(inspirational music) (music ends) >> Okay, so now that we've heard Scott talk about smart data fabrics, it's time to see this in action. Right now we're joined by Jess Jowdy, who's the manager of Healthcare Field Engineering at InterSystems. She's going to give a demo of how smart data fabrics actually work, and she's going to show how embedding a wide range of analytics capabilities, including data exploration business intelligence, natural language processing and machine learning directly within the fabric makes it faster and easier for organizations to gain new insights and power intelligence predictive and prescriptive services and applications. Now, according to InterSystems, smart data fabrics are applicable across many industries from financial services to supply chain to healthcare and more. Jess today is going to be speaking through the lens of a healthcare focused demo. Don't worry, Joe Lichtenberg will get into some of the other use cases that you're probably interested in hearing about. That will be in our third segment, but for now let's turn it over to Jess. Jess, good to see you. >> Hi, yeah, thank you so much for having me. And so for this demo, we're really going to be bucketing these features of a smart data fabric into four different segments. We're going to be dealing with connections, collections, refinements, and analysis. And so we'll see that throughout the demo as we go. So without further ado, let's just go ahead and jump into this demo, and you'll see my screen pop up here. I actually like to start at the end of the demo. So I like to begin by illustrating what an end user's going to see, and don't mind the screen 'cause I gave you a little sneak peek of what's about to happen. But essentially what I'm going to be doing is using Postman to simulate a call from an external application. So we talked about being in the healthcare industry. This could be, for instance, a mobile application that a patient is using to view an aggregated summary of information across that patient's continuity of care or some other kind of application. So we might be pulling information in this case from an electronic medical record. We might be grabbing clinical history from that. We might be grabbing clinical notes from a medical transcription software, or adverse reaction warnings from a clinical risk grouping application, and so much more. So I'm really going to be simulating a patient logging in on their phone and retrieving this information through this Postman call. So what I'm going to do is I'm just going to hit send, I've already preloaded everything here, and I'm going to be looking for information where the last name of this patient is Simmons, and their medical record number or their patient identifier in the system is 32345. And so as you can see, I have this single JSON payload that showed up here of, just, relevant clinical information for my patient whose last name is Simmons, all within a single response. So fantastic, right? Typically though, when we see responses that look like this there is an assumption that this service is interacting with a single backend system, and that single backend system is in charge of packaging that information up and returning it back to this caller. But in a smart data fabric architecture, we're able to expand the scope to handle information across different, in this case, clinical applications. So how did this actually happen? Let's peel back another layer and really take a look at what happened in the background. What you're looking at here is our mission control center for our smart data fabric. On the left we have our APIs that allow users to interact with particular services. On the right we have our connections to our different data silos. And in the middle here, we have our data fabric coordinator which is going to be in charge of this refinement and analysis, those key pieces of our smart data fabric. So let's look back and think about the example we just showed. I received an inbound request for information for a patient whose last name is Simmons. My end user is requesting to connect to that service, and that's happening here at my patient data retrieval API location. Users can define any number of different services and APIs depending on their use cases. And to that end, we do also support full life cycle API management within this platform. When you're dealing with APIs, I always like to make a little shout out on this, that you really want to make sure you have enough, like a granular enough security model to handle and limit which APIs and which services a consumer can interact with. In this IRIS platform, which we're talking about today we have a very granular role-based security model that allows you to handle that, but it's really important in a smart data fabric to consider who's accessing your data and in what context. >> Can I just interrupt you for a second, Jess? >> Yeah, please. >> So you were showing on the left hand side of the demo a couple of APIs. I presume that can be a very long list. I mean, what do you see as typical? >> I mean you could have hundreds of these APIs depending on what services an organization is serving up for their consumers. So yeah, we've seen hundreds of these services listed here. >> So my question is, obviously security is critical in the healthcare industry, and API securities are like, really hot topic these days. How do you deal with that? >> Yeah, and I think API security is interesting 'cause it can happen at so many layers. So, there's interactions with the API itself. So can I even see this API and leverage it? And then within an API call, you then have to deal with all right, which end points or what kind of interactions within that API am I allowed to do? What data am I getting back? And with healthcare data, the whole idea of consent to see certain pieces of data is critical. So, the way that we handle that is, like I said, same thing at different layers. There is access to a particular API, which can happen within the IRIS product, and also we see it happening with an API management layer, which has become a really hot topic with a lot of organizations. And then when it comes to data security, that really happens under the hood within your smart data fabric. So, that role-based access control becomes very important in assigning, you know, roles and permissions to certain pieces of information. Getting that granular becomes the cornerstone of the security. >> And that's been designed in, it's not a bolt on as they like to say. >> Absolutely. >> Okay, can we get into collect now? >> Of course, we're going to move on to the collection piece at this point in time, which involves pulling information from each of my different data silos to create an overall aggregated record. So commonly, each data source requires a different method for establishing connections and collecting this information. So for instance, interactions with an EMR may require leveraging a standard healthcare messaging format like Fire. Interactions with a homegrown enterprise data warehouse for instance, may use SQL. For a cloud-based solutions managed by a vendor, they may only allow you to use web service calls to pull data. So it's really important that your data fabric platform that you're using has the flexibility to connect to all of these different systems and applications. And I'm about to log out, so I'm going to (chuckles) keep my session going here. So therefore it's incredibly important that your data fabric has the flexibility to connect to all these different kinds of applications and data sources, and all these different kinds of formats and over all of these different kinds of protocols. So let's think back on our example here. I had four different applications that I was requesting information for to create that payload that we saw initially. Those are listed here under this operations section. So these are going out and connecting to downstream systems to pull information into my smart data fabric. What's great about the IRIS platform is, it has an embedded interoperability platform. So there's all of these native adapters that can support these common connections that we see for different kinds of applications. So using REST, or SOAP, or SQL, or FTP, regardless of that protocol, there's an adapter to help you work with that. And we also think of the types of formats that we typically see data coming in as in healthcare we have HL7, we have Fire, we have CCDs, across the industry, JSON is, you know, really hitting a market strong now, and XML payloads, flat files. We need to be able to handle all of these different kinds of formats over these different kinds of protocols. So to illustrate that, if I click through these when I select a particular connection on the right side panel, I'm going to see the different settings that are associated with that particular connection that allows me to collect information back into my smart data fabric. In this scenario, my connection to my chart script application in this example, communicates over a SOAP connection. When I'm grabbing information from my clinical risk grouping application I'm using a SQL based connection. When I'm connecting to my EMR, I'm leveraging a standard healthcare messaging format known as Fire, which is a REST based protocol. And then when I'm working with my health record management system, I'm leveraging a standard HTTP adapter. So you can see how we can be flexible when dealing with these different kinds of applications and systems. And then it becomes important to be able to validate that you've established those connections correctly, and be able to do it in a reliable and quick way. Because if you think about it, you could have hundreds of these different kinds of applications built out and you want to make sure that you're maintaining and understanding those connections. So I can actually go ahead and test one of these applications and put in, for instance my patient's last name and their MRN, and make sure that I'm actually getting data back from that system. So it's a nice little sanity check as we're building out that data fabric to ensure that we're able to establish these connections appropriately. So turnkey adapters are fantastic, as you can see we're leveraging them all here, but sometimes these connections are going to require going one step further and building something really specific for an application. So why don't we go one step further here and talk about doing something custom or doing something innovative. And so it's important for users to have the ability to develop and go beyond what's an out-of-the box or black box approach to be able to develop things that are specific to their data fabric, or specific to their particular connection. In this scenario, the IRIS data platform gives users access to the entire underlying code base. So you not only get an opportunity to view how we're establishing these connections or how we're building out these processes, but you have the opportunity to inject your own kind of processing, your own kinds of pipelines into this. So as an example, you can leverage any number of different programming languages right within this pipeline. And so I went ahead and I injected Python. So Python is a very up and coming language, right? We see more and more developers turning towards Python to do their development. So it's important that your data fabric supports those kinds of developers and users that have standardized on these kinds of programming languages. This particular script here, as you can see actually calls out to our turnkey adapters. So we see a combination of out-of-the-box code that is provided in this data fabric platform from IRIS, combined with organization specific or user specific customizations that are included in this Python method. So it's a nice little combination of how do we bring the developer experience in and mix it with out-of-the-box capabilities that we can provide in a smart data fabric. >> Wow. >> Yeah, I'll pause. (laughs) >> It's a lot here. You know, actually- >> I can pause. >> If I could, if we just want to sort of play that back. So we went to the connect and the collect phase. >> Yes, we're going into refine. So it's a good place to stop. >> So before we get there, so we heard a lot about fine grain security, which is crucial. We heard a lot about different data types, multiple formats. You've got, you know, the ability to bring in different dev tools. We heard about Fire, which of course big in healthcare. And that's the standard, and then SQL for traditional kind of structured data, and then web services like HTTP you mentioned. And so you have a rich collection of capabilities within this single platform. >> Absolutely. And I think that's really important when you're dealing with a smart data fabric because what you're effectively doing is you're consolidating all of your processing, all of your collection, into a single platform. So that platform needs to be able to handle any number of different kinds of scenarios and technical challenges. So you've got to pack that platform with as many of these features as you can to consolidate that processing. >> All right, so now we're going into refinement. >> We're going into refinement. Exciting. (chuckles) So how do we actually do refinement? Where does refinement happen? And how does this whole thing end up being performant? Well the key to all of that is this SDF coordinator, or stands for Smart Data Fabric coordinator. And what this particular process is doing is essentially orchestrating all of these calls to all of these different downstream systems. It's aggregating, it's collecting that information, it's aggregating it, and it's refining it into that single payload that we saw get returned to the user. So really this coordinator is the main event when it comes to our data fabric. And in the IRIS platform we actually allow users to build these coordinators using web-based tool sets to make it intuitive. So we can take a sneak peek at what that looks like. And as you can see, it follows a flow chart like structure. So there's a start, there is an end, and then there are these different arrows that point to different activities throughout the business process. And so there's all these different actions that are being taken within our coordinator. You can see an action for each of the calls to each of our different data sources to go retrieve information. And then we also have the sync call at the end that is in charge of essentially making sure that all of those responses come back before we package them together and send them out. So this becomes really crucial when we're creating that data fabric. And you know, this is a very simple data fabric example where we're just grabbing data and we're consolidating it together. But you can have really complex orchestrators and coordinators that do any number of different things. So for instance, I could inject SQL logic into this or SQL code, I can have conditional logic, I can do looping, I can do error trapping and handling. So we're talking about a whole number of different features that can be included in this coordinator. So like I said, we have a really very simple process here that's just calling out, grabbing all those different data elements from all those different data sources and consolidating it. We'll look back at this coordinator in a second when we introduce, or we make this data fabric a bit smarter, and we start introducing that analytics piece to it. So this is in charge of the refinement. And so at this point in time we've looked at connections, collections, and refinements. And just to summarize what we've seen 'cause I always like to go back and take a look at everything that we've seen. We have our initial API connection, we have our connections to our individual data sources and we have our coordinators there in the middle that are in charge of collecting the data and refining it into a single payload. As you can imagine, there's a lot going on behind the scenes of a smart data fabric, right? There's all these different processes that are interacting. So it's really important that your smart data fabric platform has really good traceability, really good logging, 'cause you need to be able to know, you know, if there was an issue, where did that issue happen in which connected process, and how did it affect the other processes that are related to it? In IRIS, we have this concept called a visual trace. And what our clients use this for is basically to be able to step through the entire history of a request from when it initially came into the smart data fabric, to when data was sent back out from that smart data fabric. So I didn't record the time, but I bet if you recorded the time it was this time that we sent that request in and you can see my patient's name and their medical record number here, and you can see that that instigated four different calls to four different systems, and they're represented by these arrows going out. So we sent something to chart script, to our health record management system, to our clinical risk grouping application, into my EMR through their Fire server. So every request, every outbound application gets a request and we pull back all of those individual pieces of information from all of those different systems, and we bundle them together. And from my Fire lovers, here's our Fire bundle that we got back from our Fire server. So this is a really good way of being able to validate that I am appropriately grabbing the data from all these different applications and then ultimately consolidating it into one payload. Now we change this into a JSON format before we deliver it, but this is those data elements brought together. And this screen would also be used for being able to see things like error trapping, or errors that were thrown, alerts, warnings, developers might put log statements in just to validate that certain pieces of code are executing. So this really becomes the one stop shop for understanding what's happening behind the scenes with your data fabric. >> Sure, who did what when where, what did the machine do what went wrong, and where did that go wrong? Right at your fingertips. >> Right. And I'm a visual person so a bunch of log files to me is not the most helpful. While being able to see this happened at this time in this location, gives me that understanding I need to actually troubleshoot a problem. >> This business orchestration piece, can you say a little bit more about that? How people are using it? What's the business impact of the business orchestration? >> The business orchestration, especially in the smart data fabric, is really that crucial part of being able to create a smart data fabric. So think of your business orchestrator as doing the heavy lifting of any kind of processing that involves data, right? It's bringing data in, it's analyzing that information it's transforming that data, in a format that your consumer's not going to understand. It's doing any additional injection of custom logic. So really your coordinator or that orchestrator that sits in the middle is the brains behind your smart data fabric. >> And this is available today? It all works? >> It's all available today. Yeah, it all works. And we have a number of clients that are using this technology to support these kinds of use cases. >> Awesome demo. Anything else you want to show us? >> Well, we can keep going. I have a lot to say, but really this is our data fabric. The core competency of IRIS is making it smart, right? So I won't spend too much time on this, but essentially if we go back to our coordinator here, we can see here's that original, that pipeline that we saw where we're pulling data from all these different systems and we're collecting it and we're sending it out. But then we see two more at the end here, which involves getting a readmission prediction and then returning a prediction. So we can not only deliver data back as part of a smart data fabric, but we can also deliver insights back to users and consumers based on data that we've aggregated as part of a smart data fabric. So in this scenario, we're actually taking all that data that we just looked at, and we're running it through a machine learning model that exists within the smart data fabric pipeline, and producing a readmission score to determine if this particular patient is at risk for readmission within the next 30 days. Which is a typical problem that we see in the healthcare space. So what's really exciting about what we're doing in the IRIS world, is we're bringing analytics close to the data with integrated ML. So in this scenario we're actually creating the model, training the model, and then executing the model directly within the IRIS platform. So there's no shuffling of data, there's no external connections to make this happen. And it doesn't really require having a PhD in data science to understand how to do that. It leverages all really basic SQL-like syntax to be able to construct and execute these predictions. So, it's going one step further than the traditional data fabric example to introduce this ability to define actionable insights to our users based on the data that we've brought together. >> Well that readmission probability is huge, right? Because it directly affects the cost for the provider and the patient, you know. So if you can anticipate the probability of readmission and either do things at that moment, or, you know, as an outpatient perhaps, to minimize the probability then that's huge. That drops right to the bottom line. >> Absolutely. And that really brings us from that data fabric to that smart data fabric at the end of the day, which is what makes this so exciting. >> Awesome demo. >> Thank you! >> Jess, are you cool if people want to get in touch with you? Can they do that? >> Oh yes, absolutely. So you can find me on LinkedIn, Jessica Jowdy, and we'd love to hear from you. I always love talking about this topic so we'd be happy to engage on that. >> Great stuff. Thank you Jessica, appreciate it. >> Thank you so much. >> Okay, don't go away because in the next segment, we're going to dig into the use cases where data fabric is driving business value. Stay right there. (inspirational music) (music fades)

