>> Narrator: From the Computer History Museum in the heart of Silicon Valley it's the Cube, covering food I.T., fork to farm, brought to you by Western Digital. >> Hey, welcome back, everybody. Jeffrey here with The Cube. We're at the Computer History Museum in Mountain View, California, at the Food I.T. show. About 350 people from academe, from food producers, somebody came all the way from New Zealand for this show. A lot of tech, big companies and start-ups talking about applying IT to food, everything from ag to consumption to your home kitchen to what do you do with the scraps that we all throw away. We're excited now to get to the "Big Brain" segment. We've got our Ph.D.s on here. We're excited to have Doctor Glenda Humiston. She's the V.P. of agriculture and natural resources for the University of California. Welcome. And also, Doctor Helene Dillard. She's the dean of the College of Agricultural and Environmental Sciences at UC Davis. Welcome. >> Thank you. >> So first off, we were talking a little bit before we turned the cameras on. Neither of you have been to this event before. Just kind of your impressions of the event in general? >> Glenda: I love seeing the mix of the folks here as you were saying in your intro. There's quite a diverse array of people, and I personally believe that's what's really going to help us find solutions moving forward, that cross-pollination. >> Helene: And I've enjoyed it, just seeing all the different people that are here, but then the interaction with the audience was very uniquely done, and I just think that's a real big positive for the show. >> So you guys were on a panel earlier today, and I thought one of the really interesting topics that came up on that panel was, what is good tech? You know, everybody wants it all, but unfortunately there's no free lunch, right? Something we all learned as kids. There's always a trade-off, and so people want perfect, organic, this-free, that-free, cage-free, at the same time they want it to look beautiful, be economical and delivered to their door on Amazon Prime within two hours. So it's interesting when we think of the trade-offs that we have to make in the food industry to kind of hit all these pieces, or can we hit all these pieces or how does stuff get prioritized? >> Well I think that for us, it's going to be a balance, and trying to figure out how do you provide the needs for all these different audiences and all the different things that they want and I don't think one farmer can do it for all these different groups that have different demands on what they're looking for. And some of the tradeoffs could be, as we go away from pesticides and from other things, we might have more blemishes. And those are still edible pieces of fruit and vegetables, it's just that maybe it's curly, maybe the carrot's not straight, you know, maybe it's forked, but it's still very edible. And so I think that we have to do a lot more to help educate consumers, help people understand that it doesn't have to look perfect to give you perfect nutrition. >> Right, right. >> Glenda: Yeah, yeah, Helene is absolutely right. Some of it's just education, but some of it's also us finding the new technology that is acceptable to the public. Part of the problem is we sometimes have researchers working on their own, trying to find the best solution to a problem and we're not socializing that with the public as we're moving forward. So then all of a sudden, here's this new type of technology and they're like, where did this come from? What does it mean to me? Do I need to worry about it? And that's one reason--we talked earlier on the panel too, about the need to really engage more of our citizens in the scientific process itself, and really start dealing with that scientific illiteracy that's out there. >> Because there was a lot of talk about transparency in the conversation-- >> Yes. >> Earlier today about what is transparency. Cause you always think about people complaining about genetically modified foods. Well what is genetically modified? Well, all you have to do is look at the picture of the first apple ever, and it was a tiny little nasty-looking thing that nobody would want to eat compared to what we see at the grocery store today. A different type of genetic modification, but still, you don't plant the ugly one, and you plant the ones that are bigger and have more fruit. Guess what, the next round has more fruit. So it does seem like a big education problem. >> It is, and yet, for the average human being out there, all you have to do is look at a chihuahua next to a Saint Bernard. None of that was done with a genetically modified technology and yet people just--they forget that we've been doing this for thousands of years. >> Jeffrey: Right, right. You talked about, Glenda, the VINE earlier on in the panel. What is the VINE? What's the VINE all about? >> Well, it's brand new. It's still getting rolled out. In fact, we announced it today. It's the Verde Innovation Network for Entrepreneurship. You know, you've got to think of a clever way to get that acronym in there >> Which comes first, the chicken or the egg? >> Basically it's our intent from University of California to catalyze regional innovation and entrepreneurship ecosystems. Part of what's driving that is we've got a fairly good amount of resources scattered around the state, even in some of our rural areas, on small business development centers, our community colleges, our county cooperative extension offices, and a