>> Narrator: From San Francisco, it's theCUBE covering OSIsoft PI World 2018 brought to you by OSIsoft. >> Hey welcome back, everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at OSIsoft PI World 2018 getting to the end of the day, it's been a very busy day, a lot of great conversations and about 3,000 people here talking about the industrial Internet of Things and IoT and really process improvement using data. They've been at it for almost four decades and we're excited to have a practitioner. He's Tara Rana, he is the Digital Transformation Process Control in Systems Engineering for Barrick Gold. Tara, good to see you. >> Oh, nice to meet you as well. >> Absolutely. >> Thank you. >> So, little bit of basics on Barrick Gold, kind of who are you guys, what's your business? >> All right, so, Barrick Gold Corporation, it's the largest gold producer as of today in the world. And we have about thirteen operating sites across the world. We are headquartered in Toronto, Ontario, Canada. >> Jeff: Okay. >> We are hugely focused in the Americas. About 75% of our revenue comes from the Americas, so that's North America and South America, and then we have other projects and mining operations across the world, to Australia, Chile, Zambia in Africa, Saudi Arabia, so it's global. >> So you are, you're basically getting the gold out of the dirt. >> Tara: From the rocks. >> From the rocks. >> Yeah. >> And it's pretty interesting right, we always think, we're here in San Francisco, right, in 1849 is when it all started, there was a guy with a pan, >> Tara: Oh yeah. (laughs) >> But that's not how it works anymore, right? >> Tara: No. >> Now it's a big industrial process that starts with lots of truckloads of ore, and then at the end of many many steps, out comes the gold. >> Tara: Yeah. >> And we've heard a number of times that there's so many process improvements that basically can increase the percentage of gold that you can extract out of that ore. >> So and to that note, there are a couple of things that we're actually looking at. So not only that but also as we're moving into the future, the gold grades from the ore is diminishing. And that's where I think we're at the right place, because we are looking at technology, we are looking at the buzzwords, like "artificial intelligence" to help us in that phase because all the good grades are almost gone, so to get that little gold that's in a big mass of rock, we definitely need to look at technologies. >> So the grade is the percentage of gold per unit of ore, right? Because the gold itself is the same gold, once you get it out. >> Correct, it's the ounce of gold in that mass of rock. >> So gold mining's been going on for a long time. What are some of the opportunities for you guys to use software to basically get your yield up? >> Okay, so there are a couple of things where we can look at technology. So number one is safety. So as the gold grade is going down, which also means we are actually going deeper in the mine, so as we go deeper in the mine, that means it's becoming unsafe for people operating underground. So we're looking at technology, we are looking at things like autonomous vehicles, artificial intelligence algorithms that can help us in exploration, and then other things like robotics, drones, all kinds of stuff. So, the technology space is huge for us to explore, to use. And then to go to safety, of course we're looking at reducing our operating costs, increasing productivity as much as we can, and hence, lower our AISC, which is the All-In Sustaining Cost. >> So the autonomous vehicles is an interesting one. I don't think most people are aware how many autonomous vehicles operate in mines. I don't know if it's gold mines specifically, but I think we've talked to Caterpillar before, and there's a lot of autonomous vehicles running around mining operations. >> That's the future definitely, so right now we are actually taking a couple of projects to run these autonomous mines. But yes, you're right, it's not only the gold industry, but across mining and metals industry. >> Right, and what is digital transformation in mining? 'Cause we think of big lumpy assets that are made out of rocks and steel and rubber, and you know, heavy heavy industry, heavy heavy machinery. So what does digital transformation look like in the gold industry? >> So, again, this is very interesting and also dangerous. Why I say that, because... I'll tackle the dangerous piece first. Because digital transformation is again a buzzword, we have gone through different ones in the past. What we are targeting to do through digital transformation is not new. We have attempted to do this in the past with some degree of success, but as you know, the mining industry's a very cyclical industry. So when we were in the peak of the cycle, we invested a lot of money, we did a lot of cool projects, but as soon as we moved into the downward cycle, the budgets were tighter, so some of those projects were taken off the table. But now what's happening is, we are taking it back, but we're looking at this as an enabler. What that means is we are democratizing the digital transformation laterally and vertically, which means, within the site, and also across the organization. So we are educating our operators, we are educating the metallurgists and all that, because digital transformation is more cultural transformation. You know, we all have these cool gadgets and a lot of these we use in our daily lives. But how we can use these effectively in the mining world, how we can use things like iPads, wireless technology, and bring that information, as I mentioned to you before, on the table of the operators so that they are empowered now to make decisions rather than waiting forever for their frontline supervisors to give them that information. So now with the use of digital transformation as an enabler we're hoping that A, we are making it safer, we are democratizing this, as well as making decisions faster efficient. >> So it's pretty interesting on the democratization. 