>> Okay, so now that we've heard Scott talk about smart data fabrics, it's time to see this in action. Right now we're joined by Jess Jowdy, who's the manager of Healthcare Field Engineering at InterSystems. She's going to give a demo of how smart data fabrics actually work, and she's going to show how embedding a wide range of analytics capabilities including data exploration, business intelligence natural language processing, and machine learning directly within the fabric, makes it faster and easier for organizations to gain new insights and power intelligence, predictive and prescriptive services and applications. Now, according to InterSystems, smart data fabrics are applicable across many industries from financial services to supply chain to healthcare and more. Jess today is going to be speaking through the lens of a healthcare focused demo. Don't worry, Joe Lichtenberg will get into some of the other use cases that you're probably interested in hearing about. That will be in our third segment, but for now let's turn it over to Jess. Jess, good to see you. >> Hi. Yeah, thank you so much for having me. And so for this demo we're really going to be bucketing these features of a smart data fabric into four different segments. We're going to be dealing with connections, collections, refinements and analysis. And so we'll see that throughout the demo as we go. So without further ado, let's just go ahead and jump into this demo and you'll see my screen pop up here. I actually like to start at the end of the demo. So I like to begin by illustrating what an end user's going to see and don't mind the screen 'cause I gave you a little sneak peek of what's about to happen. But essentially what I'm going to be doing is using Postman to simulate a call from an external application. So we talked about being in the healthcare industry. This could be for instance, a mobile application that a patient is using to view an aggregated summary of information across that patient's continuity of care or some other kind of application. So we might be pulling information in this case from an electronic medical record. We might be grabbing clinical history from that. We might be grabbing clinical notes from a medical transcription software or adverse reaction warnings from a clinical risk grouping application and so much more. So I'm really going to be assimilating a patient logging on in on their phone and retrieving this information through this Postman call. So what I'm going to do is I'm just going to hit send, I've already preloaded everything here and I'm going to be looking for information where the last name of this patient is Simmons and their medical record number their patient identifier in the system is 32345. And so as you can see I have this single JSON payload that showed up here of just relevant clinical information for my patient whose last name is Simmons all within a single response. So fantastic, right? Typically though when we see responses that look like this there is an assumption that this service is interacting with a single backend system and that single backend system is in charge of packaging that information up and returning it back to this caller. But in a smart data fabric architecture we're able to expand the scope to handle information across different, in this case, clinical applications. So how did this actually happen? Let's peel back another layer and really take a look at what happened in the background. What you're looking at here is our mission control center for our smart data fabric. On the left we have our APIs that allow users to interact with particular services. On the right we have our connections to our different data silos. And in the middle here we have our data fabric coordinator which is going to be in charge of this refinement and analysis those key pieces of our smart data fabric. So let's look back and think about the example we just showed. I received an inbound request for information for a patient whose last name is Simmons. My end user is requesting to connect to that service and that's happening here at my patient data retrieval API location. Users can define any number of different services and APIs depending on their use cases. And to that end we do also support full lifecycle API management within this platform. When you're dealing with APIs I always like to make a little shout out on this that you really want to make sure you have enough like a granular enough security model to handle and limit which APIs and which services a consumer can interact with. In this IRIS platform, which we're talking about today we have a very granular role-based security model that allows you to handle that, but it's really important in a smart data fabric to consider who's accessing your data and in what contact. >> Can I just interrupt you for a second? >> Yeah, please. >> So you were showing on the left hand side of the demo a couple of APIs. I presume that can be a very long list. I mean, what do you see as typical? >> I mean you can have hundreds of these APIs depending on what services an organization is serving up for their consumers. So yeah, we've seen hundreds of these services listed here. >> So my question is, obviously security is critical in the healthcare industry and API securities are really hot topic these days. How do you deal with that? >> Yeah, and I think API security is interesting 'cause it can happen at so many layers. So there's interactions with the API itself. So can I even see this API and leverage it? And then within an API call, you then have to deal with all right, which end points or what kind of interactions within that API am I allowed to do? What data am I getting back? And with healthcare data, the whole idea of consent to see certain pieces of data is critical. So the way that we handle that is, like I said, same thing at different layers. There is access to a particular API, which can happen within the IRIS product and also we see it happening with an API management layer, which has become a really hot topic with a lot of organizations. And then when it comes to data security, that really happens under the hood within your smart data fabric. So that role-based access control becomes very important in assigning, you know, roles and permissions to certain pieces of information. Getting that granular becomes the cornerstone of security. >> And that's been designed in, >> Absolutely, yes. it's not a bolt-on as they like to say. Okay, can we get into collect now? >> Of course, we're going to move on to the collection piece at this point in time, which involves pulling information from each of my different data silos to create an overall aggregated record. So commonly each data source requires a different method for establishing connections and collecting this information. So for instance, interactions with an EMR may require leveraging a standard healthcare messaging format like FIRE, interactions with a homegrown enterprise data warehouse for instance may use SQL for a cloud-based solutions managed by a vendor. They may only allow you to use web service calls to pull data. So it's really important that your data fabric platform that you're using has the flexibility to connect to all of these different systems and and applications. And I'm about to log out so I'm going to keep my session going here. So therefore it's incredibly important that your data fabric has the flexibility to connect to all these different kinds of applications and data sources and all these different kinds of formats and over all of these different kinds of protocols. So let's think back on our example here. I had four different applications that I was requesting information for to create that payload that we saw initially. Those are listed here under this operations section. So these are going out and connecting to downstream systems to pull information into my smart data fabric. What's great about the IRIS platform is it has an embedded interoperability platform. So there's all of these native adapters that can support these common connections that we see for different kinds of applications. So using REST or SOAP or SQL or FTP regardless of that protocol there's an adapter to help you work with that. And we also think of the types of formats that we typically see data coming in as, in healthcare we have H7, we have FIRE we have CCDs across the industry. JSON is, you know, really hitting a market strong now and XML, payloads, flat files. We need to be able to handle all of these different kinds of formats over these different kinds of protocols. So to illustrate that, if I click through these when I select a particular connection on the right side panel I'm going to see the different settings that are associated with that particular connection that allows me to collect information back into my smart data fabric. In this scenario, my connection to my chart script application in this example communicates over a SOAP connection. When I'm grabbing information from my clinical risk grouping application I'm using a SQL based connection. When I'm connecting to my EMR I'm leveraging a standard healthcare messaging format known as FIRE, which is a rest based protocol. And then when I'm working with my health record management system I'm leveraging a standard HTTP adapter. So you can see how we can be flexible when dealing with these different kinds of applications and systems. And then it becomes important to be able to validate that you've established those connections correctly and be able to do it in a reliable and quick way. Because if you think about it, you could have hundreds of these different kinds of applications built out and you want to make sure that you're maintaining and understanding those connections. So I can actually go ahead and test one of these applications and put in, for instance my patient's last name and their MRN and make sure that I'm actually getting data back from that system. So it's a nice little sanity check as we're building out that data fabric to ensure that we're able to establish these connections appropriately. So turnkey adapters are fantastic, as you can see we're leveraging them all here, but sometimes these connections are going to require going one step further and building something really specific for an application. So let's, why don't we go one step further here and talk about doing something custom or doing something innovative. And so it's important for users to have the ability to develop and go beyond what's an out of the box or black box approach to be able to develop things that are specific to their data fabric or specific to their particular connection. In this scenario, the IRIS data platform gives users access to the entire underlying code base. So you cannot, you not only get an opportunity to view how we're establishing these connections or how we're building out these processes but you have the opportunity to inject your own kind of processing your own kinds of pipelines into this. So as an example, you can leverage any number of different programming languages right within this pipeline. And so I went ahead and I injected Python. So Python is a very up and coming language, right? We see more and more developers turning towards Python to do their development. So it's important that your data fabric supports those kinds of developers and users that have standardized on these kinds of programming languages. This particular script here, as you can see actually calls out to our turnkey adapters. So we see a combination of out of the box code that is provided in this data fabric platform from IRIS combined with organization specific or user specific customizations that are included in this Python method. So it's a nice little combination of how do we bring the developer experience in and mix it with out of the box capabilities that we can provide in a smart data fabric. >> Wow. >> Yeah, I'll pause. >> It's a lot here. You know, actually, if I could >> I can pause. >> If I just want to sort of play that back. So we went through the connect and the collect phase. >> And the collect, yes, we're going into refine. So it's a good place to stop. >> Yeah, so before we get there, so we heard a lot about fine grain security, which is crucial. We heard a lot about different data types, multiple formats. You've got, you know the ability to bring in different dev tools. We heard about FIRE, which of course big in healthcare. >> Absolutely. >> And that's the standard and then SQL for traditional kind of structured data and then web services like HTTP you mentioned. And so you have a rich collection of capabilities within this single platform. >> Absolutely, and I think that's really important when you're dealing with a smart data fabric because what you're effectively doing is you're consolidating all of your processing, all of your collection into a single platform. So that platform needs to be able to handle any number of different kinds of scenarios and technical challenges. So you've got to pack that platform with as many of these features as you can to consolidate that processing. >> All right, so now we're going into refine. >> We're going into refinement, exciting. So how do we actually do refinement? Where does refinement happen and how does this whole thing end up being performant? Well the key to all of that is this SDF coordinator or stands for smart data fabric coordinator. And what this particular process is doing is essentially orchestrating all of these calls to all of these different downstream systems. It's aggregating, it's collecting that information it's aggregating it and it's refining it into that single payload that we saw get returned to the user. So really this coordinator is the main event when it comes to our data fabric. And in the IRIS platform we actually allow users to build these coordinators using web-based tool sets to make it intuitive. So we can take a sneak peek at what that looks like and as you can see it follows a flow chart like structure. So there's a start, there is an end and then there are these different arrows that point to different activities throughout the business process. And so there's all these different actions that are being taken within our coordinator. You can see an action for each of the calls to each of our different data sources to go retrieve information. And then we also have the sync call at the end that is in charge of essentially making sure that all of those responses come back before we package them together and send them out. So this becomes really crucial when we're creating that data fabric. And you know, this is a very simple data fabric example where we're just grabbing data and we're consolidating it together. But you can have really complex orchestrators and coordinators that do any number of different things. So for instance, I could inject SQL Logic into this or SQL code, I can have conditional logic, I can do looping, I can do error trapping and handling. So we're talking about a whole number of different features that can be included in this coordinator. So like I said, we have a really very simple process here that's just calling out, grabbing all those different data elements from all those different data sources and consolidating it. We'll look back at this coordinator in a second when we introduce or we make this data fabric a bit smarter and we start introducing that analytics piece to it. So this is in charge of the refinement. And so at this point in time we've looked at connections, collections, and refinements. And just to summarize what we've seen 'cause I always like to go back and take a look at everything that we've seen. We have our initial API connection we have our connections to our individual data sources and we have our coordinators there in the middle that are in charge of collecting the data and refining it into a single payload. As you can imagine, there's a lot going on behind the scenes of a smart data fabric, right? There's all these different processes that are interacting. So it's really important that your smart data fabric platform has really good traceability, really good logging 'cause you need to be able to know, you know, if there was an issue, where did that issue happen, in which connected process and how did it affect the other processes that are related to it. In IRIS, we have this concept called a visual trace. And what our clients use this for is basically to be able to step through the entire history of a request from when it initially came into the smart data fabric to when data was sent back out from that smart data fabric. So I didn't record the time but I bet if you recorded the time it was this time that we sent that request in. And you can see my patient's name and their medical record number here and you can see that that instigated four different calls to four different systems and they're represented by these arrows going out. So we sent something to chart script to our health record management system, to our clinical risk grouping application into my EMR through their FIRE server. So every request, every outbound application gets a request and we pull back all of those individual pieces of information from all of those different systems and we bundle them together. And for my FIRE lovers, here's our FIRE bundle that we got back from our FIRE server. So this is a really good way of being able to validate that I am appropriately grabbing the data from all these different applications and then ultimately consolidating it into one payload. Now we change this into a JSON format before we deliver it, but this is those data elements brought together. And this screen would also be used for being able to see things like error trapping or errors that were thrown alerts, warnings, developers might put log statements in just to validate that certain pieces of code are executing. So this really becomes the one stop shop for understanding what's happening behind the scenes with your data fabric. >> Etcher, who did what, when, where what did the machine do? What went wrong and where did that go wrong? >> Exactly. >> Right in your fingertips. >> Right, and I'm a visual person so a bunch of log files to me is not the most helpful. Well, being able to see this happened at this time in this location gives me that understanding I need to actually troubleshoot a problem. >> This business orchestration piece, can you say a little bit more about that? How people are using it? What's the business impact of the business orchestration? >> The business orchestration, especially in the smart data fabric is really that crucial part of being able to create a smart data fabric. So think of your business orchestrator as doing the heavy lifting of any kind of processing that involves data, right? It's bringing data in, it's analyzing that information, it's transforming that data, in a format that your consumer's not going to understand it's doing any additional injection of custom logic. So really your coordinator or that orchestrator that sits in the middle is the brains behind your smart data fabric. >> And this is available today? This all works? >> It's all available today. Yeah, it all works. And we have a number of clients that are using this technology to support these kinds of use cases. >> Awesome demo. Anything else you want to show us? >> Well we can keep going. 'Cause right now, I mean we can, oh, we're at 18 minutes. God help us. You can cut some of this. (laughs) I have a lot to say, but really this is our data fabric. The core competency of IRIS is making it smart, right? So I won't spend too much time on this but essentially if we go back to our coordinator here we can see here's that original that pipeline that we saw where we're pulling data from all these different systems and we're collecting it and we're sending it out. But then we see two more at the end here which involves getting a readmission prediction and then returning a prediction. So we can not only deliver data back as part of a smart data fabric but we can also deliver insights back to users and consumers based on data that we've aggregated as part of a smart data fabric. So in this scenario, we're actually taking all that data that we just looked at and we're running it through a machine learning model that exists within the smart data fabric pipeline and producing a readmission score to determine if this particular patient is at risk for readmission within the next 30 days. Which is a typical problem that we see in the healthcare space. So what's really exciting about what we're doing in the IRIS world is we're bringing analytics close to the data with integrated ML. So in this scenario we're actually creating the model, training the model, and then executing the model directly within the IRIS platform. So there's no shuffling of data, there's no external connections to make this happen. And it doesn't really require having a PhD in data science to understand how to do that. It leverages all really basic SQL like syntax to be able to construct and execute these predictions. So it's going one step further than the traditional data fabric example to introduce this ability to define actionable insights to our users based on the data that we've brought together. >> Well that readmission probability is huge. >> Yes. >> Right, because it directly affects the cost of for the provider and the patient, you know. So if you can anticipate the probability of readmission and either do things at that moment or you know, as an outpatient perhaps to minimize the probability then that's huge. That drops right to the bottom line. >> Absolutely, absolutely. And that really brings us from that data fabric to that smart data fabric at the end of the day which is what makes this so exciting. >> Awesome demo. >> Thank you. >> Fantastic people, are you cool? If people want to get in touch with you? >> Oh yes, absolutely. So you can find me on LinkedIn, Jessica Jowdy and we'd love to hear from you. I always love talking about this topic, so would be happy to engage on that. >> Great stuff, thank you Jess, appreciate it. >> Thank you so much. >> Okay, don't go away because in the next segment we're going to dig into the use cases where data fabric is driving business value. Stay right there.

