foreign welcome to thecube's coverage of AWS re invent 2022. this is a part of our AWS executive Summit AT AWS re invent sponsored by Accenture I'm your host Lisa Martin I've got three guests here with me Christoph schulteis head of devops and infrastructure at Vodafone Germany joins us as well as IMAP baldasare the Accenture AWS business group Europe delivery lead attic Center and Philip schmuckel senior manager at Accenture technology we're going to be talking about what Vodafone Germany is doing in terms of its agile transformation the business and I.T gentlemen it's great to have you on thecube Welcome to the program thank you thanks for having us my pleasure Kristoff let's go ahead and start with you talk to us about what Vodafone Germany is doing in its transformation project with Accenture and with AWS certainly these are but let me first start with explaining what Vodafone does in general so Vodafone is one of the leading telephone and Technology service providers in Germany half of all German citizens are Vodafone customers using Vodafone technology to access the internet make calls and watch TV in the economic sector we provide connectivity for office farms and factories so this is vodafone's largest business and I.T transformation and we're happy to have several Partners on this journey with more than a thousand people working in scaled agile framework with eight Agile Release strings and one of the largest safe implementations in Europe why are we doing this transformation well not only since the recent uncertainties the Telco Market is highly volatile and there are a few challenges that Vodafone was facing in the last years as there are Market changes caused by disruptions from technological advances in competitors or changing customer customer expectations who for example use more of the top services like Netflix or Amazon Prime video what is coming up in the next wave is unknown so Technologies evolve continual disruption from non-tel causes to be expected and being able to innovate fast is the key Focus for everyone in order to be able to react to that we need to cope with that and do so in different aspects to become the leading digital technology company therefore Vodafone Germany is highly simplifying its products as well as processes for example introducing free product upgrades for customers we're driving the change from a business perspective and modernize the it landscape which we call the technology transformation so simply business-led but it driven for that Accenture is our integration partner and AWS provides the services for our platforms got it thank you for the background on the Vodafone the impact that it's making you mentioned the volatility in the Telecom market and also setting the context for what Vodafone Germany is doing with Accenture and AWS email I want to bring you into the conversation now talk to us about the partnership between Accenture and Vodafone in AWS and how is it set up to provide maximum value for customers yeah that's a great question actually well I mean working in Partnership allows obviously to bring in transparency and trust and these are key starting points for a program of this magnitude and a program like this comes out of strong willingness to change the game both internally and on the market so as you can imagine particular attention is required that's top level alignment in general when you implement a program like this you also need to couple the long-term vision of how you want to manage your customers what are the new products that you want to bring to the market with the long-term technology roadmap because the thing that you don't want to happen is that you invest many years and a lot of efforts and then when it comes the end of the journey you figure out that you have to restart a New Journey and then you enter in the NeverEnding Loop so obviously all these things must come together and they come together in what we call the power of three and it consists in AWS Vodafone and Accenture having a strategic Vision alignment and constant updates and most importantly the best of breed in terms of technology and also people so what we do in practice is uh we bring together Market understanding business Vision technical expertise energy collaboration and what is even more important we work as a unique team everybody succeeds here and this is a true win-win partnership more specifically Vodafone leads the Strategic Direction obviously they understand the market they are close to their customers AWS provides all the expertise around the cloud infrastructure insights on the roadmap and this is a key element elasticity both technical but also Financial and the then Accenture comes with its ability to deliver with the strong industry expertise flexibility and when you combine all these ingredients together obviously you understand it's easy to succeed together the power of three it sounds quite compelling it sounds like a very partnership that has a lot of flexibility elasticity as you mentioned and obviously the customer at the end of the day benefits tremendously from that Kristoff I'd like to bring you back into the conversation talk to us about the unified unified platform approach how is walk us through how Vodafone is implementing it with AWS and with Accenture so the applications that form the basis for the transformation program were originally pursuing all kinds of approaches for deployment and use of AWS services in order to support faster adoption and optimize the usage that I mentioned before and we have provided the Vodafone Cloud framework that has been The Trusted platform for several projects within the it in Germany as a side effect the framework facilitates the compliance with Vodafone security requirements and the unified approach also has the benefit that someone who is moving from one team to another will find a structure that looks familiar the best part of the framework though is the operative rights deployment process that helps us reducing the time from implementing for example a new stage from a few weeks to me hours and that together with improvements of the cicd pipeline greatly helped us reducing the time to speed up something and deploy the software on it in order to reach our Target kpis the unified platform provides all kinds of setups like AWS eks and the ecosystem that is commonly used with coping dentists like service mesh monitoring logging and tracing but it can also be used for non-continental erased applications that we have and provide the integration with security monitoring and other tools at the moment we are in contact with other markets of Vodafone to globally share our experience in our code which makes introducing a similar system into other markets straightforward we are also continuously improving our approach and the completely new version of the framework is currently being introduced into the program Germany is doing is really kind of setting the stage as you mentioned Christopher other parts of the business who want to learn from so that's a great thing there that that what you're building is really going to spread throughout the organization and make a positive impact Philip let's bring you into the conversation now let's talk about how you're using AWS specifically to build the new Vodafone Cloud integration platform talk to us about that as part of this overall transformation program sure and let's make it even more specific let's talk API management so looking at the program and from a technology point of view what it really is it is a bold step for Vodafone it's rebuilding huge parts of the infrastructure of their business ID infrastructure on AWS it's Greenfield it's new it's a bold step I would say and then if you put the perspective of API management or integration architecture what I call it it's a unique opportunity at the same time so what it what it gives you is the the opportunity to build the API management layer or an API platform with standardized apis right from the get-go so from the beginning you can build the API platform on top which is in contrast what we see throughout the industry where we see huge problems at our clients at other engagements that try to build these layers as well but they're building them on Legacy so that really makes it unique here for Vodafone and a unique opportunity to we have this API first platform built as part of the transformation program so what we have been built is exactly this platform and as of today there is more than 50 standardized apis throughout the application landscape already available to give you a few examples there is an API where I can change customer data for instance I can change the payment method of a customer straight from an API or I can reboot a customer equipment right from it from an API to fix a network issue other than that of course I can submit an order to order one of vodafone's gigabit internet offerings so on top of the platform there's a developer portal which gives me the option to explore all of the apis yeah in a convenient way in a portal and that's yeah that's developer experience meaning I can log into this portal look through the apis understand what I what I need and just try it out directly from the portal I see the response of an API live in the portal and this is it is really in contrast to what what we've seen before where you would have a long word document a cumbersome spreadsheet a long lasting process to get your hands on and this really gives you the opportunity to just go in try out an API and see how it works so it's really developer experience and a big step forward here then yeah how have we built this platform of course it's running on AWS it's Cloud native it's using eks but what I want to point out here is three principles that that we applied where the first one is of course the cloud native principle meaning we using AKs we are using containers we have infrastructure scales so we aim for every component being Cloud native being meant to be run in the cloud so our infrastructure will sleep at night to save Vodafone cost and it will wake up for the Christmas business where Vodafone intends to do the biggest business and scale of its platform second there is the uh the aim for open API specifications what we aim for is event non-vendor-specific apis so it should not matter whether there's an mdocs backend there's a net tracker back end or an sap Behind These apis it is really meant to decouple the different Business Systems of of a Vodafone by these apis that can be applied by a new custom front-end or by a new business to business application to integrate these apis last but not least there's the automate everything so there's infrastructure as code all around our platform where where I would say the biggest magic of cloud is if we were to lose our production environment lose all apis today it will take us just a few minutes to get everything back and whatever everything I mean redeploy the platform redeploy all apis all services do the configuration again and it will be back in a few minutes that's impressive as downtime is so costly for so many different reasons I think we're gonna know when the vision of this transformation project when it's been achieved how are you going to know that okay so it's kind of flipping the perspective a bit uh maybe uh when I joined Vodafone in in late 2019 I would say the vision for Vodafone was already set and it was really well well put out there it was lived in in the organization it was for Vodafone to become a digital company to become a digital service provider to to get the engineering culture into the company and I would say this Vision has not changed until today maybe now call it a North star and maybe pointing out two big Milestones that have been achieved with this transformation program so we've talked about the safe framework already so with this program we wrote out the one of the biggest safe implementations in the industry which is a big step for Vodafone in its agile Journey as of today there's the safe framework supporting more than 1 000 FTE or 1000 colleagues working and providing value in the transformation program second example or second big milestone was the first go-life of the program so moving stuff to production really proving it works showcasing to the business that it it is actually working there is actually a value provided or constant value provided with a platform and then of course you're asking for next steps right uh talking next steps there is a renewed focus on value and A Renewed focus on value between Accenture and Vodafone means focus on what really provides the most value to Vodafone and I would like to point out two things here the first being migrate more customers scale the platform really prove the the the the the cloud native platform by migrating more customers to it and then second it enables you to decommission the Legacy Stacks decommissioning Legacy Stacks is why we are doing it right so it's migrating to the new migrating to the new platform so last but not least maybe you can hear it we will continue this journey together with with Vodafone to become a digital company or to say that their own words from Telco to TECO I love that from Telco to technology gentlemen thank you so much for joining us on thecube today talking about the power of three Accenture AWS Vodafone how you're really enabling Vodafone to transform into that digital technology company that consumers at the end of the day that demanding consumers want we appreciate your insights and your time thank you so much thank you for having us my pleasure for my guests I'm Lisa Martin you're watching thecube's coverage of the AWS executive Summit AT AWS re invent sponsored by Accenture thanks for watching

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 over the past decade cloudflare has built a Global Network that has the potential to become the fourth us-based hyperscale class cloud in our view the company is building a durable Revenue model with hooks into many important markets these include the more mature DDOS protection space to other growth sectors such as zero trust a serverless platform for application development and an increasing number of services such as database and object storage and other network services in essence cloudflare could be thought of as a giant distributed supercomputer that can connect multiple clouds and act as a highly efficient scheduling engine at scale its disruptive DNA is increasingly attracting novel startups and established Global firms alike looking for Reliable secure high performance low latency and more cost-effective alternatives to AWS and Legacy infrastructure Solutions hello and welcome to this week's wikibon Cube insights powered by ETR in this breaking analysis we initiate our deeper coverage of cloudflare we'll briefly explain our take on the company and its unique business model we'll then share some peer comparisons with both the financial snapshot and some fresh ETR survey data finally we'll share some examples of how we think cloudflare could be a disruptive force with a super cloud-like offering that in many respects is what multi-cloud should have been cloudflare has been on our peripheral radar Ben Thompson and many others have written about their disruptive business model and recently a breaking analysis follower who will remain anonymous emailed with some excellent insights on cloudflare that prompted us to initiate more detailed coverage let's first take a look at how cloudflare seize the world in terms of its view of a modern stack this is a graphic from cloudflare that shows a simple three-layer Stack comprising Storage and compute the lower level and application layer and the network and their key message is basically that the big four hyperscalers have replaced the on-prem leaders apps have been satisfied and that mess of network that you see and Security in the upper left can now be handled all by cloudflare and the stack can be rented via Opex versus requiring heavy capex investment so okay somewhat of a simplified view is those companies on the the left are you know not standing still and we're going to come back to that but cloudflare has done something quite amazing I mean it's been a while since we've invoked Russ hanneman of Silicon Valley Fame on breaking analysis but remember when he was in a meeting one of his first meetings if not the first with Richard Hendricks it was the whiz kid on the show Silicon Valley and hanneman said something like if you had a blank check and you could build anything in the world what would it be and Richard's answer was basically a new internet and that led to Pied Piper this peer-to-peer Network powered by decentralized devices and and iPhones and this amazing compression algorithm that enabled high-speed data movement and low latency uh up to no low latency access across the network well in a way that's what cloudflare has built its founding premise reimagined how the internet should be built with a consistent set of server infrastructure where each server had lots of cores lots of dram lots of cash fast ssds and plenty of network connectivity and bandwidth and well this picture makes it look like a bunch of dots and points of presence on a map which of course it is there's a software layer that enables cloudflare to efficiently allocate resources across this Global Network the company claims that it's Network utilization is in the 70 percent range and it has used its build out to enter the technology space from the bottoms up offering for example free tiers of services to users with multiple entry points on different services and selling then more services over time to a customer which of course drives up its average contract value and its lifetime value at the same time the company continues to innovate and add new services at a very rapid cloud-like Pace you can think of cloudflare's initial Market entry as like a lightweight Cisco as a service the company's CFO actually he uses that term he calls it that which really must tick off Cisco who of course has a massive portfolio and a dominant Market position now because it owns the network cloudflare is a marginal cost of adding new Services is very small and goes towards zero so it's able to get software like economics at scale despite all this infrastructure that's building out so it doesn't have to constantly face the increasing infrastructure tax snowflake for example doesn't own its own network infrastructure as it grows it relies on AWS or Azure gcp and and while it gives the company obvious advantages it doesn't have to build out its own network it also requires them to constantly pay the tax and negotiate with hyperscalers for better rental rates now as previously mentioned Cloud Fair cloudflare claims that its utilization is very high probably higher than the hyperscalers who can spin up servers that they can charge for underutilized customer capacity cloudflare also has excellent Network traffic data that it can use to its Advantage with its Analytics the company has been rapidly innovating Beyond its original Core Business adding as I said before serverless zero trust offerings it has announced a database it calls its database D1 that's pretty creative and it's announced an object store called R2 that is S3 minus one both from the alphabet and the numeric I.E minus the egress cost saying no egress cost that's their big claim to fame and they've made a lot of marketing noise around about that and of course they've promised in our a D2 database which of course is R2D2 RR they've launched a developer platform cloudflare can be thought of kind of like first of all a modern CDN they've got a simpler security model that's how they compete for example with z-scaler that brings uh they also bring VPN sd-wan and DDOS protection services that are that are part of the network and they're less expensive than AWS that's kind of their sort of go to market and messaging and value proposition and they're positioning themselves as a neutral Network that can connect across multiple clouds now to be clear unlike AWS in particular cloudflare is not well suited to lift and shift your traditional apps like for instance sap Hana you're not going to run that in on cloudflare's platform rather the company started by making websites more secure and faster and it flew under the radar and much in the same way that clay Christensen described the disruption in the steel industry if you've seen that where new entrants picked off the low margin rebar business then moved up the stack we've used that analogy in the semiconductor business with arm and and even China cloudflare is running a similar playbook in the cloud and in the network so in the early part of the last decade as aws's ascendancy was becoming more clear many of us started thinking about how and where firms could compete and add value as AWS is becoming so dominant so for instance take an industry Focus you could do things like data sharing with snowflake eventually you know uh popularized you could build on top of clouds again snowflake is doing that as are others you could build private clouds and of course connect to hybrid clouds but not many had the wherewithal and or the hutzpah to build out a Global Network that could serve as a connecting platform for cloud services cloudflare has traction in the market as it adds new services like zero trust and object store or database its Tam continues to grow here's a quick snapshot of cloudflare's financials relative to Z scalar which is both a competitor and a customer fastly which is a smaller CDN and Akamai a more mature CDN slash Edge platform cloudflare and fastly both reported earnings this past week Cloud Fair Cloud flare surpassed a billion dollar Revenue run rate but they gave tepid guidance and the stock got absolutely crushed today which is Friday but the company's business model is sound it's growing close to 50 annually it has sas-like gross margins in the mid to high 70s and it's it it's got a very strong balance sheet and a 13x revenue run rate multiple in fact it's Financial snapshot is quite close to that of z-scaler which is kind of interesting which zinc sailor of course doesn't own its own network that's a pure play software company fastly is much smaller and growing more slowly than cloudflare hence its lower multiple well Akamai as you can see is a more mature company but it's got a nice business now on its earnings call this week cloudflare announced that its head of sales was stepping down and the company has brought in a new leader to take the firm to five billion dollars in sales I think actually its current sales leader felt like hey you know my work is done here bring on somebody else to take it to the next level the company is promising to be free cash flow positive by the end of the year and is working hard toward its long-term financial model or so working towards sorry it's a long-term financial model with gross margin Targets in the mid 70s it's targeting 20 non-gaap operating margins so so solid you know very solid not like completely off the charts but you know very good and to our knowledge it has not committed to a long-term growth rate but at that sort of operating profit level you would like to see growth be consistently at least in the 20 range so they could at least be a rule of 40 company or perhaps even even five even higher if they're going to continue to command a premium valuation okay let's take a look at the ETR data ETR is very positive on cloudflare and has recently published a report on the company like many companies cloudflare is seeing an across the board slowdown in spending velocity we've reported on this quite extensively using the ETR data to quantify the degree to that Slowdown and on the data set with ETR we see that many customers they're shifting their spend to Flat spend you know plus or minus let's say you know single digits you know two three percent or even zero or in the market we're seeing a shift from paid to free tiers remember cloudflare offers a lot of free services as you're seeing customers maybe turn off the pay for a while and going with the freebie but we're also seeing some larger customers in the data and the fortune 1000 specifically they're actually spending more which was confirmed on cloudflare's earnings call they did say everything across the board was softer but they did also indicate that some of their larger customers are actually growing faster than their smaller customers and their churn is very very low here's a two-dimensional graphic we'd like to share this view a lot it's got Net score or spending momentum on the vertical axis and overlap or pervasiveness in the survey on the horizontal axis and this cut isolates three segments in the etrs taxonomy that cloudflare plays in Cloud security and networking now the table inserted in that upper left there shows the raw data which informs the position of each company in the dots with Net score in the ends listed in that rightmost column the red dotted line indicates a highly elevated Net score and finally we posted the breakdown those colors in the bottom right of cloudflare's Net score the lime green that's new adoptions the forest green is we're spending more six percent or more the gray is flat plus or minus uh five percent and you can see that the majority of customers you can see that's the majority of the customers that gray area the pink is we're spending Less in other words down six percent or worse and the bright red is churn which is minimal one percent very good indicator for for cloudflare what you do to get etr's proprietary Net score and they've done this for many many quarters so we have that time series data you subtract the Reds from the greens and that's Net score cloudflare is at 39 just under that magic red line now note that cloudflare and zscaler are right on top of each other Cisco has a dominant position on the x-axis that cloudflare and others are eyeing AWS is also dominant but note that its Net score is well above the red dotted line it's incredible Palo Alto networks is also very impressive it's got both a strong presence on the horizontal axis and it's got a Net score that's pretty comparable to cloudflare and z-scaler to much smaller companies Akamai is actually well positioned for a reasonably mature company and you can see fastly ATT Juniper and F5 have far less spending momentum on their platforms than does cloudflare but at least they are in positive Net score territory so what's going to be really interesting to see is whether cloudflare can continue to hold this momentum or even accelerate it as we've seen with some other clouds as it scales its Network and keeps adding more and more services cloudflare has a couple of potential strategic vectors that we want to talk about and it'll be going to be interesting to see how that plays out Now One path is to compete more directly as a Cloud Player offering secure access Edge services like firewall as a service and zero Trust Services like data loss prevention email security from its area one acquisition and other zero trust offerings as well as Network Services like routing and network connectivity this is The Sweet Spot of the company load balancing many others and then add in things like Object Store and database Services more Edge services in the future it might be telecom like services such as Network switching for offices so that's one route and cloudflare is clearly on that path more services more cohorts at innovating and and growing the company and bringing in more Revenue increasing acvs and and increasing long-term value and keeping retention high now the other Vector is what we're just going to refer to as super cloud as an enabler of cross-cloud infrastructure this is new value uh relative to the former Vector that we were just talking about now the title of this episode is what multi-cloud should have been meaning cloudflare could be the control plane providing a consistent experience across clouds one that is fast and secure at global scale now to give you Insight on this let's take a look at some of the comments made by Matthew Prince the CEO and co-founder of cloudflare cloudflare put its R2 Object Store into public beta this past May and I believe it's storing around a petabyte of data today I think that's what they said in their call here's what Prince said about that quote we are talking to very large companies about moving more and more of their stored objects to where we can store that with R2 and one of the benefits is not only can we help them save money on the egress fees but it allows them to then use those object stores or objects across any of the different Cloud platforms they're that they're using so by being that neutral third party we can let people adopt a little bit of Amazon a little bit of Microsoft a little bit of Google a little bit of SAS vendors and share that data across all those different places so what's interesting about this in the super cloud context is it suggests that customers could take the best of each Cloud to power their digital businesses I might like AWS for in redshift for my analytic database or I love Google's machine learning Microsoft's collaboration and I'd like a consistent way to connect those resources but of course he's strongly hinting and has made many public statements that aws's egress fees are a blocker to that vision now at a recent investor event Matthew Prince added some color to this concept when he talked about one metric of success being how much R2 capacity was consumed and how much they sold but perhaps a more interesting Benchmark is highlighted by the following statement that he made he said a completely different measure of success for R2 is Andy jassy says I'm sick and tired of these guys meaning cloudflare taking our objects away we're dropping our egress fees to zero I would be so excited because we've then unlocked the ability to be the network that interconnects the cloud together now of course it would be Adam solipski who would be saying that or maybe Andy Jesse you know still watching over AWS and I think it's highly unlikely that that's going to happen anytime soon and that of course but but in theory gets us closer to the super cloud value proposition and to further drive that point home and we're paraphrasing a little bit his comments here he said something the effect of quote customers need one consistent control plane across clouds and we are the neutral Network that can be consistent no matter which Cloud you're using interesting right that Prince sees the world that's similar to if not nearly identical to the concepts that the cube Community has been putting forth around supercloud now this vision is a ways off let's be real Prince even suggested that his initial vision of an application running across multiple clouds you know that's like super cloud Nirvana isn't what customers are doing today that's that's really hard to do and perhaps you know it's never going to happen but there's a little doubt that cloudflare could be and is positioning itself as that cross-cloud control plane it has the network economics and the business model levers to pull it's got an edge up on the competition at the edge pun intended cloudflare is the definition of Edge and it's distributed platform it's decentralized platform is much better suited for Edge workloads than these giant data centers that are you know set up to to try and handle that today the the hyperscalers are building out you know their Edge networks things like outposts you know going out to the edge and other local zones Etc now cloudflare is increasingly competitive to the hyperscalers and those traditional Stacks that it depositioned on an earlier slide that we showed but you know the likes of AWS and Dell and hpe and Cisco and those others they're not sitting in their hands they have a huge huge customer install bases and they are definitely a moving Target they're investing and they're building out their own Super clouds with really robust stacks as well let's face it it's going to take a decade or more for Enterprises to adopt a developer platform or a new database Cloud plus cloudflare's capabilities when compared to incumbent stacks and the hyperscalers is much less robust in these areas and even in storage you know despite all the great conversation that R2 generated and the buzz you take a specialist like Wasabi they're more mature they're more functional and they're way cheaper even than cloudflare so you know it's not a fake a complete that cloudflare is going to win in those markets but we love the disruption and if cloudflare wants to be the fourth us-based hyperscaler or join the the big four as the as the fifth if we put Alibaba in the mix it's got a lot of work to do in the ecosystem by its own admission as much to learn and is part of the value by the way that it sees in its area one acquisition it's email security company that it bought but even in that case much of the emphasis has been on reseller channels compare that to the AWS ecosystem which is not only a channel play but is as much an innovation flywheel filling gaps where companies like snowflake Thrive side by side with aws's data stores as well all the on-prem stacks are building hybrid connections to AWS and other clouds as a means of providing consistent experiences across clouds indeed many of them see what they call cross-cloud services or what we call super cloud hyper cloud or whatever you know Mega Cloud you want to call it we use super cloud they are really eyeing that opportunity so very few companies frankly are not going after that space but we're going to close with this cloudflare is one of those companies that's in a position to wake up each morning and ask who can we disrupt today and very few companies are in a position to disrupt the hyperscalers to the degree that cloudflare is and that my friends is going to be fascinating to watch unfold all right let's call it a wrap I want to thank Alex Meyerson who's on production and manages the podcast as well as Ken schiffman who's our newest addition to the Boston Studio Kristen Martin and Cheryl Knight help us get the word out on social media and in our newsletters and Rob Hof is our editor-in-chief over at silicon angle thank you to all remember all these episodes are available as podcasts wherever you listen all you're going to do is search breaking analysis podcasts I publish each week on wikibon.com and siliconangle.com you can email me at david.velante at siliconangle.com or DM me at divalante if you comment on my LinkedIn posts and please do check out etr.ai they got the best survey data in the Enterprise Tech business this is Dave vellante for the cube insights powered by ETR thank you very much for watching and we'll see you next time on breaking analysis