'Cause we see that in a lot of industries. So basically, giving the power, the tools, and the data to a broader group of people so they can make better decisions on the line. >> Correct. >> That's really the operator side. But you said something interesting, too, before we turned the cameras on, about transparency, not only at the site, but across the company, so that more people have more visibility into more pieces of the puzzle. >> Tara: Correct. >> So how's that been going? >> It has been going great so far. So what I meant by that was that the communities that we operate in, so Nevada in the States, Veladero, San Juan community in Argentina, communities like that... So now with the help of digital transformation we can also take this information to the community. Now they're more excited about what we're doing rather than being skeptical about us not sharing with them. >> Jeff: Right. >> So I think that is going great. The other aspect I should bring out is environmental. Environmental is a big piece. So, safety, health, and environment, we live by that because that's our license to operate. So with the help of digital transformation, and by sharing this information with our communities, I think we can reach our goal and bring everybody on board along this journey. >> Right, and I would imagine that ties directly back into trust. >> Correct, yeah. >> With the transparency, which I'm sure can be a big point of friction if you don't have that transparency. >> Tara: Absolutely. >> Especially on the environmental side, yeah. >> Tara: Yep, yeah. >> So what are you here for, what are you finding here at PI World? >> Okay, so I don't think I mentioned this, but along this journey, we are also looking for strategic partners. Because we cannot do this all by ourselves, right? And that was one of the reasons why digital transformation failed before, is we created silos, we didn't want to collaborate, we wanted to keep all the information within ourselves, and we were not sharing the information, not only publicly, but also within the organization. So what my role here in this conference is to share with all our peers in the industry what we have been doing, and also learn from others what they have been doing so that we can collaborate and make mining industry in general a very lucrative industry for everybody and make it safer and productive. >> So I would imagine there's probably a lot of sensitivity in sharing some of the operating processes, and I would imagine there's some proprietary technology in the way that you get your yield out of the ore. At the same time I would imagine safety and environmental can only benefit the industry if you share that information. >> Yes, absolutely. >> I would imagine that's not what you're going to build your competitive advantage on. >> No. >> And there's really more of an opportunity for industry sharing, if you will. >> Correct, so the point about... Sharing information about production. Yes, that is definitely sensitive, but I think what we are interested in sharing is the concepts, you know how we can do this digital transformation together, rather than the numbers that we're looking at. We're looking at percentage improvement. So even if I can share what we are doing with my peers in the industry in general, and if they are benefited, I think that's great. >> Jeff: Yeah... >> For the mining industry in general. >> Is the industry more receptive to that sharing than it has been in the past? >> Definitely there is more sharing now. But of course there are still some hurdles, and I'm hoping that attending conferences like this will make those hurdles smaller and smaller and we can do better. >> All right, well, Tara, thanks for taking a few minutes and sharing your story, and wish you obviously a lot of success on the safety and getting gold cheaper so we can all buy our wives bigger necklaces for Mother's Day, it's coming up, right? (laughs) >> Sure, absolutely, yeah. Thank you very much, and it's my pleasure to share, and let's enjoy the rest of the conference. >> Well, thanks a lot. He's Tara, I'm Jeff, you're watching theCUBE from OSIsoft PI World 2018 San Francisco, thanks for watching. (mellow techno music)

[Music] okay we're here with ceres vitale who's the chief product officer at alteryx and adam wilson the ceo of trifacta now of course part of alteryx just closed this quarter gentlemen welcome great to be here okay so rush let me start with you in my opening remarks i talked about alteryx's traditional position serving business analysts and how the hyperanna acquisition brought you deeper into the business user space what does trifacta bring to your portfolio why'd you buy the company yeah thank you thank you for the question um you know we see a we see a massive opportunity of helping brands democratize the use of analytics across their business every knowledge worker every individual in the company should have access to analytics it's no longer optional as they navigate their businesses with that in mind you know we know designer and our the products that alteryx has been selling the past decade or so do a really great job addressing the business analysts with hyper rana now kind of renamed alteryx auto insights we even speak with the business owner the line of business owner who's looking for insights that aren't revealed in traditional dashboards and so on um but we see this opportunity of really helping the data engineering teams and i.t organizations to also make better use of analytics and that's where trifacta comes in for us trifacta has the best data engineering cloud in the planet they have an established track record of working across multiple cloud platforms and helping data engineers um do better data pipelining and work better with this massive kind of cloud transformation that's happening in every business um and so trifecta made so much sense for us yeah thank you for that i mean look you could have built it yourself would have taken you know who knows