from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante earnings season has shown a conflicting mix of signals for software companies well virtually all firms are expressing caution over so-called macro headwinds we're talking about ukraine inflation interest rates europe fx headwinds supply chain just overall i.t spend mongodb along with a few other names appeared more sanguine thanks to a beat in the recent quarter and a cautious but upbeat outlook for the near term hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis ahead of mongodb world 2022 we drill into mongo's business and what etr survey data tells us in the context of overall demand and the patterns that we're seeing from other software companies and we're seeing some distinctly different results from major firms these days we'll talk more about [ __ ] in this session which beat eps by 30 cents in revenue by more than 18 million dollars salesforce had a great quarter and its diversified portfolio is paying off as seen by the stocks noticeable uptick post earnings uipath which had been really beaten down prior to this quarter it's brought in a new co-ceo and it's business is showing a nice rebound with a small three cent eps beat and a nearly 20 million dollar top line beat crowdstrike is showing strength as well meanwhile managements at microsoft workday and snowflake expressed greater caution about the macroeconomic climate and especially on investors minds his concern about consumption pricing models snowflake in particular which had a small top-line beat cited softness and effects from reduced consumption especially from certain consumer-facing customers which has analysts digging more deeply into the predictability of their models in fact barclays analyst ramo lenchow published an especially thoughtful piece on this topic concluding that [ __ ] was less susceptible to consumption headwinds than for example snowflake essentially for a few reasons one because atlas mongo's cloud managed service which is the consumption model comprises only about 60 percent of mongo's revenue second is the premise that [ __ ] is supporting core operational applications that can't be easily dialed down or turned off and three that snowflake customers it sounds like has a more concentrated customer base and due to that fact there's a preponderance of its revenue is consumption driven and would be more sensitive to swings in these consumption patterns now i'll say this first consumption pricing models are here to stay and the much preferred model for customers is consumption the appeal of consumption is i can actually dial down turn off if i need to and stop spending for a while which happened or at least happened to a certain extent this quarter for certain companies but to the point about [ __ ] supporting core applications i do believe that over time you're going to see the increased emergence of data products that will become core monetization drivers in snowflake along with other data platforms is going to feed those data products and services and become over time maybe less susceptible and less sensitive to these consumption patterns it'll always be there but i think increasingly it's going to be tied to operational revenue last two points here in this slide software evaluations have reverted to their historical mean which is a good thing in our view we've taken some air out of the bubble and returned to more normalized valuations was really predicted and looked forward to look we're still in a lousy market for stocks it's really a bear market for tech the market tends to be at least six months ahead of the economy and often not always but often is a good predictor we've had some tough compares relative to the pandemic days in tech and we'll be watching next quarter very closely because the macro headwinds have now been firmly inserted into the guidance of software companies okay let's have a look at how certain names have performed relative to a software index benchmark so far this year here's a year-to-date chart comparing microsoft salesforce [ __ ] and snowflake to the igv software heavy etf which is shown in the darker blue line which by the way it does not own the ctf does not own snowflake or [ __ ] you can see that these big super caps have fared pretty well whereas [ __ ] and especially snowflake those higher growth companies have been much more negatively impacted year to date from a stock price standpoint now let's move on let's take a financial snapshot of [ __ ] and put it next to snowflake so we can compare these two higher growth names what we've done here in this chart has taken the most recent quarters revenue and multiplied it by 4x to get a revenue run rate and we've parenthetically added a projection for the full year revenue [ __ ] as you see will do north of a billion dollars in revenue while snowflake will begin to approach three billion dollars 2.7 and run right through that that four quarter run rate that they just had last quarter and you can see snowflake is growing faster than [ __ ] at 85 percent this past quarter and we took now these most of these profit of these next profitability ratios off the current quarter with one exception both companies have high gross margins of course you'd expect that but as we've discussed not as high as some traditional software companies in part because of their cloud costs but also you know their maturity or lack thereof both [ __ ] and snowflake because they are in high growth mode