Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of DataVersity. We'd like to thank you for joining the latest installment of the DataVersity Webinar Series, Data in Science and Analytics, brought to you in partnership with First San Francisco Partners. Today, Kelly, O'Neill, and John Lathley will discuss big data as a gateway to knowledge management. Just a couple of points to get us started, due to the large number of people that attend these sessions, you will be muted during the webinar. To open the familiar chat and Q&A panels, just go to the bottom middle to find those icons. And the Q&A can be found by clicking the icon that looks like three little dots. For questions, we will be collecting them via the Q&A in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share our highlights or questions via Twitter using hashtag DI Analytics. If you'd like to chat with us or with each other, we certainly encourage you to do so. Again, just click the chat icon in the bottom middle of your screen for that feature. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and additional information requested throughout the webinar. Now, let me introduce to you our series speakers, Kelly O'Neill and John Ladly. John is a business technology thought leader and recognized Enterprise Information Management Authority. His 30 years of experience include planning, project management, implementing information systems, and improving IT functions. John writes and speaks on a variety of topics and enjoys sharing his expertise on strategic planning, data governance, and practical technology applications that solve business problems. Kelly is the founder and CEO of First-San Francisco Partners, an information management consulting firm. She is a veteran industry leader, speaker, author, and trainer. Kelly is passionate about helping companies leverage the value of data, empowering them to derive insights from that informed decision-making and improve results. And with that, I will turn it over to Kelly and John to get today's webinar started. Hello and welcome. Hello. Good morning. Good afternoon. Good evening. I hope everyone had a wonderful Halloween yesterday. The best holiday of the year. John, did you? It was the perfect kind of Halloween I like to have. Very good. We'll leave it there. Very good. Well, today is kind of a follow-up to our September webinar, actually. And so that webinar, Advanced Databases and Knowledge Management, really today we're extending that to talk specifically about how big data and knowledge management work together and how data science and artificial intelligence techniques can be used to automate that process that's traditionally been highly, highly manual. So pundits are saying that knowledge management might be one of the areas that is truly transformed through big data, AI, and machine learning. So that's all what we're going to talk about today. Really exciting to continue to dive into this subject further. So we'll do a very brief overview of what knowledge management is. In 1998, Gartner defined knowledge management as a discipline that promotes an integrated approach to identifying, capturing, evaluating, retrieving, and sharing all of an enterprise's information assets. These assets may include databases, documents, policies, procedures, and previously uncaptured expertise and experience in individual workers. So I wanted to start with that and read that because I think that that identification and definition is important. And I think also the fact that it was in 1998 is important. And we're going to talk about that a little bit in our presentation here. We're going to talk about how knowledge management has changed over the years and the scope of current knowledge management capabilities and technologies. This is going to lead us into use cases that are driven by analytics and big data. And then we'll explore a little bit about how companies are trying to experiment and innovate and so what we are seeing coming up in the future around knowledge management and usage of those techniques. And we will, of course, wrap up with best practices and takeaways. We will monitor the Q&A and the chat section throughout the webinar. And as appropriate, we can take questions within the webinar and we can also take questions at the end to make sure that we can get through as many as possible. And today we are going to follow an interview style similar to what we did with George Juha's in our October webinar. It seemed to go very well and so we thought we would repeat that again today. So I will be the interviewer, John will be the interviewee. Does that work? Works for me. Fantastic. Okay. So I started with a definition from Gartner in 1998 and knowledge management was a buzzword back then. It was getting a lot of attention from the big consulting companies, the thought leaders. You know, we see Davenport and Prusak on the slide here. So maybe give us a little bit of background on what was happening in the 90s and, you know, the early knots, I should say. Why knowledge management was really a buzzword then and was getting a lot of attention. It was kind of an offshoot of understanding the upcoming capabilities of large amounts of data and the data warehouse movement was in full force. In fact, was getting ready for an upheaval to a second generation. And I curio, no, not that's the name down there had written a bunch of seminal works about organizational learning and organizations being able to learn and matching up strategy and what you know to what you want to accomplish. Larry Davenport took that kind of wrote the seminal business version of that of that work. And it was all around what, you know, what don't we know what should we know. We need to capture it there the drivers behind that were very legitimate. We were starting to see that data volumes were going to become overwhelming the first books about how do you deal with lots and lots of data were coming out at this time. There was also this understanding that 80% of the content that we talked about in in in any organization we hadn't addressed at all during the data warehouse movement because it was unstructured.  There was a very deep concern, which has been delayed, but I think is relevant that a lot of the as the boomers retire, a lot of expertise is walking out the door. It's been delayed because they've all been hired they all leave on Friday and they come back in Monday as a consultant. But that is sooner or later that will come home to roost and then the big the big I guess the big brass ring is is reusing what you know, don't try to repeat lessons. Don't climb the same learning curves over and over and over again. So that's where it was all coming from. It was also considered for a little while the next big thing cause a lot of money was made off of data warehouse and be I know the next thing, you know, we have data then we got information. So obviously the next thing after information is knowledge and that's going to be a really, really big market as well. So a lot of people paid a lot of attention to this in the late 90s. And that's kind of where it became a relatively common word. Great. Sorry, searching for my mute button. There we go. Awesome. Well then, so knowledge management was really focused on sort of organizational learning and collaboration and pulling information from people's heads and and trying to get that tacit knowledge but then here we're looking at the how this has been transitioned into business intelligence. So can you talk a little bit about why business intelligence has become that capability that has extended the use case for knowledge management. Yeah, we're going to cover some of the some of. I'm sorry, it wasn't Larry Depport Tom Depport Larry Prusek covers Mr. Professor Davenport reasoning here a little bit. But when knowledge management burst on the scene, the solution areas over human capital which is capturing what people know and organizations getting smarter, working collaboratively and as we've said before on a serious collaboration is not the same as cooperation. It is a actual learned behavior. And then where is the stuff you know. And then it was well be I tells me this much information but there's so much more possible out there and of course even back then. I mean work I was doing in organizations in the early 90s, we were we were doing predictive and descriptive analytics so those those things aren't new, but we were beginning to see this bright light shining through the cracks right. And we had the technology to start to bring in unstructured data because the initial ideas and concepts that ended up becoming Hadoop we're getting kicked around there and there were several attempts made at text mining long, long, long before Hadoop and multiple format handling came along. The other thing was we the the be I we were getting reports through data warehouse reports, but we weren't working any better. We were getting better reports but all of the promise benefits weren't exactly happening. And with some exploration that I did some my peers back then did. We discovered that actionable was a lot more than just delivering a really really excellent excellent scorecard or something like that. You had to actually look ahead and say how are you going to behave once you get this data. And as you learn more and more about this really excellent data you're supposed to get. How do you keep that inside and a lot of terms were kicked around like collaborative be I and and things like that. Oh and that course means we got a track at management and then of course, someone says well wait a minute. We're going to have an algorithm and that algorithm will say every time these conditions exist we do this. So let's close the loop. And what will what we now call machine learning and pattern recognition and AI. We recall we were just saying develop closed loop agents back then. But and it was of course a lot cruder than what we have now. But so that was so but the technology really the whole be I world had that stuff in it. So it was really able to go so this again that's in our whole topic here. So you can see that there's a natural entry point. There's a natural intersection now between knowledge management as we had defined it. Almost 20 years ago out and and and are advanced analytics type technologies. Yeah, and I think I wanted to highlight this concept of, you know, identification tracking classification and kind of sorting and pulling together this knowledge which is one of the really difficult parts of this right so it's not just getting to the knowledge that's how do I classify it in a way that it can be consumed and like you said action. Well we were in clients all the time where a big part of data governance that's implemented is is not doing duplicate reports and duplicate scorecards and duplicating tables and stuff like that. Because we don't know what we have what we have. And short of some brute force processor procedure, some, some extension of what we do in the world of BI gives us technologies that we can now track what we've done and let people access that landscape of information assets. Great. And then if we look at and we now start going into the future usage of of where it's headed. You know, the, the role of knowledge management was to create this ability or this capability for an organization to establish kind of situational awareness. Right.  