 Hi, welcome once again to Wikibon's weekly research meeting from the Cube's Palo Alto studios I'm Peter Burris, and we're being joined as always from by Wikibon's team of analysts including George Gilbert here in the studio with me On the phone we have David Floyer, Neil Raiden, and James Kabilis And today what we're going to do is we're going to talk about some of the lessons that we learned in 2008 or 2017 Over the course of the next month Wikibon is going to put a fair amount of research into making our annual predictions And this is the first step what lessons did we learn? What is working? What isn't working as a consequence of some of the things that were tried predictions that were made and Initiatives that haven't necessarily panned out now the reason we want to do this is not just to talk about technology But we're trying to bring the idea to those users out there who are in the midst of budgeting about where they Should continue to place bets and where they might want to start thinking about ratcheting down Things that don't seem to be panning out So there's a lot of ground to cover and let's get started and I want to want to start with you David Floyer So the first thing I think we've learned in 2017 is that the cloud is not going to be homogenous. Do you agree? Absolutely it's becoming very very heterogeneous We are brought into play the the concept of true private cloud and we're seeing that develop very strongly And we're predicting that again and the future will develop Server-san a completely different way of doing storage that's coming from the cloud from the hypervisor cloud into the private clouds and in general put into general purpose and We're seeing really some very big changes in How systems are going to be developed So with that as a basis for some of the kind of macro trends the idea that Business is not going to move to the cloud as we like to say the clouds going to move to business There are a number of applications that are driving some of these changes Neil I want to start with you one of them is big data or perhaps we should finally start calling it analytics What is it about analytics that is starting to catalyze a rethinking of? The overall architecture that we're going to use to sustain some of these digital business Changes that all companies all institutions face. I don't know how it started Peter People have been doing analytics for decades while corporate IT was more or less obsessed with operations But over the last five to ten in years analytics has just become the most important thing. Well, it's not a flip-flop The problem is the approach analytics has jumped from one thing to another so quickly I don't think that anyone has had a chance to really perfect their approach. We went from predictive analytics And then we went to a data science and big data and now everything is machine learning and artificial intelligence If I were inside an organization right now, you know, my head would be spinning So we have to we have to elucidate some some clear Directions for people about what works and what doesn't and what the level of effort in the end of spend is to get things done So George over to the face is it is it safe to say is it face to say is it safe to say at this point That the kind of general purpose notion of big data where you throw everything into a single store like a data lake And then you have everybody run around looking for data Is that starting to break down and become increasingly specialized is that kind of what we learned in 2017? I think it's safe to say that big data never really crossed the chasm It was its closest application to something that would be Appealing to mainstream customers was taking ETL and offloading it from a very expensive data warehouses But the the way the open source ecosystem principally with the Hadoop distributions that curated all these open source components the way it tried to attack that problem Was so complicated in terms of administrative demands that most customers choked on it So we're seeing increasing specialization in part because of the nature of the problems that people are trying to solve but also the complexity of The underlying solution so that leads to a third question And the third question is we talked about Cloud not being homogenous. We talked about big data becoming more specialized and solution oriented outcome oriented One of the other big drivers in all this David foyer is IOT We'll talk in a second about how IOT and analytics are going to come together, but what are we learning from IOT in 2017? What we're learning is that the edge is again not homogenous and It's much better to look at the break up the edge and break up IOT at the edge into a primary layer And this and the secondary layer the primary layer is the layer that is a solution Which takes the sensors takes equipment? takes AI technologies and bring them all together as a solution to a business problem and We believe that that is a much lower cost and Volume approach to the problem than everybody every I.T. Making their own Equipment in their own in their own Factories or entrances so the primary is the where most of the data is going to be generated and Also, where most of the gender data is going to be compressed down from You know, maybe as much as a million to one to into the secondary layer And that's the interface between the primary layer and the cloud Computing whether it be quite true private cloud or public cloud or any combinations of those That that's the tertiary layer and the secondary that will be that interface at the edge between the primary devices and The cloud computing that the rest of the enterprise is dependent upon so Jim can be a list We've got three lessons learned in the table cause on homogenous analytics increasingly going to be a feature of applications But that's going to require degree of retooling IOT is not going to be homogenous is going to drive new data sources And new opportunities to create value in bespoke ways Where are the developers and all this what do we learn or what are we learning as the developer as the developer committee Starts to try to Participate more in the process of creating new levels of digitally based value in business Right what we're going to need to what developers are learning and enterprises are learning is that their current group of core Developers are not prepared for the this AI at the edge revolution not prepared in terms of skills The