how long you know but uh so definitely a great time to market move adam i wonder if we could dig into trifacta some more i mean i remember interviewing joe hellerstein in the early days you've talked about this as well on thecube coming at the problem of taking data from raw refined to an experience point of view and joe in the early days talked about flipping the model and starting with data visualization something jeff herr was expert at so maybe explain how we got here we used to have this cumbersome process of etl and you maybe and some others change that model with you know el and then t explain how trifacta really changed the data engineering game yeah that's exactly right uh dave and it's been a really interesting journey for us because i think the original hypothesis coming out of the campus research at berkeley and stanford that really birthed trifacta was you know why is it that the people who know the data best can't do the work you know why is this become the exclusive purview the highly technical and you know can we rethink this and make this a user experience problem powered by machine learning that will take some of the more complicated things that people want to do with data and really help to automate those so so a broader set of users can can really see for themselves and help themselves and and i think that um there was a lot of pent up frustration out there because people have been told for you know for a decade now to be more data driven and then the whole time they're saying well then give me the data you know in the shape that i can use it with the right level of quality and i'm happy to be but don't tell me to be more data driven and they'll don't then and and not empower me um to to get in there and to actually start to work with the data in meaningful ways and so um that was really you know what you know the origin story of the company and i think as as we saw over the course of the last five six seven years that um you know a real uh excitement to embrace this idea of of trying to think about data engineering differently trying to democratize the the etl process and to also leverage all these exciting new uh engines and platforms that are out there that allow for you know processing you know ever more diverse data sets ever larger data sets and new and interesting ways and that's where a lot of the push down or the elt approaches uh you know i think it really won the day um and that and that for us was a hallmark of the solution from the very beginning yeah this is a huge point that you're making this is first of all there's a large business probably about a hundred billion dollar tam uh and and the the point you're making is we look we've contextualized most of our operational systems but the big data pipelines hasn't gotten there but and maybe we could talk about that a little bit because democratizing data is nirvana but it's been historically very difficult you've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome but it's been hard and so what's going to be different about alteryx as you bring these puzzle pieces together how is this going to impact your customers who would like to take that one yeah maybe maybe i'll take a crack at it and adam will add on um you know there hasn't been a single platform [Music] for analytics automation in the enterprise right people have relied on different products to solve kind of smaller problems across this analytics automation data transformation domain and i think uniquely alteryx has that opportunity we've got 7000 plus customers who rely on analytics for data management for analytics for ai and ml for transformations for reporting and visualization for automated insights and so on and so by bringing trifecta we have the opportunity to scale this even further and solve for more use cases expand the scenarios where angles gets applied and serve multiple personas um and now we just talked about the data engineers they are really a growing stakeholder in this transformation of data analytics yeah good maybe we can stay on this for a minute because you're right you bring it together now at least three personas the business analyst the end user size business user and now the data engineer which is really out of an i.t role in a lot of companies and you've used this term the data engineering cloud what is that how is it going to integrate in with or support these other personas and and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores yeah you know that's great uh you know i think for us we really looked at this and said you know we want to build an open and interactive you know cloud platform for data engineers you know to collaboratively profile pipeline um and prepare data for analysis and and that really meant collaborating with the analysts that were in the line of business and so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are in the line of business that are driving a lot of the decision making and allow for that what i would describe as collaborative curation you know of the data together so that you're starting to see um uh you know increasing returns to scale as this uh as this rolls out i just think that is an incredibly uh powerful combination and frankly something that the market has not cracked the code on yet and so um i think when we when i sat down with surash and with mark and and the team at ultrix that was really part of the the big idea the big vision that that was painted and and got us really energized um about the acquisition and about the the potential of the combination yeah and you're really you're obviously riding the cloud and the cloud native wave um and but specifically we're seeing you know i almost don't even want to call it a data warehouse anyway because when you look at what princeton snowflake is doing of course their marketing is around the data cloud but i i actually think there's real justification for that because it's not like the traditional data warehouse right it's it's simplified get there fast don't necessarily