You know, I love systems. And in this episode, we are talking with Bogdan Knessovic, who is the founder of Colitiscope Bio, which is a platform that helps companies organize the workflow for getting things into the clinic and beyond, really. So we talk a lot about keeping your eye on the prize and how to break down workflows into little bite size things and keep your team all marching in the same direction without distractions, without shiny object syndrome, which we know is a problem for scientists. I am guilty of this too. And I really liked his perspective on how to trim down the operational fat so that we're saving money, we're saving time and resources, and we're getting to our milestones faster. So this is a really tactical episode where we are talking about an actual tool, which is a platform, but we're talking about the overarching need for a tool like this or systems or processes like this. Within every biotech, it doesn't matter if you've started yesterday or you're all the way in the clinic, whatever stage you're at, this episode is going to talk a lot about making systems that work for your team. Welcome to the Building Biotechs podcast. Over the years, I've helped over 90 biotech life sciences and venture capital firms strategize and hire thousands of employees to scale companies that impact human health. We speak with those at the forefront of growing biotechs to learn their tactics on building these companies from the ground up. I'm your host, Karina Klingman. I hope you enjoy the show. Bogdan, thank you so much for joining me. I'm really excited about this conversation, but we like to start at the beginning. I really want to know how you got here. So what did you want to be when you were seven? What are you now and how did you get there? Thanks so much for having me, Karina. I actually love this question because I know you ask it of all of your guests and I think it's a really fun one. I actually was disappointed because I've heard one person give this answer before, which is for me was split between archeologists and paleontologists. It was probably because as a kid, I really loved Jurassic Park and Indiana Jones and those kind of movies and that's what the characters there were. And I think for archeology, at least the person who burst the bubble for me there was like my grade five teacher who told me that it's nothing like the movies. It's grueling long hours. So that's kind of where I started thinking about other things to do. But yeah, that was me in grade school and now I am co-founder and CEO of a bio software company called Colitisco. Excellent. And so Colitisco is really a very tech enabled company. Do you want to just describe what you're doing there and what's the value proposition for small biotech specifically? When I was working as a scientist, I just felt this deep frustration at just kind of the disconnect and lag between what's possible in the world of software and how actual biotechs operate and what that kind of day to day looks like when you're doing science. And so there was, for me, this nagging feeling of this could be a lot better. People could be spending their time in smarter ways. We could be making better decisions as a field. We could be leveraging the data that we're generating in smarter ways. And so this realization that there is these pretty big inefficiencies in how R&D operates from how you do your weekly review meetings to how you set kind of monthly or quarterly milestones, how do you track progress against those? How do you connect what's happening in the lab with what you need to do as a business? That process and those frameworks, Michael Partners and I felt could really be improved. And so that's really what we're trying to productize. At Kaleidoscope, we're trying to give biotechs, whether they're small early teams or much more mature, like well-oiled machines, we give them tools to actually make their R&D dollars go farther and make their operations run faster so that you shave off months in a given year towards hitting that next milestone. Whether that's a fundraiser, whether that's getting to clinic, whether that's a drug-reaching, a patient, donor, all of those things kind of have this knock-on effect. Give me a use case for this in real-world terms. Like when would a company come to you and what would you do to streamline those processes for them? We have two flavors of companies, roughly speaking. One is the biotech that is fairly small or fairly early. They have data that they're generating. It's often coming off in spreadsheet format. And they're trying to really figure out how do we stay on top of that in a more scalable way? And maybe they've gone from just spinning out to now they're hiring and they're eight, nine, 10, 11 people. And now you're no longer at a place where you can keep things in your head. And so the value prop for that flavor of company is we've built a lot of architecture that will help them organize, catalog, track, label, all of those data points and that information in a way that's intuitive and easy for them to review. So they can get together as a team and easily see like what's the latest that we've generated? How does this track against our kind of key milestones that we need to hit? What experiments do we need to go back and rerun? And kind of giving structure to that chaos. The second flavor of company is the more like established, organization, maybe they already have an asset in clinic and then they're working on a pipeline. Maybe they're really late stage. These could be companies that are 100, 200, 300 plus people. And there it's, actually