have thin operating margins they spend nearly half or more than half of their revenue on growth that's the sg a line mostly the s the sales and marketing is really where they're spending money uh and and they're specialists so they spend a fair amount of their revenue on r d but maybe not as high as you might think but a pretty hefty percentage the free cash flow as a percentage of revenue line we calculated off the full year projections because there was a kind of an anomaly this quarter in the in the snowflake numbers and you can see snowflakes free cash flow uh which again was abnormally high this quarter is going to settle in around 16 this year versus mongo's six percent so strong focus by snowflake on free cash flow and its management snowflake is about four billion dollars in cash and marketable securities on its balance sheet with little or no debt whereas [ __ ] has about two billion dollars on its balance sheet with a little bit of longer term debt and you can see snowflakes market cap is about double that of mongos so you're paying for higher growth with snowflake you're paying for the slootman scarpelli execution engine the expectation there a stronger balance sheet etc but snowflake is well off its roughly 100 billion evaluation which it touched during the peak days of tech during the pandemic and just that as an aside [ __ ] has around 33 000 customers about five times the number of customers snowflake has so a bit of a different customer mix and concentration but both companies in our view have no lack of market in terms of tam okay now let's dig a little deeper into mongo's business and bring in some etr data this colorful chart shows the breakdown of mongo's net score net score is etr's proprietary methodology that measures the percent of customers in the etr survey that are adding the platform new that's the lime green at nine percent existing customers that are spending six percent or more on the platform that's the forest green at 37 spending flat that's the gray at 46 percent decreasing spend that's the pinkish at around 5 and churning that's only 3 that's the bright red for [ __ ] subtract the red from the greens and you net out to a 38 which is a very solid net score figure note this is a survey of 1500 or so organizations and it includes 150 mongodb customers which includes by the way 68 global 2000 customers and they show a spending velocity or a net score of 44 so notably higher among the larger clients and while it's a smaller sample only 27 emea's net score for [ __ ] is 33 now that's down from 60 last quarter note that [ __ ] cited softness in its european business on its earning calls so that aligns to the gtr data okay now let's plot [ __ ] relative to some other data platforms these don't all necessarily compete head to head with [ __ ] but they are in data and database platforms in the etr data set and that's what this chart shows it's an xy graph with net score or as we say spending momentum on the vertical axis and overlap or presence or pervasiveness in the data set on the horizontal axis see that red dotted line there at 40 that indicates an elevated level of spending anything above that is highly elevated we've highlighted [ __ ] in that red box which is very close to that 40 percent line it has a pretty strong presence on the x-axis right there with gcp snowflake as we've reported has come down to earth but still well elevated again that aligns with the earnings releases uh aws and microsoft they have many data platforms especially aws so their plot position reflects their broad portfolio massive size on the x-axis um that's the presence and and very impressive on the vertical axis so despite that size they have strong spending momentum and you can see the pack of others including cockroach small on the verdict on the horizontal but elevated on the vertical couch base is creeping up since its ipo redis maria db which was launched the day that oracle bought sun and and got my sequel and some legacy platforms including the leader in database oracle as well as ibm and teradata's both cloud and on-prem platforms now one interesting side note here is on mongo's earning call it clearly cited the advantages of its increasingly all-in-one approach relative to others that offer a portfolio of bespoke or what we some sometimes call horses for courses databases [ __ ] cited the advantages of its simplicity and lower costs as it adds more and more functionality this is an argument often made by oracle and they often target aws as the company with too many databases and of course [ __ ] makes that argument uh as well but they also make the argument that oracle they don't necessarily call them out but they talk about traditional relational databases of course they're talking about oracle and others they say that's more complex less flexible and less appealing to developers than is [ __ ] now oracle of course would retur we retort saying hey we now support a mongodb api so why go anywhere else we're the most robust and the best for mission critical but this gives credence to the fact that if oracle is trying to capture business by offering a [ __ ] api for example that [ __ ] must be doing something right okay let's look at why they buy [ __ ] here's an etr chart that addresses that question it's it's mongo's feature breadth is the number one reason lower cost or better roi is number two integrations and stack alignment is third and mongo's technology lead is fourth those four kind