(upbeat music) >> Good morning. Welcome back to theCUBE's continuing coverage of Snowflake Summit 22, live from Las Vegas. Lisa Martin, here with Dave Villante. We are at Caesar's Forum, having lots of great conversations. As I mentioned, this is just the start of day two, a tremendous amount of content yesterday. I'm coming at you today. Two guests join us from Slalom, now, we've got Chris Samuels, Principal Machine Learning, and Bethany Mudd, Senior Director, Experience Design. Welcome to theCube, guys. >> Hi, thanks for having us. >> Thank you. >> So, Slalom and Snowflake, over 200 joint customers, over 1,800 plus engagements, lots of synergies there, partnership. We're here today to talk about intelligent products. Talk to us about what- how do you define intelligent products, and then kind of break that down? >> Yeah, I can, I can start with the simple version, right? So, when we think about intelligent products, what they're doing, is they're doing more than they were explicitly programmed to do. So, instead of having a developer write all of these rules and have, "If this, then that," right, we're using data, and real time insights to make products that are more performing and improving over time. >> Chris: Yeah, it's really bringing together an ecosystem of a series of things to have integrated capabilities working together that themselves offer constant improvement, better understanding, better flexibility, and better usability, for everyone involved. >> Lisa: And there are four pillars of intelligent products that let's walk through those: technology, intelligence, experiences, and operations. >> Sure. So for technology, like most modern data architectures, it has sort of a data component and it has a modern cloud platform, but here, the key is is sort of things being disconnected, things being self contained, and decoupled, such that there's better integration time, better iteration time, more cross use, and more extensibility and scalability with the cloud native portion of that. >> And the intelligence piece? >> The intelligence piece is the data that's been processed by machine learning algorithms, or by predictive analytics that provides sort of the most valuable, or more- most insightful inferences, or conclusions. So, by bringing together again, the tech and the intelligence, that's, you know, sort of the, two of the pillars that begin to move forward that enable sort of the other two pillars, which are- >> Experiences and operations. >> Yeah. >> Perfect. >> And if we think about those, all of the technology, all of the intelligence in the world, doesn't mean anything if it doesn't actually work for people. Without use, there is no value. So, as we're designing these products, we want to make sure that they're supporting people. As we're automating, there are still people accountable for those tasks. There are still impacts to people in the real world. So, we want to make sure that we're doing that intentionally. So, we're building the greater good. >> Yeah. And from the operations perspective, it's you can think of traditional DevOps becoming MLOps, where there's an overall platform and a framework in place to manage not only the software components of it, but the overall workflow, and the data flow, and the model life cycle such that we have tools and people from different backgrounds and different teams developing and maintaining this than you would previously see with something like product engineering. >> Dave: Can you guys walk us through an example of how you work with a customer? I'm envisioning, you know, meeting with a lot of yellow stickies, and prioritization, and I don't know if that's how it works, but take us through like the start and the sequence. >> You have my heart, I am a workshop lover. Anytime you have the scratch off, like, lottery stickers on something, you know it's a good one. But, as we think about our approach, we typically start with either a discovery or mobilized phase. We're really, we're starting by gathering context, and really understanding the business, the client, the users, and that full path the value. Who are all the teams that are going to have to come together and start working together to deliver this intelligent product? And once we've got that context, we can start solutioning and ideating on that. But, really it comes down to making sure that we've earned the right, and we've got the smarts to move into the space intelligently. >> Yeah, and, truly, it's the intelligent product itself is sort of tied to the use case. The business knows what the most- what is potentially the most valuable here. And so, so by communicating and working and co-creating with the business, we can define then, okay, here are the use cases and here are where machine learning and the overall intelligent product can maybe add more disruptive value than others. By saying, let's pretend that, you know, maybe your ML model or your predictive analytics is like a dial that we could turn up to 11. Which one of those dials turning turned up to 11 could add the most value or disruption to your business? And therefore, you know, how can we prioritize and then work toward that pie-in-the-sky goal. >> Okay. So the client comes and says, "This is the outcome we want." Okay, and then you help them. You gather the right people, sort of extract all the little, you know, pieces of knowledge, and then help them prioritize so they can focus. And then what? >> Yeah. So, from there we're going to take the approach