it's actually funnily enough similar things. The main difference is that they probably already have a bunch of other tooling in their stack. And so rather than us being kind of the only platform that they turn to, the value prop there is that we will integrate with whatever tools you already have that you're kind of as point solution. So maybe the tools that your chemists are using, the tools that your biologists are using, the tools that your engineers are using, we have integrations into all of those so that we can pull kind of the key summary data that you need, again, to kind of drive that cadence of review and milestone chasing. So an example use case there in addition to kind of weekly reviews might be like your quarterly planning as a company where you need to set your budget for your R&D. And that should be tied to like tangible output that you're chasing. Whether that's against a target profile, whether that's deciding between four or five lead candidates, whatever that comparison that you're running might be, we give you really automated and intuitive ways to manage that. - That's not something that scientists really are taught to do. I mean, we may be working, especially we've got academic founders that I work with a lot. - You work toward getting a paper out the door or things like that, so you kind of know that end goal, but I don't think anyone ever really measures that output. And so now you're holding to stakeholders and investors, right? They need to know that it's your own track, that their money is being spent intelligently and toward the most important goal. And so I think that's something that is skipped over or is a shock when people start companies, especially out of academia. - Absolutely, and I think in biotech, it's a unique flavor too because you often have like a pretty formal board structure early on, which is not typical in every other kind of startup case. And so you're really early on being held responsible for like pretty existential questions. Like, you know, if you can't show a certain data point, then the company might not exist anymore. So it's very rigorous in a way that I agree with you. Like it just isn't the case coming out of academia. - Yeah, absolutely. I wanna talk about empowering teams. How do you actually help biotechs set these priorities and then establish plans to meet these milestones? - To keep it pretty simple, one thing is just being explicit about what the goals are. I actually think that it's not uncommon for companies and leaders that aren't used to operating in such a intense environment, to not realize that just saying, "Hey, we are going after X." Or like, "The purpose of this quarter is Y." That can be a really powerful thing. And then doing that at like different levels of granularity, like, you know, what's the goal for the year? What's the goal for the quarter kind of getting smaller there? Of course, those things can be changed, but just being explicit about them. I think in science, there's this interesting trap that people can fall into, which is running these unbound experiments because they're like, I don't know if it's scientific curiosity. I don't know if it's just kind of like wanting to follow the cookie trail because again, that's as academics, what we're taught to do. But there's this tendency to do that and not stop and think like, "Hey, it might be boring or uninteresting, "but the thing I need to show is actually "this metric changing." And so is the work that I'm doing moving the needle on that metric? And you only know that if you're explicit about it as a team member, as a leader. That's one thing. The other one kind of implicit to the answer I just gave is like working backwards, so starting with the end goal and kind of like breaking it down. So I think this to me was something that became very apparent, actually used to swim competitively. And so this is I think a skill that a lot of athletes, especially in sports where there is a cadence of every four years, like Olympic-based sports, where you're chasing a milestone that only comes every four years and the outcome's binary, like you make it or you don't. And so you're spending four years going after something that you have to achieve within a two-minute race in my case. And so you have to have some way to know what that end goal is, but you can't just rely on the end goal being something four years away. You actually have to break that down to like, okay, for me to get here, what do I have to have achieved the year before that and the year before that and the year before that? Okay, so within this year, what do I have to have achieved at the halfway point? Okay, and to achieve that, what do I have to have achieved by the end of this month? And it's these like compounding stepwise gains that you make that get you to that final goal. And I think not knowing what that final goal is and not having done the thoughtful work of breaking it down backwards from there is a dangerous kind of blind spot for folks. So that's another thing that we try and again, sometimes it's within our actual product. So showing people how you can structure an R&D project or a program or set milestones that might have, you know, different granularities of timelines associated with them and how you can automate it such that as you get new data, we show you your progress against those. But also just advocating for people to do that, whether it's with our tool or whether it's with other tool that they're using or other processes that they're running. How do you actually set up reliable systems where you know what you're chasing, why