tools that they're disposal the DevOps pipeline to workflows that are in place the teaming arrangements and collaborators those data lakes themselves Not prepared to do AI effectively and to drive it effectively to the edge where it can achieve the intended Results in terms of business value So what that means is in 2018 and beyond if you're an enterprise IT manager, you're an analytics manager Where do you place your budget is it skills up pretty higher the right people? Upraiser tools is somehow make do with the DevOps tools you have right now and bring more of the like for example Model governance over you know algorithms and deep learning machine learning models into the core You know governance structure you have You do your data lake idea have data lakes that can that are architected to handle machine data and great volume like Petabytes and exabytes of machine data generated by all these endpoints Okay, there's all these decisions that need to be made and there's money that needs to be spent to invest in In this entire development infrastructure ecosystem to really prepare yourself to to for the build these disruptive applications That might take your industry by storm this comes cheap so it seems like we're at a situation we're in a situation where the technology in many respects is Available to undertake and build and deploy and generate value out of some of these new classes of applications But skills are very very unevenly distributed Neil Raiden, let's talk a little bit about that. What what is the core skills challenge that businesses face today as they attempt to Explore new ways of solving problems with digitally related technologies I think that The software vendors are going to provide a tiered capability Just like we've seen in other kinds of analytical tools where you have a Small number of people at the top of the tier who have the background and the skill to understand whether this model was an appropriate Model or whether we found correlation that was spurious because they are all time series Or something like that and then you have a larger group of people who use these tools to draw the machine learning algorithm or like a company like data robot where It just runs 10 12 different algorithms that helps you find the best one and so forth But that doesn't mean that it's correct and that doesn't mean that those people Understand the statistics that are generated by the model that requires Governance of the people at the top of that tier and then of course there's the lower tier Which is how do you communicate to these people what you've done with these techniques? So this is a broad problem. It sounds like it sounds like we've got a skills deficit problem It's going to have far-reaching impacts. We'll talk more about this during the predictions But I think there's a one that's on everybody's mind right now Are we going to see? Specialist software and solution vendors emerge out of this to saw to start the process of at least Solving some of these problems and showing the industry how to go about it Or is this something at all large enterprises and mid-size enterprises are going to have to do on their own And they got to start throwing an enormous amount of money at these issues David Foyer give our CIOs a Kind of a vision of where they should be thinking right now about how to address the challenges of skills well the The big decision to make for enterprise For most enterprises is whether to the degree to which they should invest in their own solutions Their own AI solution or should they wait? until those solutions are included in ISV packages in general-purpose packages in packages they get from SAS vendors or or whatever and If you're a very large or enterprise and you can see a clear business differentiation Then clearly that investment can be justified, but I think for many enterprise CIOs they will sit back and wait and And see the degree to which they need to invest that doesn't mean to say that they shouldn't be seeing actively seeing What is available in the marketplace? But if they should be probably spending more time reaching out to potential Vendors with a solution who can generate a volume rather than trying to create snowflakes on their own So so before we get to the action item around Jim. I want to build on that very quickly So Dave's arguing essentially that it's we're moving into a buy versus build as we go through this transformation I think we all agree. That's where we are today Next question though is going to be buying software or is it going to be buying services or some combination of the two What do we learn 2017 about how the availability of increasingly advanced services? especially in the AR realm realm from some of the big cloud suppliers is Changing or altering the way businesses think about how they're going to generate value at these technologies Yeah, I think right now what we're seeing is the swing is toward buying services buying cloud services They have machine learning deep learning AI big in from you know, the usual suspects AWS Microsoft to lesser extent Google and IBM and so forth What we see right now in the whole developer wars that to win the hearts and minds of AI developers is Coming down to whose cloud are you gonna put your data in you can do your training model training and development and deployment Whose framework AWS is MXNAT? Microsoft's the NTK whatever Google's TensorFlow are you gonna use then those benders the solution providers in those frameworks provide Retrained models and all the you know, there's a lot of other capabilities to build out not only the models but to provide a DevOps pipeline for the data sizes to happen to Standardize on one solution providers environment or another George George Jim Jim, let me bring George and George. What do you have to say about I think we've seen this we've seen this movie before When enterprises started to build out their Applications at one point they were thinking of large enterprises custom data modeling how their entire enterprise Work and realize they didn't have the skills to do that. They bought packaged apps So I don't think the choice is binary between buying Services or buying apps. I think there's also Are we gonna wait for the install base of apps? the big vendors who've who've installed the the large horizontal apps to add Machine learning capabilities to those applications will we start to surround those legacy apps with more niche? packaged