of stand out with notice on the right hand side security and vision much lower there in the right that doesn't necessarily mean that [ __ ] doesn't have good security and and good vision although it has been cited uh security concerns um and and so we keep an eye on that but look [ __ ] has a document database it's become a viable alternative to traditional relational databases meaning you have much more flexibility over your schema um and in fact you know it's kind of schema-less you can pretty much put anything into a document database uh developers seem to love it generally it's fair to say mongo's architecture would favor consistency over availability because it uses a single master architecture as a primary and you can create secondary nodes in the event of a primary failure but you got to think about that and how to architect availability into the platform and got to consider recovery more carefully now now no schema means it's not a tables and rows structure and you can again shove anything you want into the database but you got to think about how to optimize performance um on queries now [ __ ] has been hard at work evolving the platform from the early days when you go back and look at its roadmap it's been you know started as a document database purely it added graph processing time series it's made search you know much much easier and more fundamental it's added atlas that fully managed cloud database uh service which we said now comprises 60 of its revenue it's you know kubernetes integrations and kind of the modern microservices stack and dozens and dozens and dozens of other features mongo's done a really fine job we think of creating a leading database platform today that is loved by customers loved by developers and is highly functional and next week the cube will be at mongodb world and we'll be looking for some of these items that we're showing here and this this chart this always going to be main focus on developers [ __ ] prides itself on being a developer friendly platform we're going to look for new features especially around security and governance and simplification of configurations and cluster management [ __ ] is likely going to continue to advance its all-in-one appeal and add more capabilities that reduce the need to to spin up bespoke platforms and we would expect enhance enhancements to atlas further enhancements there is atlas really is the future you know maybe adding you know more cloud native features and integrations and perhaps simplified ways to migrate to the cloud to atlas and improve access to data sources generally making the lives of developers and data analysts easier that's going to be we think a big theme at the event so these are the main things that we'll be scoping out at the event so please stop by if you're in new york city new york city at mongodb world or tune in to thecube.net okay that's it for today thanks to my colleagues stephanie chan who helps research breaking analysis from time to time alex meyerson is on production as today is as is andrew frick sarah kenney steve conte conte anderson hill and the entire team in palo alto thank you kristen martin and cheryl knight helped get the word out and rob hof is our editor-in-chief over there at siliconangle remember all these episodes are available as podcasts wherever you listen just search breaking analysis podcast we do publish each week on wikibon.com and siliconangle.com want to reach me email me david.velante siliconangle.com or dm me at divalante or a comment on my linkedin post and please do check out etr.ai for the best survey data in the enterprise tech business this is dave vellante for the cube insights powered by etr thanks for watching see you next time [Music] you

>>Welcome back. I really liked the drill down a data lakes with ed Walsh and Thomas Hazel. They building some cool stuff over there. The data lake we see it's evolving and chaos search has built some pretty cool tech to enable customers to get more value out of data that's in lakes so that it doesn't become stagnant. Time to dig, dig deeper, dive deeper into the water. We're here with Kevin Miller. Who's the vice president and general manager of S3 at Amazon web services. We're going to talk about activating S3 for analytics. Kevin, welcome. Good to see you again. >>Yeah, thanks Dan. It's great to be here again. So >>S3 was the very first service offered by AWS 15 years ago. We covered that out in Seattle. It was a great event you guys had, it has become the most prominent and popular example of object storage in the marketplace. And for years, customers use S3 is simple, cheap data storage, but because there's so much data now stored in S3 customers are looking to do more with the platform. So Kevin, as we look ahead to reinvent this year, we're super excited about that. What's new. What's got you excited when it comes to the AWS flagship storage offering. >>Yeah. Dan, well, that's right. And we're definitely looking forward to reinvent. We have some fun things that we're planning to announce there. So stay tuned on those, but I'd say that one of the things that's most exciting for me as customers do more with their data and look to store more, to capture more of the data that they're generating every day is our storage class that we had an announced a few years ago, but we, we actually just announced some improvements to the S3 intelligent tiering storage class. And this is really our storage class. The only one in the cloud at