'm gonna get coins and if I buy coins I know he is gonna ban me instantly my homie Andrew sold some of the players on his team and Traded me limited Paul Kraus meaning I can quick sell it for 500,000 coins meaning I can continue what we're doing here Andrew, I love you so much. You have provided for this episode Let's go okay, so I am gonna pull up our NFC championship wheel I'm gonna spin this NFC championship wheel twice.  That's absolutely beautiful Desmond Truffaut so we've got ourselves the 99 Chris Harris Jr Which used to be the 99 Dion Sanders that does suck that it can't be that way anymore But 99 Chris Harris junior 95 Desmond, and then we have Bradley Roby I know this looks kind of weird because it looks like this should be my best corner This cornerback to ends up being on the left side of your screen And that's the more important side because that's where people always throw to so I'm gonna put them in there We're 93 overall with that all right now for our final spin on the NFC championship wheel It's been a long time. Oh 95 choice 95 choice. Oh, please This is not it's not like jackpot jackpot, but any 95 overall play dude There's so many good 95 overall players in this game And it's kind of sad but I'm almost tempted to go ahead and snag an offensive lineman Because that's kind of what I really need unless I get another corner because Bradley Roby is kind of cheeks My safeties are grinding an offensive lineman. How depressing is this? I'm actually buying an offensive lineman Who's a cool 95 overall tight end? None of you are that cool. Dude imagine a duo of George Kittle and Shane and Sharpe at my tight ends though Cornerback is so important. All right, let's look at the 95 corners in this game Yo, we could get Dion back just for the mems of my boy Dion Sanders. We got to bring him back Let's get this one. Okay, so you know what boys we lost that Dion Sanders He got replaced with Chris Harris, Jr. And now we are bringing him right back. So we add to 95 overall corners I think that's one of the most important things we could have done for the playoffs Especially the NFC championship and then the Super Bowl. So this is clutch definitely putting Dion here the cornerback duo of Deion and Desmond are the additions before we head into the NFC championship The final thing that I have to do is spin the challenge wheel and the most important thing that I have to do is win Let's go win ourselves a game boys Second to last win that we need. All right, let's see what our challenge wheel brings us and let's see what opponent we've got for the NFC championship More than 35 this one is so tough because if I'm on pace to score more than 35 They're probably gonna quit unless we're in like a total gunslinging game So I'm more worried about winning but hopefully we can get this too. So let's see what happens Opponents team. Wow. That was interesting.  That's nasty We're gonna start this We were gonna start the game all the run, but that's what happened to it second and 12 I'm gonna yank this to the left side Ricky Williams. Oh my god.  Let's go one spin It's beautiful. He's gonna expect me to bounce it left. So hopefully there's a hole somewhere in the middle Yeah, he thinks it's a pass. Huh spin Jerry rice Odell and rainy moss. I mean Somebody should be open, right? Oh Look at look at why look at why My god, he caught that contested and still power through and I see championship plays right there, baby Oh, I just realized we don't have our lines jerseys on but I didn't let it jinx me Glad I didn't realize cuz seven is here I probably would have jinxed myself if I had noticed that earlier as long as this is our one game without lines jerseys Super Bowl with the lines jerseys will be cash money All right, um Ricky Williams is gonna pick that boy up bring him down Lawrence guy I accidentally have Lawrence guy in my frickin spy right now Whoa, whoa, whoa, whoa, whoa, I'd read get there. I'd read get there That's who that's what I get for a user in Lawrence guy on accident. This is a run play. No It's basically the same thing. He's gonna throw Lawrence guy I was literally usuring Lawrence guy again, but you know what? I actually made the tackle somehow I'm gonna clamp this boy guessing run up the middle pinched everyone.  