 Hello, and welcome to this CUBE conversation. I'm Natalie Erlich, your host for theCUBE. Today we're going to speak with an AI enhancing data startup that recently raised $75 million in C-Series funding. Now we're joined by the Chief Marketing Officer of Explorium, Ajay Khanna. Thank you so much for being with us today. Thank you so much, Natalie. Thanks for inviting me in. So tell us, what is Explorium? Sure, so Explorium, we provide external data platform and this platform helps you discover thousands of relevant data signals, external data signals that you can then use in your analytics or in your machine learning models and all. So what we are offering here is this unique end to end platform where you can have access to thousands and thousands of data signals and then you can take those signals and match it with your internal data. You can enrich your internal data, do the transformations and then build pipelines that business analysts can use and take it to their tool of their choice or what data scientists can do is take that enriched data and improve their ML algorithms. So that is the end to end platform that we provide. That's really fascinating. So you're constantly improving on the data and providing better analytics. Can you tell us how specifically or are you helping your customers? Absolutely, so as we jump into the customer use cases, let's first discuss the challenge with the external data. So when we refer to external data with the increase in AI and ML adoption, there has been increase in interest in external data like getting the company data from external sources, whether it is formographics, technographic data, you want socioeconomic data, you may want like for traffic data, you may want to include like data about website visits and the tons of data out there, website interaction that are not within your organization but you want to get the data to get better understanding of your customers. But the challenge is that getting external data is really hard. So and what I mean by that is that it is hard to access. First of all, you don't even know how many data sources out there. It could be thousands of data sources. If you just go to data.gov, there are like 250,000 data sources out there. So that is the first problem to tackle is where do I get the data from and how do I get? And even before that, what is the data that is going to impact my business? So having that issue of like data access is big problem. Second thing is that once you know which data you want to get, it is very hard to use within your systems. It is hard to kind of like, you're going to just directly use the data into your analytics or into machine learning. You have to clean it up. You have to evaluate the quality of the data. You have to do the proper alignment and matching and integration and so on and so forth. And by various estimates like data scientists and analysts spend 80% of their time doing just that job. And the third is around the compliance issues. We want to make sure that data is compliant with the GDPR or CCPA kind of regulations. So what we are helping our customers do is have an easy access to all these relevant data sources and where the system can recommend that, okay, this is the relevant data which is going to make an impact to your business. This is the relevant data which is going to make your ML and analytics better and then match that data with your internal data sources automatically so that you can focus on the business value that you want to generate and take that data. Once you understand the impact of the data, take it to your actual business use cases of the models that you have created. So our customers are in like various industries, right? They are in CPG, they are in retail. Our customers are coming from various fintech organizations like payments and lending and insurance. And they are using us for like various use cases like whether it is lead gen or whether it is lead enrichment, fraud and risk kind of use cases understanding the loan risk, loan application risks. And by having access to these additional data sources that helps them make better decisions about their customers, about their business. Fascinating. Well, tell us, how do you see this market evolving? Market is really dynamic and we have seen this whole market changing the whole data market kind of like changing in last year and a half with the pandemic coming in, right? So the models that we were working on for credit risk for evaluating loan applications were not working anymore. The data that we had was not really usable to make those decisions. So many of our customers, they had to depend on external data to make those credit decisions, right? I mean, if I have to approve an application for a small or a medium business restaurant and the restaurant is closed for last five months, how do I do that? So they were looking for additional data sources like for traffic data, about the Yelp reviews or about the ratings around how they're signed up for various delivery services and use those alternative sources to make those decisions. I think with these kind of like as the situation come in, companies will become much more agile to react to these kind of either data losses or changes in the data that they need. And some of the things that we also see right now is where Google is stopping the third party cookies with Chrome, right? Or Apple saying that with iOS 14, there are new transparency requirements that you have to abide by. So if those signals are gone, then how do companies better understand their customers? How do the companies will redesign their information that they are delivering to their customers or the products that they are presenting to their customers? So having that agility will be determining the competitive advantage for these companies. And once these data signal losses happen, you cannot start evaluating the alternate data at that point in time because it takes like six, seven months to kind of find the data sources, negotiate for those data sources, bring them on board and then integrate them to kind of start using them, then it is already too late. So what we are seeing is that companies will be much more agile and looking for a lot of external data sources to bring them in seamlessly and be able to make their business decision by incorporating those data sources as well. So that's how we are seeing that the use of external data is going to increase with the time. Fascinating and also that you mentioned the pandemic and the company added new data signals to help organizations understand risk. Can you explain how that actually works to our audience? Sure, so let's take couple of scenarios, right? So for example, there is a lending organization and then they are looking for approving a loan application for a small, medium business and they had like three years back revenues or three years previous employee data or their tax returns and everything, but that is irrelevant right now because the business is not running. So how can they use alternate data signals to make that loan decisions or credit decisions? So they will be relying on some of like foot traffic data. They may rely on ratings and reviews. They may rely on other delivery services, subscription that they have subscribed to and helping their customers and then use those additional signals to make those credit decisions. This is like one situation. Another situation that we came across was in CPG where food and beverages sellers, whether those are like convenience stores or whether those are like small restaurants, they're going in and out of business and now when they're coming back in or the new restaurants or convenience stores are emerging, how do these food and beverage provider find those new customers? What are the additional signals they can use to go to that customer right away and say that, okay, we are there with you. We are here to kind of like support your business. What are the additional things that you need to kind of like bring everything back to business? What are the additional shelf spaces available to place your product out there? Because now you don't have data, there is a data lag now. So you need to kind of like provide that additional data to your field operations so that they can find the right businesses. They can find, they can prioritize them and they can see that, okay, these are the businesses which are going to kind of like come back and we need to proactively go and market to them so that once we are out of this COVID which hopefully we are now and how to support these small businesses come right back on track. Very, very interesting. And recently your company Explorium closed $75 million in C-Series funding and not even a year before another $31 million. So what do you attribute to that success? I think it is the whole idea of increase in adoption of AI and ML that we are seeing in the last few years. And as this adoption increases, there is an increase in appetite for external data. So companies do realize that just having ML algorithms is not enough. That is not a competitive advantage. Everybody has the same algorithms. The advantage is the data that you have, advantage is the domain expertise that you have. And then having the wide variety of data that is really important. So what we are seeing is that there is an increased interest in getting access to these external data sources as a competitive advantage. And then having that access easily and being able to easily use that external data into your analytics, into your ML models. That's where the real kind of advantages where you can actually bring your big ideas to life and execute on those ideas but are coming from your business analysts and from your data scientists. So I think that increased interest is what we are seeing here. Well, that's a fascinating point on how data is really the central point of analytics. Really appreciate your fantastic insights on this program for this conversation on theCUBE. I'm Natalie Ehrlich, your host. And that was Ajay Khanna, the Chief Marketing Officer of Explorium. Thanks so much for joining us today.