this point that delivers automatic storage cost savings for customers where the data access patterns change. And that can happen. For example, as customers have some data that they're collecting and then a team spins up and decides to try to do something more with that data and that data that was very cool and sitting sort of idle is now being actively used. And so with intelligent tiering, we're automatically monitoring data. And then there's for customers. There's no retrieval costs and no tiering charges. We're automatically moving the data into an access tier that reduces their costs though. And that data is not being accessed. So we've announced some improvements to that just a few months ago. And I'll just say, I look forward to some more announcements at reinvent that will extend, continue to extend what we have in our intelligent tiering storage class. >>That's cool, Kevin. I mean, you've seen, you know, that technology, that tiering concept had been around, you know, but since back in the mainframe days, the problem was, it was always inside a box. So you, you didn't have the scale of the cloud and you didn't have that automation. So I want to ask you as the leader of that business, when you meet with customers, Kevin, what do they tell you that they're there they're facing as challenges when they want to do more, get better insights out of all that data that they've moved into S3? >>Well, I think that's just it, Dave. I think that most customers I speak with they, of course they have the things that they want to do with their storage costs and reducing storage costs and just making sure they have capacity available. But increasingly I think the real emphasis is around business transformation. What can I do with this data? That's very unique and different than either that unlike, you know, prior optimizations where it would just reduce the bottom line, they're saying, what can I do that will actually drive my top line more by either, you know, generating new product ideas, um, allowing for faster, you know, close, closed loop process for acquiring customers. And so it's really that business transformation and all, everything around it that I think is really exciting. And for a lot of customers, that's a pretty long journey and, and helping them get started on that, including transforming their workforce and up-skilling, you know, parts of their workforce to be more agile and more oriented around software development, developing new products using software. >>So w when I first met the folks at, at chaos search, you know, Thomas took me through sort of the architecture w with ed as well. They had me at, you don't have to move your data. That was saying that was the grabber for me. And there are a number of public customers that digital river, uh, Blackboard or Klarna, we're going to get the customer perspective little later on and others that use both AWS S3 and chaos search. And they're trying to get more out of their, their S3 data and execute analytics at scale. So wonder if you could share with us Kevin, what types of activities and opportunities do you see for customers like these that are making the move to put their enterprise data in S3 in terms of capabilities and outcomes that they are trying to achieve and are able to achieve beyond using S3 is just a Bitbucket, >>Right? Well, Dan, I think you hit the nail on the head when you talk about outcomes. Cause that I think is, is key here. Customers want to reduce the time it takes to get to a tangible result that it affects the business that improves their business. And so that's one of the things that I excites me about what CAS search is doing here specifically is that automatic indexing, being able to take the data as it is in their bucket, index it and keep that index fresh and then allow for the customers to innovate on top of that and to try to experiment with a new capability, see, see what works and then double down on the things that really do work to drive that business. And so I just think that that capability reduces the amount of what I might call undifferentiated, heavy, lifting the work to just sort of index and organize and catalog data. And instead allow customers to really focus on here's the idea. Let's try to get this into production or into a test environment as quickly as possible to see if this can really drive some value for our business. >>Yeah. So you're seeing that sort of value that you've mentioned the non-differentiated heavy lifting, moving up the stack, right. It used to just be provisioning and managing the, now it's all the layers above that and it would go and beyond that. So my question to you, Kevin, is how do you see the evolution of this, all this data at scale I'm especially interested in, as it pertains to data that's of course, an S3, which is your swim lane. When you talk to customers who want to do more with their data and analytics, and by the way, even beyond analytics, you know, where it's having conversations now in the community about, about building data products and creating new value, but how do you respond and how do you see chaos search fitting in to those outcomes? >>Well, I think that's, that's it Dave, it's about kind of going up the stack and instead of spending time organizing and cataloging data, particularly as the data volumes give much larger when the modern customers and modern data lakes that we're seeing quickly go from a few petabytes to tens, to hundreds of petabytes or more. And