live from Boston Massachusetts extracting the signal from the noise it's the kue covering HP big data conference 2015 brought to you by HP software now your host John furrier and Dave vellante okay welcome back everyone we are here live in boston massachusetts for HP's big data conference this is a special presentation of the cube our flagship program where we go out to the events and extract the season for the noise I'm John furrier with Dave allante here Wikibon down on research our next guest Stephanie McReynolds VP margon elation hot new startup that's been kind of coming out of stealth that's out there big data a lot of great stuff Stephanie welcome to the cube great see you great to be here tell us what the start at first of all because good buzz going on it's kind of stealth buzz but it's really with the fought leaders and really the you know the people in the industry who know what they're talking about like what you guys are doing so so introduce the company tells me you guys are doing and relationship with Vertica and exciting stuff absolutely a lesion is a exciting company we just started to come out of south in March of this year and we came out of self with some great production customers so eBay is a customer they have hundreds of analysts using our systems we also have square as a customer smaller analytics team but the value that you Neelix teams are getting out of this product is really being able to access their data in human context so we do some machine learning to look at how individuals are using data in an organization and take that machine learning and also gather some of the human insights about how that data is being used by experts surface that all in line with in work so what kind of data cuz Stonebreaker was kind of talking yesterday about the 3 v's which we all know but the one that's really coming mainstream in terms of a problem space is variety variety you have the different variety of schema sources and then you have a lot of unstructured exhaust or data flying around can you be specific on what you guys do yeah I mean it's interesting because there's several definitions of data and big data going around right and so I'm you know we connect to a lot of database systems and we also connect to a lot of Hadoop implementations so we deal with both structured data as well as what i consider unstructured data and i think the third part of what we do is bring in context from human created data or cumin information with which robert yesterday was talking about a little bit which is you know what happens in a lot of analytic organizations is that and there's a very manual process of documenting some of the data that's being used in these projects and that's done on wiki pages or spreadsheets that are floating around the organization and that's actually a really black base camp all these collaboration all these collaboration platforms and what you realize when you start to really get into the work of using that information to try to write your queries is that trying to reference a wiki page and then write your sequel and flip back and forth between maybe ten different documents is not very productive for the analyst so what our customers are seeing is that by consolidating all of that data and information in one place where the tables are actually reference side by side with the annotations their analysts can get from twenty to fifty percent savings and productivity and new analysts maybe more importantly new analyst can get up to speed quite a bit quicker and that square the day I was talking to one of the the data scientists and he was was talking about you know his process for finding data in the organization which prior to using elation it would take about 30 minutes going two maybe three or four people to find the data he needed for his analysis and with elation in five seconds he can run a query search for the date he wants gets it back gets all kind of all that expert annotation already around that base data said he's ready to roll he can start I'm testing some of us akashi go platform right they've heard it was it a platform and it and you said you work with a lot of database the databases right so it's tightly integrated with the database in this use case so it's interesting and you know we see databases as a source of information so we don't create copies of the data on our platform we go out and point to the data where it lies and surface that you know that data to to the end user now in the case of verdict on our relationship with Vertica and we've also integrated verdict in our stack to support we call data forensics which is the building for not an analyst who's using the system day to day but for NIT individual to understand where the behaviors around this data and the types of analysis that are being done and so verdicts a great high performance platform for dashboarding and business intelligence a back end of that providing you know quick access to aggregates so one of they will work on a vertica you guys just the engine what specifically again yeah so so we use the the vertica the vertical engine underneath our forensics product and then the that's you know one portion of our platform the rest of our platform is built out on other other technologies so verdict is part of your solution it's part of our solution it's it's one application that we part of one application we deliver so we've been talking all week about this week Colin Mahoney in his talk yesterday and I saw a pretty little history on erp how initially was highly customized and became packaged apps and he sort of pointed to a similar track with analytics although he said it's not going to be the same it's going to be more composable sort of applications I wonder and historically the analytics in the database have been closely aligned I'll say maybe not integrated you see that model continuing do you see it more packaged