• John Ennis

Lindsay Barr - Focus on Your Business


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Lindsay Barr has a decade of experience working as a Sensory Specialist for New Belgium Brewing and is a Co-Founder of DraughtLab Sensory Software. She holds a Masters degree in Food Science from UC Davis and served as the chair of the ASBC Sensory Committee for five years where she developed and published seven new beer Sensory methods. She believes flavor is the most important factor in determining food and beverage quality and continues to develop tools focused on helping businesses use their senses to inform everyday production decisions.


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DraughtLab


Transcript (Semi-automated, forgive typos!)


John: Lindsay, thanks a lot for being on the show today.


Lindsay: Thanks for having me. I'm excited to be here.


John: Wonderful. So Lindsay, something I know you're really passionate about is quality control and something we're talking about before the call here is how new technologies, for example, DraughtLab is a very nice app that helps to support quality control processes and decision making. But I would like to hear you kind of give your thoughts to start here and how you see technology helping people in the food and beverage industry with their problems of quality control.


Lindsay: Yeah. Speaking specifically about quality control, I think in the sensory world we tend to spend a lot of our energies and attention and our money on R&D, research and development is primarily what I learned about in college and where I see a bulk of businesses really focusing much of our attention. And the problem with that, I mean, it's great to be able to figure out exactly what kind of products you're going to be putting to market. But the problem with that is there needs to be a discussion very early on with quality control and R&D so that we know that we can make the product that is intended and we can do that consistently. I've seen so many times R&D just kind of like patting themselves on the back, being excited that they made this product that is specific for this market and this environment, and it's going to be successful. And production kind of looks at them and says, we can't make this and we certainly can't make this consistently. So I'm very passionate about quality control and QC being a basically having a place at the table early on in the development process to be able to ensure with the business that you can make the product that you intended to make. So as far as your second part of the question, like where do I see technologies coming into play here. I actually think that's what we were trying to do with DraughtLab. Today, I didn't really see a lot of technology that was really focused on quality control. Again, a lot of the sensory methods that exist are very intentional on helping guide product development and not necessarily that routine daily analysis. I mean, can you see, isn't that glamorous? It's not that fun. I mean, I think it's a lot of fun, but it's kind of not super glamorous, so it doesn't really get a lot of attention. So DraughtLab was really intended to kind of fit that niche where, you know, businesses primarily do QC checks about 70 percent,80 percent of their time is going to be spent doing just daily routine analysis. And we wanted to build a platform that was going to streamline that process and make it a lot easier for people to execute those daily sensory analysis, to inform those, you know, product development and to quality control decisions.


John: Yeah, I think that's really great. And for our listeners who maybe aren't familiar with DraughtLab yet, could you give a little quick summary of what DraughtLab is and what are the kind of ways that it helps businesses with their quality control problems?


Lindsay: Sure, yeah. So we developed DraughtLab in 2016. It was a collaboration between sensory scientists and and developers, software developers, the three software developers. That or on that side of the business all came from the real industry. And so they have some really unique ways of looking at running processes. So they're just a very good systems engineers. And it was just a delight to be able to work with people who are just they intuitively think in systems. And I think in sensory, we're not really calibrated to do that. We can really creatively, which is beautiful, but we don't really think in systems. And I think that we leave a lot of data potential on the table there because we're not really that great at building databases.


John: We're working on that. That's one of the biggest things like Aigora does, by the way. We're trying to fix the databases issue.


Lindsay: Yeah, see, this is why you and I are kindred spirits, because I think that that's a gap in the industry. Certainly a gap in quality control, I mean, it's really silly to run a different method. You know, every day to get the same answer, essentially. So basically, what we intended to do was to kind of fix this gap that I saw specifically in the craft brewing industry where the barrier to entry and just starting a sensory program was high. It's costly. It takes a lot of time. And then there was also a gap in the expertise and just general knowledge with a lot of crafters. You know, there are seventy five hundred crafters in the United States right now and there are not seventy five hundred sensory scientists. I can guarantee that. So it was were just really kind of trying to fill that niche where we could empower small craft food and beverage producers to do routine sensory analysis without over complicating it and making a process, you know, systematizing it in a way that allowed the user to kind of get into a routine and build that database and be able to find more power in their data set as they continue to do routine quality control analysis. So had I just been the only one developing it, I would have, you know, come up with this creative sensory platform where, you know, you could do really anything under the sun. But I think that there was a lot of wisdom from our engineering side that, you know, basically kind of directed us in too much a direction that kind of forced us to build something that had a process associated with it. That people can wrap their heads around and execute without a lot of time and resources.


