 From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Welcome back to CUBE 365. I'm your host, Rebecca Knight. Today we are with Michael Klaus. He is the CEO of Adacama. Today, Adacama has just launched Generation 2 of Adacama 1, a self-driving platform for data management and data governance. We're going to do a deep dive into the Generation 2 of Adacama 1. We're going to learn what it means to make data management and governance self-driving and the impact it will have on organizations. Thanks so much for joining us on theCUBE, Michael. Thank you, Rebecca. Thanks for having me. So you are a technology veteran. You've been CEO of this company for 13 years. Tell our viewers a little bit about Adacama. So Adacama was started as basically a spin-off of a professional services company. And I was part of the professional services company. We were doing data integrations, data warehousing, things like that. And on every project, we would struggle with data quality and actually what we didn't know what it was called but it was mastering scattered data across the whole enterprises. So after several projects, we've developed kind of little kind of utility that we would use on the projects. And it seemed to be very popular with our customers. So we decided to give it a try and spin it off as a product company. And that's how Adacama was born. That's how it all started. And... That's how it all started. And now today you're launching Generation Two of Adacama One. And this is about self-driving data management and governance. I can't hear the word self-driving without thinking about Elon Musk. Can you talk a little bit about what self-driving means in this context? So self-driving in the car industry, it will break a major shift into individual transportation. People will be able to reclaim one to two hours per day which they now spend driving, which is pretty kind of mundane low added value activity. But that's what the self-driving cars will bring. Basically people will be free to do more creative, more fun stuff, right? And we've taken this concept on a high level and we're bringing it to data management and data governance in a similar fashion, meaning organizations and people, data people, business people will be free from the mundane activity of finding data, trying to put it together. They will be able to use a readily made, let's say data product, which will be available. It will be high quality. It will be governed. So that's how we are kind of using the analogy between the car industry and the data management industry. So what was the problem that you were seeing in the space? Was it just the way that your data scientists were spending their time? Was it the cumbersome ways that they were trying to mine the data? What was the problem? What was the challenge that you were trying to solve here? So there are actually a few challenges. One challenge is basically time to value. Today, when a business decides to come up with a new product or you need a new campaign for Christmas or something like this, there is an underlying need for data product, right? And it takes weeks or months to prepare that. And that's only if you have some infrastructure. In some cases, it can take even longer. And that's one big issue. You need to be able to give non-technical users a way to instantly get the data they need. And you don't have that in organizations, basically nowhere at the moment. So that's the time to value. The other thing is basically resources, right? You have very valuable resources, data scientists, even analysts who spend, you know, there is this kind of parental law, right? They spend 80% on really preparing the data and only 20% on the value edit part of their jobs. And we are getting rid of the 80% again. And last but not least, what we've been seeing, and it's really painful for organizations, you have very kind of driven business people who just want to deliver business results. They don't want to bother with, you know, where do I get the data? How do I do it right? And then you have rightly so, people who are focused on doing things in the right way. People focus on governance in general sense, meaning, you know, we have to follow policies. We have to, when integrating data, we want to do it in the right way so that it's reusable, et cetera, et cetera. And there's a growing tension between those two views, worldviews, I would say. And it's kind of really painful creating a lot of conflict, preventing the business people to do what they want to do fast and preventing the people focused on governance, keeping things in order. And again, that's what our platform is solving or actually is actually making the gap disappear completely. It's removing that tension that you're talking about. So how is this different from the AI and machine learning that so many other companies are investing in? It is and isn't different. It isn't different in one way. Many companies, you know, in data management, outside of data management are using AI to make life easier for people and organizations. Basically, the machine learning is taking part of what people needed to be doing before that. And you have that in consumer applications, you have that in data management, B2B applications. Now, the huge difference is that we've taken the several disciplines, kind of sub-domains of data management, namely data profiling, data cataloging, data quality management, by that we also mean data cleansing and data mastering and data integration as well. So we've taken all this, we redeveloped, we had that in our platform, we redeveloped it from scratch and that allows us basically one critical thing which is different. If you only apply AI on the level of the individual, let's say modules or products, you will end up with broken processes. You will have, you know, augmented data profiling, augmented data cataloging, but you will still have the walls between the products. From a customer's view, it's kind of a wall between the processes or sub-processes, the domains. So the fact that we have redeveloped it or the reason why we have redeveloped it was to get rid of those walls, those silos. And this way, we can actually automate the whole process, not just the parts of the process. That's the biggest difference. I definitely wanna ask you about removing those silos, but I wanna get back to something you were saying before and that is this idea that you built it from scratch. That really is what sets Atacama apart, is that you architect these things in-house which is different from a lot of competitors. Talk a little bit about why you see that as such an advantage. So this has been in our DNA kind of from day one. When we started to build the core of our product, which is let's say data processing engine, we realized from day one that it needs to be high performance, powerful. It needs to support real-time scenarios. And it paid off greatly because if you have a product, for example, that doesn't have the real-time capability and slapping on the real-time, it's almost impossible, right? You end up with a not so good core with some added functionality. And this is how we build the product gradually. Around the data processing, we build the data quality, we build the data mastering, then we build metadata core next to it. And the whole platform now basically is built on basically on top of three major underlying components. One is the data processing, one is the metadata management core, and one is actually the AI core. And this allows us to do everything that I was talking about. This allows us to automate the whole process. I want to ask a little bit about the silos that you were talking about. And also the tension that you were talking about earlier in our conversation that