>>Mhm >>Yes, welcome to this cube conversation. I'm lisa martin excited about this conversation. It's combining my background in life sciences with technology. Please welcome Mike Tarsa Lee, the chief scientific officer at Tetra Science. Mike I'm so excited to talk to you today. >>Thank you lisa and thank you very much to the cube for hosting us. >>Absolutely. So we talk about cloud and data all the time. This is going to be a very interesting conversation especially because we've seen events of the last what are we on 14 months and counting have really accelerated the need for drug discovery and really everyone's kind of focused on that. But I want you to talk with our audience about Tetra science, Who you guys are, what you do and you were founded in 2014. You just raised 80 million in series B but give us an idea of who you are and what you do. >>Got it. Tetro Science, what are we? We are digital plumbers and that may seem funny but really we are taking the world of data and we are trying to resolve it in such a way that people can actually pipe it from the data sources they have in a vendor agnostic way to the data targets in which they need to consume that data. So bringing that metaphor a little bit more to life sciences, let's say that you're a chemist and you have a mass spec and an NMR and some other piece of technology and you need all of those to speak the same language. Right? Generally speaking, all of these are going to be made by different vendors. They're all going to have different control software and they're all going to have slightly different ways of sending their data in. Petro Science takes those all in. We bring them up to the cloud or cloud native solution. We harmonize them, we extract the data first and then we actually put it into what we call our special sauce are intermediate data schema to harmonize it. So you have sort of like a picture and a diagram of what the prototypical mass spec or H P. L. C. Or cell counting data should look like. And then we build pipelines to export that data over to where you need it. So if you need it to live in an L. N. Or a limb system or in a visualization tool like spot fire tableau. We got you covered. So again we're trying to pipe things from left to right from sources to targets and we're trying to do it with scientific context. >>That was an outstanding description. Data plumbers who have secret sauce and never would have thought I would have heard that when I woke up this morning. But I'm going to unpack this more because one of the things that I read in the press release that just went out just a few weeks ago announcing the series B funding, it said that that picture science is pioneering a $300 billion dollar Greenfield data market and operating this is what got my attention without a direct cloud native and open platform competitor. Why is that? >>That's right. If you look at the way pharma data is handled today, even those that long tend to be either on prem solutions with a sort of license model or a distribution into a company and therefore maintenance costs, professional services, etcetera. Or you're looking at somebody who is maybe cloud but their cloud second, you know, they started with their on prem journey and they said we should go and build out some puppies, we should go to the cloud migrate. However, we're cloud first cloud native. So that's one first strong point. And the second is that in terms of data harmonization and in terms of looking at data in a vendor agnostic way, um many companies claim to do it. But the real hard test of this, the metal, what will say is when you can look at this with the Scientific contextual ization we offer. So yes, you can collect the data and put it on a cloud. Okay great. Yes. You may be able to do an extract, transform and load and move it to somewhere else. Okay. But can you actually do that from front to back while retaining all the context of the data while keeping all of the metadata in the right place? With veracity, with G XP readiness, with data fidelity and when it gets over to the other side can somebody say oh yeah that's all the data from all the H. P. L. C. S we control. I got it. I see where it is. I see where to go get it, I see who created it. I see the full data train and validation landscape and I can rebuild that back and I can look back to the old raw source files if I need to. Um I challenge someone to find another direct company that's doing that today. >>You talk about that context and the thing that sort of surprises me is with how incredibly important scientific discovery is and has been for since the beginning of time. Why is why has nobody come out in the last seven years and tried to facilitate this for life sciences organizations. >>Right. I would say that people have tried and I would say that there are definitely strides being made in the open source community, in the data science community and inside pharma and biotech themselves on these sort of build motif, right. If you are inside of a company and you understand your own ontology and processes while you can probably design an application or a workflow using several different tools in order to get that data there. But will it be generally useful to the bioscience community? One thing we pride ourselves on is when we product eyes