John: Yeah, that's that's fascinating. So actually a bunch of questions about what you've just said, because it's a really rich answer. But I mean, as far as processes go, does seem to me that that's one of the things that data science brings. That is kind of an add on to, say, statistics. And one of the reasons why I think sensory scientists need to learn more data science is because you have these processes that really started off in computer science, like processes for workflow management that are extremely valuable to scientists. And it's interesting that it seems like you now have a process contribution coming from maybe operations research if you're talking about the railroad industry, that you got a systematic way of conducting the research. Maybe we can step back just a little bit and talk a little bit about your experience in New Belgium. Were you the first sensory scientist at New Belgium?


Lindsay: No, actually, New Belgium was kind of way ahead of the time. My predecessor there is Lauren Salazar, and now Limbach. She started the sensory program nearly 10 years before I started there and built a really wonderful program just from the ground up. So I was lucky enough to go into a really healthy program from the beginning. And she was very instrumental in my development. She had a very pragmatic approach to sensory analysis. You know, she didn't do like a degree in sensory science and all that stuff, but she intuited the proper methods to utilize and paid a lot of attention and learned what works and what doesn't work. And I was able to kind of inherit this really healthy program.


John: Yeah, that's interesting because it seems to me like, okay, almost anyone can brew something and call themselves a craft brewery. Is that correct? Or I mean, when you say craft brewery, is there some certification in that a brewery has to have or is it kind of a low barrier to entry but a high barrier to success? Like, how does that kind of like, net out?


Lindsay: I mean, I think the actual definition of a craft brewery is volume based. Yeah. I think the Brewers Association has a definition out there that has to do with barrel edge. So if you're a small producer and it's I think craft is kind of a similar term used in the food, the greater food and beverage world as well. So just to a smaller producer, not macro, you know micro.


John: I see. What's interesting about that is that, I mean, I hear a lot of helium and they make some delicious beer, but they can't make it again, right?


Lindsay: Right. Exactly.


John: And so and that's where quality control comes into all this. And where I think it's so helpful what you're doing, because if you have this basic tool, you can make some great beer once but you can't reproduce it. It's very hard to build a business around that. Whereas if you've got a strong quality control program like you're helping companies to set up, then you're gonna be able to do. Well, from what I understand, making a beer taste the same way from batch to batch is actually pretty hard. Is that correct? I mean, is it like. Yeah, it's challenging from what I understand. So I think that it's really good that you're putting the tools in the hands of these kind of small businesses. So could you describe a little bit like who is your kind of typical customer? As you know, as you go around and you're finding, you know, more and more adoption of the DraughtLab about.