host of other resources including lately, the last several years, incubators, accelerators, maker's labs. But they don't talk to each other, they don't work together. So we're trying to go in, region by region, and catalyze a coalition so that we can make sure that our innovators, our inventors out there, are able to go from idea to commercialization with all the support they need. Via just basic legal advice, on should they be patenting something. Access to people to discuss finances, access to people that can help them with business plans. Opportunities to partner with the University in joint research projects. Whatever it takes, make sure that for anybody in California they can access that kind of support. >> That's interesting. Obviously at Haas, and at Stanford, not far from here, you know, a lot of the technologies of such companies come out of, you know, kind of an entrepreneurial spin with a business-focused grad and often a tech grad in a tech world. You know, ton of stuff at Berkeley on that, but >> Yeah, but those folks this is really for ag >> are in urban areas >> If you're in a large urban area or you're near a major campus you've probably got access to most of that. If you're in agriculture, natural resources, and in particular, our more remote, rural communities, you typically have no access, or very little. >> Right. So biggest question is, Helene, so you're at Davis, right, obviously known as one of the top agricultural-focused schools certainly in the UC system, if not in the world. I mean, how is the role of academic institutions evolving in this space, as we move forward? >> I would say it's evolving in that we're getting more entrepreneurship on campus. So professors are being encouraged to look at what they're working on and see if there's patent potential for this. And also, we have a group on UC Davis campus called Innovation Access, but looking at how can they access this population of people with money and, you know, the startups to help them bring their thing to market? So that's becoming-- that's a very different campus than years ago. I think the other thing is, we're also encouraging our students to look at innovation. And so we have a competition called the Big Bang, and students participate in that. They do Hag-a-thon, they do all these kinds of things that we tend to think that only the adults are doing those but now the students are doing them as well. And so we're trying to push that entrepreneurship spirit out onto all of our campus, onto everyone on the campus. >> And I do want to emphasize that this isn't just for our students or our faculty. One of the key focuses of the VINE is all of our external partners, too. Just the farmers, the landowners, the average citizens we're working with out there. If they've got a great idea, we'd like to help them. >> Jeffrey: And what's nice about tech is, you know, tech is a vehicle you can change the world without having a big company. And I would imagine that ag is kind of-- big ag rolled up a lot of the smaller, midsize things, and there probably didn't feel like there was an opportunity that you could have this huge impact. But as we know, sitting across the street from Google, that via software and technology, you can have a huge impact far beyond the size and scope of your company. And I would imagine that this is a theme that you guys are playing off of pretty aggressively. >> Absolutely. I think that there are people on campus that are looking for small farm answers and mechanization as well as large farm answers. We have people that are working overseas in developing countries with really, really small farm answers. We have people that are working with the Driscolls and partnering up with some of these other big companies. >> We talked a little bit before we went on air about kind of the challenges of an academic institution, with some of the resources and scale. These are big, complicated problems. I mean, obviously water is kind of the elephant in the room at this conference, and it's not being talked about specifically I think they've got other water shows. Just drive up and down the valley by Turlock and Merced and you can see the signs. We want the water for the farms, not for the salmon in the streams, so where do the--the environmental impacts. So these are big, hairy problems. These are not simple solutions. So it does take a lot of the systems approach to think through, what are the tradeoffs of a free lunch? >> It really does take a systems approach, and that's one thing here in California, we're doing some very innovative work on. A great example that both UC Davis, my division, and other parts of the UC system are working on is Central Valley AgPlus Food and Beverage Manufacturing Consortium, which is 28 counties, the central valley and up into the Sierra. And what's exciting about it is, it is taking that holistic approach. It's looking at bringing around the table the folks from research and development, workforce, trained workforce, adequate infrastructure, financing, access to capital, supply chain infrastructure, and having them actually work together to decide what's needed, and leverage each other's resources. And I think that offers a lot of possibility moving forward. >> And I would say that at least in our college, and I would call the whole UC Davis, there's a lot of integration of that whole agriculture environmental space. So we've been working with the rice farmers on when can you flood the rice fields so that there's landing