So how do you see metadata as a critical component of that. Metadata is is the critical technology for this that's the one thing again where we have an intersection of analytics and advanced analytics and artificial intelligence and knowledge management because both of those are meta data driven disciplines. Right. When you talk about collecting what you know. Well, that's metadata. I mean, it's the old definition of metadata is is, you know, just a card catalog type thing, you know, and where where is it the the the next part of that metadata is is the term context and this is where we had to lead in from our September event right graph databases and being able to have almost an infinite relationship to find but take those two powerful capabilities together now I have context. Now I can in in a crude way, start to overlay this tacit knowledge and relate how things are related. I'm sorry that was that was a poor choice of words connect the dots and create the relationships that are embedded in people's minds. It's still not elegant, but we now have the ability to do that that is all metadata driven as important as the values are in the rows and the columns and the information buried in the documents and the web clicks is is this information about your information now, that is, and that was the heart and soul of knowledge management. And we'll see here in a minute that might have been an obstacle. And now it is a in the heart and soul of AI and advanced analytics. And it is it is totally embraced. Right. And I don't think many people argue about that. That's absolutely right. And I think the other thing is we look at the quote on the right hand side. And we think about knowledge management and future usage. It's moving beyond the internal assets of an organization and looking at external information as well and incorporating external information into the ability to have knowledge. Wouldn't you say? Yeah. Yeah, it's, you know, it's, it, it, it go back to some of the academic works that came out in the 80s and the 90s. And there was this sense that we should be able to really understand what we know because of all these upcoming, you know, growing infinite, seemingly infinite scalability of technology. So we should be able to, to, to be smarter about rather than just just crudely storing stuff, we should really be able to store what we've learned from that. And of course, that means the entire big picture and any evidence as we go through our daily lives are an amalgam of concrete facts we know, but also impressions and experience of what we've done as we proceed through life. And that's exactly the kind of things we want to capture in an organization as well. So, so, and that's why you need that's why it's a 360 degree view of everything that influences how anyone does anything. But that sounds kind of hard, John. Right. Yeah. It's really hard. Oh boy, did we get a smack in the face. Yeah. Some of the reasons we'll find those of us here will will will. These will sound familiar. It was too hard to change behavior. You know, the culture change, right. And that plagues anything new because people don't like to change no matter how exotic the concept seems to be. And it required an awful lot of discipline to change gears and and and and we're going to see that with AI when when the machine says go left and everyone else in the room feels they should go right. All right. So the technology, everything devolved to technology. And again, we have that problem with data science and analytics now where everyone says we can do really cool things with the data and everyone spins up the data science area and a bunch of stuff is cranked out. And if you're lucky, some of it is well, usually the first initial things show benefit. And if you're lucky, you have sustainable benefit. And if you're not, you have a pretty familiar song that we're hearing a lot lately that it's just not fulfilling any promises there at the same time this was happening. There was some technologies coming to the forefront that everyone thought had solved the problem, at least from an uninformed managerial level. And that was SharePoint and Google. And SharePoint is an excellent collaborative engine. Everyone hammers on for SharePoint, terrifically, but that's just because it's implemented poorly period. Okay, I mean, it has a great deal of power that it has features that very few organizations scratch. Google, of course, as you sit down, type in a few words and boom, there's everything you think you need to know about it. So, oh, that's knowledge management. So that was a big thing there.  And at the same time, you're trying to catalog your knowledge and the tools that we had that were coming up were really crude and labor intensive. The last thing was, and this is a hard one, and it kind of closes the loop back to behavior, is when we got things we learned back in the late 90s using crude analytics on, say, smaller data volumes or doing large data volumes but taking literally weeks to process the data sets. And we came out with big conclusions. Nobody ever did anything about it. All right, they were one-time things. There's a few examples in aerospace, which actually we'll talk about one here in a little while, where we actually did. But very rarely did anybody learn anything and then build it into their business. Just culturally, it just did not happen. So with all of that going on, it just lost interest. So Tom Davenport wrote this article in 2015. There are still big organizations that have knowledge management departments. There is still associations out there. There are still knowledge management conferences. There are still a lot of people that believe in this, but it has changed considerably from the academic pursuit that it was, and has really realigned itself more along with the technology that can deliver stuff that we can to start to talk about embedding in our organizations. And of course, that's the analytics and machine learning. So that's kind of how we ended up where we are now. Absolutely. And then as we look forward, one of the challenges that was identified on the previous slide is that everything devolved into technology. And of course, we saw Google on the previous slide. And so that was kind of how knowledge management was difficult and one of the challenges that needed to be overcome. Well, here on this slide is a slide talking about technology again. And of course, Google is in forefront. What's different now? So what's actually changed so that this technology landscape can actually make an impact on knowledge management? Well, it's really interesting. I was doing the research for this. I couldn't find any of that. I went through and found some old notes from the late 90s and early twos and some old presentations I'd done. And none of the vendors I mentioned in any of those were in existence or had been absorbed into other technologies. So we have a whole new cast of characters. But if you take a look at this cast, the characters, and you take a look at what we've been talking about in the series for the last couple of years, you'll see some familiar names. Because a lot of the really cool ideas, which is going through the analytics and then capturing using machine learning to detect the pattern and training some type of repetitive behavior is in fact some type of organizational learning. Well, and then of course, we need the metadata to go with that. And we need the collaborative tools and we need to manage the unstructured content and the digital content as well. So we've got, again, none of these I would say you can't say, I'm going to go out and buy an integrated knowledge management suite. That doesn't exist. But you have an awful lot pointing in that direction right now. Now, do you want to say, well, we have an AI program. We're doing big data and analytics. And we don't have all the we don't have all the popularity we had three years ago when it was the next big thing. So we're going to call it knowledge management and we'll be the next big thing again. I don't think that I don't think that's a brand that is worth your trouble. But I think you do have an awful lot of technology pointing in the right direction where you've got, you know, Calibra with really good solid. I put clear there because it's really good workflow.  And Elation uses AI and Watson and OpenCV are really, really smart learning technologies that will tell you what you need to do without any human intervention whatsoever. And then, of course, you have to manage it all there with all the different types of technologies. And we've got some examples there as well. And of course, the other examples for document and content management that are all connected. If you take a look at, say, Confluence, which is in the JIRA Confluence Agile universe, you go, wow, there's all kinds of workflow around technology that we're working on now. And all of this is being captured. All of these technologies allow you to capture your interactions and build enormous databases of work and that you can actually study how you did your work as well as the data that was produced by the work. So we're really getting close. It's still quite a collection. But boy, is it a lot more powerful than it was when we first started to dip our toe in the water with this. Got it. So then what's changing is that it's not, the technology now supports the process in a much better way that in fact is optimizing the manual processes through things like, you know, AI machine learning, linguistic analysis, for example. And knowledge graphs, right, what we talked about in September. I looked at my first graph database in 2001 or 2002 and I'm like, wow, this is really great, but it couldn't hold more than a few thousand relationships before it was so slow that which is just like relational was in 1984. All right. You know, that just we weren't ready. I participated in a business venture in 1998 or 99 where we tried to develop a collaborative tool that collected work. And we couldn't find anything to hold all the data. It was just we overnight we overwhelmed ourselves with the amount of data