(rhythmic techno music) >> Hey welcome back everybody, Jeff Frick here with theCube. We're at Oracle Park on the shores of McCovey Cove. We're excited to be here, it's a pretty interesting event. Sports Tech Tokyo World Demo Day. It's kind of like an accelerator but not really, it's kind of like Y Combinator but not really, it's a little bit different. But it's a community of tech start-ups focusing on sports with a real angle on getting beyond sports. We're excited to have our next guest, who's an investor and also a mentor, really part of the program to learn more about it and she is Gayatri Sarkar, the managing partner from Hype Capital. Welcome. >> Thank you. Thank you for inviting me here. >> Pretty nice, huh? (laughs) >> Oh, I just love the view. >> So you said before we turned on the cameras, well first off Hype Capital, what do you guys invest in? What's kind of your focus? >> So Hype Capital is part of one of the biggest ecosystems in sports which is Hype Sports Innovation. We have 13 accelerators all around the world. We are just launching the world's first E-sports accelerator with Epsilon and SK Gaming, one of the biggest gaming company. So we are part of the ecosystem for a pretty long time. And now, we have Hype Capital, VC Fund investing in Europe, Israel, and now in U.S. >> So you mentioned that being a mentor is part of this organization. It's something special. I think you're the first person we've had on who's been a mentor. What does that mean, what does that mean for you? But also what does it mean for all the portfolio companies? >> Sure, I'm a mentor at multiple accelerators. But being a part of Sports Tech Tokyo I saw the very inclusive community that is created by them and the opportunity to look at various portfolio companies and also including our portfolio companies as part of it. One of our portfolio company where we had the lead investors, 'Fun with Balls' they're part of this. >> What's it called, Fun with Balls? >> Fun with Balls, very interesting name. >> Good name. (laughs) >> Yeah, they're from Germany and they came all the way from Germany to here. So, yeah, I'm very excited, because as I said it's an inclusive community, and sports is big. So we are looking at opportunities where deep-techs, where it can be translated into various other verticals, but sports can also be one of the use cases, and that's our focus as investors. >> Right, you said your focus was really on AI, machine learning, you have a big data background a tech background. So when you look at the application of AI in sports what are some of the things that you get excited about. >> Yeah, so for me when I'm looking at investments definitely the diversification of sports portfolio. How can I build my portfolio from esports, gaming, behavioral science in sports to AI, ML, AR, opportunities in material science and various other cases. Coming back to your question it's like how can I look into the market and see the opportunities that, okay can I invest in this sector? Like what's the next big trend? And that's where I want to invest. Obviously, product/market fit, promise/market fit because there's a fan engagement experience that you get in sports, not in any other market the network effect is huge, and I think that's what VVC's are very excited in sports and I think this is right now the best time to invest in sports. >> So promise/market fit, I've never heard that before what does that mean when you say promise/market fit? >> Interesting question so promise/market fit was coined by Union Square Venture VC fund. And they think that where there's the network effect or your engagement with your consumers, with your clients, and with your partners can create a very loyal fan base and I think that is very important. You may see that in other technology sector but, not, it is completely unparalelled when it comes to sports. So, I request all the technologies that are actually trying to build they are use cases, they should focus on sports because the fan engagement, the loyal experience the opportunities, you will not get anywhere else. >> Right >> And I think this is the market that I, and other investors are looking for that, if deep-tech investors and deep-tech technologies are coming into this market we see the sports ecosystem not to be a trillion dollar but a multi-trillion dollar market. >> Right, but it's such a unique experience though, right? I mean some people will joke that fans don't necessarily root for the team, they root for the jersey, right? The players come and go, we're here at Oracle Park which was AT&T park, which was SBC Park, which was I can't even remember, Pac bell I think as well. So you know, is it reasonable for a regular company that doesn't have this innate connection to a fanbase that a lot of sports organizations do that's historical, and family-based, and has such deep roots that can survive maybe down years, can survive a crappy product, can survive kind of the dark days and generally they'll be there when things turn back around. Is that reasonable for a regular company to get that relationship with the customer? >> So, you asked me one of the most important questions in the investors relationship, or investors life which is the cyclicality of the industry and I feel like sports is one industry that has survived the cyclicality of that industry. Because, as you say, a crappy product will not survive you have to focus on customer service so you have to focus, that, okay even if you have the best product in the world how can I make my product sticky? These are the qualities that we are looking into when we are investing in entrepreneurs. But the idea is that if we are targeting startups and opportunities, our focus is that okay, you may have the worlds best product but the founder's should have the ability to understand the market. Okay, there are opportunities, if you look at Facebook if you look at various other companies they started with a product that was maybe like okay, friend site, dating site and they pivoted, so you need to understand the economy you need to understand the market and I think that's what we are looking into the entrepreneurs. And, to answer your question, the family offices they are actually part of this whole startup ecosystem they are saying if there is an opportunity because they are big, they are giant and they are working with legacy techs like Microsoft, Amazon. It's very difficult for the legacy techs to be agile and move fast, so it's very important for them if they can place themselves at a 45 degree angle with the startup ecosystem, and they can move faster. So that's the opportunity for them in the sport's startup ecosystem. >> All right, well Gayatri thanks for taking a few minutes and hopefully you can find some new investments here. >> No, thank you so much thank you so much for your time. >> Absolutely, she's Gayatri, I'm Jeff you're watching The Cube, we are at Oracle Park On the shores of historic McCovey Cove I got to get together with Big John and practice this line thanks for watching, and we'll see you next time. (rhythmic techno music)

>> Hey, welcome back. You're ready, Jeff? Rick! Here with the Cube were Oracle Park recently, A T and T Park just renamed. It's a beautiful day home in San Francisco Giants. They're on the road. We're here at a pretty interesting event is called Sports Tech. Tokyo World Demo Day brought together a coalition of about 100 startups. 25 of them are given demos today on technology as it relates to sports. But even more importantly, that can then be used in other in others. Beyond sports. We're excited to have an athlete on not just another tech crazy guy. He's Brendan Harris. He's an athlete in residence at 76 Capital. Brendan. Thanks for stopping by. >> Thanks for having me. >> So what is the effort, Principles and entrepreneur in residence? Where is the athlete residents do? It is >> essentially a play on the entrepreneur in residence. I was introduced to 76 Capital finished playing in 15 and I was doing my MBA at Warden and in Philly and got introduced Thio Wayne and the guys at 76. And they are kind of putting together an athlete venture group. Whether they're bringing in a lot of athletes don't wannabe investors and kind of providing them access to deal >> flow and >> um, >> and then also leveraging their social capital. So, uh, he was He was kind of tickled when he came when he coined the term athlete residents and he threw it on my business card. And and that's where we're at, >> right? So I'm just curious your perspective as an athlete as you look around at all the technology that's going into sports, right? Kind of the big categories are, you know that which helps the players play better. There's that which helps the people run, the team's better. And then there's that, which is really kind of part of the fan experience. I mean, you actually to go down and try to put wood on a ball coming at you in 90 plus miles an hour. All this other stuff. Do you see it as is it interesting is distraction. Is it entertaining? I mean, how do you look at it from an athlete's perspective? >> So yeah, so a lot to impact. So first of all, I have this ah, equally the equal view of fascination and frustration where a lot of this wasn't he wasn't around when I when I was playing it, certainly from the field. Now we're taking in things like recovery and rest and sleep. Ah, but I think players and me personally are fascinated with How can we improve on field performance? And I think baseball. It's such a perfect game and you fail so often, being able to turn to turn things that were previously subjective and applied data and in tech to make them objective and give you answers. I think it's fascinating and the ways that we can use data to to kind of promote performance and health and and all those things air Very fascinating. So from players, point of view, we're all about it. But at the same time, I think it certainly says why I've loved to get into sports. Tech is there's a lot of data that's just noise that's coming in and things. And so the tough part is, um, kind of weeding through and what is actionable info on what can actually help improve the on field performance? And then along with that, you know, we want to feel the product on the field, but also what the service is for the consumer and the fans are. And how can we improve that and then engage them? Because certainly sports are part of the culture and part of life now, and it's fascinating. These fans want to know more and more and more, certainly what's going >> on. And it's been It's been a >> great journey, >> right? So on the fan experience specifically, and we've been we've been here a number of years. Bill Styles, a good friend of mine off another word and other work. Brad and and, you know, talking about high density WiFi and you know the app on your phone and delivered, you know, food delivered to your seats. I mean, >> as a as an >> athlete on the field. Do you look at kind of all these things is as a distraction. Do you appreciate? It's kind of a more competitive environment these days in terms of people's attention and kind of that entertainment dollar. But I would imagine from between the lines it looks like Hey, you know, the game's down here people. It's been >> interesting because, um, you know, one of the problems of a major league baseball's been trying to address his pace of games right. And if you really look at the data, they're not that much longer. What's different? We're wired differently, right? So our attention spans are short and we're constantly so our technology. So these, you know, guys like Bill, you are trying to leverage that and try to have your food delivered and try to increase the social component. Increased the value in the in venue experience so that you're not only watching the game, but you're socially enjoying at the same time and kind of fill in those gaps. Ah, lot of it is yes on. And I think there's been balls flying into the stands since baseball's been playing, but they need to put the netting up. Has come a lot of times because nobody's watching. Some people aren't not nobody, but a lot of people aren't watching. The games are getting hit with a lot of these foul balls. So there is that component where you know there's there's some unbelievable things are going off on the sides. But um, you know it's baseball is still gonna be kind of very somewhat within within the confines. >> The other piece that I find really interesting on the data side, right? Is there so much data? Right? There's data data data. Obviously, baseball is built on data and arguments about data and conversations about data, but now it's kind of gone to this next Gen with, you know, wins over replacement and all these other things. But sometimes it's funny to me. It feels like they're forgetting the object of the game is to win the game. And it feels like sometimes the metadata has now become more important than the data. Did you win or lose and is not necessarily being used as a predictor for future performance? But it's almost like a standalone game in and of itself. Like we forget. The object is to win the game and win a championship, not to have the highest war number views since that frustration is that sound? Yeah, I think what you're getting >> into a lot of times is our know how are we making decisions right? And in the game? A lot of times people forget that human beings are out there performing and so I think that's how we've gotten into Moneyball 2.0, looking at development and certainly mental health in focus and game preparation have come into play more and you're seeing some managers. I mean, Mickey Callaway just came out and said 80% my, you know, Susan's go against the data, which which I thought was a little bit interesting, but, ah, so there is that fine line right where you have to filter in what's noise and what's actionable. And at the same time, um, you know, allow you know, your managers and your decision makers some flexibility to go with, You know, they're they're in the heat of the battle and they kind of know their guys. And they know the human element that's involved. So it's it's an interesting, you know, trying to balancing act, >> right? So from your from your new job in your new role, what are some of the things you hope to see today? What are some things that you're excited about? Um, you know, from kind of an investor. And having played the game as well. As you know, I'm looking forward to the evolution of sports. Two >> things specifically how the, uh certainly bias the performs on the field in the human element. And certainly everybody wants workout secrets, and I don't feel like it's whether it's athletes or the kind of weekend warrior or people that are, you know, kind of your senior citizens. And I don't think it's a simple as this has worked, and you should do this. It's a very personalized experience now. And I think some of this personalized digital fitness is fascinating to me on and then how it relates to and how your body relates to, you know, your diet and nutrition, your sleep, your recovery. I think all those air fascinating that, uh, advances that I want to look into more. And the second is a CZ, I kind of mentioned is the fan engagement aspect. How do we drive those those fans that digital, >> um, and >> make it actionable and monetize, right? So that you know, you have your fans that are following you know, your Facebook, twitter and all those things. And so how do you not only gauge them, but collect that data and then kind of personalized that experience? Engage your fan in a way that can kind of grow your brand. Yeah, it's interesting to me, >> really interesting to have to have your perspective, and I'm sure will be a great day and you see all kinds of crazy stuff. So thanks for taking a few minutes. >> Yeah, Any time. >> All right. He's Brendan. I'm Jeff. You're watching The Cube were at Oracle Park in San Francisco. Thanks for watching. We'll see you next time.

