(dramatic music) >> Live from San Francisco. It's theCUBE, covering IBM Think 2019. Brought to you by IBM. >> Welcome back to theCUBE. We are live at IBM Think 2019. I'm Lisa Martin with Dave Vellante. We're in San Francisco this year at the newly rejuved Moscone Center. Welcoming to theCUBE for the first time, Jozef de Vries, Director of IBM Cloud Databases. Jozef, it's great to have you on the program. >> Thank you very much, great to be here, great to be here. >> So as we were talking before we went live, this is, I was asking what you're excited about for this year's IBM Think. >> Yeah. >> Only the second annual IBM Think. >> Right. >> This big merger of a number of shows. >> Sure, you're right. >> Day minus one, team minus one, >> Yeah. >> everything really kicks off tomorrow. Talk to us about some of the things that you're working on. You've been at IBM for a long time. >> Mmm hmm. >> But cloud managed databases, let's talk value there for the customers. >> Yeah, definitely. Cloud managed databases really, at its core, it's about simplifying adoption of cloud provided services and reducing the capital expense that comes along with developing applications. Fundamentally what we're trying to do is abstract the overhead that is associated with running your own systems. Whether it's the infrastructure management, whether it's the network management, whether it's the configuration and deployment of you databases. Our collection of services really is about streamlining time to value of accessing and building against your databases. So we are really focused on is allowing the developer to focus on their business critical applications, their objectives, and really what they're paid for. They're paid to build applications, not paid to maintain systems. When we talk about the CIO office, the CTO office, they are looking at cost, they're looking at ways to reduce overall expenditures. And what we're able to provide with cloud managed databases is the ability not to have to staff an IT team, not to have to maintain and pay for infrastructure, not have to procure licenses, what have you, everything that goes into standing up the managing those systems yourself, we provide that and we provide the consumption based methods. So you basically pay for what you use, and we have various ways in which you can interact with your databases and the charges that are associated with that. But it really is again about alleviating all of that overhead and that expense that is associated with running systems yourself. >> 15 years ago, you're back to, before you started with IBM, >> Yeah. >> There was obviously IBM DB2, Oracle, SQL Server, >> SQL Server. >> I guess MySQL is around >> Mm hmm. >> back then, LabStack was building out the internet. But databases are pretty boring >> Yeah. >> back then. And then all of a sudden, it exploded. >> Right. >> And the NoSQL movement happened in a huge way. >> Mm hmm. >> Coincided with the big data movement. What happened? >> Yeah, I think as we saw the space of this technology evolve, and a variety of different kind of use cases cropping up. The development community kind of respond to that. And really what we try to do with our portfolio is provide that variety of database technology solutions. To me, not any number of different use cases. And we like to think about it broken down into two categories. Your primary data stores. This is where your applications are writing and reading the data that has been stored. And then particularly to your point, this is where we call the auxiliary data services, for example. These are your in memory caches, your message brokers, your search index, what have you. There is a plethora of different database technologies out there today that plug into any number of different use cases and application developers are attempting to fill. And more often than not, they're using more than one database at a time. And really what we're trying to do at IBM with our cloud managed database offering is provide a variety of those data services and database technologies to meet a variety of those use cases, whether they're mixing and matching, or different kind of applications workloads or what have you. We'd like to provide our customers with the choices that are out there today in the community at large. >> So many choices. >> Yeah. >> Am I hearing that its kind of horses for courses? I mean, you get things like, even niches like Cumulo with fine grain security. >> Yeah. >> Or Couchbase, obviously. >> Mm hmm. This one scales. And then this one is easy to use. You take Mongo, for text, really easy to use >> Yeah exactly. >> Sort of different specialized use cases. How do you squint through, and how does IBM match the right characteristics with the right technology? >> It's really, it's two-pronged. It's about understanding the user base. Understanding and listening to your customers. And really internalizing what are the use cases that they are looking to fulfill? It's also being in tune with the database technology in the market today. It's understanding where there are trends. Understanding where there are new use cases cropping up. And it's about building a deep enough engineering operations team where we can quickly spin up these new offerings. And again provide that technology to our end customers. And it's about working with our customers as well. And understanding the use cases and then sometimes making recommendations on what database technology or combination of databases would be best suited for their objectives. >> I'm curious. One of the things that you mentioned in terms of what the developer's day-to-day job should be, is this almost IBM's approach to aligning with the developer role and enabling it in new ways? >> It is really about, I think, having sympathy in delivering on solutions in regards that is simply for the pains that they had otherwise endured 10, 15 years ago. When the notion of cloud managed anything really wasn't a thing yet. Or was just starting to emerge. IBM in houses runs their own systems for years and years obviously and the folks on my team, they have come from other companies, they know that the pain, what pain is involved in trying to run services. So like I said it's a little bit out of sympathy, it's a bit out of knowing what your users need in a cloud managed service. Whether again it's security, or availability, or redundancy, you name it. It's about coming around to the other side of the table and I sat where you once sat. And we know what you need out of your data services. So trusting us to provide that for you. >> How are the requirements different? Things like recovery and resiliency. Do I need asset compliance in this new world? May be you could. >> Yeah. It's funny, that's a good question in that we don't necessarily deal so much with database specific requirements. Again as I mention we try to provide a variety of different database technologies. And by and large the users are going to know what they need, what combinations that they will need. And we'll work with them if they're navigating their way through it. Really what we see more the requirements these days are around the management characteristics. As you cited, are they highly available? Are they backed up? What's your disaster recovery policy? What security policies do you have in place? what compliance, so on and so forth. It's really about presenting the overall package of that managed solution. Not so much, whether the database is going to be high available verses consistent replication or what have you. I mean that's in there, and it's part of what we engage with our customers about, but also what we'd like to put a lot of emphasis is on providing those recognized database technologies so that there is a community behind and there's opportunity for the users to understand what it is that they need beyond just what we can sell them. It's really about selling the value proposition of again, the management characteristics of the services. >> So who do you see as the competition? Obviously the other big, the two big cloud providers, AWS and Azure. >> Yep. >> You're competing with them. >> Definitely. >> Quality of offerings. May be talk about how you fit. >> And Google's another one. Or Oracle is another emerging one. Even Alibaba is catching up quite a bit. It really feels like a neck-to-neck race in our day after day. The way we try to approach our portfolio is focusing on deep, broad and secure. Deep being that there're a core set of database technologies. We're building the database itself. Db2, Cloudant which is based off of Couchbase. Excuse me, CouchDB. And then broad. Again as I've been mentioning, having a variety of different database technologies. And they're secure across the board. Whether it's secure in how we run the systems, secure on how we certify them through external compliance certifications. Or secure in how we integrate with security based tooling that our users can take advantage of. Regarding our competitors, it really is one week it may be a new big data at scale type of database technology. Another day it may be, or another week it might be deeper integrations into the platform. It might be new open source database technologies. It might be a new proprietary database technology. But we're, it's a constant, like I say, race to who got the most robust portfolio. >> Developers are like teenagers. They're fickle. >> Yeah, that too, that too. We got to be quick in order to respond to those demands. >> In this age of hybrid multi-cloud, where the average company has five plus