Lindsay: Yeah. So to kind of go back to the first part of that question, it is certainly very difficult to produce consistent beer. And that goes with a lot of different food products, but especially fermented ones. And, you know, fermentation is just this wild process. And there are so many variables that influence the outcome of the flavor that you're going to get in your finished product. So it is quite difficult to achieve consistency in the craft brewing world, especially because a lot of the processes aren't dialed to the same degree as many of the macro breweries who have been doing this for maybe 100 years or so. And they have all of the instrumentation and tools to be able to dial in their fermentation. So there is a decent amount of inherent variability in beer. And, you know, you don't really see craft producers kind of shrug their shoulders and say like, oh, it doesn't matter. I think what's really interesting about the craft brewing world in particular is they very much understand the value of producing a consistent product from day one. It very easy conversation to have with craft beer producers because they just they get it. They understand that consistency is key. And a lot of the beer producers that have gone ahead of the craft world have put that at the very forefront of their business as well. So the value is there, they really understand that even at a very small level. If somebody comes in to get a pint of their pale ale one day and it doesn't taste the same the next day, you maybe lost a customer. So they really get that. To go back to your second question, our typical user for DraughtLab started in the beer world. That's what I knew pretty well. And what we found over the last few years is that these methods and this process doesn't just apply to beer. In fact, it's really valuable for producers of actually all sizes. We keep talking about craft, but we work with various businesses and in the beer world, in the kombucha world and in chocolate and coffee and cannabis. There are so many different applications with what we're doing. And if you're trying to make a consistent product and are intentional about doing that without a lot of cost time wise and energy wise and expertise wise in your business, and then you're a company that would it would make a lot of sense to work with DraughtLab. So, yeah.


John: Well, that leads me to a question. And there are a lot of different pieces of software for sensory data collection. So what do you see as the things that make DraughtLab special as a piece of sensory software?


Lindsay: Yeah, I think what makes us unique is that we again, we have that process. So when we were developing this, me being a sensory expert, I'm kind of just thinking about all of the various methods that you can apply. Looking through the ASTM method of analysis, and the ASPC methods of analysis, there are tons of sensory methods and we love them all and they're all very interesting and and fun to execute. So we kind of looked at all of these methods and made the conscious decision that rather than putting basically every method in there, that we really need to come up with a process. And only we basically guide the user to only utilize a handful of methods that are going to be able to answer the bulk of the questions that they have. So we really started with asking ourselves what are the questions that food and beverage producers have and what are the methods that and the process that one can execute in order to get to an answer. So that took us some time to come up with our process, and since then we've been throwing various questions at the process and we're finding it to be robust. So I think that's what makes us different. We also don't, it's not necessarily created for sensory scientists. We don't use a lot of the sensory science jargon, if you will. We just speak in plain language and have therefore kind of made it accessible while continuing to keep the methods as robust as possible.


John: So that's interesting. So you kind of, the app is I mean, one of the biggest things that people look for at ASTM is like a flowchart, though, helped to guide people's decisions through all these different methods. So you're you've kind of it sounds like you did something like 80, 20 analysis, try to figure out, Okay, which methods and which path is going to give the most value for the kind of least effort for a user. And then you put that knowledge into an app so that someone doesn't have to sensory scientists in order to get the benefit of all the accumulated wisdom of sensory science. Is that a reasonable description?


Lindsay: Yes, exactly. I mean, we that's a good way of putting it. I really like that about ASTM. The visuals of having flowcharts to kind of guide through processes is kind of at the forefront of everyone's mind. And that's essentially what we did. I mean, we basically thought, okay, well, for quality control, what are the bones that need to be in place in order to answer those routine analysis questions. And what are the steps that you can do to get there. Additionally, with a low amount of training, a lot of small businesses don't have the ability to execute hours long trainings every week to get their panelists up to a point where they have this, like, very high level of expertise and can maybe scale consistently in that kind of thing. So we looked at the methods and thought, okay, well, what what can we be doing? What kind of methods should we be applying that require the least amount of training while not leaving any kind of, not leaving any space for there to be a misinterpretation of the data.


John: Well, that's interesting because, you know, I almost hesitate to use this term because it's really overused, but it seems appropriate. That seem like you're democratizing sensory science in a way through this through this app. But it does seem like a reasonable thing to say?