we were we were there we couldn't handle it so you know our brains were 20 years ahead of our capabilities but now the capabilities are there. So a lot of kicked around back then you can really get serious about this stuff now. Right. And so, you know, Google is using knowledge graphs and text mining and machine learning, you know, translation services and speech recognition image recognition and, you know, things like that that I think we're starting to take advantage as just a given. I think cool technology just popped into my head Kelly is. I thought it was science fiction I thought it was a joke but there is actually a little Bluetooth device that you stick in your ear and it translates a conversation in another language. Now that came from hitchhiker sky to the galaxy was called the babble fish right. That might be what the technology is called I don't know. I'm not in marketing but that power that that that absolute raw power is what we need was what you need for this and we've got it. So now it's like what do we do with it. How do we go. So let's let's talk about how right so we've been talking about the what we've been talking about. You know the history. So now let's talk about the how and let's explore some of the use cases for analytics and big data that are specific to knowledge management and let's talk specifically about how AI and machine learning can automate these. You know, two of the biggest challenges that we talked about before which is what the challenges that impeded the widespread adoption and those challenges were things like extracting and categorizing and representing knowledge right. So we talked about that in the previous slide in terms of the classification. Getting it out of people's heads through the understanding of properties. So what are we seeing now then in terms of leveraging big data to do that for us if you will. Well, when you start to talk about practical use cases and big data one thing you got to obviously you're dealing with all kinds of different formats and different types of content. And what you need to though I understand it's not just it's a big lump of stuff in the lake. All right, that that can get you in trouble. The first thing you need to do is understand the spectrum of where contents coming from you looking at our little chart there. Contents goes from individual content. Things we do with our personal knowledge bases our calendars our personal spreadsheets, things like that. Then it goes all the way to global universal community type content. And there's different layers of structure and complexity within that too. And if you're going to start to say, well the technology is pretty cool I'm going to go after this.  All right. You have to understand how you're going to get visibility across all these categories classifications nooks and crannies of data. You're going to be looking at external stuff that's competitive intelligence public malabashes knowledge bases, such as a library, which has been, you know, a public knowledge base for thousands of years. All kinds of systems and things and then there's that human capital, which is your tacit knowledge that you still have to figure a way to put the veneer of human experience on top of this as well. Now, I will to a certain extent start to replicate that type of learning because machine learning will, you know, look at the patterns and you'll build repeatable models and things like that. So that gives you a use case for knowledge management in that using AI and all these technologies to process all these massive varieties of content. Now gives you a case for quote unquote, knowledge management. We're going to talk about here. Soon though this AI, there's a hook here with the AI right, you know, is AI and machine learning going to replace tacit knowledge or supplement it. And if not supplement and how do we capture it so those are some of the things that these cases have to deal with as well. Well actually a question came in that I thought was really good maybe we can take it here. So in terms of AI using the information to make sense out of this implicit and tacit knowledge. The question is, does knowledge management require some good data stewards that really know the data. So how does this concept of quality come into leveraging AI for knowledge management. There's a sideways answer to that data quality is really important for AI to be correct. We are overlooking and it's this is the scary part of AI we're overlooking the fact that a machine learning fed bad data will come up with a Frankenstein recommendation. And it will not be what you want your organization to do data quality is extremely important. So from that standpoint, you need to somehow be able to get the data quality to the appropriate position that a learning model can utilize it and that means removing all the bias out of it. We're not just talking about fat finger numbers we're talking about built in bias cultural bias in data because that bias will come out on the other side of the AI. So that's where some type of human awareness and ability to to correct that goes comes to bear. The specific question about requiring a data steward that really know the data. You're not going to like the answer the answer that one is the reason came around is we thought that was a way to shortcut having so many smart people having to know so much about the data. There's a certain twisted evil purpose to KM which was get rid of this this embedded department expert on everything we we were relying on them too much. So, so when you say no the data, you got to define what is it about the data you need to know quality contextual type behaviors of the data, but from a caretaking standpoint. And, and oh you want this data I know where it is or you want that data I know where that data is too. Those are two different things. The latter example where where someone has the the data landscape in her head, we're trying to get rid of that. KM is explicitly trying to get rid of that. But we still need what's in between people's ears, because someone knows that well on fourth quarter that number is never really accurate because it's some end of the year issue or something like that. That's the stuff that we don't know. And that's for some type of expertise or subject matter experts important why I would call that a steward or not is up to debate. But that's how that that's how I answer that question. I could go on for that one. That's a good question. I could go for two hours. That'll be another webinar. Yeah, great. Yeah. And then there was also a comment on the previous slide to mention other technologies such as Ulterics. And I guess I should purpose that previous slide to say that that's not meant to be comprehensive. It was meant to be representative and to call out some of the the names that we're seeing as leaders in each of the spaces. Ulterics absolutely the leader within that space as well. So the idea was to just kind of pepper pepper the technologies. Well, what we're trying to do is show the BI and analytics stuff how it's pushing in. And there is KM like Ulterics is a KM exclusive tool, but we didn't have time to get into that one. So that's that's a good comment. Yes. Yeah, absolutely. And then as we look at, you know, moving into these use cases and today's knowledge extraction using, you know, machine learning, linguistics analysis. It's actually becoming cheaper and, you know, a more broadly used mechanism to capture some of that domain expertise that you were just talking about to kind of capture the expert knowledge, you know, departmental expert knowledge. But I think the other thing that that comes up here is this sort of continuum and the fact that the data is generated on a regular basis too. So that you've got all of this data feeding into the potential for AI and machine learning on a regular basis versus something that's choppy in the past. Oh, yeah. Great. Well, yeah, I mean, you've kind of got the hierarchy of needs here on the right, you've got to have a well managed data supply chain. Absolutely first and foremost for any use case for this. Then you apply the AI and machine learning is a subset of of AI and when all of that happens now you can start to talk about knowledge management. Understanding how I ended kind of ties back to this question that was submitted here that you need to take to heart what people know about that that that data. All right. So I think it would be that there are data quality usefulness issues that aren't able to be detected by any type of model that there are data movement or contextual issues. Like a seasonality example I mentioned that affect the results. Do you still need to apply what people already know? I maybe I'm being a stick in the mud on that and there's some PhD and AI out there saying I'm going to eliminate people altogether fine and dandy but right now, not a chance. Okay, just not a chance. So what's tacit? Where is that tacit knowledge and where is it important? And everything else still needs to be accessible navigable and contextual. You just can't spit out the result of a machine learning pattern recognition and say this is gospel and make it so people need to be able to look at it, understand it, understand its effects and understand the context that that model was generated within.  