private cloud, public cloud, through inertia, through acquisition, et cetera. Where's IBM's advantage there as companies are, I think we heard a stat the other day, Dave, that in 2018, 80% of the companies migrated data and apps from public cloud. In terms of this reality that companies live in this multi-cloud, where is IBM's advantage there? And where does your approach to cloud managed services really differentiate IBM's capabilities? >> Really there's, for the last couple of years, a tremendous amount of investment on building on the Kubernetes open source platform. And even in particular to our cloud managed database services, we have been developing and have been recently releasing a number of different databases that run on a platform that we've developed against Kubernetes. It's a platform that allows us to orchestrate deployments, deletions of databases, backups, high availability, platform level integrations, all, a number of different things. What that has allowed us to do when concerning a hybrid type of strategy is it makes our platform more portable. So Kubernetes is something that can run on the cloud. It can run in a private cloud. It can run on premise. And this platform we're developing is something that can be deployed, which we do today for private, public cloud consumption, which can also be packaged up and deploy into a private cloud type environment. And ultimately it's portable and it's leveraging of that Kubernetes technology itself. So we're not hamstringing ourselves to purely public cloud type services, or only private cloud type services. We want to have something that is abstracted enough that again it can move around to these different kind of environments. >> How important is open source and how important is it for you to commit to the different open source projects? There are so many, >> Yeah. >> And you have limited resources. So how do you manage that? >> Open source is really critical both in what we're building and what we're also offering. As we've talked about our users out there, they know what they often want or sometimes we nudge them to the right or to the left, but generally speaking it's around all the open source technologies and whatever may be trending for that current month is often times what we're getting requested for. It could be a Postgres. It could be a RabbitMQ. It could be ElasticSearch. What have you. And really we put a lot of emphasis on embracing the open source community, providing those database technologies to our customers. And then it allows our customers to benefit from the community at large too. We don't become again the sole provider of education and information about that technology. We're able to expose the whole community to our customers and they're able to take advantage of that. >> I hear a lot of complaints sometimes, particularly from folks that might list themselves in a marketplace for one cloud or another, that they feel like the primary cloud vendor might be nudging the customer into their proprietary database. What's IBM's position on that? Is that fair? Is that overblown? >> We obviously have proprietary tech, particularly the Db2. And that's something we're continue investing in. It's what we view as one of our strategic top priority database technologies. We are very active developers in the Couch community as well. I wouldn't consider that proprietary, but again back to the point of-- >> CouchDB. You're as the steward of CouchDB. >> Exactly. >> Right. >> Right, exactly. But again, firm believers in open source. We want to give those opportunities to our customers to avoid those vendor lock-in type situations. We actually have quite a lot of interests from our EU customer base. And by and large EU policies are around anti-trust and what have you. They tend to gravitate towards open source technology because they know it's again portable. They can be used in Postgres by IBM one month and if they no longer are satisfied with that, they can take their Postgres workloads and move them into another cloud provider. Ideally they're coming from the other cloud providers onto IBM. >> Well I should be actually more specific, in fairness, Dynamo's often cited. I supposed Google's Spanner although that's sort of a more of a niche, >> Mm hmm. >> specialized database. If I understand it correctly, Db2, that's a hard core transaction >> Sure. >> system. You're not going to confused that with, I don't think, anyway CouchDB. Although, who knows? May be there are some use cases there. But it sounds like you're not nudging them to your proprietary, certainly Db2 is proprietary. CouchDB is one of many options that you offer. >> Certainly Db2 is one of our core products for our database portfolio. And we do want to push our customers to Db2 where-- >> If it makes sense. >> Exactly, where it makes sense. And where there's demand for it. If it doesn't make sense so there's not demand we will offer up any number of the other databases that we also offer. >> Excellent, here's our last question.As >> Sure. >> As IBM Think the 2nd annual kicks off really tomorrow. For this developer audience that you were talking about a lot in our conversation, what are some of the exciting things that they're going to you? Any sort of obviously not breaking news, but >> Mmm hmm. >> Where would you advise the developer community, who's attending IBM Think to go to learn more about cloud managed databases? And how they can really become far more efficient to do their jobs better. >> Sure. Databases are hard, plain and simple. They are particularly hard to run, and developers who are not necessarily database admins, they're not database operators, that they want to focus on building the applications, are going to want to find solutions that alleviate that overhead of running those systems themselves. So to your question we've got sessions all throughout the week where we're talking about our Cloudant offerings and the future of where we're going with that. We've got a couple of different sessions around our IBM cloud database portfolio. This is a lot of the open source database technology we're running. We have demos in the solution center and Db2's strided all around the conference as well. So there's lots of different sessions focused on talking the value proposition of IBM's cloud managed database portfolio across the board. >> A lot of opportunities for learning. Well, Jozef de Vries, Thank you so much for joining Dave and me on theCube this afternoon. >> Thank you very much, it was great. And for Dave Vallente, I am Lisa Martin. You're watching theCube, live from IBM Think 2019. Day 1 stick around. We'll be right back with our next guest. (upbeat music)

>> Live from Las Vegas, it's theCUBE. Covering Boomi World 2018. Brought to you by Dell Boomi. >> Welcome back to theCUBE's continuing coverage of the 2nd Annual Boomi World 2018 from Las Vegas. I am Lisa Martin with John Ferrier, and we're welcoming to theCUBE, for the first time, the chief operating officer and chief customer officer, Chris Port. Chris, thanks so much for joining us on the program today. >> Thank you for having me. >> So, 2nd Annual Boomi World. Over 1,000 people here. The keynote was streaming, in what, 17 countries this morning. Big impact, 7,500 customers. You also said, Dell Boomi, we're adding five new customers every day. >> Yes. >> You have this opportunity to get your customers together with Crass, and Analysts, and your Partner Ecosystem. Talk to us about some of the strong messages that have come out from Dell Boomi in the last couple of days about your technology partner program, how you're re-defining iPaaS. >> Yes. Yeah, I think it's about the leadership that we've talked about effectively since there was a Gartner Magic Quadrant from our space, we've been in the leader of quadrants. So, incredibly excited about that, but the goal is how do we become a leader for the next 10, 20, 30 years. And, I think this week is not just the start, it's a continuation of that. So, we talked about the new technology partner program, which, to me, is just the continued evolution. We've always had a partner program, but it's just continuing on that journey and really starting to explore ways for partners to now start to build solutions on top of Boomi that they can then take to market that we support. Obviously, leveraging Boomi's technology, but then, building on our platform. I think we're talking about exploring and expanding our GSI and SI capabilities. So that force multiplier that Chris talked about. We have a great group of Boomi team members, but we know that those SIs and GSIs provide that force multiplier. We've also launched new services around enterprise innovation and enterprise architecture. We listen, this is 100% customer-driven. Customers talk to us. They love professional services from us, but they love to see it in a much more predictable, provided deliverables, in a subscription model, so we launched that this week. And then Steve Wood's going to talk tomorrow about a multitude of things from a product perspective that we feel are really kind of, this is where the iPaaS 2.0, as Chris called it, tomorrow is the start of that, and I think you guys will see that journey. >> There's a lot of challenges in this marketplace with cloud-native and on-premise legacy applications. They have great value as they get modernized in cloud. You guys are born in the cloud. Everything that Boomi has done since the start-up days has been cloud-native. So, that's an interesting perspective. That's going to be helpful as you guys take the