(upbeat music) >> Hey welcome back everybody. Jeff Frick here with theCUBE. I wish I could give you my best John Miller impersonation but I'm just not that good. But we are at Oracle Park, home of the San Francisco Giants. We haven't really done a show here since 2014, so we're excited to be back. Pretty unique event, it's called Sports Tech Tokyo World Demo Day. About 25 companies representing about 100 different companies really demonstrating a bunch of cool technology that's used for sports as well as beyond sports, so we're excited to have one of the companies here who's demoing their software today, or their solution I should say. It's Albert Ng, he's the founder and CEO of Misapplied Sciences. Albert, great to see you. >> Great to see you, thank you for having me. >> So Misapplied Sciences. Now I want to hear about the debates on that name. So how did that come about? >> Yeah, so I used to work part time for Microsoft, at Microsoft Research, and one of the groups I worked for was called the Applied Sciences group. And so it was a little bit of a spin on that and it conveys the way that we come up with innovations at our company. We're a little bit more whimsical as a company that we take technologies that weren't intended for the ways that we apply them and so we misapply those technologies to create new innovations. >> Okay, so you're here today, you're showing a demo. So what is it? What is your technology all about? And what is the application in sports, and then we'll talk about beyond sports. >> Yeah, so Misapplied Sciences, we came up with a new display technology. Think like LED video wall, digital signage, that sort of display. But what's unique about our displays, is you can have a crowd of people, all looking at the same display at the same time, yet every single person sees something completely different. You don't need to have any special glasses or anything like that. You look at your displays with your naked eyes, except everyone gets their own personalized experience. >> Interesting. So how is that achieved? Obviously, we've all been on airplanes and we know privacy filters that people put on laptops so we know there's definitely some changes based on angle. Is it based on the angles that you're watching it? How do you accomplish that and is it completely different, or I just see a little bit of difference here, there, and in other places? >> Sure, so at the risk of sounding a little too technical, it's in the pixel technology that we developed itself. So each of our pixels can control the color of light that it sends in many different directions. So one time a single pixel can emit green light towards you, whereas red light towards the person sitting right next to you, so you perceive green, whereas the person right next to you perceives red at the same time. We can do that at a massive scale. So our pixels can control the color of light that they send between tens of thousands, up to a million different angles. So using our software, our processors on our back end, we can control what each of our pixels looks like from up to a million different angles. >> So how does it have an edge between a million points of a compass? That's got to be, obviously it's your secret sauce, but what's going on in layman's terms? >> Yeah, so it's a very sophisticated technology. It's a full stack technology, as we call it. So it's everything from new optics to new high performance computing. We had to develop our own custom processor to drive this. Computer vision, software user interfaces, everything. And so this is an innovation we can up with after four and a half years in stealth mode. So we started the company in late 2014, and we were all the way completely in stealth mode until middle of last year. So about four years just hardcore doing the development work, because the technology's very sophisticated. And I know when I say this, it does sound a little impossible, a little bit like science fiction, so we knew that. So now we have our first product coming on the market, our first public installation later next year and it's going to be really exciting. >> Great. So, obviously you're not going to have a million different feeds, 'cuz you have to have a different feed I would imagine, for each different view, 'cuz you designate this is the view from point A. This is the view from point B. Use feed A, use feed B. I assume you use something like that 'cuz obviously the controller's a big piece of the display. >> Exactly, so a lot of the technology underneath the hood is to reduce the calculations, or the rendering required from a normal computer, so you can actually drive our big displays that can control hundreds of different views using a normal PC, just using our platform. >> So what's the application. You know obviously it's cute and it's fun and I told you it's a dog, no it's a cat as you said, but what are some of the applications that you see in sports? What are you going to do in your first demo that you're putting out? >> Yeah, so what the technology enables is finally having personalized experiences when in a public environment, like a stadium, like an airport, like a shopping mall. So let me give an airport example. So imagine you go up to the giant flight board and instead of a list of a hundred flights, you see only your own flight information in big letters so you can see it from 50 feet away. You can have arrows that light your path towards your particular gate. The displays could let you know exactly how many minutes you have to board, and suggest places for you to eat and shop that are convenient for you. So the environment can be tailored just for you and you're not looking down at a smart phone, you're not wearing any special glasses to see everything that you want to see. So that ability to personalize a venue stretch, is to every single public venue, even in the stadium here, imagine the stadium knowing whether you're a home team fan or away team fan or your fantasy players. You can see it all on the jumbotron or any of the displays that are in the interstitial areas. We can have the entire stadium come alive just for you and personalize it. >> Except you're not going to have 10,000 different feeds, so is there going to be some subset of infinite that people are driving in terms of the content side? >> Mhmm. >> So on your first one, you're first installation, what's that installation going to be all about? >> The first installation is going to be at an airport, I can't see right now publicly where it's going to be or when it's going to be or what partner. But the idea is to be able to have a giant flight board that you only see your own flight information, navigating you to your particular gate. You know when you're at an airport, or any other public venue like a stadium, a lot of times you feel like cow in a herd, right? And it's not tailored for you in any way. You don't have as good of an experience. So we can personalize that for you. >> All right, Misapplied Sciences. Oh I'll come down and take a look at the booth a little bit later. And thanks for taking a few minutes. Good luck on the adventure. I look forward to watching it unfold. >> Appreciate it, thank you so much. >> All right, he's Albert I'm Jeff. You're watching theCUBE. We're at Oracle Park, on the shores of McCovey Cove. Thanks for watching, we'll see ya next time. >> Thank you. (upbeat music)

(upbeat electronic music) >> Hey welcome back everybody, Jeff Frick here with theCUBE. We are at Oracle Park in San Francisco for a really special event. It's called Sports Tech Tokyo World Demo Day, really bringing together a bunch of innovative companies in the sports tech space, really with a focus on not only sports but beyond sports. And we're happy to have really one of the key players here that made this all happen from Dentsu. He's Fumihiko Nakajima, the Senior Director of Business Development from Dentsu. Welcome. >> Hi, nice to meet you. >> Yeah absolutely. So for people that aren't familiar with Dentsu, give us a little overview of Dentsu as a company and then we'll get into the specific event. >> Yeah, Dentsu has a long history focusing on broadcasting rights and sponsorship for event globally. But combining such kind of global asset and new technology to create a new business in sports tech industry and beyond sports industry. >> Right. So pretty interesting way to do that, so you didn't just go find some interesting companies, you guys have created this event to bring a lot of companies together, demonstrate their technology. What was kind of the thinking and how did you guys get involved? >> Yeah. Combining the new asset and technologies and global asset, there are lot of the Japanese company global brand, SoftBank, ITOCHU, Sony Music, Microsoft, and CBC. Such kind of companies very interested in, create new business with innovative staff all over the world. So that's a basis of this event. >> Right, right. So, you got the Tokyo Olympics coming up in a year, so that's kind of a catalyst to make all this happen. Is there anything special that you see between, you know, kind of sports technology and managing teams, sports technology applied to the athletes, and then sports technology applied to the fan experience that you're most excited about? >> Yeah, that's correct. This is a beginning. Next month world Rugby World Cup, the next year, Tokyo Olympic and Paralympic we have. That's a beginning, so, you know the, the sports and live entertainment beyond live entertainment, health cares, biometrics, bio mechanics, from the point of sports. But we enter into the new field and explore the new business field. >> Jeff: Right. >> With the great start-ups and industry leaders on the basis, that who joining these communities. >> Right, right. No, it's pretty interesting because you know the, companies spend so much money now on the players and really look at them as investments. A lot of players get hundred million dollar contracts now. So it's pretty interesting on kind of the health care and the like we talked earlier, sleep and nutrition-- >> Yeah. >> And all these things to keep that athlete performing, are really applicable to everyday people like you and me. >> Yeah. You know that Dentsu has more than one century history in marketing and branding all over the world. And our assets, such kind, properties and, global network, will really help the new technologies and new start up, the new business field. >> Jeff: Right. >> Grow rapidly. >> Jeff: Right. >> All over the world. >> Right. It's interesting, too, that so much of the stuff around the sports, you talked about sponsorship and rights beyond just the score, you know, is so important these days. To feed the 24/7 news cycle, to do fantasy sports, the changes in the gambling law, so there's so much stuff around sports that's beyond the sport that's watched in this industry grow and grow and grow. >> Yeah it's a very interesting point. We know new, legal we will need, a new legal and a new set-up structure for the new business. >> Jeff: Right. >> In specific Tokyo, a lot of specialist joined to help such kind of structures for the futures. >> Right. So before I let you go, it's a busy day, have you been to this park before, home of the Giants, and what do you think? >> Yeah very, very, very special day. It will be very memorable day that one of the best historic venue in America, the San Francisco Giants stadium, Oracle Park. We really excited to share our progress, concrete progress, and want to expand our trial to all over the world. >> Great, well thanks for inviting us and we're, we're excited to watch the story unfold. >> Yeah, thank you. >> Alright. He's Fumihiko, I'm Jeff, you're watchin' theCUBE. We're at Oracle Park in San Francisco, thanks for watching. (upbeat music)

>> Hey, welcome back, everybody. Jeffrey here with the Cube were Rhetorical Park in San Francisco on the shores of McCovey Cove. I just love saying that we >> haven't been here since >> 2014. We're excited to be back for a really interesting event is called Sports Tech Tokyo World Demo Day. This next guest has been at it for a number of years. A really cool technology. We're excited for the conversation and to welcome Mark Philip. He's the founder and CEO of Are >> You watching this Mark? >> Great to see you. Good to see you, too. Absolutely. So, first off, you've been Thio Park before. Here I have. It's been way too long. >> There are >> a few iconic stadiums in the world, and this has got to be one of the great. So let's get into it. So what is are you watching this all about? >> We are the best friend that is >> giving the digital tap on the shoulder when it's time to run to the couch. We monitor pitch by pitch, shot by shot data to figure out when the game gets exciting. I love my Yankees till death, but the >> Yankees Red Sox occasionally tend to >> take over my entire night when they play each other. So being able to get that tap on the shoulder saying, Hey, it's time to tune in or stop raking the leaves, there's a no hitter through eight. Okay, that's what we try to do. Okay, so let's break it down before we get some of the applications into which actor doing So You guys air, You're actively watching these games. You've got some type of an algorithm based on scoring plays. Pitch count. Are we? What are some of the things that drive? Whether this is an exciting game or not, it's a great question. The easiest way to think about it is if you imagine what a win probability graph looks like. So game probably starts off in the middle. Might go up or down based on who's winning, the more violently that graph goes up and down generally, the more exciting the game is, so when probability is a big factor. But also you think about rarity whether it's we had a no hitter last night, we had the Astros with a four picture no hitter a few weeks ago. You know, those sort of things that you don't see often, even if the game's nine nothing, even if the wind probabilities and changing. If that's a no hitter, that's something you want to turn into, right? And so are you tapping into just kind of some of the feeds that are out there in terms of what's happening in the game or you actually watching and using a I in terms of actually looking at a screen and making judgments? Sure, thankfully, I'm not watching or else I would never leave the house. But for us, it's about getting that real time live data. Okay, so I can see balls and strikes on my servers faster than I can see it on live TV, which is a little bit mind bending of time. So we work with the the official data sources. So whether it's a company like sport radar or stats or opt or Abels and pretty much anyone around the globe, we pull in that real time data so we can give people that tap on. The show says Hey, run to the couch. Run to the bar, tune in. Something interesting is about to happen, right? But what's entering your B to B play. So your customers are not me. Jeff, go to the couch. You're working through other people that might be motivated to have me run to the count. So how does your business model work? Who are some of your customers? What are some of the ways that they use your service? >> I'm I'm the guy behind the guy. I'm behind the >> Red Curtain, pulling the strings, you know, for us not to paint with an overly broad brush. But we're based in Austin, Texas, and one of the big things about a city like ours versus the city like this is that our companies tend to skew very B to B versus the Bay Area, which generally excuse a lot more B to C. So pitching to the cable companies, the sports providers, probably CBS Sports is our oldest customer right now. We work with small startups, more established folks, and everyone uses this differently. But the goal is the vision. Is that whether it's your DVR recording automatically when the game gets good or just making sure that, you know, maybe you want to place a bet on the Giants or if you are, ah, glutton for punishment my lowly Knicks if the if the spreads. Good enough, you know, getting that nudge when games get exciting is an accelerant. Not just for watching in, but I think, for fandom. Yeah, well, when Kevin Durant comes back, you'll get a bit more exciting >> Nets, not Nick's. I'm gonna give you one free one. So we had a conversation >> before we turn the cameras on about, you know, kind of this. This never ending attention span competition and the never ending shrinking of consumable media. And how you guys really play an interesting role in that evolution, where if you can give us a little bit deeper background, >> I think it's fascinating. You look at >> the N B A. That really any league. If you rewind five years ago, you have to pay to 5300 bucks to get access to anything digitally, and then you got access to everything, and then the NBA's said, Well, maybe just want to buy one team, so we'll let you pay things around 80 bucks and they just want to watch. One game will sell it to you for eight. I just want 1/4 with such for dollar 99 if you just want a few minutes with silty for 99 >> cents, and now they've done that really, really quietly. >> But I think it's seismic because I think all leagues we're gonna have to follow and do this. So if you look at these snack passes and especially as thes NFL rights are coming up, I could easily imagine someone like a YouTube or, I should say, a Google if they were to grab these rights, how easy would be to go to YouTube and get a game for a few bucks and how well their entire infrastructure would work. But rewind to today when you have 10 to 20 states that are online. As far as gambling goes, you take gambling. You take excitement analytics and you take the snack passes and you kind of mix him up in a pot and you get this vision of I can send you a Texas is Hey, LeBron has 60 points with 3/4. Do you want to pay 99 cents tow, Watch the finish, or do you want, let's say, place a wager on if he's gonna be Kobe's 81 point Lakers record and then we'll let you watch for free. And so getting both sides of that equation, whether your die hard or casual fan, it's hard to say no to both those options, right? And do you see within your customer base that drive to the smaller segmentation of snack packs? Is that driven by customer demand, or are they trying to get ahead of it a little bit and offer, you know, kind of different sizes of consumption, I guess, would be the right. >> Sure, I think the horse is out of the barn. I mean, imagine if >> we were still buying complete albums. Of course, we're buying tracks when we just wanna track the idea that we have to buy an entire season. No foul, 2430 games in an MLB season. Why won't you let me buy just one game? I say MLB leaves a million dollars on the table every single time is no hit bid because there's tons of people who have cut the cord, don't want to run to the bar, but would happily pay 99 cents to stream the last inning of a game on their phone on their commute. So I think it is a combination of digital. What shoring in that We're able to do these three single track sort of purchases, but also its people continue to cut the cord and rethink about how they spend their media dollars. It makes sense really interesting. So we're here. It's sports Tech, World Demo Day. What do you hope to get out of today? Why are you here? Gosh, at least to pay homage to the reason why I went to Tokyo for the first time and had life changing Rama and I feel like I need to sort of complete >> the cycle. Uh, sports like >> Tokyo is an amazing program. There's lots of different events that have shaped different ways. But there's something really unique about this. And when we all lands in Tokyo, I think it was something like 80 different entrepreneurs that came into met to meet with all of the Japanese sponsors. Everyone had the same vibe of just really happy >> to be there. >> They didn't take a percentage of these startups coming in, so you really saw different sizes, not just early stage, but late stages well and everyone was there, too. Connects and innovate and do interesting things together. So many of us were there for the first time that there's just a vibe to this event that I haven't seen in my 10 plus years in sports. Tak interesting. Well, Mark, great to sit down with you. Really cool story. And, um, I guess I'll be watching for your watching for your app. Is the man behind the man coming through my phone? Real sand Sounds great. >> All right. He's >> Mark. I'm Jeff. You're watching the Cube World. World Tech demo today here at Oracle Park. Thanks for watching. We'll see you next time.

