>> Hi, I'm Peter Burris and welcome to another Wikibon Action Item. (electronic music) Once again, we're broadcasting from theCUBE studios in beautiful Palo Alto. Here in the studio with me are George Gilbert and David Floyer. And remote, we have Neil Raden and Jim Kobielus. Welcome everybody. >> David: Thank you. >> So this is kind of an interesting topic that we're going to talk about this week. And it really is how are we going to find new ways to generate derivative use out of many of the applications, especially web-based applications that are have been built over the last 20 years. A basic premise of digital business is that the difference between business and digital business is the data and how you craft data as an asset. Well, as we all know in any universal Turing machine, data is the basis for representing both the things that you're acting upon but also the algorithms, the software itself. Software is data and the basic principles of how we capture software oriented data assets or software assets and then turn them into derivative sources of value and then reapply them to new types of problems is going to become an increasingly important issue as we think about the world of digital business is going to play over the course of the next few years. Now, there are a lot of different domains where this might work but one in particular that's especially as important is in the web application world where we've had a lot of application developers and a lot of tools be a little bit more focused on how we use web based services to manipulate things and get software to do the things we want to do and also it's a source of a lot of the data that's been streaming into big data applications. And so it's a natural place to think about how we're going to be able to create derivative use or derivative value out of crucial software assets. How are we going to capture those assets, turn them into something that has a different role for the business, performs different types of work, and then reapply them. So to start the conversation, Jim Kobielus. Why don't you take us through what some of these tools start to look like. >> Hello, Peter. Yes, so really what we're looking at here, in order to capture these assets, the web applications, we first have to generate those applications and the bulk of that worker course is and remains manual. And in fact, there is a proliferation of web application development frameworks on the market and the range of them continues to grow. Everything from React to Angular to Ember and Node.js and so forth. So one of the core issues that we're seeing out there in the development world is... are there too many of these. Is there any prospect for simplification and consolidation and convergence on web application development framework to make the front-end choices for developers a bit easier and straightforward in terms of the front-end development of JavaScript and HTML as well as the back-end development of the logic to handle the interactions; not only with the front-end on the UI side but also with the infrastructure web services and so forth. Once you've developed the applications, you, a professional programmer, then and only then can we consider the derivative uses you're describing such as incorporation or orchestration of web apps through robotic process automation and so forth. So the issue is how can we simplify or is there a trend toward simplification or will there soon be a trend towards simplification of a front-end manual development. And right now, I'm not seeing a whole lot of action in this direction of a simplification on the front-end development. It's just a fact. >> So we're not seeing a lot of simplification and convergence on the actual frameworks for creating software or creating these types of applications. But we're starting to see some interesting trends for stuff that's already been created. How can we generate derivative use out of it? And also per some of our augmented programming research, new ways of envisioning the role that artificial intelligence machine learning, etc, can play in identifying patterns of utilization so that we are better able to target those types of things that could be used for derivative or could be applied to derivative use. Have I got that right, Jim? >> Yeah, exactly. AI within robotic process automation, anything that could has already been built can be captured through natural language processing, through a computer image recognition, OCR, and so forth. And then trans, in that way, it's an asset that can be repurposed in countless ways and that's the beauty RPA or where it's going. So the issue is then not so much capture of existing assets but how can we speed up and really automate the original development of all that UI logic? I think RPA is part of the solution but not the entire solution, meaning RPA provides visual front-end tools for the rest of us to orchestrate more of the front-end development of the application UI and interaction logic. >> And it's also popping up-- >> That's part of broader low-code-- >> Yeah, it's also popping up at a lot of the interviews that we're doing with CIOs about related types of things but I want to scope this appropriately. So we're not talking about how we're going to take those transaction processing applications, David Floyer, and envelope them and containerize them and segment them and apply a new software. That's not what we're talking about, nor are we talking about the machine to machine world. Robot process automation really is a tool for creating robots out of human time interfaces that can scale the amount of work and recombine it in different ways. But we're not really talking about the two extremes. The hardcore IoT or the hardcore systems of record. Right? >> Absolutely. But one question I have for Jim and yourself is the philosophy for most people developing these days is mobile first. The days of having an HTML layout on a screen have gone. If you aren't mobile first, that's going to be pretty well a disaster for any particular development. So Jim, how does RPA and how does your discussion fit in with mobile and all of the complexity that mobile brings? All of the alternative ways that you can do things with mobile. >> Yeah. Well David, of course, low-code tools, there are many. There are dozens out there. There are many of those that are geared towards primarily supporting of fast automated development of mobile applications to run on a variety of devices and you know, mobile UIs. That's part of the solution as it were but also in the standard web application development world. know there's these frameworks that I've described. Everything from React to Angular to Vue to Ember, everything else, are moving towards a concept, more than concept, it's a framework or paradigm called progressive web apps. And what