customers to the next level. But, this connected business market that's developing is complicated. You got smart contracts around the corner with Blockchain. You've got integrating multiple developer environments, multiple toolchains. Just on and on. A lot of complexity. And, what team leaders want is less complexity. So, they don't want more complexity to solve more complexity. So, this is the struggle. How do you guys talk to customers who come to you and say, look, I've got complexity and I want to simplify but I still want to scale. I want to do these things. I want to be prepared for Blockchain. I want to be prepared for the next level of business. >> Yeah, I mean, I would say a couple things. I think, first off, we're agnostic in terms of on-prem versus cloud from an application perspective. Our predominant use case is a SaaS-based application that's in the cloud and an on-premise application. So, I think 7,500 customers, the 10 billion minutes of experience we talked about, that experience spans both on-prem and cloud. So, I think we have a really unique opportunity to see and live in both universes. The architecture is 100% cloud-native which gives us fundamental advantages. Now, in terms of what you talk about, in terms of the simplification. That's what everybody's striving for. They want to reduce the tools sets. And, again, I think that's the power of the platform. Steve Wood talks about it, drop the mic, we're the best at integration, low-code, high productivity. It's where we were born. It's what we built the back of the company on, but that said, over the last five to seven years, we've built a true platform around that core capability to now encompass master data management with Hub, API with MIDI, EDI with Exchange, and ultimately Flow that kind of brings everything together from that workflow low-code app piece. >> So, foundationally... Congratulations by the way. It's a good job. But, that's just the foundation. >> Absolutely. >> You guys talk about the keynote today. Michael Dell kind of hit it hard with the scale and the data tsunami with AI. >> Yes. >> As IoT is right around the corner or here with edge, whole new processes are developing. That not necessarily are predictable. Sometimes architecture might change over night. This is kind of the next Boomi way that we're seeing you guys set up for. How are you guys building that out? What are the key business model components? You mentioned the community that you have now, an ecosystem that's best developed and growing. How are you guys looking at configuring the business to build on the foundation and not skip a beat? >> Yeah, I mean, I think when you start talking about kind of the tsunami of data, as you put it, or that Michael put it this morning. When you think about Boomi, and how lightweight the out-of-market texture is, it creates this really incredibly fast way to create that data fabric. The data fabric, ultimately, is what will drive AI. It's being able to aggregate and see that, and then ultimately, put it in the AI engines. As we call it the fuel, or Michael or someone, coined it this morning the fuel. And, I think our architecture, and again, this is where being cloud-native, that you talked about, this is our profound differentiation. This is why we have the advantage in that space. It's up to us to take advantage of it, but I think, first off, it's that lightweight architecture that will allow us to really work within customers to create that data fabric that then drives AI, drives it into their organizations. We just heard from the panel that Mandy was on, and Blue/Green, and the chief security officer, chief privacy officer from Dell. And, again, everybody is talking about AI and howling about data and data privacy, but Boomi's in a unique place to kind of create that data fabric. I think the second one is being able to deploy AI into our own product and into our own community. And, in talking about staying ahead of the curve, that's paramount, that's our fundamental. In my opinion, that's the fundamental differentiator. It's the moat that we have today because we are single instance multi-tenants. So, people will talk about the number of customers they have, but all of ours live on one instance of Boomi. So, that 30 terabytes of anonymous metadata, that's all on one instance. So, we see that it's our opportunity, and you see it with suggest and assure and some of the things we pioneered in AI. It's our opportunity to take advantage of that with the future of things and Steve Wood will start talking about that tomorrow. I'm excited of how we deploy AI in Arctic community and our support in a much more proactive way help our customers solve problems and opportunities that they have every day. >> Michael Dell has talked numerous times on theCUBE, and even again today, and in the keynote that companies need to express their competitive differentiation with their data. Enterprises that has mostly been the sweet spot for Dell Boomi. Large organizations not born on the cloud, many of them, have a huge advantage of having a ton of data. You guys are a great example of how you are also using almost 30 terabytes of anonymous metadata, to tune... And that's too soft of a word. To really empower the platform. So, you're an example of, with the kind of transforming, using what you're saying is what companies need to differentiate. When you're in customer conversations, as the chief customer officer, how often does sort of that Boomi on Boomi transformation story come up and help customers get even more trust in the brand? >> That's a great question. I think it comes up more and more, and I would say it's Boomi on Boomi, but it's Boomi on Dell technologies as well. Because Michael talked about it, Dell went on this acquisition bench, and if you go look at it, it started roughly nine, 10 years ago. And, Boomi was literally the second, if you go look at kind of the assets that they purchased, Boomi was the second. And it was about 12 months after the first acquisition. And everybody is learning about what it can do, and they're like, wait a minute. We acquired this other company 12 months ago, and we're still trying to figure out, simply, how to make the two instances of Salesforce talk so that sales makers can just share leads and understand what they're doing in each other's accounts. We're, like, well that's kind of what Boomi does and within six weeks that problem was solved for that acquisition, and obviously the Boomi acquisition, and then, kind of carried that on. >> So, you use your own technology to solve the internal problem. >> Exactly, drink your own champagne. And that's just become more and more. I mean, we have a multitude of people from Dell technologies, IT here, this week, talking at some of the breakouts in terms of how they leverage it. They're now leveraging that. They're now leveraging Flow for different opportunities. Dell's got one of the largest service cloud deployments in the world happening. A lot of that will be powered by Boomi. And, so, those conversations come up all the time within customers. I think the Boomi on Boomi, I think the onboarding app will certainly give us an opportunity to talk more and more about that. Obviously, our application stack underneath the covers is integrated by Boomi. So, it absolutely comes up, but I think we're kind of at this inflection point in terms of these discussions where I would tell you they come up in a step function way more today than they did when I kind of came back to Boomi three years ago. >> You know, Chris, I got to ask your perspective. You made me think of some question. You mentioned that Internally Amazon had the same challenge with AWS. They solved their internal problems. And then, the rest is history. Dell has an interesting architecture now, and if you look back at the history of Dell, I know you look at how it was built out, Michael has been very successful in merging in as an equal with EMC, the acquisitions that came in, tuck-ins, and some in storage all over the place. You guys have a culture of acting like a startup. The founder on stage is, like, I'm jazzed, I'm going to go the next 30 years. I'm like, that's 85 I'll be like... (Chris laughing) Okay, so, this is a culture of startups. How does Boomi keep that startup edge? Because they were really SaaS first, early on. How does that maintain the culture? And, now, the power of Dell technologies. VMWare, the relationships. They've got some muscle within Dell, but mostly don't want to put the wet blanket on the innovation engine of Boomi. How do you guys operate that? Because you want to tap the internal. >> Yup. >> Build that, make that, feed into growth. Same time, be nimble and fast like a startup, and grow. >> Yeah, well, this is like the unique opportunity that I've had, right? I led the strategy that ultimately led to the acquisition of Boomi, led the due diligence, and then rolled out and was part of the leadership team eight years ago. Eight years ago to the day yesterday was the anniversary. And, part of the design point of the acquisition though, part of the selling point to Michael and his leadership team at the time, was incubate Boomi. Please, don't try to integrate it. >> Don't force it too early. >> No, let's leverage the power of Dell where we can, particularly from a go-to-market perspective and a branding perspective, but in terms of truly integrating when you think about integration in terms of M&A, that wasn't the playbook that we ran. In fact, my job as kind of the chief integration officer at the time was to