places for the migrating birds? Cause this is the Pacific flyway. And can we grow baby salmonids in that ricewater and then put them back in the bay? And they figured out a way to do that, and have it actually be like a fish hatchery, only even better, because we're not feeding them little tiny pellets, they're actually eating real food, (laughs) whole foods. >> And how has an evolution changed from, again, this is no different than anyplace else, an old school intuition, the way we've always done it versus really a more data driven, scientific approach where people are starting to realize there's a lot of data out there, we've got all this cool technology with the sensors and the cloud and edge computing and drones and a whole lot of ways to collect data in ways that we couldn't do before and analyze it in ways that we couldn't do before to start to change behavior, and be more data-driven as opposed to more intuition driven. >> I would say that what we're seeing is as this data starts to come in precision gets better. And so now that we understand that this corner of the field needs more water than the other side, we don't have to flood the whole thing all at once. You can start on the dry side and work over to the other side. So I think the precision is getting much, much better. And so with that precision comes water efficiency, chemical efficiency, so to me it's just getting better every time. >> And frankly, we're just at the beginning of that. We're just starting to really use drones extensively to gather that type of data. New ways of using satellite imagery, new way of using soil sensors. But one of the problems, one of the big challenges we have, back to infrastructure, is in many parts of your agricultural areas, access to the internet. That pipeline, broadband. If you've got thousand of sensors zapping information back you can fill up that pipeline pretty fast. It becomes a problem. >> Jeffrey: That pesky soft underbelly of the cloud, right? You've got to be connected. Well, we're out of time, unfortunately. I want to give you the last word for people that aren't as familiar with this, basically, myself included, what would you like to share with people that could kind of raise their awareness of what's happening with technology and agriculture? >> Well, I guess that I would start out saying not to be afraid of it, and to look at the technology that has come. Remember when we had the rotary dial phone? My son doesn't even know what that is! (laughs) >> Jeffrey: Mom, why do you say dial them up? >> Yeah, why do you say dial people up? So I think, looking at your rotary phone, now, looking at your smart phone, which has more computing power than your first Macintosh. It's very--the world is changing, and so why do we expect agriculture to stay in the 1800s mindset? It's moving too, and it's growing too, and it's getting better just like that iPhone that you have in your hand. >> I think I would add that to that, back to the citizen science, I would love people out there, anybody, average citizens young or old to know that there's opportunities for them to engage. If they're concerned about the science or the technology come work with us! We have over twenty thousand volunteers in our programs right now. We will happily take more. And they will have a chance to see, up close and personal, what this technology is and what it can do for them. >> Alright. Well that's great advice. We're going to leave it there, and Dr. Humiston, Dr. Dillard, thank you for taking a few moments out of your day. I'm Jeffrey. You're watching the Cube. We're at the Computer History Museum. Food IT. Learning all about the IT transformation in the agriculture industry. Also to the kitchen, your kitchen, the kitchen of the local restaurant and all the stuff that can happen with those scraps that we throw away at the end of the day. Thanks for watching, and we'll be right back after this short break. (electronic music)

>>from around the globe. It's the >>cube presenting >>active de que intelligent automation for data quality brought to you by Iota Ho >>Welcome to the sixth episode of the I. O. Tahoe data automation series. On the cube. We're gonna start off with a segment on how to accelerate the adoption of snowflake with Glenn Grossman, who is the enterprise account executive from Snowflake and yusef khan, the head of data services from Iota. Gentlemen welcome. >>Good afternoon. Good morning, Good evening. Dave. >>Good to see you. Dave. Good to see you. >>Okay glenn uh let's start with you. I mean the Cube hosted the snowflake data cloud summit in November and we heard from customers and going from love the tagline zero to snowflake, you know, 90 minutes very quickly. And of course you want to make it simple and attractive for enterprises to move data and analytics into the snowflake platform but help us understand once the data is there, how is snowflake helping to achieve savings compared to the data lake? >>Absolutely. dave. It's a great question, you know, it starts off first with the notion and uh kind of, we coined it in the industry or t shirt size pricing. You know, you don't necessarily always need the performance of a high end sports car when you're just trying to go get some groceries and drive down the street 20 mph. The t shirt pricing really aligns to, depending on what your operational workload is to support the business and the value that you need from that business? Not every day. Do you need data? Every second of the moment? Might be once