you're chasing it, and you're seeing exactly how the work that you're doing in the lab does or doesn't move the needle on that as a team or a company. - I imagine when you're breaking down your goals for swimming, it's like you need to get your turns, you need to get like their little pieces that you can improve each thing. And I think that having been a very competitive athlete, you do have that in your mind, but you don't always translate that into what that actually looks like in work. And so I'm curious how you help teams balance the short-term versus long-term goals because some of these goals, if you're talking about starting at the very end, it's like patients are actually using drugs that you've discovered and commercialized, right? That's a really long-term goal. And so how do you help teams balance that because keeping your eye on that prize versus okay, but to get there, this month's goal is a very small piece of that. - It starts with just awareness and realization that's what the job will be and that it maybe is a bit counterintuitive for leaders who haven't gone through a few of these cycles because when you're in an intense period of time, you wanna focus on putting one foot in front of the other. And it's maybe counterintuitive to say like, no, you actually to lift your head up and ask those long-term questions, start asking what does this look like? If all the science works out, then what happens? Like, is there a market for this thing? What's the standard of care currently? Like will clinicians want to prescribe a new drug like under what conditions? And then those things can actually inform what you're doing in the lab pretty granularly and what experiments you're running because it'll kind of constrain your search space in a way because what you don't want to happen is that you do this five to 10 years of early in science and everything works out and you get to an approval and then the drug doesn't reach patients because no one is prescribing it. So it sounds like, again, maybe obvious, but it's definitely not to people who haven't gone through a few kind of drug approval cycles. So the awareness of like, hey, you should be asking these questions really early and thinking about them really early and not kind of kicking the can down the road or lying to yourself that like, oh, you know, aren't these going to take five to 10 years? So I'll think about those problems later. As a leader, not being afraid to kind of zoom in and out, you need to not be afraid to occasionally put yourself in the shoes of what someone else on your team is doing and what's their kind of experience in the day-to-day like, which leads me to how do you actually empower your team to do the best work? Because, like you said, you're holding in your head for something that might be a decade out at the same time as something that you're focused on this month and no one can do that indefinitely. And so you have to rely on your team here. And so I think you need to have this acute understanding of is my team set up to do the best work that they can? Is the system that we have in place conducive to us being able to move quickly on these shorter term goals? And I'll give you a very simple example here. A lot of bio CEOs probably aren't aware of this, but I'll offer it as a CEO. You'll be sent by our index maybe weekly that have a summary of a bunch of information. And if you don't stop to ask, wait, what was the process that got me this information? You could go on thinking, great, everything is easy. Every week I get this update, it's wonderful. And then if you actually go and ask your VP, SVP director, whoever it is who prepared that, how did this kind of material come together? What we often hear from folks is, oh, well, I spent six hours this week compiling it and I emailed these 15 people to get them to go and screenshot these results and send them to me and then I had to clarify. And so you learn that actually it's a very manual process, it's a very painful process. And just because you as the CDR leader don't see that doesn't mean that your team is not feeling the brunt of that. And so asking yourself, knowing that we're gonna be in this grind for a decade, is there a ways I could be making this kind of day-to-day easier for my team so that we can still focus on knocking out these short-term goals and move towards the longer-term one? - I think it's really valuable to say that that's a problem. And the typical reporting structure, the typical way that that information funnels up to the CEO and therefore to the board, like they need that information. But it doesn't work really, really well. What I think I'm hearing you say. - Yeah, that's exactly right. I think it's just it's very, often very painful for the people that are involved in compiling that and then that can complicate things that leaves you, you know, with gaps in potentially kind of bleeding knowledge, it risks burnout, it incurs opportunity costs, like all of these things kind of compound over time. - What happens when a team needs to pivot? Let's say they get a piece of data that isn't supporting the development cycle or something like that. How can teams gracefully pivot without it causing major waves within the organization? - You do have a bit of this kind of binary nature to, is the science working or is it supporting what we need it to support in a way that you just don't and let's say software where maybe the lines are blurry on whether you're on track or not. There's I think a bit more like ego in that world and people can, I think it could really affect people who decide to