>>Hi everyone. And welcome to data citizens. Thank you for making the time to join me and the over 5,000 data citizens like you that are looking to become United by data. My name is Jim Cushman. I serve as the chief product officer at Collibra. I have the benefit of sharing with you, the product, vision, and strategy of Culebra. There's several sections to this presentation, and I can't wait to share them with you. The first is a story of how we're taking a business user and making it possible for him or her data, use data and gain. And if it and insight from that data, without relying on anyone in the organization to write code or do the work for them next I'll share with you how Collibra will make it possible to manage metadata at scales, into the billions of assets. And again, load this into our software without writing any code third, I will demonstrate to you the integration we have already achieved with our newest product release it's data quality that's powered by machine learning. >>Right? Finally, you're going to hear about how Colibra has become the most universally available solution in the market. Now, we all know that data is a critical asset that can make or break an organization. Yet organizations struggle to capture the power of their data and many remain afraid of how their data could be misused and or abused. We also observe that the understanding of and access to data remains in the hands of just a small few, three out of every four companies continue to struggle to use data, to drive meaningful insights, all forward looking companies, looking for an advantage, a differentiator that will set them apart from their peers and competitors. What if you could improve your organization's productivity by just 5%, even a modest 5% productivity improvement compounded over a five-year period will make your organization 28% more productive. This will leave you with an overwhelming advantage over your competition and uniting your data. >>Litter employees with data is the key to your success. And dare I say, sorry to unlock this potential for increased productivity, huge competitive advantage organizations need to enable self-service access to data for everyday to literate knowledge worker. Our ultimate goal at Cleaver has always been to enable this self-service for our customers to empower every knowledge worker to access the data they need when they need it. But with the peace of mind that your data is governed insecure. Just to imagine if you had a single integrated solution that could deliver a seamless governed, no code user experience of delivering the right data to the right person at the right time, just as simply as ordering a pair of shoes online would be quite a magic trick and one that would place you and your organization on the fast track for success. Let me introduce you to our character here. >>Cliff cliff is that business analyst. He doesn't write code. He doesn't know Julian or R or sequel, but is data literate. When cliff has presented with data of high quality and can actually help find that data of high-quality cliff knows what to do with it. Well, we're going to expose cliff to our software and see how he can find the best data to solve his problem of the day, which is customer churn. Cliff is going to go out and find this information is going to bring it back to him. And he's going to analyze it in his favorite BI reporting tool. Tableau, of course, that could be Looker, could be power BI or any other of your favorites, but let's go ahead and get started and see how cliff can do this without any help from anyone in the organization. So cliff is going to log into Cleaver and being a business user. >>The first thing he's going to do is look for a business term. He looks for customer churn rate. Now, when he brings back a churn rate, it shows him the definition of churn rate and various other things that have been attributed to it such as data domains like product and customer in order. Now, cliff says, okay, customer is really important. So let me click on that and see what makes up customer definition. Cliff will scroll through a customer and find out the various data concepts attributes that make up the definition of customer and cliff knows that customer identifier is a really important aspect to this. It helps link all the data together. And so cliff is going to want to make sure that whatever source he brings actually has customer identifier in it. And that it's of high quality cliff is also interested in things such as email address and credit activity and credit card. >>But he's now going to say, okay, what data sets actually have customer as a data domain in, and by the way, why I'm doing it, what else has product and order information? That's again, relevant to the concept of customer churn. Now, as he goes on, he can actually filter down because there's a lot of different results that could potentially come back. And again, customer identifier was very important to cliff. So cliff, further filters on customer identifier any further does it on customer churn rate as well. This results in two different datasets that are available to cliff for selection, which one to use? Well, he's first presented with some data quality information you can see for customer analytics. It has a data quality score of 76. You can see for sales data enrichment dataset. It has a data quality score of 68. Something that he can see right at the front of the box of things that he's looking for, but let's dig in deeper because the contents really matter. >>So we see again the score of 76, but we actually have the chance to find out that this is something that's actually certified. And this is something that has a check mark. And so he knows someone he trusts is actually certified. This is a dataset. You'll see that there's 91 columns that make up this data set. And rather than sifting through all of that information, cliff is going to go ahead and say, well, okay, customer identifier is very important to me. Let me search through and see if I can find what it's data quality scores very quickly. He finds that using a fuzzy search and brings back and sees, wow, that's a really high data quality score of 98. Well, what's the alternative? Well, the data set is only has 68, but how about, uh, the customer identifier and quickly, he discovers that the data quality for that is only 70. >>So all things being equal, customer analytics is the better data set for what cliff needs to achieve. But now he wants to look and say, other people have used this, what have they had to say about it? And you can see there are various reviews for different reviews from peers of his, in the organization that have given it five stars. So this is encourages cliffs, a confidence that this is great data set to use. Now cliff wants to look a little bit more detailed before he finally commits to using this dataset. Cliff has the opportunity to look at it in the broader set. What are the things can I learn about customer analytics, such as what else is it related to? Who else uses it? Where did it come from? Where does it go and what actually happens to it? And so within our graph of information, we're able to show you a diagram. >>You can see the customer analytics actually comes from the CRM cloud system. And from there you can inherit some wonderful information. We know exactly what CRM cloud is about as an overall system. It's related to other logical models. And here you're actually seeing that it's related to a policy policy about PII or personally identifiable information. This gets cliff almost the immediate knowledge that there's going to be some customer information in this PII information that he's not going to be able to see given his user role in the organization. But cliff says, Hey, that's okay. I actually don't need to see somebody's name and social security number to do my work. I can actually work with other information in the data file. That'll actually help me understand why our customers churning in, what can I actually do about it. If we dig in deeper, we can see what is personally identifiable information that actually could cause issues. >>And as we scroll down and take a little bit of a focus on what we call or what you'll see here is customer phone, because we'll show that to you a little bit later, but these show the various information that once cliff actually has it fulfilled and delivered to him, he will see that it's actually massed and or redacted from his use. Now cliff might drive in deeper and see more information. And he says, you know what? Another piece that's important to me in my analysis is something called is churned. This is basically suggesting that has a customer actually churned. It's an important flag, of course, because that's the analysis that he's performing cliff sees that the score is a mere 65. That's not exactly a great data quality score, but cliff has, is kind of in a hurry. His bosses is, has come back and said, we need to have this information so we can take action. >>So he's not going to wait around to see if they can go through some long day to quality project before he pursues, but he is going to come up and use it. The speed of thinking. He's going to create a suggestion, an issue. He's going to submit this as a work queue item that actually informs others that are responsible for the quality of data. That there's an opportunity for improvement to this dataset that is highly reviewed, but it may be, it has room for improvement as cliff is actually typing in his explanation that he'll pass along. We can also see that the data quality is made up of multiple components, such as integrity, duplication, accuracy, consistency, and conformity. Um, we see that we can submit this, uh, issue and pass it through. And this will go to somebody else who can actually work on this. >>And we'll show that to you a little bit later, but back to cliff, cliff says, okay, I'd like to, I'd like to work with this dataset. So he adds it to his data basket. And just like if he's shopping online, cliff wants that kind of ability to just say, I want to just click once and be done with it. Now it is data and there's some sensitivity about it. And again, there's an owner of this data who you need to get permission from. So cliff is going to provide information to the owner to say, here's why I need this data. And how long do I need this data for starting on a certain date and ending on a certain date and ultimately, what purpose am I going to have with this data? Now, there are other things that cliff can choose to run. This one is how do you want this day to deliver to you? >>Now, you'll see down below, there are three options. One is borrow the other's lease and others by what does that mean? Well, borrow is this idea of, I don't want to have the data that's currently in this CRM, uh, cloud database moved somewhere. I don't want it to be persistent anywhere else. I just want to borrow it very short term to use in my Tablo report and then poof be gone. Cause I don't want to create any problems in my organization. Now you also see lease. Lease is a situation where you actually do need to take possession of the data, but only for a time box period of time, you don't need it for an indefinite amount of time. And ultimately buy is your ability to take possession of the data and have it in perpetuity. So we're going to go forward with our bar use case and cliff is going to submit this and all the fun starts there. >>So cliff has actually submitted the order and the owner, Joanna is actually going to receive the request for the order. Joanna, uh, opens up her task, UCS there's work to perform. It says, oh, okay, here's this there's work for me to perform. Now, Joanna has the ability to automate this using incorporated workflow that we have in Colibra. But for this situation, she's going to manually review that. Cliff wants to borrow a specific data set for a certain period of time. And he actually wants to be using in a Tablo context. So she reviews. It makes an approval and submits it this in turn, flips it back to cliff who says, okay, what obligations did I just take on in order to work for this data? And he reviews each of these data sharing agreements that you, as an organization would set up and say, what am I, uh, what are my restrictions for using this data site? >>As cliff accepts his notices, he now has triggered the process of what we would call fulfillment or a service broker. And in this situation we're doing a virtualization, uh, access, uh, for the borrow use case. Cliff suggests Tablo is his preferred BI and reporting tool. And you can see the various options that are available from power BI Looker size on ThoughtSpot. There are others that can be added over time. And from there, cliff now will be alerted the minute this data is available to them. So now we're running out and doing a distributed query to get the information and you see it returns back for raw view. Now what's really interesting is you'll see, the customer phone has a bunch of X's in it. If you remember that's PII. So it's actually being massed. So cliff can't actually see the raw data. Now cliff also wants to look at it in a Tablo report and can see the visualization layer, but you also see an incorporation of something we call Collibra on the go. >>Not only do we bring the data to the report, but then we tell you the reader, how to interpret the report. It could be that there's someone else who wants to use the very same report that cliff helped create, but they don't understand exactly all the things that cliff went through. So now they have the ability to get a full interpretation of what was this data that was used, where did it come from? And how do I actually interpret some of the fields that I see on this report? Really a clever combination of bringing the data to you and showing you how to use it. Cliff can also see this as a registered asset within a Colibra. So the next shopper comes through might actually, instead of shopping for the dataset might actually shop for the report itself. And the report is connected with the data set he used. >>So now they have a full bill of materials to run a customer Shern report and schedule it anytime they want. So now we've turned cliff actually into a creator of data assets, and this is where intelligent, it gets more intelligence and that's really what we call data intelligence. So let's go back through that magic trick that we just did with cliff. So cliff went into the software, not knowing if the source of data that he was looking for for customer product sales was even available to him. He went in very quickly and searched and found his dataset, use facts and facets to filter down to exactly what was available. Compare to contrast the options that were there actually made an observation that there actually wasn't enough data quality around a certain thing was important to him, created an idea, or basically a suggestion for somebody to follow up on was able to put that into his shopping basket checkout and have it delivered to his front door. >>I mean, that's a bit of a magic trick, right? So, uh, cliff was successful in finding data that he wanted and having it, deliver it to him. And then in his preferred model, he was able to look at it into Tableau. All right. So let's talk about how we're going to make this vision a reality. So our first section here is about performance and scale, but it's also about codeless database registration. How did we get all that stuff into the data catalog and available for, uh, cliff to find? So allow us to introduce you to what we call the asset life cycle and some of the largest organizations in the world. They might have upwards of a billion data assets. These are columns and tables, reports, API, APIs, algorithms, et cetera. These are very high volume and quite technical and far more information than a business user like cliff might want to be engaged with those very same really large organizations may have upwards of say, 20 to 25 million that are critical data sources and data assets, things that they do need to highly curate and make available. >>But through that as a bit of a distillation, a lifecycle of different things you might want to do along that. And so we're going to share with you how you can actually automatically register these sources, deal with these very large volumes at speed and at scale, and actually make it available with just a level of information you need to govern and protect, but also make it available for opportunistic use cases, such as the one we presented with cliff. So as you recall, when cliff was actually trying to look for his dataset, he identified that the is churned, uh, data at your was of low quality. So he passed this over to Eliza, who's a data steward and she actually receives this work queue in a collaborative fashion. And she has to review, what is the request? If you recall, this was the request to improve the data quality for his churn. >>Now she needs to familiarize herself with what cliff was observing when he was doing his shopping experience. So she digs in and wants to look at the quality that he was observing and sure enough, as she goes down and it looks at his churn, she sees that it was a low 65% and now understands exactly what cliff was referring to. She says, aha, okay. I need to get help. I need to decide whether I have a data quality project to fix the data, or should I see if there's another data set in the organization that has better, uh, data for this. And so she creates a queue that can go over to one of her colleagues who really focuses on data quality. She submits this request and it goes over to, uh, her colleague, John who's really familiar with data quality. So John actually receives the request from Eliza and you'll see a task showing up in his queue. >>He opens up the request and finds out that Eliza's asking if there's another source out there that actually has good is churned, uh, data available. Now he actually knows quite a bit about the quality of information sturdiness. So he goes into the data quality console and does a quick look for a dataset that he's familiar with called customer product sales. He quickly scrolls down and finds out the one that's actually been published. That's the one he was looking for and he opens it up to find out more information. What data sets are, what columns are actually in there. And he goes down to find his churned is in fact, one of the attributes in there. It actually does have active rules that are associated with it to manage the quality. And so he says, well, let's look in more detail and find out what is the quality of this dataset? >>Oh, it's 86. This is a dramatic improvement over what we've seen before. So we can see again, it's trended quite nicely over time each day, it hasn't actually degraded in performance. So we actually responds back to realize and say, this data set, uh, is actually the data set that you want to bring in. It really will improve. And you'll see that he refers to the refined database within the CRM cloud solution. Once he actually submits this, it goes back to Eliza and she's able to continue her work. Now when Eliza actually brings this back open, she's able to very quickly go into the database registration process for her. She very quickly goes into the CRM cloud, selects the community, to which she wants to register this, uh, data set into the schemas community. And the CRM cloud is the system that she wants to load it in. >>And the refined is the database that John told her that she should bring in. After a quick description, she's able to click register. And this triggers that automatic codeless process of going out to the dataset and bringing back its metadata. Now metadata is great, but it's not the end all be all. There's a lot of other values that she really cares about as she's actually registering this dataset and synchronizing the metadata she's also then asked, would you like to bring in quality information? And so she'll go out and say, yes, of course, I want to enable the quality information from CRM refined. I also want to bring back lineage information to associate with this metadata. And I also want to select profiling and classification information. Now when she actually selects it, she can also say, how often do you want to synchronize this? This is a daily, weekly, monthly kind of update. >>That's part of the change data capture process. Again, all automated without the require of actually writing code. So she's actually run this process. Now, after this loads in, she can then open up this new registered, uh, dataset and actually look and see if it actually has achieved the problem that cliff set her out on, which was improved data quality. So looking into the data quality for the is churn capability shows her that she has fantastic quality. It's at a hundred, it's exactly what she was looking for. So she can with confidence actually, uh, suggest that it's done, but she did notice something and something that she wants to tell John, which is there's a couple of data quality checks that seem to be missing from this dataset. So again, in a collaborative fashion, she can pass that information, uh, for validity and completeness to say, you know what, check for NOLs and MPS and send that back. >>So she submits this onto John to work on. And John now has a work queue in his task force, but remember she's been working in this task forklift and because she actually has actually added a much better source for his churn information, she's going to update that test that was sent to her to notify cliff that the work has actually been done and that she actually has a really good data set in there. In fact, if you recall, it was 100% in terms of its data quality. So this will really make life a lot easier for cliff. Once he receives that data and processes, the churn report analysis next time. So let's talk about these audacious performance goals that we have in mind. Now today, we actually have really strong performance and amazing usability. Our customers continue to tell us how great our usability is, but they keep asking for more well, we've decided to present to you. >>Something you can start to bank on. This is the performance you can expect from us on the highly curated assets that are available for the business users, as well as the technical and lineage assets that are more available for the developer uses and for things that are more warehoused based, you'll see in Q1, uh, our Q2 of this year, we're making available 5 million curated assets. Now you might be out there saying, Hey, I'm already using the software and I've got over 20 million already. That's fair. We do. We have customers that are actually well over 20 million in terms of assets they're managing, but we wanted to present this to you with zero conditions, no limitations we wouldn't talk about, well, it depends, et cetera. This is without any conditions. That's what we can offer you without fail. And yes, it can go higher and higher. We're also talking about the speed with which you can ingest the data right now, we're ingesting somewhere around 50,000 to a hundred thousand records per and of course, yes, you've probably seen it go quite a bit faster, but we are assuring you that that's the case, but what's really impressive is right now, we can also, uh, help you manage 250 million technical assets and we can load it at a speed of 25 million for our, and you can see how over the next 18 months about every two quarters, we show you dramatic improvements, more than doubling of these. >>For most of them leading up to the end of 2022, we're actually handling over a billion technical lineage assets and we're loading at a hundred million per hour. That sets the mark for the industry. Earlier this year, we announced a recent acquisition Al DQ. LDQ brought to us machine learning based data quality. We're now able to introduce to you Collibra data quality, the first integrated approach to Al DQ and Culebra. We've got a demo to follow. I'm really excited to share it with you. Let's get started. So Eliza submitted a task for John to work on, remember to add checks for no and for empty. So John picks up this task very quickly and looks and sees what's what's the request. And from there says, ah, yes, we do have a quality check issue when we look at these churns. So he jumps over to the data quality console and says, I need to create a new data quality test. >>So cliff is able to go in, uh, to the solution and, uh, set up quick rules, automated rules. Uh, he could inherit rules from other things, but it starts with first identifying what is the data source that he needs to connect to, to perform this. And so he chooses the CRM refined data set that was most recently, uh, registered by Lysa. You'll see the same score of 86 was the quality score for the dataset. And you'll also see, there are four rules that are associated underneath this. Now there are various checks that, uh, that John can establish on this, but remember, this is a fairly easy request that he receives from Eliza. So he's going to go in and choose the actual field, uh, is churned. Uh, and from there identify quick rules of, uh, an empty check and that quickly sets up the rules for him. >>And also the null check equally fast. This one's established and analyzes all the data in there. And this sets up the baseline of data quality, uh, for this. Now this data, once it's captured then is periodically brought back to the catalog. So it's available to not only Eliza, but also to cliff next time he, uh, where to shop in the environment. As we look through the rules that were created through that very simple user experience, you can see the one for is empty and is no that we're set up. Now, these are various, uh, styles that can be set up either manually, or you can set them up through machine learning again, or you can inherit them. But the key is to track these, uh, rule creation in the metrics that are generated from these rules so that it can be brought back to the catalog and then used in meaningful context, by someone who's shopping and the confidence that this has neither empty nor no fields, at least most of them don't well now give a confidence as you go forward. >>And as you can see, those checks have now been entered in and you can see that it's a hundred percent quality score for the Knoll check. So with confidence now, John can actually respond back to Eliza and say, I've actually inserted them they're up and running. And, uh, you're in good status. So that was pretty amazing integration, right? And four months after our acquisition, we've already brought that level of integration between, uh, Colibra, uh, data intelligence, cloud, and data quality. Now it doesn't stop there. We have really impressive and high site set early next year. We're getting introduced a fully immersive experience where customers can work within Culebra and actually bring the data quality information all the way in as well as start to manipulate the rules and generate the machine learning rules. On top of it, all of that will be a deeply immersive experience. >>We also have something really clever coming, which we call continuous data profiling, where we bring the power of data quality all the way into the database. So it's continuously running and always making that data available for you. Now, I'd also like to share with you one of the reasons why we are the most universally available software solutions in data intelligence. We've already announced that we're available on AWS and Google cloud prior, but today we can announce to you in Q3, we're going to be, um, available on Microsoft Azure as well. Now it's not just these three cloud providers that were available on we've also become available on each of their marketplaces. So if you are buying our software, you can actually go out and achieve that same purchase from their marketplace and achieve your financial objectives as well. We're very excited about this. These are very important partners for, uh, for our, for us. >>Now, I'd also like to introduce you our system integrators, without them. There's no way we could actually achieve our objectives of growing so rapidly and dealing with the demand that you customers have had Accenture, Deloitte emphasis, and even others have been instrumental in making sure that we can serve your needs when you need them. Uh, and so it's been a big part of our growth and will be a continued part of our growth as well. And finally, I'd like to actually introduce you to our product showcases where we can go into absolute detail on many of the topics I talked about today, such as data governance with Arco or data privacy with Sergio or data quality with Brian and finally catalog with Peter. Again, I'd like to thank you all for joining us. Uh, and we really look forward to hearing your feedback. Thank you..