a day, once a week through that t shirt size price and we can align for the performance according to the environmental needs of the business. What those drivers are the key performance indicators to drive that insight to make better decisions, It allows us to control that cost. So to my point, not always do you need the performance of a Ferrari? Maybe you need the performance and gas mileage of the Honda Civic if you would just get and deliver the value of the business but knowing that you have that entire performance landscape at a moments notice and that's really what what allows us to hold and get away from. How much is it going to cost me in a data lake type of environment? >>Got it. Thank you for that yussef. Where does Io Tahoe fit into this equation? I mean what's, what's, what's unique about the approach that you're taking towards this notion of mobilizing data on snowflake? >>Well, Dave in the first instance we profile the data itself at the data level, so not just at the level of metadata and we do that wherever that data lives. So it could be structured data could be semi structured data could be unstructured data and that data could be on premise. It could be in the cloud or it could be on some kind of SAAS platform. And so we profile this data at the source system that is feeding snowflake within snowflake itself within the end applications and the reports that the snowflake environment is serving. So what we've done here is take our machine learning discovery technology and make snowflake itself the repository for knowledge and insights on data. And this is pretty unique. Uh automation in the form of our P. A. Is being applied to the data both before after and within snowflake. And so the ultimate outcome is that business users can have a much greater degree of confidence that the data they're using can be trusted. Um The other thing we do uh which is unique is employee data R. P. A. To proactively detect and recommend fixes the data quality so that removes the manual time and effort and cost it takes to fix those data quality issues. Uh If they're left unchecked and untouched >>so that's key to things their trust, nobody's gonna use the data. It's not trusted. But also context. If you think about it, we've contextualized are operational systems but not our analytic system. So there's a big step forward glen. I wonder if you can tell us how customers are managing data quality when they migrate to snowflake because there's a lot of baggage in in traditional data warehouses and data lakes and and data hubs. Maybe you can talk about why this is a challenge for customers. And like for instance can you proactively address some of those challenges that customers face >>that we certainly can. They have. You know, data quality. Legacy data sources are always inherent with D. Q. Issues whether it's been master data management and data stewardship programs over the last really almost two decades right now, you do have systemic data issues. You have siloed data, you have information operational, data stores data marks. It became a hodgepodge when organizations are starting their journey to migrate to the cloud. One of the things that were first doing is that inspection of data um you know first and foremost even looking to retire legacy data sources that aren't even used across the enterprise but because they were part of the systemic long running operational on premise technology, it stayed there when we start to look at data pipelines as we onboard a customer. You know we want to do that era. We want to do QA and quality assurance so that we can, And our ultimate goal eliminate the garbage in garbage out scenarios that we've been plagued with really over the last 40, 50 years of just data in general. So we have to take an inspection where traditionally it was E. T. L. Now in the world of snowflake, it's really lt we're extracting were loading or inspecting them. We're transforming out to the business so that these routines could be done once and again give great business value back to making decisions around the data instead of spending all this long time. Always re architect ng the data pipeline to serve the business. >>Got it. Thank you. Glenda yourself of course. Snowflakes renowned for customers. Tell me all the time. It's so easy. It's so easy to spin up a data warehouse. It helps with my security. Again it simplifies everything but so you know, getting started is one thing but then adoption is also a key. So I'm interested in the role that that I owe. Tahoe plays in accelerating adoption for new customers. >>Absolutely. David. I mean as Ben said, you know every every migration to Snowflake is going to have a business case. Um uh and that is going to be uh partly about reducing spending legacy I. T. Servers, storage licenses, support all those good things um that see I want to be able to turn off entirely ultimately. And what Ayatollah does is help discover all the legacy undocumented silos that have been built up, as Glenn says on the data estate across a period of time, build intelligence around those silos and help reduce those legacy costs sooner by accelerating that that whole process. Because obviously the quicker that I. T. Um and Cdos can turn off legacy data sources the more funding and resources going to be available to them to manage the new uh Snowflake based data estate on the cloud. And so turning off the old building, the new go hand in hand to make sure those those numbers stack up the program is delivered uh and