foreign okay we're back at Falcon 2022 crowdstrike's big user conference first time in a couple of years obviously because of kova this is thecube's coverage Dave vellante and Dave Nicholson wall-to-wall coverage two days in a row Michael Rogers the series the newly minted vice president of global alliances at crowdstrike Michael first of all congratulations on the new appointment and welcome to the cube thank you very much it's an honor to be here so dial back just a bit like think about your first hundred days in this new role what was it like who'd you talk to what'd you learn wow well the first hundred days were filled with uh excitement uh I would say 18 plus hours a day getting to know the team across the globe a wonderful team across all of the partner types that we cover and um just digging in and spending time with people and understanding uh what the partner needs were and and and and it was just a it was a blur but a blast I agree with any common patterns that you heard that you could sort of coalesce around yeah I mean I think that uh really what a common thing that we hear at crowdstrike whether it's internal is extra external is getting to the market as fast as possible there's so much opportunity and every time we open a door the resource investment we need we continue to invest in resources and that was an area that we identified and quickly pivoted and started making some of those new investments in a structure of the organization how we cover Partners uh how we optimize uh the different routes to Market with our partners and yeah just a just a it's been a wonderful experience and in my 25 years of cyber security uh actually 24 and a half as of Saturday uh I can tell you that I have never felt and had a better experience in terms of culture people and a greater mission for our customers and our partners you'll Max funny a lot of times Dave we talk about this is we you know we learned a lot from Amazon AWS with the cloud you know taking something you did internally pointing it externally to Pizza teams there's shared responsibility model we talk about that and and one of the things is blockers you know Amazon uses that term blocker so were there any blockers that you identified that you're you're sort of working with the partner ecosystem to knock down to accelerate that go to market well I mean if I think about what we had put in place prior and I had the benefit of being vice president of America's prior to the appointment um and had the pleasure of succeeding my dear friend and Mentor Matthew Pauley um a lot of that groundwork was put in place and we work collectively as a leadership team to knock down a lot of those blockers and I think it really as I came into the opportunity and we made new Investments going into the fiscal year it's really getting to Market as fast as possible it's a massive Target addressable market and identifying the right routes and how to how to harness that power of we to drive the most value to the marketplace yeah what is it what does that look like in terms of alliances alliances can take a lot of shape we've we've talked to uh service providers today as an example um our Global Systems integrators in that group also what what is what does the range look like yeah I mean alliances at crowdstrike and it's a great question because a lot of times people think alliances and they only think of Technology alliances and for us it spans really any and all routes to Market it could be your traditional solution providers which might be regionally focused it could be nationally focused larger solution providers or Lars as you noted service providers and telcos global system integrators mssps iot Partners OEM Partners um and store crouchstrike store Partners so you look across that broad spectrum and we cover it all so the mssps we heard a lot about that on the recent earnings call we've heard this is a consistent theme we've interviewed a couple here today what's driving that I mean is it the fact that csos are just you know drowning for talent um and why crowdstrike why is there such an affinity between mssps and crowdstrike yeah a great question we um and you noted that uh succinctly that csos today are faced with the number one challenge is lack of resources and cyber security the last that I heard was you know in the hundreds of thousands like 350 000 and that's an old stat so I would venture to Guess that the open positions in cyber security are north of a half a million uh as we sit here today and um service providers and mssps are focused on providing service to those customers that are understaffed and have that Personnel need and they are harnessing the crowdstrike platform to bring a cloud native best of breed solution to their customers to augment and enhance the services that they bring to those customers so partner survey what tell us about the I love surveys I love data you know this what was the Genesis of the survey who took it give us the breakdown yeah that's a great question no uh nothing is more important than the feedback that we get from our partners so every single year we do a partner survey it reaches all partner types in the uh in the ecosystem and we use the net promoter score model and so we look at ourselves in terms of how we how we uh rate against other SAS solution providers and then we look at how we did last year and in the next year and so I'm happy to say that we increased our net promoter score by 16 percent year over year but my philosophy is there's always room for improvement so the feedback from our partners on the positive side they love the Falcon platform they love the crowdstrike technology they love the people that they work with at crowdstrike and they like our enablement programs the areas that they like us to see more investment in is the partner program uh better and enhanced enablement making it easier to work with crowdstrike and more opportunities to offer services enhance services to their customers dramatic differences between the types of Partners and and if so you know why do you think those were I mean like you mentioned you know iot Partners that's kind of a new area you know so maybe maybe there was less awareness there were there any sort of differences that you noticed by type of partner I would say that you know the areas or the part the partners that identified areas for improvement were the partners that that uh either were new to crowdstrike or they're areas that we're just investing in uh as as we expand as a company and a demand from the market is you know pull this thing into these new routes to Market um not not one in particular I mean iot is something that we're looking to really blow up in the next uh 12 to 18 months um but no no Common Thread uh consistent feedback across the partner base speaking of iot he brought it up before it's is it in a you see it as an adjacency to i-team it seems like it and OT used to never talk to each other and now they're increasingly doing so but they're still it still seems like different worlds what have you found and learned in that iot partner space yeah I mean I think the key and we the way we look at the journey is it starts with um Discovery discovering the assets that are in the OT environment um it then uh transitions to uh detection and response and really prevention and once you can solve that and you build that trust through certifications in the industry um you know it really is a game changer anytime you have Global in your job title first word that comes to mind for me anyway is sovereignty issues is that something that you deal with in this space uh in terms of partners that you're working with uh focusing on Partners in certain regions so that they can comply with any governance or sovereignty yeah that's that's a great question Dave I mean we have a fantastic and deep bench on our compliance team and there are certain uh you know parameters and processes that have been put in place to make sure that we have a solid understanding in all markets in terms of sovereignty and and uh where we're able to play and how that were you North America before or Americas uh Americas America so you're familiar with the sovereignty issue yeah a little already Latin America is certainly uh exposed me plenty of plenty of that yes 100 so you mentioned uh uh Tam before I think it was total available Market you had a different word for the t uh total addressable Mark still addressable Market okay fine so I'm hearing Global that's a tam expansion opportunity iot is definitely you know the OT piece and then just working better um you know better Groove swing with the partners for higher velocity when you think about the total available total addressable market and and accelerating penetration and growing your Tam I've seen the the charts in your investor presentation and you know starts out small and then grows to you know I think it could be 100 billion I do a lot of Tam analysis but just my back a napkin had you guys approaching 100 billion anyway how do you think about the Tam and what role do Partners play in terms of uh increasing your team yeah that's a great question I mean if you think about it today uh George announced on the day after our 11th anniversary as a company uh 20 000 customers and and if you look at that addressable Market just in the SMB space it's north of 50 million companies that are running on Legacy on-prem Solutions and it really provides us an opportunity to provide those customers with uh Next Generation uh threat protection and and detection and and response partners are the route to get there there is no doubt that we cannot cover 50 50 million companies requires a span of of uh of of of a number of service providers and mssps to get to that market and that's where we're making our bets what what's an SMB that is a candidate for crowdstrike like employee size or how do you look at that like what's the sort of minimum range yeah the way we segment out the SMB space it's 250 seats or endpoints and below 250 endpoints yes right and so it's going to be fairly significant so math changes with xdr with the X and xdr being extended the greater number of endpoints means that a customer today when you talk about total addressable Market that market can expand even without expanding the number of net new customers is that a fair yeah Fair assessment yep yeah you got that way in that way but but map that to like company size can you roughly what's the what's the smallest s that would do business with crowdstrike yeah I mean we have uh companies as small as five employees that will leverage crowd strike yeah 100 and they've got hundreds of endpoints oh no I'm sorry five uh five endpoints is oh okay so it's kind of 250 endpoints as well like the app that's the sweets that's it's that's kind of the Top Line we look at and then we focus oh okay when we Define SMB it's below so five to 250 endpoints right yes and so roughly so you're talking to companies with less than 100 employees right yeah yeah so I mean this is what I was talking about before I say I look around the the ecosystem myself it kind of reminds me of service now in 2013 but servicenow never had a SMB play right and and you know very kind of proprietary closed platform not that you don't have a lot of propriety in your platform you do but you they were never going to get down Market there and their Tam is not as big in my view but I mean your team is when you start bringing an iot it's it's mind-boggling it's endless how large it could be yeah all right so what's your vision for the Elevate program partner program well I I look at uh a couple things that we've we've have in place today one is um one is we've we've established for the first time ever at crowdstrike the Alliance program management office apmo and that team is focused on building out our next Generation partner program and that's you know processes it's you know uh it's it's ring fencing but it's most important importantly identifying capabilities for partners to expand to reduce friction and uh grow their business together with crowdstrike we also look at uh what we call program Harmony and that's taking all of the partner types or the majority of the partner types and starting to look at it with the customer in the middle and so multiple partners can play a role on the journey to bringing a customer on board initially to supporting that customer going forward and they can all participate and be rewarded for their contribution to that opportunity so it's really a key area for us going forward Hub and spoke model with the center of the that model is the customer you're saying that's good okay so you're not like necessarily fighting each other for for a sort of ownership of that model but uh cool Michael Rogers thanks so much for coming on thecube it was great to have you my pleasure thank you for having me you're welcome all right keep it right there Dave Nicholson and Dave vellante we'll be right back to Falcon 22 from the Aria in Las Vegas you're watching thecube foreign [Music]

>>Hey, everyone. Welcome back to accelerating analytics, maturity. I'm your host. Lisa Martin, Alan Jacobson joins me next. The chief data and analytics officer at Altrix Ellen. It's great to have you on the program. >>Thanks Lisa. >>So Ellen, as we know, everyone knows that being data driven is very important. It's a household term these days, but 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. What's your advice, your recommendations for organizations who are just starting out with analytics >>And you're spot on many organizations really aren't leveraging the, the full capability of their knowledge workers. And really the first step is probably assessing where you are on the journey, whether that's you personally, or your organization as a whole, we just launched an assessment tool on our website that we built with the international Institute of analytics, that in a very short period of time, in about 15 minutes, you can go on and answer some questions and understand where you sit versus your peer set versus competitors and kind of where you are on the journey. >>So when people talk about data analytics, they often think, ah, this is for data science experts like people like you. So why should people in the lines of business like the finance folks, the marketing folks, why should they learn analytics? >>So domain experts are really in the best position. They, they know where the gold is buried in their companies. They know where the inefficiencies are, and it is so much easier and faster to teach a domain expert a bit about how to automate a process or how to use analytics than it is to take a data scientist and try to teach them to have the knowledge of a 20 year accounting professional or a, or a logistics expert of your company. It much harder to do that. And really, if you think about it, the world has changed dramatically in a very short period of time. If, if you were a marketing professional 30 years ago, you likely didn't need to know anything about the internet, but today, do you know what you would call that marketing professional? If they didn't know anything about the internet, probably unemployed or retired. And so knowledge workers are having to learn more and more skills to really keep up with their professions. And analytics is really no exception. Pretty much in every profession, people are needing to learn analytics, to stay current and, and be capable for their companies. And companies need people who can do that. >>Absolutely. It seems like it's table stakes. These days, let's look at different industries. Now, are there differences in how you see analytics in automation being employed in different industries? I know Altrix is being used across a lot of different types of organizations from government to retail. I also see you're now with some of the leading sports teams, any differences in industries. >>Yeah. There's an incredible actually commonality between domains industry to industry. So if you look at what an HR professional is doing, maybe attrition analysis, it's probably quite similar, whether they're in oil and gas or in a high tech software company. And so really the similarities are, are much larger than you might think. And even on the, on, on the, on the sports front, we see many of the analytics that sports teams perform are very similar. So McLaren is one of the great partners that we work with and they use TRICS across many areas of their business from finance to production, extreme sports, logistics, wind tunnel engineering, the marketing team analyzes social media data, all using Altrics. And if I take as an example, the finance team, the finance team is trying to optimize the budget to make sure that they can hit the very stringent targets that F1 sports has. And I don't see a ton of difference between the optimization that they're doing to hit their budget numbers and what I see fortune 500 finance departments doing to optimize their budget. And so really the, the commonality is very high. Even across industries. >>I bet every F fortune 500 or even every company would love to be compared to the same department within McLaren F1, just to know that wow, what they're doing is so in incre incredibly important as is what we are doing. Absolutely. So talk about lessons learned, what lessons can business leaders take from those organizations like McLaren, who are the most analytically mature >>Probably first and foremost, is that the ROI with analytics and automation is incredibly high. Companies are having a ton of success. It's becoming an existential threat to some degree, if, if your company isn't going on this journey and your competition is it, it can be a, a huge problem. IDC just did a recent study about how companies are unlocking the ROI using analytics. And the data was really clear organizations that have a higher percentage of their workforce using analytics are enjoying a much higher return from their analytic investment. And so it's not about hiring two double PhD statisticians from Oxford. It really is how widely you can bring your workforce on this journey. Can they all get 10% more capable? And that's having incredible results at businesses all over the world. An another key finding that they had is that the majority of them said that when they had many folks using analytics, they were going on the journey faster than companies they didn't. And so picking technologies, that'll help everyone do this and, and do this fast and do it easily. Having an approachable piece of software that everyone can use is really a key, >>So faster able to move faster, higher ROI. I also imagine analytics across the organization is a big competitive advantage for organizations in any industry. >>Absolutely the IDC or not. The IDC, the international Institute of analytics showed huge correlation between companies that were more analytically mature versus ones that were not. They showed correlation to growth of the company. They showed correlation to revenue and they showed correlation to shareholder values. So across really all of the, the, the key measures of business, the more analytically mature companies simply outperformed their competition. >>And that's key these days is to be able to outperform your competition. You know, one of the things that we hear so often, Alan, is people talking about democratizing data and analytics. You talked about the line of business workers, but I gotta ask you, is it really that easy for the line of business workers who aren't trained in data science, to be able to jump in, look at data, uncover and extract business insights to make decisions. >>So in, in many ways, it really is that easy. I have a 14 and 16 year old kid. Both of them have learned Altrics they're, Altrics certified. And, and it was quite easy. It took 'em about 20 hours and they were, they, they were off to the races, but there can be some hard parts. The hard parts have more to do with change management. I mean, if you're an accountant, that's been doing the best accounting work in your company for the last 20 years. And all you happen to know is a spreadsheet for those 20 years. Are you ready to learn some new skills? And, and I would suggest you probably need to, if you want, keep up with your profession. The, the big four accounting firms have trained over a hundred thousand people in Altrix just one firm has trained over a hundred thousand. >>You, you can't be an accountant or an auditor at some of these places with, without knowing Altrix. And so the hard part, really in the end, isn't the technology and learning analytics and data science. The harder part is this change management change is hard. I should probably eat better and exercise more, but it's, it's hard to always do that. And so companies are finding that that's the hard part. They need to help people go on the journey, help people with the change management to, to help them become the digitally enabled accountant of the future. The, the logistics professional that is E enabled that that's the challenge. >>That's a huge challenge. Cultural, cultural shift is a challenge. As you said, change management. How, how do you advise customers? If you might be talking with someone who might be early in their analytics journey, but really need to get up to speed and mature to be competitive, how do you guide them or give them recommendations on being able to facilitate that change management? >>Yeah, that's a great question. So, so people entering into the workforce today, many of them are starting to have these skills Altrics is used in over 800 universities around the globe to teach finance and to teach marketing and to teach logistics. And so some of this is happening naturally as new workers are entering the workforce, but for all of those who are already in the workforce have already started their careers, learning in place becomes really important. And so we work with companies to put on programmatic approaches to help their workers do this. And so it's, again, not simply putting a box of tools in the corner and saying free, take one. We put on hackathons and analytic days, and it can, it can be great fun. We, we have a great time with, with many of the customers that we work with helping them, you know, do this, helping them go on the journey and the ROI, as I said, you know, is fantastic. And not only does it sometimes affect the bottom line, it can really make societal changes. We've seen companies have breakthroughs that really make great impact to society as a whole. >>Isn't that so fantastic to see the, the difference that that can make. It sounds like you're, you guys are doing a great job of democratizing access to alter X to everybody. We talked about the line of business folks and the incredible importance of enabling them and the, the ROI, the speed, the competitive advantage. Can you share some specific examples that you think of Alter's customers that really show data breakthroughs by the lines of business using the technology? >>Yeah, absolutely. So, so many to choose from I'll I'll, I'll give you two examples. Quickly. One is armor express. They manufacture life saving equipment, defensive equipments, like armor plated vests, and they were needing to optimize their supply chain, like many companies through the pandemic. We, we see how important the supply chain is. And so adjusting supply to, to match demand is, is really vital. And so they've used all tricks to model some of their supply and demand signals and built a predictive model to optimize the supply chain. And it certainly helped out from a, a dollar standpoint, they cut over a half a million dollars of inventory in the first year, but more importantly, by matching that demand and supply signal, you're able to better meet customer customer demand. And so when people have orders and are, are looking to pick up a vest, they don't wanna wait. >>And, and it becomes really important to, to get that right. Another great example is British telecom. They're, they're a company that services the public sector. They have very strict reporting regulations that they have to meet and they had, and, and this is crazy to think about over 140 legacy spreadsheet models that they had to run to comply with these regulatory processes and, and report, and obviously running 140 legacy models that had to be done in a certain order and linked incredibly challenging. It took them over four weeks, each time that they had to go through that process. And so to, to save time and have more efficiency in doing that, they trained 50 employees over just a two week period to start using Altrix and, and, and learn Altrix. And they implemented an all new reporting process that saw a 75% reduction in the number of man hours. >>It took to run in a 60% runtime performance. And so, again, a huge improvement. I can imagine it probably had better quality as well, because now that it was automated, you don't have people copying and past data into a spreadsheet. And that was just one project that this group of, of folks were able to accomplish that had huge ROI, but now those people are moving on and automating other processes and performing analytics in, in other areas, you can imagine the impact by the end of the year that they will have on their business, you know, potentially millions upon millions of dollars. This is what we see again. And again, company after company government agency, after government agency is how analytics are really transforming the way work is being done. >>That was the word that came to mind when you were describing the all three customer examples, the transformation, this is transformative. The ability to leverage alters to, to truly democratize data and analytics, give access to the lines of business is transformative for every organization. And, and also the business outcomes. You mentioned, those are substantial metrics based business outcomes. So the ROI and leveraging a technology like alri seems to be right there, sitting in front of you. >>That's right. And, and to be honest, it's not only important for these businesses. It's important for, for the knowledge workers themselves. I mean, we, we hear it from people that they discover Alrich, they automate a process. They finally get to get home for dinner with their families, which is fantastic, but, but it leads to new career paths. And so, you know, knowledge workers that have these added skills have so much larger opportunity. And I think it's great when the needs of businesses to become more analytics and analytic and automate processes actually matches the needs of the employees. And, you know, they too wanna learn these skills and become more advanced in their capabilities, >>Huge value there for the business, for the employees themselves to expand their skillset, to, to really open up so many opportunities for not only the business to meet the demands of the demanding customer, but the employees to be able to really have that breadth and depth in their field of service. Great opportunities there. Alan, is there anywhere that you wanna point the audience to go, to learn more about how they can get started? >>Yeah. So one of the things that we're really excited about is how fast and easy it is to learn these tools. So any of the listeners who wanna experience Altrix, they can go to the website, there's a free download on the website. You can take our analytic maturity assessment, as we talked about at the beginning and, and see where you are on the journey and just reach out. You know, we'd love to work with you and your organization to see how we can help you accelerate your journey on, on analytics and automation, >>Alan, it was a pleasure talking to you about democratizing data and analytics, the power in it for organizations across every industry. We appreciate your insights and your time. >>Thank you so much >>In a moment, Paula Hanson, who is the president and chief revenue officer of ultras and Jackie Vander lay graying. Who's the global head of tax technology at eBay will join me. You're watching the cube, the leader in high tech enterprise coverage.

>> It's no surprise that 73% of organizations indicate analytics spend will outpace other software investments in the next 12 to 18 months. After all as we know, data is changing the world and the world is changing with it. But is everyone's spending resulting in the same ROI? This is Lisa Martin. Welcome to "theCUBE"'s presentation of democratizing analytics across the enterprise, made possible by Alteryx. An Alteryx commissioned IDC info brief entitled, "Four Ways to Unlock Transformative Business Outcomes from Analytics Investments" found that 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. On this special "CUBE" presentation, Jason Klein, product marketing director of Alteryx, will join me to share key findings from the new Alteryx commissioned IDC brief and uncover how enterprises can derive more value from their data. In our second segment, we'll hear from Alan Jacobson, chief data and analytics officer at Alteryx. He's going to discuss how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. And then in our final segment, Paula Hansen, who is the president and chief revenue officer of Alteryx, and Jacqui Van der Leij Greyling, who is the global head of tax technology at eBay, they'll join me. They're going to share how Alteryx is helping the global eCommerce company innovate with analytics. Let's get the show started. (upbeat music) Jason Klein joins me next, product marketing director at Alteryx. Jason, welcome to the program. >> Hello, nice to be here. >> Excited to talk with you. What can you tell me about the new Alteryx IDC research, which spoke with about 1500 leaders, what nuggets were in there? >> Well, as the business landscape changes over the next 12 to 18 months, we're going to see that analytics is going to be a key component to navigating this change. 73% of the orgs indicated that analytics spend will outpace other software investments. But just putting more money towards technology, it isn't going to solve everything. And this is why everyone's spending is resulting in different ROIs. And one of the reasons for this gap is because 93% of organizations, they're still not fully using the analytics skills of their employees, and this widening analytics gap, it's threatening operational progress by wasting workers' time, harming business productivity and introducing costly errors. So in this research, we developed a framework of enterprise analytics proficiency that helps organizations reap greater benefits from their investments. And we based this framework on the behaviors of organizations that saw big improvements across financial, customer, and employee metrics, and we're able to focus on the behaviors driving higher ROI. >> So the info brief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the info brief that nearly three quarters plan on spending more on analytics than any other software. And can you unpack, what's driving this demand, this need for analytics across organizations? >> Sure, well first there's more data than ever before, the data's changing the world, and the world is changing data. Enterprises across the world, they're accelerating digital transformation to capitalize on new opportunities, to grow revenue, to increase margins and to improve customer experiences. And analytics along with automation and AI is what's making digital transformation possible. They're providing the fuel to new digitally enabled lines of business. >> One of the things that the study also showed was that not all analytics spending is resulting in the same ROI. What are some of the discrepancies that the info brief uncovered with respect to the changes in ROI that organizations are achieving? >> Our research with IDC revealed significant roadblocks across people, processes, and technologies. They're preventing companies from reaping greater benefits from their investments. So for example, on the people side, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% from our survey, are still not using the full breadth of data types available. Yet data's never been this prolific, it's going to continue to grow, and orgs should be using it to their advantage. And lastly organizations, they need to provide the right analytics tools to help everyone unlock the power of data. >> So they- >> They instead rely on outdated spreadsheet technology. In our survey, nine out of 10 respondents said less than half of their knowledge workers are active users of analytics software beyond spreadsheets. But true analytic transformation can't happen for an organization in a few select pockets or silos. We believe everyone regardless of skill level should be able to participate in the data and analytics process and be driving value. >> Should we retake that, since I started talking over Jason accidentally? >> Yep, absolutely we can do so. We'll just go, yep, we'll go back to Lisa's question. Let's just, let's do the, retake the question and the answer, that'll be able to. >> It'll be not all analytics spending results in the same ROI, what are some of the discrepancies? >> Yes, Lisa, so we'll go from your ISO, just so we get that clean question and answer. >> Okay. >> Thank you for that. On your ISO, we're still speeding, Lisa, so give it a beat in your head and then on you. >> Yet not all analytics spending is resulting in the same ROI. So what are some of the discrepancies that the info brief uncovered with respect to ROI? >> Well, our research with IDC revealed significant roadblocks across people, processes, and technologies, all preventing companies from reaping greater benefits from their investments. So on the people side, for example, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% in our survey, are still not using the full breadth of data types available. Data has never been this prolific. It's going to continue to grow and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytic tools to help everyone unlock the power of data, yet instead they're relying on outdated spreadsheet technology. Nine of 10 survey respondents said that less than half of their knowledge workers are active users of analytics software. True analytics transformation can't happen for an organization in a few select pockets or silos. We believe everyone regardless of skill level should be able to participate in the data and analytics process and drive value. >> So if I look at this holistically, then what would you say organizations need to do to make sure that they're really deriving value from their investments in analytics? >> Yeah, sure. So overall, the enterprises that derive more value from their data and analytics and achieve more ROI, they invested more aggressively in the four dimensions of enterprise analytics proficiency. So they've invested in the comprehensiveness of analytics across all data sources and data types, meaning they're applying analytics to everything. They've invested in the flexibility of analytics across deployment scenarios and departments, meaning they're putting analytics everywhere. They've invested in the ubiquity of analytics and insights for every skill level, meaning they're making analytics for everyone. And they've invested in the usability of analytics software, meaning they're prioritizing easy technology to accelerate analytics democratization. >> So very strategic investments. Did the survey uncover any specific areas where most companies are falling short, like any black holes that organizations need to be aware of at the outset? >> It did, it did. So organizations, they need to build a data-centric culture. And this begins with people. But what the survey told us is that the people aspect of analytics is the most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone in the organization has access to the data and analytics technology they need. And then the organizations also have to align their investments with upskilling in data literacy to enjoy that higher ROI. Companies who did so experience higher ROI than companies who underinvested in analytics literacy. So among the high ROI achievers, 78% have a good or great alignment between analytics investment and workforce upskilling compared to only 64% among those without positive ROI. And as more orgs adopt cloud data warehouses or cloud data lakes, in order to manage the massively increasing workloads- Can I start that one over. >> Sure. >> Can I redo this one? >> Yeah. >> Of course, stand by. >> Tongue tied. >> Yep, no worries. >> One second. >> If we could do the same, Lisa, just have a clean break, we'll go your question. >> Yep, yeah. >> On you Lisa. Just give that a count and whenever you're ready. Here, I'm going to give us a little break. On you Lisa. >> So are there any specific areas that the survey uncovered where most companies are falling short? Like any black holes organizations need to be aware of from the outset? >> It did. You need to build a data-centric culture and this begins with people, but we found that the people aspect of analytics is most heavily skewed towards low proficiency. In order to maximize ROI organizations need to make sure everyone has access to the data and analytics technology they need. Organizations that align their analytics investments with upskilling enjoy higher ROI than orgs that are less aligned. For example, among the high ROI achievers in our survey, 78% had good or great alignment between analytics investments and workforce upskilling, compared to only 64% among those without positive ROI. And as more enterprises adopt cloud data warehouses or cloud data lakes to manage increasingly massive data sets, analytics needs to exist everywhere, especially for those cloud environments. And what we found is organizations that use more data types and more data sources generate higher ROI from their analytics investments. Among those with improved customer metrics, 90% were good or great at utilizing all data sources, compared to only 67% among the ROI laggards. >> So interesting that you mentioned people, I'm glad that you mentioned people. Data scientists, everybody talks about data scientists. They're in high demand, we know that, but there aren't enough to meet the needs of all enterprises. So given that discrepancy, how can organizations fill the gap and really maximize the investments that they're making in analytics? >> Right, so analytics democratization, it's no longer optional, but it doesn't have to be complex. So we at Alteryx, we're democratizing analytics by empowering every organization to upskill every worker into a data worker. And the data from this survey shows this is the optimal approach. Organizations with a higher percentage of knowledge workers who are actively using analytics software enjoy higher returns from their analytics investment than orgs still stuck on spreadsheets. Among those with improved financial metrics, AKA the high ROI achievers, nearly 70% say that at least a quarter of their knowledge workers are using analytics software other than spreadsheets compared to only 56% in the low ROI group. Also among the high ROI performers, 63% said data and analytic workers collaborate well or extremely well compared to only 51% in the low ROI group. The data from the survey shows that supporting more business domains with analytics and providing cross-functional analytics correlates with higher ROI. So to maximize ROI, orgs should be transitioning workers from spreadsheets to analytics software. They should be letting them collaborate effectively and letting them do so