 Why don't I spend just a couple minutes talking about what we mean by digital enactment, turning data into models and models into action. And then we'll jump directly into, I'll introduce the panelists after that, and we'll jump directly into the questions. So Wikibon SiliconANGLE has been on a mission for quite some time now to really understand what is the nature of digital transformation or digital disruption. And historically when we've talked about digital, people talk about a variety of different characteristics of it. So we'll talk about new types of channels and activity on the web and any number of other things. But to really make sense of this, we kind of felt that we had to go to a set of basic principles and utilize those basic principles to build our observations up. So what we started with is a simple observation that if it's not digital, if it's not data, it ain't digital. By that we mean fundamentally the idea of digital business is how are we going to use data as an asset to differentially drive our business forward. And if we borrowed from Drucker, Drucker used to like to talk about the idea that business exists to create sustained customers. And so we would say that digital business is about applying data assets to differentially create sustained customers. Now to do that successfully, we have to be able to, as businesses, be able to establish a set of strategic business capabilities that will allow us to differentially use data assets. And we think that there are a couple of core strategic business capabilities required. One is human beings and most businesses operate in the analog world. So it's how do we take that analog data and turn it into digital data that we can then process. So that's the first one, that notion of IOT as a transducer of information so that we can generate these very rich data streams. Secondly, we have to be able to do something with those data streams and that's the basis of big data. So we utilize big data to create models, to create insights, and increasingly through a more declarative style, actually create new types of software systems that will be crucial to driving the business forward. That's a second capability. The third capability is one that we're still coming to understand and that is we have to take the output of those models, the output of those insights, and then turn them back into some event that has a consequential moment in the real world or what we call systems of an action. And so the three core business capabilities that have to be built are this capture data through IOT, big data to process it, systems of an action, also through IOT through actuators to actually then have a consequential action in the real world. So that's the basis of what we're talking about. We're going to take Fabio's vision that he just laid out and then we're in this panel going to talk about some of the business capabilities necessary to make that happen. And then after this, David Foyer will lead a panel on specifically some of the lower-level technologies that are going to make it work. Make sense, guys? Okay. So let me introduce the panelists. Over down there on the end, Ted Connell. Ted is from Intel. I don't know if we can get the slide up that has their names and their titles. Ted, why don't you very quickly introduce yourself? Yeah. Thank you very much. I run Solution Architecture for the Manufacturing and Industrial Vertical where we put together end-to-end ecosystem solutions that solve our clients' business problems. So we're not selling silicon or semiconductors. We're solving our clients' problems, which, as Fabio said, requires ecosystem solutions of software, systems integrators, and other partners to come together to put together end-to-end solutions. Excellent. Next to Ted is Steve Madden of Equinix. Hello. Yeah. Steve Madden. Equinix is the largest global interconnection company, and a lot of the ecosystems that you will be hearing about come together inside our locations. And one of the things I do in there is work with our big customers on industry vertical level solutions, IIT being one of them. Fu Huang from DataTorrent. Hi. My name is Fu Huang. I'm co-founder and chief strategy of a company called DataTorrent. And DataTorrent, our mission is really to build out solutions to allow enterprises to process big data in a streaming fashion. So that whole theme around ingestion, transformation, analytics, and taking action in sub-second on massive data is what we're focusing on. And you're familiar with Fabio. Fabio, take a second to... Yes. Thank you. I am leading a company that is trying to manifest the vision I highlighted here, building a platform. Not so much the applications. We are hosting the applications, the intelligence, the data management, and so forth. And trying to apply it to the industrial vertical first, big enough to keep us busy for quite a while. So in case you didn't notice, we have an interesting panel. We have use case, application, technology, infrastructure, and platform. So what we'll try to do is over the next, say, 10 minutes or so, we're going to spend a little bit of time, again, talking about some of these business capabilities. So let me start off by asking each of you a question. And I will take... If anybody has... It's really burning to ask a question, raise your hand, I'll do my best to see you. And I'll share the microphone for just long enough for you to ask. Okay. So first question, digital