like pivot away from a project that they've, you know, been working on passionately that they still maybe believe deeply is like the right one to make. Whereas in science, I think it does come down to what's the evidence showing us. Like if what we're seeing is that there's just not a result here or like the talks is just not good, then we just have to move into a different direction. And again, I think it's us, you know, how do you avoid having a ripple effects through the team? I think one, it's framing it as what's the alternative? The alternative is that we spend the next five years pursuing this and we get maybe really far and then the rug is pulled from under us. So that's not a great alternative. And then I think also just reminding people like even if it's not glamorous and even if we have to do this, like what seems like repeat work because we're pivoting and starting from scratch, the reason why we're doing it is because we're ultimately chasing a drug that we want to get to the market. And so it is a bit of the like putting of the ego aside and saying we need to, we have X amount of dollars that we have to put toward advancing this R&D as far as possible and get it to clinic. And so we just have to make pretty hard decisions sometimes to maximize the likelihood of that actually happening. - One thing that I think your software does is to help to reduce some of the operational overhead for teams. Can you tell us a little bit about how that works and why that's important? - Something that I've seen effective leaders do really well is actually map what the cost of any given thing is really well in their organization. So the kind of sharpest, whether they're CEO, CXOs, SAPs, whatever, that I've spoken with know exactly how much a new employee costs them, how much it costs to find one that can do the job, how much it costs to train a person, what it costs in salary and benefits and XYZ, what the expected output of work is and versus what the reality usually is factoring and all the setbacks. So they're really good at getting it down to numbers in a sense of, okay, if we run a month and a half of R&D with this big of a team focused on this part of our pipeline, it's going to cost us X dollar. So I think it starts with building that awareness and understanding how your actual operations look like today. And then also what I've seen really sharp people do well is be able to project out quite a bit. And so I'll give you a very concrete example here based on a conversation I actually recently had where someone was able to on the spot kind of do the math of, if we get to clinic six to seven months faster, that means that we have six to seven months more of patent life, which means we have six to seven months more of sales once the approved drug is on the market, which is X dollars of more revenue. And so they were able to like take something from the like early R&D stage and extrapolate that out and say, you know, not just are we saving, you know, six buns of whatever FD costs and ops and whatever today, but we're also seeing on the other end of this, like a pretty massive bump up in revenue if we can save that today. So that kind of clear mapping is a pretty important exercise to go through. And that's, you know, really the beginning of how you, you can start understanding how much are your operations costing you and therefore is it worth investing in XYZ to move the needle on that. - Once you've assessed that overhead and you kind of know what that looks like, then what are some data points and strategies that will enable your team to work in a more lean and focused way so that you can maximize your runway? That is a huge topic right now because money is not flowing the way it used to be. And so we're trying to figure out how can we do more with less and make that runway longer? And yeah, obviously it would be awesome to bring things to market sooner so that we could get the patent life and make more money there. But often the teams I'm working with are focused on how do we just get enough data so that we can make it to Series B and we can de-risk to that point. - We put out a piece several months ago where we just crowd sourced some thoughts from investors on what they're delegencing at SEAD at Series A and at Series B when they're doing biotech diligence and breaking it down to like platform versus asset companies. It touches on a couple of stuff that we've already talked about so one is being clear I think with your team on the like what's the thing that you're chasing ultimately and what are those inflection points along the way that unlock either the new round of funding or your ability to file an IND or getting into clinic. So being very explicit with like what are those things and then how do you actually translate that to the framework I have is like what milestones you need to hit, what data package do you need to support that milestone and then what experiments do you need to run to generate the data to support that milestone. And so again it sounds simplistic what I think like the exercise of doing this is a really powerful thing. And what that lets you do is keep your team focused on not running experiments just for the sake of kind of scientific curiosity but like having a purpose in mind for them. It keeps your team accountable for what are those specific readouts that you need from your assays, from your screens, from whatever it is so that you know okay this step is a check we can kind of like move on and do this next step. So that framework I think is something I've seen really lean and productive teams use whether