>>Good morning. It's the cube live from Las Vegas. This is day four of our coverage at AWS reinvent 2021. I'm Lisa Martin with Dave Nicholson. We have had since Monday two live sets two remote studios over 100 guests on the program. This is a 10th annual reinvent. We're talking about the next decade of cloud innovation and we're pleased to welcome Ashley Kramer, the chief product officer and chief marketing officer of Sisense to the program. Welcome Ashley, thanks for having me today. So you own marketing and product. Tell me a little bit about your role. Obviously that's done. >>It's a big role. It has a big role, but I think as the analytics ecosystem has evolved, it makes sense to bring the product you're building the platform you're building and the messaging that you're taking to market together in one. And so I've been in size since almost two years, and I am responsible for both the messaging and the building of the product. >>Awesome. Talk to me a little bit about the next generation of business intelligence. Define Gen-Z one, two and where we are with three. >>Yeah. So when we think about the generations of analytics and we think about how the world is evolved, we're here clearly at a cloud conference, data apps, and the way people work has evolved over time. And I think analytics hasn't quite kept up and I'll explain what I mean about that. The first generation of analytics was really about, you know, that we're talking by the way, late nineties, early two thousands, big on-premise servers of data, things that would make people here sort of cringe, right? And the way to extract value was to put an analytics server right next to it, go wait in line, ask her it, Hey, I need a report. And then wait a few weeks. That report gets delivered. It's the wrong data. It's now stale. You've got to go get back in line, enter gen two, which by the way, I was a part of in my career at Tableau. >>And what we said was let's put data at everybody's fingertips. Let's allow people via desktop tool to drag and drop and build the perfect, beautiful dashboard. And then we can deploy it to everybody and we will break down that barrier to it. And that was successful. But the one thing that we didn't understand was not, everybody's an analyst, not everybody's data literate and dashboards could be very, very intimidating to the everyday worker. And so now we're on the cusp of what we call gen three and sysynced is well-positioned to really nail this market, which is make data invisible, make analytics invisible, and bring it to where people are. And that's what we consider gen three. And I of course can talk about that for hours. So I'd love to talk more about it. >>So are the lines blurred between what people think of as artificial intelligence and BI, because you're talking about, um, you know, making this invisible or transparent, you know, frictionless access, um, are you talking recommendations or just a presentation of raw information? How are those two topics, interleaved >>Talking both. And so what's, what's really beautiful about this is the dashboard doesn't have to go away, but you have to break it apart and you have to make it less intimidating, more approachable, more understandable, which is where AI comes in natural language generation, natural language querying maybe for some people, maybe for a doctor, they want to see the data presented in plain text, plain English. Great. Let them do that. And so AI is a big, big piece of this gen three. When we think about where BI and analytics is evolving to. So >>Like it, from a customization perspective, you're going to be able to allow people in healthcare, finance, marketing, sales, operations products, be able to have data at their fingertips when they need it. Because one of the things that we learned in the pandemic is that access to real-time data is no longer a nice to have it's required. But to your point, if it's intimidating or if it's inaccessible, or if it's too complicated, it's not useful. >>That's right. And what we also learned during the pandemic is people are busy and they don't have time to change what they're doing. They don't have time to leave their everyday workflow and process to go look for something. They don't have time to look for an unnatural experience and try to interpret what it says. So customization is a huge piece of this, make it look and feel the way that a healthcare worker needs it to look and feel, make it look and feel the way that, you know, a construction worker makes it as part of their everyday job. And that's a core piece of gen three as well. >>What are some of the things that you guys are doing with AWS? Obviously, AWS, very, very customer focused. They always talk about working backwards from the customer, really this customer obsession. What are some of the things that you guys are working on together that your joint customers will benefit from? >>Fun fact, I was Amazonian as part of my career. So I grew up with Amazon in the early days of AWS. And we are very close partners with them from two really big perspectives. The first is the data's moving there, all of the data, particularly things like red shift. And that is a perfect place for size