>>Data is at the heart of transformation and the change. Every company needs to succeed, but it takes more than new technology. It's about teams, talent and cultural change. Empowering everyone on the front lines to make decisions all at the speed of digital. The transformation starts with you. It's time to lead the way it's time for thought leaders. >>Welcome to thought leaders, a digital event brought to you by ThoughtSpot. My name is Dave Volante. The purpose of this day is to bring industry leaders and experts together to really try and understand the important issues around digital transformation. We have an amazing lineup of speakers and our goal is to provide you with some best practices that you can bring back and apply to your organization. Look, data is plentiful, but insights are not. ThoughtSpot is disrupting analytics by using search and machine intelligence to simplify data analysis and really empower anyone with fast access to relevant data. But in the last 150 days, we've had more questions than answers. Creating an organization that puts data and insights at their core requires not only modern technology, but leadership, a mindset and a culture that people often refer to as data-driven. What does that mean? How can we equip our teams with data and fast access to quality information that can turn insights into action. >>And today we're going to hear from experienced leaders who are transforming their organizations with data insights and creating digital first cultures. But before we introduce our speakers, I'm joined today by two of my cohosts from ThoughtSpot first chief data strategy officer, the ThoughtSpot is Cindy Hausen. Cindy is an analytics and BI expert with 20 plus years experience and the author of successful business intelligence unlock the value of BI and big data. Cindy was previously the lead analyst at Gartner for the data and analytics magic quadrant. And early last year, she joined ThoughtSpot to help CDOs and their teams understand how best to leverage analytics and AI for digital transformation. Cindy. Great to see you welcome to the show. Thank you, Dave. Nice to join you virtually. Now our second cohost and friend of the cube is ThoughtSpot CEO, sedition air. Hello. Sudheesh how are you doing today? I am validating. It's good to talk to you again. That's great to see you. Thanks so much for being here now Sateesh please share with us why this discussion is so important to your customers and of course, to our audience and what they're going to learn today. >>Thanks, Dave. >>I wish you were there to introduce me into every room that I walk into because you have such an amazing way of doing it. It makes me feel also good. Um, look, since we have all been, you know, cooped up in our homes, I know that the vendors like us, we have amped up know sort of effort to reach out to you with invites for events like this. So we are getting very more invites for events like this than ever before. So when we started planning for this, we had three clear goals that we wanted to accomplish. And our first one that when you finish this and walk away, we want to make sure that you don't feel like it was a waste of time. We want to make sure that we value your time. Then this is going to be used. Number two, we want to put you in touch with industry leaders and thought leaders, generally good people that you want to hang around with long after this event is over. >>And number three, has we planned through this? You know, we are living through these difficult times. You want an event to be this event, to be more of an uplifting and inspiring event. Now, the challenge is how do you do that with the team being change agents? Because teens can, as much as we romanticize it, it is not one of those uplifting things that everyone wants to do, or like through the VA. I think of it changes sort of like if you've ever done bungee jumping and it's like standing on the edges waiting to make that one more step, uh, you know, all you have to do is take that one step and gravity will do the rest, but that is the hardest step to take change requires a lot of courage. And when we are talking about data and analytics, which is already like such a hard topic, not necessarily an uplifting and positive conversation, most businesses, it is somewhat scary. >>Change becomes all the more difficult, ultimately change requires courage, courage. To first of all, challenge the status quo. People sometimes are afraid to challenge the status quo because they are thinking that, you know, maybe I don't have the power to make the change that the company needs. Sometimes they feel like I don't have the skills. Sometimes they've may feel that I'm, I'm probably not the right person to do it. Or sometimes the lack of courage manifest itself as the inability to sort of break the silos that are formed within the organizations, when it comes to data and insights that you talked about, you know, that are people in the company who are going to have the data because they know how to manage the data, how to inquire and extract. They know how to speak data. They have the skills to do that, but they are not the group of people who have sort of the knowledge, the experience of the business to ask the right questions off the data. >>So there is the silo of people with the answers, and there is a silo of people with the questions. And there is gap. This sort of silos are standing in the way of making that necessary change that we all know the business needs. And the last change to sort of bring an external force. Sometimes it could be a tool. It could be a platform, it could be a person, it could be a process, but sometimes no matter how big the company is or how small the company is, you may need to bring some external stimuli to start the domino of the positive changes that are necessarily the group of people that we are brought in. The four people, including Cindy, that you will hear from today are really good at practically telling you how to make that step, how to step off that edge, how to trust the rope, that you will be safe. And you're going to have fun. You will have that exhilarating feeling of jumping for a bungee jump. >>So we're going to take a hard pivot now and go from football to Ternopil Chernobyl. What went wrong? 1986, as the reactors were melting down, they had the data to say, this is going to be catastrophic. And yet the culture said, no, we're perfect. Hide it. Don't dare tell anyone which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure, the additional thousands, getting cancer and 20,000 years before the ground around there and even be inhabited again, this is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with. And this is why I want you to focus on having fostering a data driven culture. I don't want you to be a laggard. I want you to be a leader in using data to drive your digital transformation. >>So I'll talk about culture and technology. Isn't really two sides of the same coin, real world impacts. And then some best practices you can use to disrupt and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology. And recently a CDO said to me, you know, Cindy, I actually think this is two sides of the same coin. One reflects the other. What do you think? Let me walk you through this. So let's take a laggard. What does the technology look like? Is it based on 1990s BI and reporting largely parameterized reports on premises, data, warehouses, or not even that operational reports at best one enterprise, nice data warehouse, very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to, or is there also a culture of fear, afraid of failure, resistance to change complacency. >>And sometimes that complacency it's not because people are lazy. It's because they've been so beaten down every time a new idea is presented. It's like, no we're measured on least cost to serve. So politics and distrust, whether it's between business and it or individual stakeholders is the norm. So data is hoarded. Let's contrast that with a leader, a data and analytics leader, what is their technology look like? Augmented analytics search and AI driven insights, not on premises, but in the cloud and maybe multiple clouds. And the data is not in one place, but it's in a data Lake and in a data warehouse, a logical data warehouse, the collaboration is being a newer methods, whether it's Slack or teams allowing for that real time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust. There is a trust that data will not be used to punish that there is an ability to confront the bad news. >>It's innovation, valuing innovation in pursuit of the company goals, whether it's the best fan experience and player safety in the NFL or best serving your customers. It's innovative and collaborative. None of this. Oh, well, I didn't invent that. I'm not going to look at that. There's still proud of that ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas, fail fast, and they're energized knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized and double monetized, not just for people, how are users or analysts, but really at the of impact what we like to call the new decision makers or really the front line workers. So Harvard business review partnered with us to develop this study to say, just how important is this? We've been working at BI and analytics as an industry for more than 20 years. >>Why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager, a warehouse manager, a financial services advisor, 87% said they would be more successful if frontline workers were empowered with data driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools. The sad reality only 20% of organizations are actually doing this. These are the data driven leaders. So this is the culture and technology. How did we get here? It's because state of the art keeps changing. So the first generation BI and analytics platforms were deployed on premises on small datasets, really just taking data out of ERP systems that were also on premises. And state-of-the-art was maybe getting a management report, an operational report over time, visual based data discovery vendors disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data sometimes coming from a data warehouse, the current state of the art though, Gartner calls it augmented analytics at ThoughtSpot, we call it search and AI driven analytics. >>And this was pioneered for large scale data sets, whether it's on premises or leveraging the cloud data warehouses. And I think this is an important point. Oftentimes you, the data and analytics leaders will look at these two components separately, but you have to look at the BI and analytics tier in lockstep with your data architectures to really get to the granular insights and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot, I'll just show you what this looks like. Instead of somebody's hard coding of report, it's typing in search keywords and very robust keywords contains rank top bottom, getting to a visual visualization that then can be pinned to an existing Pinboard that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non analyst to create themselves modernizing the data and analytics portfolio is hard because the pace of change has accelerated. >>You use to be able to create an investment place. A bet for maybe 10 years, a few years ago, that time horizon was five years now, it's maybe three years and the time to maturity has also accelerated. So you have these different components, the search and AI tier the data science, tier data preparation and virtualization. But I would also say equally important is the cloud data warehouse and pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So thoughts about was the first to market with search and AI driven insights, competitors have followed suit, but be careful if you look at products like power BI or SAP analytics cloud, they might demo well, but do they let you get to all the data without moving it in products like snowflake, Amazon Redshift, or, or Azure synapse or Google big query, they do not. >>They re require you to move it into a smaller in memory engine. So it's important how well these new products inter operate the pace of change. It's acceleration Gartner recently predicted that by 2022, 65% of analytical queries will be generated using search or NLP or even AI. And that is roughly three times the prediction they had just a couple years ago. So let's talk about the real world impact of culture. And if you read any of my books or used any of the maturity models out there, whether the Gardner it score that I worked on, or the data warehousing Institute also has the maturity model. We talk about these five pillars to really become data driven. As Michelle spoke about it's focusing on the business outcomes, leveraging all the data, including new data sources, it's the talent, the people, the technology, and also the processes. >>And often when I would talk about the people in the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for thought leaders, you have told me now culture is absolutely so important. And so we've pulled it out as a separate pillar. And in fact, in polls that we've done in these events, look at how much more important culture is as a barrier to becoming data driven. It's three times as important as any of these other pillars. That's how critical it is. And let's take an example of where you can have great data, but if you don't have the right culture, there's devastating impacts. And I will say, I have been a loyal customer of Wells Fargo for more than 20 years. But look at what happened in the face of negative news with data, it said, Hey, we're not doing good cross selling customers do not have both a checking account and a credit card and a savings account and a mortgage. >>They opened fake accounts, basing billions in fines, change in leadership that even the CEO attributed to a toxic sales culture, and they're trying to fix this. But even recently there's been additional employee backlash saying the culture has not changed. Let's contrast that with some positive examples, Medtronic, a worldwide company in 150 countries around the world. They may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker spinal implant diabetes, you know, this brand and at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients. They took the bold move of making their IP for ventilators publicly available. That is the power of a positive culture or Verizon, a major telecom organization looking at late payments of their customers. And even though the us federal government said, well, you can't turn them off. >>He said, we'll extend that even beyond the mandated guidelines and facing a slow down in the business because of the tough economy, he said, you know what? We will spend the time upskilling our people, giving them the time to learn more about the future of work, the skills and data and analytics for 20,000 of their employees, rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions, bring in a change agent, identify the relevance, or I like to call it with them and organize for collaboration. So the CDO, whatever your title is, chief analytics, officer chief, digital officer, you are the most important change agent. And this is where you will hear that. Oftentimes a change agent has to come from outside the organization. So this is where, for example, in Europe, you have the CDO of just eat a takeout food delivery organization coming from the airline industry or in Australia, national Australian bank, taking a CDO within the same sector from TD bank going to NAB. >>So these change agents come in disrupt. It's a hard job. As one of you said to me, it often feels like Sisyphus. I make one step forward and I get knocked down again. I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is with them, what is in it for me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline, as well as those analysts, as well as the executives. So if we're talking about players in the NFL, they want to perform better and they want to stay safe. That is why data matters to them. If we're talking about financial services, this may be a wealth management advisor, okay. We could say commissions, but it's really helping people have their dreams come true, whether it's putting their children through college or being able to retire without having to work multiple jobs still into your seventies or eighties for the teachers, teachers, you ask them about data. They'll say we don't, we don't need that. I care about the student. So if you can use data to help a student perform better, that is with them. And sometimes we spend so much time talking the technology, we forget, what is the value we're trying to deliver with this? And we forget the impact on the people that it does require change. In fact, the Harvard business review study found that 44% said lack of change. Management is the biggest barrier to leveraging both new technology, but also being empowered to act on those data driven insights. >>The third point organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC a BI competency center was considered state of the art. Now for the biggest impact, what I recommend is that you have a federated model centralized for economies of scale. That could be the common data, but then in bed, these evangelists, these analysts of the future within every business unit, every functional domain. And as you see this top bar, all models are possible, but the hybrid model has the most impact the most leaders. So as we look ahead to the months ahead to the year ahead and exciting time, because data is helping organizations better navigate a tough economy, lock in the customer loyalty. And I look forward to seeing how you foster that culture. That's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at thought leaders. And next I'm pleased to introduce our first change agent, Tom Masa, Pharaoh, chief data officer of Western union. And before joining Western union, Tom made his Mark at HSBC and JP Morgan chase spearheading digital innovation in technology, operations, risk compliance, and retail banking. Tom, thank you so much for joining us today. >>Very happy to be here and, uh, looking forward to, uh, to talking to all of you today. So as we look to move organizations to a data-driven, uh, capability into the future, there is a lot that needs to be done on the data side, but also how did it connect and enable different business teams and technology teams into the future. As we look across, uh, our data ecosystems and our platforms and how we modernize that to the cloud in the future, it all needs to basically work together, right? To really be able to drive an organization from a data standpoint into the future. That includes being able to have the right information with the right quality of data at the right time to drive informed business decisions, to drive the business forward. As part of that, we actually have partnered with ThoughtSpot to actually bring in the technology to help us drive that as part of that partnership. >>And it's how we've looked to integrate it into our overall business as a whole we've looked at how do we make sure that our, that our business and our professional lives right, are enabled in the same ways as our personal lives. So for example, in your personal lives, when you want to go and find something out, what do you do? You go on to google.com or you go on to being, you gone to Yahoo and you search for what you want search to find an answer ThoughtSpot for us, it's the same thing, but in the business world. So using ThoughtSpot and other AI capability is it's allowed us to actually enable our overall business teams in our company to actually have our information at our fingertips. So rather than having to go and talk to someone or an engineer to go pull information or pull data, we actually can have the end users or the business executives, right. >>Search for what they need, what they want at the exact time that action needed to go and drive the business forward. This is truly one of those transformational things that we've put in place on top of that, we are on the journey to modernize our larger ecosystem as a whole. That includes modernizing our underlying data warehouses, our technology or our Elequil environments. And as we move that we've actually picked to our cloud providers going to AWS and GCP. We've also adopted snowflake to really drive into organize our information and our data then drive these new solutions and capabilities forward. So the portion of us though, is culture. So how do we engage with the business teams and bring the, the, the it teams together to really hit the drive, these holistic end to end solution, the capabilities to really support the actual business into the future. >>That's one of the keys here, as we look to modernize and to really enhance our organizations to become data driven. This is the key. If you can really start to provide answers to business questions before they're even being asked and to predict based upon different economic trends or different trends in your business, what does this is maybe be made and actually provide those answers to the business teams before they're even asking for it, that is really becoming a data driven organization. And as part of that, it's really then enables the business to act quickly and take advantage of opportunities as they come in based upon industries, based upon markets, as upon products, solutions or partnerships into the future. These are really some of the keys that, uh, that become crucial as you move forward, right, uh, into this, uh, into this new age, especially with COVID with COVID now taking place across the world, right? >>Many of these markets, many of these digital transformations are celebrating and are changing rapidly to accommodate and to support customers. And these, these very difficult times as part of that, you need to make sure you have the right underlying foundation ecosystems and solutions to really drive those, those capabilities. And those solutions forward as we go through this journey, uh, boasted both of my career, but also each of your careers into the future, right? It also needs to evolve, right? Technology has changed so drastically in the last 10 years, and that change has only a celebrating. So as part of that, you have to make sure that you stay up to speed up to date with new technology changes both on the platform standpoint tools, but also what our customers want, what our customers need and how do we then surface them with our information, with our data, with our platform, with our products and our services to meet those needs and to really support and service those customers into the future. >>This is all around becoming a more data driven organization, such as how do you use your data to support the current business lines, but how do you actually use your information, your data, to actually better support your customers and to support your business there's important, your employees, your operations teams, and so forth, and really creating that full integration in that ecosystem is really when he talked to get large dividends from his investments into the future. But that being said, uh, I hope you enjoyed the segment on how to become and how to drive a data driven organization. And I'm looking forward to talking to you again soon. Thank you, >>Tom. That was great. Thanks so much. Now I'm going to have to brag on you for a second as a change agent. You've come in this rusted. And how long have you been at Western union? >>Uh, well in nine months. So just, uh, just started this year, but, uh, there'd be some great opportunities and great changes and we were a lot more to go, but we're really driving things forward in partnership with our business teams and our colleagues to support those customers going forward. >>Tom, thank you so much. That was wonderful. And now I'm excited to introduce you to Gustavo Canton, a change agent that I've had the pleasure of working with meeting in Europe, and he is a serial change agent most recently, Schneider electric, but even going back to Sam's clubs. Gustavo. Welcome. >>So hi everyone. My name is Gustavo Canton and thank you so much, Cindy, for the intro, as you mentioned, doing transformations is a high effort, high reward situation. I have empowerment transformations and I have less many transformations. And what I can tell you is that it's really hard to predict the future, but if you have a North star and you know where you're going, the one thing that I want you to take away from this discussion today is that you need to be bold to evolve. And so in today I'm going to be talking about culture and data, and I'm going to break this down in four areas. How do we get started barriers or opportunities as I see it, the value of AI, and also, how do you communicate, especially now in the workforce of today with so many different generations, you need to make sure that you are communicating in ways that are nontraditional sometimes. >>And so how do we get started? So I think the answer to that is you have to start for you yourself as a leader and stay tuned. And by that, I mean, you need to understand not only what is happening in your function or your field, but you have to be very into what is happening, society, socioeconomically speaking, wellbeing. You know, the common example is a great example. And for me personally, it's an opportunity because the number one core value that I have is wellbeing. I believe that for human potential, for customers and communities to grow wellbeing should be at the center of every decision. And as somebody mentioned is great to be, you know, stay in tune and have the skillset and the Koresh. But for me personally, to be honest, to have this courage is not about Nadina afraid. You're always afraid when you're making big changes in your swimming upstream. >>But what gives me the courage is the empathy part. Like I think empathy is a huge component because every time I go into an organization or a function, I try to listen very attentively to the needs of the business and what the leaders are trying to do. What I do it thinking about the mission of how do I make change for the bigger, eh, you know, workforce? So the bigger, good, despite the fact that this might have a perhaps implication. So my own self interest in my career, right? Because you have to have that courage sometimes to make choices that are not well seeing politically speaking, what are the right thing to do and you have to push through it. So the bottom line for me is that I don't think they're transforming fast enough. And the reality is I speak with a lot of leaders and we have seen stories in the past. >>And what they show is that if you look at the four main barriers that are basically keeping us behind budget, inability to add cultural issues, politics, and lack of alignment, those are the top four. But the interesting thing is that as Cindy has mentioned, these topic about culture is sexually gaining, gaining more and more traction. And in 2018, there was a story from HBR and he wants about 45%. I believe today it's about 55%, 60% of respondents say that this is the main area that we need to focus on. So again, for all those leaders and all the executives who understand and are aware that we need to transform, commit to the transformation in set us state, eh, deadline to say, Hey, in two years, we're going to make this happen. Why do we need to do, to empower and enable this change engines to make it happen? >>You need to make the tough choices. And so to me, when I speak about being bold is about making the right choices now. So I'll give you examples of some of the roadblocks that I went through. As I think the transformations most recently, as Cindy mentioned in Schneider, there are three main areas, legacy mindset. And what that means is that we've been doing this in a specific way for a long time. And here is how having successful while working the past is not going to work. Now, the opportunity there is that there is a lot of leaders who have a digital mindset and their up and coming leaders that are perhaps not yet fully developed. We need to mentor those leaders and take bets on some of these talents, including young talent. We cannot be thinking in the past and just wait for people, you know, three to five years for them to develop because the world is going to in a, in a way that is super fast, the second area, and this is specifically to implementation of AI is very interesting to me because just the example that I have with ThoughtSpot, right? >>We went on implementation and a lot of the way the it team function. So the leaders look at technology, they look at it from the prison of the prior auth success criteria for the traditional BIS. And that's not going to work again, your opportunity here is that you need to really find what success look like. In my case, I want the user experience of our workforce to be the same as this experience you have at home is a very simple concept. And so we need to think about how do we gain that user experience with this augmented analytics tools and then work backwards to have the right talent processes and technology to enable that. And finally, and obviously with, with COVID a lot of pressuring organizations and companies to do more with less. And the solution that most leaders I see are taking is to just minimize costs sometimes and cut budget. >>We have to do the opposite. We have to actually invest some growth areas, but do it by business question. Don't do it by function. If you actually invest. And these kind of solutions, if you actually invest on developing your talent, your leadership to see more digitally, if you actually invest on fixing your data platform, it's not just an incremental cost. It's actually this investment is going to offset all those hidden costs and inefficiencies that you have on your system, because people are doing a lot of work in working very hard, but it's not efficiency, and it's not working in the way that you might want to work. So there is a lot of opportunity there. And you just to put into some perspective, there have been some studies in the past about, you know, how do we kind of measure the impact of data? And obviously this is going to vary by your organization. >>Maturity is going to be a lot of factors. I've been in companies who have very clean, good data to work with. And I've been with companies that we have to start basically from scratch. So it all depends on your maturity level, but in this study, what I think is interesting is they try to put a tagline or attack price to what is the cost of incomplete data. So in this case, it's about 10 times as much to complete a unit of work. When you have data that is flawed as opposed to have imperfect data. So let me put that just in perspective, just as an example, right? Imagine you are trying to do something and you have to do a hundred things in a project, and each time you do something, it's going to cost you a dollar. So if you have perfect data, the total cost of that project might be a hundred dollars. >>But now let's say you have 80% perfect data and 20% flow data by using this assumption that Florida is 10 times as costly as perfect data. Your total costs now becomes $280 as opposed to a hundred dollars. This just for you to really think about as a CIO CTO, CSRO CEO, are we really paying attention and really close in the gaps that we have on our data infrastructure. If we don't do that, it's hard sometimes to see this snowball effect or to measure the overall impact. But as you can tell, the price tag goes up very, very quickly. So now, if I were to say, how do I communicate this? Or how do I break through some of these challenges or some of these various, right. I think the key is I am in analytics. I know statistics obviously, and, and, and love modeling and, you know, data and optimization theory and all that stuff. >>That's what I came to analytics. But now as a leader and as a change agent, I need to speak about value. And in this case, for example, for Schneider, there was this tagline coffee of your energy. So the number one thing that they were asking from the analytics team was actually efficiency, which to me was very interesting. But once I understood that I understood what kind of language to use, how to connect it to the overall strategy and basically how to bring in the right leaders, because you need to focus on the leaders that you're going to make the most progress. You know, again, low effort, high value. You need to make sure you centralize all the data as you can. You need to bring in some kind of augmented analytics solution. And finally you need to make it super simple for the, you know, in this case, I was working with the HR teams and other areas, so they can have access to one portal. >>They don't have to be confused and looking for 10 different places to find information. I think if you can actually have those four foundational pillars, obviously under the guise of having a data driven culture, that's where you can actually make the impact. So in our case, it was about three years total transformation, but it was two years for this component of augmented analytics. It took about two years to talk to, you know, it, get leadership support, find the budgeting, you know, get everybody on board, make sure the success criteria was correct. And we call this initiative, the people analytics, I pulled up, it was actually launched in July of this year. And we were very excited and the audience was very excited to do this. In this case, we did our pilot in North America for many, many manufacturers. But one thing that is really important is as you bring along your audience on this, you know, you're going from Excel, you know, in some cases or Tablo to other tools like, you know, you need to really explain them. >>What is the difference in how these two can truly replace some of the spreadsheets or some of the views that you might have on these other kinds of tools? Again, Tableau, I think it's a really good tool. There are other many tools that you might have in your toolkit. But in my case, personally, I feel that you need to have one portal going back to Cindy's point. I really truly enable the end user. And I feel that this is the right solution for us, right? And I will show you some of the findings that we had in the pilot in the last two months. So this was a huge victory, and I will tell you why, because it took a lot of effort for us to get to the station. Like I said, it's been years for us to kind of lay the foundation, get the leadership in shape the culture so people can understand why you truly need to invest, but I meant analytics. >>And so what I'm showing here is an example of how do we use basically to capture in video the qualitative findings that we had, plus the quantitative insights that we have. So in this case, our preliminary results based on our ambition for three main metrics, our safe user experience and adoption. So for our safe or a mission was to have 10 hours per week per employee save on average user experience or ambition was 4.5 and adoption, 80% in just two months, two months and a half of the pilot, we were able to achieve five hours per week per employee savings. I used to experience for 4.3 out of five and adoption of 60%, really, really amazing work. But again, it takes a lot of collaboration for us to get to the stage from it, legal communications, obviously the operations teams and the users in HR safety and other areas that might be, eh, basically stakeholders in this whole process. >>So just to summarize this kind of effort takes a lot of energy. You hire a change agent, you need to have the courage to make this decision and understand that. I feel that in this day and age, with all this disruption happening, we don't have a choice. We have to take the risk, right? And in this case, I feel a lot of satisfaction in how we were able to gain all these very souls for this organization. And that gave me the confidence to know that the work has been done and we are now in a different stage for the organization. And so for me, it says to say, thank you for everybody who has believed, obviously in our vision, everybody wants to believe in, you know, the word that we were trying to do and to make the life for, you know, workforce or customers that in community better, as you can tell, there is a lot of effort. >>There is a lot of collaboration that is needed to do something like this. In the end, I feel very satisfied. We, the accomplishments of this transformation, and I just, I just want to tell for you, if you are going right now in a moment that you feel that you have to swim upstream, you know, what would mentors, where we, people in this industry that can help you out and guide you on this kind of a transformation is not easy to do is high effort bodies, well worth it. And with that said, I hope you are well. And it's been a pleasure talking to you. Take care. Thank you, Gustavo. That was amazing. All right, let's go to the panel. >>I think we can all agree how valuable it is to hear from practitioners. And I want to thank the panel for sharing their knowledge with the community. And one common challenge that I heard you all talk about was bringing your leadership and your teams along on the journey with you. We talk about this all the time, and it is critical to have support from the top. Why? Because it directs the middle and then it enables bottoms up innovation effects from the cultural transformation that you guys all talked about. It seems like another common theme we heard is that you all prioritize database decision making in your organizations and you combine two of your most valuable assets to do that and create leverage employees on the front lines. And of course the data, as you rightly pointed out, Tom, the pandemic has accelerated the need for really leaning into this. You know, the old saying, if it ain't broke, don't fix it. We'll COVID is broken everything. And it's great to hear from our experts, you know, how to move forward. So let's get right into, so Gustavo, let's start with you. If, if I'm an aspiring change agent and let's say I'm a, I'm a budding data leader. What do I need to start doing? What habits do I need to create for long lasting success? >>I think curiosity is very important. You need to be, like I say, in tune to what is happening, not only in your specific field, like I have a passion for analytics, I can do this for 50 years plus, but I think you need to understand wellbeing other areas across not only a specific business, as you know, I come from, you know, Sam's club, Walmart, retail, I mean energy management technology. So you have to try to push yourself and basically go out of your comfort zone. I mean, if you are staying in your comfort zone and you want to use lean continuous improvement, that's just going to take you so far. What you have to do is, and that's what I try to do is I try to go into areas, different certain transformations that make me, you know, stretch and develop as a leader. That's what I'm looking to do. So I can help to inform the functions organizations and do the change management decision of mindset as required for these kinds of efforts. A thank you for that, that is inspiring. And, and Sydney, you love data. And the data's pretty clear that diversity is a good business, but I wonder if you can add your perspective to this conversation. >>Yeah. So Michelle has a new fan here because she has found her voice. I'm still working on finding mine. And it's interesting because I was raised by my dad, a single dad. So he did teach me how to work in a predominantly male environment, but why I think diversity matters more now than ever before. And this is by gender, by race, by age, by just different ways of working in thinking is because as we automate things with AI, if we do not have diverse teams looking at the data and the models and how they're applied, we risk having bias at scale. So this is why I think I don't care what type of minority you are finding your voice, having a seat at the table and just believing in the impact of your work has never been more important. And as Michelle said more possible, >>Great perspectives. Thank you, Tom. I want to go to you. I mean, I feel like everybody in our businesses in some way, shape or form become a COVID expert, but what's been the impact of the pandemic on your organization's digital transformation plans. We've seen a massive growth actually in a digital business over the last 12 months, really, uh, even in celebration, right? Once, once COBIT hit, uh, we really saw that, uh, that, uh, in the 200 countries and territories that we operate in today and service our customers. And today that, uh, been a huge need, right? To send money, to support family, to support, uh, friends and loved ones across the world. And as part of that, uh, we, you know, we we're, we are, uh, very, uh, honored to get to support those customers that we across all the centers today. But as part of that acceleration, we need to make sure that we had the right architecture and the right platforms to basically scale, right, to basically support and provide the right kind of security for our customers going forward. >>So as part of that, uh, we, we did do some, uh, some the pivots and we did, uh, a solo rate, some of our plans on digital to help support that overall growth coming in there to support our customers going forward, because there were these times during this pandemic, right? This is the most important time. And we need to support those, those that we love and those that we care about and doing that it's one of those ways is actually by sending money to them, support them financially. And that's where, uh, really our part that our services come into play that, you know, we really support those families. So it was really a, a, a, a, a great opportunity for us to really support and really bring some of our products to the next level and supporting our business going forward. Awesome. Thank you. Now, I want to come back to Gustavo, Tom. I'd love for you to chime in too. Did you guys ever think like you were, you were pushing the envelope too much in, in doing things with, with data or the technology that was just maybe too bold, maybe you felt like at some point it was, it was, it was failing or you're pushing your people too hard. Can you share that experience and how you got through it? >>Yeah, the way I look at it is, you know, again, whenever I go to an organization, I ask the question, Hey, how fast you would like to conform. And, you know, based on the agreements on the leadership and the vision that we want to take place, I take decisions. And I collaborate in a specific way now, in the case of COVID, for example, right? It forces us to remove silos and collaborate in a faster way. So to me, it was an opportunity to actually integrate with other areas and drive decisions faster, but make no mistake about it. When you are doing a transformation, you are obviously trying to do things faster than sometimes people are comfortable doing, and you need to be okay with that. Sometimes you need to be okay with tension, or you need to be okay, you know, the varying points or making repetitive business cases onto people, connect with the decision because you understand, and you are seeing that, Hey, the CEO is making a one two year, you know, efficiency goal. >>The only way for us to really do more with less is for us to continue this path. We cannot just stay with the status quo. We need to find a way to accelerate it's information. That's the way, how, how about Utah? We were talking earlier was sedation Cindy, about that bungee jumping moment. What can you share? Yeah. You know, I think you hit upon, uh, right now, the pace of change will be the slowest pace that you see for the rest of your career. So as part of that, right, that's what I tell my team. This is that you need to be, need to feel comfortable being uncomfortable. I mean, that we have to be able to basically, uh, scale, right, expand and support that the ever changing needs in the marketplace and industry and our customers today, and that pace of change that's happening. >>Right. And what customers are asking for and the competition in the marketplace, it's only going to accelerate. So as part of that, you know, as you look at what, uh, how you're operating today and your current business model, right. Things are only going to get faster. So you have to plan into align and to drive the actual transformation so that you can scale even faster in the future. So as part of that is what we're putting in place here, right. Is how do we create that underlying framework and foundation that allows the organization to basically continue to scale and evolve into the future? Yeah, we're definitely out of our comfort zones, but we're getting comfortable with it. So, Cindy, last question, you've worked with hundreds of organizations, and I got to believe that, you know, some of the advice you gave when you were at Gartner, which is pre COVID, maybe sometimes clients didn't always act on it. You know, they're not on my watch for whatever variety of reasons, but it's being forced on them now. But knowing what you know now that you know, we're all in this isolation economy, how would you say that advice has changed? Has it changed? What's your number one action and recommendation today? >>Yeah. Well, first off, Tom just freaked me out. What do you mean? This is the slowest ever even six months ago. I was saying the pace of change in data and analytics is frenetic. So, but I think you're right, Tom, the business and the technology together is forcing this change. Now, um, Dave, to answer your question, I would say the one bit of advice, maybe I was a little more, um, very aware of the power and politics and how to bring people along in a way that they are comfortable. And now I think it's, you know, what? You can't get comfortable. In fact, we know that the organizations that were already in the cloud have been able to respond and pivot faster. So if you really want to survive as, as Tom and Gustavo said, get used to being uncomfortable, the power and politics are gonna happen. Break the rules, get used to that and be bold. Do not, do not be afraid to tell somebody they're wrong and they're not moving fast enough. I do think you have to do that with empathy, as Michelle said, and Gustavo, I think that's one of the key words today besides the bungee jumping. So I want to know where's the dish gonna go on to junk >>Guys. Fantastic discussion, really, thanks again, to all the panelists and the guests. It was really a pleasure speaking with you today. Really virtually all of the leaders that I've spoken to in the cube program. Recently, they tell me that the pandemic is accelerating so many things, whether it's new ways to work, we heard about new security models and obviously the need for cloud. I mean, all of these things are driving true enterprise wide digital transformation, not just as I said before, lip service is sometimes we minimize the importance and the challenge of building culture and in making this transformation possible. But when it's done, right, the right culture is going to deliver tournament, tremendous results. Know what does that mean? Getting it right? Everybody's trying to get it right. My biggest takeaway today is it means making data part of the DNA of your organization. >>And that means making it accessible to the people in your organization that are empowered to make decisions, decisions that can drive you revenue, cut costs, speed, access to critical care, whatever the mission is of your organization. Data can create insights and informed decisions that drive value. Okay. Let's bring back Sudheesh and wrap things up. So these please bring us home. Thank you. Thank you, Dave. Thank you. The cube team, and thanks. Thanks goes to all of our customers and partners who joined us and thanks to all of you for spending the time with us. I want to do three quick things and then close it off. The first thing is I want to summarize the key takeaways that I had from all four of our distinguished speakers. First, Michelle, I was simply put it. She said it really well. That is be brave and drive. >>Don't go for a drive along. That is such an important point. Often times, you know that I think that you have to make the positive change that you want to see happen when you wait for someone else to do it, not just, why not you? Why don't you be the one making that change happen? That's the thing that I picked up from Michelle's talk, Cindy talked about finding the importance of finding your voice, taking that chair, whether it's available or not, and making sure that your ideas, your voices are heard, and if it requires some force and apply that force, make sure your ideas are we start with talking about the importance of building consensus, not going at things all alone, sometimes building the importance of building the Koran. And that is critical because if you want the changes to last, you want to make sure that the organization is fully behind it, Tom, instead of a single take away. >>What I was inspired by is the fact that a company that is 170 years old, 170 years sold 200 companies, 200 countries they're operating in and they were able to make the change that is necessary through this difficult time. So in a matter of months, if they could do it, anyone could. The second thing I want to do is to leave you with a takeaway that is I would like you to go to topspot.com/nfl because our team has made an app for NFL on snowflake. I think you will find this interesting now that you are inspired and excited because of Michelle stock. And the last thing is these go to topspot.com/beyond our global user conferences happening in this December, we would love to have you join us. It's again, virtual, you can join from anywhere. We are expecting anywhere from five to 10,000 people, and we would love to have you join and see what we've been up to since last year, we, we have a lot of amazing things in store for you, our customers, our partners, our collaborators, they will be coming and sharing. You'll be sharing things that you have been working to release something that will come out next year. And also some of the crazy ideas or engineers. All of those things will be available for you at hotspot beyond. Thank you. Thank you so much.

