 Good afternoon everyone, welcome back to Las Vegas. Viva Las Vegas, it's theCUBE Live, covering Clickworld 23 at Mandalay Bay. We're excited to be here. Lisa Martin with Dave Vellante. We have a great guest lineup today. We're talking with ClickExecs, customers, analysts, partners, we've got a partner, customer, no, a ClickEmployee turned partner next as our guest. Please welcome Chris Parara, partner and general manager at SDG Group. Welcome Chris, great to have you on the program. Thank you, happy to be here. Yeah, so tell the audience a little bit about SDG Group, what you guys do, and then we'll kind of dig into the Click partnership. Sure, so SDG Group, we're a global systems integrator. We specialize in only data and analytics. We work together globally, it's a unique thing about us. Even though we have several offices and over 1800 people, we operate as an integrated single entity. So when we staff our projects, it's people from all over the world working to execute those projects. I mentioned we're data and analytics, so we have services, everything from data management and engineering into business analytics, advanced and predictive analytics, and then also advisory services around the data and analytics function. And we cross all industries. The US entity, which I represent, is largely focused on life sciences, consumer products, manufacturing and healthcare. Got it, okay. So larger companies generally, is that fair to say? It's a bit of a mix. We do work primarily with larger organizations, but we have a separate part of the organization that handles, let's say, the commercial or the mid-tier organization. And I have to say, because it's really an important thing, we really pride ourselves in treating a company, no matter how small or how large, the same. So that's what, when we break our groups up, so we have a team of people focused in commercial and a team focused in enterprise, it allows us to give the dedication, regardless of their size. And do you serve small businesses as well? Absolutely, small and non-profit. We have customers that are a few million in revenue, so very small to very large. So what's the maturity model look like in today's data world? Can you paint a picture of the spectrum from the sort of least sophisticated to the most sophisticated? Yeah, that's actually a great question. So the maturity model is interesting in that it varies significantly from company to company. So you can have a large organization that's lower on the maturity curve or very far along, and the opposite with smaller companies. So it is very much an individual journey. What we often find is, and this is where Click had a lot of value, or has a lot of value, a company that's, let's say, a little bit lower on the maturity curve has data still, a lot of data, data all over in many different sources, still has the same need to be able to take that data and do something with it. So they're able to maybe move forward with something like, let's say, visual analytics as a starting point to be able to get moving and use analytics. Even though they haven't really had the time or the wherewithal to put together an entire data architecture. So they get moving, and that helps to move on the maturity curve, and then they'll start to eventually put more attention to the data architecture so that they can build it in a more scalable way, and in a way that can handle maybe more volumes of data, let's say. And then in the larger organizations, the challenge they have is you might have many different departments, and you can have a department that's much further along than another. So wrangling that across a massive enterprise becomes challenging, and that's where you see the large centers of excellence or IT organizations building programs that are repeatable so that they can spread it through the various departments in an organization to bring the maturity up. Talk a little bit about some of the trends that you're seeing from a data integration and analytics perspective, and how is SDG Group with Click helping customers really ride the waves so that they can actually leverage data to drive business effects? Yeah, so great question. I would say the major trend that we've seen is the growth of, let's say, the hyperscalers or the large cloud-based data warehouses that are now able to handle a lot more data effectively than they could not more than five, six years ago. So very recently, companies were still struggling with how do we handle all of this data. So what we've really seen is much more attention now is put on, now that we can put the data in one spot, how do we handle it in a way that's automated? How do we handle it in a way that brings, let's say AI and ML intelligence into the process so that you don't need as many people to manage a system that's growing much larger than it had in the past? So you're seeing a lot more around data ops, around DevOps, around different, let's say metadata-driven data integration so it's driven in a way that you can automate it. You could bring intelligence that detects data quality and helps to improve it automatically. So that's a lot of the trend, build the intelligence into the process that allows you to get that data. And then on the other side, let's say, well, the front-end side or the visual side, we're seeing a lot of attention put to also building intelligence to understanding what the data has to say. Because there's so much more volume, this system can help to identify anomalies, bring insights out, and automate that to an audience that's very broad, right across different