business is data. That means we have to think about data differently. Ted at Intel, what is Intel doing when they think about data as an asset? So Intel has been working on what is now being called Fog and Big Data Analytics for over a generation. The modern Xeon server we're selling, the wire in the electronics, if you will, is 10 silicon atoms wide. So to control that process, we've had to do what is called Industry 4.0 20 years ago. Our production equipment has been connected for 20 years. We're running... One of our factories will produce a petabyte of data a day, and we're running Big Data Analytics including machine learning on the stuff currently. If you look at an Intel factory, we have 2,000 thick clients on the factory floor supported by 600 servers in our data center at the factory just to control the process and run predictive yield analytics. So that's your itch? Our competitive advantage at Intel is the factory. We are a manufacturer, we're a world class manufacturer. Our front end factories have zero people in it. Not that we don't like people, but we had to fully automate the factory because as I speak, tens of thousands of water molecules are leaving my mouth, and if one of those water molecules lands on the silicon, it ain't going to work. So we had to get people physically out of the factory. And so we were forced by Moore's law and the product we build to build out what became Fog when they came up with the term seven years ago. We just came to that conclusion because of cost, latency, and security. It made sense to look, you got data, you got compute. There's a network between. It doesn't matter where you do the compute. Bring the compute to the data or the data to the compute. You're doing a compute function. It doesn't matter where you do it. So Fog is not complicated. It's just a distributed data center. So when we think about some of the technologies necessary to make this work, it's not just batch. We're going to be doing a lot of stuff in real time continuously. So Fog, talk a little bit about the system software, the infrastructure software that has to be put in place to ensure that this works well. I think that's great. You know, a little bit about our background. The company was founded by a bunch of ex-yahoo's that have been at yahoo for, you know, 12, 15 years from the early days. So we sort of grew up in that period where we had to learn about big data, learn about making all the mistakes of big data. And really seeing that nowadays it's not good enough to get insight. You have to get insight in a timely fashion enough to actually do something about it. And for a lot of enterprise, especially with human beings carrying around mobile phones and moving around all over the place and sensors sending thousands if not millions of events per second. The need for the business to understand what's going on and have insight and react sub-second is crucial. And what that means is the stuff that used to be batch offline, you know, can kind of kind of go down now has to be continuous, 24 by 7. You can't lose data. You've got to be a recover and come back to where you were as if nothing has happened with no human intervention. There's a lot of theme around no human intervention because this stuff is so fast, you can't involve human beings in it. Then you're not reacting fast enough. Can I real quickly add one thing? We think of data at Intel in half-life terms. The data has valuable right now. If you wait a second, literally a second, the data has a little bit of value. You wait two seconds. It's historical data. You can run regressions and tell you why you screwed up, but you ain't going to fix anything. If you want to do anything with your data, you've got to do it now. Ultimately, we need to develop experience, create experience about what we're doing, and the stuff that we're doing in applications will eventually find itself into platforms. So, Flavia, talk to us a little bit about the types of things that are going to end up in the platform to ensure that these use cases are made available to certainly businesses that perhaps aren't as sophisticated as Intel. Yes. In many ways, we are learning from what is going on in the cloud and has to come through this continuum all the way into the machines. This break between what's going on inside the machine and all the 1980 microprocessor and the server and the cloud server with the virtualization on the other side cannot leave. So, it has to be a continuum of computing so you can move the same function, the same container all the way through. First, second, you really have to take the real time very, very seriously, particularly at the edge, but even in the back. So that when you have this end-to-end continuum, you can decide where you do what. And I think that one of the models that was in that picture with the concentric circle is really telling what. We need to learn first, bring the data back and learn, and that can take time. But then you can have models that are lightweight that can be brought down to the front and impact the reaction to the data there. And we heard from a car company, a big car company, how powerful this was when they learned that the angle of a screwdriver and a few other parameters can determine the success of screwing something into a body of a car that could go well or could go very, very badly and be very costly. So all the learning, massive data can come down to a simple model