(upbeat music) >> Hello, everyone, welcome to this CUBE conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We're here with a very special guest, Amit Walia CEO of Informatica. Newly appointed CEO, about a month ago, a little bit over a month ago. Head of product before that. Been with Informatica since 2013. Informatica went private in 2015, and has since been at the center of the digital transformation around data, data transformation, data privacy, data everything around data and value and AI. Amit, great to see you, and congratulations on the new CEO role at Informatica. >> Thank you. Always good to be back here, John. >> It's been great to follow you, and for the folks who don't know you, you've been a very product centric CEO. You're a product set CEO, as they call it. But also now you have a company in the middle of the transformation. CloudScale is really mainstream. Enterprise is looking to multicloud, hybrid cloud. This is something that you've been on for many, many years. We've talked about it. So now that you're in charge, you've got the ship, the wheel in your hands. Where are you taking it? What is the update of Informatica? Give us the update. >> Well, thank you. So look, business couldn't be better. I think to give you a little bit of color where we're coming from the last couple of years Informatica went through a huge amount of transformation. All things trying to transform a business model, pivoting to subscription, all things have really been into Cloud, the new workloads as we talked about and all things new like AI. To give a little bit of color, we basically exited last year with a a billion dollars of ARR, not just revenues. So we had a billion dollar ARR company and as we pivoted to subscription, our subscription business for the last couple of years has been growing North of 55%. So that's the scale at which we are running multimillion dollars and if you look at the other two metrics which we keep very clicked near and dear to heart, one is innovation. So we are participating in five Magic Quadrants and we are the leader in all five Magic Quadrants. Five on five as we like to call it Gartner Magic Quadrants, very critical to us because innovation in the tech is very important. Also customer loyalty, very important to us. So we again, we're the number one in customer sat from a TSI survey and Gartner publishes the vendor ratings. We basically have a very strong positioning in that. And lastly, our market share continues to grow. So last IDC survey, our market share continued to grow and with the number one in all our markets. So business couldn't be at a better place where we are right now. >> I want to get into some of the business discussion. We first on the Magic Quadrant front, it's very difficult for the folks that aren't in the Cloud as to understand that to participate in multiple Magic Quadrants, what many do is hard because Clouds horizontally scalable Magic Quadrants used to be old IT kind of categories but to be in multiple Magic Quadrants is the nature of the beast but to be a leader is very difficult because Magic Quarter doesn't truly capture that if you're just a pure play and then try to be Cloud. So you guys are truly that horizontal brand and technology. We've covered this on theCUBE so it's no secret, but I want to get your comments on to be a leader in today, in these quadrants, you have to be on all the right waves. You've got data warehouses are growing and changing, you got the rise of Snowflake. You guys partner with Databricks, again, machine learning and AI, changing very rapidly and there's a huge growth wave behind it as well as the existing enterprises who were transforming analytics and operational workloads. This is really, really challenging. Can you just share your thoughts on why is it so hard? What are some of the key things behind these trends? We've got analytics, I guess you can do if it's just Analytics and Cloud, great, but this is a, this horizontal data play Is not easy. Can you share why? >> No, so yes, first we are actually I would say a very hidden secret. We're the only software company and I'll say that again, the only software company that was the leader in the traditional workloads legacy on premise and via the leader and the Cloud workloads. Not a single software company can say that they were the leader of and they were started 27 years ago and they're still the leader in the Magic Quadrants today. Our Cloud by the way runs at 10 trillion transactions a month scale and obviously we partnered with all the hyperscalers across the board and our goal is to be the Switzerland of data for our customers. And the question you ask is is a critical one, when you think of the key business drivers, what are customers trying to do? One of them is all things Cloud, all things AI is obviously there but one is all data warehouses are going to Cloud, we just talked about that. Moving workloads to Cloud, whether it is analytical, operational, basically we are front and center helping customers do that. Second, a big trend in the world of digital transformation is helping our customers, customer experience and driving that, fueling that is a master data management business, so on and so products behind that, but driving customer experiences, big, big driver of our growth and the third one is no large enterprise can live without data governance, data privacy. Even this is a thing today. You going to make sure that you would deliver a good governance, whether it's compliance oriented or brand oriented, privacy and risk management. And all three of them basically span the business initiatives that featured into those five Magic Quadrants. Our goal is to play across all of them and that's what we do. >> Pat Gelsinger here said a quote on theCUBE, many years ago. He said, "If you're not on the right wave, your could be driftwood," meaning you're going to get crashed over. >> He said very well. >> A lot of people have, we've seen a lot of companies have a good scale and then get washed away, if you will, by a wave. You're seeing like AI and machine learning. We talked a little bit about that. You guys are in there and I want to get your thoughts on this one. Whenever this executive changes, there's always questions around what's happening with the company. So I want you to talk about the state of Informatica because you're now the CEO, there's been some changes. Has there been a pivot? Has there been a sharpening focus? What is going on with Informatica? >> So I think our goal right now is to scale and hyperscale, that's the word. I mean we are in a very strong position. In fact, we use this phrase internally within the company, the next phase of great. We're at a great place and we are chartering the next phase of great for the company. And the goal that is helping our customers, I talked about these three big, big initiatives that companies are investing in, data warehousing and analytics, going to the Cloud, transforming customer experiences and data governance and privacy. And the fourth one that underpins all of them is all things AI. I mean, as we've talked about it before, right? All of these things are complex, hard to do. Look at the volume and complexity of data and what we're investing in is what we call native AI. AI needs, data, data needs AI, as I always said, right? And we had investing in AI to make these things easy for our customers, to make sure that they can scale and grow into the future. And what we've also been very diligent about is partnering. We partnered very well with the hyperscalers, like whether it's AWS, Microsoft, whether it's GCP, Snowflake, great partner of ours, Databricks great partner of ours, Tablo, great partners of ours. We have a variety of these partners and our goal is always customer first. Customers are investing in these technologies. Our goal is to help customers adopt these technologies, not for the sake of technologies, but for the sake of transforming those three business initiatives I talked about. >> You brought up, I was going to ask you the next question about Snowflake and Databricks. Databricks has been on theCUBE, Ali, >> And here's a good friend of ours. And he's got chops, I mean Stanford, Berkeley, he'll kill me with that, he's a cowl at Stanford but Databricks is doing well. They made some good bets and it's paying off for them. Snowflake, a rising star, Frank Slootman's over there now, they are clearly a choice for modern data warehouses as is, inhibits Redshift. How are you working with Snowflake? How do you take advantage of that? Can you just unpack your relationship with Snowflake? >> It's a very deep partnership. Our goal is to help our customers as they pick these technology choices for data warehousing as an example where Snowflake comes into play to make sure that the underlying data infrastructure can work seamlessly for them. See, customers build this complex logic sitting in the old technologies. As they move to anything new, they want to make sure that their transition, migration is seamless, as seamless as it can be. And typically they'll start something new before they retire to something old. With us, they can carry all of that business logic for the last 27 years, their business logic seamlessly and run natively in this case, in the Cloud. So basically we allow them this whole from-to and also the ability to have the best of new technology in the context of data management to power up these new infrastructures where they are going. >> Let me ask you the question around the industry trends, what are the top trends, industry trends that are driving your business and your product direction and customer value? >> Look, digital transformation has been a big trend and digital transformation has fueled all things like customer experiences being transformed, so that remains a big vector of growth. I would say Clouded option is still relatively that an early innings. So now you love baseballs, so we can still say what second, third inning as much as we'd like to believe Cloud has been there. Customers more with that analytical workloads first, still happening. The operational workloads are still in its very, very infancy so that is still a big vector of growth and and a big trend to BC for the next five plus years. >> And you guys are in the middle of that because of data? >> Absolutely. Absolutely because if you're running a large operation workload, it's all about the data at the end of the day because you can change the app, but it's the data that you want to carry, the logic that you've written that you want to carry and we participate in that. >> I've asked you before what I want to ask you again because I want to get the modern update because PureCloud, born in the Cloud startups and whatever, it's easy to say that, do that, everyone knows that. Hybrid is clear now, everyone that sees it as an architectural thing. Multicloud is kind of a state of, I have multiple Clouds but being true multicloud a little bit different maybe downstream conversation but certainly relevant. So as Cloud evolves from public Cloud, hybrid and maybe multi or certainly multi, how do you see those things evolving for Informatica? >> Well, we believe in the word hybrid and I define hybrid exactly as these two things. One is hybrid is multicloud. You're going to have hybrid Clouds. Second is hybrid means you're going to have ground and Cloud inter-operate for a period of time. So to us, we in the center of this hybrid Cloud trail and our goal is to help customers go Cloud native but make sure that they can run whatever was the only business that they were running as much possible in the most seamless way before they can at some point contour. And which is why, as I said, I mean our Cloud native business, our Cloud platform, which we call Informatica Intelligent Cloud Services, runs at scale globally across the globe by the way, on all hyperscalers at 10 plus trillion transactions a month. But yet we've allowed customers to run their on-prem technologies as much as they can because they cannot just rip the bandaid over there, right? So multicloud, ground Cloud, our goal is to help customers, large enterprise customers manage that complexity. Then AI plays a big role because these are all very complex environments and our investment in AI, our AI being called Clare is to help them manage that as in an as automated way, as seamless a way and to be honest, the most important with them is, in the most governed way because that's where the biggest risk or risks come into play. That's when our investments are. >> Let's talk about customers for a second. I want to get your thoughts on this 'cause at Amazon reinvent last year in December, there was a meme going around that we starred on theCUBE called, "If you take the T out of Cloud native, it's Cloud naive," and so the point was is to say, hey, doing Cloud native makes sense in certain cases, but if you'd not really thinking about the overall hybrid and the architecture of what's going on, you kind of could get into a naive situation. So I asked Andy this and I want to ask you any chance and I want to ask you the same question is that, what would be naive for a customer to think about Cloud, so they can be Cloud native or operated in a Cloud, what are some of the things they should avoid so they don't fall into that naive category? Now you've being, hi, I am doing Cloud for Cloud's sake. I mean, so there's kind of this perception of you got to do Cloud right, what's your view on Cloud native and how does people avoid the Cloud naive label? >> It's a good question. I think to me when I talk to customers and hundreds of them across the globe as I meet them in a year, is to really think of their Cloud as a reference architecture for at least the next five years, if not 10. I mean technology changes think of a reference architecture for the next five years. In that, you've got to think of multiple best of breed technologies that can help you. I mean, you've got to think of best of breed as much as possible. Now, you're not going to go have hundreds of different technologies running around because you've got to scale them. But think as much as possible that you are best of breed yet settled to what I call a few platforms as much as possible and then make sure that you basically have the right connection points across different workloads will be optimal for different, let's say Cloud environments, analytical workload and operational workload, financial workload, each one of them will have something that will work best in somewhere else, right? So to me, putting the business focus on what the right business outcome is and working your way back to what Cloud environments are best suited for that and building that reference architecture thoughtfully with a five year goal in mind then jumping to the next most exciting thing, hot thing and trying to experiment your way through it that will not scale would be the right way to go. >> It's not naive to be focusing on the business problems and operating it in a Cloud architecture is specifically what you're saying. Okay so let's talk about the customer journey around AI because this has become a big one. You guys been on the AI wave for many, many years, but now that it's become full mainstream enterprise, how are the applications, software guys looking at this because if I'm an enterprise and I want to go Cloud native, I have to make my apps work. Apps are driving everything these days and you guys play a big role. Data is more important than ever for applicants. What's your view on the app developer DevOps market? >> So to me the big chains that we see, in fact we're going to talk a lot about that in a couple of months when we are at Informatica World, our user conference in May is how data is moving to the next phase. And it's what developers today are doing is that they are building the apps with data in mind first, data first apps. I mean if you're building, let's say a great customer service app, you've got to first figure out what all data do you need to service that customer before you go build an app. So that is a very fundamental shift that has happened. And in that context what happens is that in a Cloud native environment, obviously you have a lot of flexibility to begin with that bring data over there and DevOps is getting complimented by what we see is data Ops, having all kinds of data available for you to make those decisions as you're building an application and in that discussion you and me are having before is that, there is so much data that you would not be able to understand that investing in metadata so you can understand data about the data. I call metadata as the intelligent data. If you're an intelligent enterprise, you've got to invest in metadata. Those are the places where we see developers going first and from there ground up building what we call apps that are more intelligent apps on the future not just business process apps. >> Cloud native versus Cloud naive discussion we were just having it's interesting, you talk about best of breed. I want to get your thoughts on some trends we're seeing you seeing even in cybersecurity with RSA coming up, there's been consolidation. You saw Dell just sold RSA to a private equity company. So you starting to see a lot of these shiny new toy type companies being consolidated in because there's too much for companies to deal with. You're seeing also skills gaps, but also skill shortages. There's not enough people. >> That is true. >> So now you have multiple Clouds, you got Amazon, you got Azure, you got Google GCP, you got Oracle, IBM, VMware, now you have a shortage problem. >> True. So this is putting pressure on the customers. So with that in mind, how are the customers reacting to this and what is best of breed really mean? >> So that is actually a really good one. Look, we all live in Silicon Valley, so we get excited about the latest technology and we have the best of skills here, even though we have a skills problem over here, right? Think about as you move up here from Silicon Valley and you start flying and I fly all over the world and you start seeing that if you're in the middle of nowhere, that is not a whole lot of developers who understand the latest cutting edge technology that happens here. Our goal has been to solve that problem for our customers. Look, our goal is to help the developers but as much as possible provide the customers the ability to have a handful of skilled developers but they can still take our offerings and we abstract away that complexity so that they are dealing only at a higher level. The underlying technology comes and goes and it'll come and go a hundred times. They don't have to worry about that. So our goal is abstract of the underlying changes in technology, focus at the business logically and you could move, you can basically run your business for over the course of 20 years. And that's what we've done for customers. Customers have invested with us, have run their businesses seamlessly for two decades, three decades while so much technology has changed over a period of time. >> And the Cloud is right here scaling up. So I want to get your thoughts on the different Clouds, I'll say Amazon Web Services number one in the Cloud, hyperscaler we're talking pure Cloud, they've got more announcements, more capabilities. Then you've got Azure again, hyperscale trying to catch up to Amazon. More enterprise-focused, they're doing very, very well in the enterprise. I said on Twitter, they're mopping up the enterprise because it's easy, they have an install base there. They've been leveraging it very well. So I think Nadella has done the team, has done a great job with that. You had Google try to specialize and figure out where they're going to fit, Oracle, IBM and everyone else. As you'd have to deal with this, you're kind of an arms dealer in a way with data. >> I would love to say I dance with it, not an arms dealer. >> Not an arms dealer, that's a bad analogy, but you get my point. You have to play well, you have to. It's not like an aspiration, your requirement is you have to play and operate with value in all the Clouds. One, how is that going and what are the different Clouds like? >> Well, look, I always begin with the philosophy that it's customer first. You go where the customers are going and customers choose different technologies for different use cases as deems fit for them. Our job is to make sure our customers are successful. So we begin with the customer in mind and we solve from there. Number two, that's a big market. There is plenty of room for everybody to play. Of course there is competition across the board, but plenty of room for everybody to play and our job is to make sure that we assist all of them to help at the end of the day, our joint customers, we have great success stories with all of them. Again, within mind, the end customer. So that has always been Informatica's philosophy, customer first and we partner with a critical strategic partners in that context and we invest and we've invested with all of them, deep partnerships with all of them. They've all been at Informatica well you've seen them. So again, as I said and I think the easiest way we obviously believe that the subset of data, but keep the customer in mind all the time and everything follows from there. >> What is multicloud mean to your customers if your customer century house, we hear people say, yeah, I use this for that and I get that. When I talk to CIOs and CSOs where there's real dollars and impact on the business, there tends to be a gravitational pull towards one Cloud. Why do people are building their own stacks which is why in-house development is shifted to be very DevOps, Cloud native and then we'll have a secondary Cloud, but they recognize that they have multiple Clouds but they're not spreading their staff around for the reasons around skill shortage. Are you seeing that same trend and two, what do you see is multicloud? >> Well, it is multicloud. I think people sometimes don't realize they're already in a multicloud world. I mean you have so many SaaS applications running around, right? Look around that, so whether you have Workday, whether you have Salesforce and I can keep going on and on and on, right. There are multiple, similarly, multi platform Clouds are there, right? I mean people are using Azure for some use cases. They may want to go AWS for certain other native use cases. So quite naturally customers begin with something to begin with and then the scale from there. But they realize as we, as I talked to customers, I realize, hey look, I have use cases and they're optimally set for some things that are multicloud and they'll end up there, but they all have to begin somewhere before they go somewhere. >> So I have multipleclouds, which I agree with you by the way and talking about this on theCUBE a lot. There's multi multiple Clouds and then this interoperability among Clouds. I mean, remember multi-vendor back in the old days, multicloud, it kind of feels like a multi-vendor kind of value proposition. But if I have Salesforce or Workday and these different Clouds and Amazon where I'm developing or Azure, what is the multi-Cloud interoperability? Is it the data control plane? What problems are the customers facing and the challenge that they want to turn into opportunities around multicloud. >> See a good example, one of the biggest areas of growth for us is helping our customers transform their customer experience. Now if you think about an enterprise company that is thinking about having a great understanding of their customer. Now just think about the number of places that customer data sits. One of our big areas of investment for data is a CRM product called salesforce.com right? Good customer data sits there but there could be where ticketing data sits. There could be where marketing data sits. There could be some legacy applications. The customer data sits in so many places. More often than not we realize when we talked to a customer, it sits in at least 20 places within an enterprise and then there is so much customer data sitting outside of the firewalls of an enterprise. Clickstream data where people are social media data partner data. So in that context, bringing that data together becomes extremely important for you to have a full view of your customer and deliver a better customer experience from there. So it is the customer. >> Is that the problem? >> It's a huge problem right now. Huge problem right now across the board where our customer like, hey, I want to serve my customer better but I need to know my customer better before I can serve them better. So we are squarely in the middle of that helping and we being the Switzerland of data, being fully understanding the application layer and the platform layer, we can bring all that stuff together and through the lens of our customer 360 which is fueled by our master data management product, we allow customers to get to see that full view. And from there you can service them better, give them a next best offer or you can understand the full lifetime value for customer, so on and so forth. So that's how we see the world and that's how we help our customers in this really fragmented Cloud world. >> And that's your primary value proposition. >> A huge value proposition and again as I said, always think customer first. >> I mean you got your big event coming up this Spring, so looking forward to seeing you there. I want to get your take as now that you're looking at the next great chapter of Informatica, what is your vision? How do you see that 20 mile stare out in the marketplace? As you execute, again, your product oriented CEO 'cause your product shops, now you're leading the team. What's your vision? What's the 20 mile stare? >> Well as simple as possible, we're going to double the company. Our goal is to double the company across the board. We have a great foundation of innovation we've put together and we remain paranoid all the time as to where and we always try to look where the world is going, serve our customers and as long as we have great customer loyalty, which we have today, have the foundations of great innovation and a great team and culture at the company, which we fundamentally believe in, we basically right now have the vision of doubling the company. >> That's awesome. Well really appreciate you taking the time. One final question I want to get your thoughts on the Silicon Valley and in the industry, is starting to see Indian-American executives become CEO. You now see you have Informatica. Congratulations. >> Amit: Thank you. >> Arvind over at IBM, Satya Nadella. This has been a culture of the technology for generations 'cause I remember when I broke into the business in the late 80s, 90s, this is the pure love of tech and the meritocracy of technology is at play here. This is a historic moment and it's been written about, but I want to get your thoughts on how you see it evolving and advice for young entrepreneurs out there, future CEOs, what's it take to get there? What's it like? What's your personal thoughts? >> Well, first of all, it's been a humbling moment for me to lead Informatica. It's a great company and a great opportunity. I mean I can say it's the true American dream. I mean I came here in 1998. As a lot of the immigrants didn't have much in my pocket. I went to business school, I was deep in loans and I believed in the opportunity. And I think there is something very special about America. And I would say something really special about Silicon Valley where it's all about at the end of the day value, it's all about meritocracy. The color of your skin and your accent and your, those things don't really matter. And I think we are such an embracing culture typically over here. And, and my advice to anybody is that look, believe, and I genuinely used that word and I've gone through stages in my life where you sometimes doubt it, but you have to believe and stay honest on what you want and look, there is no substitute to hard work. Sometimes luck does play a role, but there is no substitute for hard work. And at the end of the day, good things happen. >> As we say, the for the love of the game, love of tech, your tech athlete, loved it, loved to interview and congratulate, been great to follow your career and get to know you and, and Informatica. It's great to see you at the helm. >> Thank you John, pleasure being here. >> I'm John Furrier here at CUBE conversation at Palo Alto, getting the update on the new CEO from Informatica, Amit Walia, a friend of theCUBE and of course a great tech athlete, and now running a great company. I'm John Furrier. Thanks for watching. (upbeat music)

>>Live from Las Vegas. It's the cube covering UI path forward Americas 2019 brought to you by UI path. >>We're back in Las Vegas. UI path forward three. You're watching the cube, the leader in live tech coverage. Bobby Patrick is here. He's the COO of UI path. Welcome. Hi Dave. Good to see it to be here. Wow. Great to have the cube here again. Right? Q loves these hot shows like this. I mean this is, you've said Gardner hasn't done the fastest growing software segment you've seen in the data that we share from ETR. You guys are off the chart in terms of net score. It's happening. I hanging onto the rocket ship. How's it feel? Well it's crazy. I mean it's great. You all have seen some of the growth along the way too, right? I mean we had our first forward event less than two years ago and you know about 500 plus plus non UI path and people then go year later. It was Miami USY. >>There's probably a lot. Cube I think was Miami right yet and a, and that was a great event, but that was more in the 13 1400 range. This one's almost 3000 and the most amazing part about it was we had 8% attrition from the registrations. Yeah. That's never seen that we're averaging 18% of 20% for all of our, most of our events worldwide. But 8% the commitment is unbelievable. Even 18 to to 20% is very good. I mean normally you'll see 25 to sometimes as high as 50% yeah. It just underscores the heat. >> Well I think what's also great, other stats that you might find interesting. So over 50% of the attendees here are exec. Our senior executives, like for the first time we actually had S you know, C level executive CHRs and CEOs on stage. Right. You could feel the interest level. Now of course we want RPA developers at events too, right? >>But this show really does speak, I think to the bigger value propositions and the bigger business transformation opportunity from RPA. And I mean, you've come so far where no one knew RPA two years ago to the CIO of Morgan Stanley on stage, just warning raving about it. That's, we've come a long way in two years. >> Well, and I saw a lot of the banks here hovering around, you know, knocking on your door so they, they know they are like heat seeking missiles, you know, so, but the growth has been amazing. I mean I think ARR in 2017 was what, 25 million at this time. Uh, at the end of 17 it was 43 and 43 and 25 and now you're at 12 times higher now 1212 X solve X growth, which is the fastest growing software company. I think in that we know from one to 100 we were, we did that in 21 months and all that. >>And we had banks who now we're not really counting anymore and we're kind of, you know, now focus more on customer expansion. Even though we hit 5,000 customers, which we started the year at 2050 ish. We just crossed 5,000. I mean, so the number of customers is great, but there's no question. This conference is focused on scaling, helping them grow at enterprise wide with, with, with RPA. So I think our focus will be in to shift a bit, you know, to really customer expansion. Uh, and that's a lot of what this announcements, the product announcements were about a lot of what the theme here is about. We had four dozen customers on, on stage, you know, the Uber's of the world, the Amazons of the world. It's all about how they've been scaling. So that's the story now. Well, you know, we do a lot of these events and I go back to some of the, uh, when the cube first started, companies like Tablo, Dallas Blunck great service. >>Now, I mean, these you can, and when you talk to customers, first of all, it's easy to get customers to come talk about RPA. Yeah. And they're, they're all saying the same thing. I mean, Jeanne younger said she's never been more excited in her career from security benefit. But the thing is, Bobby, it's, I feel like they're, they're really just getting started. Yeah. I mean most of the use cases that you see are again, automating mundane task. We had one which was the American fidelity, which is a really bringing in AI. Right. But they're really just getting started. It's like one to 3% penetration. So what are your thoughts on that to kind of land and expand, if you will? I think, you know, look, last year we announced our vision of a robot for every person. At that point we had SNBC on stage and they were the one behind it. >>And they are an amazing story. Now we have a dozen or so that are onstage talking about a robot for every person like st and others. And so, but that, that, that's a pretty, pretty, pretty bold vision I think. Look, I think it's important to look at it both ways. Um, there's huge gold and applying RPA to solve real problems. There's a big opportunity, enterprise wide, no question. We've got that. But I look New York Foundling was on stage yesterday. We have New York Foundling is a 150 year old associate. Our charity in New York focused on child welfare, started by three fishers of charity. They focused on infants. And anyway, it's an amazing firm. Just the passion that New York family had on stage with Daniel yesterday was amazing. But what they flew here because for once they found a technology that actually makes a huge difference for them and what in their mission. >>So their first RPA operation was they have 850 clinicians every week. They spend four hours a week moving their contact, uh, a new contact data associate with child child issues from system to system to spreadsheet and paper to system, right? They use RPA and they now say for a 200,000 hours a year. But more importantly, those clinicians spend those four hours every week with children not moving. So I'm still taking, I think Daniel had a bit of a tear in his eye, hearing them talk about it on stage, but I'm still taken by, by the, by the sheer massive opportunity for RPA in, in a particular to solve some really amazing things. Now on a mass scale, a company can drive, you know, 10, 15, 20% productivity by every employee having a robot. Yes, that's true on a mass scale. They can completely transform their business, your transform customer experience, transform the workplace on a mass scale. >>And that, that is, that's a sea level GFC level goal and that's a big deal. But I love the stories that are very real. Um, and, and I think those are important to still do plug some great tech for good story. Look, tech gives, you know, the whole Facebook stuff and the fake news got beat up and it had Benny come out recently say, Hey, it's, it's not just about increasing the value to shareholders, you know, it's about tech for good and doing other things affecting lifestyle's life changing. And Michael Dell is another one. Now I've, I've, I've kind of said tongue in cheek, you know, show me the CEO misses is four quarters in a row and see if that holds up. But nonetheless, you love to see successful companies giving back. It seems to be, it's part of your, well look I've been part of hardware companies and I met you all through a few of them and others they have good noble causes but it was hard to really connect the dots. >>Yes there CPS underneath a number of these things. But I think judging by the emotional connection that these customers have on stage, right and these are the Walmarts and Uber's and others in the world judging by the employee and job satisfaction that they talk about the benefits there. I just, I my career, I have not seen that kind of real direct impact from you know, from B2B software for example on the lives of people both everyday at work but also just solving the solving, you know, help accelerate human achievement. Right. And so many amazing ways. We had the CEO of the U N I T shared services group on stage yesterday and they have a real challenge with, you know, with the growth of refugees worldwide and he would express them and they can't hit keep up. They don't have the funding, which is, you know, with everybody and, and Trump and others trying to hold back money. >>But they had this massive charter for of good, the only way they get there is through digital. The new CEO, the new head of the U N is a technology engineer. He came in and said, the way we solve this is with templates, with technology. And they decided, they said on stage yesterday that RPA and RPA has the path to AI and the greater, the greater new technologies and that's how they're going to do it. And it's just a, it's a really, it's, I think it's, it feels really great. You know, it's funny too, one of the things we've been talking about this week is people might be somewhat surprised that there's so much head room left for automation because the boy, 50 years of tech, Kevin, we automated everything. That's the other, but, and Daniel put forth the premise last night, it actually, technology is created more process problems or inefficiencies. >>So it's almost like tech has created this new problem. Can tech get us out of the problem? Well, essentially you think about all the applications we use in our lives, right? Um, you know, although people do have, you know, a Salesforce stack and sometimes in this SAP, the reality is they have a mix of a bunch of systems and then we add Slack to it and we add other tools and we add all the tools alone, have some great value. But from a process perspective of how we work everyday, right? How a business user might work at a call center, they have to interact then. And the reality is they're often interacting with old systems too because moving them is not easy, right? So now you've got old systems, new systems and, and really the only way to do that is to put a layer on top of the systems of engagement and the systems of record, right? >>A layer on top that's easy to actually build an application that goes between all of these different, these different applications, outlook, Excel, legacy systems and salesforce.com and so on and so on and, and build an app that solves a real problem, have it have outcomes quickly. And this is why, Dave, we unveiled the vision here that we believe that automation is the application. And when you begin to think about I could solve a problem now without requiring a bunch of it engineers who already are maxed out, right? Uh, I can solve a problem that can directly impact the businesses or directly impact customers. And I can do that on top of these old technologies by just dragging and dropping and using a designer tool like studio or studio X in a business user can do that. That's, that's a game changer. I think what's amazing is when you go to talk to a CIO who says, I've been automating for 20 years, you know, take up the ROI. >>Once they realize this is different, the light bulb goes off. We call it the automation first mindset. A light bulb goes off and you realize, okay, this is a very different whole different way of creating value for, for an organization. I think about how people weigh the way that people work today. You're constantly context switching. You're in different systems. Like you said, Slack, you're getting texts and you want to be responsive. You want to be real time. I know Jeff Frick who was the GM of the cube has got two giant screens right on his desk. I myself, I always have 1520 tabs open if I go, Oh you got so many tabs on my, yeah. Cause I'm constantly context switching, pulling things out of email, going back and forth and so and so. I'm starting to grok this notion of the automation is the app. >>At first I thought, okay, it's the killer app, but it's not about stitching things together with through API APIs. It's really about bringing an automation perspective across the organization. We heard it from Pepsi yesterday. Yeah, right. Sort of the fabric, the automation fabric throughout the organization. Now that's aspirational for most companies today, but that really is the vision. Well, I think you had Layla from Coca-Cola also on, right. And her V their vision there and they actually took the CDO role of the CIO and put them together. And they're realizing now that that transformation is driven by this new way of thinking. Yeah, I think, you know, look, we introduced a whole set of new brand new products and capabilities around scaling around helping build these applications quicker. I, I think, you know, fast forward one year from now, the, you know, the vision we outlined will be very obvious the way people interact with, you know, via UI path to build applications, assault come, the speed to