[Music] welcome everyone to thecube's presentation of the aws startup showcase data as code this is season two episode two of our ongoing series covering exciting startups in the aws ecosystem to talk about data and analytics i'm your host lisa martin i have a cube alumni here with me socket sarah the ceo and founder of nexla he's here to talk about a future of automated data engineering socket welcome back great to see you lisa thank you for having me pleasure to be here again let's dig into nexla's mission ready to use data in the hands of every user what does that mean that means that you know every organization what what are they trying to do with data they want to make use of data they want to make decisions from data they want to make data a part of their business right the challenge is that every function in an organization today needs to leverage data whether it is finance whether it is hr whether it is marketing sales or product the problem for companies is that for each of these users into each of these teams the data is not ready for them to use as it is there is a lot that goes on before the data can be in their hands and it's in the tools that they like to work with and that's where a lot of data engineering happens today i would say that is by far one of the biggest bottlenecks today for companies in accelerating their business and being you know truly data-driven so talk to me about what makes nexla unique when you're in customer conversations as every company these days in every industry has to be a data company what do you tell them about what differentiates you yeah one of the biggest challenges out there is that the variety of data that companies work with is growing tremendously you know every sas application you use becomes a data source every type of database every type of user event anything can be a source of data now it is a tremendous engineering challenge for companies to make the data usable and the biggest challenge there is people companies just cannot have enough people to write that code to make the data engineering happen and where we come in with a very unique value is how to start thinking about making this whole process much faster much more automated at the end of the day lisa time to value and time to results is by far the number one thing on top of mind for customers time to value is critical we're all thin on patients these days whether we're in our consumerizer our business lives but being able to get access to data to make intelligent decisions whether it's on something that you're going to buy or a product or service you're going to deliver is really critical give me a snapshot of some of the users of nexla yeah the users of nexla are actually across different industries one of the main one of the interesting things is that the data challenges whether you are in financial services whether you are in retail e-commerce whether you are in healthcare they are very similar is basically getting connected to all these data systems and having the data now what people do with the data is very specific to their industry so for example within the e-commerce world or retail world itself you know companies from the likes of bed bath beyond and forever 21 and poshmark which are retailers or e-commerce companies they use nexla today to bring a lot of data in uh so do delivery companies like dodash and instacart and you know so do for example logistics providers like you know narwhal or customer loyalty and customer data companies like yacht pro so across the board for example just in retail we cover a whole bunch of companies got it now let's dig into you're here to talk about the future of automated data engineering talk to me about data engineering what is it let's define it and crack it open yeah um data engineering is i would say by far one of the hottest areas of work today the one of the hardest people to hire if you're looking for one data engineering is basically um all the code you know the process and the people that is basically connecting to their system so just to give a very practical example right for um for somebody in e-commerce let's say a take-off case of door dash right it's extremely important for them to have data as to which stores have what products what is available is this something they can list for people to go and buy is this something that they can therefore deliver right this is data that changes all the time now imagine them getting data from hundreds of different merchants across the board so it is the task of data engineering to then consume that data from all these different places different formats different apis different systems and then somehow unify all the data so that it can be used by the applications that they are building so data engineering in this case becomes taking data from different places and making it useful again back to what i was talking about ready to use data it is a lot of code it's a lot of people not just that it is something that runs every single day so it means it has monitoring it has reliability um it has performance it has every aspect of engineering as we know going into it you mentioned it's a hot topic which it is but it's also really challenging to accomplish how does nexla help enable that yeah data engineering is quite interesting in that one it is difficult to implement you know the the necessary sort of pieces but it is also very repetitive at some level right i mean when you connect to say 10 systems and get data from them you know that's not the end of it you have 10 more and 10 more and 10 more and then at some point you have thousands of such you know data connectivity and data flows happening it's hard to maintain them right as well so the way nexla gets into the whole picture is looking at what can we understand about data what can we observe about the data systems what can be done from that and then start to automate certain pieces of data engineering so that we are helping those teams just accelerate a lot faster and it i would say comes down to more people being able to do these tasks rather than only very very specialized