a connector we call or an integration, we actually do it with a many different companies, generic cases in mind. So we say, OK, you have an h p l C problem over at this top pharma, you have an HPC problem with this biotech and you have another one of the C R. O. Okay. What are the common points between all of those? Can we actually distill that down to a workflow? Everyone's going to need, for example a compliance workflow. So everybody needs compliance. Right. So we can actually look into an empower or a unicorn operation and we can say, okay, did you sign off on that? Did it come through the right way? Was the data corrupted etcetera? That's going to be generically useful to everybody? And that's just one example of something we can do right now for anybody in bio pharma. >>Let's talk about the events of the last 14 months or so mentioned 10 X revenue growth in 2020. Covid really really highlighted the need to accelerate drug discovery and we've seen that. But talk to me about some of the things that Tetra science has seen and done to facilitate that. >>Yeah, this past 14 months. I mean um I will say that the global pandemic has been a challenge for everyone involved ourselves as well. We've basically gone to a full remote workforce. Um We have tried our very best to stay on top of it with remote collaboration tools with vera, with GIT hub with everything. However, I'll say that it's actually been some of the most successful time in our company's history because of that sort of lack of any kind of friction from the physical world. Right? We've really been able to dig down and dig deep on our integrations are connections, our business strategy. And because of that, we've actually been able to deliver a lot of value to customers because, let's be honest, we don't actually have to be on prem from what we're doing since we're not an on prem solution and we're not an original equipment manufacturer, we don't have to say, okay, we're going to go plug the thing in to the H. P. L. C. We don't have to be there to tune the specific wireless protocols or you're a W. S. Protocols, it can all be done remotely. So it's about building good relationships, building trust with our colleagues and clients and making sure we're delivering and over delivering every time. And then people say great um when I elect a Tetra solution, I know what's going right to the cloud, I know I can pick my hosting options, I know you're going to keep delivering more value to me every month. Um Thanks, >>I like that you make it sound simple and that actually you bring up a great point though that the one of the many things that was accelerated this last year Plus is the need to be remote that need to be able to still communicate, collaborate but also the need to establish and really foster those relationships that you have with existing customers and partners as everybody was navigating very, very different challenges. I want to talk now about how you're helping customers unlock the problem that is in every industry data silos and point to point integration where things can talk to each other, Talk to me about how you're helping customers like where do they start with? Touch? Where do you start that? Um kind of journey to unlock data value? >>Sure. Journey to unlock data value. Great question. So first I'll say that customers tend to come to us, it's the oddest thing and we're very lucky and very grateful for this, but they tend to have heard about what we've done with other companies and they come to us they say listen, we've heard about a deployment you've done with novo Nordisk, I can say that for example because you know, it's publicly known. Um so they'll say, you know, we hear about what you've done, we understand that you have deep expertise in chromatography or in bio process. And they'll say here's my really sticky problem. What can you do here? And invariably they're going to lay out a long list of instruments and software for us. Um we've seen lists that go up past 2000 instruments. Um and they'll say, yeah, they'll say here's all the things we need connected, here's four or five different use cases. Um we'll bring you start to finish, we'll give you 20 scientists in the room to talk through them and then we to get somewhere between two and four weeks to think about that problem and come back and say here's how we might solve that. Invariably, all of these problems are going to have a data silos somewhere, there's going to be in Oregon where the preclinical doesn't see the biology or the biology doesn't see the screening etcetera. So we say, all right, give us one scientist from each of those, hence establishing trust, establishing input from everybody. And collaboratively we'll work with, you will set up an architecture diagram, will set up a first version of a prototype connector, will set up all this stuff they need in order to get moving, we'll deliver value upfront before we've ever signed a contract and will say, is this a good way to go for you? And they'll say either no, no, thank you or they'll say yes, let's go forward, let's do a pilot a proof of concept or let's do a full production rollout. And invariably this data silos problem can usually be resolved by again, these generic size connectors are intermediate data schema, which talks and moves