Lindsay: I'm going to have to sit with that for a minute. So, yeah, maybe. I think, though, in a way, I think that a lot of us are going to be a lot stronger as an industry if we're kind of speaking the same language and utilizing more or less the same methods to get to an end result. So, yeah. I mean, in some ways. But if you think about like what our platform looks like, you can utilize it to answer different questions if you can kind of look at it from a creative angle. So we don't really strong arm our users into doing just exactly one thing with the platform. There's certainly a decent amount of room for creativity. But yeah, rather than kind of just leaving it open for the interpretation of the end user, we put some guardrails in place that help, like, teach the user while they're finding value in it. I mean, one of the more important things that we learned from the very beginning is that you really have to, that programs have to realize the value of running sensory analysis methods from the very beginning. The business itself has to see the value right away. It's a very difficult value prop to for a sensory scientist to go to a business owner and say it's going to take me four or five months to train my panel to get to the point where we can start doing these kind of analysis. And then I'm going to give you this very specific outcome. Rather, businesses are looking for value right this minute and how do we get there. So the platform is really intended to grow alongside of the business as the panel continues to develop. But you should be able to see value from your analysis from day one.


John: Yeah, that's great. I think one of things I really admire actually about your development of this app is that it really is connected to the real world. It's not like you had some awesome technology and thought, okay, what? There's not a solutionism in the tech world where people will develop awesome technology and then try to look for problems to solve with the technology versus you know, seeing problems and saying, okay, how can we build a tool that that solves these problems, which I think is really the more entrepreneurial approach, right? I still don't quite understand the origin story. So it was DraughtLab a side project when you're working in Belgium, you saw problems at New Belgium that you realized other people had, too? What was the kind of process that led to the genesis of jefa?


Lindsay: Yeah, yeah. That's pretty much it. You know, working for New Belgium. It's a very great sensory program and so I kind of had this we had three sensory scientists in Fort Collins. Our main facility and two in Ashville at our other facility. And so we were staffed and we had expertise and we had the panelists and we had the time to train like we had all of those resources. And as I got more and more into the industry, I realized that most craft breweries did not have the kinds of resources that we had. And I overheard, yeah, I should be starting a sensory program, but we're just not there yet. We just don't have the time or the expertise. I know that we should be doing it, but we're not going to we're not there. And so I always kind of scratch my head at that because I thought, well, I mean, you're tasting your beer and making decisions, right? Like there is a craft producers are already doing sensory science without actually calling it that. So we just wanted to kind of a degree of robustness to that data set to give a platform to where you're just, you know, able to put down what you are already doing and be able to build off of that. So that's it kind of came from my observations in the craft brewing world that a lot of small producers were saying that they couldn't do it, but they were, in fact, doing it. So they were doing it just didn't look like there. The vision of, you know, a very calibrated, highly and tuned sensory program. So we kind of just wanted to make that accessible.


John: Helping them do what they're already doing, but doing it better, like just improving the quality that they already were doing, sensory in a way. But you just helped them to do it in a more scientific way.


Lindsay: Yeah, that was the thought.


John: Yeah. That's fascinating. Okay, so then, so right now you're said you're starting to branch out. You started off. I mean, I think this story is interesting, too, because I think it's a model for other people who would like to start businesses. You know, there's a lot of food tech companies out there. And what I think is really inspiring about your story is that you saw it like you're connected to an industry. You had like concrete problems that you knew people in the industry had you built the tool to help solve it and you kind of gradually, you know, growing out from there. So what are your plans for the future of DraughtLab? Do you see as something that's going to be applicable just throughout sensory maybe even not even in food and beverage? Like how widely applicable do you believe that the platform can be?


Lindsay: Yeah, well, we've been proving out that it's quite flexible. So we've been getting into a whole bunch of different industries and having the same kind of conversations that we were having early on when we were developing DraughtLab intentional for beer. I think one of the things that you had mentioned earlier on is that we didn't start with a solution and just tried to apply it, find an application. We really did kind of go out and start we'd talked to a whole bunch of different producers to understand what their questions were. And we found those similarities and then we created something around that. And one of the more important factors with that was our developers went out and did that same kind of method. So they were the ones who were new to the sensory as a discipline, but understood processes. And they went out and essentially worked for craft breweries running their sensory programs because they needed to learn and know what was going to be most valuable. So we're kind of doing that now. Again, all over again with a bunch of different food and beverage producers. And what we're finding this time around is that everybody, more or less has the same questions. I mean, the basic questions of what do I make and why am I going to make that product and what are the attributes that are going to kind of drive the success of that product. And then how do I measure those attributes moving forward? And then how do I troubleshoot my product? Should we flag an example? So, yeah, I think that's the main thing that most most producers are doing, and I've not really been taken off by surprise from any conversation that I've had with any industry, more or less. It's the same questions. So I don't really see. I mean, if there are products to be felt or smelled or tasted or consumed.