(upbeat music) >> Hey, welcome back everybody. Jeff Frick with The Cube. If you can't tell over my shoulder, we are at Oracle Park. It's a glorious day. The marine layer is burning off and it is really spectacular. We're happy to be here. Haven't been here since, I think 2014. It's an interesting event called Sports Tech Tokyo World Demo Day. About 25 technology companies in the sports area are giving demos all day today. It's a huge program, and we're excited to have our next guest coming from the analytics side. She's Sezin Aksoy, Global Data Strategy and Analytics for AXS. >> Correct. >> Welcome. >> Thank you. >> Absolutely. >> Glad to be here. >> So Global Data Strategy. Everything's all about data. >> Correct. >> So, somebody's really happy to have you on board. What are so... What do you, what are you working on, what was top of line. >> Sure, so it's going to sound cheesy but data is the power of the world. >> Yes. >> It's going to empower people making better decisions, so that's kind of my role is at AXS. So AXS is the ticketing platform for live entertainment events. We operate in the US, Europe, as well as in Japan. And, if you think about it, when a consumer comes to your website, that's the first touchpoint that you have. Whether they buy the ticket or don't. Whether they buy or sell, and transfer the ticket, or they attend the event, all those are various touchpoints that we are collecting. So that we can inform our clients to make better decisions with data. >> Right. >> Whether it's pricing decisions, or marketing decisions, or scanning an event, which gates will be more busier than others. So, that's kind of what my team works on. >> Excellent. So, let's jump into a little bit on the dynamic pricing. >> Sizen: Hm mm. >> Because we saw, we've seen dynamic pricing. And you said you were in the airline industry. >> Correct. >> We've seen it in the hotel industry. >> Yup. >> My father in law talks about when he was doing dynamic pricing as a young kid. >> Sizen: Okay. Just making a call when somebody came through the door, at eleven o'clock. >> Sizen: Yeah. (laughs) >> Jeffrey: What's my marginal cost... >> Okay, yep. >> Jeffrey: with somebody in that room or not. There's really slow to get beyond, kind of the entertain, oh excuse me, the travel industry for other people... >> Hm mm. Yep. >> To kind of get on board the dynamic pricing. >> Yeah. We saw the Giants here... >> Yep. >> Actually a couple of years ago. We came by, they were starting to do dynamic pricing. >> Sizen: Hm mm. >> A Friday night Dodger game, compared to a Tuesday day... >> Sizen: Yep. >> Milwaukee game, very, very different. >> Sizen: Hm mm. >> So, what are some of the factors going in, what are some of the resistance, >> Sizen: Yeah. >> that had to be overcome for people to actually accept that it's okay to charge more for a Friday night Dodger game, than a Tuesday afternoon Milwaukee game. >> Yep, so yeah, so my background start with the airlines, which is where dynamic pricing, revenue management started at, specifically the American Airlines. If you think about there are a lot of similarities between airlines and live entertainments. Fixed costs, you have to, flight has to go, or the game has to be played no matter how many people are there. So, you really have a limited time to really maximize your revenue. And you kind of have a product that the demand level is different by day, whether it's a Tuesday game or Friday game. It really something you have to study the sort of the behavior from the consumers when they buy their tickets. What are the factors they put into play to make that decision? And in that mix, San Francisco Giants was one of the first teams that actually incorporated dynamic pricing about ten years ago, that slowly. The challenges with it is we are not as the consumer, not as trained to know that the price may change. Hotels, airlines been doing it for years and years. >> Right. >> And for them, also it didn't start from like doing all the flights in day one. So it's really needs to be a phased approach. It needs to be a lot of education for the public, and to think about the right way to think about it is, you want incentivize people to buy early. And you want to make sure they are the ones that getting the best price, and not necessarily the people that are buying last minute. >> Right. >> If you're buying last minute, then you must accept that it maybe the available today you're not looking for or the price not you looking for. But I will say though that plans change, people decide to not attend the game. The reason is that, potential for finding other seats for that similar game. But, really for you, have your plans. It's better to buy early, and that's kind of what the industries needs to be trained on, more and more. >> Right. >> Was there more opportunity in getting additional value out of that high demand game? Or was the bigger opportunity in getting, kind of lowering the prices on the less desirable games, and getting kind of marginal revenue on that side. Where was the easy money made, >> Yeah. >> Jeffrey: On dynamic pricing? I mean the immediate impact is from the high value seats for the high value games, cause that's really is your premium product at that point. But in the meantime, there's always a low number of seats that you have in your premium area. And if you find the right price, and if you start earlier. And really the goal is to sell all the seats, and to fill all the seats. >> Right. >> Also, just selling the seats is not, doesn't get you far enough. You want to make sure people actually come to the game, and they're the people that are going to attend the game. Right? >> Right. >> So, if you kind of, the lower level has many more seats, so it's really has to be both ways. It can't be in one area, either dynamic pricing and you don't do it. It's just all about training the public and consumers. >> Right. Now, the other interesting you said in your kind of intro, was keeping track of... What are the busiest turnstiles? And where people coming? And the flow within the game. >> Sizen: Yep. >> What are some of the analytics that you do there, >> Sizen: Yep. >> And how are teams using those... >> Sizen: Yep. >> that information to provide a better fan experience? >> Yeah, so we have scanned data, and we actually have it real time. So, we are able to provide the teams. We have kineses streams, not to go too technical, to kind of empower them to do their game operations in a certain way. So example would be, you could study the past games and understand where people came from. Typically for a Friday game verse a Tuesday game, your crowd will look different, right. The Friday game, maybe the more the families or Saturday or Sunday. But Tuesday may be more corporate world, right. So understanding they're patterns, but also than having that data accessible to you to real time. So, that way you're able to see how many people are coming in from this one gate to other. You can man the gates differently that way. And the real time data is not something that comes just easily. There's a lot of infrastructure built for it. >> Right. >> But we've done it at AXS, and we've been able to provide to the teams so they can manage their getting in better. >> Right. >> So real time's interesting cause you know a lot of these conversations about real time, and I would say, "How do you define real time?" And in my mind, it's in time to do something about it. >> Exactly. >> So, using real time, I mean are there things they can do in real time to either lighten the load at an overdone gate, or... >> Sizen: Yeah. >> What are some of the real time impacts that people are using this data to do? >> Yeah, so exactly the example you provided. Like making sure there are more people at this one gate as opposed to others. But also, like knowing who's coming into the arena. So AXS's I-D ticketing, I-D based ticketing platform, so we actually know who's coming in. It's a rotating barcode, so if you just copy-paste the ticket, and text your friend. That doesn't work, that eliminates fraud as well. But because we know who's coming in, you can actually empower your sales reps as a team to make sure you are, you know, if they are coming to a suite or a premium area. So in so actually just scanned in, so you kind of come up with ideas for sales reps. As well as some of the marketing activations, like... It could be that you have people that typically come in late. You want to incentivize them. You could actually come up with promotions on merch and food and beverage to incentivize them early, right? Or at the same time you can actually, there are some platforms that do marketing activation. You may have had a lot of hotdogs left that you couldn't sell. Towards the late quarter, you could send a message to everyone saying, "Okay, ya know, hot dogs are 20 percent off." >> Right, right. >> So that, you need real time for it, for data for that. Cause you again need to know how many people scanned in. You may want to know how many people scanned out. So for some conferences and other type events, you want to make sure there's a Fire Marshall rules, so you want to make sure. So all the real time data is helpful for that if you just look at the purchaser data, you're not going to get that specifically there. >> That's really interesting cause I was going to say, What are some of the next things that we can expect to see dynamic pricing applied to, and you just went through them which are really situational specific. >> Yep. >> Opportunities to clear inventory, to do whatever. >> Exactly, it's not just a ticket purchase. It could be applied to other things as well. >> Right, Right. >> Yeah. >> How cool. So what other kind of data sets are you looking at to help teams that maybe we're not thinking about. >> Sure, just when people buy their tickets. What marketing may have they done, so that we can understand the web traffic, and did they buy the ticket when you send out that email. Or did they buy it three days later. So that's one area. As well as sort of, the inventory that you have available for that game. Does it sell faster for that Friday game versus a Tuesday game? We also, we're a comprehensive marketplace where we have both primary and secondary in the same map. To give the convenience back to the consumers, so you kind of have a chance to see all the inventory available in front of you. So, a bit of understanding how tickets transact in the secondary marketplace is helpful for the teams to really price their product better. Cause sometimes we have... I work for a team, so I have that background where you may have just 20 price points, and you've done it for 20 years but it's been certainly changing then. But now that you have all these different data points on the second, you also you kind of maybe is like, 'Okay I need 40 price points really because there's that much differentiation demand. >> Wow, really sophisticated analysis... >> Yeah, it's a passion area for me, so... >> And doing the real time, real time data flow and everything. >> Yeah, yeah. A really interesting, interesting conversation. >> Yeah. >> To go so far beyond just dynamic pricing. >> Exactly. >> It uses more sophisticated methods to get more value, provide better experience for the fans. >> And actually in Japan, they do more about dynamic pricing. So they utilize our platform to actually able to price every seat differently if they wanted to. We've just went out with on sales for Big League teams, and that's how they apply that. So it's been used elsewhere, maybe in the U-S in sports. It's definitely catching up, and it's much much big difference from the 10 years ago. But, I think Japan has already been kind of doing that. >> Excellent. >> Mm hm. >> Well Sizen, thanks for taking a few minutes, and sharing those stories. There's a lot going on behind the scenes that may not be conscious of, but hopefully we're getting the benefit of. >> Yeah, thank you. >> All right. Sizen, and I'm Jeff. Yes, we're live. They're banging on something down there. I'm not sure what, but keep watching. We'lls be here at Oracle Park in San Francisco. Thanks for watching, and see ya next time. (upbeat music)