the operate will be transformational and, and so, you know, and you see this conference hear me walk around. >>I mean you saw last year in the year before you see the year before, but it's, it's a whole, the speed at which we're evolving here, I think it's unprecedented. And so I'll talk a little bit about the market for has Crigler killer was awesome this morning. He really knows his stuff now. Last year I saw some data from him and said the market by 2020 4 billion, and I said, no way. It's going to be much larger than that. Gonna be 10 billion by 2020 I did Dave Volante fork, Becca napkin by old IDC day forecast. Now what he, what he showed today is data. It actually was 10 billion by 2020 because he was including services, the services, which is what I was including in my number as well, but the of it, which was so good for him now, but the only thing is he had this kind of linear growth and that's not how these rocket ship Marcus grow. >>They're more like an old guy for an S curve. You're going to get some steep part now, so I'd love to see like a longer term forecast because that it feels like that's how this is going to evolve. Right now it's like you've seated the base and you can just feel the momentum building and then I would expect you're going to see massive steep sort of exponential growth. Steeper. There may be, you know, nonlinear because that's how these markets go >> to come from the expansion potential, right? And none of our customers are more than 1% audit automated from an RPA perspective. So that shows you the massive opportunity. But back to the market site, data size, Craig and I and the other analysts, we talk often about this. I think the Tam views are very low and you'll look at our market share, let's just get some real data out there, right? >>Our market share in 2017 was 5% let's use Craig's linear data for now. You know, our market share this year is over 20% our market share applying, and I don't want to give the exact numbers as you don't provide guidance anymore, is substantially we're substantially gaining share now. I believe that's the reality of the market. I think because we know blue prisms numbers, we go four times faster than the every quarter automation. The world won't share their numbers. But you know, I can make some guesses, but either way I think, you know, I think we're gaining share on them significantly. I think, you know, Craig's not gonna want us to be 50% of the market two years, he's just not. And so he's going to have to figure out how to identify how to think. That brought more broadly about, about that market trend. He talked about it on stage today about how does he calculate the AI impact and the other pieces now the process mining now that now that we are integrating process mining into RPA, right? >>It's strategic component of that. How does that also involve the market? So I think you have both the expansion and the plot product portfolio, which drives it. And then you have the fact that customers are going to add more automations at faster pace and more robots and that's where the expansion really kicks in. And we often say, you know, look as a, as a, as a, as a company that, you know, one day we'll be public company, our ARR numbers. Very important. We do openly transparently share that. But you know, the other big metric will be, you know, dollar based net expansion rate that shows really how customers are expanding. I think that, I know it, our numbers, we haven't shared it yet. I know all the SAS companies, the top 10 I can tell you, you know we're higher than all of them. >>The market projections are low. And I think he knows it well. >> Speaking of Tam, and when we, I saw this with, with service now, now service now the core was it right? So the, the ROI was not as obvious with, with, with you guys, you're touching business process. And so, so in David Flory are way, way back, did an analysis of service and now he said, wow, the Tam is way being way under counted by everybody. That wall street analyst Gardner, it feels like the same here because there are so many adjacencies and just talk to the customers and you're seeing that the Tam could be enormous, much bigger than the whatever 16 billion a Daniel show, the other Danielson tangles, the guy's balls. He said, Oh that's 16 billion. That's you. I pass this data. And you know, we laugh, but I'm, I'm like listening. Say I wonder if he's serious cause this guy thinks big. >>I mean, who would've thought that he'd be at this point by now? And you're just getting started? Well, I think, you know, one thing I think is, you know, we're, we're, you know, we were a little bit kind of over a little less humble when we talked about things like valuation over the last few years. We were trying to show this market's real, you know, we want to now focus more on outcomes and things get a little less from around those numbers. And I think that shows the evolution of a company's maturity, um, that we, I think we're going through right now. Uh, you know, the outcomes of, you know, Walmart on stage saying, you know, their first robot that was, this was, this was two years ago, delivered 360,000 hours of capacity for them in, in, in, in, in HR, right? That, you know, I think those, that's where we're gonna be focused because the reality is if we can deliver these big outcomes and continue them and we can go company-wide deliver on the robot for every, every, every, every person, then you know, the numbers follow along with it. >>Well we saw some M and a this week as well, which again leads me to the larger Tam cause we had PD on, um, with Rudy and you can start to see how, okay now we're going to actually move into that vision that the guy from PepsiCo laid out this, this fabric of this automation fabric across the organization. So M and a is, is a part of that as well. That starts to open up new Tam. Opportunity does. And I think, you know, a process mind is a great example of a market that is pretty well known in Europe, not so much in the U S um, and there are really only a few players in that, in that market today. Look, we're going to do what we did in RPA. We're going to do the same thing. You're process mining. We're going to just say anything we're doing in it, not as democratization, you'll our strategy will be to go mass market with these technologies, make it very easy for accessibility for every single person in the case of process mining, every business analyst to be able to mind their processes for them and, and ultimately that flows through to drive faster implementations and then faster, faster outcomes. >>I think our approach, again, our approach to the business users, our approach to democratization, um, you know it's very different than our competitors. A lot of these low code companies, I won't name a number cause I don't remember our partners here at our conference. They're IT-focused their services heavy and, and you know, their growth rates I'll be at okay are 30% year over year in this market. That shouldn't be the case at all. I mean we're a 200 plus a year. We are still and we've got big numbers and we have a whole different approach to the market. I don't think people have figured it out yet, Dave. Exactly, exactly. The strategy behind which is, which is when you have business users, subject matter experts and citizen developers that can access our technology and build automations quickly and deliver value proof for their company. And you do that in mass scale. >>Right. And then you will now allow with our apps for your end users, I get a call center to engage with a robot as part of their daily operation that none of the other it vendors who are all kind of conventional thinking and that's not, our models are very different, which I think shows in our numbers and and, and the growth rates. Yeah. Well you bet on simplicity early on. In fact, when you join you iPad, you challenged me so you have some of your Wiki bond analysts go out. I remember head download our stuff and then try to download the competitors and they'll tell us, you know how easy it as well we were able to download UI path. We, we built some simple automations. We couldn't get ahold of the other other, other companies products we tried. We were told we'll go to the reseller or how much did you have to spend and okay so you bet on simplicity, which was interesting because Daniel last night kind of admitted, look, he tracked the audience. >>He said thank you for taking a chance on us because frankly a couple of years ago this wasn't fully baked right and and so, so I want to talk about last, the last topic is sort of one of the things Craig talked about was consolidation and I've been saying that all week and said this, this market is going to consolidate. You guys are a leader now you've got to get escape velocity cause the leader makes a lot of money and becomes, gets big. The number two does. Okay, number three man, everybody else and the big guys are starting to jump in as well. You saw SAP, you know, makes an announcement and you guys are specialists and so your thoughts on hitting escape velocity, I wouldn't say you're quite there yet. I want to see more on the ecosystem. There's maybe, who knows, maybe there's an IPO coming. I've predicted that there is, but your thoughts on achieving escape velocity and some of the metrics around there, whether it's customer adoption penetration, what are your thoughts? >>Yeah, I mean we definitely don't have a timetable on an IPO, but we have investors, public investors and VCs that at some point are going to want, this is the reality of how, of how it works. Right. Um, you know, I think the, uh, you know, I think the numbers to focus right now are on around, you know, customer outcomes. I think the ecosystem is a good one. Right? You know, we have, I'd say the biggest ecosystem for us to date has been the SAP ecosystem. When we look at our advisory board members, for others, that's really where, where the action is. Supply chain management, ERP, you know, certainly CRM and others, we don't have a view that, so our competitors have, but we have chosen not to take money from our, from ecosystem companies because we don't, our customers here are building processes, all the automation across ecosystems. >>Right? So you know, we don't want to go bet on say just one like Salesforce or Workday. We want to help them across all the ecosystem now. So I think it's a little bit of a different strategy there. Look, I think the interesting thing is the SAP is the world. They bought a small company in France called contexture. They're trying to do this themselves. Microsoft, Microsoft didn't in Mark Benioff and Salesforce are asked on every earnings call now what are you doing for RPA? So they've got pressure. So maybe they invest in one of our competitors or maybe they, you'll take flow in Microsoft and expanded. I think we can't move fast enough because you know, I don't know if Microsoft has, I mean they're a great sponsor by the way. So I don't want to only be careful we swept with what I say. But you know, strategically speaking, these larger companies operate in 18 months, 12 1824 months kind of planning cycles. >>If he did that, he will never keep up with us. There's no one at any of our traditional large enterprise software companies that ever would have bet that we would come out and say that the best way to build applications right to solve problems will be through RPA. Either there'll be a layer on top of all their technologies that makes it easier than ever for business users to build applications and solve problems, that's going to scare them to death. Why? Because you don't have to move all your legacy systems anymore. Yes, you've got tons of databases, but guess what? Don't worry about it. Leave him alone. Stop spending money on ridiculous upgrades right now. Just build a new layer and I'm telling you I there. As they figured this out, they're going to keep looking back and say, Oh my God, why didn't we know? >>Why did we know there's it looked I hopefully we could all partner. We're going to try to go down that route, but there's something much bigger going on here and they haven't figured it out. Well, the SAP data is very interesting to me that I'm starting to connect the dots. I just did a piece on my breaking analysis and SAP, they thank you. They, they've acquired 31 companies over the last nine years, right? And they've not bit the bullet on integration the way Oracle had to with fusion. Right? And so as a result, there's this, they say throw everything into HANA. It's a memory that's not going to work from an integration standpoint, right? Automation is actually a way to connect, you know, the glue across all those disparate systems, right? And so that makes a lot of sense that you're having success inside SAP and there's no reason that can't continue. >>Why there's, you know, there's a number of major kind of trends we've outlined here. One of, uh, we call human in the loop. And you know, today, you know, when each, when an unattended robot could actually stop a process and instead of sending the exception to a, an it person who monitoring, say, orchestrator actually go to an inbox, a task and box of that business user in a call center or wherever, and that robot can go do something else because it's so, so efficient and productive. But once that human has to solve that problem, right, that robot or a robot will take that back on and keep going. This human and robot interaction, it doesn't exist today and we know we're rolling that out in our UI path apps. I think you know that that's kind of mind blowing and then when you add a, I can't go too far into our roadmap and strategy or when you added the app programming layer and you add data science, that's a little bit of a hint into where we're going because we're open and transparent. >>Our data science connection, it's, it's this platform here, this kind of, I'd like to still call it all RPA. I think that that's a good thing, but the reality is this platform does Tam. What it can do is nothing like it was a year ago and it won't be like where it is today. A year from now you've got the tiger by the tail, Bobby, you got work to do, but congratulations on all the success. It's really been great to be able to document this and cover it, so thanks for coming on the cube. Thank you. All right. Thank you for watching everybody back with our next guest. Right after this short break, you're watching the cube live from UI path forward three from Bellagio in Vegas right back.

(upbeat music) >> Live, from Las Vegas it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host Rebecca Knight along with my co-host John Furrier. We are joined by Adam Mariano, he is the Vice-President Health Informatics at HighPoint Solutions. Thanks for coming on theCUBE! >> Thank you for having me. >> So tell our viewers a little bit about HighPoint Solutions, what the company does and what you do there. >> Sure, HighPoint is a consulting firm in the Healthcare and Life Sciences spaces. If it's data and it moves we probably can assist with it. We do a lot of data management, we implement the full Infomatica stack. We've been an Infomatica partner for about 13 years, we were their North American partner of the year last year. We're part of a much larger organization, IQVIA, which is a merger of IMS quintiles, large data asset holder, big clinical research organization. So we're very much steeped in the healthcare data space. >> And what do you do there as Vice President of Health and Formatics? >> I'm in an interesting role. Last year I was on the road 51 weeks. So I was at over a hundred facilities, I go out and help our customers or prospective customers or just people we've met in the space, get strategic about how they're going to leverage data as a corporate asset, figure out how they're going to use it for clinical insight, how they're going to use it for operational support in payer spaces. And really think about how they're going to execute on their next strategy for big data, cloud strategy, digital re-imaginment of the health care space and the like. >> So we know that healthcare is one of the industries that has always had so much data, similar to financial services. How are the organizations that you're working with, how are they beginning to wrap their brains around this explosion of data? >> Well it's been an interesting two years, the last augur two years there isn't a single conversation that hasn't started with governance. And so it's been an interesting space for us. We're a big MDM proponent, we're a big quality proponent, and you're seeing folks come back to basics again, which is I need data quality, I need data management from a metadata perspective, I need to really get engaged from a master data management perspective, and they're really looking for integrated metadata and governance process. Healthcare's been late to the game for about five or six years behind other industries. I think now that everybody's sort of gone through meaningful use and digital transformation on some level, we're now arcing towards consumerism. Which really requires a big deep-dive in the data. >> Adam, data governance has been discussed at length in the industry, certainly recently everyone knows GDPR's one year anniversary, et cetera, et cetera. But the role of data is really critical applications for SAS and new kinds of use cases, and the term Data Provisioning as a service has been kicked around. So I'd love to get your take on what that means, what is the definition, what does it mean? Data Provisioning as a service. >> The industry's changed. We've sort of gone through that boomerang, alright, we started deep in the sort of client server, standard warehouse space. Everything was already BMS. We then, everybody moved to appliances, then everybody came back and decided Hadoop, which is now 15 year old technology, was the way to go. Now everybody's drifting to Cloud, and you're trying to figure out how am I going to provision data to all these self-service users who are now in the sort of bring your own tools space. I'd like to use Tablo, I'd like to use Click. I like SAS. People want to write code to build their own data science. How can you provision to all those people, and do so through a standard fashion with the same metadata with the same process? and there isn't a way to do that without some automation at this point. It's really just something you can't scale, without having an integrated data flow. >> And what's the benefits of data provisioning as a service? What's the impact of that, what does it enable? >> So the biggest impact is time to market. So if you think about warehousing projects, historically a six month, year-long project, I can now bring data to people in three weeks. In two days, in a couple of hours. So thinking about how I do ingestion, if you think about the Informatica stack, something like EDC using enterprise data catalog to automatically ingest data, pushing that out into IDQ for quality. Proving that along to AXON for data governance and process and then looking at enterprise data lake for actual self-service provisioning. Allowing users to go in and look at their own data assets like a store, pick things off the shelf, combine them, and then publish them to their favorite tools. That premise is going to have to show up everywhere. It's going to have to show up on AWS, and on Amazon, and on Azure. It's going to have to show up on Google, it's going to have to show up regardless of what tool you're using. And if you're going to scale data science in a real meaningful way without having to stack a bunch of people doing data munging, this is the way it's going to have to go. >> Now you are a former nurse, and you now-- >> I'm still a nurse, technically. >> You're still a nurse! >> Once a nurse, always a nurse. Don't upset the nurses. >> I've got an ear thing going on, can you help me out here? (laughter) >> So you have this really unique vantage point, in the sense that you are helping these organizations do a better job with their data, and you also have a deep understanding of what it's like to be the medical personnel on the other side, who has to really implement these changes, and these changes will really change how they get their jobs done. How would you say, how does that change the way you think about what you do? And then also what would you say are the biggest differences for the nurses that are on the floor today, in the hospital serving patients? >> I think, in America we think about healthcare we often talked about Doctors, we only talk about nurses in nursing shortages. Nurses deliver all the care. Physicians see at this point, the way that medicine is running, physicians see patients an average two to four minutes. You really think about what that translates to if you're not doing a surgery on somebody, it's enough time to talk to them about their problem, look at their chart and leave. And so nursing care is the point of care, we have a lot of opportunity to create deflection and how care is delivered. I can change quality outcomes, I can change safety problems, I can change length of stay, by impacting how long people keep IVs in after they're no longer being used. And so understanding the way nursing care is delivered, and the lack of transparency that exists with EMR systems, and analytics, there's an opportunity for us to really create an open space for nursing quality. So we're talking a lot now to chief nursing officers, who are never a target of analytics discussion. They don't necessarily have the budget to do a lot of these things, but they're the people who have the biggest point of control and change in the way care is delivered in a hospital system. >> Care is also driven by notifications and data. >> Absolutely. >> So you can't go in a hospital without hearing all kinds of beeps and things. In AI and all the things we've been hearing there's now so many signals, the question is what they pay attention to? >> Exactly. >> This becomes a really interesting thing, because you can get notifications, if everything's instrumented, this is where kind of machine learning, and understanding workflows, outcomes play a big part. This is the theme of the show. It's not just the data and coding, it's what are you looking for? What's the problem statement or what's the outcome or scenario where you want the right notification, at the right time or a resource, is the operating room open? Maybe get someone in. These kinds of new dynamics are enabled by data, what's your take on all this? >> I think you've got some interesting things going on, there's a lot of signal to noise ratio in healthcare. Everybody is trying to build an algorithm for something. Whether that's who's going to overstay their visit, who's going to be readmitted, what's the risk for somebody developing sepsis? Who's likely to follow up on a pharmacy refill for their medication? We're getting into the space where you're going to have to start to accept correlation as opposed to causation, right? We don't have time to wait around for a six month study, or a three year study where you employ 15,000 patients. I've got three years of history, I've got a current census for the last year. I want to figure out, when do I have the biggest risk for falls in a hospital unit? Low staffing, early in their career physicians and nurses? High use of psychotropic meds? There are things that, if you've been in the space, you can pretty much figure out which should go into the algorithm. And then being pragmatic about what data hospitals can actually bring in to use as part of that process. >> So what you're getting at is really domain expertise is just as valuable as coding and wrangling data, and engineering data. >> In healthcare if you don't have SMEs you're not going to get anything practical done. And so we take a lot of these solutions, as one of the interesting touch points of our organization, I think it's where we shine, is bringing that subject matter expertise into a space where pure technology is not going to get it done. It's great if you know how to do MDM. But if you don't know how to do MDM in healthcare, you're going to miss all the critical use cases. So it really - being able to engage that user base, and the SMEs and bring people like nurses to the forefront of the conversation around analytics and how data will be used to your point, which signals to pay attention to. It's critical. >> Supply chains, another big one. >> Yeah. >> Impact there? >> Well it's the new domain in MDM. It's the one that was ignored for a long time. I think people had a hard time seeing the value. It's funny I spoke at 10 o'clock today, about supply chain, that was the session that I had with Nathan Rayne from BJC. We've been helping them embark on their supply chain journey. And from all the studies you look at it's one of the easiest places to find ROI with MBM. There's an unbelievable amount of ways- >> Low hanging fruit. >> $24.5 billion in waste a year in supply chain. It's just astronomical. And it's really easy things, it's about just in time supplies, am I overstocking, am I losing critical supplies for tissue samples, that cost sometimes a $100,000, because a room has been delayed. And therefore that tissue sits out, it ends up expiring, it has to be thrown away. I'll bring up Nathan's name again, but he speaks to a use case that we talked about, which is they needed a supply at a hospital within the system, 30 miles away another hospital had that supply. The supply costs $40,000. You can only buy them in packs of six. The hospital that needed the supply was unaware that one existed in the system, they ordered a new pack of six. So you have a $240,000 price that you could have resolved with a $100 Uber ride, right? And so the reality is that supply could have been shipped, could have been used, but because that wasn't automated and because there was no awareness you couldn't leverage that. Those use cases abound. You can get into the length of stay, you can get into quality of safety, there's a lot of great places to create wins with supply chain in the MDM space. >> One of the conversations we're having a lot in theCUBE, and we're having here at Informatica World, it centers around the skills gap. And you have a interesting perspective on this, because you are also a civil rights attorney who is helping underserved people with their H1B visas. Can you talk a little bit about the visa situation, and what you're seeing particularly as it relates to the skills gap? >> We're in an odd time. We'll leave it at that. I won't make a lot of commentary. >> Yes. >> I'm a civil rights and immigration attorney, and on the immigration side I do a lot of pro bono work with primarily communities of color, but communities at risk looking to help adjust their immigration status. And what you've had is a lot of fear. And so you have, well you might have an H1B holder here, you may have somebody who's on a provisional visa, or family members, and because those family members can no longer come over, people are going home. And you're getting people who are now returning. So we're seeing a negative immigration of places like Mexico, you're seeing a lot of people take their money, and their learnings and go back to India and start companies there and work remotely. So we're seeing a big up-tick in people who are looking for staffing again. I think the last quarter or so has been a pretty big ramp-up. And I think there's going to continue to be this hole, we're going to have to find new sources of talent if we can't bring people in to do the jobs. We're still also, I think it just speaks to our STEM education the fact that we're not teaching kids. I have a 28 year old daughter who loves technology, but I can tell you, her education when she was a kid, was lacking in this technology space. I think it's really an opportunity for us to think about how do we train young people to be in the new data economy. There's certainly an opportunity there today. >> And what about the, I mean you said you were talking about your daughter's education. What would you have directed her toward? What kinds of, when you look ahead to the jobs of the future, particularly having had various careers yourself, what would you say the kids today should be studying? >> That's two questions. So my daughter, I told her do what makes you happy. But I also made her learn Sequel. >> Be happy, but learn Sequel. >> But learn sequel. >> Okay! >> And for kids today I would say look, if you have an affinity and you think you enjoy the computer space, so you think about coding, you like HTML, you like social media. There are a plethora of jobs in that space and none of them require you to be an architect. You can be a BA, you can be a quality assurance person, you can be a PM. You can do analysis work. You can do data design, you can do interface design, there's a lot of space in there. I think we often reject kids who don't go to college, or don't have that opportunity. I think there's an opportunity for us to reach down into urban centers and really think about how we make alternate pathways for kids to get into the space. I think all the academies out there, you're seeing rise, Udemy, and a of of these other places that are offering academy based programs that are three, six months long and they're placing all of their students into jobs. So I don't think that the arc that we've always chased which is you've got to come from a brand named school to get into the space, I don't think it's that important. I think what's important is can I get you the clinical skill, so that you've understood how to move data around, how to process it, how to do testing, how to do design, and then I can bring you into the space and bring you in as an entry level employee. That premise I think is not part of the American dream but it should be. >> Absolutely, looking for talent in these unexpected places. >> College is not the only in point. We're back to having I think vocational schools for the new data economy, which don't exist yet. That's an opportunity for sure. >> And you said earlier, domain expertise, in healthcare as an example, points to what we've been hearing here at the conference, is that with data understanding outcomes and value of the data actually is just as important, as standing up, wrangling data, because if you don't have the data-- >> You make a great point. The other thing I tell young people in my practice, young people I interact with, people who are new to the space is, okay I hear you want to be a data scientist. Learn the business. So if you don't know healthcare get a healthcare education. Come be on this project as a BA. I know you don't want to be a BA, that's fine. Get over it. But come be here and learn the business, learn the dialogue, learn the economy of the business, learn who the players are, learn how data moves through the space, learn what is the actual business about. What does delivering care actually look like? If you're on the payer side, what does claims processing look like from an end to end perspective? Once you understand that I can put you in any role. >> And you know digital four's new non-linear ways to learn, we've got video, I see young kids on YouTube, you can learn anything now. >> Absolutely. >> And scale up your learning at a pace and if you get stuck you can just keep getting through it no-- >> And there are free courses everywhere at this point. Google has a lot of free courses, Amazon will let you train for free on their platform. It's really an opportunity-- >> I think you're right about vocational specialism is actually a positive trend. You know look at the college University scandals these days, is it really worth it? (laughter) >> I got my nursing license through a vocational school originally. But the nursing school, they didn't have any technology at that point. >> But you're a great use case. (laughter) Excellent Adam, thank you so much for coming on theCUBE it's been a pleasure talking to you. >> Thank you. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE. (techno music)

(bright classical music) >> Hi, I'm Peter Burris and welcome to another CUBE Conversation from our studios here in Palo Alto, California. We've got another great conversation today, specifically we're going to talk about some of the trends and changes in data catalogs, which were emerging as a crucial technology to advance data-driven business on a global scale. And to do that, we've got Alation here, specifically Stephanie McReynolds who's the Vice-President of Marketing at Alation. Stephanie, welcome back to theCUBE. >> Thank you, it's great to be here again. >> So Stephanie, before we get into this very important topic of the increasing, obviously role or connection between knowing what your data is, knowing where it is, and business outcomes in a data-driven business world, let's talk about Alation. What's the update? >> Yeah, so we just celebrated, yesterday in fact, the sixth anniversary of incorporation of the company. And upon, reflecting on some of the milestones that we've seen over those six years, one of the exciting developments is we went from initially about seven production implementations a couple years after we were founded, to now over a hundred organizations that are using Alation. And in those organizations over the last couple of years, we've seen many organizations move from hundreds of users, to now thousands of users. An organization like Scout24 has 70 percent of the company as self-servicing analytics users and a significant portion of those users now using Alation. So we're seeing companies in Europe like Scout24 who's in Germany. Companies like Pfizer in the United States. Munich Reinsurance in the financial services industry. Also hit about 2000 users of Alation, and so it's exciting to look at our origins with eBay as our very first customer, who's now up to about 3000 users. And then these more recent companies adopt Alation all of them now getting to a point where they really have a large population that's using a data catalog to drive self-service analytics and business outcomes out of those self-serving analytics. >> So a hundred first-rate brands as users, it's international expansion. Sounds like Alation's really going places. What I want to do though, is I want to talk a little bit about some of the outcomes that these companies are starting to achieve. Now we have been on the record here at circling the angle with theCUBE wiki bomb for quite some time, trying to draw a relationship between business, digital business, and the role that data plays. Digital business transformation, in many respects, is about how you evolve the role the data plays in your business to become more data-driven. It's hard to do without knowing what your data is, where it is, and having some notion of how it's being used in a verified trusted way. How are you seeing your company's start to tie the use of catalogs to some of these outcomes? What kind of outcomes are folks trying to achieve first off? >> Yeah, you're right. Just basic table stakes for turning an organization into an organization that relies on data-driven decision-making rather than intuitive-decision making requires an inventory. And so that's table stakes for any catalog, and you see a number of vendors out there providing data inventories. But what I think is exciting with the customers that we work with, is they are really undertaking transformative change, not just in the tooling and technology their company uses, but also in the organizational structure, and data literacy programs, and driving towards real business impact, and real business outcomes. An example of an Alation customer, who's been talking recently about outcomes, is Pfizer. Pfizer was covered in a Wall Street Journal article, recently. Also was speaking at TABLO Conference, about how they're using a combination of the Alation data catalog with TABLO on the front end, and a data science platform called Data IQ, in an integrated analytics workbench that is helping them with new drug discovery. And so, for populations of ill individuals, who may have a rare form of heart disease, they're now able to use machine learning and algorithms that are informed by the data catalog to catch one percent, two percent of heart disease patients who have a slight deviation from the norm, and can deliver drugs appropriately to that population. Another example of the business outcome would be with an insurance company; very different industry, right? But, Munich Reinsurance is a huge global reinsurance company, so you think about hurricanes or the fires we had here in the United States, they actually support first line insurers by reinsuring them. They're also founding new business units for new types of risks in the market. An example would be a factory that is fully controlled by robots. Think about the risks of having that factory be taken over by hackers in the middle of the night, where there's not a lot of employees on the floor. Munich Reinsurance is leveraging the data catalog as a collaboration platform between actuaries and individuals that are knowledgeable in the business to define what are the data products that could support an entirely new business units, like for cyber crimes. And investing in those business units based on the innovation they're doing using the data catalog as a collaboration platform. So these are two great examples of organizations that, a couple years ago started with a data catalog, but have driven so many more initiatives than just analyst productivity off of that implementation. >> Oh, those are great outcomes. One of them talking about robots in the factory, automated factory, one thing, if they went haywire, would make for some interesting viral video. (gently laughs) >> That's right. That's right. >> But coming back, but the reason I say that is because in many respects, these practices, these relations with the outcomes, the outcomes are the real complex thing. You talked about becoming more familiar with data, using data differently, becoming more data driven. That requires some pretty significant organizational change. And it seems to me, and I'm querying you on this, that the bringing together these users to share their stories about how to achieve these data driven outcomes, made more productive by catalogs and related technologies. Communities must start to be forming. Are you seeing communities form around achieving these outcomes and utilizing these types of technologies to accelerate the business change? >> So what's really interesting at an organization like Munich Reinsurance or at Pfizer, is there's an internal community that is using the data catalog as a collaboration platform and as kind of a social networking platform for the data nerds. So if I am a brand new user of self-service analytics, I may be a product manager who doesn't know how to write a sequel query yet. Who doesn't know how to go and wrangle my own data. >> Yeah, may never want to. (playfully laughs) >> May never want to. May never want to. Who may not know how to go and validate data for quality or consistency. I can now go to the data catalog to find trusted resources of data assets, be that a dashboard to report that's already been written or be that raw data that someone else has certified, or just has used in the past. So we're seeing this social influence happen within companies that are using data catalogs, where they can see for the data catalog pages, who's used, who's validated this data set so that I now trust the data. And then, what we've seen happen, just within the last year and-a-half or so, is these organizations, the sponsors of the data of these organizations, are starting to share best practices naturally with one another, and saying, hey >> Across organizations. >> Across organizations. And so there has been a demand for Alation to get out into the market and help catalyze the creation of communities across different organizations. We kicked off, within the last two months, a series of meetings that we've called RevAlation. >> R-E-V-A-L >> That's right >> A-T-I-O-N >> R-E-V-A-L-A-T-I-O-N And the thing behind the name is, if you can start to share best practices in terms of how you create a data-driven culture across organizations, you can begin to really get breakthrough speed, right? In making this transformation to a data-driven organization. And so, I think what's interesting at the RevAlation events, is folks are not talking just about how they're using the tool, how they're using technology. They're actually talking about how do we improve the data literacy of our organizations and what are the programs in place that leverage, maybe the data catalog, to do that. And so they're starting to really think about, how does, not just the technical architecture and the tooling change in their organizations, but how do we close this gap between having access to data and trusting the data and getting folks who maybe aren't, too familiar with the technical aspects of the data supply chain. How do we make them comfortable in moving away from intuitive decisions to data-driven decisions? >> Yeah, so the outcome really is not just the application of the tool, it's the new behaviors in the business that are associated with data-driven. But to do that, you still have to gain insight and understand what kinds of practices are best used with the tool itself. >> That's right. >> So it's got to be a combination. But, you know, Alation has been, if I can say this. Alation's been on this path for a while. Not too long ago, you came on theCUBE and you talked about trust check. >> Right. >> Which was an effort to establish conventions and standards for how data could be verified and validated so that it would be easy to use, so that someone could use the data and be certain that it is what it is, without necessarily having to understand the data. Something that could be very good for, for example, for folks who are very focused on the outcome, and not focused on the science of the data associated with it. >> That's right. >> So, is this part of, it's RevAlation, it's trust check. Is this part of the journey you're on to try to get people to see this relation between data-driven business and knowing more about your data? >> It absolutely is. It's a journey to get organizations to understand what is the power that they have internally, within this data. And close the gap on, which is in part organizational, but in part for individuals user's psychological and how do you get to a trusted decision. And so, you'll continue to see us invest in features like trust check that highlight how technology can make recommendations; can help validate and verify what the experts in the organization know and propagate that more widely. And then you'll also see us share more best practices about how do you start to create the right organizational change, and how do you start to impact the psychology of fear that we've had in many organizations around data. And I think that's where Alation is uniquely placed, because we have the highest number of data catalog customers of any other vendor I'm familiar with in this space. And we also have a unique design approach. When we go into organizations and talk about adopting a data catalog, it's as much about, how do our products support psychological comfort with data as well as, how do they support the actual workflow of getting that query completed, or getting that data certified. And so I think we've taken a bit of a unique approach to the market from the beginning where we're really designing holistically. We're not just, how do you execute a software program that supports workflow? But how do you start to think about how the data consumer actually adopts that best practices and starts to think differently about how they use data in a more confident way? >> Well I think the first time that you and I talked in theCUBE was probably 2016, and I was struck by the degree to which Alation as a tool, and the language that you used in describing it was clearly designed for human beings to use it. >> Right. >> As opposed to for data. And I think that, that is a unique proposition, because at the end of the day, the goal here, is to have people use data to achieve outcomes and not just to do a better job of managing data. >> And that doesn't mean that, I mean we have a ton of machine learning, >> Sure. >> And AI in the products. That doesn't take away from the power of those algorithms to speed up human work and human behavior. But we really believe that the algorithms need to compliment human input and that there should be a human in the loop with decision-making. And then the algorithms propagate the knowledge that we have of experts in the organization. And that's where you get the real breakthrough business outcomes, when you can take input from a lot of different human perspectives and optimize an outcome by using technology as a support structure to help that. >> In a way that's familiar and natural and easy for others in your organization. >> That's right. That seems, you know, if you go back to. >> It makes sense. >> When we were all introduced to Google it was a little bit of an odd thing to go ask Google questions and get results back from the internet. We see data evolving in the same way. Alation is the Google for your data in your organization. At some point it'll be very natural to say, 'Hey Alation, what happened with revenue last month?' And Alation will come back with an answer. So I think that, that future is in sight, where it's very easy to use data. You know you're getting trusted responses. You know that they're accurate because there's either a certification program in place that the technology supports, or there's a social network that's bubbling this information up to the top, that is a trusted source. And so, that evolution in data needs to happen for our organizations to broadly see analytic driven outcomes. Just as in our consumer or personal life, Google had to show us a new way to evolving, you know, to a kind of answering machine on the internet. >> Excellent. Stephanie McReynolds, Vice-President of Marketing Alation, talked to us about building communities, to become more of a, to achieve data-driven outcomes, utilizing data catalog technology. Stephanie, thanks very much for being here. >> Thanks for inviting me. >> And once again, I'm Peter Burris, and this has been another CUBE Conversation until next time. (bright classical music)