really protect versus integrating. And, I would argue that that's kind of carried on eight years later. And, Chris McNabb and the team have, you know, Chris has built an incredible culture at Boomi. And, it's probably the first thing that we talk about at every leadership meeting which is we're trying to grow heads, and grow team members, and grow Boomers, 40, 50, 60% year-over-year in terms of our hiring. The one thing that we cannot relax on is that culture. And, Chris has infused that in us. Michael's absolutely an incredible backer of that. >> So, strategic since day one. >> Absolutely. >> You know that cloud's around the corner, but still you know you're early, so you probably got a good price on the deal anyway. But, you said, okay, cloud-native. You got VM, you got Pivotal. >> Yup. >> It's maturing in real-time every day. So, you guys had a plan from day one to be strategic that way. Not jam the revenue up and try to get the numbers up. >> No, and I would say even today, I think we're absolutely, we think there's incredible opportunities with partnerships with, obviously, Dell technologies, but with Pivotal, with Vitrustream, with potentially VMware. I think you'll continue to see us announce things and explore those, but Michael, he holds Chris, and ultimately the Boomi team, accountable to our P&L. We have to go meet our numbers. And, there is no forcing of partnerships. It's, like, it's where it makes sense, and there absolutely are things where there's logical sense. >> Well, now you're in the inflection point. You got to grow the business. But, the data is still going to be, that could be the next kick up. You don't know where you are in the inflection point, I'd imagine. Are you down here or is it hockey sticking up? Because if the data comes home, and you're a trust platform for the data, that feeds into the apps. >> Absolutely. >> That feeds into the API 2.0 economy. >> Yeah, yeah, yeah. And, I mean, yeah, it's a fair question. I don't know that we'll know until five years from now where we are today in terms of that inflection point. I would say typically we're actually seeing acceleration in our space, right? Like, usually, when you look at the Gartner, the Forrester stuff, that I stared at eight years ago. Usually they're very aggressive on their expectations. Their expectations for iPaaS were actually lower than what we've seen. And, we're actually seeing even acceleration and growth of the space. So, we know that we have this opportunity, I think, with data and the ability to create this data fabric and really drive those business results and insights into our customers. I think that's what puts us somewhere on that inflection point, but I would argue that it's more like this today than it is that. But, time will tell. >> So, customers, the bread and butter, the reason we're all here, right? 7,500 plus I mentioned in the beginning, five a day. You just today, Chris, recognized the first customer awards for Boomi customers, and you had some really cool categories, change agent, emerging technologies, innovator and ROI. Talk to us about the genesis of this customer awards program and how is that really kind of even internalized with the Boomi folks going, look at what we're enabling, so many different types of businesses to achieve. >> That's a great question. I mean, since I've been back, one thing that we try to instill in the sales cycle is really talking to customers, understanding what is the business value? What are you trying to get out of this? We're typically an ingredient of a broader project, so how do we articulate? What is that business value? What's the business outcome that you're trying to achieve? And, I think today was a way for us to talk aloud, and, ultimately, reward people that are leveraging technology. Boomi's a part of that, but, ultimately, what is the business value they're driving it? And, in a profound way, that's even amongst our 7,500 customers are unique in some way across those different four categories. So, that was really the genesis of the customer awards. It was trying to go find those types of customers that were somewhere much further along in their journey across one of those four pillars, but about their business outcomes. What they were trying to drive. Whether it be having a trading partner take six to 10 weeks down to three days. Whether it be driving better customer experience within customers trying to seek out advertising with charter. And, ultimately, get them, but, again, generating bottom-line results and top-line results. So it's about the business outcome, the business result. >> Final question, I know we got to break, but I want to get it out on the record. What are you investing in? What are you doubling down on? Obviously you're on a growth curve right now, so you can look back where you are in the next couple years, but certainly it's working. So, what are you doubling down on? Where is your key investment areas as you look at the next years, 24 months out. What's going down? How are you operating the business? >> Yeah, and maybe I'll highlight three things. I think first and foremost, it's our product, and I think you'll hear from Steve Wood tomorrow. So not just me, when you ask me that question, I'm going to talk about Boomi's investment priorities. So, first and foremost, the product. I think you'll see tomorrow. We started, I mean, look, three years ago we kind of did this separation from Dell technologies, where we're 100% owned, but that in terms of the profound impact and investment of the business, that's where we started this journey. But, in terms of the next 12 to 18 months, I'd tell you product, and you'll start to see that tomorrow, and how it's manifested itself, and where we're headed in the next 12 to 18 months. I'd tell you our go-to-market activity and there it's continuing to build out as global capabilities. It's continuing to really hone and focus our partner capabilities, and that's also figuring out how to leverage Dell technologies and really drive that, particularly to help bring us into those opportunities as we scale and continue to grow. And, then, I think the third is our customer success equation that I talked about this morning. Chris has been incredible. I genuinely mean it, success is a Boomi-wide initiative. We're only as good as our customer's experience today, and we invest in that every single day and that's been a profound investment area that we'll continue to ramp up to really plow down on that success equation we talked about. >> Well, Chris, thanks so much for joining John and me on the program. COO, chief customer officer and dare I also add chief listening officer. I've heard a lot about your listening to customers as well as employees. Thanks so much for your time, Chris. >> Thank you so much. >> I'm Lisa Martin with John Ferrier. You're watching theCUBE live from Boomi World 2018 in Las Vegas. John and I will be right back with our next guest. (upbeat music)

>> Narrator: Live from Austin, Texas. It's theCUBE, covering KubeCon and CloudNativeCon 2017. Brought to you by Red Hat, the Linux Foundation, and theCUBE's ecosystem partners. >> Hey, welcome back everyone, we're here live at theCUBE in Austin, Texas for KubeCon 2017, 2nd annual conference of the Kubernetes Conference, I'm John Furrier, here with my co-host, Stu Miniman, Ben Sigelman, who's the CEO of LightStep, welcome to theCUBE. >> Thank you so much. >> So you're also involved in open tracer, all this stuff with service mesh, really instrumental tech work going on right now. >> Mmhmm, yep. >> With this KubernetesCon, I mean Kubernetes has been successful. People are now learning for, the first time in mainstream, but it's really galvanized the community. At many levels, and I haven't seen this much action and so fast, up and down the stack. You know, you got the infrastructure plumbing guys, and you got the app plumbing guys all building really, really fast. What's the state of the union? Give us a peak of what's happening, what's solid, what's foundational? What are the building blocks that are being built on and what's the current task of jobs being worked on projects and what not? >> Yeah, and that's a great question. I was, emerged my hotel room yesterday just to get on the elevator and Kelsey Hightower emerged from his hotel room, turns out two doors down from me, and we're walking to the elevator together, I'm like, "Hey! You know, so, what's your big announcement?" He's so good on stage, he's a brilliant communicator, and he's like, you know, honestly, the big news right now, is that actually there's not that much news from a release standpoint about Kubernetes, which is actually a really big deal. It's gotten to the point where it's feature set is actually appropriate and somewhat stable. And now we finally are at the point where it's, I think, it has a really natural architecture for plugins and extensions and now we can build this entire ecosystem around it, instead of building around something that's a bit of a moving target. I think it's incredible how, it is truly incredible, to see this conference over the last couple of years. >> So Pete's foundational elements are in place. >> Yeah. >> That's his, kind of his... >> Yeah, exactly. And it's incredible to see how much of, not just a commercial ecosystem, but a technology ecosystem, that's built around those primitives, and so I think those really are the right primitives, to democratize the pieces that should be democratized, and to centralize the pieces that should be centralized. So to me, this year is