He just got him again Outside run let's go Okay, we stopped him goal line stand fourth and goal he's taking a field goal That is some NFC championship plays right there boys. I really want to throw a bomb I know this is so impatient, but I want to throw a bomb. It's seven to three Matt.   Oh my god Takes it to the 28 and we're into the second quarter.  I Completely sad you are not quitting you are not quitting did I just go three rage quits to the Super Bowl? Okay, he's not quitting. Oh, okay. I'm gonna send Jerry rice underneath I'm gonna send kiddle underneath and I'm a look and see if there's a cute. There is a QB spy. I Can still get it look at that speed Even with this QB spy and the user on me Lamar Jackson grabs that first down clutch. Okay inside zone to fill up Lindsay Got a lot of room That's it that's gotta be it you're gone God we are inevitable I couldn't I stop it.  What was my challenge 35 points? No, I didn't but see like I was on pace for it So 92 overall team we'll again. All right, so let's hope this can snag me an offensive lineman All right, so I get 192 overall player from the Eagles. Oh, that could be good I don't know if they're gonna have an offensive lineman. That'll be a 92 overall though. I'm hoping 92 overall Lane Johnson That's perfect. I'll just have to put him at a different position. All right 50 cats kind of expensive But yeah that Lane Johnson will fit very neatly at left tackle because my left tackle is kind of shitty right now Yeah, let's be great 88 overall right tackle now my left tackle and that left guard that I did have in there I think I should put him at center, which is Roger Saffold All right We upgraded our offensive line a little bit with that rage quit wheel got ready moss Odell Jerry Rice Phillip Lindsay Lamar Jackson stellar Defensive units Shazir Talvin Smith Bobby Wagner focus out of Luke on the go Justin Simmons Ed Reid Chris Harris Kalil Mack Erin Donna Lawrence guy who is an amazing player this game Desmond Truffaut and Deon Sanders going into the Super Bowl and We will have spins on the Super Bowl wheel ladies and gentlemen Thank you for joining me on this incredible journey that has been a wheel of mud I love you all for watching my videos It feels so good to make videos and be appreciated and that's how I feel when I make content like this So I love you guys with one episode left and that'll probably be the last man video that you guys see for me for at least two months so Hopefully it's awesome. I love you guys. I will catch you in the next video. Peace out


on Fandle.com for a wide variety of ways to play this weekend, including our Saturday and Sunday Million Contest. JJ has given us his best three wide receivers in these divisional matchups. And JJ, got to ask you who you got. Yeah, my number three wide receiver this week, Devonte Adams. It's really hard to not like Devonte Adams, just given how he's force fed in that offense. And he was really force fed down the stretch. Over the final three games of the season, he averaged over 100 yards per game and a 36 and a half percent target share, which is absolutely absurd. So I love Devonte Adams in this matchup. My number two is Debo Samuel. He's only $6,100. He averaged about a 19 percent target share over the second half of the season. That's a really strong number. And he's gotten some juice in the run game recently too. The 49ers have given Debo Samuel nine rushes over his last five games. And he's added almost five fantasy points per game via the ground over this time. So I like Debo Samuel. My number one play though this week, Tyreek Hill. We saw him go off against Houston the last time Kansas City played them. He had five catches for 80 yards and two touchdowns. We know he's a deep ball threat and Houston's allowed the fourth highest average depth of target this season. So I like Tyreek Hill to have a great game against Houston.