You are getting into knowledge management, which is, you know, that's pretty cool stuff. Right. So the idea of big data and data science has given us some tools to get us, you know, a little bit further up that ladder, if you will. And we can leverage the ability to identify classified information in an organized way via AI and machine learning, but applying the contextual analysis around it is still one of these more sort of human intervention requirements. So it's helping us along that chain. Okay, well, let's let's as we dive into this how this is one of my favorite slides here on this is a great representation that you put together to show, you know, really what does this look like and how does this fit into, you know, a process and how, you know, how do we go through this so maybe John, why don't you walk us through this because I think that this is a really great way to make it real for the folks on the webinar here. Yeah, so you've got big data and so that's the big disk symbol over there. And, and it has subsets within it you've got structured data, which we call data and content which might be a digital media or a document or an email or, or something like that. And starting from the top and working our way down to the bottom. The beginning of this was you have your data you do be I am reporting, you look at the report, you know what it means, the person the individual knows the context. From this I get new information and new experiences and that needs to go into the future knowledge base I need to store an insight as to what happened in response to the information and enable the action so I need to capture the work that happened as a result of this report and report output I need to those arrows going into knowledge base need to be a new type I say it's new information. It's not what's on the report or the scorecard. It's, it's a combination of what I've done with the report and recordings of the action in the book. For example, if I have a workflow engine, and I have a report I, I say hey look at this number and it goes to the workflow, and, and then someone else says oh my that's quite a number and in the workflow and as always tracked and trace and help desk software is a perfect example of that type of metaphor. Now, we take the bottom thing you have content and very, very similar we we do analytics on the unstructured used to be called text mining or something like that now we just use analytics, and we have the same meaning in context but we because we've got already the stuff is already going through an algorithm I can actually tag it now, because I because of the document is in I can actually get some sense of knowledge and learning from that. But I also will have this new information because someone's going to use the result of that text analysis or studying the web clicks or something and, and do something and will, you know, we've got to put that somewhere as well. Then, of course, in the middle is our new world of advanced analytics or relatively new will comes brand new insight. Wow, we didn't know this before that every time that you know the sun comes up over here. This happens over there. Alright, so I have, you know, new context and new information that I have to ask and I use the word as scribe. That's the right word for it, the meaning and context of these new insights. And because our algorithmic I can tag them, and I can take the experience from the work we're doing with that, and I can load that into the knowledge base as well. So the, the, the ultimate output, the brass ring is, is insight. And that can be accessed then through a knowledge map. And I have a very crude image of a knowledge map and there was no intent for that to look like mouse's ears. I really, I just noticed that and I just didn't, there's no intent there at all. That's funny. If we, if we take an example, kind of a managing human capital human interaction managing work. Alright, so we're in a very sophisticated organization Kelly and we're doing. It's a work that first San Francisco's familiar with is a product management product MDM. And there's a lot of work that goes in creating a new product, creating the packaging around the new product, and it happens again and again and again in a sophisticated consumer products company right. So, and what often happens is they do a really great product launch and in two years later they have another one and guess what. Can you guess what happens Kelly. They lose all the information that they learned in the process. They have to do it all over again and someone says upstairs, looking at this cost of launching a new product goes what the heck. I'm going to start a knowledge management project and that's how, you know, some of this got started and we're going to capture the way everyone works. So, as products are set up, we go with something that's maybe more collaborative and there are specific software products now that are intersect with workflow with product management. And you build a metadata layer there so I have my emails and stuff so that's my content I can analyze. I have maybe external data from marketing on what products work and what don't and activities on the website and things like that so I can do some some blended analytics there and come out with some new insights on products and then I can just do some plain old grinding what sold last year what didn't sell last year, etc, etc, etc. All of this goes into that knowledge, knowledge base, and I can using analytics and using the insights of AI and stuff like that I can start to put some ideas together like if you do this product this way at this time of year it probably won't sell too well. All right, or if you don't get this product out in a certain amount of time you probably won't sell as many as you wanted to sell. And at some point in time, this type of structure and this type of knowledge base you build allows you to do that closed loop agent we used to call it, but it's really a machine learning. You can use AI taking over a decision point in your business and taking over a business process where you start to say well based on all this data and all these patterns. This product is an awful lot like a product we did five years ago. Here is the workflow for that product. I recommend that we do the product this way. Or somebody is going to do a new product and they sit down and type in we're going to do a new product and it's kind of like this and kind of like that. And the AI matches that up with the old workflows and find something similar based on all these data and research that it's got and recommends a process to move this new product to market. That's how this type of running all these what we used to think of as distinct past data, big data and content together in and and then dumping that into the knowledge base and remember what's also in this content. And also there in terms of of experience on a couple of those lines is is tracking what people are actually doing and how they're interacting with various systems and measuring their actual work. That's all going into the knowledge base to you get all that one big pile and wow you can do really awesome things with that. Absolutely. And so if we think of so this is kind of a process lens if we think about it as an architecture lens and look at our reference architecture here. That knowledge base is really what we're seeing on the right hand side represented as a knowledge lake or even a knowledge graph right isn't that how we're kind of viewing it here. It's kind of the circle and like you had indicated the closed loop where you're also using it's not only pulling the data from a big data environment but it's also using the big data to be that that knowledge lake not just the data lake. Absolutely that is a knowledge lake is, if you think a data lake is sophisticated and and really it isn't it's just a big place you put stuff. But the knowledge lake is has a lot more agents in it and it has those things you've learned and those behaviors and those responses and basically rules are created to respond to certain scenarios. The knowledge graph is again using graph technology and hyperbolic trees and all those cool interfaces this stuff has is how I would navigate that. Because remember if I'm a user that I need to find stuff on both sides of the lake. And I might want to look at source documents and I might want to drill all the way back to data sources I might want to drill all the way back to the old ERP system on the vintage side of this thing to hence the red box the abstraction engine. And this is something that is not on our normal reference architecture. But that's that is an arbitrator and a translator between what is structured unstructured explicit knowledge and then positions it in such a way to make it useful to the AI. Because again, if you just take the raw data and and you just push it into the AI, you're likely to induce bias into those models and and you've got to have some way to to balance that out. Also notice we have some things appearing on the vintage side and that is work people are still doing work on the vintage side. But now we're going to try to actual capture vintage work, especially if they're doing it on an intranet, you know, and doing web stuff, web technologies, then we can capture that work. Also collaboration now enters as a formal technology to use data. And instead of just output in data usage, I now have the actual work being done on the data and capturing actual work. So there's a lot of extra things coming into your architecture here. But you know, is anyone to throw the switch and do this tomorrow probably not but this is a bit of a target. If you're starting to think about these things and metadata comes back into play here right we started the conversation in the in the beginning about how metadata is a critical component. So, so maybe also talk about how metadata fits into this to John. You know, we were drawn a slide and I was working on I didn't know what I have to metadata an old metadata and a new metadata. But really, it's not. Don't let one red box indicated it's one metadata product there this is a metadata capability. Right, I think that's probably the right word for it. And it is a capability of knowing where everything is who touches everything. What is the context of things. And it is the it is it is the driving engine it is the heart of of all your data and content management for an architecture like this. In fact, it should be so ubiquitous at some point in time that we don't even talk about it. It's when we get into a car, we know that when we turn the key this thing under the hood called an engine does something. I'm getting to the point now, well, we don't really care whether that was a hybrid or gas or diesel or all electric. We just turn the key or push a button and we go and at some point in time metadata is going to be like that. It's going to be the engine that drives all of this and it's it will be invisible. But for now, you know, you're going to be blending several products to get those capabilities. Absolutely. And it needs to be explicit. Let's start to wrap up. We want to make sure that we're sensitive to time here. Let's look at how this fits into the operating framework. And so again, kind of circling back to some of the slides that we saw in the beginning where we looked at how knowledge management enabled certain aspects of the organization. And then we talked about what's changed in knowledge management. So as we circle back now what's new about these supporting programs on the right hand side and the way that it can support things like human capital. Yeah, and I'll go through this one pretty quickly. Basically, what when I look across areas that say they're doing knowledge management or doing something like KM and not calling that they're supporting innovative efforts and trying to remember how they are innovative. How do we how do we be innovative? They're trying to capture those smarts and there's lots of cool things happening there to do that and you all can read that. All right. And then there's they're also supporting conventional efforts, which might be more complicated, really, really large in terms of impact