exists between business people and the data scientists, the ones who want to make sure we're getting everything right and fidelity and that we're paying attention to governance and then the people who are more focused on business outcomes, particularly at this time where we're all enduring a global pandemic which has changed everything about the way we live and the way we work. Have you, do you think that the silos have gotten worse during this pandemic when people are working from home, working asynchronously, working remotely? And how do you think this generation two about a comma one can help ease those challenges and those struggles that so many teams are having? Yeah, thank you for the question. It's kind of been on my mind for almost a year now. And actually in two ways. One way is how governments, our governments, how they're dealing with the pandemic because there the data is also the key to everything, right? It's the critical factor there. And I have to say the governments are not doing exactly the great job. Also in the way they are managing the data and governing the data because at the end of the day what will be needed to fight the pandemic for good is a way to predict on a very highly granular basis what is and what is not happening in each city in each county and tighten or release the measures based on that. And of course you need very good data science for that but you also need very good data management below that to have real-time granular data. So that's one kind of thing that's been a little bit frustrating for me for a long time. Now, if we look at our customers, organizations and users, what's happening there is that of course we all see the shift to work from home and we also see the needs to better support cooperation between the people who are not in one place anymore, right? So on the level of let's say the user interface what we brought to Arakama one generation two is a new way users will be interacting with the platform basically because of the self-driving nature the users will more or less be confirming what the platform is suggesting that's one major shift. And the other thing is there is kind of implicitly built in collaboration and governance process within the platform. So we believe that this will help the whole data democratization process emphasize now by the pandemic and work from home and all these drivers. So what is the impact? We hear a lot about data democratization. What do you think the impact that we'll have going forward in terms of what we'll be driving companies and how will that change the way employees and colleagues interact with and collaborate with each other? We've been hearing about digital transformation for quite a few years, all of us. And I guess you know the joke, right? Who is driving the digital transformation for you today? Is it CEO, COO or CFO? No, it's COVID, right? It really accelerated a transformation in ways we couldn't imagine. Now, what that means is that if organizations are to succeed bringing all the processes to the digital realm and all processes means everything from the market facing, customer facing, customer service, but also all the internal processes you have to bring to the digital. What that really means is you also have to be able to give data to the people throughout the company. And you have to be able to do it in a way that's kind of on one hand, safe. So you need to be able to define who can do what, who can see what in the data. On the other hand, you need to have kind of the courage simply to give the data to people and let them do what they understand best, which is their local kind of part of the organization, right? Local part of the process. And that's the biggest value we think our platform is bringing to the market, meaning it will allow exactly what I was talking about, not to be afraid to give the data to the people, give the high quality, instantly available data to the people and at the same time, be assured that it is safe from the governance perspective. So it's helping companies think about problems differently, think about potential solutions differently, but most importantly, it's empowering the employees to be able to have the data themselves and getting back to the self-driving car example, where we don't need to worry about driving places, we can use our own time for much more value added things in our lives and those employees can do the much more value added things in their jobs. Yes, absolutely. You're absolutely right. The digital transformation is kind of followed or maybe led by the change organizations are managed, right? If you look at the successful, digital first organizations, like the big tech, right, Google, et cetera, you can see that their organization is very flat, which is something else than what you have in the traditional brick and mortar companies. So I think the shift from hierarchical organization to the more flat, more decentralized way of managing things, companies, needs to be also accompanied by the data availability for people. And you have to empower, as you say, everyone through the organization. How do you foresee the next 12 to 24 months playing out as we all adjust to this new normal? Oh, that's a pretty interesting question. I won't talk about what I think will be happening with the pandemic. I think we will see, I will talk about it a little bit. I think we will see the waves, hopefully with the amplitudes kind of narrowing. So that's on that side. What I think we will see, let's say in the economy and in the industry, I can comment on from the data management perspective. I think organizations will have to adopt the new way of working with data, giving the data to the people, empowering the people. If you don't do it, there is of course some, let's say momentum, right? When you're large enterprise with a lot of, let's say, big customer base, a lot of contracts accumulated, it won't go away that fast, but those who will not adapt, they will see a small, like longer gradual decline in their revenues and their competitiveness in reality. Whereas those small and big ones who will adopt this new way of working with data, we will see them growing faster than the other ones. So for our viewers who want to know more about Atacama's launch, it is www.atacama.com backslash self-driving. What is next for this platform? I want you to close us out here and tell us, what is next for a generation two of Atacama one? So we have just launched the platform. It is available to limited number of customers in the beta version. The GA version is going to be available in spring, in February next year. And we will be kind of speeding up with additional releases of the platform that will gradually make the whole suite of functionality available in the self-driving fashion so that, let's say a year from now, you will really be able to go to your browser and actually speak to the platform, speak your wish, which we call intent. We call the principle from intent to result. So for example, you'll be able to say, I need all my customer and product ownership data as an API, which is updated every two hours. And without having to do anything else, you will be able to get that API, which means really complex thing, right? You need to be able to map the sources, translate the data, transform it, probably the API, basically build the integration and governance pipeline. So we think we will get to this point about the same time Elon Musk will actually deliver the full self-driving capability to the cars. That's an exciting future that you're painting right now. We think so too, we think so too. Excellent, Michael Klaus, thank you so much for joining us today. Thank you, Rebecca and everyone. Stay tuned for more of CUBE 365. Thank you.