that can save a lot of money and improve efficiency. But that has to be hosted along this continuum. So from the continuum it means we still have to have machines somewhere to do something. So Steve- Touching the ground, touching the physical world requires machines, actuators. So Steve, what is Equinix doing to simplify the thinking through of some of these infrastructure issues? Yeah, I mean the biggest thing that people find when they start looking at millions of devices, millions of data capture points, transferring all this data, real time and streaming it, is one thing hasn't changed and that's physics. So where those things are, where they need to go, where the data needs to move to and how fast starts with having to figure out your own topology of how are you moving that data. As much as it's easy to say we're just going to buy a platform and choose a device and we'll link them together, there's still a lot of other things that need to be solved. Physics being the first one, the second one primarily is volumes. So how much bandwidth and throughput are you going to require? How much of that data are you going to backhaul to your centralized data center before you send it up to a cloud? How much of it are you going to leave at the edge? Where do you place that? Sometimes it's a bigger deal and the third one is pretty much every industry has to deal with regulations. Regulations control what you can and can't do in terms of IT delivery, where you can place stuff, where you can not place stuff, data that can leave the country, data that can't. So all these things mean that you need to have a thought through process of where you're placing certain functions and what you're defining as your edge between the digital and physical world and Equinex is an interconnection company that's sitting there as a neutral party across all the networks, all the clouds, all the enterprises, all the providers to help people figure that out. So before I ask the audience a question, now that I'm down here so I can see you, so be prepared. I'm going to ask some of you a question. When you think about the strategic business capabilities necessary to succeed, what is the first thing that the business has to do? So why don't just take Ted and just go right on down the line. Yeah, so I think this is really, really important. I work with many, many clients around the world who are doing five, ten, fifteen POCs, pilots and the Internet of Things and they haven't thought through a codified strategy. So they're doing five things that will never fit together, that you will never scale and the learnings you're using you really can't do that much with. So coming up with what is my architecture? What is my stack going to look like? How am I going to push data? What is my data, you know, because when you connect to these things, I can't tell you how much data you're going to get. You're going to be overwhelmed by the data and that's why we all go to the edge. And I got to process data real time. And oh, by the way, if I only have one source of data like I'm connecting to production equipment, you're not going to learn anything. 98% of data is useless. You've got to contextualize the data with either an inspection step or some kind of contextualization that tells you if this then that. You need that then that. Without that, your data is basically worthless. So now you're pooling multiple sources of data together in real time to make an understanding. And so understanding what that architecture looks like, spend the time up front. Look, most of us are engineers. You know, 5% additional work up front saves you 95% on the back end. That's true here. So think through the architecture, talk to some of us who have been working in this area for a long time and we'll share our architecture. We have reference architecture that we're working with companies. How do you go from industry 2.0 or industry 3.0 to industry 4.0? And there is a logical path to do it. But ultimately, where we're going to end up is a software-defined universe. I mean, what's a cloud? It's a software-defined data center. Now we're doing software-defined networks, software-defined storages. Ultimately, we're going to be doing software-defined systems because it's cheaper. You get better capital utilization, better asset utilization. So we will go there. So what does that mean for your infrastructure? And what are you going to do for an architecture perspective? Then take all of your POCs and pilots and force them to do that, specifically around security. People are doing POCs with security that they don't even have any protocols. They're violating all their industry standards doing POCs. And that's going to get thrown out. It's wasted time, wasted effort. Don't do it. Steve, a couple of sentences. Yeah. Essentially, it's not going to be any prizes for me saying think into connection first. A lot of our customers, if we look at what they've done with us, everyone from GE to real-time facial recognition at the edge, it all comes down to how are you wired, topology-wise, first. You can't use the internet for risk reasons. You can't necessarily pay for multiple MPLS, bandwidth costs, et cetera. So low latency, 80% low latency, seven times the bandwidth at half the cost is a scalable infrastructure to move data around the planet. And if you don't have that, the rest of the stuff starts to break down. Who? And I would say that analytics is hard. Analytics