Thank you for attending this session on sliced bread and libraries, putting the federated identity pieces together. For many of you, you've been following the story of federated identity for the last twenty years and slowly but surely the pieces are coming together to deliver the type of integrated user-friendly service that we'd like to have. This session will cover that. In particular, we're going to look at some of the problems that are out there today, the issues with proxies, some missed opportunities for privacy-preserving personalization, and finally the increasing sets of requirements that privacy regulations are putting on access. We'll put the pieces together now. We're going to talk about IDP, identity-provided discovery via seamless access. We'll briefly touch on the federated identity space. We'll talk about some normative bundles of attributes, which is a way to scale access control and preserve privacy at the same time, and how we might achieve that, and finally how users can be involved in the experience and provide consent and control over what information is being passed. There's still a few small pieces missing, and we'll mention those, including the selective release of values from a multi-valued attribute. The unfortunate example for that is group memberships, all of your group memberships are contained in a single attribute that is multi-valued, and we only want to pass selected group memberships to relying parties, depending upon the relying party. There's a similar set of needs that need to be addressed around signaling what attributes various parts of an entity might need, again with the intention of preserving privacy. Some community standards on how we'd choose the perimeter between what are required and optional attributes. And finally, reporting utilities that would allow management of access control and content. The problems to solve, again, we know about the proxy issues and the theft of content that was certainly a prime motivator in the move away from IP-based access control and proxies towards something more secure. That also can be privacy preserving. The lack of privacy preserving personalization, we'll touch on that in a second, as well as the regulations. There are several ways in which we can personalize experiences and still preserve privacy, and we now have the technologies to do that, and we're looking for opportunities to use those technologies. Certainly accessibility, the ability to customize a screen for blindness, for color sensitivities, for cognitive issues, all of those things can be managed using privacy preserving approaches. There are many use cases around having a gated community where only certain members of the general public can get in, but once you're in, you're privacy preserved, as if you were wearing a mask in the old definition of masks over the eyes. And finally, there are many places where role playing games, et cetera, where using vetted avatars to preserve privacy, but offer continuity of the user, and then those can allow other kinds of identity vetting, such as reputation systems, opportunities. Finally privacy regulations, there's a number of them that have been on the radar now for several years. A GDPR is the most notable one, it covers Europe and by some extension, the rest of the world who interacts with Europeans. It was passed several years ago, it took a couple of years for adoption, it's in the last year that real fines are being leveled, and most interestingly for this talk, in May of 2020, the European Court of Justice tightened release and purpose of use issues, and you can see that in the revised key structures that are now out there on most marketing and business sites with different kinds of cookies and different fine grain cookie control, that's an outcome of that ruling by the EU Court of Justice. Issues that are central in this ongoing privacy space is the basis for release, why is the information being released by the identity provider, and for what purpose will the relying party use those attributes, and again, the cookie paradigm that you see now with I believe three or four categories of use is an example of that. I won't touch on the California Consumer Privacy Act and other state-based initiatives other than to say that they're out there, they're inconsistent, and it's just not clear yet what consequence they have for our communities. One act that is very interesting that you might want to follow both as a consumer of health care and perhaps a librarian of electronic health information is a very large piece of legislation that was passed in 2016, the Cures Act. It's guided by the Office of the National Coordinator in HHS and you will see major impacts both on your use of medical services personally and on issues that institutions that manage electronic health information will need to pay attention to, and there are certainly pockets of those in our universities. I do want to close the survey of drivers with a mention of the Canadian Digital Identity Initiative. In my eyes, it's the gold standard. It has a trust framework that is built on a very simple and useful model. Terms of defined assessment capabilities are specified against the trust framework. The trust framework itself consists of six layers and I appreciate that notice and consent have been pulled out as their own particular layer in a trust framework. In regards to that, that same trust framework specifies with some degree of rigor what when consent and notice need to be used. It's a very useful set of guidelines. Notice that it's opt-in. It can be for transactions or for extended periods of time, explicit in language.  