Created with Sketch.
24 minutes | 5 months ago
Making Data Assets Profitable with VDC
21 minutes | 6 months ago
Machine Learning with Max Sklar
31 minutes | 7 months ago
Think Differently with Graph Databases
30 minutes | 8 months ago
Data, Epidemiology, and Public Health
With recent events being what they are, epidemiology has come into the spotlight. What do epidemiologists do and how does data shape their everyday experience? Sitara and Mee-a from “Donuts and Data” fill us in. Ginette: I’m Ginette, Curtis: and I’m Curtis, Ginette: and you are listening to Data Crunch, Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Ginette: Data crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. Many people are on the lookout for online math and science resources right now, particularly data and statistics courses, and whether you’re a student looking to get ahead, a professional brushing up on cutting-edge topics, or someone who just wants to use this time to understand the world better, you should check out Brilliant. Brilliant’s thought-provoking math, science, and computer science content helps guide you to mastery by taking complex concepts and breaking them up into bite-sized understandable chunks. You’ll start by having fun with their interactive explorations, over time you’ll be amazed at what you can accomplish. Sign up for free and start learning by going to Brilliant.org slash Data Crunch, and also the first 200 people that go to that link will get 20% off the annual premium subscription. Now onto the show. Curtis: I’d like to welcome Sitara and Mee-a from the Instagram account Donuts and Data to talk to us today. I guess let’s just have you guys introduce yourselves, as opposed to me trying to introduce you cause you know what you do better than I do. So maybe we just have some introductions. Sitara: So I’m Sitara one half of Donuts and Data. I’m a PhD student in epidemiology at the University of Texas Health Science Center. I’m also a research assistant in a lab that I work in. Mee-a: And I’m Mee-a. I am an infectious disease epidemiologist that works in the public sector. I actually met Sitara through the lab that she’s currently working in. Curtis: Nice. And I’m excited to have you guys on. I just, I think epidemiology is a really interesting space, especially with what, you know, with what’s going on now with COVID. I think it’s more pertinent than it ever has been. Not that it ever hasn’t been pertinent, but maybe it’s more top of mind for people. So I’d love maybe just to have you guys level set with everybody, like what is epidemiology. There’s probably some confusion about what that is and maybe how you guys got into it. And then we can get into what your day to day is and, and what it’s all about. Sitara: So, epidemiology, I think everyone’s kind of understanding is setting patterns of disease in the, in the human population. And so in that sense, what Mee-a and I do are the same, but instead of studying infectious diseases or the natural science part of epidemiology, what I focus on is how human behavior contributes to those patterns of disease. So I look for patterns in data associated like demographics or just behaviors, diet, nutrition, and how that contributes to getting diseases. Mee-a: For me in the public sector, it’s going to be a lot of looking at incidents, rates of infectious diseases. It . . . primarily with COVID-19 right now, and just different ways that we can try to possibly implement infection prevention measures. So we are dealing a little bit more with, I don’t want to say the medical side of it because we aren’t clinicians, but we are dealing more with the medical side of, of the infectious disease than we are with, with the data compared to when I was in academia, at least. Curtis: So take us through maybe the end goal, right? So what you guys are working on. You’re hoping to come out with, I think, some recommendations for people to, to take maybe a better understanding of how the disease spreads, so we get in front of it. What does that look like? Mee-a: I always thought that epidemiology’s gold standard of what we try to achieve is probably smoking cessation. So, you know, when at least growing up for me, I felt like cigarettes and smoking were very, very pervasive and widespread. And as we grew up and we started seeing more of these campaigns showing just how unhealthy smoking was and how much it can really, really be such a detriment to your health, it became a thing where now as adults, our generation looks down upon smoking. And so that’s something that I feel like epidemiology and public health in general has helped to implement that view. And so for the public sector of things, our ideal goal is to really implement infection prevention measures. So that’s going to be in light of COVID-19, that would be making masking a normal thing, making sure social distancing is the new norm, making sure that we are washing our hands for the appropriate amount of time, making sure that when you do disinfect something that you’re disinfecting it properly. If we are in large