 Well, good day. And thanks for joining us as we continue our series here on theCUBE of the AWS startup showcase featuring today, Big ID. And with us is Will Murphy, who's the vice president of business development and alliances at Big ID. And Will, good day to you. How are you going today? Thanks, John. I'm doing well. I'm glad to be here. Yeah, that's great. And at a CUBE alum too, I might add. So it's nice to have you back. Let's first off, let's share the Big ID story. You've been around for just a handful of years, accolades coming from every which direction. So obviously what you're doing, you're doing very well. But for our viewers who might not be too familiar with Big ID, just give us a 30,000 foot level of your core competence. Yeah, absolutely. So actually we just had our five year university for Big ID, which we're quite excited about. And that five year comes with some pretty big road marks. We've raised over $200 million for a unicorn now. But where that comes to and how that came about was that we're dealing with longstanding problems with modern data landscapes. Security, governance, privacy initiatives. And starting in 2016 with the authorship of GDPR, the European Privacy Law, organizations had to treat data differently than they did before. They couldn't afford to just sit on all this data that was collected for a couple of reasons, right? One of them being that it's expensive. So you're constantly storing data, whether that's on-prem or in the cloud is what we're gonna talk about. There's expense to that. You have to pay to secure the data and keep it from being leaked. You have to pay for access control. It's paid for a lot of different things. And you're not getting any value out of that. And then there's the idea of the customer trust piece, which is like if anything happens to that data, your reputation as a company and the trust you have between your customers and your organization is broken. So Big ID, what we did is we decided that there was a foundation that needed to be built. The foundation was data discovery. If an organization knows where its data is, whose data it is, where it is, and what it is and also who has access to it, they can start to make actionable decisions based on the data and based on this new data intelligence. So we're trying to help organizations keep up with modern data initiatives. And we're empowering organizations to handle their data, sensitive, personal regulated. And what's actually quite interesting is we allow organizations to define what's sensitive to them because like people, organizations are all different. And so what's sensitive to one organization might not be to another, it goes beyond the law. And so we're giving organizations that new power and flexibility. Yeah, and this is what I still find striking is that obviously with this exponential growth of data we got in machine learning just bringing billions of inputs it seems like, right? And all of a sudden you have this vast reservoir of data is that companies in large part don't know a lot about the data that they're harvesting and where it is. And so it's not actionable. It's kind of dark data, right? It's just out there reciting. And so as I understand it, this is your focus basically is to help people, hey, here's your landscape. Here's how you can better put it to action, why it's valuable and we're going to help you protect it. And they're not aware of these things which I still find a little striking in this day and age. And it goes even further. So when you start to reveal the truth and what's going on with data there's a couple of things that some organizations do and I think human instincts. Some organizations wanna bury their head in the sand like everything's fine. Which is as we know and we've seen the news frequently not a sustainable approach. There's the like let's be, we're overwhelmed. Yeah, we don't even know where to start. Then there's the natural reaction which is okay, we have to centralize and control everything which defeats the purpose of having shared drives and collaboration and geographically disparate workforces which we've seen that particularly over the last year how important that resiliency within organizations is to be able to work in different areas. And so it really restricts the value that organizations can get from their data which is important and it's important in a ton of ways. And for customers that have allowed their data to be stored and harvested by these organizations they're not getting value out of it either. It's just risk and we've got to move data from the liability side of balance sheet to the assets out of the balance sheet. And that comes first and foremost with knowledge. So everybody's on cloud, right? Just be everybody's on prem and all of a sudden we build a bigger house. And so because we build a bigger house you need better security, right? Your front-runner's got to grow. And that's why some AWS has come in with you and this is a two year partnership that you've been engaged with in AWS. So maybe shine a little light on that about the partnership that you've created with AWS and then how you then in term transition that to leverage that for the betterment of your customer base. Yeah, so AWS has been a great partner. They're very forward-looking and for an organization as large as they are very forward-looking that they can't do everything that their customers need and it's better for the ecosystem as a whole to enable small companies like us. And we were very small when we started our relationship with them to join their partner organization. So we're an advanced partner now we're part of ISV Accelerate so it's a slightly more lead partner organization. And we're there because our customers are there and in AWS like us, but we both have a customer obsessed culture but organizations are embracing the cloud and there's fear of the cloud but there really shouldn't be in the way that we thought of it maybe five or 10 years ago in that companies like AWS are spending a lot more money on security than most organizations can. So like they have huge security teams they're building massive infrastructure and then on top of that, companies themselves can use products like Big ID and other products to make themselves more secure from outside threats and from inside threats as well. So we are trying to with them approach modern data challenge as well. So even with an AWS, if you put all the information in like, let's say as three buckets, it doesn't really tell you anything. It's like, I make this analogy sometimes I live in Manhattan, if I were to collect all the keys of everybody that lived in a 10 block radius around me and put it into a dumpster and keep doing that. I would theoretically know where all the keys were. They are in the dumpster. Now, if somebody asked me, I'd like my keys back. I'd have a really hard time giving them that because I've got to sort through 10,000 people's keys and I don't really know a lot about it but those keys say a lot. It says, are you in an old building or are you in a new building? Do you have a bike? Do you have a car? Do you have a gym locker? There's all sorts of information and I think that this analogy holds up for data bitters of the way you store your data is important but you can gain a lot of theoretically innocuous but valuable information from the data that's there while not compromising the sense of the data. And AWS has been a fabulous partner in this. They've helped us build a AWS security hub integration out of the box. We now work with over 12 different AWS native applications from anything like S3, Redshift, Athena, Kinesis, as well as apps built on AWS like Snowflake and Databricks that we connect to. And AWS, the technical team, the department teams have been an enormous part of our success there. We're very proud