It's an interesting reason. So federated identity, some important pieces have slid in. It's now a somewhat complete user experience. There's identity provided discovery that is consistent among sites via the seamless access work. This federated identity itself is the emergence of some normative bundles of attributes that are of particular interest in the library space. And now there are finally the beginnings of deployment of user control attribute release capabilities. We'll cover each of those briefly and then we'll do a demo of how they fit together in something called slice bread. Identity provided discovery, seamless access has emerged as a paradigm and community standard. It was developed by content providers, identity operators, standards groups. It was a nice process. There's a standard icon that users can know to click on to discover their identity provider. And there are several valuable integration choices that a relying party and a user can opt for that can make this experience of finding your identity provider very efficient. seamlessaccess.org is the site for more information. Federated identity, 20 years in now. It's the default model for internet identity. It emerged from R&E many years ago. There are other forms of identity out there. Centralized identity still percolates the self sovereign identity crowd muddle along as well. But they just haven't gotten the traction across so many different verticals as has federated identity. It comes in two flavors, SAML, which was raised in this environment and OpenID Connect, some of the technologies that are used both in the social space and increasingly on campus as another way to manage identity flows and attribute flows. The idea of federation can either be bilateral or multilateral. This was all designed to be multilateral, such as in common and educating for international R&E activities. But bilateral is a lot easier for cloud service providers to handle. So there is a dynamic about the usefulness of implementations by cloud service providers of federated services. The original focus of this work was inter-domain exchange of attributes. Authentication was just part of the agenda, but it became the mechanism for federated single sign-on early on. And that was a very useful and important business. And so we got a very tight focus on all of the complexities and issues for federated single sign-on. We had to evolve levels of assurance, incident handling mechanisms, a dynamic metadata for the size of the federations that were growing. And now I think the community is beginning to address the exchange of attributes now sort of. Speaking of the exchange of attributes, there are now normative bundles that are used together for common use cases. How might they be used? Well, the IDP might decide that it's going to implement a bundled release and release the set of attributes if a site relying party is so tagged. It can handle requests from the SPs for attributes. And it can also be used, these normative attributes to bundles to configure end user consent mechanisms such as Cora. The need to accommodate a variety of existing campus policies has made some of these attribute bundles more complicated than they need to be because if all universities never reassigned email addresses, it would be that unique perfect identifier. But that's not a policy at all universities. Similarly, there are variations in how one defines affiliations. And sometimes these attribute bundles wind up being either this if your policy is that or do this release of attributes if your policies are these. It adds complexity to the system. The primary existing example out there is very widespread, but not widespread enough as research and scholarship or NS. It's intended for federated login use for the research community. It consists of a shared user identifier, a personal name, an email address, and optional affiliation. So you can see it's identity rich. For the access control space, it's a different story. Overall as a community, we've evolved from identity based access control mechanisms with a list of names in an access control file associated with some data to role based where those list of names might be replaced by a list of roles. But those were two coarse grains, so we went attribute based. And finally, we're starting to think about policy based where we don't get rid of the attribute base, but there's enough work there that we need to create institutional level policies and user based policies that can manage the collection of attributes. That's where we're headed. One of the most important access control attributes is affiliation. We use it a lot in the library community in the content communities. It has coarse semantics. As you can see, those are the values it can have. And so we're looking increasingly at another attribute at your person entitlements that can have a variety of values. And we're beginning to see thoughts in that space and it's very fertile ground. She pointed out that an entitlement may not exist actually in the enterprise directory. It may be a computed value for transfer to the relying party by the IDP as part of the on the wire communication. And finally, it's a member of group memberships are very handy. That's how we manage a lot of attributes on the campus side. It's just a matter of figuring out how to make it a better vehicle for institutional use. Three new bundles are now in the library space that are of interest. They've been proposed and are available for comment. They cover the use cases for authentication only anonymous authorization and Sudan misauthorization, which allows some personalization and state. The standards process for this is community based. We began in a working group convened by seamless access. The proposal was passed to the refeds, the International Army Federation Schema Group for consideration and comment and adoption. And now we're at that stage where hopefully federations and IEPs will begin to adopt it. And it's also being reflected in the contract language working group, which is out there as an activity. Metrics and usage, distance to the attributes. I raised that as a question. Turns