congregate settings, that we’re trying to do everything that we can to make sure that we don’t create a hotbed of COVID cases. So that’s all the stuff that we’re trying to do right now. That would be, if everything goes correctly, ideally we would be getting to the point where we could either (1) control COVID or (2) completely eradicate it. So that’s, that would be our goal in the public sector. Sitara: And I think, going off of that, things like seatbelts were once seen as a radical change, but that was a public health measure. That was something that epidemiologists put people in the public health world, they looked at the data of car crashes and they decided that wearing a seatbelt was a safety measure that they could implement. And a lot of people were against it, but now that’s obviously the norm that’s in it’s own every car. So I think similar to that, we hope that mask wearing becomes the norm and it becomes okay. And it’s not, it’s not scary. It’s not . . . there’s no . . . there shouldn’t be any stigma on wearing a mask. But in terms of academia, I think what we want is for people to be able to read our research and, and know that that a lot of work went into it. And a lot of, you know, the scientific method, it’s evidence-based, and we’ve done these tests over and over again, this is real science. So I think in the end, we want people to read our research and take something away from it and, and be able to live a healthier lifestyle. Mee-a: The work that Sitara does in the academic field is what we build off in the public field. So we implement the measures that she proves in her research, if that makes sense. Curtis: Yeah, no, that’s awesome. And I’d like to maybe dig into that a little bit. Sitara, can you talk to us and maybe you can just pick one or, or however you want to go about it, but I’m curious, I’d like to give people a sense for how you approach a research problem like this, how you make sure it’s rigorous, how you go about collecting the data and analyzing it. All of that would be really interesting just to kind of hear from your perspective. Sitara: Yeah. So, okay. So for example, with COVID, we can talk about COVID, one of the faculty in the lab that I work in, we had a question of, you know, what is the shelter and policies? What are they doing to people’s behaviors? How is that affecting people’s behaviors? And we had these questions, like, are people working out more? Are they working out less? Are they eating more, are they eating less? And so we formulated a survey, we wrote questions. We took, we didn’t write the questions. That’s important. We took the questions from previously validated surveys. So these are, these are questionnaires that have been validated by other scientists that they’re good measures of asking these questions and getting the information that you want. And so we created this long survey that asks questions about physical activity, diet, drug use, sleep habits, and it’s this long survey. And then we just disseminated it on the internet. We shared it on our social media. We shared it in emails to the faculty at school, to students at the school. And then we just asked everyone, you know, could you share this with your friends, your family? And in the end, we ended up getting, I think, over 4,000 responses. And so what we’re doing with that data is then. So that this, the survey was on a data management website. We specifically used Red Cap and then that data was pulled from Red Cap, downloaded into an Excel file and plugged into a statistical software. So I think we used Stata for the specific one, and stata is what I most commonly used for data analysis, and then we just run tests on that data. So we do like T tests, Chi square test, cross tabulations, regression. That’s the type of tests that we do to see if there’s any pattern in that data to see if there’s any association. And then we take those results, and we write a manuscript, we write a paper, an introduction, a methods, results, conclusion, and then we to publish that. And then once that’s published, we hope that people read that we either hope that policymakers are reading that and they’re seeing these are the effects in shelter and policies. How can we change it to make it better? Or we hope that the public reads it or, or that the news, the media catches on and, and writes an articles, studies find that people are working out less during shelter and policies. So that’s kind of, you know, in a, in a, like in a nutshell, what the process is of coming up with a question and then getting that data and publishing it, there’s so many different ways of doin
23 minutes | 8 months ago
Vast ETL Efficiency Gain with Upsolver
27 minutes | 9 months ago
Data Flexibility in Healthcare
Jason Kolaczkowski has worked in both a large-company data shop and in a company trying to help large companies fix their problems. He shares his perspective as senior director of healthcare analytics at NextHealth and former Kaiser employee on the importance of streamlining data definitions—and many other helpful insights.