have to go through this central organization to share data uh and and but it's really all about simplification right isn't that really what the democratization comes down to yeah it's simplification and collaboration right i don't want to i want to kind of just uh what what adam said resonates with me deeply um analytics is one of those massive disciplines inside an enterprise that's really had the weakest of tools um and weakest of interfaces to collaborate with and i think truly this was alteryx's end of superpower was helping the analysts get more out of their data get more out of the analytics like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources understanding data models better i think curating those insights i borrowing adam's phrase again i think that creates a real value inside the organization because frankly in scaling analytics and democratizing analytics and data we're still in such early phases of this journey so how should we think about designer cloud which is from alteryx it's really been the on-prem the server or desktop you know offering and of course trifecta is about cloud cloud data warehouses right um how should we think about those two products yeah i think i think you should think about them and as very complementary right designer cloud really shares a lot of dna and heritage with designer desktop the low code tooling and the interface that really appeals to the business analysts and gets a lot of the things that they do well we've also built it with interoperability in mind right so if you started building your workflows in designer desktop you want to share that with designer cloud we want to make it super easy for you to do that and i think over time now we're only a week into this alliance with uh with trifacta i think we have to get deeper and start to think about what does the data engineer really need what business analysts really need and how to design a cloud and try factor really support both of those requirements uh while kind of continue to build on the trifecta on the amazing trifecta cloud platform you know and i think let's go ahead i'm just to say i think that's one of the things that um you know creates a lot of opportunity as we go forward because ultimately you know trifacta took a platform uh first mentality to everything that we built so thinking about openness and extensibility and um and how over time people could build things on top of trifacta that are a variety of analytic tool chain or analytic applications and so when you think about um alteryx now starting to uh to move some of its capabilities or to provide additional capabilities uh in the cloud um you know trifacta becomes uh a a platform that can accelerate you know all of that work and create a cohesive set of of cloud-based services that share a common platform and that maintains independence because both companies um have been uh you know fiercely independent uh in really giving people choice um so making sure that whether you're uh you know picking one cloud platform another whether you're running things on the desktop uh whether you're running in hybrid environments that no matter what your decision you're always in a position to be able to get out your data you're always in a position to be able to cleanse transform shape structure that data and ultimately to deliver uh the analytics that you need and so i think in in that sense um uh you know this this again is another reason why the combination you know fits so well together giving people um the choice um and as they as they think about their analytics strategy and and their platform strategy going forward you know i make a chuckle but one of the reasons i always liked alteryx is because you kind of did did a little end run on i.t i.t can be a blocker sometimes but that created problems right because the organization said wow this big data stuff is taken off but we need security we need governance and and it's interesting because you got you know etl has been complex whereas the visualization tools they really you know really weren't great at governance and security it took some time there so that's not not their heritage you're bringing those worlds together and i'm interested you guys just had your sales kickoff you know what was the reaction like uh maybe suresh you could start off and maybe adam you could bring us home yeah um thanks for asking about our sales kickoff so we met uh for the first time in kind of two years right for as it is for many of us um in person uh um which i think was a was a real breakthrough as alteryx has been on its transformation journey uh we had a try factor to um the the party such as it were um and getting all of our sales teams and product organizations um to meet in person in one location i thought that was very powerful for us as a company but then i tell you um the reception for trifecta was beyond anything i could have imagined uh we were working adam and i were working so hard on on the the deal and the core hypotheses and so on and then you step back and kind of share the vision with the field organization and it blows you away the energy that it creates among our sellers our partners and i'm sure adam and his team were mobbed every single day with questions and opportunities to bring them in but adam maybe you should share yeah no it was uh it was through the roof i mean uh the uh the amount of energy the uh when so certainly how welcoming everybody was uh you know just i think the story makes so much sense together i think culturally the companies are very aligned um and uh it was a real uh real capstone moment uh to be able to complete the acquisition and to and to close and announce you know at the kickoff event and um i think you know for us when we really thought about it you know when we and the story that we told was just you have this opportunity to really cater to what the end users you know care about which is a lot about interactivity and self-service and at the same time and that's and that's a lot of the goodness