>> Hey, welcome back, everybody. Jefe Rick here with the Cube. Where? It Oracle Park in San Francisco, on the stork with cubby code. We're excited to be here. They're moving a lot of dirt, I think downstairs. But we're at a very cool event. It's called Sports Tech Tokyo World Demo Day. And we're excited. Have our next guest. He's Todd Simms s VP of corporate development from access taught. Great to see you. Great >> to be here. Thank you. Absolutely. So, for people are familiar with access. Give us kind of the company over here. >> We're a global ticketing company. We were launched out of ah global sports and entertainment company called E E G in 2011. And we serve live the live entertainment market and ticketing. Excellent. >> All over the world, >> different types of events. >> E e g. Is a global company with a run venues worldwide. And we serve them as well as third party clients. >> Okay, great. So we're here. It's sports tech, Tokyo. It's a little bit different. Type of an organization. Kind of an incubator. Not really an incubator kind of association, early association, but certainly a community. Why are you guys here. What is this organization mean to you? Why is that important? >> Yeah, it's really important. We We launched our ticketing service in Tokyo last year, and you know, that's a market that we love. It's a vibrant large market with super passionate fans, both on the sports side and on the music side. What it really needs is more of an ecosystem. It can't just be a new, innovative ticketing platform needs all the bells and whistles around it to really innovate the fan experience. And that's what these startups are doing. I >> just I just love this job because, you know, you think of many industries if you're not familiar with them, and they seem really simple on the outside and like everything, once you get under the covers, >> a lot more going on. So >> from the outside, looking in a ticket is a ticket. Yeah, what's the innovation and tickets? What's different about somebody in Japan buying a ticket to watch a baseball game than >> somebody find a ticket to come here to talk >> a little bit about what we're bringing to Tokyo and what we brought to our platform of clients here in the States as well as in Europe, and that's really a digital I. D based ticketing system. So when you walk into the Staples Center at L. A live in Los Angeles, that thing that's getting scanned is not a ticket. It's an identity, it's you. And what's being reviewed is whether you have access to that building on that night or not. So what that allows for is full data around the customer base. Every president of every team wants to know two things. They want to know who's in there building, and they wanna have some control, whether it's economic control or otherwise on the secondary market. Our digital I D ticketing system enables both of that, and that's kind of the innovation that we're bringing to the Tokyo market. >> But I would imagine when you say, you know it's me, you know the opportunities way beyond that because now you know what in my preference is, how often do I come? What kind of beer do I like to drink? It just opens up a whole kind of CR m ah, world of opportunity for this relationship between the team now in that person with that barker, >> absolutely, and that happens today, but what you're missing is every time someone comes in with a paper ticket, you're really not sure who's entering the building. So that eliminates that piece of that. And it gets all these teams with analytic departments to really have a full picture of their fan base. So, you know, they may have been investing in some of this and capturing 60 70% of their who's in the building. Now they have 100% right, >> and I would imagine they've been doing this for a long time, with kind of their season ticket base and knowing they're in the building. But it got a lot of data on their season ticket holders. How is that? You know, changed. What can they apply there to? The casual fan that maybe bought a ticket on the secondary market and his, you know, common is sitting in the bleachers? >> Well, it's huge >> for up sales and establishing that relationship. A lot of teams, if you've you know, just buying a single ticket off a secondary market, you're nowhere in that database now because of our I D based system. Those people are now prospects for either mini pack or a season ticket back. It's right. Just >> curious how the rise of the secondary market really impacted the teams and how they think about their own ticket based. I think the 1st 1 is probably StubHub back in the day for some, and it all happened kind of outside the purveyor of leagues and outside the purveyor of the teams. Likely, they're pretty smart and figured out we need to be a piece of this. So how did that kind of evolution change the way the teams think about their fans? Well, look, I mean, teams >> like music promoters, they Sometimes they like the brokers getting involved because it takes risk off the table. I think teams air realizing, though, that a riel yield management perspective on their ticket inventory to really revenue manage this appropriately. They have to take a holistic approach on their >> tickets, and any time you >> have a segment of your >> ticket base where you really don't have control of pricing distribution, >> all of that, it really hurts and it has an impact on your unsold primaries. So what teams are looking to do is gain more control and manages inventory more holistically to do that you really need to know all the data. And again, the I. D based ticketing system enables secondary sales. But at least you are tracking those sales and, you know, from one person to the next who who sold it, who bought it >> right? I'm curious to get your perspective on on the difference between if you arm or >> entertainment focused. So you know, the Rolling Stones were in town a couple nights ago, and it's really a one shot deal for the Rolling Stones in the Bay Area that night versus the Giants game, right where you're hoping that your people come back over and over. Did they think of it differently? Or is it Maur? You know, Jeff, you like music? You went to the Rolling Stones last night. Maybe you'll come and see somebody else tonight. Is that is that well, can't were they? No doubt, sports teams are >> a lot smarter about their fan base. They have loyalty built in. They have got history, you know there's variability. There's night of game. And then there's weather in who's on the mound and all of those factors. But promoters are, ah, lot more in the dark about, you know, Is this an artist that you know? How much credence can they put in the last two? Or they did. It's too been two years. Is that artist still going to sell appropriately or similarly than they did last time again? The secondary market on the music side is made a bigger issue because of that variability, and those promoters are willing to take risk off the table. But the same thing applies in order for them to really manage and revenue manage that tour. They really need to know who's buying and grab some of that secondary economics out of the system. Right? And that's again, what our platform enables, and that's what we're really bringing to the Tokyo market. It's really exciting. That's a great market for >> us. I was gonna say just to close. >> You know what's special about the Tokyo market either? From an opportunity side, we're kind of a unique way which they do things or unique way in which the kind of the fan experiences as you look at that market. >> Well, it's interesting. I mean, in a culture that is so reliant on such interesting technology, these ticketing technology is actually quite old, and so we're excited to bring that. We've got great partners past Revo is our partner there, and they're really selling that through the Yahoo ticketing channel. Uh, they we have we just signed the B league, which is the professional basketball league will be rolling them out in their fall season coming up soon here. But basically, they are looking for the same things. We're looking for more data and Maura capturing of the secondary market, and we can bring that to them. >> All right. Well, Todd, thanks for taking a few minutes. Pull the covers back off ticketing A lot more going on than people think. Thank you very much. All right, He's >> taught. I'm Jeff. You're watching The Cube. Were Rhetorical Park on the shores of >> McCovey Cove in San Francisco. Thanks for watching. We'll see you next time.

>> Hey, welcome back, everybody. Jefe Rick here with the Cube were at Oracle Park, and they're moving a bunch of dirt downstairs, but we're happy to be her. Anyway. We're here to really cool thing called Sports Tech Tokyo World Demo Day. And we're excited to have our next guest. He's heavier. Altamirano, the director of innovation for sport Radar of your Nice to see you. >> Hi. Nice to see you, Jeff. Thank you for having me. >> So for people aren't familiar with sport radar. What you guys all about? >> Yes, the world all about sports date on day and fan engagement. So whenever you want to place a a safe ah, bad Latina market, that's biddings. Regulated are mostly in Europe, for example. Ah, you would use ultimately our data also, whenever you're looking for first time Ah, stat line coming out or you're one to power your fantasy game. That data ultimately comes from us. So >> we talked about before we turn the cameras on. There's lots of sources of data, but your guy's unique value proposition, speed and accuracy is all right. >> Absolutely, Absolutely. You want to think >> of sports data like the same us? Your ticker from the stock market, right? You want to have it fast and reliable as possible. We've been doing that for almost two decades. We have experience, keys and experience in many different ways of collecting. Collecting data from around the world were 2000 people strong 30 offices around the world, dedicated just to collect and into into work with data and evolve and change the narrative of how people talk about sports. >> Okay. Were you guys base? Where's headquarters, >> eh? So we're ahead course in St Gallen, Switzerland, and in the US we have offices in San Francisco in a Minneapolis, uh, New York and endless magazines. All >> right, cool. So we're here in sports Tech? Tokyo will Demo day. What do you doing here? What does this event all about for you? >> Absolutely. This is >> Ah, great events. The grain. And they were shot out to Michael Pearlman and scram Ventures, and they're putting together this ecosystem, right? They want to bring all the best technology, the best sports technology that's out there in the world. Uh, you know, with Japan having all these events leading up to the Olympics next year, bowl so all the way through 2026 what they do, Jeff. They come up and they bring all of those large, great companies that they have conglomerates. And they make you make all of this, um, big opportunity for everyone who's due in something with sports technology in some way, shape or form. And then there's a lot of collaboration. There's investment. There's a lot of things happening there. We we definitely would certainly fit in, especially with our accelerator program. >> Okay. And then, are you guys already in the market in Japan, or is this just kind of a new boost? Into what? What you've already got? >> Things definitely knew boost >> for us. Uh, Asia? Absolutely. Ah, A future focus of also pressing and future focus of us. There's great things happening there, for sure. >> Okay, Now you're director of innovation. So you're actually looking for Toby to be opportunities to take your technology in some different directions, tell us a little bit more about what you're working on? >> Absolutely. Um, I leave the accelerate our program where we provide our data to Some early stage companies were doing something innovative with sports data, so that allows us to a keep tabs on, keep a pulse on innovation that's happening outside of our walls s Oh, that's our external innovation initiatives. But that allows early stage companies to get data and to use their funds into product or marketing or what have you so that they can really develop it and really, you know, uh, deliver something that which we think they can >> write. And you said, you have a couple of partner companies that are here today, correct? >> Absolutely. Absolutely. Yeah, we have two companies who made it to the finalists were absolutely, very, very proud of those that Edison and also a really So So the guy's a shock. It ah, Edison and Colin and steam, Really, they're great people, and I'm really in a really happy and on really proud to to see them here. >> Good. So what are they doing with your data that's unique in it? Different? >> Absolutely. So what? Edison? What is Zoom with our data's? Our data allows him to better tag and better identify each player that's showing on on the screen. Edison's technology allows for a personalization that its unique you and I could be watching the same game. Let's say we're watching European soccer and your run all the fun, and I'm a messy fun. So you would see targeted messaging and targeted information on Mass. And I will see targeted information on Ronaldo even though we're both watching the same game. That's what, uh, their technology allows an hour. Data propels >> them coming through the lower thirds and the graphics. And how is that house that >> excited zone over late? So it's overlays. >> Html overlays that they can. They provide. So especially for O. T t providers. >> Okay, because obviously I need to have the apse. They know it's me watching and not you for for >> what, exactly that allows for personalization. It's all about personalization, and that's that's definitely something We're very interested in a sport. Reiter. We believe that's the future personalization of the experience watching and engaging with sports. >> It's interesting, though, gives so much of the sports is the communal effect, right? I mean, so much of so much of the greatness of sports is that, you know, two people from different sides of the city can come together and stand shoulder to shoulder and root for their team. So I don't know. Is there some some downside to >> the civilization >> because they kind of or does. It doesn't support the community, because now I hang out with a bunch of other messy A fans and you hang out with their own, although family curious, kind of where personalization fits with community in kind of engaging >> with think baseball park, you know, putting the move to send that and a nice curveball, But definitely you, maybe you you have a >> lot of massive fans who, you know, But they may not be watching the game with you, right? So when you're watching at home, then you're gonna have that experience, and that can allow them for more communication with other people who like the same things that you like, right. But really, personalization is out there in and it's everywhere, right? Like you're everything that you're getting it more and more targeted and we want to avoid you was one of always spam, right? So if anything, a message, that is, if somebody wants to sell your allow those shirt while you're ah, big messy fund, you're probably not gonna like seeing that ad right. So and neither the advertiser will want to advertise you something that you? Definitely not like so that's exactly >> yeah. No, it's interesting. One of my favorite lines about Big Data, right is when it's done well, it's magic. And when it's done poorly, it's creepy. Definitely. Make sure you're gonna tell me the right jersey and other wrong. Absolutely. Alright. Well, Javier. Well, thanks for taking a few minutes. And good luck to your to your two. Ah, entrance into the finals. >> Absolutely. I Thank you so >> much for the opportunity, Jeff. And you're looking forward to seeing the finals >> here. All right. He saw me have Jeff, You're watching. The Cube were in Oracle Park on the shores of McCovey Cove. Thanks for watching. We'll see you next time.