28 minutes | 10 months ago
Education and AI
For David Guralnick, education, AI, and cognitive psychology have always held possibility. With many years of experience in this niche, David runs a company that designs education programs, which employ AI and machine learning, for large companies, universities, and everything in between. David Guralnick: Somehow what’s happened in a lot of the uses of technology and education to this point is we’ve taken the mass education system that was there only to solve a scalability problem, not because it was the best educational method. So we’ve taken that and now we’ve scaled that even further online because it’s easy to do and easy to track. Ginette Methot: I’m Ginette, Curtis Seare: and I’m Curtis, Ginette: and you are listening to Data Crunch, Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. Curtis: First off, I’d like to thank everyone who has taken the Tableau fundamentals zombie course that we announced the last episode. We’ve been getting a lot of great feedback from you. It’s fun to see how people are enjoying the course and thinking that it’s fun and also clear and it’s helping them learn the fundamentals of Tableau. The reason we made that course is because Tableau and data visualization are really important skills. They can help you get a better job, they can help you add value to your organization. And so we hope that the course is helping people out. Also, according to the feedback that we have received, we’ve made a couple of enhancements to the course, so there are now quizzes to test your knowledge. There are quick tips with each of the videos to help you go a little bit further than even what the videos teach. We’ve also included a way to earn badges and a certificate so that you can show off your skills to your employer or whoever. And we’ve also thrown in a couple other bonuses. One is our a hundred plus page manual that we actually use to train at fortune 500 companies so that’ll have screenshots and tutorials and tips and tricks on the Tableau fundamentals. And we have also included a checklist and a cheat sheet, both of which we actually use internally in our consulting practice to help us do good work. One of them will help you know which kind of chart to use in any given scenario that you may encounter, whether that’s a bar chart or a scatter plot or any number of other more advanced charts. And the other is a checklist that you can run down and say, “do I have this, this, this and this in my visualization before I take it to present to someone to make sure that that’s going to be a good experience.” So hopefully all of that equals something that is really going to help you guys. And something also where you can learn Tableau and have fun doing it, saving the world from the zombie apocalypse, and the price has risen a little bit since last time. But for our long-time listeners here, if you use the code “podcastzombie” without any spaces in the middle, then that’ll go ahead and take off 25% of the list price that is currently on the page. So hopefully more of you guys can take it and keep giving us feedback so we can keep improving it. And we would love to hear from you Ginette: Now onto the show today. We chat with David Guralnick, president and CEO of kaleidoscope learning. David: I’ve had a long time interest in both education and technology going way, way back. I was, I was lucky enough to go to an elementary school outside of Washington DC called Green acres school in Rockville, Maryland, which was very project based. So it was non-traditional education. You worked on projects, you worked collaboratively with people, your teachers’ role was almost as much an advisor and mentor as a traditional teacher. It wasn’t person in front of the room talking at you, and you learn how to, you know, you really learn how to think creatively and pursue your own interests and learn by doing, and so all of that stayed with me as I got older and I developed interest in technology from a really young age. I had my first computer at 13, which was at a time when people did not normally have a computer at 13 and was interested then through that in how computers could learn and what did artificial intelligence mean. And it was a field that was, was a bit of a mystery and ended up as I was finishing college, getting interested in the work of an artificial intelligence professional named Roger Shank who was at Yale. And Roger was just at the time leaving Yale with some faculty to start an Institute at Northwestern university that brought together a cognitive psychology, computer science and AI and education to apply artificial intelligence techniques to education. And so I did my PhD at that program and ended up being asked to focus particularly on business problems in the corporate world and work with some corporate clients through Accenture, that was in Anderson consulting and ah, it’s kind of what, what, you know, the work that continues to this day. Curtis: Yeah. That’s great. What, what year around were you’re doing your PhD, just so I get a. David: PhD for me was starting in ’89 and so wrapping up in ’94. Late ’80s early ’90s. Curtis: Before the AI wave hit everything, right. You guys were working on this stuff on the cutting edge it sounds like. David: Yeah, absolutely. It was, it was, um, we were considered on the cutting edge was a cutting edge lab. We were, you know, written up in the early days of wired magazine and all that kind of stuff. And it was really interesting place to be, it was a tremendous group of people. We had, I mean, some of them I still work with to this day. We had people who were excellent writers. We had people who were really cutting-edge thinkers in AI and in education and, and in cognitive psychology, which sometimes almost like cognitive science side sometimes gets left out, right? It’s, you know, how do you, how do you think and learn? How do you, how do you understand what your, you know, what you’re experiencing. And all of that goes into designing the experience. So yeah, those were, it was a really a really fascinating place to be and built on a lot of the principles that, that I kind of believed in from my formative years and couldn’t work out any better. Curtis: Yeah. That’s awesome. Now, now you’ve seen this whole progression of, of AI machine learning . . . What’s your perspective on that since you’ve, you’ve lived this entire cycle now? David: Yeah, I’ve lived a, yeah, I’ve lived a few cycles. When, I mean, when I first started doing it, it was kind of, you know, the, uh, you know, the almost, almost became the dying days of, of AI at one point, right? Like we were doing really interesting things I think in applying it to education. But as a field AI was considered, it was considered a failure. The years since my PhD were mostly what’s considered AI winter, you know, really it just didn’t had high hopes. We expect it to be in a Jetsons like world and we are not. What happened? And you know, now I’ve seen the Renaissance and the Renaissance has been certainly interesting to see. There’s obviously a lot more computing power now, which has helped. There’s sort of a lot more public interest and understanding of what AI could be. And some of that’s, you know, there’s probably more, more good than bad though. Sometimes it’s a little scary. We also are in danger of being over-hyped once again. And I think that’s the thing that we, we look at. I mean I’ll talk to people sometimes even about what’s possible, what kind of conversations online systems can have with people, and there’s usually an overstatement of, of what the reality is. And so I think that’s something to be cautious of as we move forward and keep thinking about where AI techniques and machine learning, which, which to me, which the traditionalist is a subset of AI can fit in and not, you know, not overstate and not necessarily feel like the goal has to be a fully functional human replacement. I don’t know that that’s a societal goal for a lot of reasons, but even in terms of technology, it’s not clear that that’s what we need. And in particular in the world of education, it’s not clear that that’s what we would want. Curtis: Right. Now, can we talk a little bit about cognitive psychology and the angle that, that that takes in your work? That’s not a topic we hit very often on this show, but I think it’s really interesting as it applies to to the work you’re doing. David: Yeah, absolutely. There’s, I mean to me, and it’s always been a critical part of what we do. You’re not looking at just putting technology out there, you’re looking at technology that in some ways on one side might mirror some of human thought processes. So that’s part of what we were doing back in my old research lab at Northwestern was thinking about how technology could, could reflect human thought processes. But then on the, on the end user side, so on the more practical side, we need to develop technology experiences that really do help people accomplish their goals, whether they’re educational goals or whether they’re otherwise. In order to do that, we need to have an understanding of how people think, how they learn, how they process information, how they acquire skills. Some of that borders on education research, but a lot of that is the cognitive side and it all, to me it really is all interdisciplinary, right? You
13 minutes | a year ago
Upskilling from Home
Many of us are stuck at home right now, due to the Covid-19 pandemic. There are pros and cons to this. We have less of a commute, more quality time with people in our households, and time to do little tasks we've been putting off. On the flip side, it can feel isolating, basic necessities are much more of a concern, and every day often feels the same. Today we talk about taking advantage of extra time by upskilling in economies that may suffer as a result of the pandemic.
24 minutes | a year ago
How to Reduce Uncertainty in Early Stage Venture Funding
Early stage venture investing has little data to draw from to make good investing decisions. So how has Connetic Ventures successfully developed a data system to inform their investment decisions? We chat with Chris Hjelm about the process they've used to develop something that does just that.