to the organization content or disruption. And I put like a disruptive regulation like GDPR is a classic thing where a KM department if it was in place could really help in an organization. But but KM areas and I called areas because they can't really find a place where they live. And I can't find a consistent place where you can say KM always is under this, the CIO or always under human capital and they're all over the place is innovation projects, basically offer a service to help you manage your large initiative, offer you a service for content management and content tagging, offer a service for searching for things, kind of an internal Google or offer services for finding the expertise to help you with your project. We know what we know. We're going to let you find out where it is so you don't have to go find it yourself and all the all this interacts with all these supporting programs on the right. You've got your basic enterprise architecture data process and your capability architectures and everyone can read all these other type of organizations and the big one at the top is organization change. Again, if you know there was a question here that came through I just comply about, you know, influencing behaviors. It kills KM, it kills governance, it kills MDM, it kills EIM, the culture, the soft issues are really important on that. That's kind of a nice, that's a very abstract view of an operating framework. Okay, absolutely. So we're going to wrap up here and just start to summarize that, you know, ultimately, this is about supporting organizational learning and the human capital development. I just saw this slide back in September. But Bill on this slide is the net new, right, and what's available now based on the technologies that are available to do some of this very sophisticated analysis. And did you want to add anything to this slide if not we can go to our... The question mark is what do you do with what you've learned? That is the real cultural aspect. Until human beings become cyborgs, there's going to be something between somebody's ears that's really important. And how do you manage that? And that's key and that's the $64 billion question. Exactly. That's right. So if we then go into, well, how do we take advantage of this from, you know, because we aren't yet cyborgs and we talk about how to make this work within your organization. So most of the companies that are on the phone have probably started to head down a data science path. Some people have jumped into the lake 100% and some are just dipping their toes into the data lake. But the idea is that, you know, as you have gone down this path to take a look at what you're doing and see if you can leverage some of those data science techniques for knowledge management also. So for example, if you are doing linguistics analysis somewhere else in your data science organization, could that be applied to an internal process that generates knowledge? For example, this is being done in decision support systems or, you know, trouble ticketing an IT system. So can you apply linguistics analysis to support calls and therefore improve the self-service component of your IT help desk? What else are you seeing this become practical from this perspective, John? Yeah, I mean, there are some good examples of the run for years. Anyone who watches television sees the little commercial where Watson is in the elevator and Watson says you got to fix the elevator because it's going to break in two days, right? That is a meantime between failure analysis that's been going on in complex aerospace and the military for decades. It is sophisticated statistical analysis. It's a lot of number crunching. But that's a classic example of learning from things and then adjusting behaviors in the organization and closing the loop because you're telling someone to replace a perfectly good operational part. But it saves you a ton of money when you do that. So that's one really good example. You mentioned help desks, also healthcare obviously, drug interactions, things like that. Those are things that are going now. So there are organizations that absolutely require knowledge management, whether they call it that or not. But anyone in any organization can find something to benefit. If you're talking data science and you're starting to whisper AI, you need to study this topic. Absolutely. And so just a reminder to get those questions in and we're going to go to our final slide of our key takeaway. So essentially, and this is a theme that you always hear from John and I, is this pragmatism. And so leveraging big data in analytics and AI to create this pragmatic gateway to knowledge management. So leverage what you're already doing in your analysis process. Leverage the data that exists in the lake already and see if there's a way you can apply it internally to improve the understanding of processes and the efficiency of processes. So John, why don't you also go through and highlight a couple of these as well. The learning organization as academically presented in the 80s is a long way off. Luckily, I'm one of the folks that I'm worried about AI. Hal is a long way off. And if you're a young person and don't know who Hal is just ask the old person next to you next cubicle over. I think it's a long way off still, but but it doesn't mean that AI isn't beneficial and useful right now. It is a tremendous supplement to what people know it is a it is a foil. So Bob over here in the corner says we got to go this way but the model says we got to go this way that is powerful in its own right, having those two options in front of you. So again, we're far away from knowledge management and impure academic view and not so much as a department or a program or a system. I view this now more of as a capability that the entire organization moves towards the same way that in its ideal sense data governance is a capability that should be embedded and go away as a separate type area. So those are those are some of the takeaways that we've got a couple more questions came in here. Can I want you to read one? I want one just popped in and we'll take a run at that. Yeah, absolutely. I think this is a really interesting question. So this one is how do we represent knowledge? Any standards or reference architecture? Yes, there is a standard. Yeah. Okay. Go for it, John. It's called the Dublin core. What's that? Have you ever been to a library? You see those numbers on the side of the books? That is a knowledge standard of how to categorize and find knowledge. It's called the Dewey Decimal System. And a lot of people don't know this but the Dewey Decimal System is licensed proprietary intellectual property in every library in the country pays for it. And it is the first knowledge management product. It goes back to the 19th century and it is still a standard reference architecture for finding stuff topical. So that's the first thing then of course the other thing is the OMG years ago and I know is the OMG still around Kelly? They had many, many, many expressions of other knowledge reference models that were based on industry data models and then they applied some XML type stuff to them and have since applied some other graph type concepts to them. And those are sitting around out there. Is there one that everyone's going to use like UML or something like that? No, there isn't. I suspect sometime the next, I'd say we have a trends talk next month and one of the trends we're going to talk about is what this might look like in a year or two. Something's going to happen there but there isn't one. Yeah. Yeah. That's right. That's really cool though. Dublin Core can be reviewed on the website. It's very, very cool. You'll look at it and you go, dang, that is really neat. It's very, very cool. So we've provided a reference architecture on how some of these repositories, if you will, could fit together but there really isn't a metadata standard for knowledge at this moment. So there's lots of different metadata standards and definitional standards and ontology standards and things like that but from what we've seen it hasn't quite yet made its way to knowledge management. And I think with that, we are at the top of the hour, are we? Yeah, we are. I just want to say if you're diving into this area, one other thing to study is ontologies and taxonomies. Those are going to be a great deal. Great. Well, thank you everyone. Shannon to you. Thank you Kelly. Thank you, John. As always, another great presentation and thanks to our attendees for being so engaged in everything we do. We just love all the questions that have come in for this session. And just a reminder, I will send a follow up email by end of day Monday for this presentation with links to the slides and links to the recording. Again, thanks everybody. I hope you all have a great day. Thanks Kelly. Thanks, John. Bye bye. Take care.


From the CUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Hi everyone, welcome to this CUBE Conversation. I'm John Furrier, host of the CUBE here in Palo Alto, California during the COVID-19 current situation. We're here with a skeleton crew, getting the content out there. I wanted to bring a special remote interview to you with two great remote guests, talking about how the digital events and how the digital interactions are evolving and how our community is reacting. Got two great guests, John Christ, the Senior Vice President of Marketing at Docker and Jenny Priscio, who's the advocate, community and DockerCon content lead at Docker. Jenny and John, thanks for joining me today. Great to be here, thank you. So you guys, Docker has been one of the most popular container platforms from the beginning of this cloud native movement. You have over millions and millions of developers out there. DockerCon is going virtual to the event that you guys have had physically over the years. Now it's going to go virtual, but this is not new to the developer world. And I want to get into that in the conversation, but first I want to give you guys a chance to take a minute and explain, John Christ, if you could take a minute to explain the new Docker because there were some changes over the past year, take a quick minute to explain that. Sure, thanks John and hello everybody. So we went through a change in November of last year to refocus Docker on, I'll say what it was the roots and the foundations of the company around developers and developer teams. And so at that time we took the action to split off the enterprise focus component of the business, sold it to a company called Brantus. And since that time, we've been working very diligently around refocusing Docker on developer tooling, developer chains and really developer productivity, individuals and