people more people being able to do the tasks more users kind of democratization of data really there can you talk to us in more detail about how naxa is automating data engineering yeah i think um you know i think this is best shared through a visual so let me walk you through that a little bit as to how we automated engineering right so if we think about data engineering three of the most core components are many parts to it but three of the most core components of that are integrating with data systems preparing and transforming data and then monitoring that right so automating data engineering happens in you know three different ways first of all connecting connecting to data is is basically about the gateway to data the ability to read and write data from different systems this is where the data journey starts but it is extremely complex because people have to write code to connect to different systems one part that we have automated is generating these connectors so that you don't have to write code for that also making them bi-directional is extremely valuable because now you can read and write from any system the second part is that the gateway the connector has read the data but how do you represent it to the user so anybody can understand it and that's where the concept of data product comes in so we also look at auto generating data products these become the common language and entity that people can understand and work with and then the third part is taking all this automation and bringing the human in the loop no automation is perfect and therefore bringing the human in the loop means that somebody who is an expert in data who can look at it and understand it can now do things which only data systems experts were able to do before so bringing in that user of data directly into the into the picture is one important part but let's not forget data challenges are very diverse and very complex so the same system also becomes accessible to the engineers who are experts in that and now both of these can work together while an engineer will come through apis and sdk and command interfaces a data user comes in through a nice no code user interface and all of these things coming together are what is accelerating back to that time to value that really everybody cares about so if i'm in marketing and i'm a data user i'm able to have a collaborative workflow with the data engineer yeah yeah for the first time that is actually possible and everybody's focuses on their expertise and their know-how so you know um somebody who for example in financial services really understands portfolio and transactions and different type of asset classes they have the data in front of them the engineers who understand the underlying real-time data feeds and those they are still involved in the loop but now they are not doing that back and forth you know as the user of data i'm not going to the engineer saying hey can you do this for me can you get the data here and that back and forth is not only time taking it's frustrating and the number one hold back right yeah that and that's time that nobody has to waste as we know for many reasons talk to me about when you look into your crystal ball which i'm sure you have one what is the future of of data engineering from nexus perspective you talked about the automation what's the future hold i think the future of data engineering becomes that we up level this at a point where um companies don't have to be slowed down for it um i think a lot of tooling is already happening the way to think about this is that here in 2022 if we think that our data challenges are you know like x they will be a thousand x in five years right i mean this complexity is just increasing very rapidly so we think that this becomes one of those fundamental layers you know and you know as i was saying maybe the last time this is like the road you know you don't feel it you just move on it you do your job you build your products deliver your services as a company this just works for you um and that's where i think the future is and that's where i think the future should be we all need to work towards that we're not there yet not there yet a lot of a lot of potential a lot of opportunity and a lot of momentum speaking of momentum i want to talk about data mesh that is a topic of a lot of excitement a lot of discussion let's unpack that yeah i think uh you know the idea that data should be democratized that people should get access to the data and it's all coming back to that sort of basic concept of scale companies can scale only when more people can do the relevant jobs without depending on each other right so the idea of data democratization has been there for a long time but you know recently in the last couple of years the concept of data mesh was introduced by zamak digani and thoughtworks and that has really caught the attention of people and the imagination of leadership as well the idea that data should be available as a product you know that democratization can happen what is the entity of the democratization that's data presented as a product that people can use and collaborate is extremely powerful um i think a lot of companies are gravitating towards that and that's why it's exciting this is promising a future that is you know possible so second speaking of data products we talked a little bit about this last time but can you really help us understand see smell touch feel what a data product is and give us that context yeah absolutely i think uh best to orient ourselves with the general thinking of how we consider something as a product right a product is something that we find ready to use for example this table that i'm using right now made out of raw materials wood metal screws somebody designed it somebody produced it and i'm using it right now when we think about data products we think about data as the raw material so for example a spreadsheet an api a database query those are the raw raw materials what is a data product is something that further enriches and enhances that entity to be much more usable ready to