functional areas, different levels, different experiences. So it really helps to create the insights that ultimately drive behavior. And that's something that I think is happening on both sides. So getting data right is so hard for organizations, which is why it's such a great business for you. And I wonder if we could talk about the sort of discussion that's going on in the industry about the failures of big data or centralized data warehouses, shove everything into one location. And you hear a lot of talk about data fabrics, and they talk about a lot here. I don't hear much data mesh topic, but Jamakthagani has coined the data mesh to decentralization of data and responsibility. That's a whole nother organizational challenge in and of itself. And then with foundation models, it's like, oh, I want all my data to be in at least one logical place so I can run AI on it. So there's this tension between having everything in one place, a single source of truth, but that generally is the state of the art, building dashboards, looking backwards, what's happening. But we always talk about this real time, talk about IoT, data at the edge, real time inferencing. And they seem to be really counter-poisoned, like this tension there. And I know it's sort of a bigger topic, but I feel like I'm always giving up on the decentralization of data. I mean, it's happening, but it's so hard that I actually think the right answer is put it all into a central location, and that's what Snowflake's trying to do, BigQuery's trying to do it. Even Databricks is saying, do it with open tooling. And so it just seems like that's the only way to make it secure, get it governed, and actually apply AI to it. What are your thoughts on that whole centralization versus decentralization topic? So it's an interesting debate. To some extent, I would almost argue it's impossible, but does it make sense to have all the data in the same place? So for example, if I'm putting all the data in one spot, which is important for your key data and the data that you, let's say, own and manage, but anything that I bring in, I'm going to have to own and manage. So let's say I'm starting to get to the more complex use cases. I'm using weather data. I'm using data that is not owned by my company. Do I want to be responsible for managing and owning that data, or do I want to be able to have that data leverageable so I can access it? So then you really start thinking about the single version of the truth is somehow related to process, making sure that you have data management, master data management, making sure the data is somehow accurate when you bring it together, and then you can get that single version of the truth. That being said, I do believe a lot of the data should come together, and that's where you see companies like Snowflake doing a lot of very innovative things. The Snowflake data cloud in the share is a perfect example of that, right? So now I have another company that wants to expose their data to their client base. They're not actually physically having to move, they're expressing that data through the share, and then they're going to be able to access that data as if it was in the same place. So you're able to leverage modern techniques like that, but I do think companies have to think about what data do I want to be in that single place? What data will never be, because it's not as easy for me to manage and own, and that's really some of the things we think about when we bring data together for our customers. But a whole industry has been born to try to avoid doing these complex joins around thousands and thousands of data sets, put it into a central and clean it, integrate it, make sure it's of high quality, and then put it into a place that you can trust. We hear a lot about trust. All to sort of avoid doing these sort of complex operations. But it feels like that the future, I mean I look at this GPT stuff, is a lot of complex operations, and it just feels like we're entering this whole new era, and this whole industry that's been built up around it is evolving. No question. 100%. And you're seeing every where you go, people talking about oh, we've been using large language models for years. But doesn't it feel like we're at this sort of weird inflection point, and it's a bit unpredictable in terms of privacy and public policy. That usually means chaos, but then great opportunity for people. Agreed. And I would say that the data and analytics space has been evolving for decades, and it feels like you get to these points of an inflection point, and then you get a significant change. And I feel like we have just come to now get things, data in the cloud in ways you can really process massive volumes of data very quickly, which you had a hard time in the past. So I think there's continual improvement, and now with GPT and other capabilities coming, I think there's another one coming. It's almost like the inflection points are coming faster than they have in the past. And from a client perspective, it's normally challenging, because you're trying to build your strategy for the long term, and all of these factors are constantly flowing around it, and you have to really think about, for example, now, if I'm going to spend a lot of money to build these systems, how do I build it in such a way that if something does change, I can pivot without having to rebuild the entire system, which would have been the case 10 years ago, right? You would have to rebuild everything and start from scratch. That's why data warehouses had earned such a bad reputation. You'd