>>from the cube studios in Palo alto in boston connecting with thought leaders all around the world. This is a cute conversation. >>Welcome to the cubes coverage of HP discoverer 2021. I'm lisa martin Janice Zenga's joins me next. The vice president of innovation for social impact in H. P. S. Office of the C T. O Janice. Welcome to the cube. Hi lisa. Great to be here. So let's talk about this. You lead H. P. S. Tech for good program. I always love talking about programs like this. Talk to me about that industry tech academia government partnering to solve key challenges that society is facing and crack that for us. Yeah. So so >>we we um are really proud to be able to look at big challenges in the world and look where our strengths, where our innovations are emerging technologies and our employee expertise could actually contribute to a problem. And so >>we began >>a program uh to actually pick some projects particularly in food systems, world hunger and Health Systems, where we thought some of our technologies could really be impactful. And so >>we have been working with a number of >>clients and partners to actually uh work on ai contributions, high performance compute contributions um and uh and a contribution around this notion of data spaces that we're talking about all of these emerged through um complex interactions around social, good engagement. >>So the concept you mentioned, data space is the concept of data spaces isn't new but do explain that. Give us an overview Janice for those folks that might not be familiar with what it is. >>So so the notion of data spaces is to connect data producers to data consumers. And so um in the past um you know connecting producers and consumers has really been limited about, you know, where is your data located? Um Do you have access to the right data? Um Is the data a good quality set of data? What's the providence of that data? What's the quality of it? And is it trustworthy? And so um >>our >>concept of data spaces is actually trying to address all of those um notions with with a new approach >>so collecting ensuring data isn't anything new. But of course what we talk about every day on this program is the volume of data in that context. What are some of the challenges that you're seeing with clients and how can you help them eliminate those challenges and be able to make data driven decisions? >>So um the first challenge is finding the data. And uh there is a big challenge. I mean there's new roles emerging called data hunters and a great amount of time being spent by data scientists just trying to find sources of data. And that's a big challenge. And then when you find this data, is it in the right format and how expensive is it to move the data so that you could have it in a place where it can actually be analyzed. So, um, so what we're working on recognizing that there is a vast amount of data at the edge, a vast amount of data that's probably never going to move from the edge and from those locations. Um but what we're trying to do is recognize that and actually work to bring the algorithms and the analytics to the data and to work with making sure that data is accessible >>and can be >>understood and >>and processed uh in a consistent way. And >>today there is um a lot >>of silos in in place around uh where data >>exists. And uh and so our approach here is to kind of address is from an open source community perspective to build uh and and provide >>a metadata layer, >>standard of all standards. Kind of a super metadata layer for a non technical way to represent that. And and then to use that to help um connect to uh analytics platforms, both citizen users. Um, you know, subject matter experts who may not be data scientists as well as the data scientists. So actually being able to connect a broader set of users into data analytics that are currently available and have the knowledge to be able to get information and insights out of that data. >>So democratizing that access to data. One of the things I'm curious about what you've seen is that's a cultural shift. You talked about some of the new rules. Data hunters and people get very sort of territorial about that. How I'm just curious what are some of the things that you've seen that where HP and data spaces have been able to help companies to be able to democratize that access and also kind of transform their culture >>Well. So um a few >>things. First of all, there has >>to be a strong motivation >>for someone to share data and in order for them to feel safe and sharing that data. Um you know there has to be security and trust established and most data producers want to control who gets to see their data and under what conditions there needs to be governance of data as well. So those are important aspects that have to be in place. Our approach is to kind of build in exchange for that so that um data consumers understand the conditions in which they can access and use the data um And and also potentially contribute back the new datasets that they're creating through their analytics back into a catalogue being a provisioning of data. This improving kind of the standardization and the simplicity Of how data gets exchanged today. In effect allows a greater democratization of access of data so so that you don't have to be a data scientist. I mean data scientist today can spend 7-8 months actually getting their data that they're going to use um into a format that they can they can actually process. And we think that that's inefficient. We think there's a lot that can be done. Um The other challenge around this is that oftentimes data is multi entity. Even inside of a company, you can find data, you >>know, in different departments and different >>businesses. Um >>But even when you think beyond a >>company, if you think about entities that are that are, you know globally >>distributed um >>and maybe multi, you know, multi entity, there are new challenges about how data can come together from those sources and still be of the right providence and be understood to be trustworthy. >>Well, one of the things that I think one of the many things I think we've learned during the last year is that the, the need and access for real time data has been a critical factor in helping businesses pivot and survive versus those that that might not. So what are you seeing in terms of like you said, data scientist spending so much time getting access to clean data, the opportunities to miss, you know, opportunities for new products and services and to and to meet customer demand in new ways to talk to me about how data spaces can facilitate that faster real time access. >>Right? So, so by having an exchange that can be implemented inside of an enterprise or across enterprises, we actually think it allows some of that kind of pre work to be done, allows that cataloging and provisions. So you can come to uh come to a place, it's a place where an exchange can occur and actually be able to, you know, um get more ready >>access to the data. You don't have to >>necessarily go through a cleansing process and through a deep investigation on providence and then, you know, oftentimes uh you learn as you process data about new data or the data sets change. Right? So, so can there be improvements around keeping those ml algorithms current and helping you that in a very efficient way without having to rerun and rewrite code and rerun your algorithms um every single time. So we think there's a lot of improvement that can be done there as well. >>So let's look at, did a great job of explaining data space is the opportunity, the challenges that we've seen the