that um that alteryx is has brought you know through you know you know years and years of of building a very vibrant community of you know thousands hundreds of thousands of users and on the other side you know trifecta bringing in this data engineering focus that's really about uh the governance things that you mentioned and the openness that that it cares deeply about and all of a sudden now you have a chance to put that together into a complete story where the data engineering cloud and analytics automation you know come together and um and i just think you know the lights went on um you know for people instantaneously and you know this is a story that um that i think the market is really hungry for and and certainly the reception we got from from the broader team at kickoff was uh was a great indication of that well i think the story hangs together really well you know one of the better ones i've seen in this space um and and you guys coming off a really really strong quarter so congratulations on that gents we have to leave it there really appreciate your time today yeah take a look at this short video and when we come back we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses you're watching the cube your leader in enterprise tech coverage [Music]

live from Las Vegas it's the cube covering AWS reinvent 2018 brought to you by Amazon Web Services Intel and their ecosystem partners hey welcome back everyone we're live here in Las Vegas with AWS Amazon webster's reinvent our 6th year I'm Jeff our table what they did six years two sets people rolling out of the keynote so much action we got another day coming tomorrow they're two great guests here we got dr. feci philomon is the general manager the machine learning and AI at Amazon Web Services and dr. Taha costs senior leader at healthcare and AI at Amazon guys welcome to the cube Thank You thanks itíd that you're here because I've been waiting to have this conversation Dave and I have been we just had an analysis of the distractions and glued up the stack around machine learning so much value now coming online that's been in the works around AI are really mainly machine learning that's creating a I like benefits and II just had to spend a lot of time with key nuts they almost a third of it around a I like capabilities and how Amazon integrates in from you know chipsets with elastic inference beautiful it's just good stuff so congratulations so what does it mean what does it mean for customers right now who want to kind of grok what's going on with Amazon and AI is that new sense the services coming online is that how long has been the works explaining yeah our mission at AWS has always been to take technologies that have been traditionally available for a few special technology companies and take that and make it available to all developers and we've done that I should say that we've done that fairly well when it comes to compute when it comes to storage when it comes to databases the analytics and we're doing the same thing for machine learning and AI and what we're doing because it's a new field is we've got to innovate at three layers of our stack to the bottom most layer as you saw in the keynote earlier has to do with frameworks and infrastructure so this is more for the people that fully understand how to deal with machine learning models and like to go in and tweak these models the middle layer then is for everyday developers and the data scientists and that's sort of where sage maker fits in and finally at the top layer of the stack is where we have our application services and this is meant for developers that don't want to get into the weeds of machine learning but they still want to use make use of all of these technologies to make their applications more smarter so they get the insight benefits get the insights have the day that without getting in town on the weeds exactly who want to get down in the weeds you can get down and dirty with all this other stuff yeah look at that right yeah and typically what we do with the top layer of the stack as we try and solve really hard problems and so customers can now take advantage of it because we've solved it for them and they can just take that and integrate it into their Apple quick what what's the hardest problem that you guys solve I mean traditionally speech recognition is a very hard problem that's one of the hard problems the other one is NLP natural language processing but I would say speech recognition is probably a hard problem and we just launched streaming transcription so you can now transcribe live as somebody speaks and of course you can connect it to translate and translate it as well live so great for our cute beers looking forward to having that on as a health care practitioner how does this all apply to that industry what kind of projects are you guys working on in that regard of course yeah so I mean to to posses point is want to continue to innovate on behalf of the customers across all layers of the stack machine learning in particular this week we launched Amazon comprehend medical particularly in a hardier heart problem where the majority of healthcare data is captured conversation and observations and unstructured formality so petabytes of data is stored across entire healthcare system that's a nun structure for form so to drive actionable insights and to be able to find the right elements to treat patients or to manage a population or even to do accurate billing it's been really an important that we can empower our customers with building blocks for them to build the right solutions to take advantage of that so Amazon comprehend Medical is able to understand the medical language and the context similar how clinicians understand the medical language and context for example if you're looking at a patient medical note Amazon campaign medicals able to with high accuracy extract medical conditions medications tests procedures being done on the patients as well as the relationship between those and understanding