20 minutes | a year ago
Data in Healthcare with Ron Vianu
If you’ve ever tried to find a doctor in the United States, you likely know how hard it is to find one who’s the right fit—it takes quite a bit of research to find good information to make an informed choice. Wouldn’t it be nice to easily find a doctor who is the right fit for you? Using data, Covera Health aims to do just that in the radiology specialty. Ron Vianu: I think the tools are really improving year over year to a significant degree, but like anything else, the tools themselves are only as useful as how you apply them. You can have the most amazing tools that could understand very large datasets, but you know how you approach looking for solutions, I think can dramatically impact. Do you yield anything useful Ginette Methot: I’m Ginette, Curtis Seare: and I’m Curtis, Ginette: and you are listening to Data Crunch, Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. If you’re a business leader listening to our podcast and would like to move 10 times faster and be 10 times smarter than your competitors, we’re running a webinar on February 13th where you can learn how to do this and more. Just go to datacrunchcorp.com/go to sign up today for free. If you’re a subject matter expert in your field, like our guest today, and you’re looking to understand data science and machine learning, brilliant.org is a great place to dig deeper. Their classes, help you understand algorithms, machine learning concepts, computer science basics, and many other important concepts in data science and machine learning. The nice thing about brilliant.org is that you can learn in bite-sized pieces at your own pace. Their courses have storytelling, code writing and interactive challenges, which makes them entertaining, challenging, and educational. Sign up for free and start learning by going to brilliant.org/data crunch. And also the first 200 people that go to that link will get 20% off the annual premium subscription. Today we chat with Ron Vianu, the CEO of Covera Health. Let’s get right to it. Curtis: What inspired you to get into what you’re doing, uh, to start Covera health? Where did the idea come from and what drives you? So if we could start there and learn a little bit about you and the beginnings of Covera health, that would be great. Ron: Sure. Uh, and I, I guess it’s important to state that, you know, I’m a problem solver by nature, and my entire professional career, I’ve been a serial entrepreneur building companies to solve very specific problems. And as it relates to Covera, the, the Genesis of it was understanding that there were two problems in the market with respect to, uh, the healthcare space, which is where we’re focused that were historically unsolved and there were no efforts really to solve them in, from my perspective, a data-driven way. And that was around understanding quality of physicians that is predictive to whether or not they’ll be successful with individual patients as they walk through their practice. And so if you, and we’re focused on the world of radiology, which today is highly commoditized and what that means is that there was a presumption that wherever you get an MRI or a CT study for some injury or illness, it doesn’t matter where you go. It’s more about convenience and price perhaps. Whereas what we understand given our research and the, the various things that we’ve published since our beginning is that one, it’s like every other medical specialty. It’s highly variable. Two, since radiology supports all other medical specialties in a, as a tool for diagnosis, diagnostic purposes, any sort of variability within that specialty has a cascading effect on patients downstream. And so for us, the beginning was, is this something that is solvable through data? Could we understand for an individual patient as they’re looking for medical care, what is the right physician for them that would yield the most accurate diagnosis related to their condition. Curtis: Got it. And I’m assuming you have some experience in the medical field. Do you usually have the companies, you’ve started been in the medical field and so you had insight into this issue or where did that come from? Ron: Yeah, I mean, my background, I was a premed student actually, uh, in New York and I, at the time, I felt like going to medical school really wouldn’t be solving problems as the way I saw, uh, the life of a physician. And so I decided that business was probably a better perspective to solve problems. And ironically I ended up solving problems within healthcare my entire professional career. And so I have a fairly deep knowledge base, if you will, around clinical medicine for a lay person and obviously a lot of experience around starting businesses and using data to solve problems. And so it really, for me it’s an interesting combination of skills that allows me to tackle these things in a way that perhaps a physician or a business person, uh, independently wouldn’t be able to do. Curtis: And where did your expertise in data come from? You seemed to approach things from a very data-driven perspective. Where did you get that from? Ron: I think that’s honestly something that one is innately born with and then one finds the tools to help them explore that. And so in college I studied chemistry and philosophy, and I think part of it is because I was trying to approach different parts of the way my brain functioned. And so when I solve problems today, I try to solve them in a very data-driven manner, generally speaking. And so when I find tools like statistical modeling or AI and so on and so forth, that can further enhance the approach that I would take in solving a problem. Those tools are extraordinarily useful for me. But I don’t think it’s something that I, you know, and one could argue, maybe others have this where you take a course and you’re like, ah, this is an interesting science and I could use this science. For me it was how do I kind of expand the very way I generally function. Curtis: One of the things that we see as the tooling and the understanding around machine learning and analytical practices is becoming better and better. As someone that didn’t study this, you know, computer science, this kind of