when you reaching that kind of scale of data, it's a single person can reasonably kind of wrap their head around all that data. You need tools as three provides a number of first party tools and, you know, we're investing in things like our S3 batch operations to really help give the end users of that data, the business owners that leverage to manage their data at scale and apply their new ideas to the data and generate, you know, pilots and production work that really drives their business forward. And so I think that, you know, cast search again, I would just say as a good example of, you know, the kind of software that I think helps go, upstack automate some of that data management and just help customers focus really specifically on the things that they want to accomplish for their, their business. >>So this is, >>I mean, we've talked for well over a decade, how to get more value out of data. And it's been challenging for a lot of organizations, but we're seeing, we're seeing themes of scale automation, fine-grain tooling ecosystem participating, uh, on top of that data and then extracting that, that data value who Kevin, I'm really excited to see you face to face at re-inventing and learn more about some of the announcements that you're going to make. We'll see you there. >>Yeah. Stay tuned. Looking forward to seeing in person absolutely >>Have Kevin on, keep it right there because in a moment we're going to get the customer perspective on how a leading practitioner is applying chaos search on top of S3 to create a business value from data you're watching the cube, your leader, digital high tech coverage.

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Hey, welcome back to the cubes. Coverage of AWS 19 from Las Vegas. This is Day two of our coverage of three days. Two sets, lots of cute content. Lisa Martin here with Justin Warren, founder and chief analyst. A pivot nine. Justin and I are joined by a couple of guests New to the Cube. We've got David Meister next to meet Global head of sales for Io Tahoe. Welcome. Eddie Edwards with a cool name. Global Data Service is director from Direct Energy. Welcome, Eddie. Thank you. Okay, So, David, I know we had somebody from Io Tahoe on yesterday, but I'd love for you to give her audience an overview of Io Tahoe, and then you gotta tell us what the name means. >>Okay. Well, day pie stir. Io Tahoe thinks it's wonderful event here in AWS and excited to be here. Uh, I, oh, Tahoe were located in downtown on Wall Street, New York on and I Oh, Tahoe. Well, there's a lot of different meanings, but mainly Tahoe for Data Lake Input output into the lake is how it was originally meant So But ah, little background on Io Tahoe way are 2014. We came out way started in stealth came out of stealth in 2017 with two signature clients. When you're going to hear from in a moment direct energy, the other one g e and we'll speak to those in just a moment I owe Tahoe takes a unique approach way have nine machine learning machine learning algorithms 14 future sets that interrogates the data. At the data level, we go past metadata, so solving that really difficult data challenge and I'm gonna let Eddie describe some of the use cases that were around data migration, P II discovery, and so over to you >>a little bit about direct energy. What, you where you're located, What you guys do and how data is absolutely critical to your business. Yeah, >>sure. So direct energy. Well, it's the largest residential energy supplier in the er us around 5000 employees. Loss of this is coming from acquisitions. So as you can imagine, we have a vast amount of data that we need some money. Currently, I've got just under 1700 applications in my portfolio. Onda a lot. The challenges We guys are around the cost, driving down costs to serve so we can pass that back onto our consumers on the challenge that with hard is how best to gain that understanding. Where I alter whole came into play, it was vainly around off ability to use the products quickly for being able to connect to our existing sources to discover the data. What, then, that Thio catalog that information to start applying the rules around whether it be legislation like GDP, are or that way gets a lot of cases where these difference between the states on the standings and definitions so the product gives us the ability to bring a common approach So that information a good success story, would be about three months ago, we took the 30 and applications for our North America home business. We were able to running through the product within a week on that gave us the information to them, consolidate the estate downwards, working with bar business colleagues Thio, identify all the data we don't see the archival retention reels on, bring you no more meaning to the data on actually improve ourselves opportunities by highlights in that rich information that was not known >>previously. Yes, you mentioned that you growing through acquisition. One thing that people tend to underestimate around I t. Is that it's not a heterogeneous. It's not a homogeneous environments hatred genius. Like as soon as you buy another company, you've got another. You got another silent. You got another day to say. You got something else. So walk us through how iota who actually deals with that very disparity set of data that you've night out inherited from just acquiring all of these different companies? >>Yeah, so exactly right. You know, every time we a private