sense to sit right on top of that data, query it live, bring that and extract it to people in the way that they need it to consume. It really make data-driven decisions. The second piece is, um, and we saw a great keynote yesterday by Swami, which is the AI piece of the story, the comprehend, the Lexio, you know, really bringing to people, the data and the information and the way they need, and that all plugs into the size sense experience. And we can be that visual front end layer on top of all of those services. >>So where you sit because of your purview, looking at product marketing, and then let's, let's make, let's make the third point in the triangle, the customer always what, from your perspective, because you're thinking in terms of product customer requirements, and then you're thinking about how do you get the message across to make sure people understand what you're doing? What does a delighted customer sound like to you? What makes you smile? When someone says, Hey, Ashley, we have this customer who absolutely loves us. And these are the things they love about us. What does, what does that sound like? >>Really a very, very simple thing to answer because through my career too often, and I've read products at all of the companies I've worked for, you sell these big deals and you help them be successful with one use case. And you come back a year later in three people in the organization are actually using that solution to make data-driven decisions. So my perfect customer is, you know, we take them by the hand, we help them deploy that we come back a year later and the entire organization, all of their customers are using the solution because we've made it more approachable, more personalized and less intimidating. >>So what's the opposite of shelf ware. That's what you just described >>The opposite of shelfware and that breaks down every stat you see out there, there's a really widely known one that, um, less than 30% of organizations are actually successful with their analytics solutions. And my theory, my thesis and the research that we've done is that's exactly why it's too intimidating. It's too clunky and it's too disjointed. >>So talk to me, I, one of the things I think is the best validation of brand can get is the voice of the customer. I agree with you that it's, it's exciting when you, cause there's, there's so many, there's so many tools and you just mentioned the stat, um, in terms of adoption, but share with us a customer example that you think really articulates the value of what you're talking about. That gen three BI customizable, personable, what customer comes to mind are customers. You have. >>So one of my favorites is a company called outreach and what outreach outreach caters to is sales. It's, you know, early sales, sales enablement, helping people understand which customers should I go target. And should I go sell to, these are not analysts using the platform. And when outreach came to us, they said, we want data and analytics to drive our experience for all of our customers. But these are young salespeople. They can't just be looking at dashboards. And so what we've done with them is we were actually the AI engine that drives the experience. And as that BDR, ADR SDR gets in there, they're actually using analytics to figure out who to call what account is hot, what to do next, and right. They're actioning right in the experience. And they have no clue that they're will using data. And that's okay. And they're optimized. They're more efficient at their job because science is powering that experience. >>So they could be in Salesforce and accessing this, like under the covers, not even knowing it, they have no idea >>Exactly >>What they're empowered with BI AI to be able to make decisions. >>And they're becoming better at their jobs because they're using data and they're not learning a new skill set because they don't have time. There's no time. >>That's a great point. One of the other points that we've heard a lot, the last three days is every company has to become a data company. One thing to say it whole other can of worms, right? To actually enable it. Because to your point earlier, you have access to data it's confusing or it's stale. There's a competitor right here. Ready to take over. Talk to me about how your customer conversations have changed, especially in the last 22 months about how do we become data really? Data-driven, >>That's, that's interesting because if you would've asked me two, three years ago, I would have given you this big pitch on, well, we need to go in and help them build this culture of data and analytics, right? We're going to go in and help them. That has changed. What we need to do now is accept that building. That whole culture is too hard to do. It requires people to go beyond their job and really learn a new skill set. So what we do is we make every company, a data company by not necessarily making them really realize that they're using analytics and data. We're making it personalized. We're removing the nuances that come with building the state of literacy culture. So yes, you still need to build the culture around it and have the support, but you need it to be less