out there's some work going on right now. The next piece in the puzzle is consent informed attribute release. We call it CAR. It's a piece of software that's been developed by internet to Duke University from a grant several years ago from NIST. It provides end user management for consent, both in line during a transaction and self service, a debility to manage your stored consents. It has effective enterprise and end user management. It has unexpected compliance benefits as open source. And it's going back in my mind to the original purpose of what we built this infrastructure for. The original shirt t-shirt said we'll work for attributes. This works for attributes. This is what the screens of CAR looks like. Everything is customizable. The name of the identity provider is Amber in this case. You'll see that there's clear guidance on whether you're committing the release or denying the release. A use of standard colors and symbols. The values being released are displayed. The purpose of use is being released. It is being displayed. And then you have the ability to suppress the consent screen and just store that as your persistent consent until you choose to change it. Privacy policies for the site you are going to are also listed. We'll see this as part of the demo. Here's another faculty example. Again, releasing lots of information about access control and nothing about identity. So that's a very privacy preserving option. This is what the self-serve console looks like. Again, we'll see it dynamically in just a minute. The ability to manage your consent policies based upon the relying party you're going to do. There are APIs. Notice there's a couple of roles here. There's the user in a transaction. There's the user doing the self-serve. There's somebody who manages policy and then there's a major admin. This is an interesting branch that we could explore in a longer talk about gathering the informed content that you just saw in those screens that helps the user to make quality decisions. CAR has the ability to consume the new attribute bundles in several different ways, including the release of, without consent by the IDP, configuring a hint to users but requiring consent, or just using it in notice and transparency mechanisms. There's lots of things we can do. So we're going to put these all together in a slice spread demo. We'll leave the PowerPoint behind and go to show you, using seamless access and federated authentication, a variety of users, an undergraduate student and a faculty member accessing different sites and finally a brief window into what the admin interface looks like. Exit this and go to the interactive demo. So first I'm going to go to the research and scholarship site. That was a tag I mentioned. And this is in fact seamless access presenting itself. A list of identity providers. In this demo we only have one. This could be a managed list. Here's the useful icon. Here's what I was trying to access. Seamless access does a good job of keeping that continuity of user experience. I'm going to log on. I'm going to log on as an undergraduate with a password. This is the federated identity and authentication. And then I get to the consent screen that CAR presents as part of the Shibboleth experience in this case. The values that are being released and their consequence. This is dynamic. I've picked something that is identity rich. Though I've concealed some information. I can suppress the experience. I can look at the privacy policy. I'm going to just save and continue. And you'll see that the relying party will acknowledge that this is the information it received, display name, et cetera. In fact, that display name is right up here to give me a customized experience. And also that I didn't release my affiliation. I'm going to sign out. I'm going to go back. And I'm going to go as a faculty member to a commercial content site. Again, I see content always is where I'm going. Seamless access as the user experience for identity discovery. I'm now going to log on as a faculty member after the password. And you can see that the information now being released to content or us has some interesting information. I've concealed identity. But I've said I'm in the school of law so I can get to departmentally licensed content. And then I have some group memberships because they're sensitive according to at least GDPR contexts. I can obfuscate them and display them and choose whether to release them or not. If I just release this information I'll be able to preserve privacy but still get to some content which is licensed for the school of law where I'm located. You can see I can get to LexisNexis and here's the information that's been released. Some groups that I'm a member of my identity provider my law school access but nothing that identifies me. You can see you know this group. I'm going to sign out. I'm going to go now back to the self-serve console and show you what we made sure that was the right sequence. I'm going to do a stack of these self-serve content. Yes, okay. We're always going back to the recording. So the last demo that we'll do is looking at the self-service interface at the Safari. We're going to log in as a faculty member and I have to release attributes to the self-service screen that was that flicker and now I'm in here and I can see my console of release policies. I can manage what's being released so if I want to change whether my permit would deny I can edit that information. However, I just wanted to show you that self-serve console. The last part of the demo is to come in to the interface the admin interface. I'm now going to log in you just saw a seamless access to the last time in this demo and I can configure relying parties with different tags so that adding one of the new attribute bundles consists of creating a new policy if I wanted to and I can create the policies for institution or