cross-functionally. >> Yeah, that cross-functional collaboration is essential for anyone in any organization and in any discipline. Another key thing that jumped out from the survey was around shadow IT. The business side is using more data science tools than the IT side. And it's expected to spend more on analytics than other IT. What risks does this present to the overall organization, if IT and the lines of business guys and gals aren't really aligned? >> Well, there needs to be better collaboration and alignment between IT and the line of business. The data from the survey, however, shows that business managers, they're expected to spend more on analytics and use more analytics tools than IT is aware of. And this isn't because the lines of business have recognized the value of analytics and plan to invest accordingly, but a lack of alignment between IT and business. This will negatively impact governance, which ultimately impedes democratization and hence ROI. >> So Jason, where can organizations that are maybe at the outset of their analytics journey, or maybe they're in environments where there's multiple analytics tools across shadow IT, where can they go to Alteryx to learn more about how they can really simplify, streamline, and dial up the value on their investment? >> Well, they can learn more on our website. I also encourage them to explore the Alteryx community, which has lots of best practices, not just in terms of how you do the analytics, but how you stand up in Alteryx environment, but also to take a look at your analytics stack and prioritize technologies that can snap to and enhance your organization's governance posture. It doesn't have to change it, but it should be able to align to and enhance it. >> And of course, as you mentioned, it's about people, process, and technologies. Jason, thank you so much for joining me today, unpacking the IDC info brief and the great nuggets in there. Lots that organizations can learn and really become empowered to maximize their analytics investments. We appreciate your time. >> Thank you, it's been a pleasure. >> In a moment, Alan Jacobson, who's the chief data and analytics officer at Alteryx is going to join me. He's going to be here to talk about how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. You're watching "theCUBE", the leader in tech enterprise coverage. >> Somehow many have come to believe that data analytics is for the few, for the scientists, the PhDs, the MBAs. Well, it is for them, but that's not all. You don't have to have an advanced degree to do amazing things with data. You don't even have to be a numbers person. You can be just about anything. A titan of industry or a future titan of industry. You could be working to change the world, your neighborhood, or the course of your business. You can be saving lives or just looking to save a little time. The power of data analytics shouldn't be limited to certain job titles or industries or organizations because when more people are doing more things with data, more incredible things happen. Analytics makes us smarter and faster and better at what we do. It's practically a superpower. That's why we believe analytics is for everyone, and everything, and should be everywhere. That's why we believe in analytics for all. (upbeat music) >> Hey, everyone. Welcome back to "Accelerating Analytics Maturity". I'm your host, Lisa Martin. Alan Jacobson joins me next. The chief of data and analytics officer at Alteryx. Alan, it's great to have you on the program. >> Thanks, Lisa. >> So Alan, as we know, everyone knows that being data driven is very important. It's a household term these days, but 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. What's your advice, your recommendations for organizations who are just starting out with analytics? >> You're spot on, many organizations really aren't leveraging the full capability of their knowledge workers. And really the first step is probably assessing where you are on the journey, whether that's you personally, or your organization as a whole. We just launched an assessment tool on our website that we built with the International Institute of Analytics, that in a very short period of time, in about 15 minutes, you can go on and answer some questions and understand where you sit versus your peer set versus competitors and kind of where you are on the journey. >> So when people talk about data analytics, they often think, ah, this is for data science experts like people like you. So why should people in the lines of business like the finance folks, the marketing folks, why should they learn analytics? >> So domain experts are really in the best position. They know where the gold is buried in their companies. They know where the inefficiencies are. And it is so much easier and faster to teach a domain expert a bit about how to automate a process or how to use analytics than it is to take a data scientist and try to teach them to have the knowledge of a 20 year accounting professional or a logistics expert of your company. Much harder to do that. And really, if you think about it, the world has changed dramatically in a very short period of time. If you were a marketing professional 30 years ago, you likely didn't need to know anything about the internet, but today, do you know what you would call that marketing professional if they didn't know anything about the internet, probably unemployed or retired. And so knowledge workers are having to learn more and more skills to really keep up with their professions. And analytics is really no exception. Pretty much in every profession, people are needing to learn analytics to stay current and be capable for their companies. And companies need people who can do that. >> Absolutely, it seems like it's table stakes these days. Let's look at different industries now. Are there differences in how you see analytics in automation being employed in different industries? I know Alteryx is being used across a lot of different types of organizations from government to retail. I also see you're now with some of the leading sports teams. Any differences in industries? >> Yeah, there's an incredible actually commonality between the domains industry to industry. So if you look at what an HR professional is doing, maybe attrition analysis, it's probably quite similar, whether they're in oil and gas or in a high tech software company. And so really the similarities are much larger than you might think. And even on the sports front, we see many of the analytics that sports teams perform are very similar. So McLaren is one of the great partners that we work with and they use Alteryx across many areas of their business from finance to production, extreme sports, logistics, wind tunnel engineering, the marketing team analyzes social media data, all using Alteryx, and if I take as an example, the finance team, the finance team is trying to optimize the budget to make sure that they can hit the very stringent targets that F1 Sports has, and I don't see a ton of difference between the optimization that they're doing to hit their budget numbers and what I see Fortune 500 finance departments doing to optimize their budget, and so really the commonality is very high, even across industries. >> I bet every Fortune 500 or even every company would love to be compared to the same department within McLaren F1. Just to know that wow, what they're doing is so incredibly important as is what we're doing. >> So talk- >> Absolutely. >> About lessons learned, what lessons can business leaders take from those organizations like McLaren, who are the most analytically mature? >> Probably first and foremost, is that the ROI with analytics and automation is incredibly high. Companies are having a ton of success. It's becoming an existential threat to some degree, if your company isn't going on this journey and your competition is, it can be a huge problem. IDC just did a recent study about how companies are unlocking the ROI using analytics. And the data was really clear, organizations that have a higher percentage of their workforce using analytics are enjoying a much higher return from their analytic investment, and so it's not about hiring two double PhD statisticians from Oxford. It really is how widely you can bring your workforce on this journey, can they all get 10% more capable? And that's having incredible results at businesses all over the world. An another key finding that they had is that the majority of them said that when they had many folks using analytics, they were going on the journey faster than companies that didn't. And so picking technologies that'll help everyone do this and do this fast and do it easily. Having an approachable piece of software that everyone can use is really a key. >> So faster, able to move faster, higher ROI. I also imagine analytics across the organization is a big competitive advantage for organizations in any industry. >> Absolutely the IDC, or not the IDC, the International Institute of Analytics showed huge correlation between companies that were more analytically mature versus ones that were not. They showed correlation to growth of the company, they showed correlation to revenue and they showed correlation to shareholder values. So across really all of the key measures of business, the more analytically mature companies simply outperformed their competition. >> And that's key these days, is to be able to outperform your competition. You know, one of the things that we hear so often, Alan, is people talking about democratizing data and analytics. You talked about the line of business workers, but I got to ask you, is it really that easy for the line of business workers who aren't trained in data science to be able to jump in, look at data, uncover and extract business insights to make decisions? >> So in many ways, it really is that easy. I have a 14 and 16 year old kid. Both of them have learned Alteryx, they're Alteryx certified and it was quite easy. It took 'em about 20 hours and they were off to the races, but there can be some hard parts. The hard parts have more to do with change management. I mean, if you're an accountant that's been doing the best accounting work in your company for the last 20 years, and all you happen to know is a spreadsheet for those 20 years, are you ready to learn some new skills? And I would suggest you probably need to, if you want to keep up with your profession. The big four accounting firms have trained over a hundred thousand people in Alteryx. Just one firm has trained over a hundred thousand. You can't be an accountant or an auditor at some of these places without knowing Alteryx. And so the hard part, really in the end, isn't the technology and learning analytics and data science, the harder part is this change management, change is hard. I should probably eat better and exercise more, but it's hard to always do that. And so companies are finding that that's the hard part. They need to help people go on the journey, help people with the change management to help them become the digitally enabled accountant of the future, the logistics professional that is E enabled, that's the challenge. >> That's a huge challenge. Cultural shift is a challenge, as you said, change management. How do you advise customers if you might be talking with someone who might be early in their analytics journey, but really need to get up to speed and mature to be competitive, how do you guide them or give them recommendations on being able to facilitate that change management? >> Yeah, that's a great question. So people entering into the workforce today, many of them are starting to have these skills. Alteryx is used in over 800 universities around the globe to teach finance and to teach marketing and to teach logistics. And so some of this is happening naturally as new workers are entering the workforce, but for all of those who are already in the workforce, have already started their careers, learning in place becomes really important. And so we work with companies to put on programmatic approaches to help their workers do this. And so it's, again, not simply putting a box of tools in the corner and saying free, take one. We put on hackathons and analytic days, and it can be great fun. We have a great time with many of the customers that we work with, helping them do this, helping them go on the journey, and the ROI, as I said, is fantastic. And not only does it sometimes affect the bottom line, it can really make societal changes. We've seen companies have breakthroughs that have really made great impact to society as a whole. >> Isn't that so fantastic, to see the difference that that can make. It sounds like you guys are doing a great job of democratizing access to Alteryx to everybody. We talked about the line of business folks and the incredible importance of enabling them and the ROI, the speed, the competitive advantage. Can you share some specific examples that you think of Alteryx customers that really show data breakthroughs by the lines of business using the technology? >> Yeah, absolutely, so many to choose from. I'll give you two examples quickly. One is Armor Express. They manufacture life saving equipment, defensive equipments, like armor plated vests, and they were needing to optimize their supply chain, like many companies through the pandemic. We see how important the supply chain is. And so adjusting supply to match demand is really vital. And so they've used Alteryx to model some of their supply and demand signals and built a predictive model to optimize the supply chain. And it certainly helped out from a dollar standpoint. They cut over a half a million dollars of inventory in the first year, but more importantly, by matching that demand and supply signal, you're able to better meet customer demand. And so when people have orders and are looking to pick up a vest, they don't want to wait. And it becomes really important to get that right. Another great example is British Telecom. They're a company that services the public sector. They have very strict reporting regulations that they have to meet and they had, and this is crazy to think about, over 140 legacy spreadsheet models that they had to run to comply with these regulatory processes and report, and obviously running 140 legacy models that had to be done in a certain order and length, incredibly challenging. It took them over four weeks each time that they had to go through that process. And so to save time and have more efficiency in doing that, they trained 50 employees over just a two week period to start using Alteryx and learn Alteryx. And they implemented an all new reporting process that saw a 75% reduction in the number of man hours it took to run in a 60% run time performance. And so, again, a huge improvement. I can imagine it probably had better quality as well, because now that it was automated, you don't have people copying and pasting data into a spreadsheet. And that was just one project that this group of folks were able to accomplish that had huge ROI, but now those people are moving on and automating other processes and performing analytics in other areas. So you can imagine the impact by the end of the year that they will have on their business, potentially millions upon millions of dollars. And this is what we see again and again, company after company, government agency after government agency, is how analytics are really transforming the way work is being done. >> That was the word that came to mind when you were describing the all three customer examples, transformation, this is transformative. The ability to leverage Alteryx, to truly democratize data and analytics, give access to the lines of business is transformative for every organization. And also the business outcome you mentioned, those are substantial metrics based business outcomes. So the ROI in leveraging a technology like Alteryx seems to be right there, sitting in front of you. >> That's right, and to be honest, it's not only important for these businesses. It's important for the knowledge workers themselves. I mean, we hear it from people that they discover Alteryx, they automate a process, they finally get to get home for dinner with their families, which is fantastic, but it leads to new career paths. And so knowledge workers that have these added skills have so much larger opportunity. And I think it's great when the needs of businesses to become more analytic and automate processes actually matches the needs of the employees, and they too want to learn these skills and become more advanced in their capabilities. >> Huge value there for the business, for the employees themselves to expand their skillset, to really open up so many opportunities for not only the business to meet the demands of the demanding customer, but the employees to be able to really have that breadth and depth in their field of service. Great opportunities there, Alan. Is there anywhere that you want to point the audience to go to learn more about how they can get started? >> Yeah, so one of the things that we're really excited about is how fast and easy it is to learn these tools. So any of the listeners who want to experience Alteryx, they can go to the website, there's a free download on the website. You can take our analytic maturity assessment, as we talked about at the beginning, and see where you are on the journey and just reach out. We'd love to work with you and your organization to see how we can help you accelerate your journey on analytics and automation. >> Alan, it was a pleasure talking to you about democratizing data and analytics, the power in it for organizations across every industry. We appreciate your insights and your time. >> Thank you so much. >> In a moment, Paula Hansen, who is the president and chief revenue officer of Alteryx, and Jacqui Van der Leij Greyling, who's the global head of tax technology at eBay, will join me. You're watching "theCUBE", the leader in high tech enterprise coverage. >> 1200 hours of wind tunnel testing, 30 million race simulations, 2.4 second pit stops. >> Make that 2.3. >> Sector times out the wazoo. >> Way too much of this. >> Velocities, pressures, temperatures, 80,000 components generating 11.8 billion data points and one analytics platform to make sense of it all. When McLaren needs to turn complex data into winning insights, they turn to Alteryx. Alteryx, analytics automation. (upbeat music) >> Hey, everyone, welcome back to the program. Lisa Martin here, I've got two guests joining me. Please welcome back to "theCUBE" Paula Hansen, the chief revenue officer and president at Alteryx, and Jacqui Van der Leij Greyling joins us as well, the global head of tax technology at eBay. They're going to share with you how Alteryx is helping eBay innovate with analytics. Ladies, welcome, it's great to have you both on the program. >> Thank you, Lisa, it's great to be here. >> Yeah, Paula, we're going to start with you. In this program, we've heard from Jason Klein, we've heard from Alan Jacobson. They talked about the need to democratize analytics across any organization to really drive innovation. With analytics, as they talked about, at the forefront of software investments, how's Alteryx helping its customers to develop roadmaps for success with analytics? >> Well, thank you, Lisa. It absolutely is about our customers' success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts of course with our innovative technology and platform, but ultimately we help our customers to create a culture of data literacy and analytics from the top of the organization, starting with the C-suite. And we partner with our customers to build their roadmaps for scaling that culture of analytics, through things like enablement programs, skills assessments, hackathons, setting up centers of excellence to help their organization scale and drive governance of this analytics capability across the enterprise. So at the end of the day, it's really about helping our customers to move up their analytics maturity curve with proven technologies and best practices, so they can make better business decisions and compete in their respective industries. >> Excellent, sounds like a very strategic program, we're going to unpack that. Jacqui, let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the IDC report that showed that 93% of organizations are not utilizing the analytics skills of their employees, but then there's eBay. How Jacqui did eBay become one of the 7% of organizations who's really maturing and how are you using analytics across the organization at eBay? >> So I think the main thing for us is when we started out was is that, our, especially in finance, they became spreadsheet professionals instead of the things that we really want our employees to add value to. And we realized we had to address that. And we also knew we couldn't wait for all our data to be centralized until we actually start using the data or start automating and being more effective. So ultimately we really started very, very actively embedding analytics in our people and our data and our processes. >> Starting with people is really critical. Jacqui, continuing with you, what were some of the roadblocks to analytics adoption that you faced and how did you overcome them? >> So I think eBay is a very data driven company. We have a lot of data. I think we are 27 years around this year, so we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and just finding those data sources and finding ways to connect to them to move forward. The other thing is that people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals. And there was no, we were not independent. You couldn't move forward, you would've put it on somebody else's roadmap to get the data and to get the information if you want it. So really finding something that everybody could access analytics or access data. And finally we have to realize is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy, and that is not so daunting on somebody who's brand new to the field? And I would call those out as your major roadblocks, because you always have, not always, but most of the times you have support from the top, and in our case we have, but at the end of the day, it's our people that need to actually really embrace it, and making that accessible for them, I would say is definitely not per se, a roadblock, but basically a block you want to be able to move. >> It's really all about putting people first. Question for both of you, and Paula we'll start with you, and then Jacqui we'll go to you. I think the message in this program that the audience is watching with us is very clear. Analytics is for everyone, should be for everyone. Let's talk now about how both of your organizations are empowering people, those in the organization that may not have technical expertise to be able to leverage data, so that they can actually be data driven. Paula. >> Yes, well, we leverage our platform across all of our business functions here at Alteryx. And just like Jacqui explained, at eBay finance is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jacqui mentioned, we have this huge amount of data flowing through our enterprise and the opportunity to leverage that into insights and analytics is really endless. So our CFO Kevin Rubin has been a key sponsor for using our own technology. We use Alteryx for forecasting all of our key performance metrics, for business planning, across our audit function, to help with compliance and regulatory requirements, tax, and even to close our books at the end of each quarter. So it's really going to remain across our business. And at the end of the day, it comes to how do you train users? How do you engage users to lean into this analytic opportunity to discover use cases? And so one of the other things that we've seen many companies do is to gamify that process, to build a game that brings users into the experience for training and to work with each other, to problem solve and along the way, maybe earn badges depending on the capabilities and trainings that they take. And just have a little healthy competition as an employee base around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jacqui mentioned, it's really about ensuring that people feel comfortable, that they feel supported, that they have access to the training that they need, and ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >> That confidence is key. Jacqui, talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >> Yeah, I think it means to what Paula has said in terms of getting people excited about it, but it's also understanding that this is a journey and everybody is at a different place in their journey. You have folks that's already really advanced who has done this every day. And then you have really some folks that this is brand new or maybe somewhere in between. And it's about how you get everybody in their different phases to get to the initial destination. I say initial, because I believe a journey is never really complete. What we have done is that we decided to invest, and built a proof of concept, and we got our CFO to sponsor a hackathon. We opened it up to everybody in finance in the middle of the pandemic. So everybody was on Zoom and we told people, listen, we're going to teach you this tool, it's super easy, and let's just see what you can do. We ended up having 70 entries. We had only three weeks. So and these are people that do not have a background. They are not engineers, they're not data scientists. And we ended up with a 25,000 hour savings at the end of that hackathon from the 70 entries with people that have never, ever done anything like this before. And there you have the result. And then it just went from there. People had a proof of concept. They knew that it worked and they overcame the initial barrier of change. And that's where we are seeing things really, really picking up now. >> That's fantastic. And the business outcome that you mentioned there, the business impact is massive, helping folks get that confidence to be able to overcome sometimes the cultural barriers is key here. I think another thing that this program has really highlighted is there is a clear demand for data literacy in the job market, regardless of organization. Can each of you share more about how you're empowering the next generation of data workers? Paula, we'll start with you. >> Absolutely, and Jacqui says it so well, which is that it really is a journey that organizations are on and we as people in society are on in terms of upskilling our capabilities. So one of the things that we're doing here at Alteryx to help address this skillset gap on a global level is through a program that we call SparkED, which is essentially a no-cost analytics education program that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this program's really developed just to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with SparkED. We started last May, but we currently have over 850 educational institutions globally engaged across 47 countries, and we're going to continue to invest here because there's so much opportunity for people, for society and for enterprises, when we close the gap and empower more people with the necessary analytics skills to solve all the problems that data can help solve. >> So SparkED has made a really big impact in such a short time period. It's going to be fun to watch the progress of that. Jacqui, let's go over to you now. Talk about some of the things that eBay is doing to empower the next generation of data workers. >> So we basically wanted to make sure that we kept that momentum from the hackathon, that we don't lose that excitement. So we just launched the program called eBay Masterminds. And what it basically is, is it's an inclusive innovation in each other, where we firmly believe that innovation is for upskilling for all analytics roles. So it doesn't matter your background, doesn't matter which function you are in, come and participate in in this where we really focus on innovation, introducing new technologies and upskilling our people. We are, apart from that, we also said, well, we shouldn't just keep it to inside eBay. We have to share this innovation with the community. So we are actually working on developing an analytics high school program, which we hope to pilot by the end of this year, where we will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, but also how to use Alteryx. And we're working with, actually, we're working with SparkED and they're helping us develop that program. And we really hope that at, say, by the end of the year, we have a pilot and then also next year, we want to roll it out in multiple locations in multiple countries and really, really focus on that whole concept of analytics for all. >> Analytics for all, sounds like Alteryx and eBay have a great synergistic relationship there that is jointly aimed at especially going down the stuff and getting people when they're younger interested, and understanding how they can be empowered with data across any industry. Paula, let's go back to you, you were recently on "theCUBE"'s Supercloud event just a couple of weeks ago. And you talked about the challenges the companies are facing as they're navigating what is by default a multi-cloud world. How does the Alteryx Analytics Cloud platform enable CIOs to democratize analytics across their organization? >> Yes, business leaders and CIOs across all industries are realizing that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organizations. Last I checked, there was 2 million data scientists in the world, so that's woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. So what we're seeing now with CIOs, with business leaders is that they're integrating data analysis and the skillset of data analysis into virtually every job function, and that is what we think of when we think of analytics for all. And so our mission with Alteryx Analytics Cloud is to empower all of those people in every job function, regardless of their skillset, as Jacqui pointed out from people that are just getting started all the way to the most sophisticated of technical users. Every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organizations. So that's our goal with Alteryx Analytics Cloud, and it operates in a multi cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyze, and report out so that we can break down data silos across the enterprise and help drive real business outcomes as a result of unlocking the potential of data. >> As well as really lessening that skill gap. As you were saying, there's only 2 million data scientists. You don't need to be a data scientist, that's the beauty of what Alteryx is enabling and eBay is a great example of that. Jacqui, let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where Alteryx fits in as that analytics maturity journey continues and what are some of the things that you are most excited about as analytics truly gets democratized across eBay? >> When we're starting up and getting excited about things when it comes to analytics, I can go on all day, but I'll keep it short and sweet for you. I do think we are on the top of the pool of data scientists. And I really feel that that is your next step, for us anyways, is that how do we get folks to not see data scientists as this big thing, like a rocket scientist, it's something completely different. And it's something that is in everybody in a certain extent. So again, partnering with Alteryx who just released the AI ML solution, allowing folks to not have a data scientist program, but actually build models and be able to solve problems that way. So we have engaged with Alteryx and we purchased the licenses, quite a few. And right now through our Masterminds program, we're actually running a four month program for all skill levels, teaching them AI ML and machine learning and how they can build their own models. We are really excited about that. We have over 50 participants without a background from all over the organization. We have members from our customer services. We have even some of our engineers are actually participating in the program. We just kicked it off. And I really believe that that is our next step. I want to give you a quick example of the beauty of this is where we actually just allow people to go out and think about ideas and come up with things. And one of the people in our team who doesn't have a data scientist background at all, was able to develop a solution where there is a checkout feedback functionality on the eBay side where sellers or buyers can verbatim add information. And she built a model to be able to determine what relates to tax specific, what is the type of problem, and even predict how that problem can be solved before we as a human even step in, and now instead of us or somebody going to verbatim and try to figure out what's going on there, we can focus on fixing the error versus actually just reading through things and not adding any value, and it's a beautiful tool and I was very impressed when I saw the demo and definitely developing that sort of thing. >> That sounds fantastic. And I think just the one word that keeps coming to mind, and we've said this a number of times in the program today is empowerment. What you're actually really doing to truly empower people across the organization with varying degrees of skill level, going down to the high school level, really exciting. We'll have to stay tuned to see what some of the great things are that come from this continued partnership. Ladies, I want to thank you so much for joining me on the program today and talking about how Alteryx and eBay are really partnering together to democratize analytics and to facilitate its maturity. It's been great talking to you. >> Thank you, Lisa. >> Thank you so much. (cheerful electronic music) >> As you heard over the course of our program, organizations where more people are using analytics who have deeper capabilities in each of the four Es, that's everyone, everything, everywhere, and easy analytics, those organizations achieve more ROI from their respective investments in analytics and automation than those who don't. We also heard a great story from eBay, great example of an enterprise that is truly democratizing analytics across its organization. It's enabling and empowering line of business users to use analytics, not only focused on key aspects of their job, but develop new skills rather than doing the same repetitive tasks. We want to thank you so much for watching the program today. Remember you can find all of the content on thecube.net. You can find all of the news from today on siliconangle.com and of course alteryx.com. We also want to thank Alteryx for making this program possible and for sponsoring "theCUBE". For all of my guests, I'm Lisa Martin. We want to thank you for watching and bye for now. (upbeat music)