progressive web apps are all about, that's really the mainstream of web application development now is blurring the distinction between mobile and web and desktop applications because you build applications, JavaScript applications for browsers. The apps look and behave as if they were real-time interactive in memory mobile apps. What that means is that they download fresh content throughout a browsing session progressively. I'm putting to determine air quotes because that's where the progressive web app comes in. And they don't require the end-user to visit an app store or download software. They don't require anything in terms of any special capabilities in terms of synchronizing data from servers to run in memory natively inside of web accessible containers that are local to the browser. They just feel mobile even though they, excuse me, they may be running on a standard desktop with narrowband connectivity and so forth. So they scream and they scream in the context of their standard JavaScript Ajax browser obsession. >> So when we think about this it got, jeez Jim it almost sounds like like client-side Java but I think you're we're talking about something, as you said, that that evolves as the customer uses it and there's a lot of techniques and approaches that we've been using to do some of those things. But George Gilbert, the reason I bring up the notion of client-side Java is because we've seen other initiatives over the years try to do this. Now, partly they failed because, David Floyer, they focused on too much and tried to standardize or presume that everything required a common approach and we know that that's always going to fail. But what are some of the other things that we need to think about as we think about ways of creating derivative use out of software or in digital assets. >> Okay, so. I come at it from two angles. And as Jim pointed out, there's been a Cambrian explosion of creativity and innovation on frankly on client-side development and server-side development. But if you look at how we're going to recombine our application assets, we tried 20 years ago with EAI but that was, and it's sort of like MuleSoft but only was for on-prem apps. And it didn't work because every app was bespoke essentially-- >> Well, it worked for point-to-point classes of applications. >> Yeah, but it required bespoke development for every-- >> Peter: Correct. >> Every instance because the apps were so customized. >> Peter: And the interfaces were so customized. >> Yes. At the same time we were trying to build higher-level application development capabilities on desktop productivity tools with macros and then scripting languages, cross application, and visual development or using applications as visual development building blocks. Now, you put those two things together and you have the ability to work with user interfaces by building on, I'm sorry, to work with applications that have user interfaces and you have the functionality that's in the richer enterprise applications and now we have the technology to say let's program by example on essentially a concrete use case and a concrete workflow. And then you go back in and you progressively generalize it so it can handle more exception conditions and edge conditions. In other words, you start with... it's like you start with the concrete and you get progressively more abstract. >> Peter: You start with the work that the application performs. >> Yeah. >> And not knowledge of the application itself. >> Yes. But the key thing is, as you said, recombining assets because we're sort of marrying the best of EAI world with the best of the visual client-side development world. Where, as Jim points out, machine learning is making it easier for the tools to stay up to date as the user interfaces change across releases. This means that, I wouldn't say this as easy as spreadsheet development, it's just not. >> It's not like building spreadsheet macros but it's more along those lines. >> Yeah, but it's not as low-level as just building raw JavaScript because, and Jim's great example of JavaScript client-side frameworks. Look at our Gmail inbox application that millions of people use. That just downloads a new version whenever they want to drop it and they're just shipping JavaScript over to us. But the the key thing and this is, Peter, your point about digital business. By combining user interfaces, we can bridge applications that were silos then we can automate the work the humans were doing to bridge those silos and then we can reconstitute workflows in much more efficient-- >> Around the digital assets, which is kind of how business ultimately evolves. And that's a crucial element of this whole thing. So let's change direction a little bit because we're talking about, as Jim said, we've been talking about the fact that there are all these frameworks out there. There may be some consolidation on the horizon, we're researching that right now. Although there's not a lot of evidence that it's happening but there clearly is an enormous number of digital assets that are in place inside these web-based applications, whether it be relative to mobile or something else. And we want to find derivative use of or we want to create derivative use out of them and there's some new tools that allow us to do that in a relatively simple straightforward way, like RPA and there are certainly others. But that's not where this ends up. We know that this is increasingly going to be a target for AI, what we've been calling augmented programming and the ability to use machine learning and related types of technologies to be able to reveal, make transparent, gain visibility into, patterns within applications and within the use of data and then have that become a crucial feature of the development process. And increasingly even potentially to start actually creating code automatically based on very clear guidance about what work needs to be performed. Jim, what's happening in that world right now? >> Oh, let's see. So basically, I think what's going to happen over time is that more of the development cycle for web applications will incorporate not just the derivative assets, the AI to be able to decompose existing UI elements and recombine them. Enable flexible and automated recombination in various ways but also will enable greater tuning of the UI in an automated fashion through A/B testing that's in line to the development cycle based on metrics that AI is able to sift through in terms of... different UI designs can be put out into production applications in real time and then really tested with different categories of users and then the best suited or best fit a design based on like reducing user abandonment rates and speeding up access to commonly required capabilities and so forth. The metrics can be rolled in line to the