spend millions of dollars to build them. They wouldn't really work effectively, and then you'd end up implementing something brand new with another technology starting from scratch. And now I think we're starting to see a little bit more freedom in the development of the code. Yeah, not to mention the ethics, the privacy, the IP rights, who owns the data, Lisa? I mean, it's just, wow, what a curve ball we've been throwing in the last 120 days. Right, well, with all this flux and the data explosion, we could talk about the data explosion every day at every event. What is, when you're talking with customers, what's the unique differentiator, the value prop of SDG Group with CLIC? How are you helping customers manage data effectively so that they can drive business outcomes like improving revenue with so much dynamics going on? Yeah, so two things. One is I would say, from an SDG perspective, our value prop is typically centered around the three pillars of expertise as we call it. And basically that is, we'll bring industry knowledge to make sure you can do it within the context of your industry. We're going to bring the technical knowledge in this case to make sure we can get deep and talk about how CLIC can be a competitive advantage for them. And then we bring practice knowledge. And that practice is more wrapped around, are we focusing on data management and engineering? Or are we focusing on visual analytics or something like that? So we typically will try and bring that as our value prop. Then with CLIC, as an example, CLIC themselves have a very powerful value proposition. One of the nice things about CLIC is that, and I'll talk in a minute about the broader tool set, but CLIC has the ability to ingest data at a visual level, which gives you the uniqueness to be able to move fast. So even if you're not building the full-on system right out of the gate, because maybe you don't have a good vision for what you need yet, but you need to address business questions and you want their input, you can start to almost in a very agile way, work with the business users to say, well, what is it that you're trying to accomplish? And in the discussions, you can literally build examples of visuals, of dashboards, of full-on applications very, very quickly to show them the art of the possible. And then as you move forward, you start to map out what the bigger, long-term, strategic system should look like, and then you execute that also in a fairly iterative way. So the value prop you get from customers, if the customers in the combination is in the context of your industry and your needs, we can bring a very flexible and agile system that can address business needs across all functional areas potentially and across all industries. So it's a really powerful value prop at the intersection. Which I imagine will expand, given the talent acquisition. So SCG Group has a very strong click practice. You just did a great job talking about the joint value prop there. You also have a very strong talent portfolio. Talk a little bit about how you expect the TAM to expand and the value prop to expand as well once that acquisition closes. Yeah, it's interesting when the acquisition, the intent to acquire was first announced, I initially thought, oh, wow. Click as a company is going to truly be a data company now. They will be seen as a strong data company. They have always been a data company, but primarily focused on visual and then over time making acquisitions, getting closer and closer to data, and now very significant. And then I thought the same thing on the talent side. When I looked at talent, I thought, okay, wait a minute. You can be perceived oftentimes, not just talent, but that space, can be perceived as almost a utility. So now, they're no longer just a utility. They have a face that brings their data and their information to life, to the customer. So I think the value proposition from the combination to a customer is enormous flexibility in where you're putting, let's say, the intelligence of your solution. Because I'm a big believer that any company, almost regardless of the level of maturity, the knowledge that goes into the systems comes from all different levels. It comes from IT. It comes from data scientists. It comes from, let's say, a power user. It comes from casual end users that know the business very well. That logic can come from all over. So now with the combined organizations, I think a customer can start building business logic at, let's say, a business level, and then they can, depending on what the specifics of the integration are, you could potentially leverage that same knowledge that's coming from the business and the actual code that's being developed in a front end solution, and almost pull it back and industrialize it across the entire organization. Imagine the power of a company that has IT people. Many companies don't know the business logic. Many they might not. But imagine being in, let's say, an IT role where you're responsible for building out that data foundation, but you don't know the business logic. Typically you'll go gather requirements. You understand what they're looking to build. You'll go through a process to develop and then code what that logic is. Now imagine if you have different stakeholders in this architecture where the business can actually put the business logic in and you can almost inherit it. Same thing, it would be enormous. It would be game changing in the market. This is so, it's such an interesting topic because you're absolutely right today, the data is embedded into