use right um let me illustrate that with a little bit of a visual actually and that might help okay um the idea of the data product and this is how a data product looks like in next lab for a user to write as you see the concept of a data product is something that first of all it's a logical entity this simply means that it's not a new copy of data just like containers or logical compute units you know these data products are logical entities but they represent data in the same consistent fashion regardless of where the data comes from what format it is in they provide the user the idea of what the structure of data is what the sample data looks like what the characteristics of data are it allows people to have some documentation around it what does the data mean what do these attributes you know mean and how to interpret them how to validate that data something that users often know in an industry how is my data looking like well this value can never be negative because it's a price for example right um then the ability to take these data products that you know we automate by generating as i was mentioning earlier automatically creating these data products taking these data products to create new data products now that's something that's very unique about data you could take data off about an order for a from a company and say well the order data has an order id and a user id but i need to look up shipping address so i can combine user and order data to get that information in one place so you know creating new data products giving people access hey i've designed a data product i think you'll find it useful you can go use that as it is you don't have to go from scratch so all of those things together make a data product something that people can find ready to use again and this is this is also usable by the again that example where i'm in marketing uh or i'm in sales this is available to me as a general user as a general user in the tool of your choice so you can say oh no i am most familiar with using data in a spreadsheet i would like it there or i prefer my data in a tableau or a looker to visualize it and you can have it there so these data products give multiple interfaces for the end user to make use of it got it i like it you're meeting the user where they are with relevant data that helps them understand so much more contextually i'm curious when you're in customer conversations customers that come to you saying saka we need to build the data mesh how is nexl relevant they're how what is your conversation like yeah when people want to build a data mesh they're really looking for how their organization will scale into the future uh there are multiple components to building a data mesh there's a tooling part of it the technology portion there are people and processes right i mean unless you train people in certain processes and say hey when you build a data product you know make sure you have taken care of privacy or compliance to certain rules or who do you give access to is something you have to follow some rules about so we provide the technology component of it and then the people and process is something that companies you know then as they adopt and do that right so the concept of data product becomes core to building the data mesh having governance on it uh having all this be self-serve it's an essential part of that so that's where we come into the picture as a as a technology component to the whole story and working to deliver on that mission to getting data in the hands of every user you mentioned i want to dig into in the last few minutes here that we have uh the target audience you mentioned a few by name big names customers that nexla has you i heard retail i heard e-commerce i think i heard logistics but talk to me about the target customer for nexla any verticals in particular or any company's sizes in particular as well yeah you know the one of the top three banks in the country is a big user of nexla as part of their data stack uh we actually sit as part of their enterprise-wide ai platform providing data to their data scientists um we're not allowed to share their name unfortunately but um you know there are multiple other companies in asset management area for example they work with a lot of data in markets portfolio and so on um the leading medical devices companies using nexla data scientists there are using data coming in real time or streaming for medical devices to train and um and combine that with other data to do sort of clinical trial related research that they do um we have you know the companies for example linkedin is an excellent customer linkedin is by far the largest social network um their marketing team leverages nexla to bring data from different type of systems together as well um you know so are companies in education space like nerdy is a public company that uses nexla for you know student enrollment education data as they collaborate with school districts for example um you know there are companies across the board in marketing live brand you know for example uses nexla so we are um we are you know from who uses nexla is today mostly mid to large to very large enterprises today leverage nexla as a very critical component and often mission critical data for which they leverage us do you see that changing anytime soon as every company these days has to be a data company we expect that as consumers whether it's my grocery store um or my local coffee shop that they've got to use data to deliver me that personalized experience do you see the target audience kind of shifting down to more into mid-market smb space for next level oh yeah absolutely look we started the journey of the company with the thinking that the most complex data challenges exist in the large enterprise and if we can make it no code self-serve easy to use for them we can bring the same high-end technology to everybody and this is exactly why we recently launched in the amazon marketplace so anybody can go there get access to nexla and start to use it and you will see more and more of that happen where we will be bringing