organization, they would have various different applications that were running in the estate. Where would be an old article? I say, Hey, sequel tap environment. What we're able to do is use the products to plug in a name profile to understand what's inside knowledge they have around their customer base and how we can number in. That's in to build up a single view and offer additional products value adding products or rewards for customers, whether that be, uh on our hay truck side our heat in a ventilation and air con unit, which again we have 4600 engineers in that space. So it's opening up new opportunities and territories to us. >>Go ahead, >>say additionally to that, we're across multiple sectors, but the problem death by Excel was in the financial service is we're located on Wall Street. As I mentioned on this problem of legacy to spirit, data, sources and understanding, and knowing your data was a common problem, banks were just throwing people at the problem. So his use case with 1700 applications, a lot of them legacy is fits right into what we d'oh and cataloging is he mentioned. We catalogue with that discover in search engine that we have. We enable search cross enterprise. But Discovery we auto tag and auto classify the sensitive data into the catalog automatically, and that's a key part of what we do. And it >>was that Dave is something in thinking of differentiation, wanting to know what is unique about Iota. What was the opportunity that you guys saw? But is the cataloging and the sensitive information one of the key things that makes it a difference >>Way enabled data governance. So it's not just sensitive information way catalog, entire data set multiple data sets. And what makes us what differentiates us is that the machine learning way Interrogate in brute force The data So every single so metadata beyond so 1,000,000,000 rose. 100,000 columns. Large, complex data sets way. Interrogate every field value. And we tell you what this looks like A phone number. This looks like an address. This looks like a first name. This looks like the last name and we tagged at to the catalog. And then anything that sensitive in nature will color coded red green, highly sensitive, sensitive. So that's our big differentiator. >>So is that like 100% visibility into the granularity of what is in this data? >>Yes, that's that's one of the issues is who were here ahead of us. We're finding a lot of folks are wanting to go to the cloud, but they can't get access to the data. They don't know their data. They don't understand it. On DSO where that bridge were a key strategic partner for aws Andi we're excited about the opportunity that's come about in the last six months with AWS because we're gonna be that key geese for migration to the cloud >>so that the data like I love the name iota, How But in your opinion, you know, you could hear so many different things about Data Lake Data's turning into data Swamp is there's still a lot of value and data lakes that customers just like you're saying before, you just don't know what they have. >>Well, what's interesting in this transition to one of other clients? But on I just want to make a note that way actually started in the relational world. So we're already a mess. We're across header genius environment so but Tahoe does have more to do with Lake. But at a time a few years back, everybody was just dumping data into the lake. They didn't understand what what was in there, and it's created in this era of privacy, a big issue, and Comcast had this problem. The large Terry Tate instance just dumping into the lake, not understanding data flows, how they're data's flowing, not understanding what's in the lake, sensitivity wise, and they want to start, you know they want enable b I. They want they want to start doing analytics, but you gotta understand and know the data, right? So for Comcast, we enable data ops for them automatically with our machine learning. So that was one of the use cases. And then they put the information and we integrated with Apache Atlas, and they have a large JW aws instance, and they're able to then better govern their data on S O N G. Digital. One other customer very complex use case around their data. 36 e. R. P s being migrated toe one virtually r p in the lake. And think about finance data How difficult that is to manage and understand. So we were a key piece in helping that migration happen in weeks rather than months. >>David, you mentioned cloud. Clearly weird. We're at a cloud show, but you mentioned knowing your data. One of the aspect of that cloud is that it moves fast, and it's a much bigger scale than what we've been used to. So I'm interested. Maybe, Eddie, you can. You can fill us in here as well about the use of a tool to help you know your data when we're not creating any less stated. There's just more and more data. So at this speed and this scale, how important is it that you actually have tooling to provide to the to the humans who have to go on that operate on all of this data >>building on what David was saying around the speed in the agility side, you know, now all our information I would know for North America home business is in AWS Hold on ns free bucket. We are already starting work with AWS connect on the call center side. Being able to stream that information through so we're getting to the point now is an organization where we're able to profile the data riel. Time on. Take that information Bolts predict what the customers