really about going a level up in the stack, and delivering value that's beyond, you know, the container, Kubernetes level, and that's what a lot of the projects that I'm excited about are doing. >> Yeah, so Ben, and that leads right into one of the things that we've been talking about all week here, service meshes. >> Ben: Yeah. >> So, you gave a keynote yesterday, maybe give our audience a little bit about service meshes, servibility, and there's something about a pigeon? >> (laughs) Yeah that was very funny. Just the reference about the pigeon, the first slide in my talk was a picture of a murmuration of starlings, this beautiful cloud of birds moving in harmony, and while I was waxing on about how this represented microservices, an actual bird flew above me on stage. There was a pigeon trapped in this room `(laughter) and so everyone started laughing, I didn't know what was so funny, I'm like... >> Jeez. What a great demo. >> ...like what did I do wrong? Do I have a note on my back or something? And then the hilarious thing is the second slide was actually the operational experience of deploying this sort of microservice technology is actually very difficult, and so it was this slide from Alfred Hitchcock's "The Birds," with these birds attacking this poor child. And so, and the bird is still circling around above me. It was perfect stagecraft, I wish I had tried to do it, it would have been amazing to take credit for arranging an actual live animal as part of my presentation. But in terms of the actual material in the presentation, which may be less entertaining than the bird flying around my head, but the material of the presentation is something I feel very strongly about, and I alluded to this a moment ago, I think that containers are incredibly important, I think Kubernetes is incredibly important, and I am extraordinarily confident that in ten years, they're going to be everywhere. That said, they're not something an application developer really should care that deeply about as part of their job of writing business logic for the service that they are maintaining and developing. That shouldn't be a layer that they care about. And there are a lot of really, really important problems that crop up at the application layer. At Google, the way we addressed this, was by having not a monolithic architecture, but a monolithic software repository where everyone developed the same code base, but one of the things that I thought was interesting was being at Google, if you wanted to deploy an application, even something that just printed out 'Hello, world' or something, it was like a 150 megabyte binary, because there's so much stuff that was crammed in to level 7, user level stuff, and that was right for Google, it's not really the best architecture for a lot of enterprises out there and I think what's so cool about service mesh, is that it's taken a bunch of really, genuinely hard computer science problems, like service discovery, connection, and load balancing, and reconnection, health checks, security and authentication, observability and tracing, these are really hard things to do well, and it's factored them off into a side car that you can run alongside ordinary applications that were not even developed with that in mind and take advantage of these application level, level 7 primitives. We've had people who are trying to build solutions for any number of managerial and monitoring tasks at the container level, where often that stuff is completely obscured. Like by the time you're at the kernel that you can't see any of this stuff. If you're up at level 7 in the service mesh, you have easy access to application level data, which makes everything a lot more elegant and straightforward for developers, so it's like, to me, it's this single point of integration that removes a bunch of hard computer science problems from ordinary application development. >> And so people were stuffing containers basically and trying to overdrive that. Makes total sense architecturally and I want you to take a step back and kind of unpack that a little bit. We didn't get here by accident. We got here through real hard work, I mean people were out there building from open-source large-scale systems. >> Yeah. >> Uber, Lyft, there's a handful of other examples. What was the driver around this, because you're talking about a really elegant architecture that allows for solving a problem for the guys that solve their own problems. Thousands, hundreds of thousands of transactions, services, millions of transactions per second. >> Yup. >> So this was not like "Hey, let's just design a new system!" It was some scar tissue. >> Yeah. >> How does that connect to like, reality now for, whether it's a start-up saying "Hey, you know, we're a couple of years old, we're on AWS, and we're growing, and I want to add more value, but I don't want to relearn machine learning, I want to build on all this stuff and create business value from my enterprise, growing an enterprise. Or, big enterprises, trying to be cloud enabled. So that's, how should someone think about that? And what specifically was the problem that was solved? >> Yes. Well, I'm an obsessive person, I'll admit that. And I'm personally obsessed with performance, and so when I think about this, I actually think about profiling the engineers who are building this stuff. You have developers, let's profile them, like what are they spending their time on? 'Cause that's really a precious resource right now, right? It's like, it's hard to even hire people fast enough, right? So if you think about profiling people, you have folks that are spending a lot of time trying to get their services communicated properly, to authenticate, to observe these systems, in a way that's sane. And so it's only natural you try to factor that out and make that factored out. You try to amortize the cost of solving that problem across your entire organization. And I think that you've seen people who've been at other companies, and want to recreate something like what they had at Google or Facebook or Twitter or what have you, but they want to do it in a way that meshes with their existing systems. I'm actually not surprised that super, super young companies that are starting with the true green field code base, move in this direction. What has been interesting to me, and although I shouldn't say surprising, this is actually very rational, but you also have companies that are much larger, and we, LightStep has, we have customers that are running a mainframe, alongside legacy Java VMs, alongside microservices, and they're all working in concert to the service application requests from end users. And these things need to talk to each other, and I think what's actually really fun for me, Google gets a lot of credit for building things the right way, I don't know if that's accurate for not, but it's really funny 'cause the problem is actually a lot more interesting outside of Google, because you have to integrate with a much larger surface area and the thing that's so exciting to me about a lot of the technologies that are really taking off here, is that they're designed for that kind of heterogeneity, certainly I've talked about service mesh a million times already here, open tracing also exists specifically because of heterogeneity, we didn't need open tracing at Google because everything was perfectly factored, so it was unnecessary. Outside of Google, it's necessary to have a common API to describe transactions as they propagate, because otherwise, you can't make sense of anything that's happening in your application. This sort of heterogeneity has encouraged projects that standardize at the right layer, and I think those are the ones that are proliferating. >> What is service mesh about now? I mean, how would you describe it, I mean, how would you define, in the world of Kubernetes, in the world we're talking about, for someone just getting, tech person, just getting started. What's the hubbub about with service mesh? What is it? >> Well, I mean, I think at the most basic level, it's something that sits in between any two processes that are communicating in your system, and it sits in between them at a layer where you can observe the application itself. Like, you're able to access application levels, security information application level, primitives like, you know, the particular path you're hitting for any HTP requests, something like that. It's something that sits in between at that layer. Because microservices, you know, I've seen Lyft up close 'cause they're also a customer for LightStep, and to see Envoy deployed at their company is really instructive. It's amazing, I mean it's really amazing. They went from having no integration with our product to having 100% integration with our product by flipping a configuration bit to on, you know. Actually it wasn't even on, they could do it by percentage, I mean, they can roll these things out with perfect, perfect precision. And, I mean, it's an incredibly powerful thing to be able to have that kind of leverage over an entire architecture and that didn't require all their developers to redeploy. This system required the service mesh to redeploy, so you make these sorts of changes without touching application CSCD stuff, you can do all these infrastructural level changes independently from application pushes-- >> All right, >> And that's very powerful. >> So, so hold on, I know Stu wants to get a question in, but let's stop there for a second. Compare and contrast what the old way would have been. >> Stu: Yeah. What would it have taken to do this similar concept that full team had met, assuming they had another architecture. >> I've seen, I mean, you know- >> John: Months, weeks, redeploys... >> So, you know, the model that I've seen at Google where would we make changes to software that was linked into every application would go out with the next release, we would make that change in some central place, I'd say 50% of the services would be deployed within a week, 90% within two weeks, but to get to 99% would take over a year, and so the issue is if you need a change that's going to cut across your entire system, it is not feasible to wait for people to redeploy because there are going to be services that are not being maintained by human beings anymore, and no one's about to volunteer for that chore- >> John: It's a nightmare basically. >> Of reintegrating, taking in months of code changes, making sure it still works and deploys. >> Yeah, they're going to quit right there. I mean, no one wants that. >> It's infeasible. >> Yeah, it's not feasible. >> Ben, I wanted you to be able to share a little bit about founding LightStep, you know what's kind of the need in the market, and what you're seeing from your early customers. >> Sure, LightStep is, it has a pretty simple mission. We aim to deliver insights about very complex production software, which is commonplace at this point. Anyone who's building a meaningful business is building meaningful production software, and that means it's complicated. So that's what we want to do. The way that we're doing that with our first product, LightStep XPM, is by delivering root cause analysis for the symptoms that are of most interest to these businesses, regardless of their application or architecture, as I said earlier, we have customers that run mainframes as well as microservices at the same time, multi-cloud, it doesn't matter. We follow transactions across these distributed services and use those to explain behaviors that they're puzzling over and help them with performance analysis and root cause analysis. >> And what's the relationship between the open source projects and... >> That's a great question. It's not a normal open core model. Open tracing is really an API project that's designed to ease integration with any number of vendors, and open tracing is supported by LightStep of course, but also by Jaeger, and CNCF, it's compatible with Zipkin, it's supported by New Relic and Datadog, I'll give a shoutout to some competitors. We're all in this together in the sense that I think we see that we all have a much bigger market as things like open tracing proliferate, and make it easier to actually observe your own system. I would love to compete in the playing field of solutions and not worry so much about integration, so open tracing is an integration project, it's not our core technology. Our core IP is something that's very powerful, that's designed to absorb a lot of information about these distributed systems and deliver value about that. >> And when I look at your website, and see kind of some of your early customers, I mean, jump out, you know, Lyft, Twilio, Digital Ocean, I mean, these are not kind of your typical companies, is it, you know, fully kind of cloud-native, you know, horn of the web, type companies? >> I'm really glad you asked that. No. >> Stu: Yeah. >> I mean, most of our customers at this point are, have actually never seen a full microservice deployment, certainly not at one of customers. It's always a combination of a monolith in the middle and microservices on the outside, but a lot of our customers are more traditional enterprises that we haven't put on our website for logo rights reasons, but they get a lot value out of the solution, I would say even more value in some cases because they're dealing with a greater diversity of technology generations they need to cut across. >> Yeah, I want to go back. You mentioned the time for people these days and you talk about developers and people building, the fight for talent is huge out there. What are you seeing in your customers? Is that something that you help? How's kind of that interaction? >> Yeah absolutely, I mean, I think, Digital Ocean says they're saving, I think 1000 engineer hours a month or something like that on LightStep. It's a huge timesaver for people who are trying to get to the bottom of issues. So it's a labor issue, but also root cause analysis, I mean, every second counts. Seconds cost hundreds of thousands of dollars for some of our customers for any big outage, and so we help people get those, Twilio's addressing the instance 92% faster after using LightStep, so it's a big change to their root cause analysis. >> Yeah, there was a great quote I saw that said, "When something goes wrong, it used to be you knew, now it turned into a murder mystery." >> Yeah. (laughter) >> Tell the story of why did you start the company. Was there an itch you were scratching? You saying, "Hey, you know, I've seen this movie before, I want to get out there, help customers, I mean, I heard, your mission is really straightforward, clean, good positioning. Why start the company? What was the rationale? What was the motivation? >> That's a very easy one for me. I mean, the reason I left Google was not necessarily to start a company per se, it was that I wanted to have as much of an impact on the industry as I could, I wanted to see things, not just make money and siphon cash away from companies, but actually to change the way that software is built. And the first act for us, this product, is a way for us to kind of get into the tendril, get our system deep into the fabric of an application, and from that point, I'd like to see LightStep really change the way people build software. I think people right now, it's almost like everyone's programming an assembly. Like we're all trying to operate this level that's totally inappropriate, and I'd love to see LightStep be a part of this story for making the industry move up the value chain and really focus on building applications, and that's what I want to see us do. >> You know, we've been saying, first, we have a similar mission along our media business, but one of the things we're seeing, we go to all the shows, sometimes it's like, why is theCUBE covering, you know, Node.js, or why are you covering Hadoop in 2010, why are you, because we see it early, we get in early, as I said, we can see the innovation, we like it, but I got to tell you, we've been seeing recently, I've been seeing it specifically, we see a huge renaissance in software development companies. >> Yeah, for sure. >> And my piece is, I want to test this with you because I think this is going to change the culture, certainly in Silicon Valley and around the world. Certainly with open source is exponentially growing, you know, Zemlin puts that stat up pretty clear. All software development models was crafty and built a product you QA and you'd ship it, it either worked or it didn't work, put some art to it, around ownership, and then AdJail derisked that risk, but you can get it to the market quicker, and you listen to the data, you learn from the data, but it kind of took the craft out of it. You know what I'm saying, almost we're coding and we're iterating, we're on a treadmill, which is good. But now, with what we're seeing here, is that you're getting back to extracting away, to your point, all these services you don't need to worry about anymore. I could actually focus all of my attention on the artisan aspect of the solution. Not UX, love UX design, not that kind of art, but something about software art. What's your reaction to that? Do you see that coming? Because if this continues, we're going to have a whole class of software developers just essentially painting software art, if you will. >> Yeah. >> I mean, that potentially is a scenario. Your thoughts. >> Yes, I agree with that scenario being feasible. I think it's probably more than a couple of weeks away, but I'm really excited about it. I think you're right on the money, I think a lot of the changes that we're seeing allow people to operate more independently and that's what motivates the transitions to microservice in the first place, it wasn't just to rewrite everyone's software for fun, it was because we want everyone to be able to be independent of each other and operate in that mode. The thing that I think is exciting about that vision which I would echo is a lot of the primitives that we see in the marketplace right now allow developers to focus on the semantics of application and the requirements of application which is where all of the interesting stuff is, and what we all get excited about. And I think we do see a lot of the, this number of people here right now, that investment as a community in allowing developers to focus on the logic and nothing more is really tremendous and exciting to me. >> How has community changed? I know you believe in community. Community's more important than ever now, in this new model, 'cause there's so much leverage going on with the software. How important is community and how is it changing and how should it evolve to handle all this awesome growth? >> Yeah I do have some thoughts about that. It's definitely important, I mean no one's going to deny that. I think one of the biggest challenges that I think about anyway in this sphere, has to do with, I referred to this earlier, it's important to figure out what problem you're solving with the community aspect of things, like with open tracing we thought really hard about