you live from Orlando Florida it's the cube coverage conf 18 got to you by Splunk welcome back to comp 2018 this is the cube the leader in live tech coverage my name is Dave Volante I'm here with my co-host Stu minimun we're gonna start the day we're going to talk to some customers we love that John Morris here is the MIS program director at UConn the Huskies welcome to the cube good to see you and he's joined by Ryan O'Connor who's the senior advisory engineer at Splunk he's got the cool hat on gents welcome to the cube great to have you thank you thank you for having us so kind of a cool setting this morning is the Stu's first conf and I said you know when you see this it's kind of crazy we're all shaking our phones we had the horse race this morning we won so that was kind of orange yeah team are and team orange as well that's great you're on Team Orange so we're in the media section and the median guys were like sitting on their hands but Stu and I were getting into it good job nice and easy so Jon let's start with you start always left to start with the customer perspective maybe you describe your role and we'll get into it sure so as you mentioned I'm the director of our undergrad program Mis management information systems business technology we're in the school of business under the operations and information management department the acronym OPI M okay cool and gesture Ryan tell us about your role explain the Hat absolutely yeah so I'm a member of an honorary member of the Splunk trust now I recently joined Splunk about a month ago back in August and yeah and outside of my full-time job working at Splunk I'm also an adjunct professor at the University of Connecticut and so I helped John in teaching and you know that's that's kind of my role and where our worlds sort of meet so John we were to when I were talking about the sort of evolution of Splunk the company that was just you know okay log file analysis kind of on-prem perpetual license model and it's really evolved and its permanent permeating throughout you know many organizations but maybe you could take us through sort of the early days and it was UConn for a while what what was life like before Splunk what prompted you to start playing around with Splunk and where have you taken it what's your journey look like so about three years ago we started looking at it through kind of an educational lens started to think of how could we tie it into the curriculum we started talking to a lot of the recruiters and companies that many of our students go into saying what skillsets are you looking for and Splunk was definitely one of those so academia takes a while to change the curriculum make that pendulum swing so it was how can we get this into students hands as quickly as possible and also make it applicable so we developed this initiative in our department called OPI M innovate which was all based around bringing emerging technology skills to students outside of the general curriculum we built an innovation space a research lab and really focused in bringing students in classes and incorporating it that way we started kind of slowly different parts of some early classes about three years ago different data analytics predictive analytics courses and then that really built into we did a few workshops with our innovate initiative which Ryan taught and then from there it kind of exploded we started doing projects and our latest one was with the Splunk mobile team okay you guys had some hard news around now well today right yeah maybe take us through that absolutely wanted sure yeah I'll take that so we we teach a course on IOT industrial IOT at the University of Connecticut and so we heard about the mobile projects and you know the basically they were doing a beta of the mobile and application so we we partnered with them this summer and they came in you know we have a Splunk Enterprise license through Splunk for good so we're able to actually ingest Splunk data and so as part of that course we can ingest IOT data and use Splunk mobile to visualize it all right right right maybe you could explain to our audience that might not know spun for good absolutely yeah so spun for good is a great initiative they offer a Splunk pledge license they call it to higher education institutions and research initiatives so we're able to have a 10 gig license for free that we can you know run our own Splunk enterprise we can have students actually get hands-on experience with it and in addition to that they also get free training so they can take Splunk fundamentals one and two and actually come out of school with hands-on experience and certifications when they go into the job market that's John name you know we talk so much about them the important role of data and you know that the tools change a lot you know when we talk about kind of the next generation of jobs you're right at that intersection maybe you can give you know what what are what are the students what are they looking for what are the people that are looking for them hoping that they come out of school with you know yeah it's it's um you have two different types of students I would say those that know what they're looking for and those that don't that I really have the curiosity they want to learn and so we we try to build this initiative around both those that maybe they're afraid of the technology and the skills so how do we bring them in how do we make a very immersive environment kind of have that aha moment quickly so we have a series of services around that we have what's called tech kits the students come in they're able to do something applicable right away and it sparks an interest and then we also kind of developed another path for those that were more interested in doing projects or they had that higher level skill set but we also wanted to cultivate an environment where they could learn more so a lot of it is being able to scaffold the learning environment based off of the different student coming in so it's interesting my son's a junior in college at GW and he's very excited he's playing around with date he says I'm learning are I'm learning Tablo I'm like great what about Splunk and he said what's that yes so yeah then though it's a little off-center from some of the more traditional visualization tools for example so it's it's interesting and impressive that you guys sort of identified that need and actually brought it to two students how did that all how what was in an epiphany or was that demand from the students how'd that come about it was a combination of a lot of things you know we were lucky Ryan and I have known each other for a long time as the director of the program trying to figure out what classes we should bring in how to build out the curriculum and we have our core classes but then we have the liberty to build out special topics things that we think are irrelevant up-and-coming we can try it out once if it's good maybe teach it a few more times maybe it becomes a permanent class and that's kind of where we were able to pull Ryan in and he had been doing consulting for Splunk for a number of years I said I think you know this is our important skill set is it something that you could help bring to the students sure yeah yeah I mean one of the big courses we looked at was a data analytics course and we were already teaching with a separate piece of software not gonna name names but essentially I looked at it one for one like what key benefits does this piece of software have you know what are the students trying to get out of it and then just compared to one for one to Splunk like could Splunk actually give them the same learning components and all that and it could and and with this one for swum for a good license and all that stuff we could give them the hands-on experience and augment our teaching with that free training so and they come out of school they have something tangible they can say you know I have this and so that would kind of snowball once that course worked then we could integrate it into multiple other courses so you were able to essentially replicate the value to the students of the legacy software and but also have a modern platform exactly exactly yep yeah you know that and that was a what was like a Doug was talking about making jokes about MDM and codifying business processes and yeah it's a little bit more of an antiquated piece of software essentially you know and I mean it was nice it did a great job but there wasn't when we were talking to recruiters and stuff it wasn't a piece of software that recruiters were actually looking at so we said we were hearing Splunk over and over again so why not just bring it into the classroom and give them that so in the keynote this morning started to give a vision I believe they call it Splunk next and mobile things like augmented reality are fitting in you know how do you look at things like this what what how's the mobile going to impact you especially I would think yeah so when we kind of came up with our initiative we identified five tracks that both skill sets we believe the students needed and that and companies were kind of looking for a lot of that was our students would go into internships and say hey you know the the set skills that were learning you know they're asking us to do all this other work in AWS and drones and VR so as again it's part of this it was identifying let's start small five tracks so we started with 3d printing virtual reality microcontrollers IOT and then analytics kind of tying that all together so we had already been building an environment to try and incorporate that and when we kind of started working with the spunk mobile team there's all these different components we wanted to not only tie into the class but tied into the larger initiative so the goal of the class is not to just get these students the skills interesting interested in it but to spread that awareness the Augmented part is just kind of another feature was the next piece that we're looking in to build activities and it just had this great synergy of coming in at the right time saying hey look at this sensor that we built and now you can look at data in an AR it's a really powerful thing to most people so yeah they showed that screenshot of AR during the keynote and one of the challenges that we have at the farm so we're teaching that this is the latest course that we're talking about on an industrial IOT one of the challenges we have at that farm is we don't have a desk we don't have a laptop but we do have a phone in our pocket and we have we can put a QR code or NFC tag anywhere inside that facility so we can actually have we have students go around and you know they can put an iPhone upto a sensor or scan a QR code and see actual live real-time data of what those sensors are doing which is it's an invaluable tool inside the classroom and in an environment like that for sure so it's interesting able to do things we never would have been able to do before I want to ask you about come back to mobile yeah as you you just saying it was a something that people have wanted for a long time it took a while yeah presumably it's not trivial to take all this data and present it in a format and mobile that's simple number one and number two is a lot of spunky users are you know they're at the command center right and they're on the grid yep so maybe that worked to your advantage a little bit and that you know you look at how quickly mobile apps become obsolete hmm so is that why it took so long because it was so complicated and you had a user profile that was largely stationary yeah and how is that change yes honestly I'm not sure in the full history of the mobile app I know there previously was a new mobile app and I are there was an old mobile app and this new one though is you use it the new one yes oh so when we're talking about augmented reality that might be we may not been clear on that augmented reality is actually part of its features and then in addition we have the Apple TV app is in our lab we have a dashboard displayed on a monitor so not only can we teach this class and have students setting up sensors and all this but we can live display it for everyone to come in and look at all the time and we know that it's a secure connection to our back-end people walk into the lab and the first thing I see is this live dashboard Splunk data from the Apple TV based off of project we've been working on what's that well that's a live feed from a farm five miles off campus giving us all these data points and it's just a talking point people are like wow how did you do that and you know it kind of goes from there yeah and the farm managers are actively looking at it too so they can see when the doors are open and closed to the facility you know the temperature gets too high all these metrics are actually used by the you know that was the important part to actually solve a business problem for them you know we we built a proof of concept for the class so the students could see it then their students are kind of replicating another final project in the class class is still ongoing but where they have to build out a sensor for for plants to so it's kind of the same type of sensor kit but it's they're more stationary plant systems and then they have to figure out how to take that data put it into Splunk and make sense of it so there's all these different components and you get for the students get the glam factor you can put it in a fishbowl have the Apple TV up there exactly and that's I mean part of it when we when we started to think about in ishutin you know it was recruitment you know how do we get students beyond that fear of technology especially kind of coming into a business school but it really went well beyond that we aligned it with the launch of our analytics minor which was open to anyone so now we're getting students from outside the school a bit liberal arts students creating very diverse teams and even in the class itself we have engineers business psychology student history student that are all looking to understand data and platforms to be able to make decisions so there's essentially one Splunk class today instead of a Splunk 101 there this semester there's there's a couple classes that are actually using Splunk inside the classroom and I mean depends on the semester how many we have going on that are actually there's three the semester I sorry I misspoke there we have a another professor as well who's also utilizing it so so yeah we have three three classes that are essentially relying on Splunk to teach different components or you know is it helped us understand is it part of almost exclusively part of the analytics you know curriculum or is it sort of permeate into other Mis and computer science or right now it's within our kind of Mis purview trying to you know build all their partners within the university and the classes they're not it's not solely on spunk spunk is a component of you the tool so it's like for example the particular industrial IOT course it is understanding microcontrollers understanding aquaponics and sustainability understanding how to look at data clean data and then using Splunk as a tool to help bring that all together yeah it's kind of the backbone you know love it and then and I mean in addition to I just wanted to mention that we've had students already go out into the field which is great and come back and tell us hey we went out to a job and we mentioned that we knew Splunk and we were you know a shoo-in for certain things once it goes up on their LinkedIn profile and start getting yeah I mean I again I would think it's right up there with I mean even even more so I mean everybody says and right and our day it was SPSS now it's our yep tableau obviously for the VIS everybody's kind of playing around but spunk is a very you know specific capability that not everybody has except every IT department on the planet exactly coming out of school you take a little bit deeper you either you find you find that out yeah cool well great work guys really thank you guys coming on the cube it was great to meet you I appreciate it incoming all right you're welcome all right keep it right - everybody stew and I will be right back after this this is day one of cough 18 from Splunk this is the cube [Music]

>> Live from New York, It's theCUBE! Covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. >> Hello and welcome back to theCUBE live in New York City, here for CUBE NYC. In conjunct with Strata Conference, Strata Data, Strata Hadoop This is our ninth year covering the big data ecosystem which has evolved into machine learning, A.I., data science, cloud, a lot of great things happening all things data, impacting all businesses I'm John Furrier, your host with Dave Vellante and Peter Burris, Peter is filling in for Dave Vellante. Next guest, Stephanie McReynolds who is the CMO, VP of Marketing for Alation, thanks for joining us. >> Thanks for having me. >> Good to see you. So you guys have a pretty spectacular exhibit here in New York. I want to get to that right away, top story is Attack of the Bots. And you're showing a great demo. Explain what you guys are doing in the show. >> Yah, well it's robot fighting time in our booth, so we brought a little fun to the show floor my kids are.. >> You mean big data is not fun enough? >> Well big data is pretty fun but occasionally you got to get your geek battle on there so we're having fun with robots but I think the real story in the Alation booth is about the product and how machine learning data catalogs are helping a whole variety of users in the organization everything from improving analyst productivity and even some business user productivity of data to then really supporting data scientists in their work by helping them to distribute their data products through a data catalog. >> You guys are one of the new guard companies that are doing things that make it really easy for people who want to use data, practitioners that the average data citizen has been called, or people who want productivity. Not necessarily the hardcore, setting up clusters, really kind of like the big data user. What's that market look like right now, has it met your expectations, how's business, what's the update? >> Yah, I think we have a strong perspective that for us to close the final mile and get to real value out of the data, it's a human challenge, there's a trust gap with managers. Today on stage over at STRATA it was interesting because Google had a speaker and it wasn't their chief data officer it was their chief decision scientist and I think that reflects what that final mile is is that making decisions and it's the trust gap that managers have with data because they don't know how the insides are coming to them, what are all the details underneath. In order to be able to trust decisions you have to understand who processed the data, what decision making criteria did they use, was this data governed well, are we introducing some bias into our algorithms, and can that be controlled? And so Alation becomes a platform for supporting getting answers to those issues. And then there's plenty of other companies that are optimizing the performance of those QUERYS and the storage of that data, but we're trying to really to close that trust gap. >> It's very interesting because from a management standpoint we're trying to do more evidence based management. So there's a major trend in board rooms, and executive offices to try to find ways to acculturate the executive team to using data, evidence based management healthcare now being applied to a lot of other domains. We've also historically had a situation where the people who focused or worked with the data was a relatively small coterie of individuals that crave these crazy systems to try to bring those two together. It sounds like what you're doing, and I really like the idea of the data scientists, being able to create data products that then can be distributed. It sounds like you're trying to look at data as an asset to be created, to be distributed so they can be more easily used by more people in your organization, have we got that right? >> Absolutely. So we're now seeing we're in just over a hundred production implementations of Alation, at large enterprises, and we're now seeing those production implementations get into the thousands of users. So this is going beyond those data specialists. Beyond the unicorn data scientists that understand the systems and math and technology. >> And business. >> And business, right. In business. So what we're seeing now is that a data catalog can be a point of collaboration across those different audiences in an enterprise. So whereas three years ago some of our initial customers kept the data catalog implementations small, right. They were getting access to the specialists to this catalog and asked them to certify data assets for others, what were starting to see is a proliferation of creation of self service data assets, a certification process that now is enterprise-wide, and thousands of users in these organizations. So Ebay has over a thousand weekly logins, Munich Reinsurance was on stage yesterday, their head of data engineering said they have 2,000 users on Alation at this point on their data lake, Fiserv is going to speak on Thursday and they're getting up to those numbers as well, so we see some really solid organizations that are solving medical, pharmaceutical issues, right, the largest re insurer in the world leading tech companies, starting to adopt a data catalog as a foundation for how their going to make those data driven decisions in the organization. >> Talk about how the product works because essentially you're bringing kind of the decision scientists, for lack of a better word, and productivity worker, almost like a business office suite concept, as a SAS, so you got a SAS model that says "Hey you want to play with data, use it but you have to do some front end work." Take us through how you guys roll out the platform, how are your customers consuming the service, take us through the engagement with customers. >> I think for customers, the most interesting part of this product is that it displays itself as an application that anyone can use, right? So there's a super familiar search interface that, rather than bringing back webpages, allows you to search for data assets in your organization. If you want more information on that data asset you click on those search results and you can see all of the information of how that data has been used in the organization, as well as the technical details and the technical metadata. And I think what's even more powerful is we actually have a recommendation engine that recommends data assets to the user. And that can be plugged into Tablo and Salesworth, Einstein Analytics, and a whole variety of other data science tools like Data Haiku that you might be using in your organization. So this looks like a very easy to use application that folks are familiar with that you just need a web browser to access, but on the backend, the hard work that's happening is the automation that we do with the platform. So by going out and crawling these source systems and looking at not just the technical descriptions of data, the metadata that exists, but then being able to understand by parsing the sequel weblogs, how that data is actually being used in the organization. We call it behavior I.O. by looking at the behaviors of how that data's being used, from those logs, we can actually give you a really good sense of how that data should be used in the future or where you might have gaps in governing that data or how you might want to reorient your storage or compute infrastructure to support the type of analytics that are actually being executed by real humans in your organization. And that's eye opening to a lot of I.T. sources. >> So you're providing insights to the data usage so that the business could get optimized for whether it's I.T. footprint component, or kinds of use cases, is that kind of how it's working? >> So what's interesting is the optimization actually happens in a pretty automated way, because we can make recommendations to those consumers of data of how they want to navigate the system. Kind of like Google makes recommendations as you browse the web, right? >> If you misspell something, "Oh did you mean this", kind of thing? >> "Did you mean this, might you also be interested in this", right? It's kind of a cross between Google and Amazon. Others like you may have used these other data assets in the past to determine revenue for that particular region, have you thought about using this filter, have you thought about using this join, did you know that you're trying to do analysis that maybe the sales ops guy has already done, and here's the certified report, why don't you just start with that? We're seeing a lot of reuse in organizations, wherein the past I think as an industry when Tablo and Click and all these B.I tools that were very self service oriented started to take off it was all about democratizing visualization by letting every user do their own thing and now we're realizing to get speed and accuracy and efficiency and effectiveness maybe there's more reuse of the work we've already done in existing data assets and by recommending those and expanding the data literacy around the interpretation of those, you might actually close this trust gap with the data. >> But there's one really important point that you raised, and I want to come back to it, and that is this notion of bias. So you know, Alation knows something about the data, knows a lot about the metadata, so therefore, I don't want to say understands, but it's capable of categorizing data in that way. And you're also able to look at the usage of that data by parsing some of sequel statements and then making a determination of the data as it's identified is appropriately being used based on how people are actually applying it so you can identify potential bias or potential misuse or whatever else it might be. That is an incredibly important thing. As you know John, we had an event last night and one of the things that popped up is how do you deal with emergence in data science in A.I, etc. And what methods do you put in place to actually ensure that the governance model can be extended to understand how those things are potentially in a very soft way, corrupting the use of the data. So could you spend a little bit more time talking about that because it's something a lot of people are interested in, quite frankly we don't know about a lot of tools that are doing that kind of work right now. It's an important point. >> I think the traditional viewpoint was if we just can manage the data we will be able to have a govern system. So if we control the inputs then well have a safe environment, and that was kind of like the classic single source of truth, data warehouse type model. >> Stewards of the data. >> What we're seeing is with the proliferation of sources of data and how quickly with IOT and new modern sources, data is getting created, you're not able to manage data at that point of that entry point. And it's not just about systems, it's about individuals that go on the web and find a dataset and then load it into a corporate database, right? Or you merge an Excel file with something that in a database. And so I think what we see happening, not only when you look at bias but if you look at some of the new regulations like [Inaudible] >> Sure. Ownership, [Inaudible] >> The logic that you're using to process that data, the algorithm itself can be biased, if you have a biased training data site that you feed it into a machine learning algorithm, the algorithm itself is going to be biased. And so the control point in this world where data is proliferating and we're not sure we can control that entirely, becomes the logic embedded in the algorithm. Even if that's a simple sequel statement that's feeding a report. And so Alation is able to introspect that sequel and highlight that maybe there is bias at work and how this algorithm is composed. So with GDPR the consumer owns their own data, if they want to pull it out from a training data set, you got to rerun that algorithm without that consumer data and that's your control point then going forward for the organization on different governance issues that pop up. >> Talk about the psychology of the user base because one of the things that shifted in the data world is a few stewards of data managed everything, now you've got a model where literally thousands of people of an organization could be users, productivity users, so you get a social component in here that people know who's doing data work, which in a way, creates a new persona or class of worker. A non techy worker. >> Yeah. It's interesting if you think about moving access to the data and moving the individuals that are creating algorithms out to a broader user group, what's important, you have to make sure that you're educating and training and sharing knowledge with that democratized audience, right? And to be able to do that you kind of want to work with human psychology, right? You want to be able to give people guidance in the course of their work rather than have them memorize a set of rules and try to remember to apply those. If you had a specialist group you can kind of control and force them to memorize and then apply, the more modern approach is to say "look, with some of these machine learning techniques that we have, why don't we make a recommendation." What you're going to do is introduce bias into that calculation. >> And we're capturing that information as you use the data. >> Well were also making a recommendation to say "Hey do you know you're doing this? Maybe you don't want to do that." Most people are using the data are not bad actors. They just can't remember all the rule sets to apply. So what were trying to do is cut someone behaviorally in the act before they make that mistake and say hey just a bit of a reminder, a bit of a coaching moment, did you know what you're doing? Maybe you can think of another approach to this. And we've found that many organizations that changes the discussion around data governance. It's no longer this top down constraint to finding insight, which frustrates an audience, is trying to use that data. It's more like a coach helping you improve and then social aspect of wanting to contribute to the system comes into play and people start communicating, collaborating, the platform and curating information a little bit. >> I remember when Microsoft Excel came out, the spreadsheet, or Lotus 123, oh my God, people are going to use these amazing things with spreadsheets, they did. You're taking a similar approach with analytics, much bigger surface area of work to kind of attack from a data perspective, but in a way kind of the same kind of concept, put the hands of the users, have the data in their hands so to speak. >> Yeah, enable everyone to make data driven decisions. But make sure that they're interpreting that data in the right way, right? Give them enough guidance, don't let them just kind of attack the wild west and fair it out. >> Well looking back at the Microsoft Excel spreadsheet example, I remember when a finance department would send a formatted spreadsheet with all the rules for how to use it out of 50 different groups around the world, and everyone figured out that you can go in and manipulate the macros and deliver any results they want. And so it's that same notion, you have to know something about that, but this site, in many respects Stephanie you're describing a data governance model that really is more truly governance, that if we think about a data asset it's how do we mediate a lot of different claims against that set of data so that its used appropriately, so its not corrupted, so that it doesn't effect other people, but very importantly so that the out6comes are easier to agree upon because there's some trust and there's some valid behaviors and there's some verification in the flow of the data utilization. >> And where we give voice to a number of different constituencies. Because business opinions from different departments can run slightly counter to one another. There can be friction in how to use particular data assets in the business depending on the lens that you have in that business and so what were trying to do is surface those different perspectives, give them voice, allow those constituencies to work that out in a platform that captures that debate, captures that knowledge, makes that debate a knowledge of foundation to build upon so in many ways its kind of like the scientific method, right? As a scientist I publish a paper. >> Get peer reviewed. >> Get peer reviewed, let other people weigh in. >> And it becomes part of the canon of knowledge. >> And it becomes part of the canon. And in the scientific community over the last several years you see that folks are publishing their data sets out publicly, why can't an enterprise do the same thing internally for different business groups internally. Take the same approach. Allow others to weigh in. It gets them better insights and it gets them more trust in that foundation. >> You get collective intelligence from the user base to help come in and make the data smarter and sharper. >> Yeah and have reusable assets that you can then build upon to find the higher level insights. Don't run the same report that a hundred people in the organization have already run. >> So the final question for you. As you guys are emerging, starting to do really well, you have a unique approach, honestly we think it fits in kind of the new guard of analytics, a productivity worker with data, which is we think is going to be a huge persona, where are you guys winning, and why are you winning with your customer base? What are some things that are resonating as you go in and engage with prospects and customers and existing customers? What are they attracted to, what are they like, and why are you beating the competition in your sales and opportunities? >> I think this concept of a more agile, grassroots approach to data governance is a breath of fresh air for anyone who spend their career in the data space. Were at a turning point in industry where you're now seeing chief decision scientists, chief data officers, chief analytic officers take a leadership role in organizations. Munich Reinsurance is using their data team to actually invest and hold new arms of their business. That's how they're pushing the envelope on leadership in the insurance space and were seeing that across our install base. Alation becomes this knowledge repository for all of those mines in the organization, and encourages a community to be built around data and insightful questions of data. And in that way the whole organization raises to the next level and I think its that vision of what can be created internally, how we can move away from just claiming that were a big data organization and really starting to see the impact of how new business models can be creative in these data assets, that's exciting to our customer base. >> Well congratulations. A hot start up. Alation here on theCUBE in New York City for cubeNYC. Changing the game on analytics, bringing a breath of fresh air to hands of the users. A new persona developing. Congratulations, great to have you. Stephanie McReynolds. Its the cube. Stay with us for more live coverage, day one of two days live in New York City. We'll be right back.