they're using it explicitly or whether it's just kind of implicit in their process like I don't actually know but that's kind of what I observe from the outside. And then like what we're trying to basically help people do within in kaleidoscope. I think another thing is if you want to keep your team lean then you have to be very robust against or what I call like data or like knowledge leakage. You can't afford to have experiments either running or not running without your knowledge I mean it sounds kind of like an obvious but the amount of times I've seen this happen in large orgs is pretty staggering. And so if you're trying to stay lean for longer which is a trend that I think I'm seeing more and more you know biotechs that are you know only 20 people and they're like entering clinic which is kind of happening more often. I think if you're gonna operate in that world you need to have you know you can't afford to have a leaky ship. And so knowing even something as simple as like knowing the status of an experiment knowing like hey is this thing that we sent to the CRO and we expected back this week like is it back or not? Or we said we were interested in assets ABC we now have enough data to decide which one do we want to prioritize rather than kind of pushing all three of them forward marginally it's about kind of allocating resource to the most exciting one. So all of those kinds of questions I think are pretty critical. And that's what again like from a product perspective I think this touches people and so just culture this touches process and this touches the tools that you use. So we're gonna focus on the tooling side and a bit of the process side but that is really what we're trying to enable teams to do. And so the way we approach it for example is one of our features as we call dashboards it's basically where you set up the kind of stage gates for your program that you need to hit so let's say get to an IND. And so we made it really easy for people to encode rules into these dashboards and to say hey for this stage gate I care about these experiments and for each of these experiments I care about these values. And so if I get data in for this asset and it's any of these experiments and it meets any of these values or doesn't meet any of these values I want to visually see that and I want to be notified hey there's data it didn't look good or hey there's data it did look good or hey there should have been data but there's no data. The idea here is you need to make it really easy for people to stay on top of what they're doing and to hold themselves and their team accountable to that so that you can move forward with kind of confidence and speed. - Hey there just a quick break. I wanted to let you know that if you're listening to this podcast because you are exploring careers in biotech which it turns out quite a few of our listeners actually are. You may be interested in the biotech career coach podcast it is brought to you by our sister company the Collaboratory Career Hub which is our career development community. If you would like actionable tips on job seeking and career development that is the place for you. It is a companion podcast to our career coach column that we write monthly in biospace but we go a little more in depth and sometimes we have special workshops and all of that good stuff. So if that sounds interesting click the link in the show notes or search for biotech career coach on Apple podcast or wherever you get your podcasts back to the show. Okay this is gonna sound really crazy but I wish I had this even back during my PhD thesis work only because what you just said was that experiments happen and there's this leaky there's a sort of leaky ship. Well that's all labs everywhere. I don't care if they're academic or industry because scientists be scientists and we're gonna follow the data but to your point that is not following the data does not always mean that we're going to get the results we need for what we're actually trying to accomplish. And I would say that most of the papers that I published probably I could have cut down the actual time to publication by a significant amount months if not in one case maybe like two years. If I had had a better, not a better plan. - Yeah, absolutely, yeah. We hear this, it's a pretty common sentiment shared. Like you said, it doesn't really matter if you're academic or small biotech or mid-cap or large public pharma. This is I think one of the universal realities of working in science. - Oh yeah, yeah. We have a, scientists are a certain phenotype. We're curious and we're experimental and that doesn't always play well in business unfortunately. I wish it did but investors really do want to see that you're making that steady incremental progress. - Exactly. - So what is your philosophy then on aligning that data with those key milestones? You said they have these dashboards. How does the data actually get into those dashboards? Who puts it in and how does that process happen? - We try and meet people where they are. Those are just scientists or scientists. One of our guiding philosophies early on was as much as we can propose like a much better way to work we have to meet people where they are because humans are humans. And also we have to make it so that people don't do any redundant work because it's already hard enough to have a scientist log their data somewhere. If you're now asking them to do that in two places I think that's a losing battle. And so regardless of whether you're one of those early companies that just has CSVs and spreadsheets of data or whether