that seeing is solving. We want to make sure that we get the right voices in the room, and we've got the right alignment. So, we're going to map out everything. We're going to diagram what that experience is going to look like, how technology's going to play into it, all of the roles and actors involved. We're going to draw a map of the ecosystem that everyone can understand, whether you're in marketing, or the IT sort of area, once again, so we can get crisp on that outcome and how we're going to deliver it. And, from there, we start building out that roadmap and backlog, and we deliver iteratively. So, by not thinking of things as getting to the final product after a three year push, we really want to shrink those build, measure, and learn loops. So, we're getting all of that feedback and we're listening and evolving and growing the same way that our products are. >> Yeah. Something like an intelligent product is is pretty heady. So it's a pretty heavy concept to talk about. And so, the question becomes, "What is the outcome that ultimately needs to be achieved?" And then, who, from where in the business across the different potentially business product lines or business departments needs to be brought together? What data needs to be brought together? Such that the people can understand how they themselves can shape. The stakeholders can, how the product itself can be shaped. And therefore, what is the ultimate outcome, collectively, for everybody involved? 'Cause while your data might be fueling, you know, finances or someone else's intelligence and that kind of thing, bringing it all together allows for a more seamless product that might benefit more of the overall structure of the organization. >> Can you talk a little bit about how Slalom and Snowflake are enabling, like a customer example? A customer to take that data, flex that muscle, and create intelligent products that delight and surprise their customers? >> Chris: Yeah, so here's a great story. We worked to co-create with Kawasaki Heavy Industries. So, we created an intelligent product with them to enable safer rail travel, more preventative, more efficient, preventative maintenance, and a more efficient and real time track status feedback to the rail operators. So, in this case, we brought, yeah, the intelligent product itself was, "Okay, how do you create a better rail monitoring service?" And while that itself was the primary driver of the data, multiple other parts of the organization are using sort of the intelligent product as part of their now daily routine, whether it's from the preventative maintenance perspective, or it's from route usage, route prediction. Or, indeed, helping KHI move forward into making trains a more software centered set of products in the future. >> So, taking that example, I would imagine when you running- like I'm going to call that a project. I hope that's okay. So, when I'm running a project, that I would imagine that sometimes you run into, "Oh, wow. Okay." To really be successful at this, the company- project versus whole house. The company doesn't have the right data architecture, the right skills or the right, you know, data team. Now, is it as simple as, oh yeah, just put it all into Snowflake? I doubt it. So how do you, do you encounter that often? How do you deal with that? >> Bethany: It's a journey. So, I think it's really about making sure we're meeting clients where they are. And I think that's something that we actually do pretty well. So, as we think about delivery co-creation, and co-delivering is a huge part of our model. So, we want to make sure that we have the client teams, with us. So, as we start thinking about intelligent products, it can be incorporating a small feature, with subscription based services. It doesn't have to be creating your own model and sort of going deep. It really does come down to like what value do you want to get out of this? Right? >> Yeah. It is important that it is a journey, right? So, it doesn't have to be okay, there's a big bang applied to you and your company's tech industry or tech ecosystem. You can just start by saying, "Okay, how will I bring my data together at a data lake? How do I see across my different pillars of excellence in my own business?" And then, "How do I manage, potentially, this in an overall MLOps platform such that it can be sustainable and gather more insights and improve itself with time, and therefore be more impactful to the ultimate users of the tool?" 'Cause again, as Bethany said that without use, these things are just tools on the shelf somewhere that have little value. >> So, it's a journey, as you both said, completely agree with that. It's a journey that's getting faster and faster. Because, I mean, we've seen so much acceleration in the last couple of the years, the consumer demands have massively changed. >> Bethany: Absolutely. >> In every industry, how do Slalom and Snowflake come together to help businesses define the journey, but also accelerate it, so that they can stay ahead or get ahead of the competition? >> Yeah. So, one thing I think is interesting about the technology field right now is I feel like we're at the point where it's not the technology or the tools that's limiting us or, you know, constraining what we can build, it's our imaginations. Right? And, when I think about intelligent products and all of the things that are capable, that you can achieve with AI and ML, that's not widely known. There's so much tech jargon. And, we put all