even some free versions of our product available so you're absolutely right every company needs to leverage data and i think people are getting much better at it you know especially in the last couple of years i've seen that teams have become much more sophisticated yes even if you are a coffee shop and you're running campaigns you know getting people yelp reviews and so on this data that you can use and understand better your demographic your customer and run your business better so one day yes we will absolutely be in the hands of every single person here a lot more opportunity to delight a lot more consumers and customers socket thank you so much for joining me on the program during the startup showcase you did a great job of helping us understand the future of automated data engineering we appreciate your insights thank you so much lisa it's a pleasure talking to you likewise for soccer sarah i'm lisa martin you're watching thecube's coverage of the aws startup showcase season two episode two stick around more great content coming up from the cube the leader in hybrid tech event coverage [Music]

>>Hey everyone. Welcome to this cube conversation featuring next law. I'm your host, Lisa Martin. And today we are joined by Sukkot Sarab CEO and founder of next, next LA Sukkot. Great to have you on the program, >>Lisa, thank you so much for having me here really excited about this. >>Tell us a little bit about next level. What is it that you guys do? >>Yeah. Um, you know, we are in the world of data and one of the biggest challenges that we face, um, as an industry is there is so much data, so much variety. How do we really get it into the hands of people who use data? And, um, the users of data are all across. You know, they shouldn't have to be engineers. They are across the board in different functions. So next, last purpose and mission has always been ready to use data in the hands of the users. So, um, what misled us today is makes it possible for users across the board, whether those are data scientists, whether those are data analysts, whether those are people in various business functions to get the data that they need in the tools that they work with. So, um, we make that possible in a very, no code way, um, for users to get access to the data. Um, and very uniquely actually do that by automating a lot of the data engineering process. We'll talk more about that, but it's an exciting space to be in. >>It is an exciting space to be in. And of course that the volumes of data could just continue to explode and there's, that will not be slowing down anytime. Soon, as we know in businesses, one of the things we saw in the last two years was businesses pivoting so many times and really needing, going from survival mode to thriving mode, but the ability to harness the power and the insights and data is critical for businesses to be successful these days as consumers. We just expect that if it's our business life or personal life, whoever we're interacting with is going to know what we want, and we're going to be able to display that to us quickly. We think about data match. It's a relatively new concept, right? Talk to me about data mesh and what differentiates next left from your competition. >>Yeah. Um, so data mesh is essentially, I would say in the lineage of the concept of democratizing data, the idea has always been that data should get to the users. Now for a long time, these users were dependent on it and engineering to get the data to them. So what the data mesh is doing is it's bringing a framework by which users of data. We call them data, the domains, the different functions that use data. They can have the data to use themselves. They can manage things on their own. And I think that is allowing for a framework in which teams can truly scale. I mean, that bottleneck of depending on engineering to do everything for you is just not going work. And I think in the last two years, even more so we saw that as companies tried to move fast, it started to break down. And I think there is a lot of momentum around this concept of data mesh. For this reason, people are finding that this concept is what can help them scale >>And how does next SLA deliver that single tool so that you can really democratize data and give people with varying levels of technical fluency, the access that they need. I can imagine finance folks with ERP data marketing folks with CRM data. How do you do that with a single tool? >>Yeah. So, um, I think the key thing about getting data in the hands of users, as we think about data democratization has been that, how does it actually happen? How do you give people access to the data? You know, simply giving them passwords to systems is not enough right now the data mesh concept comes with the understanding that there should be an entity, which we call it a data, product data, you know, a data product becomes that sort of common entity that becomes something that people can get access to. They can use, they can collaborate on. Now, what is a data product becomes an important question, of course, and how do we get a data product? So our next step comes in in a very key way is we automatically generate these data products. So again, going back to the thinking that look there, there is not going to be enough engineers to write code for everything. What we are able to do is to say that we can actually, you know, connect to data systems, look to the data, understand it, and package it up as a product, as a data product. And that data product is a core element of the damage. I'm happy to share what a data product is, if it helps people understand and of, >>Yeah. Let's double click into that a little bit. I was noticing on your website about next sets and I wanted to know what that is and how does it reimagine data, product creation. >>Yeah. Um, so let me just break down a little bit about what is the data product in the first place, right. I mean, as consumers, we use products all the time, you know, I'm, I'm, my laptop is here on our desk and that is a product. It is a product made from raw materials, like wood