for users. The policies can include what might get released and when and under what considerations. There's extensive admin capabilities some of those can be delegated out to individuals for example technical librarians to manage the attribute bundles themselves. Let's get back to the PowerPoint to talk briefly about what's missing and thank you for attending. What's missing? Well as I alluded to you saw that group memberships had a pretty clunky interface in CAR about what attributes to release if there were many different group memberships that interface would have been really ugly and that's the best one I've seen so we need to change that. We need some good purposes of use so that users can know what attributes are being released in our community what therefore taxonomy not dissimilar in nature perhaps to what's happening in the advertising industry. We need fine-grained signaling so that various parts of the Wiki can say this part of the Wiki is only open to these group memberships only send these attributes if you're these group memberships if you come into this part of the Wiki. There's community standards that would say when is something really required? When is it really necessary? We need thinking applications this is kind of reflecting some of the privacy by design there's some reporting utilities that we want to see and there's some interesting work just starting up now a consultation on some analytics that would allow of the library community to do some privacy-preserving reporting of usage. Follow FIMFA-L it's a group of librarians contact your campus IDM people so that you understand what the environment is what capabilities they have for new attribute bundles for user consent etc how they think about entitlements and then finally we're going to be working with publishers and community groups for making these new kinds of attribute bundle capabilities reflect in purpose of use in contract language and I would imagine these standard activities will continue Finally I'll put this slide up for contact information and I thank you for listening to this presentation


What's up everybody, Jason here for JaspiersCaseBricks.com. Last break of the night, guys. 2022, Penini Mosaic, football, six bucks, half case break, penalty number six, second half of the master. All right, last ball, Mojo, Michael, get the Texans and the Seahawks. All righty, and six bucks, half case, sir. Kenny Pickett introductions, and is that a gold? Just normal. Looks gold, though. Keeshawn Johnson, and center stage, Tony Pollard, 49. And Mark Dupre for the Dolphins. Miami Dolphins, Rages, and Alec Pierce. And they don't have those Colts. Colts Autos, Brian Crouch, Bailey Zappi. I could say that. I mean, it just really wasn't working for us. We were trying it like a test run. Didn't work, so we stopped.  But it just wasn't working too good for us as good as Instagram does in our own YouTube channel. So just stick to Instagram and YouTube for however long we need to. Andre Reisen, how about that? Give me a power of his angel. Kenny Pickett, and Devante Evans, purple to 49. Anna Samhall, Blue Chips, to 99. And Cade York for the Browns. Fyith and Browns, it's Jerry. Man, is that Sean Alexander? That's crazy. Blue to 99, Seahawks. This Spiller, Bo Jackson, Stairmasters to 99. Raiders is Reinaldo. And Avila's Jones Jr., purple to 49. We hit his one-on-one in the last case. Out of 49, that is for the Bears in Blake. Drake and Blake, Drake and Blake. David and Jocu, to 99. Devante Smith, Drake London Red, Tony Gonzalez. And Jake Ferguson for the Cowboys. Dallas Cowboys is Gabriel. Look, Kenny Pickett Silver, Kyle Hamilton Silver, Drake London, Tom Brady, Stairmasters. And T. Higgins White, 25 for the Bengals, 20 out of 25. Bengals is Derwin. If you don't want this, man, sell it to me. Donovan McNabb, one of my favorites. That's just a red, not numbered, but still love to add it to my PC. There you go. Tom Brady and Brad Johnson. I was hoping that would be Tom Brady. We'll take it. You don't want to super roll with them, right? 13 out of 15, one off the Jersey number. Buccaneers, that's HM's. Swagger, Josh Allen, it's 99. Britton Brown, Terry McClure, pretty tall. The top of my head, Jonathan, I do not know. I'd assume the Steelers, I think were like 200. Ford Downers probably had to be over 100. Vikings, I don't know, a no clue. Jim's watching, he can answer that. We had another one, guys. Aiden Hutchinson this time. That's a sweet-ass card, right at 3 out of 15 for the Lions, Austin Caruso. I like to say that because of your last name. Very nice hit right there. Trambick Duffy for the Kansas City Chiefs, A-A-Rond, with that one. Jameson Williams, blue, 10-99. And a Derek Henry Gold, Stairmasters, nine out of 10. Tennessee Titans, that's going to EA. Nice hit right there, buddy. And a Arnold. E-Bug-Tay, or a Big-T. Sorry about the pronunciation. That's a Falcon, though, and a Chris. Alrighty, last one here. Good luck, guys. Eli Mitchell to 49. James Cook for the Mills, Jeffrey with that one. Kyler Murray, about a 30-minute drive home, Rex. This is what's next. I don't know about for you, though. Are you at work tonight? Ace, Michael Vick. That's actually pretty cool. See the Michael Vick Animic Nav in this half. 10 out of 49. Kind of a walker. Alrighty, guys. Quick little recap here. Michael Vick to 99. James Cook here for the Falcons. Arnold, Gold, Derek, Henry, McDuffie, Brinn Brown, Jake Ferguson, Purple, Vils Jones Jr., Kade York, Andre Ryzen, Alec Pierce, Mark Dupre. They get a very nice Aiden Hutchinson here to 15. Brad Johnson, same thing. And a little Donovan McNabb. It's a little McNabb and a Vick. So there you go, folks. Like I said, I'll top it all this. That was the break. This was the second half. I'm gonna re-post up more mosaics, seven and eight right now. So if you guys wanna get some teens in that for tomorrow, jockeetscasebreak.com.