you're a much more mature shop that has six other tools that you're using we make it really easy for you to get that data into kaleidoscope. So in a world where the scientists are already logging information maybe they have any LN, maybe you have a limbs in place, maybe you have like a registry somewhere else, maybe you have a custom database. So someone is logging those things in already. In that world what we do is when we set someone up with kaleidoscope we basically the implementation is us like creating these hooks into those tools and asking the kind of leaders, the ones who are looking to kind of summarize and distill this information, hey which of these values you're not gonna need all of the raw data you're ever generating for your decision making. So of the data you have what's the stuff that's critical for you to actually track. And so we show them how you specify that let's say in a dashboard and how you point that dashboard to those sources of data. And then you can have kaleidoscope for example, update overnight every night and sync with those sources so that any new data that your scientist would have generated peers in that dashboard that can happen automatically. But we built these I would say like templates or building blocks that you can kind of modularly put together. And so we show people on that first call, hey instead of you going and opening a PowerPoint to start creating slides where you're pulling in screenshots you can within three, four clicks, have a dashboard up and running and set it up so that it auto-populates with information. The side where it's people in spreadsheets, again we tried to make it straightforward for folks so that they don't have to do a bunch of extra work on their end. And so we even have like a drag and drop flow with spreadsheets where you can just pull your spreadsheet in and then we'll do this kind of smart mapping of, hey this column here actually looks very similar to this other column already the same. So for example, I saw this in my lab all the time. I might call something mole dash ID and you might call it mole ID one word. And someone else might call it mole space ID and we don't want these being like duplicates. We also don't want scientists having to go and like edit their files and like agree on what they're gonna call it. And so what we've done is make it so that if you kind of, two, three different scientists drag things in we detect like, hey this is probably the same column like you can say no it's not but if it is we'll treat it as the same. So we'll do that kind of cleaning and curation and labeling for you so that again you as a scientist are just focused on generating results and then we handle all the kind of hard work downstream. - Yeah, that sounds very valuable. Scientists don't love doing extra data entry. - No. - You see a lot of different companies. Are there any mistakes or things that you wish leaders knew or things that they could implement earlier that would make things a little bit smoother as companies grow? I know you work with companies of all different scales but sometimes you come into companies. Do you look at them and go if you just done this thing would have been so much easier. I think it's very common for people to not realize the value of compounding gains as it's very easy to think, oh it doesn't, you know who cares if this takes us an extra several hours a week right now like we have bigger problems and we'll just deal with this again later. And so I, that's somewhere where I always bang my head against the wall and just think to myself. No, those several hours a week is gonna be huge because over a year now that's, you know that's quarter of your time potentially in a year which is a lot of R&D operation that's a lot of budget that's a lot of you know existential threat. It's to me something that's you know beautiful and scary about biotech is that you're almost constantly under existential sort of like will we insist? Like will the data come back positive? Will we get to run this experiment or will we run out of money? So I just wish that people were much more ruthless with a weight. Are we using people's time in the most effective way? And what's the opportunity cost of not doing something? I think that's one and the second that came to mind is more like software specific. This is where again leaders that have been through a few cycles, they're very aware of this. And I think those that haven't like they'll learn it eventually but when it comes to specifically software and this was one of the motivating reasons that we started to try to scope actually was when we were doing our kind of discovery work early on before we built anything. It was pretty astounding the number of biotechs that told us hey we needed a better way to kind of manage these flows. There wasn't anything built for the space. So we went ahead and hired software engineers or went ahead and repurposed a bunch of computational people and they're now building these tools for us. And I think that's a very big mistake because in the vast, vast majority of cases that software is not core to your IP. Your IP is the science that you're doing. And so everything that you're spending money and time on should be advancing that IP. And so if you're now trying to also build, maintain, effectively run a software company within a biotech that is massive upfront cost and massive maintenance cost. Again, I'm biased here but for a fraction of that price you could bring in a tool that like yes, it'll take your team a few weeks to get a handle on but it will save you so much more than those