things into a common format. Right? And then also by organizationally, since we're already connecting all these groups in this problem statement, they tend to continue working together even when we're no longer front and center, right? They say, oh we set up that thing together. Let's keep thinking about how to make our data more available to one another. >>Interesting. So culturally, within the organization it sounds like Tetra is having significant influences their, you know, the collaboration but also data ownership. Sometimes that becomes a sticky situation where there are owners and they want to read retain that control. Right? You're laughing? You've been through this before. I'd like to understand a little bit more though about the conversation because typically we're talking about tech but we're also talking about science. Are you having these technical conversations with scientists as well as I. T. What is that actual team from the customer perspective look >>like? Oh sure. So the technical conversation and science conversation are going on sometimes in parallel and sometimes in the same threat entirely. Oftentimes the folks who reach out to us first tend to be the scientists. They say I've got a problem, you know and and my research and and I. T. Will probably hear about this later. But let's go. And then we will invariably say well let's bring in your R. And D. I. T. Counterparts because we need them to help solve it right? But yes we are usually having those conversations in parallel at first and then we unite them into one large discussion. And we have varied team members here on the Tetris side we have me from science along with multiple different other PhD holders and pharma lifers in our business who actually can look at the scientific use cases and recommend best practices for that and visualizations. We also have a lot of solutions architects and delivery engineers who can look at it from the how should the platform assemble the solution and how can we carry it through? Um And those two groups are three groups really unite together to provide a unified front and to help the customer through and the customer ends up providing the same thing as we do. So they'll give us on the one call, right? Um a technical expert, a data and QA person and a scientist all in one group and they'll say you guys work together to make sure that our orders best represented here. Um And I think that that's actually a really productive way to do this because we end up finding out things and going deeper into the connector than we would have otherwise. >>It's very collaborative, which is I bet those are such interesting conversations to be a part of it. So it's part of the conversation there helping them understand how to establish a common vision for data across their organization. >>Yes, that that tends to be a sort of further reaching conversation. I'll say in the initial sort of short term conversation, we don't usually say you three scientists or engineers are going to change the fate of the entire orig. That's maybe a little outside of our scope for now. But yes, that first group tends to describe a limited solution. We help to solve that and then go one step past and then they'll nudge somebody else in the Oregon. Say, do you see what Petra did over here? Maybe you could use it over here in your process. And so in that way we sort of get this cultural buy in and then increased collaboration inside a single company. >>Talk to me about some customers that you've worked with it. Especially love to know some of the ones that you've helped in the last year where things have been so incredibly dynamic in the market. But give us an insight into maybe some specific customers that work with you guys. >>Sure. I'd love to I'll speak to the ones that are already on our case studies. You can go anytime detector science dot com and read all of these. But we've worked with Prelude therapeutics for example. We looked at a high throughput screening cascade with them and we were able to take an instrument that was basically unloved in a corner at T. Can liquid handler, hook it up into their Ln. And their screening application and bring in and incorporate data from an external party and do all of that together and merge it so they could actually see out the other side a screening cascade and see their data in minutes as opposed to hours or days. We've also worked as you've seen the press release with novo Nordisk, we worked on automating much of their background for their chromatography fleet. Um and finally we've also worked with several smaller biotechs in looking at sort of in stan shih ation, they say well we've just started we don't have an L. N. We don't have a limbs were about to buy these 50 instruments. Um what can you do with us and we'll actually help them to scope what their initial data storage and harmonization strategy should even be. Um so so we're really man, we're at everywhere from the enterprise where its fleets of thousands of instruments and we're really giving data to a large amount of scientists worldwide, all the way down to the small biotech with 50 people who were helping add value there. >>So big range there in terms of the data conversation, I'm curious has have you seen it change in the last year plus with respect to