this, like are we going to focus on, like, the bits and bytes and the wire protocols, or on the part that really needs to be standardized. I think community makes sense when standards are appropriate and standard interfaces are appropriate. I'm actually a little bit skeptical of community driven solutions where it's, you're delivering the entire package as a community because it ends up intersecting in ways that are complex I think with business motivations. I think the most successful projects are areas where the community really must collaborate, which usually has something to do with standardization. Those are the areas where I'm most excited. And then you actually literally, I was talking with Ken Goldberg yesterday, and they intentionally carved out areas for vendors to play, because they don't want to kind of meddle in that are. It's actually better not to meddle in that area. It's actually better- >> It's like microservices, you put the vendors over there and you put core commuters over there. Ben Sigelman, thanks for coming on theCUBE, I appreciate it. Congratulations on LightStep and the success and your talks here. Early community exploding, cloud native is not only a movement, it's clear to everyone, cloud and data and software and open source is making it happen, easier, accelerating velocity. It's theCUBE, doing our part, bringing you the data, here in Texas, I'm John Furrier, with Stu Miniman. We're back with more live coverage after this short break. >> Thank you. (techno music)

>> Announcer: Live, from Stanford University, it's the Cube, covering The Women in Data Science Conference, 2017. >> Welcome back to The Cube, we are live at Stanford University, at the 2nd annual Women in Data Science Conference, this great, fantastic one day technical conference. And we are so excited to be joined by Yael Garten, who was one of the career panelists. Yael, you are the Director of Data Science at LinkedIn, welcome to the cube. >> Yeah, thank you, thanks for having me. So excited to have you here, everybody knows LinkedIn. My parents even have probably multiple LinkedIn accounts, but they do. You've served, what 400 and plus million accounts, I'd love to understand, what is the role, what's the data scientist's role in the business overall? >> Yeah, so I guess when people ask me about data science, what I love to kind of start with is there are a couple different types of data science. And so I would basically say that there are two main categories by which we use data science at LinkedIn. If you think about it, there is really data science where a product of your work is for a human to consume. So using data to help inform business or product strategy, to make better products, make more informed decisions about how you're investing your resources. So that's one side, which is often called decision sciences, or advanced analytics. Another type of data science is where the consumer of the output is a machine. Alright so rather than a human, a machine. So basically they these are things like machine learning models and recommendation systems. So we have really both of those. The second category is what we call data products. And so we use those in virtually everything we do. So on the data products, much of LinkedIn is a data product, it's really based on date. Right, our profiles, our connection graph, the way that people are engaging with LinkedIn helps us improve the product for our members and clients. And then we use that data internally, to really make better decisions, to understand, you know how can we better serve the world's professionals, and make them more productive and successful? >> Right, fantastic, so tell us a little bit about your team. It sounds like it's sort of broken into those two domains. You must have quite a, a large team, or a lean team? >> So yeah, we have, the way we have our team is that we work really closely within all of our product verticals, and we embed closely with the business, to really understand kind of what are the needs. And then we work very cross-functionally. So we will typically have in any group, sort of a product manager, and engineer, a designer, a data scientist, often it's from both kinds of data scientists. So sort of one on the analytic side, one on the machine learning side. Right, marketing, business operation, so really very cross-functional teams working together, using this data. >> Very smart, it sounds very integrated from the beginning, where they kind of by design-- >> Yes. >> So that collaboration is really sort of natural within LinkedIn? >> Yes. >> That's fantastic, very progressive. And certainly it's something that everybody benefits from. >> Yes. >> Right because as whether you're on the advanced analytic side, or on the machine learning side, you're getting exposure to the business side, vice versa, which, that's really a great environment for success. >> Yes, yeah and part of, I think, what I love about LinkedIn is actually our data culture, and how kind of data is infused in the culture of how we do things. >> Right, which is really-- >> Right, not always the case. >> It's not, and it's, cultural shifts have, we were talking about that with a number of guests today, and especially the size of the organization, that's tough. >> Yael: Yes. >> So to have that built in and that integration as part of, this is how we do business is, really you can imagine all the potential and possibilities there. So would love to understand, how is LinkedIn using data to recommend ways to evolve products and services to best serve all of it's members? >> Yeah, so maybe two different examples of how we do this, one is, what we do is every launch that we have, so every feature that we generate, we really do it at an online experimentation setting. So we have a certain feature that we're about to roll out to our members. And we want to make sure that it's a better experience for our members. And better, as measured by kind of the metrics that we've defined in terms of measures of success. And so, which is really aligned to what value we believe we're delivering our members and customers. And so when we roll out features, we'll roll it out to a certain percentage of our users, test the downstream impacts of that, and then decide, based on that, whether we actually roll that feature out to 100% of members. And so that's one of the things that my team is heavily involved in, is really helping to use that data to make sure that we are structuring things in a way that's statistically sound, so that we can measure the impacts correctly, of rolling out certain features. So that's kind of one category of work. And the other category is really to, to do sort of opportunity identification, and kind of deep-dive insights into understanding into a certain product area. Where are there opportunities to improve the product? So one, let me give you a high-level example. One of the ways we might use data is to say okay, Are certain members in certain countries accessing via iOS or Android? And if so, should we be developing more in differentiating between iOS and Android apps? It's one simple example right, where we'll actually decide our R&D investments, based on the data that we're seeing in terms of how people are using our products and do we think that that's important enough of an investment to improve the products and invest in that area? >> Wow very, very smart. What are some of the basic ways that data scientists can deliver more value for their stakeholders, whether they're internal stakeholders, across different functions within the organization, or the members, the external stakeholders? >> Yeah, I think one of the most important things is to really embed closely into these kind of functional or domain areas, and understand qualitatively and quantitatively, what's important. Right, so understanding what the business context is and what problem you're trying to solve. And I think one of the most important that data scientists play a role is actually helping to ensure are we even answering the right question? So as an example, a product manager might ask a data scientist to pull certain data, or to do a certain analysis, and a part of the conversation and the culture has to be what are you trying to get at? What are you trying to understand? And really thinking through is that even the right question to be asking? Or could we ask it in a different way? Because that's going to inform what analysis you do, right what, really what, how you're delivering the results of this analysis to make better decisions. So I think that's a big part of it is, having this iterative process of doing data science. >> Really, it sounds like such and innovative culture, and you're right, looking at the data to determine is this the right next step? Is it not? How do we maybe adapt and change based on really what this data is telling us. If we kind of look at collaboration for a second. You talked about the integrated teams, but I'm wondering how do you scale collaboration within LinkedIn across so many businesses and engineering stakeholders? >> Yeah, so the way I kind of like to think about it is, there's really, you have to invest in culture, process, and tools. So let me start from the bottom up. So on the tools or technology, one of the ways to do it, is actually to create self-served tools, to really democratize the data. So first of all investing in foundations of really good data quality, right, whether you're creating that data yourself, or you're collecting that from externally, from different organizations. Once