John: Or listened to. It is suppose to be someone is trying to optimize the sounds that, you know. Car chime is making? I mean, these are sensory problems as well. Does the modality matter at all to your app? Is it kind of agnostic as far as the motive David Clarkson?


Lindsay: Oh, that's interesting. We've not thought a lot about sound, but I know that that is really an important factor in a lot of products, chips and. Yeah, cars, that kind of thing. And yeah, basically, if something can be measured and if you can describe that in normal language, then it can be measured. So but I like the idea of like adding kind of like an audio portion to it. I like it.


John: No, it's good. Honestly it's really very interesting. So actually, amazingly, Lindsay, we are now almost out of time. I didn't even get a chance to get into my questions about databasing.


Lindsay: For another time.


John: Yeah, presumably, let me just ask this, so your customers do have access to all the data they've collected, is that correct? I mean, this is a cloud based platform, I guess, and all other data goes into some database? Or how does that part?


Lindsay: Yes, absolutely. Yeah. So all of our users have access to their data. And to no one else's. That should be said. And they are able to download it into Excel, make PDF's, kind of whatever they want to do.


John: Pull all their data all at once they could. That would be fine.


Lindsay: Yeah, absolutely.


John: Interesting. Okay, that's good to know. Alright, great Lindsay. So something I would like to ask for our guests is if you have advice for young sensory scientists out there, what would you give someone starting their sensory career right now? And, you know, from where you've now been in several walks of life, what do you think young sensory scientists should be focusing over the next two years? What advice would you have for that?


Lindsay: It's a really good question. I think we as sensory scientists understand that there needs to be a little bit of a shift in our mentality that's more focused on business needs. We have to be very focused to the outcome of whatever business that we're looking for, working for. So I think that as much as you possibly can to align yourself with where your business is going and kind of construct your methodology with the focus on where you're going as a company. I know that I can kind of seem a little bit vague but I think that a lot of us are very academically focused. And so we get into these nitty-gritty research projects and might be kind of pulled off base because we're interested in some esoteric question. But if we can't answer the main questions that your business has to succeed as an organization, then we are we're just kind of off kilter there. So focus on your business.


John: Yes, that's very good. In fact, that might be the title of the show, focus on your business.


Lindsay: I like it.


John: Okay, great. So Lindsay, where can people find you if someone wants to follow up with you after they listen to this conversation? What will be a good way for them to get in touch you?


Lindsay: Yeah. I can always be contacted through directly through email. So my email address is lindsay@draughtlab.com. Yeah. Or just through our website info@draughtlab.com. But I'm always happy to respond to emails and get on phone calls with anybody who inquires.


John: Okay. And what about LinkedIn? Can someone send you a LinkedIn message if they've found your profile there?


Lindsay: Yes, I am on LinkedIn. I've finally given into the social media's. So yes, I am on LinkedIn. I don't know how you can find me, but my name is Lindsay Barr. So you can just check out my profile.


John: We'll put the links in the show notes. Are you on Twitter any of that?


Lindsay: I haven't gone that far, John.


John: I have an addictive personality, so I have to be careful.


Lindsay: Yes, I understand.


John: Okay, well, it's been great, Lindsay. Thank you so much for being on the show. Is there anything else you'd like to say to our listeners before we wrap up?


Lindsay: Thanks a lot for having me. And it was a lot of fun to chat with you and yeah. That's about all I've got. Thanks for the opportunity to chat.


John: Okay, sounds great. Alright, I will look forward to seeing you in the future. Okay. That's it. Hope you enjoyed this conversation. If you did, please help us grow our audience by telling a friend about AigoraCast and leaving us a positive review on iTunes. Thanks.



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