automation process to automatically select the best fit UI design that had been developed through automated means. In other words, this real-world experimentation of the UI has been going on for quite some time in many enterprises and it's often, increasingly it involves data scientists who are managing the predictive models to sort of very much drive the whole promotion process of promoting the best fit design to production status. I think this will accelerate. We'll take more of these in line metrics on UI and then we brought, I believe, into more RPA style environments so the rest of us building out these front ends are automating more of our transactions and many more of the UIs can't take advantage of the fact that we'll let the infrastructure choose the best fit of the designs for us without us having to worry about doing A/B testing and all that stuff. The cloud will handle it. >> So it's a big vision. This notion of it, even eventually through more concrete standard, well understood processes to apply some of these AIML technologies to being able to choose options for the developer and even automate some elements of those options based on policy and rules. Neil Raden, again, we've been looking at similar types of things for years. How's that worked in the past and let's talk a bit about what needs to happen now to make sure that if it's going to work, it's going to work this time. >> Well, it really hasn't worked very well. And the reason it hasn't worked very well is because no one has figured out a representational framework to really capture all the important information about these objects. It's just too hard to find them. Everybody knows that when you develop software, 80% of it is grunt work. It's just junk. You know, it's taking out the trash and it's setting things up and whatever. And the real creative stuff is a very small part of it. So if you could alleviate the developer from having to do all that junk by just picking up pieces of code that have already been written and tested, that would be big. But the idea of this has been overwhelmed by the scale and the complexity. And people have tried to create libraries like JavaBeans and object-oriented programming and that sort of thing. They've tried to create catalogs of these things. They've used relational databases, doesn't work. My feeling and I hate to use the word because it always puts people to sleep is some kind of ontology that's deep enough and rich enough to really do this. >> Oh, hold on Neil, I'm feeling... (laughs) >> Yeah. Well, I mean, what good is it, I mean go to Git, right. You can find a thousand things but you don't know which one is really going to work for you because it's not rich enough, it doesn't have enough information. It needs to have quality metrics. It needs to have reviews by people who have used converging and whatever. So that's that's where I think we run into trouble. >> Yeah, I know. >> As far as robots, yeah? >> Go ahead. >> As far as robots writing code, you're going to have the same problem. >> No, well here's where I think it's different this time and I want to throw it out to you guys and see if it's accurate and we'll get to the action items. Here's where I think it's different. In the past, partly perhaps because it's where developers were most fascinated, we try to create object-oriented database and object oriented representations of data and object oriented, using object oriented models as a way of thinking about it. And object oriented code and object oriented this and and a lot of it was relatively low in the stack. And we try to create everything from scratch and it turned out that whenever we did that, it was almost like CASE from many years ago. You create it in the tool and then you maintain it out of the tool and you lose all organization of how it worked. What we're talking about here, and the reason why I think this is different, I think Neil is absolutely right. It's because we're focusing our attention on the assets within an application that create the actual business value. What does the application do and try to encapsulate those and render those as things that are reusable without necessarily doing an enormous amount of work on the back-end. Now, we have to be worried about the back-end. It's not going to do any good to do a whole bunch of RPA or related types of stuff on the front-end that kicks off an enormous number of transactions that goes after a little server that's 15 years old. That's historically only handled a few transactions a minute. So we have to be very careful about how we do this. But nonetheless, by focusing more attention on what is generating value in the business, namely the actions that the application delivers as opposed to knowledge of the application itself, namely how it does it then I think that we're constraining the problem pretty dramatically subject to the realities of what it means to actually be able to maintain and scale applications that may be asked to do more work. What do you guys think about that? >> Now Peter, let me say one more thing about this, about robots. I think you're all a lot more sanguine about AI and robots doing these kinds of things. I'm not. Let me read to you have three pickup lines that a deep neural network developed after being trained to do pickup lines. You must be a tringle? 'Cause you're the only thing here. Hey baby, you're to be a key? Because I can bear your toot? Now, what kind of code would-- >> Well look, the problems look, we go back 50 years and ELIZA and the whole notion of whatever it was. The interactive psychology. Look, let's be honest about this. Neil, you're making a great point. I don't know that any of us are more or less sanguine and that probably is a good topic for a future action item. What are the practical limits of AI and how that's going to change over time. But let's be relatively simple here. The good news about applying AI inside IT problems is that you're starting with engineered systems, with engineered data forms, and engineered data types, and you're working with engineers, and a lot of that stuff is relatively well structured. Certainly more structured than the outside world and it starts with digital assets. That's why a AI for IT operations management is more likely. That's why AI for application programming is more likely to work as opposed to AI to do pickup lines, which is as you said semantically it's all over the place. There's very, very few people that are going to conform to a set of conventions for... Well, I want to move away from the concept of pickup lines and set conventions for other social interactions that are very, very complex. We don't look at a face and get excited or not in a way that corresponds to an obvious well-understood semantic problem. >> Exactly, the value that these applications deliver is in