going going to do is part that machine learning side. So we're starting to trial where we will interject into a call to say, Well, you know, a customer might be on your digital site trying to do a journey. You can see the challenges around data, and you could Then they go in with a chop using, say, the new AWS trap that's just coming through at the moment. So >>one of the things that opportunities I'm here. Sorry, Eddie is the opportunity to leverage the insights into the data to deliver more. You mentioned like customer words, are more personalized experience or a call center agent. Knowing this is the problem of this customer is experiencing this way. Have tried X, y and Z to resolve, or this customer is loyal to pay their bills on time. They should be eligible for some sort of reward program. I think consumers that I think amazon dot com has created us this demanding consumer that way expect you to know us. I expect you to serve us up things that you think we want. Talk to me about the opportunity that I owe Ty was is giving your business to be able to delight customers in ways that you probably couldn't even have predicted? >>Well, they touched on the tagging earlier, you know, survive on the stunned in the data that's coming through. Being able to use the data flow technology on dhe categorizing were able than telling kidding with wider estate. So David mentioned Comcast around 36 e. R. P. You know, we've just gone through the same in other parts of our organization. We're driving the additional level of value, turning away from being a manually labor intensive task. So I used to have 20 architects that daily goal through trying to build an understanding the relationship. I do not need that now. I just have a couple of people that are able to take the outputs and then be able to validate the information using the products. >>And I like that. There's just so much you mentioned customer 360. Example at a call centre. There's so much data ops that has to happen to make that happen on. That's the most difficult challenge to solve. And that's where we come in. And after you catalogue the data, I just want to touch on this. We enable search for the enterprise so you're now connected to 50 115 100 sources with our software. Now you've catalogued it. You profiled it. Now you can search Karen Kim Kim Smith, So your your your engineers, your architect, your data stewards influences your business analysts. This is folks can now search anything they want and find anything sensitive. Find that person find an invoice, and that helps enable. But you mentioned the customer >>360. But I can Also. What I'm hearing is, as it has the potential to enable a better relationship between I t in the business. >>Absolutely. It brings those both together because they're so siloed. In this day and age, your data siloed and your business is siloed in a different business unit. So this helps exactly collaborate crowdsource, bring it all together. One platform >>and how many you so 1700 applications. How many you mentioned the 36 or so air peace. What percentage? If you can guess who have you been able to reduce duplicate triplicate at center applications? And what are some of the overarching business benefits that direct energy is achieving? >>So incentive the direct senator, decide that we're just at the beginning about journey. We're about four months in what? We've already decommissioned 12. The applications I was starting to move out to the wider side in terms of benefits are oh, I probably around 300% of the moment >>in a 300% r A y in just a few months. >>Just now, you know you've got some of the basic savings around the story side. We're also getting large savings from some of the existing that support agreements that we have in place. David touched on data Rob's. I've been able to reduce the amount of people that are required to support the team. There is now a more common on the standing within the organization and have money to turn it more into a self care opportunity with the business operations by pushing the line from being a technical problem to a business challenge. And at the end of the day, they're the experts. They understand the data better than any IittIe fault that sat in a corner, right? So I'm >>gonna ask you one more question. What gave you the confidence that I Oh, Tahoe was the right solution for you >>purely down Thio three Open Soul site. So we come from a you know I've been using. I'll tell whole probably for about two years in parts of the organization. We were very early. Adopters are over technologies in the open source market, and it was just the ability thio on the proof of concept to be able to turn it around iTunes, where you'll go to a traditional vendor, which would take a few months large business cases. They need any of that. We were able to show results within 24 48 hours on now buys the confidence. And I'm sure David would take the challenge of being able to plug in some day. It says on to show you the day. >>Cool stuff, guys. Well, thank you for sharing with us what you guys are doing. And I have a Iot Tahoe keeping up data Lake Blue and the successes that you're cheating in such a short time, but direct energy. I appreciate your time, guys. Thank you. Excellent. Our pleasure. >>No, you'll day. >>Exactly know your data. My guests and my co host, Justin Warren. I'm Lisa Martin. I'm gonna go often. Learn my data. Now you've been watching the Cube and AWS reinvent 19. Thanks for watching