(upbeat techno music) >> Hey everyone, and welcome back to theCUBE's coverage of Snowflake Summit '22 live from Caesar's Forum in beautiful, warm, and sunny Las Vegas. I'm Lisa Martin. I got the Chris and Chris show, next. Bear with me. Chris Degnan joins us again. One of our alumni, the Chief Revenue Officer at Snowflake. Good to have you back, Chris. >> Thank you for having us. >> Lisa: Chris Grusz also joins us. Director of Business Development AWS Marketplace and Service Catalog at AWS. Chris and Chris, welcome. >> Thank you. >> Thank you. >> Thank you. Good to be back in person. >> Isn't it great. >> Chris G: It's so much better. >> Chris D: Yeah. >> Nothing like it. So let's talk. There's been so much momentum, Chris D, at Snowflake the last few years. I mean the momentum at this show since we launched yesterday, I know you guys launched the day before with partners, has been amazing. A lot of change, and it's like this for Snowflake. Talk to us about AWS working together with Snowflake and some of the benefits in it from your customer. And then Chris G, I'll go to you for the same question. >> Chris G: Yep. >> You know, first of all, it's awesome. Like, I just, you know, it's been three years since I've had a Snowflake Summit in person, and it's crazy to see the growth that we've seen. You know, I can't, our first cloud that we ever launched on top of was, was AWS, and AWS is our largest cloud, you know, in in terms of revenue today. And they've been, they just kind of know how to do it right. And they've been a wonderful partner all along. There's been challenges, and we've kind of leaned in together and figured out ways to work together, you know, and to solve those challenges. So, been a wonderful partnership. >> And talk about it, Chris G, from your perspective obviously from a coopetition perspective. >> Yep. >> AWS has databases, cloud data forms. >> Chris G: Yeah. >> Talk to us about it. What was the impetus for the partnership with Snowflake from AWS's standpoint? >> Yeah, well first and foremost, they're building on top of AWS. And so that, by default, makes them a great partner. And it's interesting, Chris and I have been working together for, gosh, seven years now? And the relationship's come a really long way. You know, when we first started off, we were trying to sort out how we were going to work together, when we were competing, and when we're working together. And, you know, you fast forward to today, and it's just such a good relationship. Because both companies work backwards from customers. And so that's, you know, kind of in both of our DNA. And so if the customer makes that selection, we're going to support them, even from an AWS perspective. When they're going with Snowflake, that's still a really good thing for AWS, 'cause there's a lot of associated services that Snowflake either integrates to, or we're integrating to them. And so, it's really kind of contributed to how we can really work together in a co-sell motion. >> Talk to us, talk about that. The joint GOTO market and the co-selling motion from Snowflake's perspective, how do customers get engaged? >> Well, I think, you know, typically we, where we are really good at co-selling together is we identify on premise systems. So whether it's, you know, some Legacy UDP system, some Legacy database solution, and they want to move to the cloud? You know, Amazon is all in on getting everyone to the cloud. And I think that's their approach they've taken with us is saying we're really good at accelerating that adoption and moving all these, you know, massive workloads into the cloud. And then to Chris's point, you know, we've integrated so nicely into things like SageMaker and other tool sets. And we, we even have exciting scenarios where they've allowed us to use, you know, some of their Amazon.com retail data sets that we actually use in data sharing via the partnership. So we continue to find unique ways to partner with our great friends at Amazon. >> Sounds like a very deep partnership. >> Chris D: Yeah. Absolutely. >> Chris G: Oh, absolutely, yeah. We're integrating into Snowflake, and they're integrating to AWS. And so it just provides a great combined experience for our customers. And again, that's kind of what we're both looking forward from both of our organizations. >> That customer centricity is, >> Yeah. >> is I think the center of the flywheel that is both that both of you, your companies have. Chris D, talk about the the industry's solutions, specific, industry-specific solutions that Snowflake and AWS have. I know we talked yesterday about the pivot from a sales perspective >> Chris D: Yes. >> That snowflake made in recent months. Talk to us about the industries that you are help, really targeting with AWS to help customers solve problems. >> Yeah. I think there's, you know, we're focused on a number of industries. I think, you know, some of the examples, like I said, I gave you the example of we're using data sharing to help the retail space. And I think it's a really good partnership. Because some of the, some companies view Amazon as a competitor in the retail space, and I think we kind of soften that blow. And we actually leverage some of the Amazon.com data sets. And this is where the partnership's been really strong. In the healthcare space, in the life sciences space, we have customers like Anthem, where we're really focused on helping actually Anthem solve real business problems. Not necessarily like technical problems. It's like, oh no, they want to get, you know, figure out how they can get the whole customer and take care of their whole customer, and get them using the Anthem platform more effectively. So there's a really great, wonderful partnership there. >> We've heard a lot in the last day and a half on theCUBE from a lot of retail customers and partners. There seems to be a lot of growth in that. So there's so much change in the retail market. I was just talking with Click and Snowflake about Urban Outfitters, as an example. And you think of how what these companies are doing together and obviously AWS and Snowflake, helping companies not just pivot during the pandemic, but really survive. I mean, in the beginning with, you know, retail that didn't have a digital presence, what were they going to do? And then the supply chain issues. So it really seems to be what Snowflake and its partner Ecosystem is doing, is helping companies now, obviously, thrive. But it was really kind of like a no-go sort of situation for a lot of industries. >> Yeah, and I think the neat part of, you know, both the combined, you know, Snowflake and AWS solution is in, a good example is DoorDash, you know. They had hyper growth, and they could not have handled, especially during COVID, as we all know. We all used DoorDash, right? We were just talking about it. Chipotle, like, you know, like (laughter) and I think they were able to really take advantage of our hyper elastic platforms, both on the Amazon side and the Snowflake side to scale their business and meet the high demand that they were seeing. And that's kind of some of the great examples of where we've enabled customer growth to really accelerate. >> Yeah. Yeah, right. And I'd add to that, you know, while we saw good growth for those types of companies, a lot of your traditional companies saw a ton of benefit as well. Like another good example, and it's been talked about here at the show, is Western Union, right? So they're a company that's been around for a long time. They do cross border payments and cross currency, you know, exchanges, and, you know, like a lot of companies that have been around for a while, they have data all over the place. And so they started to look at that, and that became an inhibitor to their growth. 'Cause they couldn't get a full view of what was actually going on. And so they did a lengthy evaluation, and they ended up going with Snowflake. And, it was great, 'cause it provided a lot of immediate benefits, so first of all, they were able to take all those disparate systems and pull that into Snowflake. So they finally had a single source of the truth, which was lacking before that. So that was one of the big benefits. The second benefit, and Chris has mentioned this a couple times, is the fact that they could use data sharing. And so now they could pull in third data. And now that they had a holistic view of their entire data set, they could pull in that third party data, and now they could get insights that they never could get before. And so that was another large benefit. And then the third part, and this is where the relationship between AWS and Snowflake is great, is they could then use Amazon SageMaker. So one of the decisions that Western Union made a long time ago is they use R for their data science platform, and SageMaker supports R. And so it really allowed them to dovetail the skill sets that they had around data science into SageMaker. They could now look across all of Snowflake. And so that was just a really good benefit. And so it drove the cost down for Western Union which was a big benefit, but the even bigger benefit is they were now able to start to package and promote different solutions to their customers. So they were effectively able to monetize all the data that they were now getting and the information they were getting out of Snowflake. And then of course, once it was in there, they could also use things like Tableau or ThoughtSpot, both of which available in AWS Marketplace. And it allowed them to get all kinds of visualization of data that they never got in the past. >> The monetization piece is, is interesting. It's so challenging for organizations, one, to get that single source view, to be able to have a customer 360, but to also then be able to monetize data. When you're in customer conversations, how do you help customers on that journey, start? Because the, their competitors are clearly right behind them, ready to take first place spot. How do you help customers go, all right this is what we're going to do to help you on this journey with AWS to monetize your data? >> I think, you know, it's everything from, you know, looking at removing the silos of data. So one of the challenges they've had is they have these Legacy systems, and a lot of times they don't want to just take the Legacy systems and throw them into the cloud. They want to say, we need a holistic view of our customer, 360 view of our customer data. And then they're saying, hey, how can we actually monetize that data? That's where we do everything from, you know, Snowflake has the data marketplace where we list it in the data marketplace. We help them monetize it there. And we use some of the data sets from Amazon to help them do that. We use the technologies like Chris said with SageMaker and other tool sets to help them realize the value of their data in a real, meaningful way. >> So this sounds like a very strategic and technical partnership. >> Yeah, well, >> On both sides. >> It's technical and it's GOTO market. So if you take a look at, you know, Snowflake where they've built over 20 integrations now to different AWS services. So if you're using S3 for object storage, you can use Snowflake on top of that. If you want to load up Snowflake with Glue which is our ETL tool, you can do that. If you want to use QuickSite to do your data visualization on top of Snowflake, you can do that. So they've built integration to all of our services. And then we've built integrations like SageMaker back into Snowflake, and so that supports all kinds of specific customer use cases. So if you think of people that are doing any kind of cloud data platform workload, stuff like data engineering, data warehousing, data lakes, it could be even data applications, cyber security, unistore type things, Snowflake does an excellent job of helping our customers get into those types of environments. And so that's why we support the relationship with a variety of, you know, credit programs. We have a lot of co-sell motions on top of these technical integrations because we want to make sure that we not only have the right technical platform, but we've got the right GOTO market motion. And that's super important. >> Yeah, and I would add to that is like, you know one of the things that customers do is they make these large commitments to Amazon. And one of the best things that Amazon did was allow those customers to draw down Snowflake via the AWS Marketplace. So it's been wonderful to his point around the GOTO market, that was a huge issue for us. And, and again, this is where Amazon was innovative on identifying the ways to help make the customer have a better experience >> Chris G: Yeah. >> Chris D: and put the customer first. And this has been, you know, wonderful partnership there. >> Yeah. It really has. It's been a great, it's been really good. >> Well, and the customers are here. Like we said, >> Yep. >> Yes. Yes they are. >> we're north of 10,000 folks total, and customers are just chomping at the bit. There's been so much growth in the last three years from the last time, I think I heard the 2019 Snowflake Summit had about 1500 people. And here we are at 10,000 plus now, and standing-room-only keynote, the very big queue to get in, people turned away, pushed back to an overflow area to be able to see that, and that was yesterday. I didn't even get a chance to see what it was like today, but I imagine it was probably the same. Talk about the, when you're in customer conversations, where do you bring, from a GTM perspective, Where do you bring Snowflake into the conversation? >> Yeah >> Obviously, there's Redshift there, what does that look like? I imagine it follows the customer's needs, challenges. >> Exactly. >> Compelling events. >> Yeah. We're always going to work backwards from the customer need, and so that is the starting point for kindling both organizations. And so we're going to, you know, look at what they need. And from an AWS perspective, you know, if they're going with Snowflake, that's a very good thing. Right? 'Cause one of the things that we want to support is a selection experience to our AWS customers and make sure that no matter what they're doing, they're getting a very good, supported experience. And so we're always going to work backwards from the customer. And then once they make that technology decision, then we're going to support them, as I mentioned, with a whole bunch of co-sell resources. We have technical resources in the field. We have credit programs and in, you know, and, of course, we're going to market in a variety of different verticals as well with Snowflake. If you take a look at all the industry clouds that Snowflake has spun up, financial services and healthcare, and media entertainment, you know, those are all very specific use cases that are very valuable to an AWS customer. And AWS is going more and more to market on a vertical approach, and so Snowflake really just fits right in with our overall strategy. >> Right. Sounds like very tight alignment there. That mission alignment that Frank talked about yesterday. I know he was talking about that with respect to customers, but it sounds like there's a mission alignment between AWS and Snowflake. >> Mission alignment, yeah. >> I live that every week. (laughter) >> Sorry if I brought up a pain point. >> Yeah. Little bit. No. >> Guys, what's, in terms of use cases, obviously we've been here for a couple days. I'm sure you've had tremendous feedback, >> Chris G: Yeah. >> from, from customers, from partners, from the ecosystem. What's next, what can we expect to hear next? Maybe give us a preview of re:Invent in the few months. >> Preview of re:Invent. Yeah. No, well, one of the things we really want to start doing is just, you know, making the use case of, of launching Snowflake on AWS a lot easier. So what can we do to streamline those types of experiences? 'Cause a lot of times we'll find that customers, once they buy a third party solution like Snowflake, they have to then go through a whole series of configuration steps, and what can we do to streamline that? And so we're going to continue to work on that front. One of the other places that we've been exploring with Snowflake is how we work with channel partners. And, you know, when we first launched Marketplace it was really more of an app store model that was ISVs on one side and channel partners on the other, and there wasn't really a good fit for channel partners. And so four years ago we retrofitted the platform and have opened it up to resellers like an SHI or SIs like Salam or Deloitte who are top, two top SIs for Snowflake. And now they can use Marketplace to resell those technologies and also sell their services on top of that. So Snowflake's got a big, you know, practice with Salam, as I mentioned. You know, Salam can now sell through Marketplace and they can actually sell that statement of work and put that on the AWS bill all by virtue of using Marketplace, that automation platform. >> Ease of use for customers, ease of use for partners as well. >> Yes. >> And that ease of use is it's no joke. It's, it's not just a marketing term. It's measurable and it's about time-to-value, time-to-market, getting customers ahead of their competition so that they can be successful. Guys, thanks for joining me on theCUBE today. Talking about AWS and >> Nice to be back. Nice to be back in person. >> Isn't it nice to be back. It's great to be actually sitting across from another human. >> Exactly. >> Thank you so much for your insights, what you shared about the partnership and where it's going. We appreciate it. >> Thank you. >> Cool. Thank you. >> Thank you. >> All right guys. For Chris and Chris, I'm Lisa Martin, here watching theCUBE live from Las Vegas. I'll be back with my next guest momentarily, so stick around. (Upbeat techno music)

(upbeat music) >> We're back with Charley Dublin. He's the Vice President of Product Management at Acquia. Great to see you, Charley welcome to theCUBE. >> Nice to meet you Dave. >> Acquia, tell us about the company. >> Sure, so Acquia is the largest and best provider of Drupal hosting capabilities. We rank number two in the digital experience platform space, just behind Adobe. Very strong business growing well and innovating every day. >> Drupal open source, super deep high quality content management system. And more experience, you call it an experience platform. >> An experience platform, open, flexible. We want our customers to have choice the ability to solve their problems how they want leveraging the power of the open source community. >> What were the big challenges? Just describe your, kind of the business drivers. We're going to talk about StormForge but the things that you were facing some of the challenges that's kind of led you to StormForge. >> Sure, so our objective first is to provide the best experience with Drupal. So that entails lots of capabilities around ease of use for Drupal itself. But that has to run on a world class platform. It has to be the most performance. It has to be the most secure. It needs to be flexible to enable customers to run Drupal however they want to run Drupal. And so that involves the ability to support thousands of different kinds of modules that come out of the community. We want our customers to have choice with Drupal and to be able to support those choices on our platform. >> So optionality is key. Sometimes that creates other challenges. Like you've got one of everything. How do you deal with that challenge? >> That's a great question. Every strength is a form of weakness. And so our objective is really first to provide that choice but to do it in a cost efficient way. So we try to provide reference architectures for customers, opinionation for our customers to standardize take out some of the complexity that they might have if everything were a snowflake. But our objective is really to support their needs and err on the side of that flexibility. >> So you guys had to go through a major replatforming effort around containers and Kubernetes can you talk about that and what role StormForge played? >> Sure, so tied to the last point, our objective is to provide customers the highest performance, most secure platform. The entire industry of course is moving to Kubernetes and leveraging containers. We are a large consumer of AWS Services and are undergoing a major replatforming away from Legacy AWS towards Kubernetes and containers. And so that major replatforming effort is intending to enable customers to run applications how they want to and the power of Kubernetes and containers is to support that. And so we looked at StormForge as a way for us to right size resource capacity to support our customer's applications. >> I love it, AWS is now Legacy. But Andy Jassy one time said that if they had to redo Amazon they'd it in Lambda using serverless and so, it's been around a long time now. Okay so what were the outcomes that you were seeking? Was it, better management, cost reduction and how'd that go? >> Our customers run a wide range of applications. We support customers leveraging Drupal in every industry. Globally we do business in 30 different countries. And so what you have is a very wide range of applications and consumer and consumption models. And so we felt that leveraging StormForge would put us in a position where we'd be able to right size resource to those different kinds of applications. Essentially let the platform align to how customers wanted to operate their applications. And so StormForge's capability in conjunction with Kubernetes and containers really puts us in a position where customers are able to get the performance that they want, and when they need it on demand. A lot of the auto scaling capabilities that you get from Kubernetes and containers supports that. And so it really enables customers to run their applications how they want to functionally, as well as from a performance perspective. >> So this move toward containers and microservices sort of modern application development coincides with a modern platform like StormForge. And so there are, I'm sure there are alternatives out there, why StormForge? Maybe you could explain a little bit more about why, from your perspective what it does and why you chose them. >> So we leverage AWS in many respects in terms of the underlying platform, but we are a very strong DIY for how that platform supports Drupal applications. We view our expertise as being the best of Drupal. And so we felt like for us to true really maximize Kubernetes and containers and the power of those underlying technologies. On the one hand allows us to automate more and do more for customers. On the other side of it, it puts a tremendous burden on the level of expertise in order to do that well for every customer every day at scale. And so that at scale part of that was the challenge. And so we leverage StormForge to enable us to rightsize applications for performance, provide us cost benefits, allocate what you need when you need it for our customers. And that at scale piece is a critical part. We could do elements of it internally. We tried to do elements of that internally, but as you start getting to scale from, a few apps to hundreds of apps to certainly across our fleet of tens of thousands of applications, you really need something that leverages machine learning. You really need a technology that's integrated well within AWS and StormForge provided that solution. >> Make sure I got this right. So it sounds like you sort of from a skill standpoint transitioned or applied your skills from turning knobs if you will, to automation and scale. >> Correct. >> And what was that like? Was the team leaning into that, loving it? Was it a, a challenging thing for you guys to get there? >> That's a good question. The benefit in the way that StormForge applies it. So they leverage machine learning to enable us to make better decisions. So we still have the control elements, but we have much greater insight into what that would mean ahead of time before customers would be affected. So we still have the knobs we need, but we're able to do it at scale. And then from the automation point, it allows us to focus our deep expertise on making Drupal and the core hosting platform capabilities awesome. Sort of the stuff and resource allocation resource consumption. That's an enabler we can outsource that to StormForge >> This is not batch it's, you're basically doing this in sort of near realtime Optimize Live, is the capability, maybe you can describe what it is. >> So Optimize Live is new, we're in testing with that. We've done extensive testing with StormForge on the core call it decision making logic that allows for the right sizing of consumption and resources for our customer application. So that has already been tested. So the core engine's been tested. Optimize Live allows us to do that in real time to make policy decisions across our fleet on what's the right trade off between performance cost, other parameters. Again, it informs our decision making and our management of our platform. That would be very, very difficult otherwise. Without StormForge we'd have to do massive data aggregation. We'd have to have machine learning and additional infrastructure to manage to derive this information, and, and, and. And that is not our core business. We don't want to be doing that. We want insights to manage our platform to enable customers and StormForge for provides that. >> So it's kind of human in the loop thing. Hey, here's what like our recommendation or here's some options that you might want to, here's a path that you want to go down, but it's not taking that action for you necessarily. You don't want that. You want to make sure that the experts are have a hand in it still, is that correct? >> Correct, you still want the experts to have a hand in it but you don't want them to have a hand in it on each individual app. You need that, that machine learning capability that insight that allows you to do that at scale. >> So if you had to step back and think about your relationship with StormForge what was the business impact of bringing them in? >> First, from a time to market perspective we're able to get to market with a higher performing more cost effective solution earlier. So there's that benefit. Second benefit to the earlier point is that we're able to make resource allocation decisions focused on where our core competency is, not into the guts of Kubernetes containers and the like. Third is that the machine learning talent that StormForge brings to the table is world class. I've run machine learning teams, data science teams and would put them in the top 1% of any team that I've worked with in terms of their expertise. The logic and decision making and insights is outstanding. So we can get to the best decision, the optimal decision much more quickly. And then when you accompany that with the newer product in Optimize Live with that automation component you mentioned, all the better. So we're able to make decisions quicker, get it implemented in our platform and realize the benefits. What customers get from that is much better performance of their applications. More real time, higher, able to scale more dynamically. What we get is resource efficiency and our network and platform efficiency. We're not over allocating a capacity that costs us more money than we should. We're under allocating capacity that could have a lower performance solution for our customers. >> So that puts money in your pocket and your customers are happier. So there are higher renewal rates, less churn, high air prices over time as you add more capabilities. >> That's correct. >> What's it like, new application approach, Kubernetes containers, fine. Okay I need a modern platform but it's a relatively new company StormForge. What's it like working with them? >> Their talent level is world class. I wasn't familiar with them when I joined Acquia came to know them and been very impressed. There's many other providers in the market that will speak to some similar capabilities and will make many claims. But from our assessment our view is that they're the right partner for us, they're the right size, they're flexible, excellent team. They've evolved their technology roadmap very quickly. They deliver on their promises and commits a very good team to work with. So I've been very impressed for such an early stage company to deliver and to support our business so rapidly. So I think that's a strength. And then I think again the quality that people that's been manifested in the product itself, it's a high quality product. I think it's unique to the market. >> So Napoleon Hill famous writer, thinker, he wrote "Think and Grow Rich." If you haven't read it, check it out. One of his concepts is this a lever, small lever can move a big rock. It can be very powerful. Do you see StormForge as having that kind of effect on your business that change on your business? >> I do. Like I said, I think the engagement with them has proven, and this isn't, debatable based on the results that we've had with them. We ran that team through the ringer to validate the technology. Again, we'd heard lots of promises from other companies. Ran that team through the ringer with extensive testing across many customers, large and small, many use cases, to really stress test their capabilities. And they came out well ahead of any metric we put forth even well ahead of claims that they had coming into the engagement. They exceeded that. And so that's why I'm here. Why I'm an advocate. Why I think they're an outstanding company with a tremendous amount of potential. >> Thinking about, what can you tell us about where you want to take the company and the partnership with StormForge. >> I think the main next step is for us to engage with StormForge to drive automation drive decisioning, as we expand and move more and more customers over to our new platform. We're going to uncover use cases, different challenges as we go. So I think the, it's a learning process for both both sides, but I think the it's been successful so far and has a lot potential. >> Sounds like you had a great business and a great new partnership. So thanks so much for coming on theCUBE, appreciate it. >> Thank you very much, appreciate your time. >> My pleasure. And thank you for watching theCUBE, you're global leader in enterprise tech coverage. (upbeat music)

(upbeat music) >> We're back with Charley Dublin. He's the Vice President of Product Management at Acquia. Great to see you, Charley welcome to theCUBE. >> Nice to meet you Dave. >> Acquia, tell us about the company. >> Sure, so Acquia is the largest and best provider of Drupal hosting capabilities. We rank number two in the digital experience platform space, just behind Adobe. Very strong business growing well and innovating every day. >> Drupal open source, super deep high quality content management system. And more experience, you call it an experience platform. >> An experience platform, open, flexible. We want our customers to have choice the ability to solve their problems how they want leveraging the power of the open source community. >> What were the big challenges? Just describe your, kind of the business drivers. We're going to talk about StormForge but the things that you were facing some of the challenges that's kind of led you to StormForge. >> Sure, so our objective first is to provide the best experience with Drupal. So that entails lots of capabilities around ease of use for Drupal itself. But that has to run on a world class platform. It has to be the most performance. It has to be the most secure. It needs to be flexible to enable customers to run Drupal however they want to run Drupal. And so that involves the ability to support thousands of different kinds of modules that come out of the community. We want our customers to have choice with Drupal and to be able to support those choices on our platform. >> So optionality is key. Sometimes that creates other challenges. Like you've got one of everything. How do you deal with that challenge? >> That's a great question. Every strength is a form of weakness. And so our objective is really first to provide that choice but to do it in a cost efficient way. So we try to provide reference architectures for customers, opinionation for our customers to standardize take out some of the complexity that they might have if everything were a snowflake. But our objective is really to support their needs and err on the side of that flexibility. >> So you guys had to go through a major replatforming effort around containers and Kubernetes can you talk about that and what role StormForge played? >> Sure, so tied to the last point, our objective is to provide customers the highest performance, most secure platform. The entire industry of course is moving to Kubernetes and leveraging containers. We are a large consumer of AWS Services and are undergoing a major replatforming away from Legacy AWS towards Kubernetes and containers. And so that major replatforming effort is intending to enable customers to run applications how they want to and the power of Kubernetes and containers is to support that. And so we looked at StormForge as a way for us to right size resource capacity to support our customer's applications. >> I love it, AWS is now Legacy. But Andy Jassy one time said that if they had to redo Amazon they'd it in Lambda using serverless and so, it's been around a long time now. Okay so what were the outcomes that you were seeking? Was it, better management, cost reduction and how'd that go? >> Our customers run a wide range of applications. We support customers leveraging Drupal in every industry. Globally we do business in 30 different countries. And so what you have is a very wide range of applications and consumer and consumption models. And so we felt that leveraging StormForge would put us in a position where we'd be able to right size resource to those different kinds of applications. Essentially let the platform align to how customers wanted to operate their applications. And so StormForge's capability in conjunction with Kubernetes and containers really puts us in a position where customers are able to get the performance that they want, and when they need it on demand. A lot of the auto scaling capabilities that you get from Kubernetes and containers supports that. And so it really enables customers to run their applications how they want to functionally, as well as from a performance perspective. >> So this move toward containers and microservices sort of modern application development coincides with a modern platform like StormForge. And so there are, I'm sure there are alternatives out there, why StormForge? Maybe you could explain a little bit more about why, from your perspective what it does and why you chose them. >> So we leverage AWS in many respects in terms of the underlying platform, but we are a very strong DIY for how that platform supports Drupal applications. We view our expertise as being the best of Drupal. And so we felt like for us to true really maximize Kubernetes and containers and the power of those underlying technologies. On the one hand allows us to automate more and do more for customers. On the other side of it, it puts a tremendous burden on the level of expertise in order to do that well for every customer every day at scale. And so that at scale part of that was the challenge. And so we leverage StormForge to enable us to rightsize applications for performance, provide us cost benefits, allocate what you need when you need it for our customers. And that at scale piece is a critical part. We could do elements of it internally. We tried to do elements of that internally, but as you start getting to scale from, a few apps to hundreds of apps to certainly across our fleet of tens of thousands of applications, you really need something that leverages machine learning. You really need a technology that's integrated well within AWS and StormForge provided that solution. >> Make sure I got this right. So it sounds like you sort of from a skill standpoint transitioned or applied your skills from turning knobs if you will, to automation and scale. >> Correct. >> And what was that like? Was the team leaning into that, loving it? Was it a, a challenging thing for you guys to get there? >> That's a good question. The benefit in the way that StormForge applies it. So they leverage machine learning to enable us to make better decisions. So we still have the control elements, but we have much greater insight into what that would mean ahead of time before customers would be affected. So we still have the knobs we need, but we're able to do it at scale. And then from the automation point, it allows us to focus our deep expertise on making Drupal and the core hosting platform capabilities awesome. Sort of the stuff and resource allocation resource consumption. That's an enabler we can outsource that to StormForge >> This is not batch it's, you're basically doing this in sort of near realtime Optimize Live, is the capability, maybe you can describe what it is. >> So Optimize Live is new, we're in testing with that. We've done extensive testing with StormForge on the core call it decision making logic that allows for the right sizing of consumption and resources for our customer application. So that has already been tested. So the core engine's been tested. Optimize Live allows us to do that in real time to make policy decisions across our fleet on what's the right trade off between performance cost, other parameters. Again, it informs our decision making and our management of our platform. That would be very, very difficult otherwise. Without StormForge we'd have to do massive data aggregation. We'd have to have machine learning and additional infrastructure to manage to derive this information, and, and, and. And that is not our core business. We don't want to be doing that. We want insights to manage our platform to enable customers and StormForge for provides that. >> So it's kind of human in the loop thing. Hey, here's what like our recommendation or here's some options that you might want to, here's a path that you want to go down, but it's not taking that action for you necessarily. You don't want that. You want to make sure that the experts are have a hand in it still, is that correct? >> Correct, you still want the experts to have a hand in it but you don't want them to have a hand in it on each individual app. You need that, that machine learning capability that insight that allows you to do that at scale. >> So if you had to step back and think about your relationship with StormForge what was the business impact of bringing them in? >> First, from a time to market perspective we're able to get to market with a higher performing more cost effective solution earlier. So there's that benefit. Second benefit to the earlier point is that we're able to make resource allocation decisions focused on where our core competency is, not into the guts of Kubernetes containers and the like. Third is that the machine learning talent that StormForge brings to the table is world class. I've run machine learning teams, data science teams and would put them in the top 1% of any team that I've worked with in terms of their expertise. The logic and decision making and insights is outstanding. So we can get to the best decision, the optimal decision much more quickly. And then when you accompany that with the newer product in Optimize Live with that automation component you mentioned, all the better. So we're able to make decisions quicker, get it implemented in our platform and realize the benefits. What customers get from that is much better performance of their applications. More real time, higher, able to scale more dynamically. What we get is resource efficiency and our network and platform efficiency. We're not over allocating a capacity that costs us more money than we should. We're under allocating capacity that could have a lower performance solution for our customers. >> So that puts money in your pocket and your customers are happier. So there are higher renewal rates, less churn, high air prices over time as you add more capabilities. >> That's correct. >> What's it like, new application approach, Kubernetes containers, fine. Okay I need a modern platform but it's a relatively new company StormForge. What's it like working with them? >> Their talent level is world class. I wasn't familiar with them when I joined Acquia came to know them and been very impressed. There's many other providers in the market that will speak to some similar capabilities and will make many claims. But from our assessment our view is that they're the right partner for us, they're the right size, they're flexible, excellent team. They've evolved their technology roadmap very quickly. They deliver on their promises and commits a very good team to work with. So I've been very impressed for such an early stage company to deliver and to support our business so rapidly. So I think that's a strength. And then I think again the quality that people that's been manifested in the product itself, it's a high quality product. I think it's unique to the market. >> So Napoleon Hill famous writer, thinker, he wrote "Think and Grow Rich." If you haven't read it, check it out. One of his concepts is this a lever, small lever can move a big rock. It can be very powerful. Do you see StormForge as having that kind of effect on your business that change on your business? >> I do. Like I said, I think the engagement with them has proven, and this isn't, debatable based on the results that we've had with them. We ran that team through the ringer to validate the technology. Again, we'd heard lots of promises from other companies. Ran that team through the ringer with extensive testing across many customers, large and small, many use cases, to really stress test their capabilities. And they came out well ahead of any metric we put forth even well ahead of claims that they had coming into the engagement. They exceeded that. And so that's why I'm here. Why I'm an advocate. Why I think they're an outstanding company with a tremendous amount of potential. >> Thinking about, what can you tell us about where you want to take the company and the partnership with StormForge. >> I think the main next step is for us to engage with StormForge to drive automation drive decisioning, as we expand and move more and more customers over to our new platform. We're going to uncover use cases, different challenges as we go. So I think the, it's a learning process for both both sides, but I think the it's been successful so far and has a lot potential. >> Sounds like you had a great business and a great new partnership. So thanks so much for coming on theCUBE, appreciate it. >> Thank you very much, appreciate your time. >> My pleasure. And thank you for watching theCUBE, you're global leader in enterprise tech coverage. (upbeat music)