their engagement with the real world of experience and that's not the, you can't encode the real world of human lived experience in a crisp clear way. It simply has to be proven out in the applications or engagement through people or not through people, with the real world outcome and then some outcomes like the ones that Neil read off there, in terms of those ridiculous pickup lines. Most of those kinds of automated solutions won't make a freaking bit of sense because you need humans with their brains. >> Yeah, you need human engagement. So coming back to this key point, the constraint that we're putting on this right now and the reason why certainly, perhaps I'm a little bit more ebullient than you might be Neil. But I want to be careful about this because I also have some pretty strong feelings about where what the limits of AI are, regardless of what Elon Musk says. That at the end of the day, we're talking about digital objects, not real objects, that are engineered, not, haven't evolved over a few billion years, to deliver certain outputs and data that's been tested and relatively well verified. As opposed to have an unlimited, at least from human experience standpoint, potential set of outcomes. So in that small world and certainly the infrastructure universe is part of that and what we're saying is increasingly the application development universe is going to be part of that as part of the digital business transformation. I think it's fair to say that we're going to start seeing AI machine learning and some of these other things being applied to that realm with some degree of success. But, something to watch for. All right, so let's do action item. David Floyer, why don't we start with you. Action item. >> In addressing this, I think that the keys in terms of business focus is first of all mobiles, you have to design things for mobile. So any use of any particular platform or particular set of tools has to lead to mobile being first. And the mobiles are changing rapidly with the amount of data that's being generated on the mobile itself, around the mobile. So that's the first point I would make from a business perspective. And the second is that from a business perspective, one of the key things is that you can reduce cost. Automation must be a key element of this and therefore designing things that will take out tasks and remove tasks, make things more efficient, is going to be an incredibly important part of this. >> And reduce errors. >> And reduce errors, absolutely. Probably most important is reduce errors. Is to take those out of the of the chain and where you can speed things up by removing human intervention and human tasks and raising what humans are doing to a higher level. >> Other things. George Gilbert, action item. >> Okay, so. Really quickly on David's point that we have many more application forms and expressions that we have to present like mobile first. And going back to using RPA as an example. The UiPath product that we've been working with, the core of its capability is to be able to identify specific UI elements in a very complex presentation, whether it's on a web browser or whether it's on a native app on your desktop or whether it's mobile. I don't know how complete they are on mobile because I'm not sure if they did that first but that core capability to identify in a complex, essentially collection and hierarchy of UI elements, that's what makes it powerful. Now on the AI part, I don't think it's as easy as pointing it at one app and then another and say go make them talk. It's more like helping you on the parts where they might be a little ambiguous, like if pieces move around from release to release, things like that. So my action item is say start prototyping with the RPA tools because that's probably, they're probably robust enough to start integrating your enterprise apps. And the only big new wrinkle that's come out in the last several weeks that is now in everyone's consciousness is the MuleSoft acquisition by Salesforce because that's going back to the EAI model. And we will see more app to app integration at the cloud level that's now possible. >> Neil Raden, action item. >> Well, you know, Mark Twain said, there's only two kinds of people in the world. The kind who think there are only two kinds of people in the world and the ones who know better. I'm going to deviate from that a little and say that there's really two kinds of software developers in the world. They're the true computer scientists who want to write great code. It's elegant, it's maintainable, it adheres to all the rules, it's creative. And then there's an army of people who are just trying to get something done. So the boss comes to you and says we've got to get a new website up apologizing for selling the data of 50 million of our customers and you need to do it in three days. Now, those are the kind of people who need access to things that can be reused. And I think there's a huge market for that, as well as all these other software development robots so to speak. >> Jim Kobielus, action item. >> Yeah, for simplifying web application development, I think that developers need to distinguish between back-end and front-end framework. There's a lot of convergence around the back-end framework. Specifically Node.js. So you can basically decouple the decision in terms of front-end frameworks from that and you need to write upfront. Make sure that you have a back-end that supports many front ends because there are many front ends in the world. Secondly, the front ends themselves seem to be moving towards React and Angular and Vue as being the predominant ones. You'll find more programmers who are familiar with those. And then thirdly, as you move towards consolidation on to fewer frameworks on the front-end, move towards low-code tools that allow you just with the push of a button, you know visual development, being able to deploy the built out UI to a full range of mobile devices and web applications. And to close my action item... I'll second what David said. Move toward a mobile first development approach for web applications with a focus on progressive web applications that can run on mobiles and others. Where they give a mobile experience. With intermittent connectivity, with push notifications, with a real-time in memory fast experience. Move towards a mobile first development paradigm for all of your your browser facing applications and that really is the simplification strategy you can and should pursue right now on the development side because web apps are so important, you need a strategy. >> Yeah, so mobile irrespective of the... irrespective of the underlying biology or what have you of the user. All right, so here's our action item. Our view on digital business is that a digital business uses data differently than a normal