opportunities. But let us help the audience understand what makes what HPV is doing with data spaces different, unique. What are some of the differentiators? There? >>A few things. One is we're approaching this from an open source approach, so we expect to be able to contribute back to the open source communities and allow for a greater ecosystem to develop around these solutions and that will enable greater sharing and trustworthy sharing. The second thing is security, we intend to apply a great security layer into this that allows data to be trusted um and then the governance capabilities, so being able to use things like our data fabric to actually help support um the governance that producers and consumers want to have uh is also important. And then finally being able to work multi cloud across um, on prem and in the cloud >>is a great >>advantage. So you don't get vendor lock in, you'll be able to be able to kind of minimize your data egress because >>maybe you're not gonna be doing data egress out of the cloud >>and instead you'll be you'll be able to process your data right where it's at without having to pay for that movement. >>And I imagine that would facilitate that speed of real time that I mentioned a minute ago. >>That's right, That's right. >>So let's now look at HP data spaces compared to data marketplace. Give me the compare and contrast with respect to those two. So, data >>marketplaces are typically very siloed and very specific to a sector or an industry today, um, and they're they're typically built on their own platforms and to end, they're not always open by design. Um so uh we expect to be able to support multiple data data marketplaces through a plug in into the data spaces um platform that that we build and that will allow greater connectivity and greater access to many different marketplaces. Um and so the data spaces is not intended to be siloed by industry or narrowly kind of focused, >>so helping to remove those silos, which we also, another thing that we talk about, what are some, I'm just curious some of the feedback from the open source community about what you're doing here building on this open foundation. >>So it's um it's actually been very positive. So the very first thing we did was because of our work as I start at the top of the conversation in agriculture, which is a great, a great example of where there's immense amounts of data that is not well standardized, are structured in a way that can be used towards addressing things like world hunger and some of the food supply and food system challenges. We have we uh in working through this >>kind of distilled some >>of the problems that to being this lack of access to data. And so one of the reasons we explored was like why is there this lack of use of data and lack of access to data? And it came down to not being able to access the data where it's generated and not being able to actually share it broadly across entities. And so, um so what we did is we joined the Linux Foundation has a new open source community called Ag stack and we are a founding company uh as part of that new community and we have shared the concepts around data spaces and the metadata layer standardisation that we've envisioned uh into the community and that's just getting kicked off. But it's also a great first step for us um to kind of build an open source community around it. >>Excellent. Sounds like you said positive feedback. If we crack open the hood of data spaces, what are some of the technologies that we see underneath that are making it and its evolution possible? >>Right? So um multi cloud uh across uh data, you know, support um edge processing um data fabric um Israel's solution as well, so being able to kind of move data and then of course, kind of a key layer. This is this notion of a metadata layer, standard on top >>of metadata layer standards. >>And what is that going to allow in terms of connecting the data consumers with the data producers? >>It's going to make it easier, it's going to make it faster, it's going to minimize costs. Uh it's gonna allow for a quality exchange with more information for consumers to have that trust and most importantly the security. Um and it will also create kind of motivation, kind of give and take because exchange has to be equitable for producers and consumers to both be at the >>table. That's a great point about about the being equitable. So this whole initiative that we've been talking about is coming out of the Office of the CTO at HP where we talked about. So the focus is on Uh projects that are emerging not yet on the road map. So what can we expect, what can your audience expect in the next 12-18 months? >>So our approach in the Office of the CTO is to take emerging technologies and ideas and actually bring them into kind of what we would call advanced development stages. So we do proof of concepts, we do a lot of piloting, we worked with customers and clients directly to kind of tune and test commercialization possibilities uh and value of a solution that we're evolving and to kind of get it ready for market if it makes sense to do that. And so We have proof of concepts with the dozens of customers right now in this topic area and more that want to join in and get involved in having access to it as well. Um so I would say most of the work we do in the coming 12 months will be driven by what these proof of concepts with these clients actually uncovered for us. Um and so we know first and foremost we're working with, you know, a large financial services company, we're looking we're working on the agricultural front with a number of important customers that are testing kind of a multi entity data sharing aspects. Were working also with the health care industry client, which is looking at extreme sets of large data that are kind of unanticipated datasets, you would normally think that would be important for disease prediction. And so all of those different kind of use cases are helping us kind of think about um, you know, which features are most important and by when I can tell you the security, the trustworthiness, the data provenance, the data governance are essential elements that are going to have to be there. >>I think those are essential elements that in any industry, especially that security front. >>Yes, very much so. >>So. In terms of the event at hp, what are some of the things that the audience is going to be able to to learn and glean about? Data sources, data spaces? >>So we've had a kind of a great three days um first starting out with Antonio neary and and and F. I. S to talk about kind of the the insight, the age of insights and and how data is actually becoming the currency of the future if you will. And so that we started that way. And then on day two we had a panel of some of our clients talking about in their particular industry, what's happening with data. So you start to see the kind of um sharing out of uh requirements and how urgent these requirements are growing. Uh And then on day three we actually go into more technology. So you'll see there. We have a number of demos and sessions Uh one specifically around agriculture use case, another around health care use case as well. And then we go into a little bit more detail around the data spaces concept in the keynote for day three. >>So action packed three days Janice. Thank you so much for joining me. Talking to us about data space is what you guys are doing for social impact out of h p. S. Office of the C t. O. We appreciate your time. >>Thank you lisa >>for Janice. Thank yous. I'm lisa martin. You're watching the cubes coverage of HP discover 2021 mm.