to have joined the marketplace to be where our customers want to buy enterprise software more and more. And that's another area that we're collaborating in joint accounts now to bring more value and simplicity to our joint customers. So what's your process in terms of your customer and evaluating your needs? You just talked about it earlier about different approaches to security. Some people put their head in the sand, right? Some people admit that there's a problem. Some people fully engaged. So I assume there's also different levels of sophistication in terms of what they already have in place and then what their needs are. So if you would shout a little out on that about assessing where they are in terms of their data landscape and how AWS and its tools, which you just touched on, multiple tools you have in your service. Now all that comes together to develop what would be, I guess, a unique program for a company's specific needs. It is. We started talking to the largest enterprise accounts when we were founded and we still have a real proclivity and expertise in that area. So the issues with the large enterprise accounts and the uniqueness there is scale. They have a tremendous amount of data, HR data, financial data, customer data. You name it, right? We could go dry mouth, talk about how many, you're saying data so many times with these large customers. For AWS scale wasn't an issue. They can store it, they can analyze it, they can do tons with it. Where we were helping is that we could make that safer. So if you want to perform data analytics, you want to ensure that sensitive data is not being part of that. You want to make sure you're not violating local, national or industry specific regulations. Financial services is a great example. There's dozens of regulations at the federal level in the United States. Each state has their own regulations. This becomes increasingly complex. So AWS handles this by allowing an amazing amount of customization for their customers. They have data centers in the right places. They have experts on vertical specific issues. Big ID handles this similarly in some ways but we handle it through ostensibility. So one of our big things is we have to be able to connect to everywhere where our customers have data. So we want to build a foundation of like, let's say first, let's understand the goals. Is the goal compliance with the law? Which it should be for everybody. That should just be like, we need to comply with the law, like that's easy. Then as the next piece, like are we dealing with something legacy? Was there a breach? Do we need to understand what happened? Are we trying to be forward-looking and understanding we want to make sure we can lock down our most sensitive data, tier our storage, tier our security, tier our analytics efforts, which also is cost-effective. So you don't have to do everything everywhere. Or is the goal a little bit, like we need to get our return on investment faster and we can't do that without de-risking some of that. So we've taken those lessons from the enterprise where it's exceedingly difficult to work because of the strict requirements because the customers expect more. And I think like AWS, we're bringing it down market. We have some new product coming out. It's exclusive for AWS now called Small ID, which is a cloud-native, smaller version, lighter-weight version of our product for customers in the more commercial space, in the SMB space where they can start to build a foundation of understanding their data for protection, for security, for privacy. Well, and before I let you hear what I'd like to hear about is a practical application. Somebody that you were able to help and assist you evaluate it is you've talked about the format here. You've talked about the process and you've talked about some future, I guess, challenges, opportunities. But just to give our viewers an idea of maybe the kind of success you've already had to give them a perspective on that to share a couple of little stories, if you wouldn't mind, whether it's some work that you guys did, what it was sleeves and created that additional value for your customers. Yeah, absolutely. So I'll give a couple of examples. I'm gonna keep everyone anonymized as a privacy-based company in many ways. But we try to respect customers. I'll give you an idea. All right. But let's talk about different types of sensitive data. So we have customers that intellectual property is their biggest concern. So they do care about compliance. They wanna comply with all the local and national laws where their company resides and all their offices are. But they were very concerned about sensitive data sprawl around intellectual property. They have a lot of patents. They have a lot of sensitive data that way. So one of the things we did is we were able to provide custom tags and classifications for their sensitive data based on intellectual property. And they could see across their cloud environment, across their on-premise environment, across shared drives, et cetera, we're sensitive data at sprawl. We're ahead and moved, who's having access to it. And they were able to start realigning their store strategy and their content management strategy, data governance strategy based on that and start to move sensitive data back to certain locations, block that down on a higher level, create more access control there, but also proliferate and share data that more teams needed access to. And so that's an example of a use case that I don't think we imagined necessarily in 2016 when we were focused on privacy, but we've seen that the value can come from it. Yeah. Please, go ahead. No, I mean, the other piece is so we've worked with some of the largest AWS customers in the world, they're concerned is how do we even start to scan the terabytes and petabytes of data in any reasonable fashion without it being out of date? If we create this data map, if we create this data inventory, it's gonna be out of date, day one. As soon as we say it's complete, we've already added more. That's where our scalability fits in. We were able to do a full scan of their entire AWS environment in months and then keep up with the new data that was going into their AWS environment. This is huge, this was groundbreaking for them. So our hyper scan capability that we brought out that we rolled out to AWS first was a game changer for them to understand what data they had and where it is, who's it is, et cetera at a way that they never thought they could keep up with. I brought back to the beginning of COVID when the British government was keeping track of all the COVID cases on spreadsheets and spreadsheet broke. It was also out of date. As soon as they entered something else, it was already out of date. They couldn't keep up with him. Like there's better ways to do that. Luckily they think they've moved on from that manual system but automation using the correct human inputs when necessary then let machine learning let big data take care of things that it can. Don't waste human hours that are precious and expensive unnecessarily and make better decisions based on that data. Yeah, you raised a great point too which I had thought of about the fact is that you do your snapshot today and you started valuing all their needs for today and by the time you're able to get that done their needs have now exponentially grown. It's like painting the Golden Gate Bridge, right? You get done, you're, and now you got to paint it again except it got painted, you know, we added lanes. But anyway, hey, well, thanks for the time. We certainly appreciate it. Thanks for joining us here on the startup showcase and just remind me that if you ever ask for my keys keep them out of that dumpster, all right? That'd be a good thing. Thanks and I'd like to be here. All right, with pleasure.