business. And a digital business transformation ultimately is about how do we increase our visibility into our data assets and find new ways of creating new types of value so that we can better compete in markets. Now, that includes data but it also includes application elements, which also are data. And we think increasingly enterprises must take a more planful and purposeful approach to identifying new ways of deriving additional streams of value out of application assets, especially web application assets. Now, this is a dream that's been put forward for a number of years and sometimes it's work better than others. But in today's world we see a number of technologies emerging that are likely, at least in this more constrained world, to present a significant new set of avenues for creating new types of digital value. Specifically tools like RPA, remote process automation, that are looking at the outcomes of an application and allow programmers use a by example approach to start identifying what are the UI elements, what those UI elements do, how they could be combined, so that they can be composed into new things and thereby provide a new application approach, a new application integration approach which is not at the data and not at the code but more at the work that a human being would naturally do. These allow for greater scale and greater automation and a number of other benefits. The reality though is that you also have to be very cognizant as you do this, even though you can find these, find these assets, find a new derivative form and apply them very quickly to new potential business opportunities that you have to know what's happening at the back-end as well. Whether it's how you go about creating the assets, with some of the front-end tooling, and being very cognizant of which front ends are going to be better or not better or better able at creating these more reusable assets. Or whether you're talking about still how relatively mundane things like how a database serialized has access to data and will fall over because you've created an automated front-end that's just throwing a lot of transactions at it. The reality is there's always going to be complexity. We're not going to see all the problems being solved but some of the new tools allow us to focus more attention on where the real business value is created by apps, find ways to reuse that, and apply it, and bring it into a digital business transformation approach. All right. Once again. George Gilbert, David Floyer, here in the studio. Neil Raden, Jim Kobielus, remote. You've been watching Wikibon Action Item. Until next time, thanks for joining us. (electronic music)

>> Hi, I'm Peter Burris, Welcome to Wikibon's Action Item. (slow techno music) Once again Wikibon's research team is assembled, centered here in The Cube Studios in lovely Palo Alto, California, so I've got David Floyer and George Gilbert with me here in the studio, on the line we have Neil Raden and Jim Kobielus, thank you once again for joining us guys. This week we are going to talk about an issue that has been dominant consideration in the industry, but it's unclear exactly what direction it's going to take, and that is the role that open source is going to play in the next generation of solving problems with technology, or we could say the role that open source will play in future digital transformations. No one can argue whether or not open source has been hugely consequential, as I said it has been, it's been one of the major drivers of not only new approaches to creating value, but also new types of solutions that actually are leading to many of the most successful technology implementations that we've seen ever, that is unlikely to change, but the question is what formal open source take as we move into an era where there's new classes of individuals creating value, like data scientists, where those new problems that we're trying to solve, like problems that are mainly driven by the role that data as opposed to code plays, and that there are new classes of providers, namely service providers as opposed to product or software providers, these issues are going to come together, and have some pretty important changes on how open source behaves over the next few years, what types of challenges it's going to successfully take on, and ultimately how users are going to be able to get value out of it. So to start the conversation off George, let's start by making a quick observation, what has the history of open source been, take us through it kind of quickly. >> The definition has changed, in its first incarnation it was fixed UNIX fragmentation and the high price of UNIX system servers, meaning UNIX the proprietary UNIX's and the proprietary servers they were built, that actually rather quickly morphed into a second incarnation where it was let's take the Linux stack, Linux, Apache, MySQL, PHP, Python, and substitute that for the old incumbents, which was UNIX, BEA Web Logic, the J2E server and Oracle Database on an EMC storage device. So that was the collapse of the price of infrastructure, so really quickly then it morphed into something very, very different, which was we had the growth of the giant Internet scale vendors, and neither on pricing nor on capacity could traditional software serve their needs, so Google didn't quite do open source, but they published papers about what they did, those papers then were implemented. >> Like Map Produce. Yeah Map Produce, Big Table, Google File System, those became the basis of Hadoop which Yahoo open sourced. There is another incarnation going, that's probably getting near its end of life right now, which is sort of a hybrid, where you might take Kafka which is open source, and put sort of proprietary bits around it for management and things like that, same what Cloudera, this is called the open core model, it's not clear if you can build a big company around it, but the principle is, the principle for most of these is, the value of the software is declining, partly because it's open source, and partly because it's so easy to build new software systems now, and the hard part is helping the customer run the stuff, and that's where some of these vendors are capturing it. >> So let's David turn our attention to how that's going to turn into actual money. So in this first generation of open source, I think up until now, certainly Red Hat, Canonical have made money by packaging and putting forward distributions, that have made a lot of money, IBM has been one of the leaders in contributing open source, and then turning that into a services business, Cloudera, Horton Works, NapR, some of these other companies have not generated the same type of market presence that a Red Hat or Canonical have put forward, but that doesn't mean there aren't companies out there that have been