the app, into the business logic, not the reverse. And what you're talking about is flipping the equation on its side. That's where data mesh gets interesting, but the tools aren't there today. It's too hard to glue everything together. You have people playing with knowledge graphs. I don't know how much you know about this, but is it an area that you've looked at? I mean, the problem with knowledge graphs basically take people, places, things, and create relationships between them and the semantic layer that there's knowledge across that layer, which is kind of cool. But the problem is you don't have the flexibility of the query flexibility of relational database. And so you have to go back pre-sequel 15 years back. And so bringing those two worlds together is really challenging. But if you could, to your point, it would absolutely change because now you can build a digital twin of your business in real time. Like the woman from Ford this morning talking about how most of the dashboards are being built by business people. Okay, great. That in and of itself is so hard. But we're talking about something much more difficult and impactful down the road. That's like data nirvana. Am I making any sense? Yeah, absolutely. And to your point with the evolution and the maturity. So a lot of those technologies are starting to become in the, let's say, in the viewpoint of a lot of companies. Still a little early on for us. We're starting to get involved, but not so much. But to your point, it does help understand the relationships in the data and then to depict it in a way that people can understand it very clearly. So getting there, at least for us, but not quite there yet. Yeah, and it's early days. But actually, the reason I'm cautious about saying it's early days, because we used to talk about, oh, it's the first inning. Well, you're blinking, it's the ninth inning these days. And so, you're seeing companies, I don't know if you do work with like DBT Labs, that sort of API-ifying the data and the data warehouse and the metrics and, you know, Mugley has got his new company. Mugley was the CEO of Snowflake, a Microsoft guy. He's a relational AI. They're kind of thinking about new data structures and new data platforms, which is kind of interesting. You got companies like AtScale out there doing semantic layers. There's all these sort of interesting developments that point to the future. It's really hard to predict right now, but there's real tech that's hard. That's all around data. And it's going to be interesting to see what these guys do, because they're very clearly smart about M&A, and they can move fast, and they're agile in that regard. And then, you know, if and when they go public, now they're going to have even greater resources. Absolutely. I think it's one of the things for me that really does differentiate click in the market. To your point, there's a lot of technologies out there and a lot of very good, very powerful technologies. There's many technologies that are popping up that focus on a specific piece of the equation, and then more and more are popping up. So the integration for customers becomes a primary focus. How do I bring all these technologies together to provide some kind of an outcome that's really hopefully differentiated? Click is taking a different tactic where they're saying, okay, we're going to try and own a bit more of that so that a client doesn't have to have as many technologies. And so as Click's acquisition strategy, to your point, has deliberately expanded and moved it across, I think back to when I was a part of Click, you know, Click was very much a visual tool and that's what it was known for. And then my coupon came on, they started making more acquisitions and some very good acquisitions that started expanding all the way. They covered the entire scope and process of data all the way from ingestion all the way through to data engineering now, data management, they have governance, they've got AIML or AutoML, they've got visual analytics. So they've really covered the whole thing. And it's funny, because I did think at one point, are they kind of picking a fight with all the other vendors that are out there and now all the vendors? That's one way to look at it. But then the other side to look at it from a customer's perspective is no, they're providing the customer with a unique value prop that makes it so that they can get as much or as little as what they need at that moment. And then if they do decide there's another technology for one of those components they want, Click is open, they're a great partner company. So they partner with a lot of these technologies, as you mentioned. So then yeah, plug them in and use them for the component that you want to use them for. So it gives both options. They're not building a database yet. So that's cool, don't go there yet. But then Mike talked about the areas that they're leading in the magic quadrant. And when I listen to you speak, it's like those magic quadrants are like these bespoke sets and they're all coming together as a platform. That's why I asked Mike, are you an ISV? Of course I knew he was going to say a platform. But it's almost like you need a magic quadrant for this data platform. And what's the data platform? Is that Snowflake? Is that the Snowflake in its ecosystem? Is that something like Click, which is really doing that data integration and the data management? But it is all coming together as the industry matures. It is true. And one thing I will say that I'm very impressed with some of the companies you're