of those statistical words on it, and you know the things you don't know. And, instead, really, what we're doing is we're providing different ways to learn and grow. So, I think if we can demystify and humanize some of that language, I really would love to see all of these companies better understand the crayons and the tools in their toolbox. >> Speaking from a creative perspective, I love it. >> No, And I'll do the tech nerd bit. So, there is- you're right. There is a portion where you need to bring data together, and tech together, and that kind of thing. So, something like Snowflake is a great enabler for how to actually bring the data of multiple parts of an organization together into, you know, a data warehouse, or a data lake, and then be able to manage that sort of in an MLOps platform, particularly with some of the press that Snowflake has put out this week. Things becoming more Python-native, allowing for more ML experimentation, and some more native insights on the platform, rather than going off Snowflake platform to do some of that kind of thing. Makes Snowflake an incredibly valuable portion of the data management and of the tech and of the engineering of the overall product. >> So, I agree, Bethany, lack of imagination sometimes is the barrier we get so down into the weeds, but there's also lack of skills, as mentioned the organizational, you know, structural issues, politics, you know, whatever it is, you know, specific agendas, how do you guys help with that? Can, will you bring in, you know, resources to help and fill gaps? >> Yeah, so we will bring in a cross-disciplinary team of experts. So, you will see an experienced designer, as well as your ML architects, as well as other technical architects, and what we call solution owners, because we want to make sure that we've got a lot of perspectives, so we can see that problem from a lot of different angles. The other thing that we're bringing in is a repeatable process, a repeatable engineering methodology, which, when you zoom out, and you look at it, it doesn't seem like that big of a deal. But, what we're doing, is we're training against it. We're building tools, we're building templates, we're re-imagining what our deliverables look like for intelligent products, just so, we're not only speeding up the development and getting to those outcomes faster, but we're also continuing to grow and we can gift those things to our clients, and help support them as well. >> And not only that, what we do at Slalom is we want to think about transition from the beginning. And so, by having all the stakeholders in the room from the earliest point, both the business stakeholders, the technical stakeholders, if they have data scientists, if they have engineers, who's going to be taking this and maintaining this intelligent product long after we're gone, because again, we will transition, and someone else will be taking over the maintenance of this team. One, they will understand, you know, early from beginning the path that it is on, and be more capable of maintaining this, and two, understand sort of the ethical concerns behind, okay, here's how parts of your system affect this other parts of the system. And, you know, sometimes ML gets some bad press because it's misapplied, or there are concerns, or models or data are used outside of context. And there's some, you know, there are potentially some ill effects to be had. By bringing those people together much earlier, it allows for the business to truly understand and the stakeholders to ask the questions that they- that need to be continually asked to evaluate, is this the right thing to do? How do I, how does my part affect the whole? And, how do I have an overall impact that is in a positive way and is something, you know, truly being done most effectively. >> So, that's that knowledge transfer. I hesitate to even say that because it makes it sound so black and white, because you're co-creating here. But, essentially, you're, you know, to use the the cliche, you're teaching them how to fish. Not, you know, going to ongoing, you know, do the fishing for them, so. >> Lisa: That thought diversity is so critical, as is the internal alignment. Last question for you guys, before we wrap here, where can customers go to get started? Do they engage Slalom, Snowflake? Can they do both? >> Chris: You definitely can. We can come through. I mean, we're fortunate that snowflake has blessed us with the title of partner of the year again for the fifth time. >> Lisa: Congratulations. >> Thank you, thank you. We are incredibly humbled in that. So, we would do a lot of work with Snowflake. You could certainly come to Slalom, any one of our local markets, or build or emerge. We'll definitely work together. We'll figure out what the right team is. We'll have lots and lots of conversations, because it is most important for you as a set of business stakeholders to define what is right for you and what you need. >> Yeah. Good stuff, you guys, thank you so much for joining Dave and me, talking about intelligent products, what they are, how you co-design them, and the impact that data can make with customers if they really bring the right minds together and get creative. We appreciate your insights and your thoughts. >> Thank you. >> Thanks for having us guys. Yeah. >> All right. For Dave Villante, I am Lisa Martin. You're watching theCUBE's coverage, day two, Snowflake Summit 22, from Las Vegas. We'll be right back with our next guest. (upbeat music)