(upbeat music) >> Welcome back, everybody, Jeff Frick here with theCube. We are at Oracle Park, formerly AT&T Park, recently named Oracle Park. Right on the shores of McCovey Cove, in downtown San Francisco. We haven't been here since Sport's Data, I think 2014. I can't believe it's been five years. So maybe now the Giants' situation will turn as we make a run for the pennant. We're here at a really interesting event, it's called Sports Tech Tokyo World Demo Day. And we're here with kind of the master of ceremonies, if you will, he's Mike Proman, the Managing Director of Scrum Ventures. Mike, great to see you. >> Great to be here. Thanks again for the time. >> Absolutely. So what is this day all about? Give us the low down. >> Yeah so, start up frenzy, right? Sports tech community's just in it's infancy right now. There's a lot of fragmentation though, in this world. And how do we best connect start ups to best-in-class companies, right? Japanese companies, there's a lot of excitement in Japan right now. We have Rugby World Cup coming up next month, we have the Olympics next year. How do we enable the start up community to realize those opportunities from a partnership perspective? So, we set out on this journey about a year ago. Bringing together companies of all different stages, all different geographic regions, and all different areas of focus within sports tech. And our job was to connect them to opportunities in Japan. What we kind of uncovered along the journey right, is that this is a community. And that we're building a platform here that transcends Asia, right. We want to help this community, and whether it's connecting them with the venture audience, or otherwise, we feel this is a great reflection of innovation coming in to this industry. >> Now you took kind of an interesting tact. You've called them, before we turned the cameras on, kind of a cohort, kind of an incubator, not really an incubator. So how is this thing structured, how do people get involved? What are some of the benefits of being part of this group versus out there slogging it on your own? >> Well, absolutely, and I think everyone's first reaction is, oh, this is just another accelerator, right? And we've really made a point of not identifying ourselves as an accelerator, for a variety of reasons. Number one, it's a stage-agnostic cohorts, right. So a lot of the companies that are representative here today, the 159 in our cohort, they've raised 10, 20, 30, $40 million. In many respects, they're all grows up, right. They don't need a quote unquote, a traditional accelerator. But our reality is, everybody needs acceleration. And particularly in Asia, Japan in particular, right? You need allies, you need advocates, you need facilitators. And people who are going to help revenue optimization, as well as just breaking the door in some cases. There's a lot of high profile content coming to that region, and if we can help people, it all comes back to us, long term. >> Right, right. And then the other piece, obviously, is the investment piece. 'Cause you work with a number of Japanese investment firms, so that's really kind of part of the, you know, we're sitting in San Franscisco, the event's called Tokyo, the Olympics are a year way, and you're from the Mid-West. So, you're kind of bringing it all together here in San Franscisco. >> You know, sport is the great unifier, right. So this is a great opportunity for us to speak to other industries, and bring the venture community into this conversation. Because, as you know, it's about top-line growth for a lot of these startups, but in many cases, they need capital to be able to accelerate into that growth. And so, you know, it's a very exciting time, and we're here to help support everybody. Our DNA, we're investors, right. We're a venture capital firm. But at the end of the day, what ends up happening is, these companies needs advocacy and connections, and that's what we're here to provide. >> Right, so, you said 100 plus companies in cohort. So, there's a lot of things going on in sports tech, but what are some of the really oddball ones that you're seeing a little further out than maybe most people aren't thinking about. >> Yeah, you know, the trends to me that I'm really excited about personally, are those opportunities that transcend the industry, right. Where is there opportunity for us to democratize things, from just a lead athletes, right, into things that you and I both need. So look at athlete performance. Look at recovery health, as an industry focus, right. Hydration, you look at mental health, sleep health, dietary health, you know. Players of the Giants, they need that, right? But you and I need that too. So where are those technologies that are innovators or thought leaders and leading the way in those spaces? The nice thing about Sports Tech Tokyo is we focus in athlete performance, stadium experience, and fan engagement, right. And there are 13 sub-categories, so it's a very broad based cohort, a lot of different areas of expertise. But bringing them all together is what's most rewarding. >> What's your favorite piece of it? I mean, it's hard to pick your favorite kid, but a couple of interesting companies in the portfolio that you'd like to highlight. >> Everyone's always saying, oh, you put me on the spot. No, absolutely not, Jeff. But in reality, my background is, I've been an entrepreneur for 10 plus years before this. And I've worked with brands like Coca Cola, and the NBA. What excites me most-- >> So we framed you up with a Coke bottle, by the way. >> Thank you very much. That was a nice product placement there. The nice thing is, I'm seeing technology today that didn't fundamentally exist a year or two ago. So I could tell you my favorite right now, in 2 weeks that might be entirely different, right. You're going to meet somebody from Misapplied Sciences, and they are doing some of the most breakthrough, cutting edge tech that, it's mind boggling, in terms of what they can do. And what's great about a company like Misapplied, is that they're doing it in sports, but they're also doing it in retail, and other high-dense environments. And so to me, those are the winners in this cohort. The ones that can transcend sport, and add value to so many other places. >> Right, so, before I let you go, you got a busy day ahead. What's the run of the day, what should people expect who are coming through the gates here at Oracle today? >> Well I said this is not your traditional accelerator. Well, this is not your traditional demo day, by any means, right. Traditionally, demo day is a bunch of company pitches, and then there's maybe some conversation afterwards. To us, this is a celebration of a broader cohort, right. Our 100 plus mentors that make up the Sports Tech Tokyo community. And we wanted to celebrate those individuals, right. The 100 mentors, the 400 plus attendees we have here today. So, think of it as an extended cocktail party, right. We want people to connect, and connect at scale. And so that's the back half of the day. The front half of the day is more content oriented. We have a lot of industry experts, again, common theme is transcending the vertical. Looking at opportunities to bring the venture community into the conversation. >> All right, well Mike, good luck and have a great and very busy day. >> Yeah, thank you so much. Appreciate it Jeff. >> He's Mike, I'm Jeff, you're watching theCube. We're at Oracle Park in San Francisco on the shores of McCovey Cove, thanks for watching. We'll see you next time. (upbeat digital music)

(upbeat music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Oracle Park on the shores of McCovey Cove. We're excited to be here. It's a pretty interesting event. Sports Tech Tokyo World Demo Day. It's kind of like an accelerator, but not really. It's kind of like YCombinator, but not really. It's a little bit different, but it's a community of tech start-ups focusing on sports with a real angle on getting beyond sports. We're excited to have our next guest who's an investor and also a mentor, really part of the program to learn more about it, and she is Gayatri Sarkar, the managing partner from HYPE Capital. Welcome. >> Thank you. Thank you for inviting me here. >> Pretty nice, huh? >> Oh, I just love the view. >> So you said before we turned on the cameras... Well, first off, HYPE Capital, what do you guys invest in? What's kind of your focus? >> So HYPE Capital is one of the biggest ecosystem in sports, which is HYPE Sports Innovation. We have 13 accelerators all around the world. We are just launching the world's first Esports accelerator with FC Koeln and SK gaming, one of the biggest gaming company. So we are part of the ecosystem for a pretty long time. And now we have HYPE Capital or VC Fund investing in Europe, Israel, and now in US. >> So you mentioned that being a mentor, as part of this organization, as something special. Think you're the first person we've had on who's been a mentor. What does that mean? What does it mean for you, but also what does it mean for all the portfolio companies? >> Sure. I'm a mentor at multiple accelerators, but being a part of Sports Tech Tokyo, I saw the very inclusive community that is created by them. And the opportunity to look at various portfolio companies and also including our portfolio companies as part of it. One of our portfolio company where we are the lead investors, Fund with Balls, they are part of this. So-- >> What's it called? Fun with Balls? >> Fun with Balls, very interesting name. >> Good name. >> Yeah. (laughing) They're from Germany and they came all the way from Germany to here. So, yeah, I'm very excited because as I said, it's an inclusive community and sports is big. So we are looking at opportunities where deep techs, where it can be translated into various other verticals, but sports can also be one of the use cases. And that's our focus as investors. >> Right. You said your focus is really on AI, machine learning. You have a big data background, a tech background. So when you look at the application of AI in sports, what are some of the things that you get excited about? >> Yeah, so for me, when I'm looking at investments, definitely the diversification of sports portfolio, how can I build my portfolio from Esports gaming, behavioral science in sports to AI, ML, AR opportunities in material science, and various other cases? Coming back to your question, it's like how can I look into the market and see the opportunities that, okay, can I invest in this sector? As I said, what's the next big trend? And that's where I want to invest. Obviously, founder market fit, product market fit, promise market fit because there's the fan engagement experience that you get in sports, not in any other market. The network effect is huge and I think that's what we VCs are very excited in sports. And I think this is, right now, the best time to invest in sports. >> So promise market fit, I've never heard that before. What does that mean when you say promise market fit? >> Interesting question. So promise market fit was coined by Union Square Venture VC Fund. And they think that where there's the network effect, or your engagement with your consumers, with your clients, with your partners, can create a very loyal fan base and I think that's very important. You may see that in other technology sector, but it is completely unparallel when it comes to sports. So I request all the technologies that are actually trying to build their use cases. They should focus on sports because the fan engagement, the loyal experience, they opportunities, you'll not get anywhere else. >> And I think this is the market that I and other investors are looking forward. If deep tech investors and deep tech technologies are coming into this market, we see the sports ecosystem, not to be a trillion-dollar, but a multi-trillion dollar market. >> Right. But it's such a unique experience, though, right? I mean, some people will joke their fans don't necessarily root for the team, they root for the jersey, right? The players come and go. We're here at Oracle Park, which was AT&T Park, which was SBC Park, which was I can't even remember. Pac Bell, I think, as well. So is it reasonable for a regular company that doesn't have this innate, kind of, a connection to a fan base that a lot of sports organizations do that's historical and family-based, and has such deep roots that can survive, maybe, down years, can survive a crappy product, can survive, kind of, the dark days and generally they'll be there when things turn back around. Is that reasonable for a regular company to try to get that relationship with a customer? >> So you asked me one of the most important question in the investor's relationship or investor's life, which is the cyclicality of the industry. And I feel like sports is one industry that has survived the cyclicality of that industry. Because, as you said, a crappy product will not survive. You have to focus on customer service. You have to focus that, okay, even if you have the best product in the world. How can I make my product sticky? I think these are the qualities that we're looking into when we are investing in entrepreneurs. But the idea is that if we are targeting start-ups and opportunities, our focus is that, okay, you may have the world's best product, but the founders should have the ability to understand the market. Okay, there are opportunities. If you look at Facebook, if you look at various other companies, they started with a product, which maybe, okay, friends saw a dating site and they pivoted. So you need to understand the economy. You need to understand the market. And I think that's what we are looking into the entrepreneurs. And as to answering your question, the family offices, they're actually part of this world start-up ecosystems. They're seeing if there's an opportunity, because they're big, they're giant, and they're working with legacy techs like Microsoft, Amazon. It's very difficult for the legacy techs to be agile and move fast. So it's very important for them if they can place themselves at a 45 degree angle with the start-up ecosystem and they can move faster. So that's the opportunity for them in the sports start-up ecosystem. >> All right. Well, Gayatri, thanks for taking a few minutes and hopefully you can find some new investments here-- >> No, thank you so much. >> over the course of the day. >> Thank you so much for your time. >> Absolutely, she's Gayatri, I'm Jeff. You're watching theCUBE. We are at Oracle Park on the shores of historic McCovey Cove. I got to get together with big John and practice this line. (laughing) Thanks for watching. We'll see you next time. (upbeat music) >> Camera Crew: Clear. >> Jeff: John Miller. >> Gayatri: Oh, yeah.

(upbeat music) >> Hey welcome back everybody, Jeff Rick here with theCUBE. We're at Oracle Park, recently AT&T Park just renamed, it's a beautiful day. Home of San Francisco Giants, they're on the road, we're here at a pretty interesting event, it's called Sports Tech Tokyo World Demo Day, brought together coalition of about 100 startups. 25 of them are giving demos today on technology as it relates to sports but even more importantly, that can then be used in others beyond sports. We're excited to have an athlete on, not just another tech, crazy guy. He's Brendan Harris, he's an athlete and resident at SeventySix Capital. Brendan, thanks for stopping by. >> Thanks for having me. >> So what is that, I've heard principles and entrepreneur residence\\\, what does a athlete residence do? >> It is essentially a play on the entrepreneuring residence. I was introduced to SeventySix Capital, I finished playing at 15 and I was doing my MBA at Wharton and in Philly, and got introduced to Wayne and the guys at SeventySix and they are kind of putting together an athlete venture group where they're bringing in a lot of athletes that want to be investors and kind of providing them access to deal flow. And then also leveraging their social capitals, so, he was kind of tickled when he coined the term athlete in residence and threw it on my business card and that's where we're at. >> Right so I'm just curious, your perspective as an athlete as you look around at all the technology that's going into sports, right. Kind of the big categories are that which helps the players play better, there's that which helps the people run the teams better, and then there's that which is really kind of part of the fan experience, I mean, you actually had to go down and try to put wood on a ball coming at you 90 plus miles an hour, all this other stuff, do you see it as interesting, is it a distraction, is it entertaining? How do you look at from an athlete's perspective? >> So, yeah, so a lot to impact, so, first of all, I have this equal view of fascination and frustration where a lot of this wasn't around when I was playing, certainly from the field, now we're taking in things like recovery and rest and sleep, but I think players and me personally, are fascinated with how can we improve on field performance and I think baseball's such an imperfect game and you fail so often. Being able to turn things that were previously subjective and apply data and tech to make them objective and give you answers, I think it's fascinating. The ways that we can use data to kind of promote performance and health and all those things are very fascinating. So from a player's point of view, we are all about it but at the same time, I think this is why I've loved to get into sports tech is there's a lot of data that's just noise that's coming in and things and so the tough part is kind of weeding through and what is actionable info and what can actually help improve beyond field performance and then, along with that, we want to feel the product on the field, but also what what the services for the consumer and the fans are and how can we improve that and then engage them because certainly sports are a part of the culture and part of life now and it's fascinating, these fans want to know more and more and more, certainly what's going on and it's been a great journey. >> Right so on the fan experience specifically, we've been here a number of years, Bill Styles' a good friend of mine, and another Wharton grad. And talking about high density WiFi and the app on your phone and food delivered to your seat, I mean as an athlete on the field, do you look at kind of of all these things as a distraction, do you appreciate it's more competitive environment these days in terms of people's attention and kind of that entertainment dollar but I would imagine from the between the lines it looks like, hey, the game's down here people. >> Yeah. (laughing) It's been interesting because one of the problems major league baseball's been trying to address is pace of games right? And if you really look at the data, they're not that much longer. What's different, we're wired differently, right? So our attention spans are shorter and we're constantly addicted to our technology. So these guys like Bill, are trying to leverage that and try to have your food delivered and try to increase the social component, increase the value in the in-venue experience so that you're not only watching the game but you're social enjoying it at the same time and kind of filling those gaps. A lot of it is, yes, and I think, there has been balls flying into the stands since baseball's been playing but the need to put the netting up has come a lot of times because nobody's watching. Some people aren't, not nobody, but a lot of people aren't watching the games are getting hit with a lot of these foul balls. So there's that component, where there's some unbelievable things are going off on the sides but it's baseball still going to be kind of very similar within the confines of lines. >> The