very successful at appropriating significant returns out of open source software, mainly however they're doing it as George said, as a service, give us some examples. >> I think the key part of open source is providing a win-win environment, so that people are paid to do stuff, and what is happening now a lot is that people are putting stuff into open source in order that it becomes a standard, and also in order that it is maintained by the community as a whole. So those two functions, those two capabilities of being paid by a company often, by IBM or by whoever it is to do something on behalf of that company, so that it becomes a standard, so that it becomes accepted, that is a good business model, in the sense that it's win-win, the developer gets recognition, the person paying for it achieves their business objective of for example getting a standard recognized-- >> A volume. >> Volume, yes. >> So it's a way to get to volume for the technology that you want to build your business around. >> Yes, what I think is far more difficult in this area is application type software, so where open source has been successful, as George said is in the stacks themselves, the lower end of the stacks, there are a few, and they usually come from very very successful applications like Word, Microsoft Word, or things like that where they can be copied, and be put into open source, but even there they have around them software from a company, Red Hat or whoever it is, that will make it successful. >> Yes but open office wasn't that successful, get to the kind of, today we have Amazon, we have some of the hyper scalars that are using that open core model and putting forward some pretty powerful services, is that the new Red Hat, is that the new Canonical? >> The person who's made most money is clearly Amazon, they took open source code and made it robust, and made it in volume, those are the two key things you to have for success, it's got to be robust, it's got to be in volume, and it's very difficult for the open source community to achieve that on its own, it needs the support of a large company to do that, and it needs the value that that large company is going to get from it, for them to put those resources in. So that has been a very successful model a lot of people decry it because they're not giving back, and there's an argument-- >> They being Amazon, have not given back quite as much. >> Yes they have relatively very few commiters. I think that's more of a problem in the T&Cs of the open source contract, so those should probably be changed, to put more onus on people to give back into the pool. >> So let me stop you, so we have identified one thing that is likely going to have to be evolved as we move forward, to prevent problems, some of the terms and conditions, we try to ensure that there is that quid pro quo, that that win-win exists. So Jim Kobielus, let me ask you a question, open source has been, as David mentioned, open source has been more successful where there is a clear model, a clear target of what the community is trying to build, it hasn't been quite successful, where it is in fact is expected that the open source community is going to start with some of the original designs, so for example, there's an enormous plethora of big data tools, and yet people are starting to ask why is big data more successful, and partly it's because putting these tools together is so difficult. So are we going to see the type of artifacts and assets and technologies associated with machine learning, AI, deep learning et cetera, easily lend themselves to an open source treatment, what do you think? >> I think were going to see open source very much take off in the niches of the deep learning and machine learning AI space, where the target capabilities we've built are fairly well understood by our broad community. Machine learning clearly, we have a fair number of frameworks that are already well established, with respect to the core capabilities that need to be performed from modeling and training, and deployment of statistical models into applications. That's where we see a fair amount of takeoff for Tensor Flow, which Google built in an open source, because the core of deep learning in terms of the algorithm, in terms of the kinds of functions you perform to be able to take data and do feature engineering and algorithm selection are fairly well understood, so those are the kinds of very discreet capabilities for which open source code is becoming standard, but there's many different alternative frameworks for doing that, Tensor Flow being one of them, that are jostling for presence in the market. The term is commoditized, more of those core capabilities are being commoditized by the fact that there well understood and agreed to by a broad community. So those are the discrete areas we're seeing the open source alternatives become predominant, but when you take a Tensor Flow and combine it with a Spark, and with a Hadoop and a Kafka and broader collections of capabilities that are needed for robust infrastructure, those are disparate communities that each have their own participants committed and so forth, nobody owns that overall step, there's no equivalent of a lamp stack were all things to do with deep learning machine learning AI on an open source basis come to the fore. If some group of companies is going to own that broadening stack, that would indicate some degree of maturation for this overall ecosystem, that's not happening yet, we don't see that happening right now. >> So Jim, I want to, my bias, I hate the term commoditization, but I Want to unify what you said with something that David said, essentially what we're talking about is the agreement in a collaborative open way around the conventions of how we perform work that compute model which then turns into products and technologies that can in fact be distributed and regarded as a standard, and regarded as a commodity around which trading can take place. But what about the data side of things George, we have got, Jim's articulated I think a pretty good case, that we're going to start seeing some tools in the marketplace, it's going to be interesting to see whether that is just further layering on top of all this craziness that is happening in the big data world, and just adding to it in the ML world, but how does the data fit into this, are we going to see something that looks like open source data in the marketplace? >> Yes, yes, and a modified yes. Let me take those in two pieces. Just to be slightly technical, hopefully not being too pedantic, software used to mean algorithms and data structures, so in other words the recipe for what to do, and the buckets for where to put the data, that has changed in the data in terms of machine learning, analytic world where the algorithms and data are so