few weeks. And so I do wish that more people kind of were open to this idea in general. And I think it's a trend that we're seeing. Like that's the bet that we're making is that again, leaders are seeing that. They know how painful it is to do once. They don't wanna do it again. - Yeah, those are both great points. I call the first one and sort of the second one too. I call that startup math. It's like time doesn't matter, but it matters actually a whole lot. And I talk about that in the context of recruiting and talent acquisition and in your team's time trying to navigate that process. But what I'm hearing in your answer is like, this could be a whole head count lost to simply doing stuff that's manual that shouldn't be done manually in the first example. And then in the second example, yeah, it's keep your eye on the prize and this is the shiny object syndrome that scientists have. It's like, well, I can envision this software tool. So let's just build this software tool. And I've had to restrain myself from doing that very thing in my company, applicant tracking systems. It's hard to find one you like and you just start to think, well, why don't I just build my own tool? But I've luckily talked myself off that ledge because that is very expensive. It's a long endeavor. And I'm not gonna do it as well as the custom built ones that were there just focusing on that tool. - Yeah, I think that those are great examples. And it's funny because I think a lot of people actually, if you like sit down with them and ask them to talk through the math, like they'll reach that conclusion. So it's not that you have to convince them that it's just that they, I guess, don't put the time to do the math. And so it's not uncommon for me to be talking to, let's say, a series, a pretty early stage biotech that tells me, yeah, we spend a million dollars a month on R&D. And so if you can save two months in a year of R&D, which we're seeing like our other customers tell us, that's $2 million a year saved. So when you frame it that way, like is spending, you know, whatever the number is on, you know, a small fraction of that on a tool that lets you do that, is it worth it? Like just the math, within a year, you'll more than paid it back. - Yep, math is hard. We don't always think about it. - That's pretty true. - What is the best book that you read recently? What do you think everyone should read? - I actually recently read Ted Chang's two collections of short stories. So one is stories of your life and others, and the other one's called "Exolation." So if you've seen the movie "Arrival," it's actually based on one of the short stories in that book. What I really love about it is it's short story format, and they're all sci-fi, sci-fi-esque. And so what that actually lets you do is free yourself with the constraints of like, what's the real world like? And so in a very short amount of time, the author can say like, okay, cool, the world looks like X. Just assume that that's what the world looks like. Now we're gonna run this like thought experiment of what would happen if X, Y, Z was true. And so it's like this really nice way, I think, to explore these concepts and ideas without being constrained to the world of nonfiction or without being the hold into a narrative that you have to maintain when it's a long-form novel where you have to do like character development and all of that. It's just exploring the ideas. Nonfiction, this book is kind of stuck with me. It's called "Let My People Go Surfing" by Mosh Winard, Patagonia founder. And I think it's just a really great book to read on people and culture and purpose. - Yeah, great recommendations. I've read "Let My People Go Surfing". I'll have to check out your fiction recommendations, but that's a great book. And I think it's on the list of must reads, I think for leaders, good perspective and a fun company. So we are going to link some resources in our show notes including the resource you talked about earlier that you recently published. How can people get a hold of you? What's the best for you that linked in your website? - Yeah, I think both work. So if you want to get ahold of either be personally or just keep up with little snippets of what we're up to as a company, LinkedIn is best. And you can always message me directly on there, give Cloudoscope a follow, whatever. And then our website's great for more keeping up with what the actual platform is doing, the kind of new features we're shipping, what our customers are kind of saying about us. So that's collidescope.byo. - Super, well, thank you so much. We'll put all that in the show notes. And this was a great conversation. I hope that folks take away a little bit about organizing their workflow from this because it is an afterthought, I think in a lot of cases and we don't want that to be the case for your company. So thanks so much. - Thank you, Kerry, and thanks so much for having me. - Building biotechs is brought to you by Recruitomics Consulting. You can find building biotechs in Apple Podcasts, Spotify, Google Podcasts or anywhere else podcasts are found. Make sure to click subscribe so you don't miss any future episodes. And join our mailing list for a weekly dose of biotech news and a podcast overview. And if you need to install a recruitment engine that saves time and money for your growing company, reach out for a free strategy session. I'm always happy to share my expertise and show you techniques to simplify your hiring process and maximize returns. On behalf of the team here at Recruitomics Consulting, thanks for listening. [BLANK_AUDIO]