(techno music) >> (announcer) From Berlin, Germany it's theCUBE covering DataWorks Summit Europe 2018. Brought to you by Hortonworks. >> Well hello, welcome to theCUBE. We're here on day two of DataWorks Summit 2018 in Berlin, Germany. I'm James Kobielus. I'm lead analyst for Big Data Analytics in the Wikibon team of SiliconANGLE Media. And who we have here today, we have Alan Gates whose one of the founders of Hortonworks and Hortonworks of course is the host of DataWorks Summit and he's going to be, well, hello Alan. Welcome to theCUBE. >> Hello, thank you. >> Yeah, so Alan, so you and I go way back. Essentially, what we'd like you to do first of all is just explain a little bit of the genesis of Hortonworks. Where it came from, your role as a founder from the beginning, how that's evolved over time but really how the company has evolved specifically with the folks on the community, the Hadoop community, the Open Source community. You have a deepening open source stack with you build upon with Atlas and Ranger and so forth. Gives us a sense for all of that Alan. >> Sure. So as I think it's well-known, we started as the team at Yahoo that really was driving a lot of the development of Hadoop. We were one of the major players in the Hadoop community. Worked on that for, I was in that team for four years. I think the team itself was going for about five. And it became clear that there was an opportunity to build a business around this. Some others had already started to do so. We wanted to participate in that. We worked with Yahoo to spin out Hortonworks and actually they were a great partner in that. Helped us get than spun out. And the leadership team of the Hadoop team at Yahoo became the founders of Hortonworks and brought along a number of the other engineering, a bunch of the other engineers to help get started. And really at the beginning, we were. It was Hadoop, Pig, Hive, you know, a few of the very, Hbase, the kind of, the beginning projects. So pretty small toolkit. And we were, our early customers were very engineering heavy people, or companies who knew how to take those tools and build something directly on those tools right? >> Well, you started off with the Hadoop community as a whole started off with a focus on the data engineers of the world >> Yes. >> And I think it's shifted, and confirm for me, over time that you focus increasing with your solutions on the data scientists who are doing the development of the applications, and the data stewards from what I can see at this show. >> I think it's really just a part of the adoption curve right? When you're early on that curve, you have people who are very into the technology, understand how it works, and want to dive in there. So those tend to be, as you said, the data engineering types in this space. As that curve grows out, you get, it comes wider and wider. There's still plenty of data engineers that are our customers, that are working with us but as you said, the data analysts, the BI people, data scientists, data stewards, all those people are now starting to adopt it as well. And they need different tools than the data engineers do. They don't want to sit down and write Java code or you know, some of the data scientists might want to work in Python in a notebook like Zeppelin or Jupyter but some, may want to use SQL or even Tablo or something on top of SQL to do the presentation. Of course, data stewards want tools more like Atlas to help manage all their stuff. So that does drive us to one, put more things into the toolkit so you see the addition of projects like Apache Atlas and Ranger for security and all that. Another area of growth, I would say is also the kind of data that we're focused on. So early on, we were focused on data at rest. You know, we're going to store all this stuff in HDFS and as the kind of data scene has evolved, there's a lot more focus now on a couple things. One is data, what we call data-in-motion for our HDF product where you've got in a stream manager like Kafka or something like that >> (James) Right >> So there's processing that kind of data. But now we also see a lot of data in various places. It's not just oh, okay I have a Hadoop cluster on premise at my company. I might have some here, some on premise somewhere else and I might have it in several clouds as well. >> K, your focus has shifted like the industry in general towards streaming data in multi-clouds where your, it's more stateful interactions and so forth? I think you've made investments in Apache NiFi so >> (Alan) yes. >> Give us a sense for your NiFi versus Kafka and so forth inside of your product strategy or your >> Sure. So NiFi is really focused on that data at the edge, right? So you're bringing data in from sensors, connected cars, airplane engines, all those sorts of things that are out there generating data and you need, you need to figure out what parts of the data to move upstream, what parts not to. What processing can I do here so that I don't have to move upstream? When I have a error event or a warning event, can I turn up the amount of data I'm sending in, right? Say this airplane engine is suddenly heating up maybe a little more than it's supposed to. Maybe I should ship more of the logs upstream when the plane lands and connects that I would if, otherwise. That's the kind o' thing that Apache NiFi focuses on. I'm not saying it runs in all those places by my point is, it's that kind o' edge processing. Kafka is still going to be running in a data center somewhere. It's still a pretty heavy weight technology in terms of memory and disk space and all that so it's not going to be run on some sensor somewhere. But it is that data-in-motion right? I've got millions of events streaming through a set of Kafka topics watching all that sensor data that's coming in from NiFi and reacting to it, maybe putting some of it in the data warehouse for later analysis, all those sorts of things. So that's kind o' the differentiation there between Kafka and NiFi. >> Right, right, right. So, going forward, do you see more of your customers working internet of things projects, is that, we don't often, at least in the industry of popular mind, associate Hortonworks with edge computing and so forth. Is that? >> I think that we will have more and more customers in that space. I mean, our goal is to help our customers with their data wherever it is. >> (James) Yeah. >> When it's on the edge, when it's in the data center, when it's moving in between, when it's in the cloud. All those places, that's where we want to help our customers store and process their data. Right? So, I wouldn't want to say that we're going to focus on just the edge or the internet of things but that certainly has to be part of our strategy 'cause it's has to be part of what our customers are doing. >> When I think about the Hortonworks community, now we have to broaden our understanding because you have a tight partnership with IBM which obviously is well-established, huge and global. Give us a sense for as you guys have teamed more closely with IBM, how your community has changed or broadened or shifted in its focus or has it? >> I don't know that it's shifted the focus. I mean IBM was already part of the Hadoop community. They were already contributing. Obviously, they've contributed very heavily on projects like Spark and some of those. They continue some of that contribution. So I wouldn't say that it's shifted it, it's just we are working more closely together as we both contribute to those communities, working more closely together to present solutions to our mutual customer base. But I wouldn't say it's really shifted the focus for us. >> Right, right. Now at this show, we're in Europe right now, but it doesn't matter that we're in Europe. GDPR is coming down fast and furious now. Data Steward Studio, we had the demonstration today, it was announced yesterday. And it looks like a really good tool for the main, the requirements for compliance which is discover and inventory your data which is really set up a consent portal, what I like to refer to. So the data subject can then go and make a request to have my data forgotten and so forth. Give us a sense going forward, for how or if Hortonworks, IBM, and others in your community are going to work towards greater standardization in the functional capabilities of the tools and platforms for enabling GDPR compliance. 'Cause it seems to me that you're going to need, the industry's going to need to have some reference architecture for these kind o' capabilities so that going forward, either your ecosystem of partners can build add on tools in some common, like the framework that was laid out today looks like a good basis. Is there anything that you're doing in terms of pushing towards more Open Source standardization in that area? >> Yes, there is. So actually one of my responsibilities is the technical management of our relationship with ODPI which >> (James) yes. >> Mandy Chessell referenced yesterday in her keynote and that is where we're working with IBM, with ING, with other companies to build exactly those standards. Right? Because we do want to build it around Apache Atlas. We feel like that's a good tool for the basis of that but we know one, that some people are going to want to bring their own tools to it. They're not necessarily going to want to use that one platform so we want to do it in an open way that they can still plug in their metadata repositories and communicate with others and we want to build the standards on top of that of how do you properly implement these features that GDPR requires like right to be forgotten, like you know, what are the protocols around PIII data? How do you prevent a breach? How do you respond to a breach? >> Will that all be under the umbrella of ODPI, that initiative of the partnership or will it be a separate group or? >> Well, so certainly Apache Atlas is part of Apache and remains so. What ODPI is really focused up is that next layer up of how do we engage, not the programmers 'cause programmers can gage really well at the Apache level but the next level up. We want to engage the data professionals, the people whose job it is, the compliance officers. The people who don't sit and write code and frankly if you connect them to the engineers, there's just going to be an impedance mismatch in that conversation. >> You got policy wonks and you got tech wonks so. They understand each other at the wonk level. >> That's a good way to put it. And so that's where ODPI is really coming is that group of compliance people that speak a completely different language. But we still need to get them all talking to each other as you said, so that there's specifications around. How do we do this? And what is compliance? >> Well Alan, thank you very much. We're at the end of our time for this segment. This has been great. It's been great to catch up with you and Hortonworks has been evolving very rapidly and it seems to me that, going forward, I think you're well-positioned now for the new GDPR age to take your overall solution portfolio, your partnerships, and your capabilities to the next level and really in terms of in an Open Source framework. In many ways though, you're not entirely 100% like nobody is, purely Open Source. You're still very much focused on open frameworks for building fairly scalable, very scalable solutions for enterprise deployment. Well, this has been Jim Kobielus with Alan Gates of Hortonworks here at theCUBE on theCUBE at DataWorks Summit 2018 in Berlin. We'll be back fairly quickly with another guest and thank you very much for watching our segment. (techno music)

>> Announcer: From San Jose, it's theCUBE. (upbeat music) Presenting Big Data, Silicon Valley, brought to you by Silicon Angle Media and its ecosystem partners. >> Welcome back to theCUBE. We are live on our first day of coverage of our event, Big Data SV. This is our tenth Big Data event. We've done five here in Silicon Valley. We also do them in New York City in the fall. We have a great day of coverage. We're next to where the Startup Data conference is going on at Forger Tasting Room and Eatery. Come on down, be part of our audience. We also have a great party tonight where you can network with some of our experts and analysts. And tomorrow morning, we've got a breakfast briefing. I'm Lisa Martin with my co-host, Peter Burris, and we're excited to welcome to theCUBE for the first time the CEO of Kinetica, Paul Appleby. Hey Paul, welcome. >> Hey, thanks, it's great to be here. >> We're excited to have you here, and I saw something marketer, and terms, I grasp onto them. Kinetica is the insight engine for the extreme data economy. What is the extreme data economy, and what are you guys doing to drive insight from it? >> Wow, how do I put that in a snapshot? Let me share with you my thoughts on this because the fundamental principals around data have changed. You know, in the past, our businesses are really validated around data. We reported out how our business performed. We reported to our regulators. Over time, we drove insights from our data. But today, in this kind of extreme data world, in this world of digital business, our businesses need to be powered by data. >> So what are the, let me task this on you, so one of the ways that we think about it is that data has become an asset. >> Paul: Oh yeah. >> It's become an asset. But now, the business has to care for, has to define it, care for it, feed it, continue to invest in it, find new ways of using it. Is that kind of what you're suggesting companies to think about? >> Absolutely what we're saying. I mean, if you think about what Angela Merkel said at the World Economic Forum earlier this year, that she saw data as the raw material of the 21st century. And talking about about Germany fundamentally shifting from being an engineering, manufacturing centric economy to a data centric economy. So this is not just about data powering our businesses, this is about data powering our economies. >> So let me build on that if I may because I think it gets to what, in many respects Kinetica's Core Value proposition is. And that is, is that data is a different type of an asset. Most assets are characterized by, you apply it here, or you apply it there. You can't apply it in both places at the same time. And it's one of the misnomers of the notion of data as fuels. Because fuel is still an asset that has certain specificities, you can't apply it to multiple places. >> Absolutely. >> But data, you can, which means that you can copy it, you can share it. You can combine it in interesting ways. But that means that the ... to use data as an asset, especially given the velocity and the volume that we're talking about, you need new types of technologies that are capable of sustaining the quality of that data while making it possible to share it to all the different applications. Have I got that right? And what does Kinetica do in that regard? >> You absolutely nailed it because what you talked about is a shift from predictability associated with data, to unpredictability. We actually don't know the use cases that we're going to leverage for our data moving forward, but we understand how valuable an asset it is. And I'll give you two examples of that. There's a company here, based in the Bay Area, a really cool company called Liquid Robotics. And they build these autonomous aquatic robots. And they've carried a vast array of senses and now we're collecting data. And of course, that's hugely powerful to oil and gas exploration, to research, to shipping companies, etc. etc. etc. Even homeland security applications. But what they did, they were selling the robots, and what they realized over time is that the value of their business wasn't the robots. It was the data. And that one piece of data has a totally different meaning to a shipping company than it does to a fisheries companies. But they could sell that exact same piece of data to multiple companies. Now, of course, their business has grown on in Scaldon. I think they were acquired by Bowing. But what you're talking about is exactly where Kinetica sits. It's an engine that allows you to deal with the unpredictability of data. Not only the sources of data, but the uses of data, and enables you to do that in real time. >> So Kinetica's technology was actually developed to meet some intelligence needs of the US Army. My dad was a former army ranger airborne. So tell us a little bit about that and kind of the genesis of the technology. >> Yeah, it's a fascinating use case if you think about it, where we're all concerned, globally, about cyber threat. We're all concerned about terrorist threats. But how do you identity terrorist threats in real time? And the only way to do that is to actually consume vast amount of data, whether it's drone footage, or traffic cameras. Whether it's mobile phone data or social data. but the ability to stream all of those sources of data and conduct analytics on that in real time was, really, the genesis of this business. It was a research project with the army and the NSA that was aimed at identifying terrorist threats in real time. >> But at the same time, you not only have to be able to stream all the data in and do analytics on it, you also have to have interfaces and understandable approaches to acquiring the data, because I have a background, some background in that as well, to then be able to target the threat. So you have to be able to get the data in and analyze it, but also get it out to where it needs to be so an action can be taken. >> Yeah, and there are two big issues there. One issue is the inter-offer ability of the platform and the ability for you to not only consume data in real time from multiple sources, but to push that out to a variety of platforms in real time. That's one thing. The other thing is to understand that in this world that we're talking about today, there are multiple personas that want to consume that data, and many of them are not data scientists. They're not IT people, they're business people. They could be executives, or they could be field operatives in the case of intelligence. So you need to be able to push this data out in real time onto platforms that they consume, whether it's via mobile devices or any other device for that matter. >> But you also have to be able to build applications on it, right? >> Yeah, absolutely. >> So how does Kinetica facilitate that process? Because it looks more like a database, which is, which is, it's more than that, but it satisfies some of those conventions so developers have an afinity for it. >> Absolutely, so in the first instance, we provide tools ourselves for people to consume that data and to leverage the power of that data in real time in an incredibly visual way with a geospatial platform. But we also create the ability for a, to interface with really commonly used tools, because the whole idea, if you think about providing some sort of ubiquitous access to the platform, the easiest way to do that is to provide that through tools that people are used to using, whether that's something like Tablo, for example, or Esri, if you want to talk about geospatial data. So the first instance, it's actually providing access, in real time, through platforms that people are used to using. And then, of course, by building our technology in a really, really open framework with a broadly published set of APIs, we're able to support, not only the ability for our customers to build applications on that platform, and it could well be applications associated with autonomous vehicles. It could well be applications associated with Smart City. We're doing some incredible things with some of the bigger cities on the planet and leveraging the power of big data to optimize transportation, for example, in the city of London. It's those sorts of things that we're able to do with the platform. So it's not just about a database platform or an insights engine for dealing with these complex, vast amounts of data, but also the tools that allow you to visualize and utilize that data. >> Turn that data into an action. >> Yeah, because the data is useless until you're doing something with it. And that's really, if you think about the promise of things like smart grid. Collecting all of that data from all of those smart sensors is absolutely useless until you take an action that is meaningful for a consumer or meaningful in terms of the generational consumption of power. >> So Paul, as the CEO, when you're talking to customers, we talk about chief data officer, chief information officer, chief information security officer, there's a lot, data scientist engineers, there's just so many stakeholders that need access to the data. As businesses transform, there's new business models that can come into development if, like you were saying, the data is evaluated and it's meaningful. What are the conversations that you're having, I guess I'm curious, maybe, which personas are the table (Paul laughs) when you're talking about the business values that this technology can deliver? >> Yeah, that's a really, really good question because the truth is, there are multiple personas at the table. Now, we, in the technology industry, are quite often guilty of only talking to the technology personas. But as I've traveled around the world, whether I'm meeting with the world's biggest banks, the world's biggest Telco's, the world's biggest auto manufacturers, the people we meet, more often than not, are the business leaders. And they're looking for ways to solve complex problems. How do you bring the connected card alive? How do you really bring it to life? One car traveling around the city for a full day generates a terabyte of data. So what does that really mean when we start to connect the billions of cars that are in the marketplace in the framework of connected car, and then, ultimately, in a world of autonomous vehicles? So, for us, we're trying to navigate an interesting path. We're dragging the narrative out of just a technology-based narrative speeds and feeds, algorithms, and APIs, into a narrative about, well what does it mean for the pharmaceutical industry, for example? Because when you talk to pharmaceutical executives, the holy grail for the pharma industry is, how do we bring new and compelling medicines to market faster? Because the biggest challenge for them is the cycle times to bring new drugs to market. So we're helping companies like GSK shorten the cycle times to bring drugs to market. So they're the kinds of conversations that we're having. It's really about how we're taking data to power a transformational initiative in retail banking, in retail, in Telco, in pharma, rather than a conversation about the role of technology. Now, we always needs to deal with the technologists. We need to deal with the data scientists and the IT executives, and that's an important part of the conversation. But you would have seen, in recent times, the conversation that we're trying to have is far more of a business conversation. >> So if I can build on that. So do you think, in your experience, and recognizing that you have a data management tool with some other tools that helps people use the data that gets into Kinetica, are we going to see the population of data scientists increase fast enough so our executives don't have to become familiar with this new way of thinking, or are executives going to actually adopt some of these new ways of thinking about the problem from a data risk perspective? I know which way I think. >> Paul: Wow, >> Which way do you think? >> It's a loaded question, but I think if we're going to be in a world where business is powered by data, where our strategy is driven by data, our investment decisions are driven by data, and the new areas of business that we explored to creat new paths to value are driven by data, we have to make data more accessible. And if what you need to get access to the data is a whole team of data scientists, it kind of creates a barrier. I'm not knocking data scientists, but it does create a barrier. >> It limits the aperture. >> Absolutely, because every company I talk to says, "Our biggest challenge is, we can't get access to the data scientists that we need." So a big part of our strategy from the get go was to actually build a platform with all of these personas in mind, so it is built on this standard principle, the common principles of a relational database, that you're built around anti-standard sequel. >> Peter: It's recognizable. >> And it's recognizable, and consistent with the kinds of tools that executives have been using throughout their careers. >> Last question, we've got about 30 seconds left. >> Paul: Oh, okay. >> No pressure. >> You have said Kinetica's plan is to measure the success of the business by your customers' success. >> Absolutely. >> Where are you on that? >> We've begun that journey. I won't say we're there yet. We announced three weeks ago that we created a customer success organization. We've put about 30% of the company's resources into that customer success organization, and that entire team is measured not on revenue, not on project delivered on time, but on value delivered to the customer. So we baseline where the customer is at. We agree what we're looking to achieve with each customer, and we're measuring that team entirely against the delivery of those benefits to the customer. So it's a journey. We're on that journey, but we're committed to it. >> Exciting. Well, Paul, thank you so much for stopping by theCUBE for the first time. You're now a CUBE alumni. >> Oh, thank you, I've had a lot of fun. >> And we want to thank you for watching theCUBE. I'm Lisa Martin, live in San Jose, with Peter Burris. We are at the Forger Tasting Room and Eatery. Super cool place. Come on down, hang out with us today. We've got a cocktail party tonight. Well, you're sure to learn lots of insights from our experts, and tomorrow morning. But stick around, we'll be right back with our next guest after a short break. (CUBE theme music)

>> Announcer: Live from San Francisco, it's The Cube. Covering Spark Summit 2017. Brought to you by Databricks. >> The Cube is live once again from Spark Summit 2017, I'm David Goad, your host, here with George Gilbert, and we are interviewing many of the speakers that we saw on stage this morning at the keynote. Happy to introduce our next guest on the show, his name is Matt Fryer, Matt, how're you doing? >> Matt: Very well. >> You're the chief, Chief Data Science Officer, I don't see many CDSOs out there, is that a common-- >> I think to say, it's a newer title, and it's coming, I think, where companies that feel the use of data, data science and algorithms, are fundamental to their, their futures. They're creating both the mix of commercial, technical, and algorithmic skill sets, this one team, and to execute together, and that's where the title came from. There's more coming, there's a number of-- Facebook have a few, that's one for example, but it's a newer title, I think it's going to become larger and larger, as time goes on. >> David: So, the CDSO for Hotels.com, something else we learned about you that you may not want me to reveal, but I heard you were the inspiration for Captain Obvious, is that true? >> Uh, that's not true. (laughter) I think Captain Obvious is only an expression of my brand, so there's an awesome brand team, at our office in Dallas. (crosstalk) We all love the captain, he has some good humorous moments, and he keeps us all kind of happy. >> Oh, yeah, he states the obvious, we're going to talk about some of the obvious, and maybe some of the not-so obvious here in this interview. So let's talk a little bit about company culture, because you talked a lot on the stage this morning about customer-first kind of approach, rather than a, "Ooh, look what I can do with the technology." Talk a little bit more about the culture at Hotels.com. >> And that's important, and I think, we're a very data-driven culture, I think most tech companies, and travel, technology companies have that kind of ethos. But fundamentally, the focus and the reason we exist is for the customer. So we want to bring, and actually-- in even better ways than that, I think it's the people. So whether it's the focus on the customer, if we did the right thing by the customer, we fundamentally want you to use our platform time and time again. Whatever need you have, booking, lodging and travel, please use our platform. That's the crucial win. So, to do that, we have to always delight you in every experience you have with us. And equally about people, it's about the team, so we have an internal concept called being supportive. So the whole part of our team culture, is that everybody helps everybody else out, we don't single things out, we're all part of the same team, and we all win if all of us pull together. That makes it a great place, a fun place to work, we're going to play with some new technologies, tech is important to us, but actually the people are even more important to us. >> In part why you love the Spark Summit then, huh? Same kind of spirit here, right? >> It's great, I think it's my third Spark Summit, my second time over in San Francisco, and the size of it is very impressive now. I just love meeting other people learning about some of the things they're up to, how we can apply those back to our business, and hopefully sharing a little bit of what we're up to. >> David: Let's dive into how you're applying it to your business, you talked about this evolution toward becoming an algorithm business, what does that mean and what part does Spark play in that? >> Matt: I think what it is, is about how do you, if you think about a bit of the journey, historically, a lot of the opportunity came in building new features, constantly building it, it's almost like a semi arms race, about how to build more and more features. The crucial thing I think going forward, and particularly with mobile devices now, we have over half our traffic, comes from people using smartphones, on both the app and mobile web. That bringing together means that, be more targeted, in understanding your journey, and people are, last on to time, speed is much more important, people expect things to be right there when they need it, relevance is much more important to people, so we need to bring all those things together to offer a much more targeted experience, and a much more real-time experience. People expect you to have understood what they did milliseconds ago, and respond to that. The only way you can do that is using data science and algorithms. You balance out on a business operation side, just how do you scale? The analogy I use with, say, anomaly detection, which is a crucial feature for enterprises. Used to have a large business intelligence, lots of reports, pages of paper, now people have things like Tablo, Power BI, those are great and you need those to start with, but really as a business leader, you want to know, "Tell me what's broken, tell me what's changed, "because if it's changed something caused the change, "tell me why it's slowly moving, and most importantly, "tell me where the opportunity is." And that transforms the conversation where algorithms can really surface that to users, and it's about organic intelligence, it's not about artificial intelligence, it's about how would you bring together the people, and the advance in technology to really do a great job for customers. >> David: Well, you mentioned AI, you made a big bold claim about AI, I'm going to ask George to weigh in on this in just a moment, you said AI was going to be the next big thing in the travel industry, can you explain? >> One of the next big things, I think. Yeah, I think it's already happening, in fact, our chairman, Mr. Diller made that statement very recently, also backed up by both the CEO and the brand president, where it's... If you think about 20 years ago, one of the things both Expedia and Hotels.com, and travel online space did, were democratize price information, and made it transparent to users. So previously, the power was with the travel agents, that power moved to the user, they had the information. And that's evolved over time, and what we feel with artificial intelligence, particularly organic intelligence, enablers like mobile, messaging and having conversations, have a machine learning how to make this happen, that you can turn the screen around and actually empower users always with the second revolution. They actually have the advice, and the benefits you had a number of years ago from travel agents: A, they had the price transparency, they have the other part now, which is the content, advice, and what's the most relevant to help them. And you can listen to what they're saying to you, as a customer, and actually we can now replay the perfect information back to them, or increasingly perfect as time goes on. (crosstalk) >> That is fascinating, 'cause in the way you broke that out, with--it wasn't actually only travel, but over the last couple decades, price transparency became an issue for many industries, but what you're saying now is, by giving the content to surprise and delight the customer, as long as you're collecting the data breadcrumbs to help you do that, you're not giving up control, you're actually creating stickiness. >> Matt: We're empowering, is the language I use. And if you empower the user, the more likely to come back to use your service in the future, and that's really what we want, we want happy customers. >> George: Tell us a little bit, at the risk of dropping a little in the wait, tell us a little bit about how you empower, in other words, how do you know what type of content to serve up, and how do you measure how they engage with it? >> It's a great question, and I think it's quite embryonic, part of the world right now. I don't think anybody's-- have we made some great developments? I said it was a long journey we have, but it's a lot about how do you, and this is true across data science machine learning, great data science is fundamental to having great feedback loops. So, there's lots of different techniques and tactics around how you might discover those feedback loops, and customers demand that you use their data to help them. So, we need to get faster, and streaming is one way, that's becoming feasible, and the advances in streaming and it's great Databricks are working on that, but the advances in streaming allows it to feed