tied together, the instances of the data, not the buckets, that the data changed the algorithms, the algorithms change the data, the significance of that is, when we build applications now, it's never done, and so you go, the construct we've been focusing on is the digital twin, more broadly defined than a smart device, but when you go from one vendor and you sort of partially build it, it's an evergreen thing, it's never done, then you go to the next vendor, but you need to be able to backport some core of that to the original vendor, so for all intents and purposes that's open source, but it boils down to actually the original Berkeley license for open source, not the Apache one everyone is using now. And remind me of the other question? >> The other issue is are we going to see datasets become open source like we see code bases and code fragments and algorithms becoming open source? >> Yes this is also, just the way Amazon made infrastructure commoditized and rentable, there are going to be many datasets were they used to be proprietary, like a Google web crawl, and Google knowledge graph of disambiguation people, places and things, some of these things are either becoming open source, or openly accessible by API, so when you put those resources together you're seeing a massive deflation, or a massive shrinkage in the capital intensity of building these sorts of apps. >> So Neil, if we take a look at where we are this far, we can see that there is, even though we're moving to a services oriented model, Amazon for example is a company that is able to generate commercial rents out of open source software, Jim has made a pretty compelling case that open source software can be, or will emerge out of the tooling world for some of these new applications, there are going to be some examples of datasets, or at least APIs to datasets that will look more open source like, so it's not inconceivable that we'll see some actual open source data, I think GDPR, and some other regulations, we're still early in the process of figuring out how we're going to turn data into commodity, using Jim's words. But what about the personnel, what about the people? There were reasons why developers moved to open source, some of the soft reasons that motivated them to do things, who they work with, getting the recognition, working on relevant projects, working with relevant technologies, are we going to see a similar set of soft motivators, diffuse into the data scientist world, so that these individuals, the real ones who are creating the real value, are going to have some degree of motivation to participate with each other collaborate with each other in an open source way, what do you think? >> Good question, I think the answer is absolutely true, but it's not unique to data scientists, academics, scientists in molecular biology, civil engineers, they all wannabe recognized by their peers, on some level beyond just their, just what they're doing in their organization, but there is another segment of data scientists that are just guys working for a paycheck, and generating predictive analysis and helping the company along and so forth, and that's what they're going to do. The whole open source thing, you remember object programming, you remember JavaBeans, you remember Web Services, we tried to turn developers into librarians, and when they wanted to develop something, you go to Github, I go to Github right now and I say I'm looking for a utility that can figure out why my face is so pink on this camera, I get 1000 listings of programs, and have no idea which ones work and which ones don't, so I think the whole open source thing is about to explode, it already has, in terms of piece parts. But I think managing in an organization is different, and when I say an organization, there's the Googles and the Amazons and so forth of the world, and then there's everybody else. >> Alright so we've identified an area where we can see some consequence of change where we can anticipate some change will be required to modernize the open source model, the licensing model, we see another one where the open source communities going to have to understand how to move from a product and code to a data and service orientation, can we think of any others? >> There is one other that I'd like to add to that, and that is compliance. You addressed it to some extent, but compliance brings some real-world requirements onto code and data, and you were saying earlier on that one of the options is bringing code and data so that they intermingle and change each other, I wonder whether that when you look at it from a compliance point of view will actually pass muster, because you need from a compliance point of view to prove, for example, in the health service, that it works, and it works the same way every time, and if you've got a set of code and data that doesn't work the same every time, you probably are going to get pushed back from the people who regularly health that this is not, you can't do it that way, you'll have to find another way to do it. But that again is, is at the same each time, so the point I'm making-- >> This is a bigger issue than just open source, this is an issue where the idea if continuous refinement of the code, and the data-- >> Automatic refinement. >> Automatic refinement, could in fact, we're going to have to change some compliance laws, is open source, is it possible the open source community might actually help us understand that problem? >> Absolutely, yes. >> I think that's a good point, I think that's a really interesting point, because you're right George, the idea of a continuous development, is not something that for example Serr Banes actually says I get this, Serr Banes actually says "Oh yeah, I get this." Serr Banes actually is like, yes the data, I acknowledge that this date is right, and I acknowledge the process by which it was created was read, now this is another subject, let's bring this up later, but I think it's relevant here, because in many respects it's a difference between an income statement and balance sheet right? Saying it's good now, is kind of like the income statement, but let's come back to this, because I think it's a bigger issue. You're asserting the open source community in fact may help solve this problem by coming up with new ways of conceiving say versioning of things, and stamping things and what is a distribution, what isn't a distribution, with some of these more tightly bound sets of-- >> What we find normally is that-- >> Jim: I think that we are going to-- >> Peter: Go on Jim. >> Just to elaborate on what Peter was talking about, that whole theme, I think what we're going to see is more open source governance of models and data, within distributed development environments, using technologies like block chain as a core enabler