apps or will thus what Collins calling composable apps what's the relationship between your platforming and the application yeah so our platform is is really more tooling for those individuals that are building or creating those applications so we're helping data scientists and analysts find what algorithms they want to use as a foundation for those applications so a little bit more on the discovery side where folks are doing a lot of experiment and experimentation they may be having to prepare data in different ways in order to figure out what might work for those applications and that's where we fit in as a vendor and what's your license model and so you know we're on a subscription model we have customers that have data teams in the in the hundreds at a place like eBay you know the smaller implementations could be maybe just teams of five analyst 10a analyst fairly small spatial subscription and it's a seat base subscription but we can run in the cloud we can run on premise and we do some interesting things around securing the data where you can and see your columns bommana at the data sets for financial services organizations and our customers that have security concerns and most of those are on premise top implementation 70 talk about the inspiration of the company in about the company he's been three years since then came out of stealth what's the founders like what's the DNA the company what do you guys do differently and what was the inspiration behind this yeah what's really what's really interesting I think about the founding of the company is that and the technical founders come from both Google and Apple so you have an interesting observation that both individuals had made independently hardcore algorithmic guy and then like relevant clean yeah and both those kind of made interesting observations about how Google and Apple two of the most data-driven companies you know on the planet we're struggling and their analytics teams were struggling with being able to share queries and share data sets and there was a lot of replication of work that was happening and so much for the night you know but both of these folks from different angles kind of came together at adulation said look there's there's a lot of machine learning algorithms that could help with this process and there's also a lot of good ways with natural language processing to let people interact with their data in more natural ways the founder from from Apple Aaron key he was on the Siri team so we had a lot of experience designing products for navigability and ease of use and natural language learning and so those two perspectives coming together have created some technology fundamentals in our product and it's an experience to some scar tissue from large-scale implementations of data yeah very large-scale implementations of data and also a really deep awareness of what the human equation brings to the table so machine learning algorithms aren't enough in and of themselves and I think ken rudin had some interesting comments this morning where you know he kind of pushed it one step further and said it's not just about finding insight data science about is about having impact and you can't have impact unless you create human contacts and you have communication and collaboration around the data so we give analyst a query tool by which we surface the machine learning context that we have about the data that's being used in the organization and what queries have been running that data but we surface in a way where the human can get recommendations about how to improve their their sequel and drive towards impact and then share that understanding with other analysts in the organization so you get an innovation community that's started so who you guys targets let's step back on the page go to market now you guys are launched got some funding can you share the amount or is it private confidential or was how much did you raise who are you targeting what's your go-to market what's the value proposition give us the give us this data yeah so its initial value proposition is just really about analyst productivity that's where we're targeted how can you take your teams of analysts and everyone knows it's hard to hire these days so you're not going to be able to grow those teams out overnight how do you make the analyst the data scientist the phd's you have on staff much more productive how do you take that eighty to ninety percent of the time that they make them using stuff sharing data because I stuff you in the sharing data try to get them out of the TD of trying to just find eight in the organization and prepare it and let them really innovate and and use that to drive value back to the to the organization so we're often selling to individual analysts to analytics teams the go to market starts there and the value proposition really extends much further in the organization so you know you find teams and organizations that have been trying to document their data through traditional data governance means or ETL tools for a very long time and a lot of those projects have stalled out and the way that we crawl systems and use machine learning automation and to automate some of that documentation really gives those projects and new life in our enterprise data has always been elusive I mean do you go back decades structured day to all these pre pre built databases it's been hard right so it's you can crack that nut that's going to be a very lucrative in this opportunity I got the Duke clusters now storing everything I mean some clients we talked to here on the key customers of a CHP or IBM big companies they're storing everything just because they don't know they do it again yeah I mean if the past has been hard in part because we in some cases over manage the modeling of the data and I think what's exciting now about storing all your data in Hadoop and storing first