that context at this condition and this treatment go together as well as the nuances for example you know a patient has no family history of X or there's no smoking history all those are things in relation in the past or in the future or other members and this is really what we're really proud about launched an Amazon comprehend medical talk about how it works because you know I Healthcare has been a great field around where a is old-fashioned a is a queer when I wasn't doing it in the 80s early 90s ontologies were really popular and it's linguistics is kind of known but now that but you need that linguistics guru to do that he mentioned streaming the transcribed got metadata how do you guys get this kind of benefit when the balls moving so fast around these rapidly changing and verticals like healthcare because healthcare is got a big problem like other verticals where it's too many notifications what I pay attention to so much data how do you put the puzzle together let me first give you some context here as you probably we're at last reinvent we launched Amazon comprehend right comprehend is a text analytics service it helps you look into text and understand what's in there right we started out with general things that we could detect like people places things sentiment the language the text is written in and so on but when we started customers are picked on it and they're using it a lot but as they keep using it they came back to us and said hey it's great that you guys have this this you're giving us the capability to understand general language but some of our domains have some special language like jargon like yeah like take the legal domain for example right it's got charges and defendants and very particular things that are very relevant to the legal domain so they were asking us for a capability to sort of extend the comprehend to include their custom domain terms and phrases as well right so last week we actually launched a custom custom entities feature that allows them to bring in their custom domain into comprehend so the comprehend be extended to include their domain the so legal language is difficult to understand but medical language on the other hand is even more harder to understand that quick right acronyms jargon absolutely what is an entity looks like extracting that and extracting it uses alone yeah miss spells right but relating those entities together is super important because you could in one clinical note you could have multiple drugs in there with different dosages different frequencies and so you need to be able to relate those entities together right and that's the sort of thing that comprehend Medical allows our customers to do to solve some really so you're doing one of that entity extraction is under the covers is that right has it were I mean how does comprehending the medical work I mean just out of the box you have to train it there's no training meet needed know machine learning expertise needed so the algorithm extract these entities as well as the relationship between those entities and then also extracts any attributes that might be related such as negation or past and future or what's anatomy of the body relates one now all that is done out of the box and that's super important you want to know whether the patient's stopped taking a medication right yeah so negation things like that you want to know because that gives you the context just getting the terms alone doesn't really tell you much it each has had a great video about the f1 point of ethics imagine that for personal that's right you're not doing good right now take a break yeah so I feel like we're kind of now scratching the service of stress in the surface of health care yeah information yeah think about the health care industry for years it's been compliance-driven yeah whether it's hip Affordable Care Act yeah EMR and meaningful use right but the industry hasn't been you know dramatically transformed and disrupted and it kind of needs to be yeah how do you guys see that evolving I feel like you're now beginning to see that see change and that's going to take a while it's a high-risk business obviously but what's your sort of prognosis for that transformation and what's the vision as to the outcome yes now that's a really great question I mean one thing I mean one great things happen over the last decade is the digitization of your medical record so and that's really wonderful because before was all paper-based primarily unless you were an acute setting so now the majority of the US for example and globally there's this huge adopt adoption and propagation of these electronic medical records the issue there remains now when the majority of that data is observations and conversations as well as unstructured that that creates a different kind of roadblock for our customers and this is what we're hoping for service like Amazon comprehend medical that's HIPPA eligible means a lot of the early the compliance or help our customer meet their compliance needs that we'll be able to remove the heavy lifting of this undepreciated task about you know having in a large amount of time being spent on analyzing this text and extracting very low we're now with Amazon company and medical be able to really fast track that and be able to elevate it hit the nail on the head of the undifferentiated heavy lifting right that's the ethos of DevOps is that yeah let me give you some stats actually there are one point two billion medical documents that are generated every year in the US and 80% of them it's unstructured text so to make sense of that it's going to enable our customers to do some really amazing things one of the things one of the use cases that we see is its clinical trial recruitment so Fred Hutchinson which is one of the yeah the nation's top cancer research centers they recruit patients for clinical trials if you go to clinical trials.gov you'll see like 290 thousand four and 50 clinical trials open and typically from history we know that most of