for these workflows, for these as it were general distributed hyper ledgers indicate the latest and greatest version of a given dataset, or a given model being developed somewhere around some common solution domain, I think those kinds of environments for governance will become critically important, as this pipeline for development and training and deployment of these assets, gets ever more distributed and virtual. >> By the way Jim I actually had a conversation with a very large open source distribution company a few months ago about this very point, and I agree, I think blockchain in fact could become a mechanism by which we track intellectual property, track intellectual contributions, find ways to then monetize those contributions, going back to what you were saying David, and perhaps that becomes something that looks like the basis of a new business model, for how we think about how open source goes after these looser, goosier problems. >> But also to guarantee integrity without going through necessarily a central-- >> Very important, very important because at the end of the day George-- >> It's always hard to find somebody to maintain. >> Right, big companies, one of the big challenges that companies today are having is that they do open source is that they want to be able to keep track of their intellectual property, both from a contribution standpoint, but also inside their own business, because they're very, very concerned that the stuff that they're creating that's proprietary to their business in a digital sense, might leave the building, and that's not something a lot of banks for example want to see happen. >> I want to stick one step into this logic process that it think we haven't yet discussed, which is, we're talking about now how end customers will consume this, but there still a disconnect in terms of how the open source software vendor's or even hybrid ones can get to market with this stuff, because between open source pricing models and pricing levels, we've seen a slow motion price collapse, and the problem is that, the new go to market motion is actually made up of many motions, which is discover, learn, try, buy, recommend, and within each of those, the motion was different, and you hear it's almost like a reflex, like when your doctor hit you on the knee and your leg kind of bounced, everybody says yeah we do land and expand, and land was to discover, learn, try augmented with inside sales, the recommend and standardizes still traditional enterprise software where someone's got to talk to IT and procurement about fitting into the broader architecture, and infrastructure of the firm, and to do that you still need what has always been called the most expensive migratory workforce in the world, which is an enterprise sales force. >> But I would suggest there's a big move towards standardization of stacks, true private cloud is about having a stack which is well established, and the relationship between all the different piece parts, and the stack itself is the person who is responsible for putting that stack and maintaining that stack. >> So for a moment pretend that you are a CIO, are you going to buy OpenStack or are you going to buy the Vmware stack? >> I'm going to buy Vmware stack. >> Because that's about open source? >> No, the point I'm saying is that those open source communities or pieces, would then be absorbed into the stack as an OEM supplier as opposed to a direct supplier and I think that's true for all of these stacks, if you look at the stack for example and you have code from Netapp or whatever it is that's in that code and they're contributing It You need an OEM agreement with that provider, and it doesn't necessarily have to be open source. >> Bottom line is this stuff is still really, really complicated. >> But this model of being an OEM provider is very different from growing an enterprise sales force, you're selling something that goes into the cost of goods sold of your customer, and that the cost of goods sold better be less than 15 percent, and preferably less than five percent. >> Your point is if you can't afford a sales force, an OEM agreement is a much better way of doing it. >> You have to get somebody else's sales force to do it for you. So look I'm going to do the Action Item on this, I think that this has been a great conversation again, David, George, Neil, Jim, thanks a lot. So here's the Action Item, nobody argues that open source hasn't been important, and nobody suggests that open source is not going to remain important, what we think based on our conversation today is that open source is going to go through some changes, and those changes will occur as a consequence of new folks that are going to be important to this like data scientists, to some of the new streams of value in the industry, may not have the same motivations that the old developer world had, new types of problems that are inherently more data oriented as opposed process-oriented, and it's not as clear that the whole concept of data as an artifact, data as a convention, data as standards and commodities, are going to be as easy to define as it was in the cold world. As well as ultimately IT organizations increasingly moving towards an approach that focused more on the consumption of services, as opposed to the consumption of product, so for these and many other reasons, our expectation is that the open source community is going to go through its own transformation as it tries to support future digital transformations, current and future digital transformations. Now some of the areas that we think are going to be transformed, is we expect that there's going to be some pressure on licensing, we think there's going to be some pressure in how compliance is handled, and we think the open source community may in fact be able to help in that regard, and we think very importantly that there will be some pressure on the open source community trying to rationalize how it conceives of the new compute models, the new design models, because where open source always has been very successful is when we have a target we can collaborate to replicate and replace that target or provide a substitute. I think we can all agree that in 10 years we will be talking about how open source took some time to in fact put forward that TPC stack, as opposed to define the true private cloud stack. So our expectation is that open source is going to remain relevant, we think it's going to go through some consequential changes, and we look forward to working with our clients to help them navigate what some of those changes are, both as commiters, and also as consumers. Once again guys, thank you very much for this week's Action Item, this is Peter Barris, and until next week thank you very much for participating on Wikibon's Action Item. (slow techno music)