and then asking questions later is you're able to take a more discovery oriented hypothesis testing iterative approach and if you think about how true innovation works you know you build insights on top of one another to get to the big breakthrough concepts and so I think we're at an interesting point in the market for a solution like this that can help with that increasing complexity of data environment so you just raise your series a raised nine million you maybe did some seed round before that so pretty early days for you guys you mentioned natural language processing before one of your founders are you using NLP and in your solution in any way or so we have a we have a search interface that allows you to look for that technical data to look for metadata and for data objects and by entering a simple simple natural language search terms so we are using that as part of our interface in solution right and so kind of early customer successes can you talk about any examples or yeah you know there's some great examples and jointly with Vertica square is as a customer and their analytics team is using us on a day-to-day basis not only to find data sets and the organization but to document those those data sets and eBay has hundreds of analysts that are using elation today in a day to day manner and they've seen quite a bit of productivity out of their new analysts that are coming on the system's it used to take analysts about 18 months to really get their feet around them in the ebay environment because of the complexity of all of the different systems at ebay and understanding where to go for that customer table you know that they needed to use and now analysts are up and running about six months and their data governance team has found that elation has really automated and prioritized the process around documentation for them and so it's a great light a great foundation for them there and data curators and data stewards to go in and rich the data and collaborate more with the analysts and the actual data users to get to a point of catalogued catalog data disease so what's the next you guys going to be on the road in New York Post Radek hadoop world big data NYC is coming up a big event in New York I'm Cuba visa we're getting the word out about elation and then what we're doing we have customers that are you know starting to speak about their use cases and the value that they're seeing and will be in New York market share I believe will be speaking on our behalf there to share their stories and then we're also going to a couple other conferences after that you know the fall is an exciting time which one's your big ones there so i will be at strada in New York and a September early October and then mid-october we're going to be at both teradata partners and tableaus conference as well so we connect not only to databases of all set different sorts but also to go with users are the tools yeah awesome well anything else you'd like to add share at the company is awesome we're some great things about you guys been checking around I'll see you found out about you guys and a lot of people like the company I mean a lot of insiders like moving little see you didn't raise too much cash that's raised lettin that's not the million zillion dollar round I think what led you guys take nine million yeah raised a million and I you know I think we're building this company in a traditional value oriented way great word hey stay long bringing in revenue and trying to balance that out with the venture capital investment it's not that we won't take money but we want to build this company in a very durable so the vision is to build a durable company absolutely absolutely and that may be different than some of our competitors out there these days but that's that we've and I have not taken any financing and SiliconANGLE at all so you know we're getting we believe in that and you might pass up some things but you know what have control and you guys have some good partners so congratulations um final word what's this conference like you go to a lot of events what's your take on this on this event yeah I do i do end up going to a lot of events that's part of the marketing role you know i think what's interesting about this conference is that there are a lot of great conversations that are happening and happening not just from a technology perspective but also between business people and deep thinking about how to innovate and verticals customers i think are some of the most loyal customers i've seen in the in the market so it's great in their advanced to they're talking about some pretty big problems but they're solving it's not like little point solutions it's more we architecting some devops i get a dev I'm good I got trashed on Twitter private messages all last night about me calling this a DevOps show it's not really a DevOps cloud show but there's a DevOps vibe here the people who are working on the solutions I think they're just a real of real vibe people are solving real problems and they're talking about them and they're sharing their opinions and I I think that's you know that's similar to what you see in DevOps the guys with dev ops are in the front line the real engineers their engineering so they have to engineer because of that no pretenders here that's for sure are you talking about it's not a big sales conference right it's a lot of customer content their engineering solutions talking to Peter wants a bullshit they want reaiah I mean I got a lot on the table i'm gonna i'm doing some serious work and i want serious conversations and that's refreshing for us but we love love of hits like it's all right Stephanie thinks for so much come on cubes sharing your insight congratulations good luck with the new startup hot startups here in Boston hear the verdict HP software show will be right back more on the cube after this short break you you