(upbeat music) >> Hi, today I'm going to talk about network analytics and what that means for telecommunications as we go forward, thinking about 5G, what the impact that's likely to have on network analytics and the data requirement, not just to run the network and to understand the network a little bit better, but also to inform the rest of the operation of the telecommunications business. So as we think about where we are in terms of network analytics and what that is over the last 20 years, the telecommunications industry has evolved its management infrastructure to abstract away from some of the specific technologies in the network. So what do we mean by that? Well, in the, when initial telecommunications networks were designed there were management systems that were built in. Eventually fault management systems, assurance systems, provisioning systems, and so on, were abstracted away. So it didn't matter what network technology had, whether it was a Nokia technology or Erickson technology or Huawei technology or whoever it happened to be. You could just look at your fault management system and understand where faults were happened. As we got into the last sort of 10, 15 years or so telecommunication service providers become, became more sophisticated in terms of their approach to data analytics and specifically network analytics and started asking questions about why and what if in relation to their network performance and network behavior. And so network analytics as a sort of an independent functioning was born and over time more and more data began to get loaded into the network analytics function. So today just about every carrier in the world has a network analytics function that deals with vast quantities of data in big data environments that are now being migrated to the cloud. As all telecommunications carriers are migrating as many IT workloads as possible to the cloud. So what are the things that are happening as we migrate to the cloud that drive enhancements in use cases and enhancements in scale in telecommunications network analytics? Well, 5G is the big thing, right? So 5G, it's not just another G in that sense. I mean, in some cases, in some senses it is 5G means greater bandwidth and lower latency and all those good things. So, you know, we can watch YouTube videos with less interference and, and less sluggish bandwidth and so on and so forth. But 5G is really about the enterprise and enterprise services transformation. 5G is a more secure kind of a network, but 5G is also a more pervasive network. 5G has a fundamentally different network topology than previous generations. So there's going to be more masts. And that means that you can have more pervasive connectivity. So things like IOT and edge applications, autonomous car, current smart cities, these kinds of things are all much better served because you've got more masts, that of course means that you're going to have a lot more data as well and we'll get to that. The second piece is immersive digital services. So with more masts, with more connectivity, with lower latency, with higher bandwidth with the potential is immense for services innovation. And we don't know what those services are going to be. We know that technologies like augmented reality, virtual reality, things like this have great potential, but we have yet to see where those commercial applications are going to be, but the innovation and the innovation potential for 5G is phenomenal. It certainly means that we're going to have a lot more edge devices. And that again is going to lead to an increase in the amount of data that we have available. And then the idea of pervasive connectivity when it comes to smart cities, autonomous cars, integrated traffic management systems, all of this kind of stuff, those kind of smart environments thrive where you've got this kind of pervasive connectivity, this persistent connection to the network. Again, that's going to drive more innovation. And again, because you've got these new connected devices, you're going to get even more data. So this rise, this exponential rise in data is really what's driving the change in network analytics. And there are four major vectors that are driving this increase in data in terms of both volume and in terms of speed. So the first thing is more physical elements. So we said already that 5G networks are going to have a different topology. 5G networks will have more devices, more masts. And so with more physical elements in the network, you're going to get more physical data coming off those physical networks. And so that needs to be aggregated and collected and managed and stored and analyzed and understood so that we can have a better understanding as to, you know, why things happened the way they do, why the network behaves in which they do in ways that it does and why devices that are connected to the network and ultimately of course, consumers, whether they be enterprises or retail customers behave in the way they do in relation to their interaction with the network. Edge nodes and devices. We're going to have an explosion in terms of the number of devices. We've already seen IOT devices with your different kinds of trackers and sensors that are hanging off the edge of the network, whether it's to make buildings smarter or car smart or people smarter in terms of having the measurements and the connectivity and all that sort of stuff. So the numbers of devices on the edge and beyond the edge are going to be phenomenal. One of the things that we've been trying to wrestle with as an industry over the last few years is where does a telco network end and where does the enterprise, or even the consumer network begin? It used to be very clear that, you know, the telco network ended at the router but now it's not that clear anymore because in the enterprise space, particularly with virtualized networking, which we're going to talk about in a second, you start to see end to end network services being deployed. And so are they being those services in some instances that are being managed by the service provider themselves, and in some cases by the enterprise client. Again, the line between where the telco network ends and where the enterprise or the consumer network begins is not clear. So those edge, the proliferation of devices at the edge, in terms of, you know, what those devices are, what the data yield is and what the policies are that need to govern those devices in terms of security and privacy and things like that, that's all going to be really, really important. Virtualized services, we just touched on that briefly. One of the big, big trends that's happening right now is not just the shift of IT operations onto the cloud, but the shift of the network onto the cloud, the virtualization of network infrastructure. And that has two major impacts. First of all, it means that you've got the agility and all of the scale benefits that you get from migrating workloads to the cloud, the elasticity and the growth and all that sort of stuff, but arguably more importantly for the telco it means that with a virtualized network infrastructure, you can offer entire networks to enterprise clients. So, you know, selling to a government department, for example, who's looking to stand up a system for, you know, certification of, you know, export certification, something like that. You can not just sell them the connectivity, but you can sell them the networking and the infrastructure in order to serve that entire end to end application. You could send, you could offer them in theory, an entire end-to-end communications network. And with 5G network slicing, they can even have their own little piece of the 5G bandwidth that's been allocated to gets a carrier and have a complete end to end environment. So the kinds of services that can be offered by telcos given virtualize network infrastructure are many and varied and it's an outstanding opportunity. But what it also means is that the number of network elements virtualized in this case is also exploding. And that means the amount of data that we're getting on, informing us as to how those network elements are behaving, how they're performing is going to go up as well. And then finally, AI complexity. So on the demand inside while historically network analytics, big data has been driven by returns in terms of data monetization, whether that's through cost avoidance or service assurance, or even revenue generation through data monetization and things like that. AI is transforming telecommunications and every other industry. The potential for autonomous operations is extremely attractive. And so understanding how the end-to-end telecommunication service delivery infrastructure works is essential as a training ground for AI models that can help to automate a huge amount of telecommunications operating processes. So the AI demand for data is just going through the roof. And so all of these things combined to mean that big data is getting explosive. It is absolutely going through the roof. So that's a huge thing that's happening. So as telecommunications companies around the world are looking at their network analytics infrastructure, which was initially designed for service insurance primarily and how they migrate that to the cloud. These things are impacting on those decisions because you're not just looking at migrating a workload to operate in the cloud that used to work in the data center. Now you're looking at migrating a workload but also expanding the use cases in that workload. And bear in mind many of those workloads are going to need to remain on-prem. So they'll need to be within a private cloud or at best a hybrid cloud environment in order to satisfy regulatory jurisdiction requirements. So let's talk about an example. So LG Uplus is fantastic service provider in Korea, huge growth in that business over the last, over the last 10, 15 years or so. And obviously most people would be familiar with LG, the electronics brand, maybe less so with, with LG Uplus, but they've been doing phenomenal work and were the first business in the world to launch commercial 5G in 2019. And so a huge milestone that they achieved. And at the same time they deployed the Network Real-time Analytics Platform or NRAP from a combination of Cloudera and our partner Caremark . Now, there were a number of things that were driving the requirement for the analytics platform at the time. Clearly the 5G launch was the big thing that they had in mind, but there were other things that were at play as well. So within the 5G launch, they were looking for a visibility of services and service assurance and service quality. So, you know, what services have been launched? How are they being taken up? What are the issues that are arising? Where the faults happening? Where are the problems? Because clearly when you launch a new service like that you want to understand and be on top of the issues as they arise. So that was really, really important. A second piece was and, you know, this is not a new story to any telco in the world, right? But there are silos in operation. And so it taking advantage of, or eliminating redundancies through the process of digital transformation it was really important. And so particular, the two silos between wired and the wireless sides of the business needed to come together so that there would be an integrated network management system for LG Uplus as they rolled out 5G. So eliminating redundancy and driving cost savings through the integration of the silos was really, really important. And that's a process and the people think every bit, as much as it is a systems and a data thing so another big driver. And the fourth one, you know, we've talked a little bit about some of these things, right? 5G brings huge opportunity for enterprise services innovation. So industry 4.0, digital experience, these kinds of use cases were very important in the South Korean market and in the business of LG Uplus And so looking at AI and how can you apply AI to network management? Again, there's a number of use cases, really, really exciting use cases that have gone live now in LG Uplus since we did this initial deployment and they're making fantastic strides there. Big data analytics for users across LG Uplus, right? So it's not just for, it's not just for the immediate application of 5G or the support or the 5G network, but also for other data analysts and data scientists across the LG Uplus business. Network analytics while primarily it's, it's primary use case is around network management. LG Uplus or network analytics has applications across the entire business, right? So, you know, for customer churn or next best offer for understanding customer experience and customer behavior really important there for digital advertising, for product innovation, all sorts of different use cases and departments within the business needed access to this information. So collaboration sharing across the network, the real-time network analytics platform it was very important. And then finally, as I mentioned, LG group is much bigger than just LG Uplus. It's got the electronics and other pieces, and they had launched a major group wide digital transformation program in 2019. And so being a part of that was important as well. Some of the seems that they were looking to address. So first of all, the integration of wired and wireless data sources, and so getting your assurance data sources, your network data sources and so on integration was really, really important. Scale was massive for them. You know, they're talking about billions of transactions in under a minute being processed and hundreds of terabytes per day. So, you know, phenomenal scale that needed to be, you know, available out of the box as it were. Real time indicators and alarms. And there was lots of KPIs and thresholds set that, you know, to make, made to meet certain criteria, certain standards. Customer specific real time analysis of 5G, services particularly for the launch, root cause analysis and AI based prediction on service anomalies and service issues was a core use case. As I talked about already the provision of service, of data services across the organization. And then support for 5G, served the business service impact was extremely important. So it's not just understand, well, you know, that you have an outreach in a particular network element, but what is the impact on the business of LG Uplus, but also what is the impact on the business of the customer from an outage or an anomaly or a problem on the network. So being able to answer those kinds of questions really, really important too. And as I said between Cloudera and Caremark and LG Uplus they have already, themselves an intrinsic part of the solution, this is what we ended up building. So a big, complicated architecture side. I really don't want to go into too much detail here. You can see these things for yourself, but let me skip through it really quickly. So, first of all, the key data sources. You have all of your wireless network information, other data sources, this is really important 'cause sometimes you kind of skip over this. There are other systems that are in place like the enterprise data warehouse that needed to be integrated as well. Southbound and northbound interfaces. So we get our data, yo know, from the network and so on and network management applications through both file interfaces, Kafka, NiFi are important technologies. And also the RDBMS systems that, you know, like the enterprise data warehouse that we're able to feed that into the system. And then northbound, you know, we spoke already about making network analytics services available across the enterprise. So, you know, having both a file and API interface available for other systems and other consumers across the enterprise is very important. Lots of stuff going on then in the platform itself. Two petabytes and persistent storage, Cloudera HDFS, 300 nodes for the raw data storage and then Kudu for real time storage for, you know, real-time indicator analysis around generation and other real time processes. So there was the core of the solution spark processes for ETL, key quality indicators and alarming, and also a bunch of work done around data preparation, data generation for transferal to, for party systems through the northbound interfaces. Impala API queries for real-time systems there on the right hand side and then a whole bunch of clustering classification, prediction jobs through the ML processes, the machine learning processes. Again, another key use case, and we've done a bunch of work on that, and I encourage you to have a look at the Cloudera website for more detail on some of the work that we did here. Some pretty cool stuff. And then finally, just the upstream services, some of these, there's lots more than simply these ones, but service assurance is really, really important so SQM, CEM and ACD right to the service quality management customer experience autonomous control is really, really important consumers of the real-time analytics platform and your conventional service assurance functions like faulted performance management. These things are as much consumers of the information and the network analytics platform as they are providers of data to the network analytics platform. So some of the specific use cases that have been stood up and that are delivering value to this day and there's lots of more besides, but these are just three that we pulled out. So, first of all, sort of specific monitoring and customer quality analysis care and response. So again, growing from the initial 5G launch, and then broadening into broader services, understanding where there are issues so that when people complain, when people have an issue that we can answer the concerns of the client in a substantive way. AI functions around root cause analysis understanding why things went wrong when they went wrong and also making recommendations as to how to avoid those occurrences in the future. So, you know, what preventative measures can be taken. And then finally, the collaboration function across LG Uplus extremely important and continues to be important to this day where data is shared throughout the enterprise, through the API Lira, through file interfaces and other things and through interface integrations with upstream systems. So that's kind of the real quick run through of LG Uplus. And the numbers are just staggering. You know, we've seen upwards of a billion transactions in under 40 seconds being tested. And we've gone through beyond those thresholds now already, and we're start, and this isn't just a theoretical sort of a benchmarking test or something like that. We're seeing these kinds of volumes of data and not too far down the track. So with those things that I mentioned earlier or with the proliferation of network infrastructure in the 5G context with virtualized elements, with all of these other bits and pieces are driving massive volumes of data towards the network analytics platform. So phenomenal scale. This is just one example. We work with service providers all over the world is over 80% of the top 100 telecommunication service providers run on Cloudera. They use Cloudera in the network and we're seeing those customers all migrating Legacy Cloudera platforms now onto CDP onto the Cloudera Data Platform. They're increasing the jobs that they do. So it's not just warehousing, not just ingestion of ETL and moving into things like machine learning. And also looking at new data sources from places like NW DAF the network data analytics function in 5G or the management and orchestration layer in software defined network function virtualization. So, you know, new use cases coming in all the time, new data sources coming in all the time, growth in the application scope from, as we say, from edge to AI. And so it's really exciting to see how the footprint is growing and how the applications in telecommunications are really making a difference in facilitating network transformation. And that's covering, that's me covered for today. I hope you found that helpful. By all means please reach out. There's a couple of links here. You can follow me on Twitter. You can connect to the telecommunications page. Reach out to me directly at Cloudera I'd love to answer your questions and talk to you about how big data is transforming networks and how network transformation is accelerating telcos throughout the world.

>> Narrator: From around the globe it's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Hello and welcome back to the cube's ongoing coverage of IBM Think 2021, the virtual edition. My name is Dave Vellante and I'm excited to introduce our next segment. We're going to dig into the intersection of machines and humans and the changing nature of work, worker productivity, and the potential of humans. With me is Glenn Finch, who is the global managing partner for data and AI at IBM Glenn, great to see you again. Thanks for coming on. >> Dave. Good to be with you, always a lot of fun to chat. >> I'm interested in this concept that you've been working on about amplifying worker potential. You've got humans, you've got digital workers coming together. Maybe you could talk a little bit about what you're seeing at that intersection. >> You know, it's interesting for most of my career I've always thought about amplifying human worker potential. And, you know, I would say over the last five years, you know we start to think about this concept of digital workers and amplifying their potential so that human potential can extend even further. What's cool is when we get them both to work together: amplifying digital worker potential, amplifying human worker potential, to radically change how service is experienced by an end consumer. I mean, that's really the winner is when you start seeing the end consumer, the end user fundamentally feeling the difference in the experience. >> I mean, a lot of the, you see a lot of the trade press and the journalists, they like to focus on the sort of the negative of automation. But when you talk to people who have implemented things take, for example, RPA, they're so happy that they're not having to do these menial tasks anymore. And then it's sort of the interesting discussion is, okay, well, what are you doing with your free time? What are you doing with your weekends? So how should we be thinking about that? What you, what you called amplifying human worker potential, what has to occur for that outcome? >> And you know, that all my life I've spent time making money for people, right? And this last year I was involved in a project where it, it fundamentally changed is tied to answer that exact question. You know, the service men and women in America who are willing to risk their lives, you know for our country, they file claims for medical benefits. And on average, it would take 15 days to get a response. We actually, for about 70 or 80% of them we've taken that down to like 15 minutes. And to do that, you can't just drop in a RPA. You can't just drop in AI. You, it's not one thing, right? It's this, it's this seamless interaction between digital workers and human workers, right? So that a lot of the more routine mundane tasks can be done by AI and, and robotics, but all of the really hard complex cases that only a human being can adjudicate, that's what the folks that were doing the more mundane work can go focus on. So, I mean, that's what makes me come to work every day is if I can change the life of a service man or woman that was willing to risk their lives for our country. So that that's, that's the concept. Now, the critical piece of what I said, it's not about implementing AI and robotics anymore, because a lot of that starts to get very rote, but picking up on, okay, we've liberated this block of human capability. How do we reposition it? How do we re-skill it? How do we get them to focus on new things? That's just as important the human change aspect, incredibly important. >> Yeah. I mean, that's interesting, because you're right. I mean, the downside, you mentioned RPA a lot of it is paving the cow path and you know the human in the loop piece has been has been missing and that's obviously changing. But what about the flip side of that equation? Where, you know, you asked the question, okay what can humans do that machines can't do? That equation you know, continues to evolve, but maybe you could talk about where you've amplified the digital worker potential. >> Yeah. So, you know, one of our clients has Anthem and you know, they've been on a variety of programs with us to talk about this, but, you know we just recorded, you know, another session with them for Think where the Chief Technology Officer came and talked about how they wanted to radically change their member experience. And when you think about the last year, I mean, I don't know, Dave, I know you travel a lot cause I see you in all the places that I'm in. But I don't know if you remember, like 15 months ago if you had to wait on the phone for two minutes you thought it was an eternity, right? You're like, what's the matter with me? I'm a frequent flyer. I deserve a better service than this. Then as COVID started roll around, those wait times were two hours, and then 30 days into COVID, if you got a call back within two days or two weeks, it was a blessing, right? So all of our expectations changed in an instant, right? So I have to say over the last 12 to 15 months that's where we've been spending a lot of our time in all of those human contact, human touch places to radically transition the ability to be responsive and touch people with the same experience that we had 15 months ago to get an answer back in two minutes. You can't get enough people right now to do that. And so we're forced to make sure that the digital experience is what that needs to be. So the digital worker has to be up and on, and extending the brand experience the same way that the human worker was back when everybody could be at a call center. That make sense, Dave? >> Yeah. I mean, what I think I like about this conversation Glenn is it's not an either/or, it's not a zero-sum game, which it kind of, they sort of used to be, I mean we've talked about this before humans and machines have always replaced humans at certain tasks, but it never really had cognitive tasks. And that's why I think there's a lot of fear out there, but what you're talking about is, is a potential to amplify both human and digital capabilities. And I think that people might look at that and say, well, wait a minute. Isn't it a zero-sum game, but it but it's not. Explain why. >> Yeah. So we're never finding the zero-sum game, because there is always something for people to do, right? And so, you know I talked about the one amplification of digital worker at Anthem. Let me switch to an amplification of a human worker. So state of Rhode Island, you know, we had the great honor to work with their governor and their department of health and human services around again, around the whole COVID thing. We started out just answering basic questions and helping with contact racing. And then, from there, we moved into, you know helping them with their data in AI, being able to answer questions. Why are there hotspots? Why, you know, should I shut this portion of the city down? Should I shut bars down? Should I do this? And the governor and the health and human services director were constantly saying in press briefings in the morning. Well, you know, we learned from our partners IBM that we want to consider this, right? And we did pinpoint vaccinations and other things like that. To me, that's that whole continuum. So, you know, we liberated some people from one spot. They went to work in another spot, all human beings guided by AI. So, you know, I think this is all about, you know for the first time in our lives, being able to realize sort of the, the vaulted member experience or client experience that everybody's already talked about using a blend of digital workers and human workers. It's just, it's all about the experience I think. >> I mean, you're, you're laying out some really good outcomes and you mentioned some of the, you know, the folks in the military, the healthcare examples and I'm struck because if you think about the, look at the numbers, I mean the productivity gains over the last 20 years particularly in the US and Europe, it's not the case for China because their productivity is exploding, but but it's gone down. And so when you think about the big problems that we face in society: climate change, income inequality, I mean these are big chewy problems that, you know aren't going to, humans, you just can't throw humans at the problem that's, that's been been proven. And I'm curious as to if, you know how you see it in terms of some of those other outcomes of, and the potential that is there. And, can you give us a glimpse as to what tech is involved underneath all of this? >> Sure. So, you know the first one outcomes you know that whole picture changes with the business cycle, right? I'd love to tell you that it's always these three outcomes, but, you know during downturns and business cycles cost-based outcomes are, you know, are paramount, because people are thinking about survival, right? In upticks, people are worried about, you know converting new business, growth, they're worried about net promoter score, they're worried about experience score. And then over the last 12 to 18 months, you know we've seen this whole concept of carbon footprint and sustainability all tied into the outcomes. So, hey, did you realize that shifting these 22 legacy applications from here to the cloud would reduce your carbon footprint by 3%? No. Right? And so, the big hitters are always, you know, the, the cost metric, the sort of time to value or the whole cycle time and the process and net promoter score. Those are generally in all of the, you know all the plays, obviously the bookends, you know around what's happening with, you know, the the economy, what's happening with carbon, what's happening with sustainability are always in there. Now on the technology side, boy, that's the cool part about working for IBM, right? Is that there's a new thing that shows up on my door every two weeks from either the math and science labs, or from a new ecosystem partner. And that's one of the things that I will say about you know, over the last 12 to 15 months, you've seen this massive shift from IBM to go away from pure blue, to embrace the whole ecosystem. So, you know, Dave the stuff I work with every day is you know, AI, computer vision, blockchain, automation, quantum, connected operations, not just software robots but now human robots, digital twin, all these things where we are digitally rendering what used to be a very paper-based legacy, right? So, boy, I couldn't be more excited to be a part of that. And then now with the opening up to all the hyperscalers, the Microsoft, the Google, the Amazon, the, you know, Salesforce, Adobe, all those folks, it's like a candy store. And quite honestly, my single greatest challenge is to kind of bring all of that together and point it at a series of three or four buyers at a chief marketing officer, experience officer for the whole customer piece. At a chief human resource officer around the town piece and at a CFO or a chief procurement officer for finance and supply chain. I'm sorry to answer, so, you know, long-winded, but it's, it's awesome out there. >> That was a great answer. And I think, you know, I joked the other day, Glenn that Milton Friedman must be turning over in his grave because he said, you know the only job of a company is to make profits for its shareholders and increase shareholder value. But, ironically, you know things like ESG, sustainability, climate change, they actually make business sense. So it's really not antithetical to, you know Friedman economics necessarily, but it's a good business. And I think, I think the other thing that I'm excited about is that there is some like deep tech we're seeing an explosion of of something as fundamental as processing power like we've never seen before, but he talks about, you know Moore's law being dead well, okay. With the doubling of of processor performance every 24 months, we're now at a quadrupling when you include GPU's and NPUs and accelerators and all that. I mean, that is going to power the the next wave of machine intelligence. And that really is exciting. >> Yeah. I, you know, it's I feel blessed every day to come to work that you know, I can, amass all these technologies and change how human beings experience service. I mean that's, man, that whole service experience that's what I've lived for, for, you know two and a half decades in my career, is to not to just to make and deploy stuff that's cool technically, but to change people's lives. I mean, that's it for me, that's, you know, that's that's the way that I want to ride, so I couldn't be more excited to do that stuff. >> Well Glenn thanks so much for coming on your passion shows right through the camera. And hopefully we're, face-to-face, you know, sometime soon maybe, maybe later on this year, but for sure. Knock on wood, 2022. All right. Hey, great to see you, thank you so much >> Dave. Same to you, thanks. Have a great rest of the day. >> All right, thank you. And thanks for following along with our continuing broadcast of IBM Think 2021, you're watching the cube the leader, digital tech coverage, be right back.