you have really good data quality, making sure that you have foundations that enable self-serve data basically. So for example, some of the things that data scientists are used today in various companies, really doesn't need a data scientist if you've invested in ways where business partners, let's say, can quarry that data themselves. So they don't need a data scientist to be doing this role. So that's an important investment on the technology side. In addition, making data scientists really productive, by using and investing in tools that will enable them to access the data is really important. So once you have that sort of technology, it enables your data scientist to be productive. The process is really important. So just as an example we have a sort of playbook in terms of how do we launch features? And part of that is kind of bring in data insights, in terms of which features we should be building. And then once you've determined how using the data on those insights, it's okay how are we going to launch this in terms of experimental design and setting? And then what are the success metrics? How are we going to know that this actually a good-- (speaker drowned out by crashing sound) And then once we've launched the experiment, analyzing that, where all of the stakeholders are part of this right? The project manager, the executive, the engineer, the data scientist, and then kind of iterating on the results and deciding what the decision is. So having actually a process that the whole team or the company abides by, really helps at having this collaboration where it's clear what everyone is doing and kind of what's the process by which we use data to develop and to innovate? And then finally culture, I think that's such an important part, and that really needs to be sort of bottoms up, top down, everywhere. It really needs to be a community and a culture where data is discussed and where data is expected, and where decision making really is grounded on, on data. I fundamentally believe that any product being developed, or any decision being made really should be data informed if not data driven. >> Right absolutely. One of the things that I'm hearing in what you're doing is enabling some of business users to be self-sufficient. So you're taking that feedback and that input from the business side to be able to determine what tools they need to have and how you need to enable them so that you've got your resources aligned on certain products. >> Yeah, just as an example, one of the things that we do for example, is we realized over time that, this isn't actually productive, and how do we make ourselves scale, so we started doing data boot camps, for example. >> Interviewer: Okay. >> Where we'll actually train new people coming into the company, on data, and on self-serve tools, and on how to run experiments. And so a variety of different kind of aspects, and even how to work with data scientists productively. So we have actually train that >> fantastic. >> So this data boot camp really helps us to instill a data culture, and it rally empowers the team. >> So this is, anybody coming in, whether they're coming in for a marketing role, or a sales ops role, they get this data boot camp? >> Yeah. >> Wow. >> And it's open to anyone and you know, it yeah, typically is going to be a certain subset of those people, but it really is open to anyone, and we're talking about more ways of how do we scale that and maybe how we put that on LinkedIn learning and make that more broadly accessible. >> Yeah. >> Yeah. >> So you have quite a big team, how do you keep all of the data scientists that you've got happy, what are the challenges that they face, how do you evaluate those challenges and move forward so that they have an opportunity to make an impact at LinkedIn? >> Yeah, so part of the things are actually the things that I mentioned right? So a culture of data so a, it's really important when we see that this is not happening, actually addressing that. So data scientists are going to thrive in a community where data is valued, and where data scientists are valued, so that's actually a really important aspect. And you know luckily people come to use because they know that we do value data. But I think that that's very important for any company and so, I advise startups as well, and this is one of the things that I tell people that are founding companies, is you have to have a culture which values data to attract data scientists, because otherwise they have other options. The other thing is having these, these foundations that enable them to be productive. Right, so these tools and these systems that enable them to really do high-value work, and invest in the right areas. So start graduating from doing things that are more, maybe repetitive or low-level and figure out how do you scale that so that you can have data scientists really, efficiently using their time for things that only they can do? >> Right, I love that this culture is sort of grooming them. One of the things that, a couple things I read recently. One, was that, I think it was Forbes that said, 2017, the best job to apply for is data scientist. But, from an trends perspective, it's looking that by 2018, there's going to be a demand so high, there's not going to be enough talent. How are, what's your perspective on LinkedIn? Are you, have you, it sounds like from a foundational perspective, it is a data driven company that really values data, is that something that you see as a potential issue or you really have built a culture of such, not just collaboration and innovation, but education that LinkedIn is in a very good position? >> Yeah, well so one thing is that, I didn't mention in terms of the happiness factor right? Is that it is actually a place where data scientists look for a place where they can also grow and learn and be with other like-minded data scientists. So I think that's something that we strongly support, again for companies that, people that may be viewing this and are not in such environments, there are a lot of ways to do this. So keeping data scientists happy also can be facilitating meetups, right with data scientists from your local region, and so those are ways that people share information and share techniques and share challenges even right? >> Interviewer: Yeah. >> Because this a growing and evolving field. And so that's, having that community and one of the things that's amazing about this conference is that it's creating this community of data scientists that are all sharing successes and failures as data science is evolving. The other thing is that data science draws from so many different backgrounds right? >> Yeah. >> It's a broad field, right, and there's so many different kinds of data science, and even that is getting both more specialized and more broad. So I think that part of it is also looking at different backgrounds, different educational backgrounds and figuring out how can you expand the pool of people that you're looking at, you know that are data scientists? >> Interviewer: Right. >> And how do you augment what skills they may not have yet, you know, on the job or through training or through online education, and so we're looking at all of these ways so. >> That's fantastic, we've heard a lot of that today. The fact that, the core data science skills are still absolutely vital, but there's some other sort of softer skills, you talked about sharing. Communication has come up a number of times today. It's really a key, not only to be able to understand and interpret the data from a creative perspective and communicate what the data say. But to your point, to grow and learn and keep the data scientists happy, that social skill element is quite important. >> Yael: Yes. >> So that was, that was an interesting learning that I heard today, and I'm sure you've heard many interesting things today that have inspired you as well. >> Yeah, and that's something that you know, creating this culture is something that even data science leaders around the world, where we're discussing this and talking about this, you know what are the challenges? And how do we evolve this field? And how do we help define and help kind of groom the next generation of data scientists? >> Interviewer: Right. >> And to be in a more stable and be in a better place than where we were and to help to continue to evolve it, and so it is yeah. >> Evolution, it's a great word. I think that that's another theme that we've heard today and as much as I'm sure you've inspired and educated these women that are here. Not just in person today, but all the what 70, 70 cities and 25 countries it's being live streamed. >> Yael: Yeah, it was 80 cities and six continets. >> It's growing it's amazing. >> And yeah. >> And I'm sure that they'd vote a 10 from you, but it's probably just in the little bit that we've had a time to chat, I'm sure that you're probably gleaning a lot from them as well. >> Yeah, definitely, absolutely. >> And it's the, we're scratching the surface. >> Yes, absolutely and so there are many more years to come. >> Interviewer: Exactly, Yeal thank you so much for joining us on The Cube. >> Thank you, it's pleasure. >> It's a pleasure talking to you, we wish you continued success at LinkedIn. >> Thank you, it's a pleasure. >> And we want to thank you for watching The Cube. We've had a great day at the 2nd annual Women in Data Science conference at Stanford University. Join the conversation #wids2017. Thanks so much for watching, we'll see ya next time. (rhythmic music) >> Voiceover: Yeah.