elevating to the C suite level or the board saying we've got to be able to figure this out because as we saw, you know, the race for the Covid 19 vaccine for example. Time to value and and to discovery is so critical. Is that C suite or board involved in having conversations with you guys? >>It's funny because they are but they are a little later. Um we tend to be a scientist and user driven um solution. So at the beginning we get a power user, an engineer or a R and D I. T. Person in who really has a problem to solve. And as they are going through and developing with us, eventually they're going to need either approval for the time, the resources or the budget and then they'll go up to their VP or their CIA or someone else at the executive level and say, let's start having more of this conversation. Um, as a tandem effort, we are starting to become involved in some thought leadership exercises with some larger firms. And we are looking at the strategic aspect through conferences, through white papers etcetera to speak more directly to that C suite and to say, hey, you know, we could fit your industry for dato motif. And then one other thing you said, time to value. So I'll say that the Tetro science executive team actually looks at that as a tract metric. So we're actually looking at driving that down every single week. >>That's outstanding. That's a hard one to measure, especially in a market that is so dynamic. But that time to value for your customers is critical. Again, covid sort of surfaced a number of things and some silver linings. But that being able to get hands on the day to make sure that you can actually pull insights from it accelerate facilitate drug discovery. That time to value there is absolutely critical. >>Yeah. I'll say if you look at the companies that really, you know, went first and foremost, let's look at Moderna right? Not our customer by the way, but we'll look at Madonna quickly as an example as an example are um, everything they do is automated, right? Everything they do is cloud first. Everything they do is global collaboration networks, you know, with harmonized data etcetera. That is the model we believe Everyone's going to go to in the next 3-5 years. If you look at the fact that Madonna went from sequence to initial vaccine in what, 50, 60 days, that kind of delivery is what the market will become accustomed to. And so we're going to see many more farmers and biotechs move to that cloud first. Distributed model. All data has to go in somewhere centrally. Everyone has to be able to benefit from it. And we are happy to help them get >>Well that's that, you know, setting setting a new record for pace is key there, but it's also one of those silver linings that has come out of this to show that not only was that critical to do, but it can be done. We have the technology, we have the brain power to be able to put those all user would harmonize those together to drive this. So give me a last question. Give me an insight into some of the things that are ahead for Tetra science the rest of this year. >>Oh gosh, so many things. One of the nice parts about having funding in the bank and having a dedicated team is the ability to do more. So first of course our our enterprise pharma and BioPharma clients, there are plenty more use cases, workflows, instruments. We've just about scratch the surface but we're going to keep growing and growing our our integrations and connectors. First of all right we want to be like a netflix for connectors. You know we just want you to come and say look do they have the connector? No well don't worry. They're going to have it in a month or two. Um so that we can be basically the almost the swiss army knife for every single connector you can imagine. Then we're going to be developing a lot more data apps so things that you can use to derive value from your data out. And then again, we're going to be looking at helping to educate everybody. So how is cloud useful? Why go to the system with harmonization? How does this influence your compliance? How can you do bi directional communication? There's lots of ways you can use. Once you have harmonized centralized data, you can do things with it to influence your order and drive times down again from days and weeks, two minutes and seconds. So let's get there. And I think we're going to try doing that over the next year. >>That's awesome. Never a dull moment. And I, you should partner with your marketing folks because we talked about, you talked about data plumbing the secret sauce and becoming the netflix of connectors. These are three gems that you dropped on this this morning mike. This has been awesome. Thank you for sharing with us what teacher science is doing, how you're really helping to fast track a lot of the incredibly important research that we're all really um dependent on and helping to heal the world through data. It's been a pleasure talking with you. >>Haley says I'm a real quickly. It's a team effort. The entire Tetro science team deserves credit for this. I'm just lucky enough to be able to speak to you. So thank you very much for the opportunity. >>And she about cheers to the whole touch of science team. Keep up the great work guys. Uh for mike Roselli, I'm lisa martin. You're watching this cube conversation. >>Mhm.