teams. And that's something that has really revitalized our community, I'll say. The community has been strong and has come with us and I think is reacting very, very positively to this change. So that's something that we've been going on for about five months into that change and that's been positive so far. So it's a startup, kind of a reset, kind of a startup again, it's a startup roots, but with a huge community, millions of developers. Massive community, that's right. Millions of developers, more than two million Docker desktop downloads and installed on developer machines. Huge community around Docker hub, using that as their central collaboration point for developers for developing applications as they move those applications to the web and to the cloud infrastructure. So really just a tremendous community. It is a refocused company, but one that really is starting with a phenomenal foundation, as you mentioned in terms of the community that we have with us. It's a second chance to ride the big wave, certainly the waves are bigger now. I don't want to make this a real commercial about Docker or DockerCon, we're going to do a special video promo reel on that event, which we're kind of co-producing with you guys on a new collaboration. So look for the folks watching, look for a nice promo intro video on what's going to happen at DockerCon which will be a whole nother track. The reason why I wanted to bring you guys here is that this highlights to me the core impact of part of this crisis, the current situation around coronavirus, COVID-19, people are working at home more. So the mainstream world is seeing what it's like to work at home, people are understanding some of the pressures and the dynamics around how people communicate and work. And we, Jenny, we're talking about this from a community standpoint, open source community. They've been working at home for a long time. It's been one of the most biggest success stories hasn't been written is the success of how software developers have been productive in working in these groups on big projects with people from around the world. This isn't new. I'd love to get your perspective on how, what's your reaction to everyone else's reaction of the whole work at home digital world? Yeah, so I mean, if you look at what open source and what engineers have done generally, innovation doesn't happen within an office from nine to five and whatever time zone you're in. And so there's been with, you know, Docker, GitHub, Zoom, right? A number of tools in place and not just the ones I named that really allow anyone anywhere in the world to contribute their ideas and respond real time. We're not going through a huge change even, you know, within Docker or in tech of having to work from home, maybe kids in the calls is a little bit different. But for many of us, we're lucky to continue marching on during this time. What do you guys see as best practices of the work at home crisis or some of the collaboration techniques?  I mean, trolls just get put it out or moderated out of groups. Is there a certain best practice that you could share with folks that aren't learning this for the first time? Sure. So I highly recommend having a code of conduct and living by that code of conduct. So making it very visible to whomever you're working with, both internally at the company and, you know, an open source externally to anyone wanting to contribute to a project, giving grace in this time. As we all know, there's stress much beyond what's happening in our day to day work for all the community right now and writing things as much as possible. And I think particularly as in the last couple of weeks, there's been a real need to keep the written record of decisions and conversations out and make it out there and open so anyone can kind of participate. And even to that end, Dr. announced a public roadmap earlier this month. So now our entire community can jump on and vote for what they want to see or provide input and ideas on what we would do next. John, I want to ask you around DockerCon coming up. As you guys look to this being a first virtual event, digital event, we call it, it's more digital than virtual but I guess people use the word virtual more now but it's really digital. The content value has always been king on physical events, but as you move over to virtual events, you just can't make the same people make the same decisions around breakout room or what assignment on the calendar and just ship it over to digital as a whole. Another roles, new rules, new roles, new dynamics. What's your view on this as a marketer? Because you've been on both sides, successful on both sides of the table there. Yeah, it is a in a way whole new world. I mean, I've participated in virtual events in the past but I think this, the kind of scenario that we're in, it's a whole new impetus on making sure that these events as much as you can emulate the in-person experience, I think it's important that the experience you provide to your audience allows them to interact in a number of different ways above and beyond just simply watching and consuming content but really allows them to interact with each other that makes it so they can interact with speakers and other users and the kinds of people that they want to have. And one of the things that we're thinking about, for example, for DockerCon coming up is how do we emulate that hallway experience, right? That you're walking down the hall, you see somebody, you've been wanting to talk to them and you have a quick five or 10 or 20 minute conversation that allows you to have a really good, rich exchange. And that's something that we're working hard, Jenny's working hard on and the team's really worked hard to provide. So in this new world, it's how do we bring some of those things that make a great in-person event to the virtual world. And there's fortunately a lot of great tools out there now that do make that possible. You just have to bring them together in the right way. Yeah, I know that's something that we've been working on together with you guys. And everyone knows my rant. I think that the format's going to be multiple different types of formats from chats are different. We were chatting around, there's been a stream chat like on YouTube and Twitch versus threaded like Reddit and the hallway grabs those hallway tracks. It's all about the content of the people. And I want to get your thoughts on, as you guys look to take this asynchronous approach, try to make it synchronous with Dr. Kahn, has it changed some of your thinking around call for papers? I mean, call for papers is almost like an editorial call for a blood post now. So is it changing how you guys are thinking? Is there any insight that you could share as you guys are preparing? Also, you got to still got to get sponsors. You still got to get some funding, maybe not huge amounts because the physical space is venues not there. It's digital now. So can you guys just share your thinking, your reaction and any insights you can glean from those two dynamics, the format, the call for papers and the sponsors and things of that nature. That were proven methods the old way.  There it is. And everyone shows up, but not anymore. What's different? So with virtual, we really have the opportunity to take the serendipity out of the conversations and the learning that happens at a in-person event. We ran a traditional call for papers. It closed on Friday. We've got a number of responses and great content that came in. But we're not going to set a speaker up to deliver that live in a session at DockerCon. We're going to prerecord their talks and have the speaker there live to actually chat with anyone in the audience. So answer questions. So you'll have actually a much greater opportunity to talk to that expert via this virtual event than you would in-person listening to that person speak. I think it really helps first-time speakers and speakers who aren't as confident to get up in a huge room to have the opportunity to prerecord their talk. So it adds to kind of the diversity and inclusion of the event to bring on some new speakers for sure. From a location standpoint, right? Now you don't have to give up a week to show up somewhere. You can spend that time working on your talk or whatever else to kind of share your knowledge. And then the conversation doesn't have to end there. You really, everyone has a way to connect with each other after the fact, which as event marketers, you're always looking for what is that way that you continue the wonderful connections and learning that happens at a live event beyond.  It actually makes it more interesting because you think about it, you give your talk and you're there after either giving people high fives or signing autographs or getting tomatoes thrown at you. So it's there, right?  That's the content product. You can engage with the audience after to take that follow-up, that sidebar, maybe the conversations. How about John sponsors? I mean, obviously, we have to include sponsors into these events. I've seen some software out there that's pretty old school. It's like, oh, here's a digital rendering of our booth. I mean, I personally think that's horrible. I think that's the wrong direction. But the content value of a booth is an event within the event. So there is a way to weave this in. What's your vision of that? How do you see the inclusion of a sponsor and how is it more intimate and more authentic for them? Yeah, I think there's multiple aspects in terms of benefits for a sponsor that we are thinking about. Certainly, as you said, you don't really get that walk into the booth kind of experience, but given that it's all digital, you actually have a much more scalable way to enable sponsors to interact. Firstly, just with how we're promoting the event, going into it, and the fact that as they create an asset, it can live in perpetuity, that we can continue to push out there to viewers. And we know that people can come and look at that content afterwards, and that gives yet another opportunity for those sponsors to interact with the people who are consuming it. So everybody has to really think a little bit differently, both the sponsors of these events and the hosts, as Docker is doing now, to how and what kinds of interactions. So we're thinking, how do we allow them to capture an interaction, what kinds of calls to action can they include within their digital content? And so everybody's got to think a little bit more digitally and more forward than just, hey, let me have people walk in my booth and pick up some swag. You know, it's interesting, we have this conversation that's still looking at you all the time. If you think about the end user, the consumer of the content, if