intimidating and you need it to just be part of the everyday workflow, the everyday process, the everyday experience, regardless of job title. There's another interesting stat that there's over 1 billion knowledge workers out there that are underserved. That's the barrier we need to break down. Next analysts are happy. Data scientists are happy. They have what they need. How do we get what they're doing to those 1 billion plus underserved knowledge workers. >>So when you're in customer conversations and they're like, Ashley, help us figure this out. >>You say, we go in and we show, you know, we, we figure out what their business case is. And they very often say, okay, let's start with a big, huge dashboard. Step them back and say, what are you really trying to solve? Okay. You want that doctor to be more efficient? You want to triage more properly, maybe right there within your system, within your medical system. We're just going to pop, you know, we're going to pull data out of red shift and we're just going to pop some insights there. Some recommendations, as you mentioned earlier, some plain text, we'll give you a search experience so you can search, you know, what beds are open and it will bring it back to you the way that you understand how to work, you don't have to change. >>And that's critical because one of the things that we talk about all the time is change management, cultural change. It's really hard to do, especially given the dynamics of the environment that we're in. People are still scattered, working from anywhere. That's going to persist for a while. We need to meet them where they are >>Absolutely a hundred percent nailed it. And I'm going to steal that in my marketing material. Thank you. I'm a marketer trademarks. Um, but absolutely meet them where they are. And you know, everybody wants to evolve. Everybody wants to upscale and you can help them, but don't expect it to happen overnight. And don't expect they're going to take it on as a second job because their core job function is the most critical. >>It's interesting from a marketer's perspective. Um, it's always great to have people running around wearing size sense, hoodies on the customer site what's even better is having them using the product. And maybe they don't even know as long as key stakeholders in the organization knows so that you can drive, you know, drive into the market. But is there anything disheartening about sort of being toiling in obscurity at times? >>It is the hardest part of the CMO hat that I wear is you both are very likely using size sense in something that you're doing and you have no idea. And that is a brand's nightmare. So yes, checking my pockets. In fact, we are giving away Fanny packs. And as soon as I'm done, I will be over here with two Fanny packs for you. Apparently that's the new thing. That's what the kids are doing. Um, but it is very hard. It's um, we have to do more because lots of people are using size sense and actually lots of people at this conference right now, a lot of these vendors have size sense embedded, and they don't necessarily know they're using it. It's a double-edged sword though, >>Because you're saying, you know, the whole point is making analytics invisible. So, but it is, but I'll take the Fanny pack. >>I'll be out. Don't worry about that. Don't you worry? >>So here we are wrapping up 10th annual at reinvent. You were an Amazonian. So you've been to many of these, obviously the first one in two years, there's nothing like the conversations that are going on behind it. There's nothing like an in-person interview to have a really a conversation about the technology. What are some of the things that have you heard at the conference that excite you going into 2022? >>My excitement going in is the focus that everybody's putting just beyond what's next beyond data like the AI, right? The AI perspective of everything, the way that AWS is evolving their data story, lots of serverless spoken by Adam in the first day. And I think there's really, really big things coming. You see the three big clouds competing and making each other better and better. You see vendors like size sense working cross-cloud because everybody has something best in class. And so I am one very excited to be in person and to be shaking hands and hugging friends that I have not seen except over zoom in two years. But I'm really excited for the direction. Particularly AWS is taking the data ecosystem inside sense plans to be a core part of that. >>Awesome. It's exciting. The amount of innovation that has gone on is we think, you know, the next 10 years is we're going to see far more in the next, probably five than we did in the previous 10. Actually. Thank you so much for joining us, talking to us about science. We'll have to, we'll have to think about that. Well, when we get our Fanny pack, so we can talk about science, how we're using it, but awesome. To be able to bring analytics to everyone so that it is invisible, usable, and we can actually extract value from data in real time. Thank you for >>Having me today. Our >>Pleasure for Dave Nicholson. I'm Lisa Martin. You're watching the cube, the global leader in live tech coverage.