in real-time is even harder. And I think with us talking to our customers, I feel for them. They're confused. There's like a million solutions out there. Everybody's trying to claim to do the same thing. I think it's both sides. Consumers have to get more educated. They've got to be more intelligent about their POCs. But as an industry, we also have to get better at thinking about how do we help our customers succeed? It's not about, oh, let me give you some open source, and then let me spend the next 10 months charging you professional services to help you. We got to think about software tools and enterprise tools to really help the customer be able to think about their total cost of ownership and time to value to handle this thing, because it's not easy. We are facing an interesting situation where the customers are ready. The needs are there. The market is going to be huge. But the plot, the solution is not trivial. It is maturing. And we are all trying to understand how to do it. And this is the confusion that you see in many of these half-baked solutions, the security and everything is coming together. And you have to go up the stack and down the stack with full confidence. Not easy. So we all have to really work together, give ourselves time, be feeling that we are in a pre-competitive world, preparing for addressing together a huge market, and trying to mature these solutions that then will be replicated more and more. But we have to be patient with each other and with the technologies that are maturing and they are not fully there and understood. But the market is amazing. So we have a Twitter question. I was being live-streamed but the audience is really engaged online as well, digital. So we have a question from Twitter from Lauren Cooney saying, would like to know what industries will be most impacted with digitization of the next five years? Which one won't be? Yeah. Exactly. All of them. I mean, we have seen, you know, the business model is the data. I mean, we are, you know, our CEO is calling data the new gold. I mean, it is the new oil. I don't know of anything unless you are doing something that is just physical therapy. But that even data. You could do data on that. Yeah. Everything. Yeah. I don't know of anything that won't be. I think the real question is how is it going to move through industries? Yeah. Obviously it is going to start with some of the digital native. It is already deep into that. Deep into media. We are moving through the media right now. Intel is clearly a digital company and you have been working on this path for quite some time. Let me give you a stat. Intel has 105,000 people and 144,000 servers. So we are about 1.5 server to people. That is what kind of computation we are doing. We can help you work on that. But you are right though, networking started by scaling out the internet, then content delivery and media, you know, hard media, et cetera is gone. Financial services and trading exchanges pretty much show what digital markets are going to be in the future. Clouds showed up and now I think he is right. It is affecting every industry, manufacturing, industrial health and professional services are the top three right now. But people who show up to ask for help from every industry and every country for that matter. Our customers are, you know, the top players in almost every vertical. And then, you know, you start out as a small company thinking that you are going to attack one vertical. But as you start to talk about the capability, everybody thinks, wait, you are solving my problem. Leaders are followers. What business would say, hey, I don't want to know what is going on with my business and I don't want to take any action. Add to that that it is an ecosystem of ecosystems. No one by themselves is going to solve anything. They have to partner and connect with other people to solve the solution. So I will close the panel by making these kind of summary comments. The business capabilities that we think are going to be most important are first off, when we talk about the internet of things, we like to talk about the internet of things in people, that the people equation doesn't go away. So we are building on mobile, we are building on other things. But if there is a strategic capability that is going to be required, it is going to be how is this going to impact folks who actually create value in the business. The second one I will turn around is that IT organizations have gone through a number of different range wars, if you will, over the past 20 years. I have lived through IT versus telecom, for example. The IT-OT conflict, or potential conflict, is not trivial. There is going to be some serious work that has to be done. So I would add to the conversation that we have heard this far, the answers that we have heard this far, is the degree to which people are going to be essential to making this work and how we diffuse this knowledge into our employees and into our IT and professional communities is going to be crucial, especially with developers. Because Flavio, if we are right now trying to figure stuff out, it really matures when we think about the developer world. So I want to close the first panel and get ready for the second panel. So thank you very much, and thank you very much to our panelists. And if we could bring David Foyer and the second panel up, we will get going on panel two. Oh, we are going to get together for a picture.