>>from >>Around the globe. It's the cube with digital coverage of IBM think 2021 brought to you by IBM >>Hello and welcome back to the cubes ongoing coverage of IBM Think 2021. The virtual edition, my name is David and I'm excited to introduce our next segment. We're going to dig into the intersection of machines and humans and the changing nature of work, worker productivity and the potential of humans with me is Glenn Finch, who's the global managing partner for data and ai at IBM Glenn great to see you again. Thanks for coming on. >>Dave good to be with you. Always a lot of fun to chat. >>So I'm interested in this concept that you've been working on about amplifying worker potential. You've got humans, you've got digital workers coming together and maybe you could talk a little bit about what you're seeing at that intersection. >>You know, it's um it's interesting for most of my career, I've always thought about um amplifying human worker potential. And you know I would say over the last five years we start to think about this concept of digital workers and amplifying their potential so that human potential can extend even further. What's cool is when we get them both to work together, amplifying digital worker potential. Amplifying human worker potential to radically change how services experienced by an end consumer. I mean that's really the winner is when you start seeing the end consumer, the end user fundamentally feeling the difference in the experience. >>I mean a lot of the you see a lot of the trade press and the journalists they like to focus on the sort of the negative of automation. But when you talk to people who have implemented things they take it, for example R. P. A. They're so happy that they're not have to do these menial tasks anymore. And then it sort of the interesting discussion is, okay well what are you what are you doing with your free time? What are you doing with your weekend? So how should we be thinking about that? What you what you called? Amplifying human worker potential? What has to occur for that outcome? >>You know? Um The all my life I've spent time making money for people, right? And this uh last year I was involved in a project where it fundamentally changed. It's tied to answer that exact question. You know the servicemen and women in America who are willing to risk their lives. Um you know for our country um they file claims for medical benefits. And on average it would take 15 days to get a response Actually for about 70 or 80 of them. We've taken that down to like 15 minutes and to do that. You can't just drop in a R. P. A. You can't just drop in a. I. It's not one thing right? It's this it's this seamless interaction between digital workers and human workers right? So that a lot of the more routine mundane tasks can be done by ai and robotics. But all of the really hard complex cases that only a human being can adjudicate. That's what the folks that were doing, the more monday work can can go focus on. So I mean God that's what makes me come to work every day is if I can change the life of a serviceman or woman that was willing to risk their lives for our country. So that's that's the concept now. The critical piece of what I said, it's not about implementing Ai and robotics anymore because a lot of that started to get very wrote but picking up on okay we've liberated this block of human capability. How do we reposition it? How do we re skillet? How do we get them to focus on new things? That's just as important. The human change aspect incredibly important. >>Yeah, I mean that's interesting because you're right. I mean the downside, you mentioned our P. A. A lot of it is paving the cow path and you know the human in the loop piece has been it's been missing and that's obviously changing. But what about the what about the flip side of that equation? Where you know you ask the question okay what can humans do that machines can't do that equation continues to evolve. But maybe you could talk about where you have amplified the digital worker potential. >>Yeah. So you know um one of our clients is anthem and you know we've you know they've been on a variety of programs with us to talk about this. But you know, we just recorded, um, you know, another session with them for think where, um, the chief technology officer came and talked about how they wanted to radically change their member experience. And when you think about the last year, I mean, I don't know. Dave, I know you travel a lot because I see you in all the places that I'm in, right? But I don't remember like 15 months ago, if you had to wait on the phone for two minutes, you thought it was an eternity, right? You're like, what's the matter with me? I'm a frequent flyer. I deserve a better service on this. Then as Covid started to roll around those wait times or two hours and then 30 days into Covid. If you got a call back within two days or two weeks, it was a blessing. Right? So all of our expectations changed in an instant. Right? So I have to say, over the last 12-15 months, that's where we've been spending a lot of our time in all of those human contact human touch places to radically transition the ability to be responsive, touch people with With the same experience that we had 15 months ago to get an answer back in two minutes. You can't get enough people right now to do that. And so we're forced to make sure that the digital experience is what that needs to be. So the digital worker has to be up and on and extending the brand. Experience the same way that the human worker was back when everybody could be at a call center. That makes sense. Yeah. I >>mean, I think I like about this conversation, Glenn is it's not an either or. It's not a zero sum game, which is kind of, it's sort of used to be. I mean, we've talked about this before. Humans have machines have always replaced humans at certain tasks, but never really a cognitive task. And that's why I think there's a lot of fear out there. But what you're talking about is is the potential to amplify both human and digital capabilities. And I think people might look at that and say, well wait a minute, is it isn't a zero sum game, but it's not explain why. >>Yes, So we're never finding the zero sum game because there is um there is always something for people to do, right? And so, you know, I talked about the one an amplification of digital worker at anthem, let me let me switch to an amplification of a human worker, right? So state of Rhode Island, Um you know, we had the great honor to work with their governor and their Department of Health and Human Services, around again, around the whole covid thing. We started out just answering basic questions and helping with contact tracing. And then from there we moved into helping them with their data and ai being able to answer questions. Why are there are hotspots? Why should I shut this person of the city down? Should I shut fires down? Should I do this? And the Governor and Health and human Services Director were constantly saying on press briefings in the morning. Well, you know, we learned from our partners, IBM, that we want to consider this, right? And we we did pinpoint vaccinations and and other things like that. To me, that's that whole continuum. So, you know, we liberated some people from one spot. They went to work in another spot. All human beings guided by ai so, you know, I think this is all about, you know, for the first time in our lives being able to realize sort of the vaulted member experience or client experience that everybody has already talked about using a blend of digital workers and human workers. It's just it's all about the experience. I think >>you're laying out some really good outcomes. You mentioned some of the folks in the military, the healthcare examples. Um and I'm struck because if you think about look at the numbers, I mean the productivity gains over the last 20 years, particularly in the US. and Europe, doesn't it's not the case for China the productivity exploding, but but it's gone down. And so when you think about the big problems that we face in society, um climate change, income inequality, I mean, these are big, chewy problems that, you know, what kind of humans, you just can't throw humans at the problem that's, that's been proven. Um, and I'm curious as to if you know how you see it in terms of some of those other outcomes of the potential that is there and, and, and can you give us a glimpse as to what tech is involved underneath all this? Sure. >>So, you know, um, the first of all on outcomes, you know, that whole picture changes with the business cycle, right? I'd love to tell you that it's always these three outcomes, but you know, during downturns in business cycles, costs based outcomes are, you know, are paramount because people are thinking about survival right? In upticks, people are worried about, you know, converting new business growth, they're worried about net promoter score, they're worried about experience score. And then Over the last 12 to 18 months, you know, we've seen this whole concept of carbon footprint and sustainability All tied into the outcomes. So hey, did you realize that shifting these 22 legacy applications from here to the cloud would reduce your carbon footprint by 3%? No. Right. And so, so you know, the big hitters are always, you know, the cost metric, the sort of time to value or the whole cycle time of the process and net promoter score. Those are generally in all of the, you know, all the plays, obviously the book ends, you know, around, um, what's happening with, you know, the, the economy, what's happening with carbon, what's happening with sustainability are always in there. Now, the technology side boy, that's the cool part about working for IBM, right, is that there is a new thing that shows up on my door every two weeks from either the math and science labs or from a new ecosystem partner. Right. And that's one of the things that I will say about over the last 12 to 15 months, you've seen this massive shift from IBM to to go away from pure blue to embrace the whole ecosystem. So you know, Dave the stuff I work with every day is, you know, ai computer vision, Blockchain automation, quantum uh connected operations. Uh not just software robots, but now human robots, Digital Twin, all these things where we are digitally rendering um what used to be a very paper based legacy. Right. So boy, I couldn't be more excited to be a part of that. And then now with the opening up to all the hyper scholars, the Microsoft, the google the amazon, the, you know, uh salesforce adobe, all those folks. It's like a candy store. And quite honestly, my single greatest challenge is to kind of bring all of that together and point it at a series of three or 4 buyers at a chief marketing officer experience officer for the whole customer piece, at a chief human resource officer around the town peace and at a CFO or a chief procurement officer for finance and supply chain. I'm sorry to answer. So, you know, long winded, but it's it's awesome out there. >>It was a great answer. And I think, you know, I joke the other day, glenn that Milton Friedman must be turned over his grave because he said, you know, the only job of a company has to make profits for shareholders and increase shareholder value. But but you're but but ironically, you know, things like E. S. G. Sustainability, his climate change, he said they actually make business sense. So it's really not antithetical to Friedman economics necessarily but it's good business. And I think I think the other thing that I'm excited about is that there is some like deep tech we're seeing an explosion of of something as fundamental as processing power like we've never seen before but he talks about Moore's law being dead. Well okay with the doubling of of of of processor performance every 24 months. We're now at a quadrupling when you include GPU S. And N. P. U. S. And accelerators and all. I mean that is gonna power the next wave of machine intelligence and that really is exciting. >>Yeah I am. You know it's I feel blessed every day to come to work that you know I can you know a mass all these technologies and change how human beings experience service. I mean that's man, that whole service experience. that's what I've lived for for, you know, 2.5 decades in my career is to not just just to make and deploy stuff. That's cool, technically, but to change people's lives. I mean, that's it for me. That's you know, that's that's the way that I want to ride. So I couldn't be more excited to do that stuff. Well, glad >>Thanks so much for coming on. Is your your passion shows right through the camera and hopefully we'll face to face, you know, sometime soon, maybe, maybe later on this year. But for sure Lockwood 2022. All right. Hey, great to see you. Thank you so much. >>Dave same to you. Thanks have a great rest of the day. >>All right. Thank you. And thanks for following along with our continuing broadcast of IBM think 2021 you're watching the cube the leader in digital tech coverage right back. Mhm. Yeah.

[Music] 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 top security pros indicate that the solar winds hack on top of the pandemic have further heightened a change in how they think about security not only musciso secure an increasingly distributed workforce and network infrastructure but they now must be wary of software code coming from reputable vendors including the very patches designed to protect them against cyber attacks hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll summarize cso sentiments from a recent etr venn session and provide our quarterly update of the cyber security sector now in an upcoming episode we'll be inviting eric bradley of etr to provide deeper analysis and insights on these trends but we wanted to give you a preliminary preview of what's happening in the sector as we start off 2021. now the solar winds attack was like nothing we've ever seen before it's been covered quite widely in the press but in case you don't know the details solarwinds is a company that provides software to monitor many aspects of largely on-prem infrastructure including things like network performance log files configuration data storage servers and the like now as with all software companies solarwinds sends out regular updates and patches hackers were able to infiltrate the update and trojanize the software meaning when customers installed the updates the malware just went along for the ride now the reason this is so insidious is that often hackers they're going to target installations that haven't installed patches or updates and identified vulnerabilities in the infrastructure that haven't been addressed doors that are open that haven't been closed if you will now here the very code designed to protect against the breach actually facilitated that breach now according to experts this was quite a sophisticated attack that most believe was perpetrated by the russian hacker group cozy bear an advanced persistent threat or apt as classified by the u.s government now it's suspected that somehow they fished their way into a github repo and stole username and password access to allow them to penetrate the supply chain of software that's delivered over the internet but public information on this attack it's still spotty people are still learning now what is known is that the attackers have been lurking since march of last year and they exfiltrated lots of information from the u.s government and many other high-profile companies now here's what the csos and the etr van had to say about it let me just read some of the quotes the impact of this breach is profound it really turned a lot of heads and conventions about cyber security i don't think this threat has been exaggerated in the media we're now in a situation where we have to monitor the monitors this attack didn't have any signatures of a previous attack so you got down to the code level 80 to 90 of that code is being downloaded from the internet it's bringing devops security processes and making us rethink how to reinvent security and i'll add my business friend val berkovici said to me on twitter last year that he thinks the government hack is going to have permanent implications on how organizations approach cyber security it seems these cisos agree now the one question is what can be done about this and when you talk to security pros they'll definitely tell you they're rethinking security practices but look there's only so much you can do here's a tag cloud summarizing some of what we hear in the cube community and in the venn from etr practitioners you hear a lot about xero trust many csos are really leaning into identity access management and pam and mandates around two-factor authentication we've talked a lot about firms like octa sale point cyber arc software and microsoft is coming up more and more in this conversation especially as octa is seen as setting a price umbrella there's definitely some frustration amongst csos about octa's pricing strategies and auth 0 which does authentication as a service that's hitting our radar as well now of course endpoint security is something we've talked a lot about as the work from home trend hit during the pandemic it's become much much more important and you can see in the growth of crowdstrike and as you see in a moment we're getting some traction with vmware and carbon black in the survey data and of course titanium is another company that we've talked about csos look they're not just going to rip out what they have so companies like cisco especially with umbrella and duo they come up in the conversation as does palo alto networks we've said many times palo alto is seen as a thought leader csos like them they also like fortinet especially those that may be more cost cost conscious we see that a lot in mid-market and so on with analytics micro-segmentation cloud security with z-scaler and even rpa to automate certain tasks uipath has come up in the conversation more and more in a security context so you look at this tag cloud and there's no one answer as is often the case case with cyber security lots of tools lots of disciplines and a very capable adversary who has learned to as they say live off the land using your own infrastructure and tooling against you now the common narrative is that security is a top priority with cios and csos and budgets are going to be up so let's take a look at that well kind of here's a chart that shows the net scores or spending momentum for various sectors of the etr tech taxonomy and we've highlighted the information security segment yes it's up relative to the october survey but it really doesn't stand out i mean everything's up as we've reported coming off a down year in tech spending minus four percent last year and we're forecasting a plus six to seven percent increase this year really depending on on the pace of their recovery but the point is cyber is one of many budget organizations and organizations they're simply not going to open up a blank check to the cso now part of the reason is they're heavily invested in cyber this graphic shows several sectors in context and we've highlighted security in the red box the vertical axis that shows spending velocity and the horizontal axis is market share or presence in the data set and you can see the security it's got a big presence it's pervasive of course but it lags some of the top sectors in terms of spending velocity because look organizations they've got lots of priorities and as you'll see in a moment this space like most mature markets has some companies with off the charts spending patterns and others that lag so let's dig into that a little bit here you see that same xy graphic and we've plotted a number of security players so there's a couple of points here that we want to make first microsoft as usual is off the charts to the right and amazingly has a net score of 48 percent so highly elevated octa continues to lead this pack in net score as it has the last several surveys it's got a net score of 61.5 percent up from last quarter survey octa crowdstrike cyberark fortinet proof point and splunk are all up nicely from last quarter's survey we also really want to highlight carbon black the company's net score last quarter was 23.9 percent with 134 mentions in this quarter its net score shot up to nearly 38 so a very meaningful and noticeable move for vmware's 2.1 billion dollar acquisition that it made in the summer of 2019. so a number of companies that have momentum which stems from a rebound in tech spending but also a shift in security spend that we've highlighted and you can see a couple of legacy security firms that are also there in the chart losing momentum we've highlighted fireeye and rsa okay so now let's dig deeper into the data and the vendor performance here's a view of the data that we first showed you in 2019 it shows the net score and the shared n which identifies the number of mentions within the sector and it's an indicator of presence in the marketplace the leftmost chart is sorted by netscore and the right-hand chart is sorted by shared n so to make this chart you had to have at least an n of 50 in the survey again you can see octa sale and sale point lead in net score and microsoft has the biggest presence in the right hand side along with cisco and palo alto and something we started two years ago was if a vendor shows up in the top 10 for both net score and shared n we anointed them with four stars so these are the four star companies microsoft palo alto octa and crowdstrike which crouch by the way it fell off but it's back on and i think that was probably a survey anomaly because based on the company's financials there has been no loss of momentum for crowdstrike and we give two stars to those companies that make the top 20 in both categories so cisco because of umbrella and duo splunk proofpoint fortinet z z-scaler cyborg and carbon black vmware carbon black is new to the two-star list due to its rapid rise in net score that we just talked about now just a quick aside on carbon black at vmworld 2019 pat gelsinger told john furrier and me that he felt like he got a great deal picking up carbon black for 2.1 billion dollars now his logic was in part based on the valuation of crowdstrike at the time which is of course carbon black competitor crowdstrike as you can see on this chart had a valuation that was at nine times higher than that of carbon black and you can see from the trailing 12-month revenue that crowdstrike was a significantly larger company by more than 100 million dollars in revenue so the real story though was the company's growth crowdstrike at the time was growing much much faster than carbon black at more than a hundred percent compared to carbon blacks 22 roughly now in vmware's recent earnings call they said that carbon black had good bookings performance so who knows exactly what that means but if it were more than 22 my guess is that vmware vmware would have been more effusive in its commentary so let's assume that since the acquisition carbon black growth has been flattish you know maybe down maybe up but probably flat so vmware they're figuring out how to integrate the company and we think that as it does that it's going to use its channel of distribution and global presence to really drive carbon black sales now nonetheless we would still peg carbon black's valuation of having increased pretty substantially since the time of the acquisition perhaps in the three to five billion range we don't know for sure so but a nice pickup in our view for vmware and it'll likely grow from here based on the etr data then that's very encouraging for carbon black now let's look at how the valuations in this sector have changed since before covid here's an updated view of our valuation matrix since just before the pandemic hit in the u.s as you can see the s p is up 16 from that time frame the nas composite up 43 percent wow now look at the others only splunk really hasn't seen a huge uptick in valuation but the others have either risen noticeably like proof point cyber arc sail point they bounced up like palo alto or fortinet or exploded like crowd chat octa and z scalar you combine all these and you're talking about 114 billion dollar increase in market cap for these so one would think carbon black as a vmware asset has done pretty well along with these names and we would expect that the tech spending rebound this year combined with the heightened concerns over the solar winds hack and the tectonic shifts from the accelerated work from home and digital business transformations will continue to bode well for many of these names for quite some time all right let's wrap it up with some of the things we're watching in this space as we exit the pandemic and experience a new digital reality cyber threats have never been greater look each january if you look back on the prior year you'd be able to say the same thing for the last couple of decades and the reality is the budgets and spending on cyber they're asymmetric to the economic risks we just don't spend enough and probably can't spend enough to solve this problem csos they have to balance their legacy legacy install base security infrastructure with the shift to zero trust accelerated endpoint new access management challenges the ever expanding cloud and dot dot dot lack of talent remains the single biggest challenge for organizations which are stretch thin making investments in automation a trend that is not going to abate anytime soon in cyber all the cliches apply there is no silver bullet there is no rest for the weary the adversary they are well funded and extremely capable and they only have to succeed once to create a business disaster for an organization that has to succeed every day 24 hours a day so expect more of the same with no end in sight in terms of complexity fragmentation and whack-a-mole approaches to fighting cyber crime i hate to say this but it just means the fundamentals for the sector just keep getting better and better sorry okay that's it for this week remember all these episodes are available as podcasts wherever you listen so please subscribe i publish weekly on wikibon.com and siliconangle.com and don't forget to check out etr.plus for all the survey data and the analytics i appreciate the comments on my linkedin post you can dm me at [Music] you

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. >>We continue >>with Cuban Cloud. We're here with Milan Thompson Bukovec, who's the vice president? Block and object storage at A W s, which comprise comprises elastic block storage, AWS s three and Amazon Glacier. Milan. Great to see you again. Thanks so much for coming on the program. >>Nice to be here. Thanks for having me, David. >>You're very welcome it So here we are. We're unpacking the future of cloud. And we'd love to get your perspectives on how customers should think about the future of infrastructure, things like applying machine intelligence to their data. But just to set the stage when we look back at the history of storage in the Cloud is obviously started with us three. And then a couple years later was introduced CBS for block storage. And those are the most well known services in the portfolio. But there's there's Mawr, this cold storage and new capabilities that you announced recently. It reinvent around, you know, super duper block storage and in tearing is another example. But it looks like AWS is really starting to accelerate and pick up the pace of customer >>options in >>storage. So my first question is, how should we think about this expanding portfolio? >>Well, I think you have to go all the way back to what customers air trying to do with their data. Dave, The path to innovation is paved by data. If you don't have data, you don't have machine learning. You don't have the next generation of analytics applications. That helps you chart a path forward into a world that seems to be changing every week. And so in orderto have that insight in orderto have that predictive forecasting that every company needs, regardless of what industry that you're in today. It all starts from data, and I think the key shift that I've seen is how customers are thinking about that data about being instantly usable, whereas in the past it might have been a backup. Now it's part of a data lake, and if you could bring that data into a data lake, you can have not just analytics or machine learning or auditing applications. It's really what does your application do for your business, and how can it take advantage of that vast amount of shared data set in your business. Awesome. >>So thank you. So I wanna I wanna make sure we're hitting on the big trends that you're seeing in the market. That kind of informing your strategy around the portfolio and what you're seeing with customers Instant usability. You you bring in machine learning into the equation. I think, um, people have really started to understand the benefits of of of cloud storage as a service on the pay paid by the drink and that whole whole model, obviously co vid has accelerated that cloud migration has accelerated. Anything else we're missing there. What are the other big trends that you see if any? >>Well, Dave, you did a good job of capturing a lot of the drivers. The one thing I would say that just sits underneath All of it is the massive growth of digital data year over year I. D. C. Says digital data is growing at a rate of 40% year over year, and that has been true for a while. And it's not going to stop. It's gonna keep on growing because the sources of that data acquisition keeps on expanding and whether it's coyote devices whether it is content created by users. That data is going to grow, and everything you're talking about depends on the ability to not just capture it and store it. But as you say, use it well, >>you know, and we talk about data growth a lot, and sometimes it becomes bromide. But I think the interesting thing that I've observed over the last a couple of decades really is that the growth is nonlinear on. It's really the curve is starting. Thio used to shape exponentially. You guys always talk about that flywheel. Effect it. It's really hard to believe, You know, people say trees don't grow to the moon. It seems like data does. >>It does. And what's interesting about working in the world of AWS storage Dave is that it's counterintuitive. But our goal without data growth is to make it cost effective. And so year over year, how could we make it cheaper and cheaper? Just have customers store more and more data so they can use it. But it's also to think about the definition of usage. And what kind of data is that? Eyes being tapped by businesses for their insights and make that easier than it's ever been before. Let me ask >>you a follow up question on that my life could I get asked this a lot? Or guy here comments a lot that yes, A W s continuously and rigorously reduces pricing. But it's just >>kind of >>following the natural curve of Moore's law or, you know, whatever. How >>do you >>respond to that? And there are other factors involved. Obviously, labor is another cost reducing factor. But what's the trend line say, >>Well, cost efficiencies in our DNA, Dave. We come to work every day and aws across all of our services, and we ask ourselves, How can we lower our costs and be able to pass that along to customers? As you say, there are many different aspects to cost. There's the cost of the storage itself is the cost of the data center. And that's really what we've seen impact a lot of customers that were slower or just getting started with removed. The cloud is they entered 2020 and then they found out exactly how expensive that data center was to maintain because they had to put in safety equipment and they had to do all the things that you have to do in a pandemic in a data center. And so sometimes that cost is a little bit hidden or won't show up until you really don't need to have it land. But the cost of managing that explosive growth of data is very riel. And when we're thinking about cost, we're thinking about cost in terms of how can I lower it on a per gigabyte per month basis? But we're also building into the product itself adaptive discounts like we have a storage class in S three that's called intelligent hearing. And in intelligence hearing, we have built in monitoring where, if particular objects aren't frequently accessed in a given month, ah, customer will automatically get a discounted price for that storage or a customer Can you know, as of late last year, say that they wanna automatically move storage in the storage class that has been stored, for example, longer than 100 and 80 days and saves 95% by moving it into archive storage, deep archives storage? And so it's not just, you know, relentlessly going after and lowering the cost of storage. It's also building into the products these new ways where we can adaptive Lee discount storage based on what a customer's storage is actually doing >>well. And I would, I would add to our audience, is the other thing that does has done is it's really forced transparency almost the same way that Amazon has done on retail. And now my mom, When we talked last I mentioned that s three was an object store. And of course, that's technically technically correct. But your comment to me was Dave. It's more than that. And you started to talk about sage Maker and AI and bringing in machine learning. And I wonder if you could talk a little bit about the future of how storage is gonna be leveraged in the cloud that's may be different than what we've been, you know, used to in the early days of s three and how your customers should be thinking about infrastructure not as bespoke services but as a suite of capabilities and maybe some of those adjacent adjacent services that you see as most leverage a ble for customers And why? >>Well, to tell this story, dude, we're gonna have to go a little bit back in time all the way back to the 19 nineties. Or before then, when all you had waas, a set of hardware appliance vendors that sold you appliances that you put in your data center and inherently created a data silo because those hardware appliances were hardwired to your application. And so an individual application that was dealing with auditing as an example wouldn't really be able to access the storage for another application. Because you know, the architecture er of that legacy world is tied to a data silo and s tree came out launched in 2000 and six and introduced very low cost storage. That is an object. And I'll tell you, Dave, you know, over the last 10 plus years, we have seen all kinds of data come into us three, whereas before it might have been backups or it might have been images and videos. Now a pretty substantial data set is our parquet files and orc files. Thes files are there for business analytics for more real time type of processing. And that has really been the trend of the future. Is taking these different files putting them in a shared file layer, So any application today or in the future can tap into that data. And so this idea of the shared file layer is a major trend that has been taking off for the last. I would say five or six years, and I expect that to not only keep on going, but to really open up the type of services that you can then do on that shared file layer and whether that sage maker or some of the machine learning introduced by our connect service, it's bringing together the data as a starting point. And then the applications can evolve very rapidly. On top of that, I want to >>ask your opinion about big data architectures. One of our guests, Jim Octagon E. She's amazing, uh, data architect, and she's put forth this notion of a distributed global mesh, and I picked him picking up on some of the comments. Andy Jassy made it at reinvent How essentially Hey, we're bringing a W s to the edge. We see the data center is just another edge. Notes. You're seeing this massive distributed system evolving. You guys have talked about that for a while, and data by its very nature is distributed. But we've had this tendency to put into it monolithic Data Lake or a data warehouse on bits sort of antithetical to that distributed nature. So how >>do >>you see that playing out? What do you see customers in the future doing in terms of their big data architectures? And what does that mean for storage? >>It comes down to the nature of the data and again, the usage and Dave. That's where I see the biggest difference in these modern data architectures from the legacy of 20 years ago is the idea that the data need drives the data storage. So let's taken example of the type of data that you always wanna have on the edge. We have customers today that need tohave storage in the field and whether the field of scientific research or oftentimes, it's content creation in the in the film industry or if it's for military operations. There's a lot of data that needs to be captured and analyzed in the field and for us, what that means is that you know we have a suite of products called Snowball and whether it's snowball or snow cone, take your pick. That whole portfolio of AWS services is targeted at customers that need to do work with storage at the edge. And so it you know, if you think about the need for multiple applications acting on the same data set, that's when you keep it in an AWS region. And what we've done in AWS storage is we've recognized that depending on the need of usage, where you put your data and how you interactive, it may vary. But we've built a whole set of services like data transfer to help make sure that we can connect data from, for example, that new snow cone into a region automatically. And so our goal Dave, is to make sure that when customers air operating at the edge or they're operating in the region, they have the same quality of storage service, and they have easy ways to go between them. You shouldn't have to pick. You should be able to do it all. >>So in the spirit of do it all, this is sort of age old dynamic in the tech business, where you've got the friction between the the best of breed and the integrated suite, and my question is around what you're optimizing for for customers. And can you have your cake and eat it too? In other words, why A W S storage does what makes a compelling? Is it because it's kind of a best of breed storage service? Or is it because it's integrated with a W S? Would you ever sub optimize one in in order to get an advantage to the other? Or can you actually, >>you >>know, have your cake and eat it, too? >>The way that we build storage is to focus on being both the breath of capabilities on the depth of capabilities. And so where we identify ah, particular need where we think that it takes a whole new service to deliver, we'll go build that service and example for that is FTP, our AWS sftp service, which you know there's a lot of sftp usage out there and there will be for a while because of the you know, the Legacy B two b type of architectures that still live in the business world today. And so we looked at that problem. We said, How are we gonna build that in the best depth way and the best focus? And we launched a separate service for them. And so our goal is to take the individual building blocks of CBS and Glacier and s three and make the best of class and the most comprehensive in the capabilities of what we can dio and where we identify very specific need. We'll go build a service for. But, Dave, you know, as an example for that idea of both depths and breath s three storage lands is a great example of that s three storage lands is a new capability that we launched last year. And what it does is it