other piece that I find really interesting on the data side right, is there's so much data, right? There's data, data, data. Obviously baseball's built on data and arguments about data and conversations about data. But now it's kind of gone to this next gen with wins over replacement and all these other things, but sometimes it's funny to me. It feels like they're forgetting the object of the game is to win the game and it feels like sometimes the metadata has now become more important than the data. Did you win or lose and it's not necessarily being used as a predictor for future performance but it's almost like a stand alone game in and of itself. We forget the object is to win the game and win a championship, not to have the highest award number. Do you sense that frustration, does that sound like something you see-- >> Yeah, I think what you're getting into a lot of times is how are we making decisions, right and in the game a lot of times people forget that human beings are out there performing and so I think that's how we've gotten into Moneyball 2.0 when looking at development. Certainly mental health in focus and game preparation have come into play more and you're seeing some managers, Mickey Callaway just came out said 80% of my distances go against the data which I thought was a little bit interesting but so there is that fine line where you have to filter in what's noise and what's actionable and at the same time, allow your managers and your decision makers some flexibility to go with they're there in the heat of the battle and they kind of of know their guys and they know the human element that's involved. It's an interesting balancing act. >> Right so from your new job and your new role, what are some of the things you hope to see today, what are somethings that you're excited about from an investor and in having played the game as well as looking forward to the evolution of sports? >> Two things, specifically how the, I'm certainly biased to the performance on the field, and the human element and certainly, everybody wants workout secrets and I don't feel like it's, whether it's athletes or the kind of weekend warrior or people that are senior citizens. I don't think it's as simple as, this is work and you should do this, it's a very personalized experience now and I think some of this personalized digital fitness is fascinating to me and then how it relates to and how your body relates to your diet, your nutrition, your sleep, your recovery, I think all those are fascinating that advances that I want to look into more. And then second is, as I kind of mentioned, is the fan engagement aspect and how do we drive those fans, that digital, and make it actionable and monetized, right. So that you have your fans that are following your Facebook, your Twitter, and all those things and so how do you, not only engage them but collect that data and then kind of personalize that experience, engage your fan in a way that can kind of grow your brand. It will be interesting to me. >> Really interesting to have your perspective and I'm sure it will be a great day and you'll see all kind of crazy stuff. So thanks for taking a few minutes. >> Yeah, anytime, thanks for having me. >> All right, he's Brendan, I'm Jeff, you're watching theCUBE. We are at Oracle Park in San Francisco, thanks for watching, we'll see you next time. (upbeat music)

(upbeat techno music) >> Hello everyone, I'm John Furrier, the co-founder and co-CEO of SiliconANGLE Media. I'm often asked a lot about our business and what our business model is. In the wake of media these days, media businesses are not doing well. Some of them are doing better than others. And today a whole new model of media is changing. I get the question all the time from people, what is theCUBE, what is SiliconANGLE? What is Wikibon? You guys have all the software. I want to take some time to explain what the SiliconANGLE Media business is about. And I'm often asked many times, how does it all work together? So I want to take some time to review that. And I've prepared some slides to take us through this, but I also wanted to show you how it works. Some of the technology that we've built, and also some of the things that we offer to our clients and advertisers or marketers, although we don't have any advertising per say. We do have an interesting business model. I want to share that with you. So let's take a look at some of the slides. SiliconANGLE Media is a new model for media, digital TV, journalism, and research. We provide a unique formula that all works together, but yet individually. We have three major parts to our business. We have theCUBE, which is our digital TV interview show where we go out to events and extract the signal from the noise. We have SiliconAngle.com. News and event coverage. This is our technology journalism. This is the site that really focuses on you know editorial, high quality news and analysis, kind of what's happening, instructing the signal of what's going on in the industry. It really is a short cut to the truth of what's happening. And then Wikibon is or consulting and research team that focuses on the key areas that we program into. And all three of them work together. Think of SiliconANGLE Media as three legs of the stool. SiliconANGLE.com is the journalism, which engages in with the industry. Getting all the top stories, telling the most important stories in Enterprise technology, working with public relations professionals and people in the industry to source the best interviews and get the best content, objective and truthful and again, pure editorial. This site has no advertising on it, and it's completely supported by the sponsorship and business model from theCUBE and our Wikibon research team. TheCUBE is a interesting dynamic because we've been for nine years going to events and going to the front lines where the communities are. And theCUBE has become a community brand, a community content source. But we co-create content in the front lines during an event with the community to tell the best stories. Not only news and editorial, but really what's going on in the people's lives and what's happening in technology. And finally Wikibon is where all the action happens. That's our big brains in our organization that dig in and do the analysis from (the data) that. And then this really kind of gets rendered itself into a couple different sites. SiliconANGLE.com and theCUBE.net. And our coverage areas are really focused in and around  key areas and digital. Our content revolves around the core content markets and communities we cover. Infrastructure, Cloud, AI, big data software, blockchain and crypto. And the intersection of these markets is security data and IOT, but this is really the digital landscape. There is no circulation in digital. There is not real boundaries to content, but for us we focus and use our technology to understand where these lines are in the industry, and we program to them. And we program in a deep targeted way that creates network effects in each community. So if you look at this, we interview the most important people we can and the smartest people we can. And that creates a beautiful network effect. And we create community by streaming live event coverage for major events. That's what we're most well known for is theCUBE. 110 events last year. Our ninth year covering all the top enterprise, all the top Cloud events, all the top big data events, and soon all the top block chain events. Our formula drives activation, but because the content is so targeted around communities, it really creates a targeted network effect because everyone we interview becomes a Cube alumni, and everyone that consumes the content becomes part of our community. So content and community drives engagement. Let's take a look at what this means for our customers. Our audiences go to siliconANGLE.com, where on this site all these stories are led by Rob Hof, editor in chief. And this content here is the best of the best. Everything is editorially vetted. Nothing is paid for in this site. It's completely editorial. We have multiple sections. We have research. A section dedicated to our research analysis. This is where we do deep dives and provide special reporting around all the top important areas. Cube coverage is the section of SiliconANGLE that puts all theCUBE event coverage in one spot. If you want to see the stories that our writers cover from theCUBE, which is separate from theCUBE event itself, but our live writers look at the activity on SiliconANGLE, CUBE and cover it as best they can. And if an important story is happening at a CUBE event, it'll be on the front page of SiliconANGLE, and the editors will pick the best, most important stories here at SiliconANGLE. TheCUBE.net is our site where we have all theCUBE content, a featured section here. There's a live event going on. The content will be played right here in the screen. If there's multiple events going on, then the right hand side they'll be there. Upcoming events are here. You can view more, and of course if you missed an event, you can always look for more here and browse the site for all the events that have happened. And of course if you want to search, we have an alumni database to search all the most important people in tech. If you want to search all the people from say, you know Google, you can browse here and find people to connect with. And this is the beginning of some of our technology that we've built, that you're only see more of. Connecting people around content, people around community, and people around topics and interests. And of course if you want to meet our hosts, they're all listed on there too. TheCUBE.net site is software written by our software engineering teams that's built for fully Cloud horizontally scalable systems, asynchronous technologies, APIs, and a lot more will be coming. You'll see social network, you'll see video clips and other variety of things. Some of the most important technology that we have at SiliconANGLE that no one knows about with theCUBE is we have a variety of technologies. You look at this site here, we have a full dashboard of things that we've built for ourselves using Amazon web services. We built our own content Cloud for our business. We can do search, analyze, visualization. We can detect humans from bots, text analytics, entity extractions, machine learning, leader boards, CUBE leader boards, LinkedIn profiles, who, what, and where, trend analysis, influence or overlaps, really in-depth analysis where I can say give me all the AWS reinvent community with VM World, as an example. I'll type it in here, VM World. Type my email address. And our influence overlap engine will go out and determine who are the influences that overlap between those two communities. I can do that for many more communities. This helps us figure out what's going on. And of course we built our own custom listening engine that listens to every tweet of every single person in the Twitter fire hose by community. And we have hundreds of hundreds of communities. And to give you a taste of how much this is, you look at the stats, 62 million total people over 700 million signals, and we're pulling in 292 signals per minute into our ingestion, into or community. That's driving a lot of our engagement, and again, going back to here we can see we can do full search, all kinds of cool things, trending hashtags. This gives our writers and our community more insight into what's happening so we can bring the most important content to people and connect people to the content. Some of our digital services include video clip, a service that we built with our team, that allows us to search and clip videos. So let's take an example. Here's an interview I did at Google Cloud, and here's our Video Clipper service. Here's the YouTube video and a full transcript. I can put it into different languages. Looks like we have a Korean interest here. I can turn this into Korean or English or Chinese. Or I can say, highlight the summary for me. Every CUBE video gets a full transcript. It says, takes advantage of it here. I can come down here. Every piece of the transcript is linked to the video. So if I want to highlight something, like this, I can highlight this. And here's an example of a clip. Thank you very much. I can share this on Twitter instantly. Or Facebook or LinkedIn. So we can, we index every single video from, like it's uploaded to YouTube, into a full transcript. And that transcript is available for that. We can run machine learning and AI techniques, do any of the extractions, transcripts, and we're starting to do that so we can drive more community around the video. Let's go look at my Twitter feed and show where that clip came up. So the ability to clip videos is super important. There's the video, Google spanner in production. So this video was clipped from a YouTube video that has a unique URL, cube365.net that now we can measure that metadata and offer that nugget of that video and share that to the world. This is unique in that you can take pieces of the video and share them throughout the social web, allows for videos to be merchandised. So a CUBE interview that could be 15 or 20 minutes can now be cut down into multiple nuggets. This is great value, and you can roll these clips up from different videos into a highlight reel by the click of the button. We've automated the hard part of using video so that we can bring video onto the marketing mix for our clients and bring video in the center of the user experience for content consumption. Okay, so here's a real life example of how the Clipper tool can work, as these clips can be merchandised down into gold nuggets or pieced down by part of a bigger video. Certainly it changes the nature of video, whether it's in the marketing mix for a marketer or brand or for us as content developers serving audiences. If you have a piece of content that's in video form, it's a data asset. That data asset then can be used. Here's an example. On Twitter we were having an argument, as usual on Twitter, about who's number one in Cloud. My friend, Bob Evans, said Microsoft is number one in Cloud. And that's his position, like him, but I'm not, you know that's him. We disagree, I said Amazon. An ongoing Twitter battle ensued. He called me out, I called him out. We're all friends, but it's all good fun. And you can see here, what's happening. Hey John, if you're going to go down that type of path, you know how about taking some koolaid injection from the Silicon Valley world. Right, and so I come back. And he goes back again. So finally what's interesting is that Dave Vellante, co-host of theCUBE and my business partner, realized and remembered that he was with me during theCUBE in Washington DC and had a clip, and he sent it here. Furrier, the pressure to catch up with the Amazon experience. And here is an example of why these clips are so powerful. During this conversation that could have gone anywhere, the content needed information. And Dave Vellante then injected content from a video clip of a long interview, and that was a 15 minute interview. And a short sound byte, here it is. >> You say you're doing Cloud, but as they teach you in business school, there's dis-economies of scale trying to match a trajectory of an experienced Cloud vendor. You just mentioned that. Let's explore that. If I want to match Amazon's years of experience, I can say I'm up there with all these services, but you can't just match that over night. It's just dis-economy of scale. Reverse proxies, technical debt, all kinds of stuff. So Microsoft, although looking good on paper, is under serious pressure, and those dis-economy scales creates more risk. That more risk is more down time. We just saw 11 hours of down time on Microsoft Azure than Europe, 11 hours. 11 hours, it's massive, it's not like oh, something just happened. >> Hey, there it is, a clip that was short, part of a longer video. You can always watch it here, that we cut up and created. It instantly changed the nature of the conversation. That's a great example of other things. Let me show you some other tools here, with Video Clipper. That's one example. Certainly we have the notion of creating clip lists. So here's a highlight reel that I put together of Pat Gelsinger's best highlights. I took three, five, four clips and I made it into one beautiful asset. That's Andy Jassy's keynote from VM World. >> Today I'm excited to announce the availability of our, let's talk about that one. We've received hundreds of priorities. >> This is an example. I took a keynote and broke it down into a highlight reel there. There's other clip lists, other CUBE videos, got great stuff, here's the highlights from VM World 2017 that was put together. Look at all those clips. These are different clips. You check a box and you said clip list, creates a highlight reel. You can do this for things like sales enablement. A sales rep could put some clips together and send it to a prospect via email and say here's a minute and a half of our smartest person talking about x. See ya in later for a meeting. It could be used for content to support an article. It could be used to support an argument. It could be used to support a positive thing. This is content for good. This is what we do, and of course, this is all available to our team and also our customers. The best part of all, if I want to find out what's going on with block chain, I can just type into the search engine. We solved the video search problem. I can click on a link and find all I want to do about block chain. Like I say, well, just give me all the clips that have block chain in it. Or give me when there's a block chain mentioned in all the transcripts. So anytime the word block chain is mentioned in any of our videos, we can surface that quickly. 220 clips, I can type in backup. If you're interested in backup and recovery, you can do that. Multi Cloud. Making videos more productive, integral part of the marketing mix is what the purpose of this is. And this is all part of comprehensive back end technology that we're using for our system. So SiliconANGLE Media is not just three properties. It's a coverage area that has technology behind it that you can look at and say, we cover Cloud, we go to the top events in Cloud, we go to the top events in Infrastructure, the top events in AI and big data, and the top events in each of these markets. And we share as much content as possible with theCUBE, SiliconANGLE, and Wikibon. The fastest, most relevant content and engage the community, and we collaborate with them. It's a co-creation business model that has monetization and money making around sponsorships and co-creation. And we make money by monetizing our digital services via our content Cloud, Video Clipper, and data services that help marketers with the co-creation and help them find community, grow community, and create a content market with community. Content plus community equals engagement. Those are the things that are mattering right now. And all of this is happening off someone's website, in the wild, organic discovery. This is the new marketing model that we're taking advantage of, creating a network effect with great content. That's how it works. And of course, we're excited to continue to push the envelope and grow. If you have any questions, I'm happy to talk at any time. You can reach out to me, Dave Vellante, Stu Miniman, Greg Ontario, and any of our team. Kent Libbey, Jeff Rick, and our entire sales organization. Of course, Rob Hof, editor in chief. Peter Burris at Wikibon, and Jeff Rick at theCUBE. Thanks for watching. If you have any more questions, happy to do this next time. We'll give you an update on what's going on with or crypto currency community that we're doing. Thanks for watching. (techno music)