mentioning, a Click, a Snowflake, AWS, a lot of these firms, they do a very good job of working together for the benefit of the customer. And I've seen it time and time again where they will make sure that they're supporting the integration, they're making sure the customer gets the right level of service and support that they need. And even with partners, frankly, as everything has shifted into the cloud, it's more important that for the vendors that they have some stake in the game. So they work very closely with us as well, just to make sure that the customers are successful. And that's important because this integration is pretty complex at times. Well, it's interesting to watch AWS kind of gluing together all the bespoke, you know, primitives, right? They're clearly doing that now, they have to do that. Whereas you see Microsoft taking a different sort of more abstracted view. Now with ChatGPT, it's a heavily abstracted view of the world. I'm not saying one's right or wrong. There's needs for both. Some customers, you know, the AWS customers, the developers, you know, first, and the enterprise, you know, Microsoft, maybe the other end of the spectrum, Google's kind of, I guess, I don't know, in the middle. But it's, you know, it's such a big market. You know, there's no one right answer. That's exactly right. There's different ways to approach it. Wrap us up with a customer example that you think really demonstrates very clearly the value crop, the differentiation, the STG group and click bring to the table. Yeah, so I guess I'll use one that we're presenting with here today. Oh, well, technically tomorrow. But we are presenting here and I think it's a great example of a good joint value prop. So we're presenting with Novartis tomorrow. Hopefully you'll be able to join us one o'clock. And we're talking about their use of click, click sense being rolled out to tens of thousands of users across the globe. And in their case, it spans many functional areas, but their commercial organization has done a phenomenal job working together with their IT organization to build a platform that leverages various technologies that allow them to then surface those insights to end users across the globe. And they've been working with click for several years now. They have leading edge tactics for implementing it and integrating it with other capabilities. So really powerful user interfaces that can support different users based on who they are. So one person logs in, they'll see a completely different environment than the other. But behind the scenes, you're leveraging the same architecture. So it's again, one of the other value props that click brings. Click is not just, and I'm referring to click sense in this context, it's not just a traditional reporting tool. It's an entire platform that is connected to the rest of the technologies that are there. So from a delivery perspective, you can get in and you can build any custom system you want, leveraging common architecture. So I think that adds a lot of scale. It adds a lot of ease of maintenance for the customer. And most importantly, it gives the end users a really flexible and intuitive way to consume the information. Chris, what do you see in the spending climate? I mean, you know, this morning Mike, he's optimist. Okay, cool. Nobody wants to talk about the macro here. I get it, but still security and analytics are top two. You know, you see that in clouds there as well. But even those are coming down a little bit since last fall, really. Are you seeing sort of people look at analytics as semi-discretionary? Being careful about that work. Let's say they're able, because there's so much cloud action going on, they're able to dial it down just like they were able to dial it up. What are you seeing in terms of the spending climate? That's an excellent question. So, and it's very timely. So we are seeing a lot of pressure on finances across the board. However, what we're noticing is the use of analytics is still important. It's still a priority and it's still a factor in how they move forward with their spending. I will say though, their clients are much more focused on how do we do things efficiently? How do we pay attention to the costs? How do we keep them at bay, make sure they don't become too much? And maybe that's using some more people internally and then working with consultants in a different way to empower their people. So it's shifting a little bit of the dynamics. In some cases, it's the opposite. They struggle with turnover or they have to let go of people. In those cases, they'll rely a little bit more on service providers like us. And then we provide ourselves in bringing a global model that can help them adjust to the cost pressures that they've got. But to answer your question, I still see clients focusing and prioritizing on analytics as a key spend during this time. Awesome. This has been such a great conversation, guys. We could keep going, we're out of time, but you've done such a great job, Chris, of talking about what you guys are doing together with Click, how you're helping customers manage the data explosion really efficiently. High fives all around. That was awesome. Thank you so much for joining us and sharing your insights. We really appreciate that. Thank you for having me. And we want to thank you for watching. If you are our at Click World, you can catch Chris and Novartis tomorrow at 1 p.m. We appreciate you watching for Dave Vellante and our guests. I'm Lisa Martin. We're going to be back with our next guest in just a minute, so don't go anywhere.