and metal and screws, right. And somebody designed the product, somebody built it and I'm using it. So if we think of the same parallel in the world of data, then API APIs and files and database tables, those are the raw materials. Um, if somebody takes that and packages that up into something that other people can use easily, that is the concept of the data product. Now, what, how is it different from data? Well, you take the core data and you put things around it. Like, what is the distribution of data? >>What is the structure of it? You know, what are the validations that make it work, how to better manage that, who has access to it when you take that raw material and put all of those other structures, it that's when it sort of becomes a data product. And the next step concept in next door is essentially a manifestation of that. It is the concept that these data products do not need to be new copies of data, which is a huge pain by the way. But instead they can be these logical entities. So if I can take us back to the world of compute, where we understand the concept of containers, no, these containers are basically a logical NPP that gives us access to the computation resources. Think of next set as a very similar thing, a logical entity that gives us access to the data resources. And, um, this is something that, you know, we have been able to innovate and automate in such a way that today, when people think of the data mesh and they want to build that, they see us as a component in that whole framework. So data mesh is a much broader framework, but we are sort of the building block for that, through this concept >>Building block. Got it. Talk to me about where your customer conversations are happening. Are they within chief data officer chief information officer? Is it within the C-suite as data is every company these days has to be a data company. >>Oh yeah. Very much in the C-suite. Right. So again, this changes a little bit industry by industry because every industry is organized differently. Um, for example, you know, we have some amazing customer international services there. The conversation often is this the chief data officer or the chief analytics officer. And the key thing that the C suite is thinking is how does this work in the future? How do you know the scale of data challenges are, is the growth of that is so fast. How do we handle things to three years, five years from now? And that's where the strategic conversation is. And that's where things like data mesh become extremely important for companies where we talked to them about, you know, how our technology sort of enables that, right? Um, across other industries, the functions may vary. And one of the things which is very interesting with data, um, compared to other technologies is that it touches almost every aspect of business. It's not limited to engineering. It is your person in HR who is doing HR analytics, source candidates, and profiles are reviewing and all that stuff to finance, to operations, every aspect of business does touch data. So this has to be done in a language and a mechanism that's much more approachable. >>It's gotta be horizontal for all of those different types of users, right. To be able to understand so that ultimately not only did they get access to the data, but they can pull out those insights faster than their competition, whether it's to develop new revenue, streams, new products, new services, you know, the, the person on the other end or the companies on the other end are expecting that real-time interaction. >>Yeah. Yeah. But that's >>No longer a nice to have >>No longer likes to have. And to clarify, right. I mean, the use of data is in multiple ways, right? So analytics is a big use of data, which is how is my business doing and running. Um, we have customers like, um, you know, um, Marchex and Poshmark and bed bath beyond and so on. We'll use us heavily to bring data for the analytics use cases as our companies, for example, like a door dash or Instacart, but that data feeds operational purposes, operational purposes, meaning, understanding the availability of inventory or products across different stores. Not that data has functioned to say, well, if I know what products are available, then I can list them. Then I can go pick them up. And that's not a analytics use case alone. It has a, um, you know, it has an operational use case, right? Um, similarly we see that in audit tracking, we have customers, for example, like Narvar that use us to connect to different shipping tracking system. So the applications of data are in analytics. There are certainly also in operations, which is core business. And they're also, of course, in data science. There's no question that the extension of analytics from looking back on how business is doing to data science, which is, you know, what should we be doing and how should we be more intelligent? So it's across the board, >>Across the board, horizontal, all industries really need to do this, but one of the things that pop into my mind as you were walking through that example was the supply chain challenges that we're all experiencing right now. How can next help organizations mitigate some of the challenges that are going on? >>I think what happens is, you know, technologies like ours, which are the data layer are at a fundamental foundational level. One of the things about next slide is that we are able to bring a data into a much more real time usage. So where in companies, but traditionally moving data on a much more sort of periodic basis. We are our plumbing under the hood. We are completely in real time, which means that we are allowing companies to now get access to that data in a faster way where possible. So again, this is not something that can be fixed overnight, but the role that data can play in is better visibility. Um, and better visibility means those business decisions are being made earlier at the right time. It's more insight. And hopefully that eventually leads to sort of, um, much more efficient, actual on the ground, some movement of products and so on. >>Yeah. That