that loop, to take that much--those real-time signals, as well as previous signals, to really help figure out what you're trying to do today, what content-- interesting thing is, Netflix and Amazon were some pioneers in this space, where if you use Netflix service, often you go, "How the hell did they know "this video was going to be right for me?" And, some of the comments, and you can say, well, what they're actually doing is they're looking at microsegments, so previously everyone talked about custom segments as these very large groups, and they have their place, but increasing machine learning allows you to build microsegments. What I can start to do is actually discover from the behavior of others, things you likely-- very relevant things that you're going to be very interested in, and actually help inspire you and discover things you didn't even know existed. And by filling that gap and using those microsegments as well as put truly personal, personalization, I can bring that together to offer you a much more enhanced service. >> George: And so, help make that concrete in terms of, what would I as a potential--I want to plan a vacation for the summer, I have my five and a half inch or, five-seven iPhone, and that's my primary device. And in banking, it's moved from tying everything to the checking account, to tying every interaction to your mobile device. So what would you show me on my mobile device, that would get me really engaged about going to some location? >> So I think a lot of it is about where you are in that journey. So, you think, there's so many different routes customers can take, through that buying decision. And depends on the trip type, whether it's a leisure trip, seeing your family and friends, how much knowledge you may have about them, have you been there before? We look for all those signals, to try and help inspire. So a great example might be, if you stayed in a hotel on our site before, and you liked that hotel, and you come back and do a search again, we try and make it easy to continue by putting that hotel at the top. Trying to make it easy to task-complete. We have a trip planner capability you'll see on the home screen, which allows you to record and play back some of your previous searches, so you can quickly see and compare where you've been, and what's interesting for you. But on top of that, we can then use the signals, and increasingly, we have a very advanced filter list, and that's a key, and we're looking in stuff, how we do conversations in the chatbox, is this sort of future, how to have a conversation to say, "Hey, here's a list of hotels, which we used a mix of your, "the types of preferences understood about you, "and the wider thing, where you are in the world, "what's going on, what time of day." We take hundreds of different signals to try and figure out what the right list is for you, and from that list, the great thing is most people interact with that list and give us more signals, exactly what you wanted. We can hone and hone and hone, and repeat, 'cause I said at the start, for example, those majority of customers will do multiple searches. They want to understand what the market is, they may not be interested in one particular place, they may have a sweeter place there instead. Even now, where we've moved further up the funnel, investing behind, how can you figure out what destination you're interested in? So you may not even know what destination you're interested in, or there might be other destinations that you didn't know--with a very relevant for your use case, particularly if you're going on vacation, we can help inspire you to find that hidden gem, that hidden great prize, you may not even know it existed. Being the much better job, but to show you how busy the market is, to how fast you should be looking to book there, if it's a very compressed, busy market, you need to get in there quick to lock your price in, and we're now providing that information to help you make a better decision. And we can mine all that data, to empower you to make smart decisions with smart data. >> I want to clarify something I saw in your demonstration this morning, you were talking about detecting the differences between photos and user-generated content, so do you have users actually posting their own photos of the hotel, right next to the photoshopped pictures of the hotel? >> Matt: We do, yeah. >> David: What are the ramifications of that? >> So it's an interesting advancement we've made, so we've... In the last of the year, we now offer and asking users to submit their photos, to help other users. I think one of the crucial things is about how to be authentic. Over the years, we've had tens of millions of testimonial reviews, text reviews, and we can see they're really, crucially important to users, and their buying decisions. >> David: It scares the hotel owners to death though, doesn't it? >> Matt: Well, I think it does, but I think the testimony of the customer, could be one of the key things we call them, as we have verified reviews, so to leave a review on our site, you've had to stay in that hotel. We think that's a crucial step in really helping to say, "These are your customers." In recent times, we've taken that product further, to now when you actually arrive at the hotel within a few hours, We'll ask you what your first impressions were. We would ask if you want to share that with the hotel owner. To get the hotel owner a chance to actually rectify any early challenges, so you can have a great stay. And one of the crucial things we have is that, what's really, really important, is that users and customers have a great stay, that reflects on our Net Promoter score, and their view of us, and we need to fill that cycle and make sure we have happy users. So that real-time review is super crucial, in basing how can hotels--if they want happy users and customers as well, it helps them to cut a course correct, if there's an issue, and we can step in as well to help the user if it's a really deep issue. And then with the photos, the key to think is how to navigate and understand what the photo is, so the user helps us by tagging that, which is great, but how we-- >> David: Possibly mistagging it. >> Possibly mistagging it on occasion, that's something we've, we've built in some skill as you've heard, on how to tackle that, but the crucial thing is how to bring these together, if you're on a mobile device, you've got to scan through each photo, and in places around the world have limited bandwidth, a limited time to go through them, so what we're now working on is how to assess the quality of those photos, to try and make sure we authentically--what we want to do, is get the customer the most lively experience they will have. As I said before, we're on the customer's kind of focus, we want to make sure they get the best photos, the most realistic of what's going to happen, and doing the most diverse. You want to see three photos, exactly the same, and we're working on the moment, you can swipe left and swipe right, we're working on how that display evolves over time, but it's exciting. >> David: Very exciting, fascinating stuff. Sorry that we're up against a hard break, coming here in just a moment, but I wanted to give you just 30 seconds to kind of sum up, maybe the next big technical challenge you're looking at that involves Spark, and we'll close with that. >> Cool, it's a great question. I think I talked a little about that in the keynote, totally caught the kind of out challenge. How to scale a mountain, which has been-- there's been great advance on how to stream data into platforms, Spark is a core part of that, and the platforms that we've been building, both internally, and partnering with Databricks and using their platform, has really given us a large boost going forwards, but how you turn those algorithms and that competitive algorithmic advantage, into a live production environment, whether it's marketplaces, Adtech marketplaces or websites, or in call centers, or in social media, wherever the platform needs to go, that's a hard problem right now. Or, I think it's too hard a problem right now. And I'd love to see--and we're going to invest behind that, a transformation, that hopefully this time next year, that is no longer a problem, and is actually an asset. >> David: Well I hope I'm not Captain Obvious to say, I know you're up to the challenge. Thank you so much, Matt Fryer, we appreciate you being on the show, thank you for sharing what's going on at Hotels.com. And thank you all for watching The Cube, we'll be back in a few moments with our next guest, here at Spark Summit 2017. (electronic music) (wind blowing)

>>Live from Seattle, Washington. It's the queue at Tableau conference 2014 brought to you by headline sponsor Tableau.. >>Here are your hosts, John furrier and Jeff Kelly. >>Okay, welcome back. And when we hear live in Seattle, Washington for the cube, this is our flagship program. We go out to the events, expect to see with the noise. I'm John furrier, my coach Jeff Kelly, analysts that we bond.org and we'd love to go talk to the senior leaders of the companies that are hosting the event, the Tablo data 14 conference and Kelly, right EVP of sales for Tableau software. Welcome to the cube. >>Thank you. Thank you for having me. >>So, uh, you're under the, you're in the pressure cooker seat. So sales is everything, right? You know, you guys are a public company and you have to perform. Performance is happy customers, they pay you money, you collect the cash, you put it in the bank and invested into your business and do it again and again. Um, you've done very well as a company. You guys have been great. So I got to ask you, um, about where Chad blow is today. Share with the folks a little bit of the history. Um, you know, we've been big fans of the company actually. We are, uh, you know, me personally being an entrepreneur, I love when companies get built by the founders and don't have to raise money to start the company. They get critical mass and take the extra growth capital. And you guys have done that. You've been in real big success story is an entrepreneurial venture. So share the culture and kind of where you guys are now and with the customer base, the culture. >>Oh, that's a lot of questions all in one. Uh, well thank you for having me. It's a pleasure being here. You know, you asked about what it's been like on this whole journey and a lot of the people that were here at the beginning, we're all still here, right? So I was the first salesperson at Tableau. I joined a month before we started version one. And I've seen how things have changed and evolved. And the truth of the matter is we have a lot more people. We have more customers, but the culture of the company has stayed really sound from the beginning. We were a bunch of people who were very, very passionate about this mission to help people see and understand data. And that's still our mission today. So from the day I started to now, it's all been focused on empowering people to answer their questions more. And so the culture of the people that started were very passionate, really excited about the mission, really a group of company builders who wanted to roll up their sleeves and go make things happen. And yes, we're a bigger company now. Now we're a public company, but we're still just barely, barely scratching the surface. I mean, they're 55 million companies out there in the world. We have 20,000 customers. So we have a long, long way to go. >>I love that you're a senior lead as a company. You've been there as the first is awesome. So I've got to ask you, I mean there's always a moment in time where you go, Oh, will we make it? Or that moment where you going? We've the flywheels going. Could you share just some color around because startups are very hard. Think they're easy all yet. Anyone can do that. So share with a moment where you go, Oh my God, it's gonna be tough shipping where they're shipping a product or hiring or personnel or, and an aha moment where you said, Oh my God, we're doing it. Well, >>when, when you're in this company building mode, it's just you put your head down and you go and you're just go, go, go. And it's always about going and finding the next customer, making sure that customer is excited, ecstatic, hiring more people on the team, making sure that culture is still vibing. And we really just took the focus of doing things one day at a time and treating each customer like their goals. And that's still what we do. Our customers are our lifeblood, right? And that's what's keeping us going. So there were certain times at during during the whole journey, I mean, I remember 2009 when the economy was slowing down. Tableau actually still grew at a really healthy clip, but it was harder. But there was really no time that I felt, Oh, this is a huge uphill battle. I, it was an uphill battle all the time. >>We're still kind of the underdogs, right, where there's tons of customers to help. We haven't helped tons of them yet. And it's just doing things to make sure that we're building good products, empowering people to you go, wow, we're really doing this well. Did you take a break and pause and say, Hey, we're doing it, we're making it. Well, you know, I think one of the moments that really resonated for me is we worked so long to say is Tao, is Tablo gonna make it just keep doing what we're doing and believe in what we're doing. Believe in that mission. And for a long time it was, can we make it to be a public company? Can we ever get to that moment? And I remember the day, it was May 17th last year, 2013 when we were on the floor of the New York stock exchange. And we had brought tons of customers. I mean not customers. We had a lot of employees. So we had over a hundred employees filling out the floor. And in that moment when we had the management team and Christian was ringing the bell, just looking out at all these people who had helped us build Tableau and get to that day. I think that was a moment of real. A lot of pride. And it's funny talking about it right now because where I just came from is gesturing in the bell again at the, at the closing bell. So >>cause that's a lot of those steps are very hard. I mean Jeff and I talked to special all the time. We'll get a big pile of money from the VCs. Four or five guys. >>Well we didn't get a big pile of, >>I know, I just, why I was thinking why it's such a great story because the pilot money could complicate it. Being hungry actually is motivating. So, and then having that customer product successes is a great testimony. So we, I mean I think you guys are a great testimonial to successful startups. Thank you. So let's dig into the sales strategy a little bit. So as you've grown up Tableau, when you started off you really, this is you know, this very nimble underdog. You were kind of going in there with really disrupting the old guard BI players. A lot of, more of a kind of I think a desktop focus, a single user kind of focus. You've expanded, you've got enterprise licenses, now you've got cloud, now you've got mobile. How has the sales strategy evolved over that time period to, to adopt or to adjust to these new, uh, Kevin, the new ways of reaching your customer? >>Well, you know, our model is actually really quite simple. I'll go back to what I had talked about before. We help people see and understand data. So everything about what we're trying to do is to help people to be able to answer their own questions and to empower them with flexibility and agility and self service. And as we add additional products, it's really just extending the number of people that we can help. Some people want to work in the cloud, so Tableau online's better. Some people want to do it on their desktop so they're doing it more with tablet, desktop, some people out in the server and so as long as our salespeople are are looking for what is the best way that I can help this customer to be able to be more self sufficient in answering their own question and then we really hear what's the customer's use case. >>Then to answer that we have different products that actually fit that in. So in terms of how our sales strategy is working, the sales strategy is the same as it always is so we don't really focus on what to do with this product line versus that product line or this product line or small customers versus big customers. It's really all in this landed expand, let the customer buy as big or as little as they want to get started. We'll work with them very closely to make them successful and then as they're successful, they'll come back to buy more. And we have all these different ways that they can buy software and types of software that they can buy to be able to address their needs of self service agility and answering their own questions. >>The buyer, the profile of the buyer changed at all. So I know obviously Tableau is all about the end user, the person who's interacting with the software interact with the data as you'd like to focus on. But as you move to larger accounts, larger enterprises, are you still dealing directly with that user when you sell? Are you dealing with essential it more often? Right, right. >>And I guess that was kind of my question. You evolve to that, you know, I think that's a great, it's a great question because if I were to roll back the clock to almost 10 years ago when I was starting, we were, we were actually interacting mostly with the business user. So the end user and over time we're interacting with the C level, the C suite, we're interacting with the VP of it, we're interacting with the business users. And actually we're, we're working with both groups a lot. So what happened early on was we'd start with the business and over time as they bought more and more and more, they would bring us into it. And now actually we're seeing a shift that sometimes it's the it and the C suite that's coming to us and they're saying, Hey, we want to be able to empower our user community answered their own questions, but we need to be able to do that in a more secure governed control type of way. >>And is there a way that we can balance with Tableau? So we see it happening in both. I think one of the interesting changes that we're seeing is there is a cultural shift that's going on right now and companies are now starting to realize that the way that the past is very different than the wave of the future. So the wave of the past was if you had a question, you threw it over the fence to this central group that was report writers and these report writers knew how to code and they were very, very specialized. And the user that had the question, they had absolutely no idea how to operate those systems well. Now that companies are saying as data's coming in at such a fast clip, it just takes too long. They have to empower people to be able to answer their own questions, otherwise they end up being at a standstill. And so as we start having more discussions with the enterprise in the C suite, those folks who are in it and the CIO who realize, Hey, there's a shift that's going on and we need to be doing things in the way of where the world is going, not the way that we've done it in the past. It makes that conversation quite a bit easier. And so now we're seeing more and more conversations that are along those lines of how are we going to keep our organization to be competitive going into the. >>So I've got to ask you about the international expansion. We were talking earlier with your colleague Dave Martin, um, and also move at the HP big data event. And I had also had a conversation with Dave, CEO firearm, huge international. He says, John, my big growth happened. He's public company. You got you guys, he says international huge growth opportunity for us. So you have a Tam, then you have 55 million customers. You have one of those unique products at all customers need. So that's good. Check growth is on the horizon. How are you going to attack that new territory? I mean international and to grow, I mean channel strategy, indirect big part of it. I mean you guys are enabling people to create value. That seems to be the formula for a great indirect strategy. You've built a successful direct sales force graduations, but that's can take time. >>Yeah. Well you know, our model for international international is a huge opportunity for us. So we are putting a lot of resources and time into expanding internationally. We have our headquarters over in AMEA, we have headquarters over an APAC. We're now just w we opened up offices in Japan and in Germany we opened up operations in India. We are opening up another, a bigger office in, in Australia and even in Latin America, Brazil and Mexico. There's a fair amount going on now as we're going to market. It actually is pretty similar, so we're building direct sales force in all of those regions. But international, as you start doing more international, the channel becomes even increasingly important and it is, we're focusing a lot of time and energy on the channel here in the States. But in places like AMEA and certain locations over an APAC and and certainly in Latin America there is just the way of doing business tends to be more around the channel. >>Equalization has always been a nice thing of having in country operations. So that's always been kind of the international playbook. But with data I can be complicated. So having people in country, in a channel delivering value, is that the preferred way you guys, is that what you're saying? Is that, is that kind of? >>You know what I th th well the interesting part about Tableau is as we talked about, it's agnostic. Anyone can use it. And so when we go into a new country, there's two ways that we can go in. We can go on with our directing and we can go in with empowering our channel. And we actually have customers in over a hundred countries throughout the world, right? And we have partners operating in a large number of those. So our partners often are the ones that are the local feet on the street. They're going and they're having the conversations and, and they're providing the local support in the language and in the culture that it is now. When we actually open up offices in those different regions, we try to be very aligned, not only just putting our salespeople in, but having our entire company all lined up behind it. So we have our sales team, we have our marketing, we have our product. So when we go into Japan, for instance, we want to be able to have the website in Japanese. We want to be able to have the product localized in Japanese, we want to be able to have support staff that can help. And, and then of course having the partner ecosystem where the partners are able to help us make those customers all realistic. >>Flip yet in the U S I mean, as you guys get the channel going, has there been some channel conflict on order orders and who owns the accounts? >>Yeah, well you know what, our channel, we were developing a lot in the channel, but we're still pretty early in the, in our channel development and we're spending a lot of time to make sure that our channel is really successful as well as our, as well as our customers being successful. And the truth of the matter is we can't, we can't go and help all the people that we want to help without embracing the channel. And they're system integrators that they're in there and they're doing huge multi-year projects and we're working closely with them. And when we talk about the channel, we're working with resellers but also OEM and technology partners and system integrators. So lots and lots of channel activity going on. >>Yeah, I think you just touched on, well I think is one of the going to be one of the challenges for Tableau is that you can't, as you expand so fast, you can't keep your finger or your pulse on the customer quite as quite as closely as maybe you'd like. You've got to, you've got to count on the channel to do some of that. So that, and Tableau is of course known for being very customer focused. I mean the show here, you know, the crowds are cheering and Christian as he's giving his keynote and different visualizations are being demoed on stage and the crowds standing on their feet, you know, to keep that kind of customer focus as you expand. I think it's a challenge. It sounds like you really got to focus on those relationships with your partners and your OEM partners, et cetera. So they kind of understand that the Tableau approach is that, yeah, >>I I, I totally agree. Actually. I think you can even see at the show today, if you go down to that partner expo hall, there are so many partners, you're way more partners than we've ever had before. And when I was checking in with them, even yesterday where the show hadn't even started, they're getting a huge number of leads that are coming in and they're, there's so many opportunities for us to work together with our partners. In fact, this year, not only did we build of being really growing our partner sales team, but we had a whole series of partner summits this year and we traveled around the world. We had one in AMEA, one in APAC, one here in the States of being able to really train and enable our partners not only how to sell Tableau, but to work with them in a conversation of what's the best way that we can engage with them and make them really successful. So when we think about our ecosystem, it's not just about our customers, it's now about our customers and about our partners. And we're all part of the Tableau >>here. So obviously one of the things that you guys have done, you do a great job because you're such walking testimonials as customers. Um, what channel partners do you have as customers and that are top references now that you're showcasing and what end users are you showcasing here at this event? Can you name names and? >>Yeah, well I think you can, you can actually go downstairs and look in the partners of who we are and we're doing Watson, lots of, uh, partner with, with whether it's Vertica or with Alteryx or with data, uh, where we're doing joint sales and a lot of those, a lot of the that you'll see here, they're using Tableau internally in a pretty big way. And then in terms of customers, and we have showcases all over the place. I think we have a hundred customer speakers that are here. So there are there hospitals, we have Barnes, Jewish and Seattle children's who are talking about how they're using Tableau actually in the operating rooms and with nurses. And to be able to help save lives. We have education institutions who are using Tableau for how they can teach better in school, how the teachers can have their administration going. Uh, and we also have a number of corporate customers who are helping with that as well. >>So one of the things that we always talk about when we talk about startups, you guys want to start certainly, but company building is a great team. You guys are on that next generation of building out. Um, you always get the question, um, high touch sales, indirect low cost, our automated self-service if you're, you know, kind of a platform, um, inside sales is a great strategy for expanding out growth. Um, but it's hard. Um, do you guys have an inside sales organization? You, are you building it out? Is that a big part of your increase in your customer service? Cause a lot of you got great fans. Loyalties, high products is good. So are you building out? >>Yeah. You know, we actually, we got predominantly with inside sales, so we started with inside sales and then enterprise sales came later. And with our inside sales, we still have a very, very robust inside sales. We have kind of both models, some customers prefer to be interacted with field, face to face. And so we have field folks that are all over, uh, in our, all our major regions and we have a lot of inside folks. And the same is true when we look at how we're going to support them. So we have technical folks and services folks in training folks that will go out and meet the customer on their site, help to enable them setting up center of excellence, all that. And then we have a large number of that is that is done remotely. The benefit we have at Tableau is actually tablets, pretty easy to use. >>And so we don't always have to sit down and do it beside them. So how about sales compensation, if you will? Not with numbers, but like, I mean culturally is it, is it, we're hiring you killed like in the early days of Cisco sales guys were making zillions of dollars. Um, there's Tableau have, um, the kind of product pricing mix where you guys have a lot of like huge compensation, uh, rewards. So how does that work? You know, what we focus on having our salespeople be really excited about working here, having it be a very good as you know, right. I mean, compensation drives behavior. How do you guys, we have a lot of salespeople that have been here for a very long period of time. So we have a huge opportunity and we focus on the opportunity to help more customers and then the opportunity to have a really good career progression path. >>You know? Yes. I'm not going to answer your question, but you can keep on top a little bit about the competitive landscape. So, and again, maybe you know, because you've been with Tableau since the beginning, how has it evolved again, when you guys started, you were very much the disruptor going in. Yeah. Let's name some names, the disruptor, SAP business objects. You had Cognos, Hyperion, you guys are going in there and say, no, that's the old way. This is the new way. Um, since then you've now that some of those old players are started, they're focusing now on you know, being very self service, kind of emulating a lot of the things top load yet now you've got also kind of even newer companies, newer startups out there that are coming, even some are maybe mobile focused or cloud focused. What's the competitive landscape look like for you and from a sales perspective, again, how do you adapt as you got to come in from, you know, from the, from the new guys, you've got to come in from the old guard, you guys are targeted. >>When you're this successful you're always going to be a target. What it's like from your perspective. You know what, one of the things that we actually really focused on at Tableau, cause we talk about this a lot internally with our team is we can only control what we can control. We can control what our products are, we can control what our customer success is, we can control how we engage with our customers. And so we spend a lot of time just focusing on what it is that Tableau can do. And as we're now talking more about data discovery and agile and analytics and self-service, there's a lot of noise out there. A lot of other players who are saying that they can do the same thing and that they can do it as well. And our strategy is really, if you think you can use that, so why don't you go download their product and download our product and see how long it takes. And we actually encourage people to go out and test it out and try. And what we find is when someone is really interested in self service and helping people to answer their own questions, then the answer to them becomes really clear when it is an a question of we just want traditional old pixel perfect reporting you have. There are a lot of people that can play in that game. Uh, but we're finding the conversations changing quite a bit when they really want self-service. Then we actually feel like we're, we're pretty well positioned competitively. >>So are your lottery, your deals going up in, you know, competitive environments where you've got Tableau lined up against business objects against, I don't know. Good data against whoever. Is it a lot of that or do you have a lot of, you know, people who are trying the product love it and just say, Hey, we want to go with Tableau. >>You know, there's both, but the majority of our deals are actually when we're competing against the status quo, they actually aren't even looking at other business intelligence. They might have it in their company but it's not solving their need and their requirement. So a lot of people are just using what is already commissioned on their computer. Now there are situations where there is a competitive bake-off and we love competition. I mess with salespeople. Do we go and compete? Uh, but we're finding that the conversation is shifting and where we tend to really focus our time and energy is with those companies that are really looking for the new way. >>Kelly, you got to get the, I got to get the hook here, but I want to ask you two final questions. One is an easy one. What's it like working with Christian? >>It's great working with Christen. You know what? We've worked together all for so long and it's, it's really, we say it's like we're a family, right? We, we know each other, we know each other's families, we know each other's kids and it's pretty much the same as it was when I started almost 10 years ago. Nothing's really >>the second question. Share with the folks out there watching what is the culture of Tablo, if you could. Every culture has their own little weird tweak that makes them so unique. Intel, it's Moore's law. What's Tableau's cultural? >>Well, you have to go ask all the Tablo people if they think our culture is weird, probably not like a unique tweak that makes them so successful. The Moore's law was first called the weird, you know, people that work here are really, really passionate about what we do. We're passionate, we're mission focus and people have a lot of fun at what they do. They work hard and they play hard and it's, it's a very fun place to be. But we go fast. Yeah, certainly not weird, that's for sure. I didn't mean that, but I want a good way, a good thing. And it's usually the, it's the ones that the best deals are the ones that no one sees that doesn't look like it's going to be. And you guys were certainly a great winner of our hiring, so everyone in the world were hiring. We couldn't get the sales comp out of her, but we, you know, we tried our best, uh, Kelly, seriously, thanks for coming on cue. Really appreciate it. We know the journey you've been on has fantastic. It's a >>whirlwind now. You just got to go to the next leg of the journey, which is build a global 50 million customer business. Congratulations. Thank you for having me. We'll be right back with our next guest after this short break live in Seattle, Washington to the cube. Thank you.