lets you look across all your regions and all your accounts and get a summary view of all your s three storage and whether that's buckets or, you know, the most active prefixes that you have and be able to drill down from that and that is built in to the S three service and available for any customer that wants to turn it on in the AWS Management Council. >>Right? And we we saw just recently made I called it super duper block storage. But you made some, you know, improvements and really addressing the highest performance. Um, I want to ask you So we've all learned about an experience the benefits of cloud over the last several years, and especially in the last 10 months during the pandemic. But one >>of >>the challenges, and it's particularly acute with bio is, of course, Leighton see and moving data around and accessing data remotely. It's It's a challenge for customers, you know, due to speed of light, etcetera. So my question is, how was a W s thinking about all that data that still resides on premises? I think we heard that reinvent. That's still 90% of the opportunities or or the workloads. They're still on Prem that live inside a customer's data center. So how do you tap into those and help customers innovate with on Prem data, particularly from a storage >>angle? Well, we always want to provide the best of class solution for those little Leighton see workloads, and that's why we launched Block Express just late last year. It reinvent and Black expresses a new capability and preview on top of our Iot to provisioned eye ops volume type, and what's really interesting about Block Express Dave, is that the way that we're able to deliver the performance of Block Express, which is sound performance with cloud elasticity, is that we went all the way down to the network layer and we customize the hardware software. And at the network Lehrer, we built a Block Express on something called SRD, which stands for a scalable, reliable diagrams. And basically, what is letting us to do is offload all of our EBS operations for Block Express on the Nitro card on hardware. And so that type of innovation where we're able Thio, you know, take advantage of modern cop commodity, multi tenant data center networks where we're sending in this new network protocol across a large number of network paths, and that that type of innovation all the way down to that protocol level helps us innovate in a way that's hard. In fact, I would say impossible for for other sound providers to kind of really catch up and keep up. And so we feel that the amount of innovation that we have for delivering those low latency workloads in our AWS cloud storage is is unlimited, really, Because of that ability to customize software, hardware and network protocols as we go along without requiring upgrades from a customer it just gets better and the customer benefits. Now if you want to stay in your data center, that's why we built outposts. And for outpost, we have EBS and we have s three for outposts. And our goal there is that some customers will have workloads where they want to keep them resident in the data center And for those customers, we want to give them that AWS storage opportunities as well. So >>thank you for coming back to block Express. So you call it in sand in the cloud eso Is that essentially you've you've comprises a custom built, essentially storage storage network. Is that is that right? What kind of what you just described? SRD? I think you call it. >>Yeah, it's SRT is used by other AWS services as well, but it is a custom network protocol that we designed to deliver the lowest latency experience on We're taking advantage of it with Block Express >>sticking with traditional data centers for a moment, I'm interested in your thoughts on the importance of the cloud you know, pricing approach I e. The consumption model to paid by the drink. Obviously, it's one of the most attractive features But But And I ask that because we're seeing what Andy Jassy first, who is the old Guard Institute? Flexible pricing models. Two of the biggest storage companies HP with Green Lake and Dell has this thing called Apex. They've announced such models for on Prem and and presumably, Cross Cloud. How >>do you think >>this is going to impact your customers Leverage of AWS cloud storage? Is it something that you have ah, opinion on? >>Yeah, I think it all comes down to again that usage of the storage And this is where I think there is an inherent advantage for our cloud storage. So there might be an attempt by the old guard toe lower prices or add flexibility. But the end of the day it comes down to what the customer actually needs to to. And if you think about gp three, which is the new E. B s volume, the idea with GP three is we're gonna pass along savings to the customer by making the storage 20% cheaper than GP two. And we're gonna make the product better by giving a great, reliable baseline performance. But we're also going to let customers who want to run work clothes like Cassandra on TBS tune their throughput separately, for example, from their capacity. So if you're running Cassandra, sometimes you don't need to change your capacity. Your storage capacity works just fine, but what happens with for example, Cassandra were quote is that you may need more throughput. And if you're buying hardware appliance, you just have to buy for your peak. You have to buy for the max of what you think, your throughput in the max of what your storage is and this inherent flexibility that we have for AWS storage and being able to tune throughput separate from IOP, separate from capacity like you do for GP three. That is really where the future is for customers having control over costs and control over customer experience without compromising or trading off either one. >>Awesome. Thank you for that. So another time we have remaining my line. I want to talk about the topic of diversity. Uh, social impact on Daz. Ah, woman leader, women executive on. I really wanna get your perspectives on this, and I've shared with the audience previously. One of my breaking analysis segments your your boxing video, which is awesome and eso so you've got a lot of unique, non traditional aspects to your to your life, and and I love it. But I >>want to >>ask you this. So it's obviously, you know, certainly politically and socially correct to talk about diversity, the importance of diversity. There's data that suggests that that that diversity is good both economically, not just socially. And of course, it's the right thing to do. But there are those. Peter Thiel is probably the most prominent, but there are others who say, You know what, >>But >>get that. Just hire people just like you will be able to go faster, ramp up more quickly, hit escape velocity. It's natural. And that's what you should dio. Why is that not the right approach? Why is diversity both course socially responsible, but also good for business? >>For Amazon, we think about diversity as something that is essential toe how we think about innovation. And so, Dave, you know, as you know, from listening to some of the announcements I reinvent, we launched a lot of new ideas, new concepts and new services in AWS and just bringing that lends down to storage U. S. Tree has been reinventing itself every year since we launched in 2000 and six. PBS introduced the first Son on the Cloud late last year and continues to reinvent how customers think about block storage. We would not be able Thio. Look at a product in a different way and think to ourselves Not just what is the legacy system dio in a data center today. But how do we want to build this new distributed system in a way that helps customers achieve not just what they're doing today, but what they want to do in five and 10 years? You can't get that innovative mindset without bringing different perspectives to the table. And so we strongly believe in hiring people who are from underrepresented groups and whether that's gender or it's related racial equality or if its geographic, uh, diversity and bringing them in tow have the conversation. Because those divers viewpoints inform how we can innovate at all levels in a W s >>right. And so I really appreciate the perspectives on that, and we've had a zoo. You probably know the Cube has been, you know, a very big advocate of diversity, you know, generally, but women in tech Specifically, we participated a lot. And you know, I often ask this question is, you know, as a smaller company, uh, I and some of my other colleagues in in small business Sometimes we struggle. Um and so my question is, how >>how do >>you go beyond What's your advice for going beyond, you know, the good old boys network? I think its large companies like AWS and the big players you've got a responsibility to that. You can put somebody in charge and make it you know, their full time job. How should smaller companies, um, that are largely white, male dominated? How should they become more diverse? What should they do? Thio increase that diversity? >>Well, I think the place to start his voice. A lot of what we try to dio is make sure that the underrepresented voice is heard. And so, Dave, any small business owner of any industry can encourage voice for your under represented or your unheard populations. And honestly, it is a simple as being in a meeting and looking around that table, we're on your screen as it were and asking yourself Who hasn't talked? Who hasn't weighed in particularly if the debate is contentious or even animated. And you will see, particularly if you note this. Over time you will see that there may be somebody and whether it's an underrepresented, a group or its ah woman whose early career or it's it's not. It's just a member of your team who happens to be a white male to who's not being hurt. And you can ask that person for their perspective. And that is a step that every one of us can and should do, which is asked toe, have everyone's voice at the table, toe listen and to weigh in on it. So I think that is something everyone should dio. I think if you are a member of an underrepresented groups, as for example, I'm Vietnamese American and I'm the female in Tech. I think it z something to think about how you can make sure that you're always taking that bold step forward. And it's one of the topics that we covered it at reinvent. We had a great discussion with a group of women CEOs, and a lot of it we talked about is being bolt, taking the challenge of being bold in tough situations, and that is an important thing, I think, for anybody to keep in mind, but especially for members of underrepresented groups, because sometimes Dave, that bold step that you kind of think of is like, Oh, I don't know if I should ask for that promotion or I don't know if I should volunteer for that project It's not. It's not a big ask, but it's big in your head. And so if you can internalize as a member of some, you know, a group that maybe hasn't heard or seen as much how you can take those bold challenges and step forward and learn, maybe fell also because that's how you learn. Then that is a way toe. Also have people learn and develop and become leaders in whatever industry it ISS. It's >>great advice, and I reminds me of, I mean, I think most of us can relate to that my land, because when we started in the industry, we may be timid. You didn't want to necessarily speak up, and I think it's incumbent upon those in a position of power. And by the way, power might just be running a meeting agenda to maybe calling those folks that are. Maybe it's not diversity of gender or, you know, our or race. And maybe it's just the underrepresented. Maybe that's a good way to start building muscle memory. So that's unique advice that I hadn't heard before. So thank you very much for that. Appreciate it. And, uh hey, listen, thanks so much for coming on the Cuban cloud. Uh, we're out of time and and really, always appreciate your perspectives. And you're doing a great job, and thank you. >>Great. Thank you, Dave. Thanks for having me and have a great day. >>All right? And keep it right, everybody. You're watching the cube on cloud right back.

>>from around the globe. It's the Cube presenting Cuban cloud brought >>to you by silicon angle. Okay, we're back. Here. Live Cuban Cloud. And this is Dave. Want with my co host, John Ferrier Were all remote. We're getting into the analyst power half hour. Really pleased to have Maribel Lopez here. She's the principal and founder of Lopez Research and Zias Caraballo, who is the principal and founder of ZK research. Guys, great to see you. Let's get into it. How you doing? >>Great. How you been? Good, >>thanks. Really good. John's hanging in there quarantining and, uh, all healthy, So I hope you guys are too. Hey, Mary, But let's start with you. You know, here we are on 2021 you know, just exited one of the strangest years, if not the strangest year of our lives. But looking back in the past decade of cloud and we're looking forward. How do you see that? Where do we come from? Where we at and where we going >>When we obviously started with the whole let's build a public cloud and everything was about public cloud. Uh, then we went thio the notion of private cloud than we had hybrid cloud and multi cloud. So we've done a lot of different clouds right now. And I think where we are today is that there's a healthy recognition on the cloud computing providers that you need to give it to the customers the way they want it, not the way you've decided to build it. So how do you meet them where they are so that they can have a cloud like experience wherever they want their data to be? >>Yes and yes, you've, you know, observed, This is well, in the early days of cloud, you heard a lot of rhetoric. It was private cloud And and then now we're, you know, hearing a lot of multi cloud and so forth. But initially, a lot of the traditional vendors kind of pooh poohed it. They called us analysts. We said we were all cloud crazy, but they seem to have got their religion. >>Well, everything. Everyone's got a definition of cloud, but I actually think we are right in the midst of another transformation of clouds Miracle talked about. We went from, you know, private clouds, which is really hosting the public cloud to multi cloud hybrid cloud. And if you look at the last post that put on Silicon Angle, which was talking about five acquisition of Volterra, I actually think we're in the midst of the transition to what's called distributed Club, where if you look at modernized cloud apps today, they're actually made up of services from different clouds on also distributed edge locations. And that's gonna have a pretty profound impact on the way we build out, because those distributed edges be a telco edge, cellular vagina. Th whatever the services that lived there are much more ephemeral in nature, right? So the way we secure the way we connect changes quite a bit. But I think that the great thing about Cloud is we've seen several several evolutionary changes. So what the definition is and we're going through that now, which is which is pretty cool to think about, right? It's not a static thing. Um, it's, uh, you know, it's a it's an ongoing transition. But I think, uh, you know, we're moving into this distributed Cloudera, which to me is a lot more complex than what we're dealing with in the Palace. >>I'm actually pretty excited about that because I think that this move toe edge and the distribution that you've talked about, it's like we now have processing everywhere. We've got it on devices, we've got it in, cars were moving, the data centers closer and closer to where the action's happening. And I think that's gonna be a huge trend for 2021. Is that distributed that you were talking about a lot of edge discussion? You >>know what? The >>reason we're doing This, too, is we want. It's not just we're moving the data closer to the user, right? And some. If you think you brought up the autonomous vehicle right in the car being an edge, you think of the data that generates right? There's some things such as the decision to stop or not right that should be done in car. I don't wanna transport that data all the way back to Google him back to decide whether I want to stop. You could also use the same data determine whether drivers driving safely for insurance purposes, right? So the same data give me located at the edge or in a centralized cloud for different purposes, and I think that's what you know, kind of cool about this is we're being able to use our data and much different ways. Now. >>You know, it's interesting is it's so complex. It's mind blowing because this is distributed computing. Everyone kind of agrees this is where it is. But if you think about the complexity and I want to get your guys reaction to this because you know some of the like side fringe trend discussions are data sovereignty, misinformation as a vulnerability. Okay, you get the chips now you got gravitas on with Amazon in front. Apple's got their own chips. Intel is gonna do a whole new direction. So you've got tons of computer. And then you mentioned the ephemeral nature. How do you manage those? What's the observe ability look like? They're what's the trust equation? So all these things kind of play into it. It sounds almost mind blowing, just even thinking about it. But how do you guys, this analyst tryto understand where someone's either blowing bullshit or kind of like has the real deal? Because all those things come into play? I mean, you could have a misinformation campaign targeting the car. Let's say Hey, you know that that data is needs to be. This is this is misinformation who's a >>in a lot of ways, this creates almost unprecedented opportunity now for for starts and for companies to transform right. The fundamental tenet of my research has always been share shifts happen when markets transition and we're in the middle of the big one. If the computer resource is we're using, John and the application resource will be using or ephemeral nature than all the things that surrounded the way we secured the way we connect. Those also have to be equal, equally agile, right, So you can't have, you know, you think of a micro services based application being secured with traditional firewalls, right? Just the amount of, or even virtual the way that the length of time it takes to spend those things up is way too long. So in many ways, this distributed cloud change changes everything in I T. And that that includes all of the services in the the infrastructure that we used to secure and connect. And that's a that is a profound change, and you mentioned the observe ability. You're right. That's another thing that the traditional observe ability tools are based on static maps and things and, you know, traditional up, down and we don't. Things go up and down so quickly now that that that those don't make any sense. So I think we are going to see quite a rise in different types of management tools and the way they look at things to be much more. I suppose you know Angela also So we can measure things that currently aren't measurable. >>So you're talking about the entire stack. Really? Changing is really what you're inferring anyway from your commentary. And that would include the programming model as well, wouldn't it? >>Absolutely. Yeah. You know, the thing that is really interesting about where we have been versus where we're going is we spent a lot of time talking about virtual izing hardware and moving that around. And what does that look like? And that, and creating that is more of a software paradigm. And the thing we're talking about now is what is cloud is an operating model look like? What is the manageability of that? What is the security of that? What? You know, we've talked a lot about containers and moving into a different you know, Dev suck ups and all those different trends that we've been talking about, like now we're doing them. So we've only got into the first crank of that. And I think every technology vendor we talked to now has to address how are they going to do a highly distributed management and security landscape? Like, what are they gonna layer on top of that? Because it's not just about Oh, I've taken Iraq of something server storage, compute and virtualized it. I now have to create a new operating model around it. In a way, we're almost redoing what the OS I stack looks like and what the software and solutions are for that. >>So >>it was really Hold on, hold on, hold on their lengthened. Because that side stack that came up earlier today, Mayor. But we're talking about Yeah, we were riffing on the OSC model, but back in the day and we were comparing the S n a definite the, you know, the proprietary protocol stacks that they were out there and someone >>said Amazon's S N a. Is that recall? E think that's what you said? >>No, no. Someone in the chest. That's a comment like Amazon's proprietary meaning, their scale. And I said, Oh, that means there s n a But if you think about it, that's kind of almost that can hang. Hang together. If the kubernetes is like a new connective tissue, is that the TCP pipe moment? Because I think Os I kind of was standardizing at the lower end of the stack Ethernet token ring. You know, the data link layer physical layer and that when you got to the TCP layer and really magic happened right to me, that's when Cisco's happened and everything started happening then and then. It kind of stopped because the application is kinda maintain their peace there. A little history there, but like that's kind of happening now. If you think about it and then you put me a factor in the edge, it just kind of really explodes it. So who's gonna write that software? E >>think you know, Dave, your your dad doesn't change what you build ups. It's already changed in the consumer world, you look atyou, no uber and Waze and things like that. Those absolute already highly decomposed applications that make a P I calls and DNS calls from dozens of different resource is already right. We just haven't really brought that into the enterprise space. There's a number, you know, what kind of you know knew were born in the cloud companies that have that have done that. But they're they're very few and far between today. And John, your point about the connectivity. We do need to think about connectivity at the network layer. Still, obviously, But now we're creating that standardization that standardized connectivity all the way a player seven. So you look at a lot of the, you know, one of the big things that was a PDP. I calls right, you know, from different cloud services. And so we do need to standardize in every layer and then stitch that together. So that does make It does make things a lot more complicated. Now I'm not saying Don't do it because you can do a whole lot more with absolute than you could ever do before. It's just that we kind of cranked up the level of complexity here, and flowered isn't just a single thing anymore, right? That's that. That's what we're talking about here It's a collection of edges and private clouds and public clouds. They all have to be stitched together at every layer in orderto work. >>So I was I was talking a few CEOs earlier in the day. We had we had them on, I was asking them. Okay, So how do you How do you approach this complexity? Do you build that abstraction layer? Do you rely on someone like Microsoft to build that abstraction layer? Doesn't appear that Amazon's gonna do it, you know? Where does that come from? Or is it or is it dozens of abstraction layers? And one of the CEO said, Look, it's on us. We have to figure out, you know, we get this a p I economy, but But you guys were talking about a mawr complicated environment, uh, moving so so fast. Eso if if my enterprise looks like my my iPhone APs. Yes, maybe it's simpler on an individual at basis, but its app creep and my application portfolio grows. Maybe they talk to each other a little bit better. But that level of complexity is something that that that users are gonna have to deal >>with what you thought. So I think quite what Zs was trying to get it and correct me if I'm wrong. Zia's right. We've got to the part where we've broken down what was a traditional application, right? And now we've gotten into a P. I calls, and we have to think about different things. Like we have to think about how we secure those a p I s right. That becomes a new criteria that we're looking at. How do we manage them? How do they have a life cycle? So what was the life cycle of, say, an application is now the life cycle of components and so that's a That's a pretty complex thing. So it's not so much that you're getting app creep, but you're definitely rethinking how you want to design your applications and services and some of those you're gonna do yourself and a lot of them are going to say it's too complicated. I'm just going to go to some kind of SAS cloud offering for that and let it go. But I think that many of the larger companies I speak to are looking for a larger company to help them build some kind of framework to migrate from what they've used with them to what they need tohave going forward. >>Yeah, I think. Where the complexities. John, You asked who who creates the normalization layer? You know, obviously, if you look to the cloud providers A W s does a great job of stitching together all things AWS and Microsoft does a great job of stitching together all things Microsoft right in saying with Google. >>But >>then they don't. But if if I want to do some Microsoft to Amazon or Google Toe Microsoft, you know, connectivity, they don't help so much of that. And that's where the third party vendors that you know aviatrix on the network side will tear of the security side of companies like that. Even Cisco's been doing a lot of work with those companies, and so what we what we don't really have And we probably won't for a while if somebody is gonna stitch everything together at every >>you >>know, at every layer. So Andi and I do think we do get after it. Maribel, I think if you look at the world of consumer APS, we moved to a lot more kind of purpose built almost throwaway apps. They serve a purpose or to use them for a while. Then you stop using them. And in the enterprise space, we really haven't kind of converted to them modeling on the mobile side. But I think that's coming. Well, >>I think with micro APS, right, that that was kind of the issue with micro APS. It's like, Oh, I'm not gonna build a full scale out that's gonna take too long. I'm just gonna create this little workflow, and we're gonna have, like, 200 work flows on someone's phone. And I think we did that. And not everybody did it, though, to your point. So I do think that some people that are a little late to the game might end up in in that app creep. But, hey, listen, this is a fabulous opportunity that just, you know, throw a lot of stuff out and do it differently. What What? I think what I hear people struggling with ah lot is be to get it to work. It typically is something that is more vertically integrated. So are you buying all into a Microsoft all you're buying all into an Amazon and people are starting to get a little fear about doing the full scale buy into any specific platform yet. In absence of that, they can't get anything to work. >>Yeah, So I think again what? What I'm hearing from from practitioners, I'm gonna put a micro serve. And I think I think, uh, Mirabelle, this is what you're implying. I'm gonna put a micro services layer. Oh, my, my. If I can't get rid of them, If I can't get rid of my oracle, you know, workloads. I'm gonna connect them to my modernize them with a layer, and I'm gonna impart build that. I'm gonna, you know, partner to get that done. But that seems to be a a critical path forward. If I don't take that step, gonna be stuck in the path in the past and not be able to move forward. >>Yeah, absolutely. I mean, you do have to bridge to the past. You you aren't gonna throw everything out right away. That's just you can't. You can't drive the bus and take the wheels off that the same time. Maybe one wheel, but not all four of them at the same time. So I think that this this concept of what are the technologies and services that you use to make sure you can keep operational, but that you're not just putting on Lee new workloads into the cloud or new workloads as decomposed APS that you're really starting to think about. What do I want to keep in whatever I want to get rid of many of the companies you speak Thio. They have thousands of applications. So are they going to do this for thousands of applications? Are they gonna take this as an opportunity to streamline? Yeah, >>well, a lot of legacy never goes away, right? And I was how companies make this transition is gonna be interesting because there's no there's no really the fact away I was I was talking to this one company. This is New York Bank, and they've broken their I t division down into modern I t and legacy I t. And so modern. Everything is cloud first. And so imagine me, the CEO of Legacy i e 02 miracles. But what they're doing, if they're driving the old bus >>and >>then they're building a new bus and parallel and eventually, you know, slowly they take seats out of the old bus and they take, you know, the seat and and they eventually start stripping away things. That old bus, >>But >>that old bus is going to keep running for a long time. And so stitching the those different worlds together is where a lot of especially big organizations that really can't commit to everything in the cloud are gonna struggle. But it is a It is a whole new world. And like I said, I think it creates so much opportunity for people. You know, e >>whole bus thing reminds me that movie speed when they drive around 55 miles an hour, just put it out to the airport and just blew up E >>got But you know, we all we all say that things were going to go away. But to Zia's point, you know, nothing goes away. We're still in 2021 talking about mainframes just as an aside, right? So I think we're going to continue tohave some legacy in the network. But the But the issue is ah, lot will change around that, and they're gonna be some people. They're gonna make a lot of money selling little startups that Just do one specific piece of that. You know, we just automation of X. Oh, >>yeah, that's a great vertical thing. This is the This is the distributed network argument, right? If you have a note in the network and you could put a containerized environment around it with some micro services um, connective tissue glue layer, if you will software abstract away some integration points, it's a note on the network. So if in mainframe or whatever, it's just I mean makes the argument right, it's not core. You're not building a platform around the mainframe, but if it's punching out, I bank jobs from IBM kicks or something, you know, whatever, Right? So >>And if those were those workloads probably aren't gonna move anywhere, right, they're not. Is there a point in putting those in the cloud? You could say Just leave them where they are. Put a connection to the past Bridge. >>Remember that bank when you talk about bank guy we interviewed in the off the record after the Cube interviews like, Yeah, I'm still running the mainframe, so I never get rid of. I love it. Run our kicks job. I would never think about moving that thing. >>There was a large, large non US bank who said I buy. I buy the next IBM mainframe sight unseen. Andi, he's got no choice. They just write the check. >>But milliseconds is like millions of dollars of millisecond for him on his back, >>so those aren't going anywhere. But then, but then, but they're not growing right. It's just static. >>No, no, that markets not growing its's, in fact. But you could make a lot of money and monetizing the legacy, right? So there are vendors that will do that. But I do think if you look at the well, we've already seen a pretty big transition here. If you look at the growth in a company like twilio, right, that it obviates the need for a company to rack and stack your own phone system to be able to do, um, you know, calling from mobile lapse or even messaging. Now you just do a P. I calls. Um, you know, it allows in a lot of ways that this new world we live in democratizes development, and so any you know, two people in the garage can start up a company and have a service up and running another time at all, and that creates competitiveness. You know much more competitiveness than we've ever had before, which is good for the entire industry. And, you know, because that keeps the bigger companies on their toes and they're always looking over their shoulder. You know what, the banks you're looking at? The venues and companies like that Brian figure out a way to monetize. So I think what we're, you know well, that old stuff never going away. The new stuff is where the competitive screen competitiveness screen. >>It's interesting. Um IDs Avery. Earlier today, I was talking about no code in loco development, how it's different from the old four g l days where we didn't actually expand the base of developers. Now we are to your point is really is democratizing and, >>well, everybody's a developer. It could be a developer, right? A lot of these tools were written in a way that line of business people create their own APs to point and click interface is, and so the barrier. It reminds me of when, when I started my career, I was a I. I used to code and HTML build websites and then went to five years. People using drag and drop interface is right, so that that kind of job went away because it became so easy to dio. >>Yeah, >>sorry. A >>data e was going to say, I think we're getting to the part. We're just starting to talk about data, right? So, you know, when you think of twilio, that's like a service. It's connecting you to specific data. When you think of Snowflake, you know, there's been all these kinds of companies that have crept up into the landscape to feel like a very specific void. And so now the Now the question is, if it's really all about the data, they're going to be new companies that get built that are just focusing on different aspects of how that data secured, how that data is transferred, how that data. You know what happens to that data, because and and does that shift the balance of power about it being out of like, Oh, I've created these data centers with large recommend stack ums that are virtualized thio. A whole other set of you know this is a big software play. It's all about software. >>Well, we just heard from Jim Octagon e You guys talking earlier about just distributed system. She basically laid down that look. Our data architectures air flawed there monolithic. And data by its very nature is distributed so that she's putting forth the whole new paradigm around distributed decentralized data models, >>which Howie shoe is just talking about. Who's gonna build the visual studio for data, right? So programmatic. Kind of thinking around data >>I didn't >>gathering. We didn't touch on because >>I do think there's >>an opportunity for that for, you know, data governance and data ownership and data transport. But it's also the analytics of it. Most companies don't have the in house, um, you know, data scientists to build on a I algorithms. Right. So you're gonna start seeing, you know, cos pop up to do very specific types of data. I don't know if you saw this morning, um, you know, uniforms bought this company that does, you know, video emotion detection so they could tell on the video whether somebody's paying attention, Not right. And so that's something that it would be eso hard for a company to build that in house. But I think what you're going to see is a rise in these, you know, these types of companies that help with specific types of analytics. And then you drop you pull those in his resource is into your application. And so it's not only the storage and the governance of the data, but also the analytics and the analytics. Frankly, there were a lot of the, uh, differentiation for companies is gonna come from. I know Maribel has written a lot on a I, as have I, and I think that's one of the more exciting areas to look at this year. >>I actually want to rip off your point because I think it's really important because where we left off in 2020 was yes, there was hybrid cloud, but we just started to see the era of the vertical eyes cloud the cloud for something you know, the cloud for finance, the cloud for health care, the telco and edge cloud, right? So when you start doing that, it becomes much more about what is the specialized stream that we're looking at. So what's a specialized analytic stream? What's a specialized security stack stream? Right? So until now, like everything was just trying to get to what I would call horizontal parody where you took the things you had before you replicated them in a new world with, like, some different software, but it was still kind of the same. And now we're saying, OK, let's try Thio. Let's try to move out of everything, just being a generic sort of cloud set of services and being more total cloud services. >>That is the evolution of everything technology, the first movement. Everything doing technology is we try and make the old thing the new thing look like the old thing, right? First PCs was a mainframe emulator. We took our virtual servers and we made them look like physical service, then eventually figure out, Oh, there's a whole bunch of other stuff that I could do then I couldn't do before. And that's the part we're trying to hop into now. Right? Is like, Oh, now that I've gone cloud native, what can I do that I couldn't do before? Right? So we're just we're sort of hitting that inflection point. That's when you're really going to see the growth takeoff. But for whatever reason, and i t. All we ever do is we're trying to replicate the old until we figure out the old didn't really work, and we should do something new. >>Well, let me throw something old and controversial. Controversial old but old old trope out there. Consumerism ation of I t. I mean, if you think about what year was first year you heard that term, was it 15 years ago? 20 years ago. When did that first >>podcast? Yeah, so that was a long time ago >>way. So if you think about it like, it kind of is happening. And what does it mean, right? Come. What does What does that actually mean in today's world Doesn't exist. >>Well, you heard you heard. Like Fred Luddy, whose founder of service now saying that was his dream to bring consumer like experiences to the enterprise will. Well, it didn't really happen. I mean, service not pretty. Pretty complicated compared toa what? We know what we do here, but so it's It's evolving. >>Yeah, I think there's also the enterprise ation of consumer technology that John the companies, you know, you look a zoom. They came to market with a highly consumer facing product, realized it didn't have the security tools, you know, to really be corporate great. And then they had to go invest a bunch of money in that. So, you know, I think that waken swing the pendulum all the way over to the consumer side, but that that kind of failed us, right? So now we're trying to bring it back to center a little bit where we blend the two together. >>Cloud kind of brings that I never looked at that way. That's interesting and surprising of consumer. Yeah, that's >>alright, guys. Hey, we gotta wrap Zs, Maribel. Always a pleasure having you guys on great great insights from the half hour flies by. Thanks so much. We appreciate it. >>Thank >>you guys. >>Alright, keep it right there. Mortgage rate content coming from the Cuban Cloud Day Volonte with John Ferrier and a whole lineup still to come Keep right there.