visibility is absolutely critical regardless of the global climate. Right. Talk to me last question here, since we're almost out of time, give me a little bit about your AWS partnership and then talk to me about what's next for next time. >>Um, you know, as, as a technology provider, we ended up, um, running a lot of our own infrastructure on AWS as do many of our customers. And, uh, we have been an AWS partner for multiple years, but very decently, we actually made our product available on AWS marketplace, which means that the access to our technology has become so much easier for companies. Now, uh, next law has started its journey focusing on mid to large enterprises and some of the most complex use cases out there from some of the biggest banks to some of the biggest companies in marketing to some of the core companies in retail, logistics and so on. Now what is happening is that the powerful nature of our product and the ease of use that we have given that need is coming further and further earlier in the life cycle of companies, right? So today new companies are starting up, which said, which are saying that we need to make that sort of investment in data infrastructure earlier. You know, and that's why we have seen even some very small, early startups starting to use next level to come to us for our technology. So we are very much partnered up with AWS because AWS covers the whole gamut from companies that were started yesterday to extremely large enterprises, um, and bring our technology accessible to them. >>Excellent. Well, thank you so much for joining me. It sounds like a tremendous amount of momentum and opportunity at Nexa. We appreciate your insights and best of luck to you. We look forward to hearing more. >>Thank you, Lisa. It's a pleasure talking to it's an exciting space. So time flies, when we talked about that, >>Doesn't it, it really does for sockets sound room. I'm Lisa Martin. You're watching the queue, leave it here for more coverage and a leader in live tech hybrid events.

(upbeat music) [Announcer] Live from the Wigwam in Pheonix, Arizona, it's the Cube. Covering Data Platforms 2017. Brought to you by Cue Ball. >> Hey welcome back everybody, Jeff Frick here with the Cube. We are coming down to the end of a great day here at the historic Wigwam at the Data Platforms 2017, lot of great big data practitioners talking about the new way to do things, really coining the term data ops, or maybe not coining it but really leveraging it, as a new way to think about data and using data in your business, to be data-driven, software-defined, automated solution and company. So we're excited to have Saket Saurabh, he is the, and I'm sorry I butchered that, Saurabh. >> Saurabh, yeah. >> Saurabh, thank you, sorry. He is the co-founder and CEO of Nexla, and welcome. >> Thank you. >> So what is Nexla, tell us about Nexla for those that aren't familiar with the company. Thank you so much. Yeah so Nexla is a data operations platform. And the way we look at data is that data is increasingly moving between companies and one of the things that is driving that is the growth in machine learning. So imagine you are an e-commerce company, or a healthcare provider. You need to get data from your different partners. You know, suppliers and point-of-sale systems, and brands and all that. And the companies, when they are getting this data, from all these different places, it's so hard to manage. So we think of, you know just like cloud computing, made it easy to manage thousands of servers, we think of data ops as something that makes it easy to manage those thousands of data sources coming from so many partners. So you've jumped straight past the it's a cool buzz term in way to think about things, into the actual platform. So how does that platform fit within the cloud, and on Prim, is it part of the infrastructure, sits next to the infrastructure, is it a conduit? How does that work? >> Yeah, we think of it as, if you think of maybe machine learning or advanced analytics as the application, then data operations is sort of an underlying infrastructure for it. It's not really the hardware, the storage, but it's a layer on top. The job of data operations is to get the data from where it is to where you need it to be, and in the right form and shape. So now you can act on it. >> And do you find yourself replacing legacy stuff, or is this a brand new demand because of all the variant and so many types of datasets that are coming in that people want to leverage. >> Yeah, I mean to be honest, some of this has always been there in the sense that the day you connected a database to a network data started to move around. But if you think of the scale that has happened in the last six or seven years, none of those existing systems were ever designed for that. So when we talk about data growing at at a Moore's Law rate, when we talk about everybody getting into machine learning, when we talk about thousands of data sets across so many different partners that you work with, and when we think that reports that you get from your partners is no more sufficient, you need that underlying data, you can not basically feed that report into an algo. So when you look at all of these things we feel like it is a new thing in some ways. >> Right. Well, I want to unpack that a little bit because you made an interesting comment, before you turned on the cameras you just repeated, that you can't run an algorithm on a report. And in a world where we've got all the shared data sets, and it's funny too right, because you used to run a sample, now you want, you said, the raw. Not only all, but the raw data, so that you can do with it what you wish. Very different paradigm. >> Yeah. >> It sounds like there's a lot more, and you're not just parsing what's in the report, but you have to give it structure that can be combined with other data sources. And that sounds like a rather challenging task. Because the structure, all the metadata, the context that gives the data meaning that is relevant to other data sets, where does that come from? >> Yeah, so what happens, and