these clinical trials don't end up recruiting they don't end up meeting their recruiting goals because it's very hard to figure out which patients fit the clinical trial that you're actually trying to perform so comprehend medical helps these customers to very quickly narrow it down expand on the involvement of people in the community mentioned Fred hutch Roach has also been involved what I heard yeah what who was involved in this project sound it was a collaboration take a minute to explain that right I mean it's very similar to a lot of other services that we put it into the market we collaborate a lot with customers 90% of what we do is really coming from customers so we've collaborated with people like Fred hutch and some of the nation's top institutions to help us validate the service that we've built to actually make sure that its meeting sort of the requirements for those use cases that they are thinking of so we collaborate closely with them to get the service to where this today and we announced it as generally available yesterday ok so what's the use case I'll go ahead yeah I can expand a little bit some of the customers as well their use cases we're talking anywhere from hospital systems that when I use or take advantage of their unstructured text for things such as identify people who are for their follow-up appointments or stopping treatments or find an alternative routes to billers we're trying to identify it is accurate procedures were done if we account for all the procedures or care for all the billing which often time is hidden in those unstructured text and require a lot of manual process and often time the rules that can't really scale to things such as clinical trials recruitment how can you if example in Fred Hutchinson Cancer Institute use case for identify a patient and match them to the right clinical trial these patients often time have Harry Potter's worth of clinical notes down on the minute their longitudinal journey and to go from one institution another another and be able to really find it's no longer needed a haystack it's like a needle in the bottom of Atlantic Ocean and then be able to really do that match from hours and months down to a few seconds and that's really the beauty about the service John likes to talk about the 20 mile stare and I wonder if we could just look ahead how far can we take AI and machine learning in in healthcare and how far should we take it and maybe a more specific question as as a practitioner you know when do you think machines might make better diagnosis than doctors if ever how do you feel about that where do you see this all going I think I mean the whole idea about machine learning the beauty about it I mean the seta scope was introduced or how the thermometer was introduced in medicine and these are tools that we use to our advantage to really provide better care and and better outcomes and that's really what we're that's the mission that our health IT and customers and wanna are really driving tower's machine learning can do a lot of great things for routine things that human being can't can go and focus their attention to other things such as the Fred Hutchinson instead of going and mining these diagnoses in mountain amounts of data a machine learning will be able to identify that with a clinical staff can focus on care and that's really where I think I mean over the next decade and so we can see a lot of this advancement in in these building blocks as well as what Amazon's offering from forecasting and prediction algorithms Rana will be able to find you know fine-tune our capabilities to help customers achieve even precision medicine real-world impact because you're changing the workflow I mean someone's within the wrong line or the wrong process based upon their history yeah HIPPA HIPPA requirements really cause a lot of this record sharing thing to be a problem from what we've been reporting over the years it's kind of a solution to that so if I move to a service medical service I get all that records with me it's just kind of how you see going and how does other regulations that are holding you back that are blockers is that clear now how does that solve the industry challenge it's of privacy and if you look at the healthcare system today there are lots of inefficiencies in there right in the end this is all about improving patient outcomes and making sure that we reduce costs and that's what this boils down to and these are tools that allow our customers to do exactly that well guys thanks for sharing this insight comprehend medicals really awesome opportunities I think it's early days day one is you guys think right I think there's so much more that could be there I'd love to see the industry just from the personal is decided change it's just get out of the way of all these pretty broad hurdles get the data out there expose the data check the privacy box would be good right this is gonna change the game yeah maybe we should say a little bit about the how we built the service in terms of that right as you know at AWS security and privacy is number one for us right so this service is HIPAA eligible it's a stateless service what that means is nothing gets stored this is not the data is not used to improve the models or anything like that the only person that can actually see the data is the customer he's got the keys he's the only one that's sending the data to the endpoint and whatever he gets back only he can decrypt it so we've taken care to make sure that we can remove some of those hurdles that people have always been worried about well doctors take you so much for sharing thank you so much for having us here we are bringing you all the action here from 80s reinvent again as the compute power is increased as software is written with new apps a eyes changing the game of course the cube a lot of video we don't need some of these services to make these transcribes on the fly they succumb and I really appreciate it you think back on the more after this short break [Music]