 Good morning. Welcome back to theCUBE's continuing coverage of Snowflake Summit 22 live from Las Vegas Lisa Martin here with Dave Vellante. 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 the queue guys. Thanks for having us. So Slalom and Snowflake over 200 joint customers over 1800 plus engagements lots of synergies their 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 performant and improving over time 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 better understanding better flexibility and better usability For for everyone involved 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 here the key is sort of things being disconnected things being self-contained and and and D 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 provide sort of the most valuable or more and most insightful inferences or conclusions So so by bringing together again the tech and the intelligence That's sort of the two of the pillars that that begin to move forward that enable sort of the other Two pillars which are Experiences and operations. Yeah, 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 it's you can think of traditional dev ops becoming ml ops where there's an overall platform and a Framework in place to manage not only the software components of it But but the overall workflow and the data flow in the model lifecycle such that we have tools and people from different backgrounds and different teams developing and maintaining this than you would Previously see with with something like product engineering. 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 I don't know if that's how it works, but let's take us through like the the start and the sequence you have my heart I am a workshop lover anytime you have this scratch and 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 mobilize phase We're really we're starting by gathering context and really Understanding the business the client the users and that full path of value Who are all the teams that are gonna 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 this space Intelligently yeah, and truly it's it's the intelligent product itself is sort of tied to the use case The business knows what the most what is 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 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 that 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 is 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 pretty heady So it's pretty it's 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 different business product lines or business Business departments need to be brought together. What data needs to be brought together such that the people can understand how they themselves can shape This the stakeholders can can how the product itself can be shaped and therefore What is the ultimate outcome collectively for everybody involved because because while while your data might be fueling, you know Finances or someone else's intelligence and that kind of thing bring it all together allows for a more seamless product It might benefit more more of the overall structure 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 Yeah, so so so here's a great story. We we worked to co-create with Kawasaki heavy industries So we created the intelligent product with them to 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 so in this case we brought yeah, the 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 as Part of their now daily 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 what I'm gonna call that a project I hope that's you know, 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. Yeah, 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 it? 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 there's a big bang applied to you and your company's tech industry tech 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 ML apps 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 Because again as Bethany said that without use these these things are just just tools on the shelf somewhere that have a 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 years the consumer demands have massively changed Absolutely in every industry. How do slalom and stuff that 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 from a creative perspective. I love it No, and I'll do the tech nerd bit So 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 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 mlabs 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 of the data management and of the tech and of the engineering of the overall product. So I Agree about the need lack of imagination sometimes is the barrier we get so down into the weeds But there's also lack of skills that mention the organizational, you know structural issues Politics, you know, whatever it is, you know specific agendas How do you guys help with that? Can you bring in, you know resources to help and fill gaps? 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 reimagining what our deliverables look like for intelligent products Just so we're not only speeding up the development and and getting to those outcomes faster but we're also continuing to grow and we can gift those things to our clients and and Support them as well and not only that What what we do at Salamis is we want to think about transition from the beginning Yeah, 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 and someone else will be taking over the maintenance of this team One they will understand, you know early early from beginning the the path that that is on and be more capable of Maintaining this and to understand sort of the ethical concerns behind Okay, here's how parts of your system affect this other parts of the system and now some sometimes ML gets some bad press because it's Misapplied or there are concerns or or or models or data are used outside of context and there's 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 and How do I have an overall impact that is in a positive way and is something? You know truly be being done most effectively. So that's that knowledge transfer I mean I hesitate to even say that 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 Yeah, not you know gonna ongoing you know do the fishing for them 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 to they engage? Solemn snowflake. Can I do both? You definitely can it's you can come through. I mean we're we're fortunate that snowflake has has Blessed us with the title of partner of the year again for the fifth time. Congratulations. Thank you We are we are incredibly humbled in that so so we do a lot of work with snowflake You could certainly come to slalom any one of our local markets or or or build or it emerge And 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? 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 that they really bring The right minds together and get creative. We appreciate your insights and your thoughts Thank you guys. Yeah, all right for Dave Vellante. I'm Lisa Martin You're watching the cubes coverage day two snowflake summit 22 from Las Vegas. We'll be right back with our next guest