this has been how technology companies have started to evolve. You want to focus on your core business. And therefore you will use a provider that processes your payments, you will use a provider that gives you search. You will use a provider that provides you the data for example for your e-commerce system. So there are different types of vendors you're working with. Which means that there's different types of data being involved. So when I look at for example a brand today, you could be say, a Nike, and your products are being sold on so many websites. If you want to really analyze your business well, you want data from every single one of those places, where your data team can now access it. So yes, it is that raw data, it is that metadata, and it is the data coming from all the systems that you can look at together and say when I ran this ad this is how people reacted to it, this was the marketing lift from that, this is the purchase that happened across these different channels, this is how my top line or bottom line was affected. And to analyze everything together you need all the data in a place. >> I'm curious on what do you find on the change in the business relationship. Because I'm sure there were agreements structured in another time which weren't quite as detailed, where the expectations in terms of what was exchanged wasn't quite this deep. Are you seeing people have to change their relationships to get this data? Is it out there that they're getting it, or is this really changing the way that people partner in data exchange, on like the example that you just used between say Nike and Foot Locker, to pick a name. >> Yeah, so I think companies that have worked together have always had reports come in, so you would get a daily report of how much you sold. Now just a high-level report of how much you sold is not sufficient anymore. You want to understand where was it bought, in which city, under what weather conditions, by what kind of user and all that stuff. So I think what companies are looking at, again, they have built their data systems, they have the data teams, unless they give the data their teams cannot be effective and you cannot really take a daily sales report and feed that into your algorithm, right? So you need very fine-grained data for that. So I think companies are doing this where, hey you were giving me a report before, I also need some underlying data. Report is for a business executive to look at and see how business is doing, and the underlying data is really for that algorithm to understand and maybe identify things that a report might not. >> Wouldn't there have been already, at least in the example of sell-through, structured data that's been exchanged between partners already like vendor-managed inventory, or you know where like a downstream retailer might make their sell-through data accessible to suppliers who actually take ownership of the inventory and are responsible for stocking it at optimal levels. >> Yeah, I think Walmart was the innovator in that, with the POS link system, back in the day, for retail. But the point is that this need for data to go from one company to their partners and back and forth is across every sector. So you need that in e-commerce, you need that in fintech, we see companies who have to manage your portfolio needs to connect with different banks and brokerages you work with to get the data. We see that in healthcare across different providers and pharmaceutical companies, you need that. We see that in automotive. If every care generates data, an insurance company needs to be able to understand that and look at it. >> This, it's a huge problem you're addressing, because this is the friction between inter-company applications. And we went through this with the B2B marketplaces, 15 plus years ago. But the reason we did these marketplace hubs was so that we could standardize the information exchange. If it's just Walgreens talking to Pfizer, and then doing another one-off deal with, I don't know, Lily, I don't know if they both still exist, it won't work for connecting all of pharmacy with all of pharma. How do you ensure standards between downstream and upstream? >> Yeah. So you're right, this has happened. When we do a wire transfer from one person to another, some data goes from a bank to another bank, still takes hours to get that, it's very tiny amount of data. That has all exploded, we are talking about zetabytes of data now every year. So the challenge is significantly bigger. Now coming to standards, what we have found, that two companies sitting together and defining a standard almost never works. It never works because applications change, systems change, the change is the only constant. So the way we've approached it at our company is, we monitor the data, we sit on top of the data and just learn the structure as we observe data flowing through. So we have tons of data flowing through and we're constantly learning the structure, and are identifying how the structure will map to the destination. So again, applying machine learning to see how the structure is changing, how the data volume is changing. So you are getting data from somewhere say every hour, and then it doesn't show up for two hours. Traditionally systems will go down, you may not even find for five days that the data wasn't there for that. So we look at the data structure, the amount of data, the time when it comes, and everything to instantly learn and be able to inform the downstream systems of what they should be expecting, if there is a change that somebody needs to be alerted about. So a lot of innovation is going in to doing this at scale without necessarily having to predefine something in a tight box that cannot be changed. Because it's extremely hard to control. >> All right, Saket, that's a great explanation. We're going to have to leave it there, we're out of time. And thank you for taking a few minutes out of your day to stop by. >> Thank you. >> All right. Jeff Frick with George Gilbert, we are at Data Platforms 2017, Pheonix Arizona, thanks for watching. (electronic music)