>>The cube at big data SV 2014 is brought to you by headline sponsors. When disco we make Hadoop invincible and Aptean accelerating big data, 2.0, >>Okay. We're back here, live in Silicon valley. This is big data. It has to be, this is Silicon England, Wiki bonds, the cube coverage of big data in Silicon valley and all around the world covering the strata conference. All the latest news analysis here in Silicon valley, the cube was our flagship program about the events extract the signal from noise. I'm John furrier, the founders of looking angle. So my co-host and co-founder of Wiki bond.org, Dave Volante, uh, George Matthew CEO, altruist on the cube again, back from big data NYC just a few months ago. Um, our two events, um, welcome back. Great to be here. So, um, what fruit is dropped into the blend or the change, the colors of the big data space this this time. So we were in new Yorkers. We saw what happened there. A lot of talk about financial services, you know, big business, Silicon valley Kool-Aid is more about innovation. Partnerships are being formed, channel expansion. Obviously the market's hot growth is still basing. Valuations are high. What's your take on the current state of the market? >>Yeah. Great question. So John, when we see this market today, I remember even a few years ago when I first visited the cave, particularly when it came to a deep world and strata a few years back, it was amazing that we talked about this early innings of a ballgame, right? We said it was like, man, we're probably in the second or third inning of this ball game. And what has progressed particularly this last few years has been how much the actual productionization, the actual industrialization of this activity, particularly from a big data analytics standpoint has merged. And that's amazing, right? And in a short span, two, three years, we're talking about technologies and capabilities that were kind of considered things that you play with. And now these are things that are keeping the lights on and running, you know, major portions of how better decision-making and analytics are done inside of organizations. So I think that industrialization is a big shift forward. In fact, if you've listened to guys like Narendra Mulani who runs most of analytics at Accenture, he'll actually highlight that as one of the key elements of how not only the transformation is occurring among organizations, but even the people that are servicing a large companies today are going through this big shift. And we're right in the middle of it. >>We saw, you mentioned a censure. We look at CSC, but service mesh and the cloud side, you seeing the consulting firms really seeing build-out mandates, not just POC, like let's go and lock down now for the vendors. That means is people looking for reference accounts right now? So to me, I'm kind of seeing the tea leaves say, okay, who's going to knock down the reference accounts and what is that going to look like? You know, how do you go in and say, I'm going to tune up this database against SAP or this against that incumbent legacy vendor with this new scale-out, all these things are on in play. So we're seeing that, that focus of okay, tire kicking is over real growth, real, real referenceable deployments, not, not like a, you know, POC on steroids, like full on game-changing deployments. Do you see that? And, and if you do, what versions of that do you seeing happening and what ending of that is that like the first pitch of the sixth inning? Uh, w what do you, how would you benchmark that? >>Yeah, so I, I would say we're, we're definitely in the fourth or fifth inning of a non ballgame now. And, and there's innings. What we're seeing is I describe this as a new analytic stack that's emerged, right? And that started years ago when particularly the major Hadoop distro vendors started to rethink how data management was effectively being delivered. And once that data management layer started to be re thought, particularly in terms of, you know, what the schema was on read what the ability to do MPP and scale-out was in terms of how much cheaper it is to bring storage and compute closer to data. What's now coming above that stack is, you know, how do I blend data? How do I be able to give solutions to data analysts who can make better decisions off of what's being stored inside of that petabyte scale infrastructure? So we're seeing this new stack emerge where, you know, Cloudera Hortonworks map are kind of that underpinning underlying infrastructure where now our based analytics that revolution provides Altrix for data blending for analytic work, that's in the hands of data analysts, Tableau for visual analysis and dashboarding. Those are basically the solutions that are moving forward as a capability that are package and product. >>Is that the game-changing feature right now, do you think that integration of the stack, or is that the big, game-changer this sheet, >>That's the hardening that's happening as we speak right now, if you think about the industrialization of big data analytics that, you know, as I think of it as the fourth or fifth inning of the ballgame, that hardening that ability to take solutions that either, you know, the Accentures, the KPMGs, the Deloitte of the world deliver to their clients, but also how people build stuff internally, right? They have much better solutions that work out of the box, as opposed to fumbling with, you know, things that aren't, you know, stitched as well together because of the bailing wire and bubblegum that was involved for the last few years. >>I got it. I got to ask you, uh, one of the big trends you saw in certainly in the tech world, you mentioned stacks, and that's the success of Amazon, the cloud. You're seeing integrated stacks being a key part of the, kind of the, kind of the formation of you said hardening of the stack, but the word horizontally scalable is a term that's used in a lot of these open source environments, where you have commodity hardware, you have open source software. So, you know, everything it's horizontally scalable. Now, that's, that's very easy to envision, but thinking about the implementation in an enterprise or a large organization, horizontally scalable is not a no brainer. What's your take on that. And how does that hyperscale infrastructure mindset of scale-out scalable, which is a big benefit of the current infrastructure? How does that fit into, into the big day? >>Well, I think it fits extremely well, right? Because when you look at the capabilities of the last, as we describe it stack, we almost think of it as vertical hardware and software that's factually built up, but right now, for anyone who's building scale in this world, it's all about scale-out and really being able to build that stack on a horizontal basis. So if you look at examples of this, right, say for instance, what a cloud era recently announced with their enterprise hub. And so when you look at that capability of the enterprise data hub, a lot of it is about taking what yarn has become as a resource manager. What HDFS has been ACOM as a scale-out storage infrastructure, what the new plugin engines have merged beyond MapReduce as a capability for engines to come into a deep. And that is a very horizontal description of how you can do scale out, particularly for data management. >>When we built a lot of the work that was announced at strata a few years ago, particularly around how the analytics architecture for Galerie, uh, emerged at Altryx. Now we have hundreds of, of apps, thousands of users in that infrastructure. And when we built that out was actually scaling out on Amazon where the worker nodes and the capability for us to manage workload was very horizontal built out. If you look at servers today of any layer of that stack, it is really about that horizontal. Scale-out less so about throwing more hardware, more, uh, you know, high-end infrastructure at it, but more about how commodity hardware can be leveraged and use up and down that stack very easily. So Georgia, >>I asked you a question, so why is analytics so hard for so many companies? Um, and you've been in this big data, we've been talking to you since the beginning, um, and when's it going to get easier? And what are you guys specifically doing? You know, >>So facilitate that. Sure. So a few things that we've seen to date is that a lot of the analytics work that many people do internal and external to organizations is very rote, hand driven coding, right? And I think that's been one of the biggest challenges because the two end points in analytics have been either you hard code stuff that you push into a, you know, a C plus plus or a Java function, and you push it into database, or you're doing lightweight analytics in Excel. And really there needs to be a middle ground where someone can do effective scale-out and have repeatability in what's been done and ease of use. And what's been done that you don't have to necessarily be a programmer and Java programmer in C plus plus to push an analytic function and database. And you certainly don't have to deal with the limitations of Excel today. >>And really that middle ground is what Altryx serves. We look at it as an opportunity for analysts to start work with a very repeatable re reasonable workflow of how they would build their initial constructs around an analytic function that they would want to deploy. And then the scale-out happens because all of the infrastructure works on that analyst behalf, whether that be the infrastructure on Hadoop, would that be the infrastructure of the scale out of how we would publish an analytic function? Would that be how the visualizations would occur inside of a product like Tableau? And so that, I think Dave is one of the biggest things that needs to shift over where you don't have the only options in front of you for analytics is either Excel or hard coding, a bunch of code in C plus plus, or Java and pushing it in database. Yeah. >>And you correct me if I'm wrong, but it seems to be building your partnerships and your ecosystem really around driving that solution and, and, and really driving a revolution in the way in which people think about analytics, >>Ease of use. The idea is that ultimately if you can't get data analysts to be able to not only create work, that they can actually self-describe deploy and deliver and deliver success inside of an organization. And scale that out at the petabyte scale information that exists inside of most organizations you fail. And that's the job of folks like ourselves to provide great software. >>Well, you mentioned Tableau, you guys have a strong partnership there, and Christian Chabot, I think has a good vision. And you talked about sort of, you know, the, the, the choices of the spectrum and neither are good. Can you talk a little bit more about that, that, that partnership and the relationship and what you guys are doing together? Yeah. >>Uh, I would say Tableau's our strongest and most strategic partner today. I mean, we were diamond sponsors of their conference. I think I was there at their conference when I was on the cube the time before, and they are diamond sponsors of our conference. So our customers and particular users are one in the same for Tablo. It really becomes a, an experience around how visual analysis and dashboard, and can be very easily delivered by data analysts. And we think of those same users, the same exact people that Tablo works with to be able to do data blending and advanced analytics. And so that's why the two software products, that's why the two companies, that's where our two customer bases are one in the same because of that integrated experience. So, you know, Tableau is basically replacing XL and that's the mission that thereafter. And we feel that anyone who wants to be able to do the first form of data blending, which I would think of as a V lookup in Excel, should look at Altryx as a solution for that one. >>So you mentioned your conference it's inspire, right? It >>Is inspiring was coming up in June, >>June. Yeah. Uh, how many years have you done inspire? >>Inspire is now in its fifth year. And you're gonna bring the >>Cube this year. Yeah. >>That would be great. You guys, yeah, that would be fun. >>You should do it. So talk about the conference a little bit. I don't know much about it, but I mean, I know of it. >>Yeah. It's very centered around business users, particularly data analysts and many organizations that cut across retail, financial services, communications, where companies like Walmart at and T sprint Verizon bring a lot of their underlying data problems, underlying analytic opportunities that they've wrestled with and bring a community together this year. We're expecting somewhere in the neighborhood of 550 600 folks attending. So largely to, uh, figure out how to bring this, this, uh, you know, game forward, really to build out this next rate analytic capability that's emerging for most organizations. And we think that that starts ultimately with data analysts. All right. We think that there are well over two and a half million data analysts that are underserved by the current big data tools that are in this space. And we've just been highly focused on targeting those users. And so far, it's been pretty good at us. >>It's moving, it's obviously moving to the casual user at some levels, but I ended up getting there not soon, but I want to, I want to ask you the role of the cloud and all this, because when you have underneath the hood is a lot of leverage. You mentioned integrates that's when to get your perspective on the data cloud, not data cloud is it's putting data in the cloud, but the role of cloud, the role of dev ops that intersection, but you're seeing dev ops, you know, fueling a lot of that growth, certainly under the hood. Now on the top of the stack, you have the, I guess, this middle layer for lack of a better description, I'm of use old, old metaphor developing. So that's the enablement piece. Ultimately the end game is fully turnkey, data science, personalization, all that's, that's the holy grail. We all know. So how do you see that collision with cloud and the big, the big data? >>Yeah. So cloud is basically become three things for a lot of folks in our space. One is what we talked about, which is scale up and scale out, uh, is something that is much more feasible when you can spin up and spin down infrastructure as needed, particularly on an elastic basis. And so many of us who built our solutions leverage Amazon being one of the most defacto solutions for cloud based deployment, that it just makes it easy to do the scale-out that's necessary. This is the second thing it actually enables us. Uh, and many of our friends and partners to do is to be able to bring a lower cost basis to how infrastructure stood up, right? Because at the end of the day, the challenge for the last generation of analytics and data warehousing that was in this space is your starting conversation is two to $3 million just in infrastructure alone before you even buy software and services. >>And so now if you can rent everything that's involved with the infrastructure and the software is actually working within days, hours of actually starting the effort, as opposed to a 14 month life cycle, it's really compressing the time to success and value that's involved. And so we see almost a similarity to how Salesforce really disrupted the market. 10 years ago, I happened to be at Salesforce when that disruption occurred and the analytics movement that is underway really impacted by cloud. And the ability to scale out in the cloud is really driving an economic basis. That's unheard of with that >>Developer market, that's robust, right? I mean, you have easy kind of turnkey development, right? Tapping >>It is right, because there's a robust, uh, economy that's surrounding the APIs that are now available for cloud services. So it's not even just at the starting point of infrastructure, but there's definite higher level services where all the way to software as industry, >>How much growth. And you'll see in those, in that, as that, that valley of wealth and opportunity that will be created from your costs, not only for the companies involved, but the company's customers, they have top line focus. And then the goal of the movement we've seen with analytics is you seeing the CIO kind of with less of a role, more of the CEO wants to the chief data officer wants most of the top line drivers to be app focused. So you seeing a big shift there. >>Yeah. I mean, one of the, one of the real proponents of the cloud is now the fact that there is an ability for a business analyst business users and the business line to make impacts on how decisions are done faster without the infrastructure underpinnings that were needed inside the four walls in our organization. So the decision maker and the buyer effectively has become to your point, the chief analytics officer, the chief marketing officer, right. Less so that the chief information officer of an organization. And so I think that that is accelerating in a tremendous, uh, pace, right? Because even if you look at the statistics that are out there today, the buying power of the CMO is now outstrip the buying power of the CIO, probably by 1.2 to 1.3 X. Right. And that used to be a whole different calculus that was in front of us before. So I would see that, uh, >>The faster, so yeah, so Natalie just kind of picked this out here real time. So you got it, which we all know, right. I went to the it world for a long time service, little catalog. Self-service, you know, Sarah's already architectures whatever you want to call it, evolve in modern era. That's good. But on the business side, there's still a need for this same kind of cataloguing of tooling platform analytics. So do you agree with that? I mean, do you see that kind of happening that way, where there's still some connection, but it's not a complete dependency. That's kind of what we're kind of rethinking real time you see that happen. >>Yeah. I think it's pretty spot on because when you look at what businesses are doing today, they're selecting software that enables them to be more self-reliant the reason why we have been growing as much among business analysts as we have is we deliver self-reliance software and in some way, uh, that's what tablet does. And so the, the winners in this space are going to be the ones that will really help users get to results faster for self-reliance. And that's, that's really what companies like Altrix Stanford today. >>So I want to ask you a follow up on that CMOs CIO discussion. Um, so given that, that, that CMOs are spending a lot more where's the, who owns the data, is that, is we, we talk, well, I don't know if I asked you this before, but do you see the role of a chief data officer emerging? And is that individual, is that individual part of the marketing organization? Is it part of it? Is it a separate parallel role? What are you, >>One of the things I will tell you is that as I've seen chief analytics and chief data officers emerge, and that is a real category entitled real deal of folks that have real responsibilities in the organization, the one place that's not is in it, which is interesting to see, right? Because oftentimes those individuals are reporting straight to the CEO, uh, or they have very close access to line of business owners, general managers, or the heads of marketing, the heads of sales. So I seeing that shift where wherever that chief data officer is, whether that's reporting to CEOs or line of business managers or general managers of, of, you know, large strategic business units, it's not in the information office, it's not in the CEO's, uh, purview anymore. And that, uh, is kind of telling for how people are thinking about their data, right? Data is becoming much more of an asset and a weapon for how companies grow and build their scale less. So about something that we just have to deal with. >>Yeah. And it's clearly emerging that role in certain industry sectors, you know, clearly financial services, government and healthcare, but slowly, but we have been saying that, >>Yeah, it's going to cross the board. Right. And one of the reasons why I wrote the article at the end of last year, I literally titled it. Uh, analytics is eating the world, is this exact idea, right? Because, uh, you have this, this notion that you no longer are locked down with data and infrastructure kind of holding you back, right? This is now much more in the hands of people who are responsible for making better decisions inside their organizations, using data to drive those decisions. And it doesn't matter the size and shape of the data that it's coming in. >>Yeah. Data is like the F the food that just spilled all over it spilled out from the truck and analytics is on the Pac-Man eating out. Sorry. >>Okay. Final question in this segment is, um, summarize big data SV for us this year, from your perspective, knowing what's going on now, what's the big game changer. What should the folks know who are watching and should take note of which they pay attention to? What's the big story here at this moment. >>There's definite swim lanes that are being created as you can see. I mean, and, and now that the bigger distribution providers, particularly on the Hadoop side of the world have started to call out what they all stand for. Right. You can tell that map are, is definitely about creating a fast, slightly proprietary Hadoop distro for enterprise. You can tell that the folks at cloud era are focusing themselves on enterprise scale and really building out that hub for enterprise scale. And you can tell Horton works is basically embedding, enabling an open source for anyone to be able to take advantage of. And certainly, you know, the previous announcements and some of the recent ones give you an indicator of that. So I see the sense swimlanes forming in that layer. And now what is going to happen is that focus and attention is going to move away from how that layer has evolved into what I would think of as advanced analytics, being able to do the visual analysis and blending of information. That's where the next, uh, you know, battle war turf is going to be in particularly, uh, the strata space. So we're, we're really looking forward to that because it basically puts us in a great position as a company and a market leader in particularly advanced analytics to really serve customers in how this new battleground is emerging. >>Well, we really appreciate you taking the time. You're an awesome guest on the queue biopsy. You know, you have a company that you're running and a great team, and you come and share your great knowledge with our fans and an audience. Appreciate it. Uh, what's next for you this year in the company with some of your goals, let's just share that. >>Yeah. We have a few things that are, we mentioned a person inspired coming up in June. There's a big product release. Most of our product team is actually here and we have a release coming up at the beginning of Q2, which is Altryx nine oh. So that has quite a bit involved in it, including expansion of connectivity, uh, being able to go and introduce a fair degree of modeling capability so that the AR based modeling that we do scales out very well with revolution and Cloudera in mind, as well as being able to package into play analytic apps very quickly from those data analysts in mind. So it's, uh, it's a release. That's been almost a year in the works, and we're very much looking forward to a big launch at the beginning of Q2. >>George, thanks so much. You got inspire coming out. A lot of great success as a growing market, valuations are high, and the good news is this is just the beginning, call it mid innings in the industry, but in the customers, I call the top of the first lot of build-out real deployment, real budgets, real deal, big data. It's going to collide with cloud again, and I'm going to start a load, get a lot of innovation all happening right here. Big data SV all the big data Silicon valley coverage here at the cube. I'm Jennifer with Dave Alonzo. We'll be right back with our next guest. After the short break.

okay we're back this is Dave Volante with Jeff Kelly we're with Ricky bond on organ this is the cubes silicon angles flagship product we go out to the events we extract the signal from the noise we bring you the tech athletes who are really changing the industry and we have one here today christiane sabo is the CEO the leader the spiritual leader of of this conference and of Tablo Kristin welcome to the cube thanks for having me yeah it's our pleasure great keynote the other day I just got back from Italy so I'm full of superlatives right it really was magnificent I was inspired I think the whole audience was inspired by your enthusiasm and what struck me is I'm a big fan of simon Sinek who says that people don't buy what you do they buy why you do it and your whole speech was about why you're here everybody can talk about their you know differentiators they can talk about what they sell you talked about why you're here was awesome so congratulations I appreciate that yeah so um so why did you start then you and your colleagues tableau well it's how below really started with a series of breakthrough research innovations that was this seed there are three co-founders of tableau myself dr. crystal T and professor Pat Hanrahan and those two are brilliant inventors and designers and researchers and the real hero of the tableau story and the company formed when they met on entrepreneur and a customer I had spent several years as a data analyst when I first came out of college and I understood the problems making sense of data and so when I encountered the research advancements they had made I saw a vision of the future a much better world that could bring the power of data to a vastly larger number of people yeah and it's really that simple isn't it and and so you gave some fantastic examples them in the way in which penicillin you know was discovered you know happenstance and many many others so those things inspire you to to create this innovation or was it the other way around you've created this innovation and said let's look around and see what others have done well I think the thing that we're really excited about is simply put as making databases and spreadsheets easy for people to use I can talk to someone who knows nothing about business intelligence technology or databases or anything but if I say hey do you have any spreadsheets or data files or databases you you just feel like it could it could get in there and answer some questions and put it all together and see the big picture and maybe find a thing or two everyone not everyone has been in that situation if nothing else with the spreadsheet full of stuff like your readership or the linkage the look the the traffic flow on on the cube website everyone can relate to that idea of geez why can't I just have a google for databases and that's what tableau is doing right right so you've kind of got this it's really not a war it's just two front two vectors you know sometimes I did I did tweet out they have a two-front war yeah what'd you call it the traditional BI business I love how you slow down your kids and you do that and then Excel but the point I made on Twitter in 140 characters was you it will be longer here I'm a little long-winded sometimes on the cube but you've got really entrenched you know bi usage and you've got Excel which is ubiquitous so it sounds easy to compete with those it's not it's really not you have to have a 10x plus value problem solutely talked about that a little bit well I think the most important thing we're doing is we're bringing the power of data and analytics to a much broader population of people so the reason the answer that way is that if you look at these traditional solutions that you described they have names like and these are the product brand names forget who owns them but the product brand names people are used to hearing when it comes to enterprise bi technology our names like Business Objects and Cognos and MicroStrategy and Oracle Oh bi and big heavy complicated develop intensive platforms and surprise surprise they're not in the hands of very many people they're just too complicated and development heavy to use so when we go into the worlds even the world's biggest companies this was a shocker for us even when we go into the world's most sophisticated fortune 500 companies and the most cutting-edge industries with the top-notch people most of the people in their organization aren't using those platforms because of theirs their complication and expense and development pull and so usually what we end up doing is just bringing the power of easy analytics and dashboards and visualization and easy QA with data to people who have nothing other than maybe a spreadsheet on their desk so in that sense it's actually a little easier than it sounds well you know I have to tell you I just have a cio consultancy and back in the day and we used to go in and do application portfolio analysis and we would look at the applications and we always advise the CIOs that the value of an application is a function of its use how much is being adopted and the impact of that use you know productivity of the users right and you'd always find that this is the dss system the decision support system like you said there were maybe 3 to 15 users yeah and an organization of tens of thousands of people yeah if they were very productive so imagine if you can you can permeate the other you know hundreds of thousands of users that are out there do you see that kind of impact that productivity impact as the potential for your marketplace absolutely I you know the person who I think said it best was the CEO of Cisco John Chambers and I'll paraphrase him here but he has this great thing he said which is he said you know if I can get each of the people on my team consulting data say oh I don't know twice per day before making a decision and they do the same thing with their people and their people and so you know that's a million decisions a month you did the math better made than my competition I don't want people waiting around for top top management to consult some data before making a decision I want all of our people all the time Consulting data before making a decision and that's the real the real spirit of this new age of BI for too long it's been in the hands of a high priesthood of people who know how to operate these complicated convoluted enterprise bi systems and the revolution is here people are fed up with it they're taking power into their hands and they're driving their organizations forward with the power of data thanks to the magic of an easy-to-use suite like tableau well it's a perfect storm right because everybody wants to be a data-driven organization absolutely data-driven if you don't have the tools to be able to visualize the data absolutely so Jeff if you want to jump in well Christian so in your keynote you talked for the majority of the keynote about human intuition and the human element talk a little bit about that because when we hear about in the press these days about big data it's oh well the the volume of data will tell you what the answer is you don't need much of the human element talk about why you think the human element is so important to data-driven decision-making and how you incorporate that into your design philosophy when you're building the product and you're you know adding new features how does the human element play in that scenario yeah I mean it's funny dated the data driven moniker is coming these days and we're tableaus a big big believer in the power of data we use our tools internally but of course no one really wants to be data driven if you drive your company completely based on data say hello to the cliff wall you will drive it off a cliff you really want people intelligent domain experts using a combination of act and intuition and instinct to make data informed decisions to make great decisions along the way so although pure mining has some role in the scheme of analytics frankly it's a minor role what we really need to do is make analytic software that as I said yesterday is like a bicycle for our minds this was the great Steve Jobs quote about computers that their best are like bicycles for our mind effortless machines that just make us go so much faster than any other species with no more effort expended right that's the spirit of computers when they're at our best Google Google is effortless to use and makes my brain a thousand times smarter than it is right unfortunately over an analytic software we've never seen software that does tap in business intelligence software there's so much development weight and complexity and expense and slow rollout schedules that were never able to get that augmentation of the brain that can help lead to better decisions so at tableau in terms of design we value our product requirements documents say things like intuition and feel and design and instinct and user experience they're focused on the journey of working with data not just some magic algorithm that's gonna spit out some answer that tells you what to do yeah I mean I've often wondered where that bi business would be that traditional decision support business if it weren't for sarbanes-oxley I mean it gave it a new life right because you had to have a single version of the truth that was mandated by by the government here we had Bruce Boston on yesterday who works over eight for a company that shall not be named but anyway he was talking about okay Bruce in case you're watching we're sticking to our promise but he was talking about intent desire and satisfaction things those are three things intent desire and satisfaction that machines can't do like the point being you just you know it was the old bromide you can't take the humans in the last mile yeah I guess yeah do you see that ever changing no I mean I think you know I I went to a friend a friend of mine I just haven't seen in a while a friend of mine once said he was an he was an artificial intelligence expert had Emilie's PhD in a professorship in AI and once I naively asked him I said so do we have artificial intelligence do we have it or not and we've been talking about for decades like is it here and he said you're asking the wrong question the question is how smart our computers right so I just think we're analytics is going is we want to make our computers smarter and smarter and smarter there'll be no one day we're sudden when we flip a switch over and the computer now makes the decision so in that sense the answer to your question is I keep I see things going is there is it going now but underneath the covers of human human based decision making it are going to be fantastic advancements and the technology to support good decision making to help people do things like feel and and and chase findings and shift perspectives on a problem and actually be creative using data I think there's I think it's gonna be a great decade ahead ahead of us so I think part of the challenge Christian in doing that and making that that that evolution is we've you know in the way I come the economy and and a lot of jobs work over the last century is you know you're you're a cog in a wheel your this is how you do your job you go you do it the same way every day and it's more of that kind of almost assembly line type of thinking and now we're you know we're shifting now we're really the to get ahead in your career you've got to be as good but at an artist you've got to create B you've got to make a difference is the challenge do you see a challenge there in terms of getting people to embrace this new kind of creativity and again how do you as a company and as a you know provider of data visualization technology help change some of those attitudes and make people kind of help people make that shift to more of less of a you know a cog in a larger organization to a creative force inside that organ well mostly I feel like we support what people natively want to do so there are there are some challenges but I mostly see opportunity there in category after category of human activity we're seeing people go from consumers to makers look at publishing from 20 years ago to now self-publishing come a few blogs and Twitter's Network exactly I mean we've gone from consumers to makers everyone's now a maker and we have an ecosystem of ideas that's so positive people naturally want to go that way I mean people's best days on the job are when they feel they're creating something and have that sense of achievement of having had an idea and seeing some progress their hands made on that idea so in a sense we're just fueling the natural human desire to have more participation with data to id8 with data to be more involved with data then they've been able to in the past and again like other industries what we're seeing in this category of technology which is the one I know we're going from this very waterfall cog in a wheel type process is something that's much more agile and collaborative and real-time and so it's hard to be creative and inspired when you're just a cog stuck in a long waterfall development process so it's mostly just opportunity and really we're just fueling the fire that I think is already there yeah you talked about that yesterday in your talk you gave a great FAA example the Mayan writing system example was fantastic so I just really loved that story you in your talk yesterday basically told the audience first of all you have very you know you have clarity of vision you seem to have certainty in your vision of passion for your vision but the same time you said you know sometimes data can be confusing and you're not really certain where it's going don't worry about that it's no it's okay you know I was like all will be answered eventually what but what about uncertainty you know in your minds as the you know chief executive of this organization as a leader in a new industry what things are uncertain to you what are the what are the potential blind spots for you that you worry about do you mean for tableau as a company for people working with data general resource for tableau as a company oh I see well I think there's always you know I got a trip through the spirit of the question but we're growing a company we're going a disruptive technology company and we want to embrace all the tall the technologies that exist around us right we want to help to foster day to day data-driven decision-making in all of its places in forms and it seems to me that virtually every breakthrough technology company has gone through one or two major Journal technology transformations or technology shocks to the industry that they never anticipated when they founded the company okay probably the most recent example is Facebook and mobile I mean even though even though mobile the mobile revolution was well in play when when Facebook was founded it really hadn't taken off and that was a blind Facebook was found in oh seven right and look what happened to them right after and here's that here's new was the company you can get it was founded in oh seven yeah right so most companies I mean look how many companies were sort of shocked by the internet or shocked by the iPod or shocked by the emergence of a tablet right or shocked by the social graph you know I think for us in tableaus journey if this was the spirit of the thought of the question we will have our own shocks happen the first was the tablet I mean when we founded tableau like the rest of the world we never would have anticipated that that a brilliant company would finally come along and crack the tablet opportunity wide open and before in a blink of an eye hundreds of millions of people are walking around with powerful multi-touch graphic devices in their I mean who would have guessed people wouldn't have guessed it no six let alone oh three know what and so luckily that's what that's I mean so this is the good kind of uncertainty we've been able to really rally around that there are our developers love to work on this area and today we have probably the most innovative mobile analytics offering on the market but it's one we never could have anticipated so I think the biggest things in terms of big categories of uncertainty that we'll see going forward are similar shocks like that and our success will be determined by how well we're able to adapt to those so why is it and how is it that you're able to respond so quickly as an organization to some of those tectonic shifts well I think the most important thing is having a really fleet-footed R&D team we have just an exceptional group of developers who we have largely not hired from business technology companies we have something very distributed going a tableau yeah one of the amazing things about R&D key our R&D team is when we decided to build just this amazing high-wattage cutting-edge R&D team and focus them on analytics and data we decided not to hire from other business intelligence companies because we didn't think those companies made great products so we've actually been hiring from places like Google and Facebook and Stanford and MIT and computer gaming companies if you look at the R&D engineers who work on gaming companies in terms of the graphic displays and the response times and the high dimensional data there are actually hundreds of times more sophisticated in their thinking and their engineering then some engineer who was working for an enterprise bi reporting company so this incredible horsepower this unique team of inspired zealots and high wattage engineers we have in our R&D team like Apple that's the key to being able to respond to these disruptive shocks every once in a while and rule and really sees them as an opportunity well they're fun to I mean think of something on the stage yesterday and yeah we're in fucky hats and very comfortable there's never been an R&D team like ours assembled in analytics it's been done in other industries right Google and Facebook famously but in analytics there's never been such an amazing team of engineers and Christian what struck me one of the things that struck me yesterday during your keynote or the second half of the keynote was bringing up the developers and talking about the specific features and functions you're gonna add to the product and hearing the crowd kind of erupt at different different announcements different features that you're adding and it's clear that you're very customer focused at this at tableau of you I mean you're responding to the the needs and the requests of your customers and I that's clearly evident again in the in the passion that these customers have for your for your product for your company how do you know first I'm happy how do you maintain that or how do you get get to that point in the first place where you're so customer focused and as you go forward being a public company now you're gonna get pressure from Wall Street and quarter results and all that that you know that comes with that kind of comes with the territory how do you remain that focused on the customer kind of as your you know you're going to be under a lot of pressure to grow and and you know drive revenue yeah I keep that focus well there's two things we do it's a it's always a challenge to stay really connected to your customers as you get big but it's what we pride ourselves on doing and there's two specific things we do to foster it the first is that we really try to focus the company and we try to make a positive aspect of the culture the idea of impact what is the impact of the work we're having and in fact a great example of how we foster that is we bring our entire support and R&D team to this conference no matter where it is we take we fly I mean in this case we literally flew the entire R&D team and product management team and whatnot across country and the time they get here face to face face to face with customers and hearing the customer stories and the victories and actually seeing the feedback you just described really inspires them it gives them specific ideas literally to go back and start working on but it also just gives them a sense of who comes first in a way that if you don't leave the office and you don't focus on that really doesn't materialize and the way you want it the second thing we do is we are we are big followers of I guess what's called the dog food philosophy of eat your own dog so drink your own champagne and so one of our core company values that tableau is we use our products facility a stated value of the company we use our products and into an every group at tableau in tests in bug regressions in development in sales and marketing and planning and finance and HR every sip marketing marketing is so much data these these every group uses tableau to run our own business and make decisions and what happens Matt what's really nice about a company because you know we're getting close to a thousand people now and so it's keeping the spirit you just described alive is really important it becomes quite challenging vectors leagues for it because when that's one of your values and that's the way the culture has been built every single person in the company is a customer everyone understands the customer's situation and the frustrations and the feature requests and knows how to support them when they meet them and can empathize with them when they're on the phone and is a tester automatically by virtue of using the product so we just try to focus on a few very authentic things to keep our connection with the customer as close as possible I'll say christen your company is a rising star we've been talking all this week of the similarities that we were talking off about the similarities with with ServiceNow just in terms of the passion within the customer base we're tracking companies like workday you know great companies that are that are that are being built new emerging disruptive companies we put you in that in that category and we're very excited for different reasons you know different different business altogether but but there are some similar dynamics that we're watching so as observers it's independent observers what kinds of things do you want us to be focused on watching you over the next 12 18 24 months what should we be paying attention to well I think the most important thing is tableau ultimately is a product company and we view ourselves very early in our product development lifecycle I think people who don't really understand tableau think it's a visualization company or a visualization tool I don't I don't really understand that when you talk about the vision a lot but okay sure we can visualization but there's just something much bigger I mean you asked about people watching the company I think what's important to watch is that as I spoke about makino yesterday tableau believes what is called the business intelligence industry what's called the business analytics technology stack needs to be completely rewritten from scratch that's what we believe to do over it's a do-over it's based on technology from a prior hair prior era of computing there's been very little innovation the R&D investment ratios which you can look up online of the companies in this space are pathetically low and have been for decades and this industry needs a Google it needs an apple it's a Facebook an RD machine that is passionate and driven and is leveraging the most recent advances in computing to deliver products that people actually love using so that people start to enjoy doing analytics and have fun with it and make data-driven driven decision in a very in a very in a way that's just woven into their into their into their enjoyment and work style every every single day so the big series of product releases you're going to see from us over the next five years that's the thing to watch and we unveiled a few of them yesterday but trust me there's a lot more that's you a lot of applause christina is awesome you can see you know the passion that you're putting forth your great vision so congratulations in the progress you've made I know I know you're not done we'll be watching it thanks very much for coming to me I'm really a pleasure thank you all right keep right there everybody we're going wall to wall we got a break coming up next and then we'll be back this afternoon and this is Dave Volante with Jeff Kelly this is the cube we'll be right back