you work on the strive for the content value, everybody wins. So it's like an upstream project in open source. If done well, everyone can reap the benefits. If the shared mission is audience satisfaction around the content, that's contextually relevant to the people at any given time, which is what digital is beautiful for. You can really create an environment for great activation and full on demand, consumer experience advantage, either learning or engaging or whatever. If you do that, if everyone shares in that mission, that's a success formula, whether you're a sponsor or an attendee or producer. Do you agree with that? That's what we were saying earlier today. This format makes content even more of the king, right? Like the way that you're going to get attention is by delivering value through that content and you'll probably have a better result of someone stopping by to learn what you're offering is or what knowledge you're bringing to table versus what that piece of swag plus that they're getting scanned for. And there's a role for everybody. I remember when back on the glory days when I used to do all code, I used to go show my peers, my software, they're like, yeah, John, the code's just not that good. But no, no, but it was a role for me. I wasn't the best coder, but if you have good code, you rise right to the top of the pecking where people recognize your software and open source. And content's kind of the same way. Everyone can produce content and some will be better than others, but it doesn't mean that it's just about the content producer, this curation, this other roles. Do you guys see some parallels between content development in this kind of way in similar fashion that say software, just making that metaphor, but it's just riffing out loud. It's a similar construct. Good software wins the day. Good community makes it all work. Sure. If your end goal is to educate others and share something that is of value, then it's going to be picked up. And of course, creating content takes practice, just like becoming an excellent coder. And so the stakes aren't as high in a virtual event that especially with the recording and some of the other things that you're doing, blog about it, do a video, do a session, take that content, deliver it different ways and practice, particular to DockerCon, both at our live events and what we will do moving forward. We have an extensive support system for all of our speakers. We assign a number of people internally to review outline, review talk tracks, review slides and run through actual practices so that our speakers are very attuned to what our audience is going to be expecting and feel very comfortable delivering their session because their success is our success. And ultimately we're looking for delivering that value to Docker's developers. I love the format you mentioned earlier pre-record but also there's a new format emerging that's very popular in the Twitch world which is streaming your video game. I still predict that people will be streaming their coding sessions, but you guys have a Docker captain in Brett who has his own streaming rig and he does the Docker birthday party recently. I think that's going to be a future format streaming to an endpoint, not just for gaming but for just life, life casting as some people call it but that's a good format. It fits perfectly into these digital events to host and emcee these sessions. So you can do the record on demand, record in advance but it's also there's a role for streaming doing the demos, doing the tech talks. I mean, think about your audience, right? They need something both in the moment and after the fact and sessions are going to get or something that you can watch now or later but running through an event with our captain like Brett Fisher. You want to be there to see what's going on. We did a birthday live stream on Thursday to celebrate a Docker's seventh birthday and it was amazing because we had so many members of our community come on. They can't go to meetups anymore with everything that's happening but we found a way to all connect, all chat, have a great time and have this group experience both fun and learning. And I think we will continue to see that not just in the conference form but increasingly now with COVID, right? People can't get together. People are zooming with their high school friends to make up for time loss. So I think beyond our industry, the world is going to get very used to connecting virtually. I'm going to have a Zoom session tonight, seven o'clock on my Facebook page. It can be interesting to see all my high school friends come out and who knows those words about that? There's kind of no moderator button on Facebook. I have to figure out, make sure they're all there. Final question on this whole event thing that we can get to the last section around DockerCon. John and Jenny, we both have friends that are in digital, have done events. I'm hearing a lot of pressure is on these digital teams because the physical events have proven a lot of great business value. Most companies know the economic value of physical event. Again, it's been standardized over decades but now all of a sudden these new teams, digital teams are being asked to provide the same business value that these physical events have provided. And these teams aren't equipped for it. So I'm getting a lot of phone calls and a lot of outreach for theCUBE saying we need help. That's the event or a digital team and the demand generation marketing teams. They're under a lot of pressure. Are you guys seeing the same thing? And what would you, if you are, what advice would you give the people out there? Because they're under a massive amount of pressure to deliver. Yeah. Yeah, it's a new world in that regard. I mean, yeah, there are a few platforms out there but in terms of something for demand marketers that emulates that live event, there's really not. I mean, as you know, we're innovating in multiple ways with you to bring a different kind of experience. But we're also having to think about how do we convert that into some kind of economic value? I mean, for example, DockerCon is a free event this year. A lot of the costs are lower, but it's a free event. That sort of changes that aspect of it. But the other part is, how do we make sure we connect with that audience so that we have an ongoing relationship? And the way we're looking at it, and I think one recommendation for other companies is, it is a component in a series of engagements. It's a very big one, one that we're investing quite a bit in in terms of resources, but it's really just a series of, one component of a series of engagements that we have digitally. And there's a lot of other ways that you can do it. And fortunately, but Jenny has online meetups or already has virtual meetups as component of our virtual experience. This is one that we're sliding in with that. And based on how we're expecting it to go, we'll continue to invest in it in the future. Jenny, 365 days in a year, that's digital. It's always on, right? It's like, you got to think holistically, not just have an event, stand it up, tear it down, move to the next one, you activate, and you got to keep it going always on. You have to keep a pulse, keep the community rolling. Yes, and whether it was a physical event or a virtual event, that's your goal anyways, is to continue that momentum and keep the community going. We are innovating on that with you, with Dr. Kahn, but we're also very much listening to our community and what their needs are and trying to figure out how to support their connections with their local community. Dr. has a pretty extensive meetup network all around the world. And the rise of virtual really allows us to take the physical limitations of local meetups out. And if they want to run virtual events, then great, how can we support them as well? That's awesome. And you know our mission from this area for the folks watching is to create the best experience possible for audiences. And that means putting the right content in front of them that matters or having them choose their own content, meet the right people, find out if their friends are there, make it a great engaging experience because if that happens, everybody wins. So we're looking forward to Dr. Kahn. If you guys could just give a highlight, quick teaser, John, give it quick teaser on Dr. Kahn and then Cheney, give the community update of what do you guys expect to have happen? What are you hoping for? What are you nervous about? What's the excitement? What's going on? John, we'll start with you. Yes, it's just, yeah, thanks, John. So just a brief on Dr. Kahn. It's May 28th of this year. It is a free event that is going to run for I think it's eight hours during the day. There's multimodal kind of consumption models. We're thinking in terms of different channels that people can come and consume. We talked a little bit about the live channel with our captains. There's a live channel with theCUBE with you guys. There's also the prerecorded track content. So there's a way for people to come and interact, come and participate in the chats and consume content that should be highly educational and focused. And we hope that it'll be a great experience we're really focused on the content and making sure that it's a great experience for our users and our audience. Jenny, how about the community? What's your take? What's your goal and aspirations? What are you hoping for? Hoping for the community to be able to connect both with the speakers, experts, captains, get their questions answered, have conversations with people on stage, if you will, but also with each other and just kind of strengthen the bonds of the community and getting everybody to a better place with developing with Docker and DevOps and kind of create those pathways beyond May 28. Yeah, it's a DevOps world. We're going to do our best, hopefully put a kick-ass program together. It's going to be fun and we hope we have good bandwidth. John went out a couple of times there one time but we're going to have some good time and hopefully learn a lot and iterate and just raise the bar on it and just get it going. So really appreciate collaborating with you guys and really thank you for your insight on this real, I think a clear vision for how digital is going to shape how people engage and how events will go. Even when they come back, I think this point in time, this current situation is going to emphasize the role of digital isn't just about marketing to people and getting them to come to an event. I think it's going to be a real productive network effect where there's value created. And I think the silver lining in all of this is, this is going to be now the new path for it. So thank you for sharing your cutting edge insights and I appreciate your time. Thank you. Thanks so much for having us. Okay, this is a CUBE conversation. I'm John Furrier here in the CUBE Studios in Palo Alto with the remote interviews during this time of crisis of COVID-19 current situation. I'm John Furrier at the CUBE. Thanks for watching.