the benefits are delivered. And so what we're doing here with a Tahoe is improving the customers are y by accelerating their ability to adopt Snowflake. >>Great. And I mean we're talking a lot about data quality here but in a lot of ways that's table stakes like I said, if you don't trust the data, nobody's going to use it. And glenn, I mean I look at Snowflake and I see obviously the ease of use the simplicity you guys are nailing that the data sharing capabilities I think are really exciting because you know everybody talks about sharing data but then we talked about data as an asset, Everyone so high I to hold it. And so sharing is is something that I see as a paradigm shift and you guys are enabling that. So one of the things beyond data quality that are notable that customers are excited about that, maybe you're excited about >>David, I think you just cleared it out. It's it's this massive data sharing play part of the data cloud platform. Uh you know, just as of last year we had a little over about 100 people, 100 vendors in our data marketplace. That number today is well over 450 it is all about democratizing and sharing data in a world that is no longer held back by FTp s and C. S. V. S and then the organization having to take that data and ingested into their systems. You're a snowflake customer. want to subscribe to an S and P data sources an example, go subscribe it to it. It's in your account there was no data engineering, there was no physical lift of data and that becomes the most important thing when we talk about getting broader insights, data quality. Well, the data has already been inspected from your vendor is just available in your account. It's obviously a very simplistic thing to describe behind the scenes is what our founders have created to make it very, very easy for us to democratize not only internal with private sharing of data, but this notion of marketplace ensuring across your customers um marketplace is certainly on the type of all of my customers minds and probably some other areas that might have heard out of a recent cloud summit is the introduction of snow park and being able to do where all this data is going towards us. Am I in an ale, you know, along with our partners at Io Tahoe and R. P. A. Automation is what do we do with all this data? How do we put the algorithms and targets now? We'll be able to run in the future R and python scripts and java libraries directly inside Snowflake, which allows you to even accelerate even faster, Which people found traditionally when we started off eight years ago just as a data warehousing platform. >>Yeah, I think we're on the cusp of just a new way of thinking about data. I mean obviously simplicity is a starting point but but data by its very nature is decentralized. You talk about democratizing data. I like this idea of the global mesh. I mean it's very powerful concept and again it's early days but you know, keep part of this is is automation and trust, yussef you've worked with Snowflake and you're bringing active D. Q. To the market what our customers telling you so far? >>Well David the feedback so far has been great. Which is brilliant. So I mean firstly there's a point about speed and acceleration. Um So that's the speed to incite really. So where you have inherent data quality issues uh whether that's with data that was on premise and being brought into snowflake or on snowflake itself, we're able to show the customer results and help them understand their data quality better Within Day one which is which is a fantastic acceleration. I'm related to that. There's the cost and effort to get that insight is it's a massive productivity gain versus where you're seeing customers who've been struggling sometimes too remediate legacy data and legacy decisions that they've made over the past couple of decades, so that that cost and effort is much lower than it would otherwise have been. Um 3rdly, there's confidence and trust, so you can see Cdos and see IOS got demonstrable results that they've been able to improve data quality across a whole bunch of use cases for business users in marketing and customer services, for commercial teams, for financial teams. So there's that very quick kind of growth in confidence and credibility as the projects get moving. And then finally, I mean really all the use cases for the snowflake depend on data quality, really whether it's data science, uh and and the kind of snow park applications that Glenn has talked about, all those use cases work better when we're able to accelerate the ri for our joint customers by very quickly pushing out these data quality um insights. Um And I think one of the one of the things that the snowflake have recognized is that in order for C. I. O. Is to really adopt enterprise wide, um It's also as well as the great technology with Snowflake offers, it's about cleaning up that legacy data state, freeing up the budget for CIA to spend it on the new modern day to a state that lets them mobilise their data with snowflake. >>So you're seeing the Senate progression. We're simplifying the the the analytics from a tech perspective. You bring in Federated governance which which brings more trust. Then then you bring in the automation of the data quality piece which is fundamental. And now you can really start to, as you guys are saying, democratized and scale uh and share data. Very powerful guys. Thanks so much for coming on the program. Really appreciate your time. >>Thank you. I appreciate as well. Yeah.