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE in ETR. This is Breaking Analysis with Dave Vellante. >> Snowflake is not going to grow into its valuation by stealing the croissant from the breakfast table of the on-prem data warehouse vendors. Look, even if snowflake got 100% of the data warehouse business, it wouldn't come close to justifying its market cap. Rather Snowflake has to create an entirely new market based on completely changing the way organizations think about monetizing data. Every organization I talk to says it wants to be, or many say they already are data-driven. why wouldn't you aspire to that goal? There's probably nothing more strategic than leveraging data to power your digital business and creating competitive advantage. But many businesses are failing, or I predict, will fail to create a true data-driven culture because they're relying on a flawed architectural model formed by decades of building centralized data platforms. Welcome everyone to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, I want to share some new thoughts and fresh ETR data on how organizations can transform their businesses through data by reinventing their data architectures. And I want to share our thoughts on why we think Snowflake is currently in a very strong position to lead this effort. Now, on November 17th, theCUBE is hosting the Snowflake Data Cloud Summit. Snowflake's ascendancy and its blockbuster IPO has been widely covered by us and many others. Now, since Snowflake went public, we've been inundated with outreach from investors, customers, and competitors that wanted to either better understand the opportunities or explain why their approach is better or different. And in this segment, ahead of Snowflake's big event, we want to share some of what we learned and how we see it. Now, theCUBE is getting paid to host this event, so I need you to know that, and you draw your own conclusions from my remarks. But neither Snowflake nor any other sponsor of theCUBE or client of SiliconANGLE Media has editorial influence over Breaking Analysis. The opinions here are mine, and I would encourage you to read my ethics statement in this regard. I want to talk about the failed data model. The problem is complex, I'm not debating that. Organizations have to integrate data and platforms with existing operational systems, many of which were developed decades ago. And as a culture and a set of processes that have been built around these systems, and they've been hardened over the years. This chart here tries to depict the progression of the monolithic data source, which, for me, began in the 1980s when Decision Support Systems or DSS promised to solve our data problems. The data warehouse became very popular and data marts sprung up all over the place. This created more proprietary stovepipes with data locked inside. The Enron collapse led to Sarbanes-Oxley. Now, this tightened up reporting. The requirements associated with that, it breathed new life into the data warehouse model. But it remained expensive and cumbersome, I've talked about that a lot, like a snake swallowing a basketball. The 2010s ushered in the big data movement, and Data Lakes emerged. With a dupe, we saw the idea of no schema online, where you put structured and unstructured data into a repository, and figure it all out on the read. What emerged was a fairly complex data pipeline that involved ingesting, cleaning, processing, analyzing, preparing, and ultimately serving data to the lines of business. And this is where we are today with very hyper specialized roles around data engineering, data quality, data science. There's lots of batch of processing going on, and Spark has emerged to improve the complexity associated with MapReduce, and it definitely helped improve the situation. We're also seeing attempts to blend in real time stream processing with the emergence of tools like Kafka and others. But I'll argue that in a strange way, these innovations actually compound the problem. And I want to discuss that because what they do is they heighten the need for more specialization, more fragmentation, and more stovepipes within the data life cycle. Now, in reality, and it pains me to say this, it's the outcome of the big data movement, as we sit here in 2020, that we've created thousands of complicated science projects that have once again failed to live up to the promise of rapid cost-effective time to insights. So, what will the 2020s bring? What's the next silver bullet? You hear terms like the lakehouse, which Databricks is trying to popularize. And I'm going to talk today about data mesh. These are other efforts they look to modernize datalakes and sometimes merge the best of data warehouse and second-generation systems into a new paradigm, that might unify batch and stream frameworks. And this definitely addresses some of the gaps, but in our view, still suffers from some of the underlying problems of previous generation data architectures. In other words, if the next gen data architecture is incremental, centralized, rigid, and primarily focuses on making the technology to get data in and out of the pipeline work, we predict it's going to fail to live up to expectations again. Rather, what we're envisioning is an architecture based on the principles of distributed data, where domain knowledge is the primary target citizen, and data is not seen as a by-product, i.e, the exhaust of an operational system, but rather as a service that can be delivered in multiple forms and use cases across an ecosystem. This is why we often say the data is not the new oil. We don't like that phrase. A specific gallon of oil can either fuel my home or can lubricate my car engine, but it can't do both. Data does not follow the same laws of scarcity like natural resources. Again, what we're envisioning is a rethinking of the data pipeline and the associated cultures to put data needs of the domain owner at the core and provide automated, governed, and secure access to data as a service at scale. Now, how is this different? Let's take a look and unpack the data pipeline today and look deeper into the situation. You all know this picture that I'm showing. There's nothing really new here. The data comes from inside and outside the enterprise. It gets processed, cleanse or augmented so that it can be trusted and made useful. Nobody wants to use data that they can't trust. And then we can add machine intelligence and do more analysis, and finally deliver the data so that domain specific consumers can essentially build data products and services or reports and dashboards or content services, for instance, an insurance policy, a financial product, a loan, that these are packaged and made available for someone to make decisions on or to make a purchase. And all the metadata associated with this data is packaged along with the dataset. Now, we've broken down these steps into atomic components over time so we can optimize on each and make them as efficient as possible. And down below, you have these happy stick figures. Sometimes they're happy. But they're highly specialized individuals and they each do their job and they do it well to make sure that the data gets in, it gets processed and delivered in a timely manner. Now, while these individual pieces seemingly are autonomous and can be optimized and scaled, they're all encompassed within the centralized big data platform. And it's generally accepted that this platform is domain agnostic. Meaning the platform is the data owner, not the domain specific experts. Now there are a number of problems with this model. The first, while it's fine for organizations with smaller number of domains, organizations with a large number of data sources and complex domain structures, they struggle to create a common data parlance, for example, in a data culture. Another problem is that, as the number of data sources grows, organizing and harmonizing them in a centralized platform becomes increasingly difficult, because the context of the domain and the line of business gets lost. Moreover, as ecosystems grow and you add more data, the processes associated with the centralized platform tend to get further genericized. They again lose that domain specific context. Wait (chuckling), there are more problems. Now, while in theory organizations are optimizing on the piece parts of the pipeline, the reality is, as the domain requires a change, for example, a new data source or an ecosystem partnership requires a change in access or processes that can benefit a domain consumer, the reality is the change is subservient to the dependencies and the need to synchronize across these discrete parts of the pipeline or actually, orthogonal to each of those parts. In other words, in actuality, the monolithic data platform itself remains the most granular part of the system. Now, when I complain about this faulty structure, some folks tell me this problem has been solved. That there are services that allow new data sources to really easily be added. A good example of this is Databricks Ingest, which is, it's an auto loader. And what it does is it simplifies the ingestion into the company's Delta Lake offering. And rather than centralizing in a data warehouse, which struggles to efficiently allow things like Machine Learning frameworks to be incorporated, this feature allows you to put all the data into a centralized datalake. More so the argument goes, that the problem that I see with this, is while the approach does definitely minimizes the complexities of adding new data sources, it still relies on this linear end-to-end process that slows down the introduction of data sources from the domain consumer beside of the pipeline. In other words, the domain experts still has to elbow her way into the front of the line or the pipeline, in this case, to get stuff done. And finally, the way we are organizing teams is a point of contention, and I believe is going to continue to cause problems down the road. Specifically, we've again, we've optimized on technology expertise, where for example, data engineers, well, really good at what they do, they're often removed from the operations of the business. Essentially, we created more silos and organized around technical expertise versus domain knowledge. As an example, a data team has to work with data that is delivered with very little domain specificity, and serves a variety of highly specialized consumption use cases. All right. I want to step back for a minute and talk about some of the problems that people bring up with Snowflake and then I'll relate it back to the basic premise here. As I said earlier, we've been hammered by dozens and dozens of data points, opinions, criticisms of Snowflake. And I'll share a few here. But I'll post a deeper technical analysis from a software engineer that I found to be fairly balanced. There's five Snowflake criticisms that I'll highlight. And there are many more, but here are some that I want to call out. Price transparency. I've had more than a few customers telling me they chose an alternative database because of the unpredictable nature of Snowflake's pricing model. Snowflake, as you probably know, prices based on consumption, just like AWS and other cloud providers. So just like AWS, for example, the bill at the end of the month is sometimes unpredictable. Is this a problem? Yes. But like AWS, I would say, "Kill me with that problem." Look, if users are creating value by using Snowflake, then that's good for the business. But clearly this is a sore point for some users, especially for procurement and finance, which don't like unpredictability. And Snowflake needs to do a better job communicating and managing this issue with tooling that can predict and help better manage costs. Next, workload manage or lack thereof. Look, if you want to isolate higher performance workloads with Snowflake, you just spin up a separate virtual warehouse. It's kind of a brute force approach. It works generally, but it will add expense. I'm kind of reminded of Pure Storage and its approach to storage management. The engineers at Pure, they always design for simplicity, and this is the approach that Snowflake is taking. Usually, Pure and Snowflake, as I have discussed in a moment, is Pure's ascendancy was really based largely on stealing share from Legacy EMC systems. Snowflake, in my view, has a much, much larger incremental market opportunity. Next is caching architecture. You hear this a lot. At the end of the day, Snowflake is based on a caching architecture. And a caching architecture has to be working for some time to optimize performance. Caches work well when the size of the working set is small. Caches generally don't work well when the working set is very, very large. In general, transactional databases have pretty small datasets. And in general, analytics datasets are potentially much larger. Is it Snowflake in the analytics business? Yes. But the good thing that Snowflake has done is they've enabled data sharing, and it's caching architecture serves its customers well because it allows domain experts, you're going to hear this a lot from me today, to isolate and analyze problems or go after opportunities based on tactical needs. That said, very big queries across whole datasets or badly written queries that scan the entire database are not the sweet spot for Snowflake. Another good example would be if you're doing a large audit and you need to analyze a huge, huge dataset. Snowflake's probably not the best solution. Complex joins, you hear this a lot. The working set of complex joins, by definition, are larger. So, see my previous explanation. Read only. Snowflake is pretty much optimized for read only data. Maybe stateless data is a better way of thinking about this. Heavily right intensive workloads are not the wheelhouse of Snowflake. So where this is maybe an issue is real-time decision-making and AI influencing. A number of times, Snowflake, I've talked about this, they might be able to develop products or acquire technology to address this opportunity. Now, I want to explain. These issues would be problematic if Snowflake were just a data warehouse vendor. If that were the case, this company, in my opinion, would hit a wall just like the NPP vendors that proceeded them by building a better mouse trap for certain use cases hit a wall. Rather, my promise in this episode is that the future of data architectures will be really to move away from large centralized warehouses or datalake models to a highly distributed data sharing system that puts power in the hands of domain experts at the line of business. Snowflake is less computationally efficient and less optimized for classic data warehouse work. But it's designed to serve the domain user much more effectively in our view. We believe that Snowflake is optimizing for business effectiveness, essentially. And as I said before, the company can probably do a better job at keeping passionate end users from breaking the bank. But as long as these end users are making money for their companies, I don't think this is going to be a problem. Let's look at the attributes of what we're proposing around this new architecture. We believe we'll see the emergence of a total flip of the centralized and monolithic big data systems that we've known for decades. In this architecture, data is owned by domain-specific business leaders, not technologists. Today, it's not much different in most organizations than it was 20 years ago. If I want to create something of value that requires data, I need to cajole, beg or bribe the technology and the data team to accommodate. The data consumers are subservient to the data pipeline. Whereas in the future, we see the pipeline as a second class citizen, with a domain expert is elevated. In other words, getting the technology and the components of the pipeline to be more efficient is not the key outcome. Rather, the time it takes to envision, create, and monetize a data service is the primary measure. The data teams are cross-functional and live inside the domain versus today's structure where the data team is largely disconnected from the domain consumer. Data in this model, as I said, is not the exhaust coming out of an operational system or an external source that is treated as generic and stuffed into a big data platform. Rather, it's a key ingredient of a service that is domain-driven and monetizable. And the target system is not a warehouse or a lake. It's a collection of connected domain-specific datasets that live in a global mesh. What is a distributed global data mesh? A data mesh is a decentralized architecture that is domain aware. The datasets in the system are purposely designed to support a data service or data product, if you prefer. The ownership of the data resides with the domain experts because they have the most detailed knowledge of the data requirement and its end use. Data in this global mesh is governed and secured, and every user in the mesh can have access to any dataset as long as it's governed according to the edicts of the organization. Now, in this model, the domain expert has access to a self-service and obstructed infrastructure layer that is supported by a cross-functional technology team. Again, the primary measure of success is the time it takes to conceive and deliver a data service that could be monetized. Now, by monetize, we mean a data product or data service that it either cuts cost, it drives revenue, it saves lives, whatever the mission is of the organization. The power of this model is it accelerates the creation of value by putting authority in the hands of those individuals who are closest to the customer and have the most intimate knowledge of how to monetize data. It reduces the diseconomies at scale of having a centralized or a monolithic data architecture. And it scales much better than legacy approaches because the atomic unit is a data domain, not a monolithic warehouse or a lake. Zhamak Dehghani is a software engineer who is attempting to popularize the concept of a global mesh. Her work is outstanding, and it's strengthened our belief that practitioners see this the same way that we do. And to paraphrase her view, "A domain centric system must be secure and governed with standard policies across domains." It has to be trusted. As I said, nobody's going to use data they don't trust. It's got to be discoverable via a data catalog with rich metadata. The data sets have to be self-describing and designed for self-service. Accessibility for all users is crucial as is interoperability, without which distributed systems, as we know, fail. So what does this all have to do with Snowflake? As I said, Snowflake is not just a data warehouse. In our view, it's always had the potential to be more. Our assessment is that attacking the data warehouse use cases, it gave Snowflake a straightforward easy-to-understand narrative that allowed it to get a foothold in the market. Data warehouses are notoriously expensive, cumbersome, and resource intensive, but they're a critical aspect to reporting and analytics. So it was logical for Snowflake to target on-premise legacy data warehouses and their smaller cousins, the datalakes, as early use cases. By putting forth and demonstrating a simple data warehouse alternative that can be spun up quickly, Snowflake was able to gain traction, demonstrate repeatability, and attract the capital necessary to scale to its vision. This chart shows the three layers of Snowflake's architecture that have been well-documented. The separation of compute and storage, and the outer layer of cloud services. But I want to call your attention to the bottom part of the chart, the so-called Cloud Agnostic Layer that Snowflake introduced in 2018. This layer is somewhat misunderstood. Not only did Snowflake make its Cloud-native database compatible to run on AWS than Azure in the 2020 GCP, what Snowflake has done is to obstruct cloud infrastructure complexity and create what it calls the data cloud. What's the data cloud? We don't believe the data cloud is just a marketing term that doesn't have any substance. Just as SAS is Simplified Application Software and iOS made it possible to eliminate the value drain associated with provisioning infrastructure, a data cloud, in concept, can simplify data access, and break down fragmentation and enable shared data across the globe. Snowflake, they have a first mover advantage in this space, and we see a number of fundamental aspects that comprise a data cloud. First, massive scale with virtually unlimited compute and storage resource that are enabled by the public cloud. We talk about this a lot. Second is a data or database architecture that's built to take advantage of native public cloud services. This is why Frank Slootman says, "We've burned the boats. We're not ever doing on-prem. We're all in on cloud and cloud native." Third is an obstruction layer that hides the complexity of infrastructure. and fourth is a governed and secured shared access system where any user in the system, if allowed, can get access to any data in the cloud. So a key enabler of the data cloud is this thing called the global data mesh. Now, earlier this year, Snowflake introduced its global data mesh. Over the course of its recent history, Snowflake has been building out its data cloud by creating data regions, strategically tapping key locations of AWS regions and then adding Azure and GCP. The complexity of the underlying cloud infrastructure has been stripped away to enable self-service, and any Snowflake user becomes part of this global mesh, independent of the cloud that they're on. Okay. So now, let's go back to what we were talking about earlier. Users in this mesh will be our domain owners. They're building monetizable services and products around data. They're most likely dealing with relatively small read only datasets. They can adjust data from any source very easily and quickly set up security and governance to enable data sharing across different parts of an organization, or, very importantly, an ecosystem. Access control and governance is automated. The data sets are addressable. The data owners have clearly defined missions and they own the data through the life cycle. Data that is specific and purposely shaped for their missions. Now, you're probably asking, "What happens to the technical team and the underlying infrastructure and the cluster it's in? How do I get the compute close to the data? And what about data sovereignty and the physical storage later, and the costs?" All these are good questions, and I'm not saying these are trivial. But the answer is these are implementation details that are pushed to a self-service layer managed by a group of engineers that serves the data owners. And as long as the domain expert/data owner is driving monetization, this piece of the puzzle becomes self-funding. As I said before, Snowflake has to help these users to optimize their spend with predictive tooling that aligns spend with value and shows ROI. While there may not be a strong motivation for Snowflake to do this, my belief is that they'd better get good at it or someone else will do it for them and steal their ideas. All right. Let me end with some ETR data to show you just how Snowflake is getting a foothold on the market. Followers of this program know that ETR uses a consistent methodology to go to its practitioner base, its buyer base each quarter and ask them a series of questions. They focus on the areas that the technology buyer is most familiar with, and they ask a series of questions to determine the spending momentum around a company within a specific domain. This chart shows one of my favorite examples. It shows data from the October ETR survey of 1,438 respondents. And it isolates on the data warehouse and database sector. I know I just got through telling you that the world is going to change and Snowflake's not a data warehouse vendor, but there's no construct today in the ETR dataset to cut a data cloud or globally distributed data mesh. So you're going to have to deal with this. What this chart shows is net score in the y-axis. That's a measure of spending velocity, and it's calculated by asking customers, "Are you spending more or less on a particular platform?" And then subtracting the lesses from the mores. It's more granular than that, but that's the basic concept. Now, on the x-axis is market share, which is ETR's measure of pervasiveness in the survey. You can see superimposed in the upper right-hand corner, a table that shows the net score and the shared N for each company. Now, shared N is the number of mentions in the dataset within, in this case, the data warehousing sector. Snowflake, once again, leads all players with a 75% net score. This is a very elevated number and is higher than that of all other players, including the big cloud companies. Now, we've been tracking this for a while, and Snowflake is holding firm on both dimensions. When Snowflake first hit the dataset, it was in the single digits along the horizontal axis and continues to creep to the right as it adds more customers. Now, here's another chart. I call it the wheel chart that breaks down the components of Snowflake's net score or spending momentum. The lime green is new adoption, the forest green is customers spending more than 5%, the gray is flat spend, the pink is declining by more than 5%, and the bright red is retiring the platform. So you can see the trend. It's all momentum for this company. Now, what Snowflake has done is they grabbed a hold of the market by simplifying data warehouse. But the strategic aspect of that is that it enables the data cloud leveraging the global mesh concept. And the company has introduced a data marketplace to facilitate data sharing across ecosystems. This is all about network effects. In the mid to late 1990s, as the internet was being built out, I worked at IDG with Bob Metcalfe, who was the publisher of InfoWorld. During that time, we'd go on speaking tours all over the world, and I would listen very carefully as he applied Metcalfe's law to the internet. Metcalfe's law states that the value of the network is proportional to the square of the number of connected nodes or users on that system. Said another way, while the cost of adding new nodes to a network scales linearly, the consequent value scores scales exponentially. Now, apply that to the data cloud. The marginal cost of adding a user is negligible, practically zero, but the value of being able to access any dataset in the cloud... Well, let me just say this. There's no limitation to the magnitude of the market. My prediction is that this idea of a global mesh will completely change the way leading companies structure their businesses and, particularly, their data architectures. It will be the technologists that serve domain specialists as it should be. Okay. Well, what do you think? DM me @dvellante or email me at david.vellante@siliconangle.com or comment on my LinkedIn? Remember, these episodes are all available as podcasts, so please subscribe wherever you listen. I publish weekly on wikibon.com and siliconangle.com, and don't forget to check out etr.plus for all the survey analysis. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching. Be well, and we'll see you next time. (upbeat music)