Hello everybody and welcome to another hobby cheating video today. We're gonna get into brown hair Something I used to have so I know a little bit about let's get into it Let's strict techno man sir. That is Vincy V. Let us get to the technique and learn it Vincy V style Brown hair is one of the more common hair colors in the world And so it's only makes sense that we should finally get into it One of the reasons I haven't deep dove it for a long time is because I think it's honestly sometimes fairly obvious Or at least I think that was my perception But as I've talked to people and come to evolve the way I think about painting over time I've come to see how much nuance there is in painting brown hair all the different various tones and hues and values and shades We can use and as a result. I wanted to do a new video really taking you through brown hair We're gonna paint it on this model And I'm gonna show you kind of how to create the light how to do interesting things and how you can play with the tones To create everything you might want from very light sandy brown hair to something very dark Even though it might have been almost 20 years since I had any brown hair up there I can still look at those old pictures of mine as a good reference for what we're supposed to do So let's head over to the desk. Let's get into it All right, let's start with the paints everything we do here is going to be these five paints There's nothing magic about these five you could use anything But generally you want something in a nice brown tone It's gonna be your mid tone you want to be able to go a little bit down and a lot up and I'm gonna start By actually laying down just a nice base coat of something that's going to be darker than my mid tone Here's what I mean by that imagine you have a sort of color spectrum from one to five one being the brightest five being the darkest We're actually gonna start with the hair in the four We're gonna build up that halo effect and you'll see how we build back in saturation and things like that later So once I've got the base coat, I then block in some rough shadows Here I'm working very thin. I want it to flow down into the recesses and I'm not washing it everywhere This is the first mistake. I see people make when they when they shade their their hair They put the dark shadow everywhere. It doesn't go everywhere The upper parts of the hair do not have the darkest shadow in them Only the parts that are in sort of the shadow areas go down into this dark color Now this is going to be my actual like Realistically brown hair mid tone with some light and so this is my mahogany and we're gonna focus here on two things One establishing the actual halo of light So that is the big broad section that you saw me place across the crown of the head and then to Extending that down Into the appropriate areas so notice that I first applied it and I made sure in those in the area where it's going to be the Primary color I covered everything including the recesses, but then I stretch out from it and Don't cover everything It's the same with this even though I'm doing these lines as I continue to add lighter color and the When I'm my first pass is going to cover everything in the area including the recesses Then I start working out from there doing these longer lines that stretch and extend their way down into The other color so every color is extending out of its sort of spectrum down into the other colors Every time the primary light the primary band of light that I'm creating here Operates completely within the one above it and when I get up to this highlight, you'll know he'll notice here I'm not going quite as deep in the resource the recesses The shadow color for my highlight areas is effectively a slightly brighter version of my mid-tone But nothing even in the recesses at the highlight goes darker Then something slightly above my mid-tone and nothing in the shadow area Goes brighter as a highlight than the mid-tone. I cannot stress this enough as You're working the halo as you're sort of shaping and building the light that's going to be on the hair In this whole upper area where the light is is actually showing You don't have any of your deepest shadow. It does not exist Only the mid-tone exists and Then when we get down to these very bright highlights where I'm really trying to establish that light line Remember hair is satin. That means it's highly reflective So to establish that highlight we have to come in and do the same thing here up to very very bright tones This should be a very small volume i.e. not a lot of space is taken up on the manager But it should run very bright. There's nothing brown about these last couple colors. I'm painting but that's appropriate That's the light a Bunch of the hair is still brown So it will still read as brown just because it says brown hair doesn't mean you have to use paints that only say brown on the Model and as I get to these very highest highlights, what I'm doing is just now We're only doing the sort of staccato lines Only in these very Central small teeny tiny areas the very precise middle of the halo, right? My last and final brightest highlight. You notice how sometimes I'm just literally stippling it. It's a dot It's nothing more than a dot. Sometimes it's a line that streaks, but mostly it's just a dot Now this has all just been layering But I am getting progressively thinner as I go and that journey will continue with our next step Remember the tones I'm working with here are not somehow sacred this to me is sort of a good Neutral brown it'll run you and and serve you well, you know run through your colors and serve you well But it's not the only thing you can use any Series of brown paints you could shift more into red if you wanted to feel a little warmer a little more strawberry a little closer To red hair you can shift it more into ochres if you want it to be much more sandy Brown hair you can shift more into deep purples or or blue blacks if you want it to be much darker hair It's really just about controlling those individual Volumes you want that nice tight light halo to be created Regardless of how dark or light it is the rest is just setting the individual hues that you think are credible for your miniature or For what you picture it to be at least any of those color ranges will work And in fact most of them can be combined in really interesting ways As I mentioned we are working progressively thinner So when I started with the base coat I was completely normal layer consistency and I was mostly using layers But they got to be thinner and thinner layers as I worked my way up the the light spectrum And I did that so I could build it up over multiple passes slowly instantiating that light and as I draw that thin translucent paint down Over the other areas it wouldn't be as intense and bright But instead a lot of the undertone would show and so we get that natural blend and overlap Here I'm actually building in the light on her shoulder, which is like a sticky outie bit. I missed earlier But now what I'm going to do is mostly glaze either the mahogany or the warm brown Some of both sometimes a mix sometimes I bring in a little of flesh and glaze it This is very much a back-and-forth process But I'm going to seek to smooth to some degree the transition between those layers hide those Individual brushstrokes and transitions and make them look like a more natural transition of the light from one to the other You'll also occasionally see me take some of that glaze and Run like I am here all the way down into the shadow. This doesn't have much effect But again, I don't want all of the hair to fall into black in the shadow In much the same way in the light We have some of the mid-tone still represented in the recesses In the shadow we need some of the mid-tone represented in the highlights This is a little important step Some figures have this sort of hair where it extends out like this on the side of the face You have to treat this in the same way and it kind of over highlight this especially if it's facing your light Because that hair in this case that that hair is facing toward the light And so it would actually be quite translucent and quite reflective and showing a lot of light So I have to really make sure I get some good highlights on there and then perform all the same steps You saw me doing otherwise to smooth it out Most of this then is refinement from this point Do I have enough of this other color wherever there's a part sticking out or a natural fold to the hair? Remember the highlight is specular So that's the halo, but then we also want to get the sticky outty bits where there would naturally be light catching And it's really just working back and forth and you'll see how I sometimes push a little of that glaze High up into the into almost into the highlight area not into the very center But up higher And I do that because I want a little bit of that mid-tone represented In the deeper recesses of the highlight It's really a sort of balancing act I wish I could say that doing hair any color hair was just a matter of applying a straight set of layers, but it's not The final step I'm doing with the airbrush But you could just do this more with glazes and that's instantiating saturation This can be where you tint the hair a certain color. So here I'm turning it just a tad more into the red tone But through this like a thin glaze either through brush or airbrush You can bring back the saturation that life that intensity and even Shift the color just a little and with that that hair's done There you go. The brown hair is all done As I said, you could continue to filter this and run it out any way you want Including by toning it through additional like Contrast paints run through as a thin filter with the airbrush or very thin with a glaze or a brush as well So if you liked this, hey, give it a like subscribe for additional hobby cheating in the future We have new videos here every saturday if you want to support the channel There's lots of ways you can do so hit that share button or tell somebody about the channel It's free and it really helps. There's a merch store down there. And of course, there's a patreon Patreon is where you can go. It's focused on review and feedback and taking your next step on your hobby journey As always though, I appreciate so much you watching this one And we'll see you next time You