solutions and then the third one is will we see Vendors like IBM and maybe Accenture Which have a mix of services and some repeatable IP great So the one I'll add to this before we do the action on guys is I think one of the more important things that we're facing in the industry Right now is as it becomes evident per David's earlier point that the cause not going to be homogenous Are we moving into another round of platform wars where users have to be very very smart about What platform they choose yes, but increasingly having the options to do the appropriate level of integration Across whatever arrangement of cloud services on-premise true product cloud, etc Probably something a lesson that we've learned and one that our clients are increasingly tell us they have to focus on Okay action item around guys David Foyer want to talk with you David Foyer action item Yeah, I don't for me is actually in infrastructure. There is a tremendous opportunity evolving to develop be able to put applications with far more data onto the systems and Those are based on a change in architecture, which we're calling UniGrid, which is tripping away the storage and the networking completely from the Processes and being able to assemble systems. We should do things which are just unimaginable just five years ago George Gilbert action item. I'd go back to picking how you're going to divide your efforts among extending your existing package apps with machine learning capabilities And finding where the highest ROI areas for those are look at the emerging Sort of I would I don't want to say startups, but younger companies that are adding these complementary capabilities Okay, good next Jim Cabello's action item Yeah, well action that was explore the new generation of high-level Development abstraction framework for AI and deep learning like the new blue on framework that Microsoft and AWS released a couple of weeks ago That will enable the rest of us developers to be able to do deep learning AI development Using code and visual paradigms that they've grown to love and use in their core development Initiative new Raiden action item Title that maybe it doesn't deserve. It's not that complicated But more importantly it creates opportunities for organizations to do things that really can help them I think we've had too much time talking about AI and I think the average organization needs a Computer that thinks like a human being about as much as we need airplanes and flap their wings There's there's too much time on AI, which is a very essential area You know the facial recognition and all that other stuff That's going to be package things if you need it but companies don't need to worry about finding people who can develop that No need to anthropomorphize what does need to be anthropomorphized? Okay, so here's our here's our overall action item in 2017 or 2017 has been a year of significant success in the computing industry as Business increasingly woke up to the idea that the transformation of digital business is not just about taking cost out of IT It's about doing things differently and specifically doing more with data. We've seen a lot of leaders in this realm Companies that have been called digital natives have paved the way But a lot of other industries are now recognizing that the role of data as an asset is crucial to their future And they want to find ways of appropriating that In particular, we think that there are three lessons that have been learned at the technology level Lesson number one the cloud is not going to be homogenous The cloud is going to be a combination of technologies each optimized to handle data as It pertains to particular uses application forms and workloads in the unnatural and appropriate way Data will drive workload will drive cloud implementation Number two is that one of the key issues or one of the key areas of that changes the transformation from big data Concepts to analytic practicalities We've got years of working with analytics the technologies improving the hardware is improving and now we can apply it new and interesting ways and very importantly that includes Applying it to existing legacy applications to extend their useful life as well a lot of it's going to go on into this But the good news ultimately is the technology is becoming increasingly usable and increasingly useful to business Third the IOT or Internet of Things is going to have an enormous consequence in how we consider the arrangement of IT assets IT investments and IT personnel and our expectation ultimately is that that will continue to be a Crucial determinant of the decisions that ultimately get made if success is a criteria because our observation is yes Software is going to eat the world, but it's going to eat it at the edge The last point that we want to make here ultimately is that a lot of IT organizations have to fess up to the reality that they're not Skilled to do a lot of these things. They're not skilled to fully support the business's needs in these transformations We are no longer in control of the speed of Transformation in our industries that's being set by our competitors who may be better or worse than us at Introducing some of these new technologies and taking advantage of them and introducing new business model and customer experience capabilities as a consequence there's going to be a new round of value being created by solution providers utilizing different cloud options different IOT options and different AI options in response to expertise about how those solutions need to be deployed and IT has to accept that Sooner rather than later and start the process of establishing the frameworks for strategic Management of those suppliers so they can appropriately weave them into the business in ways that serve the business's long-term needs It's going to be a buy versus build world for the next few years and significant emphasis on buying services Which will have dramatic Changes of dramatic consequences for the types of partnerships that we put in place Once again, this has been the wikibon research meeting. I want to thank everybody on the analyst team for participating We're far flung this week roll over the place We're in a lot with a lot of different Conversations are a lot of different conferences. So thanks everybody for participating and we hope to see you next week From or here at the wikibon weekly research meeting from our from the Cube studios in Palo Alto, California