stuff. Have you found it accessible? Sort of easy to pick up and apply to problems? Ron: Right. So I guess two points I would make there. One, I’m, I’m not a data scientist per se in any traditional way. My background is comp sci meaning back in a, in kind of an untraditional way, meaning both in college and pre college I was programming. And so I have a little bit of that background even though I didn’t study it in a formal setting, but I think the tools are really, uh, improving year over year to, uh, to a significant degree. But like anything else, the tools themselves are only as useful as how you apply them. And so I think, you know, you can have the most amazing tools that could understand very large datasets, but you know, how you approach looking for solutions I think can dramatically impact do you yield anything useful. Curtis: And do you have a specific approach that you do? Is this, does it come naturally to you or do you have some sort of framework or approach that you use to look at things and figure out how you, how you could solve it? Ron: Right. So I’m agnostic from a data science perspective with respect to the actual approach we’re taking, uh, meaning what tools are we going to be using? But moving aside technically, you know, there are two different approaches one can take when one broadly thinks about data science and analytics. And you know, the, the big approach that I think has been very popular over the last, call it five, seven years, is around big data as people call it, which is now that we have access to lots of data and we have access to all these interesting tools and algorithms that can analyze that data, what can we ultimately understand from that data, what patterns can emerge that perhaps we haven’t seen in the past? And I think that’s very productive and useful in many contexts in healthcare where it’s very difficult to understand what data you’re looking at to begin with. And so you have very dirty datasets and cleaning those up becomes half the challenge. And so for me, my approach with respect to healthcare data analytics has been more hypothesis driven rather than that big data approach. And what I mean by that is if you speak to physicians around this thing called quality, which is what we’re trying to solve, how do you understand what physician is ideally suited for a particular patient in order to yield the best outcome? And so as we approach that problem, we work with many experts across the field and we ask to understand their intuition around quality, what makes a good physician. And once we have a unified sense of what the experts think, then we start attacking the data in a way that explores those theories and understands if we can ultimately find some signal with respect to those theories or rather correlations with respect to those theories. And so it’s, it’s a little bit of a different approach, much more hypothesis driven than big data-driven. Curtis: So instead of sifting through the data to find random signals and then seeing if those are useful for some application, you then make some hypotheses, uh, bring domain knowledge and then see if you can find some signals in data that, that, that you have available. Is that accurate? Ron: That’s accurate. And, and I can give you a concrete
30 minutes | a year ago
Data Literacy with Ben Jones
We talk with Ben Jones, CEO of Data Literacy, who's on a mission to help everyone understand the language of data. He goes over some common data pitfalls, learning strategies, and unique stories about both epic failures and great successes using data in the real world. Ginette Methot: I’m Ginette, Curtis Seare: and I’m Curtis, Ginette: and you are listening to Data Crunch, Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. It’s becoming increasingly important in our world to be data literate and to understand the basics of AI and machine learning, and Brilliant.org is a great place to dig deeper into this and related topics. Their classes help you understand algorithms, machine learning concepts, computer science basics, and many other important concepts in data science and machine learning. The nice thing about Brilliant.org is that you can learn in bite-sized pieces at your own pace. Their courses have storytelling, code-writing, and interactive challenges, which makes them entertaining, challenging, and educational. Sign up for free and start learning by going to Brilliant.org/DataCrunch, and also the first 200 people that go to that link will get 20% off the annual premium subscription. Curtis: Ben Jones is here with me on the podcast today. This is a couple months coming. Excited to have him on the show. He's well known in the data visualization community, he's done a lot of great work there. Uh, used to work for Tableau. Now he's off doing his own thing, has a company called Data Literacy, which is interesting. We're going to dig into that and also has a new book out called Avoiding Data Pitfalls. So all of this is really great stuff and we're happy to have you here, Ben. Before we get going, just give yourself a brief introduction for anyone who may not know you and we can go from there. Ben: Yeah, great. Thanks Curtis. You mentioned some of the highlights there. I uh, worked for Tableau for about seven years running the Tableau public platform, uh, in which time I wrote a book called Communicating Data with Tableau. And the fun thing was for me that launched kind of a teaching, um, mini side gig for me at the University of Washington, which really made me fall in love with this idea of just helping people get excited about working with data. Having that light bulb moment where they feel like they've got what it takes. And so that's what caused me to really want to lead Tableau and launch my own company Data Literacy at dataliteracy.com which is where I help people, you know, as I say, learn the language of data, right? Whether that's reading charts and graphs, whether that's exploring data and communicating it to other people through training programs to the public as well as working one on one with clients and such. So it's been a been an exciting year doing that. Also, other things about me, I live here in Seattle, I love it up here and go hiking and backpacking when I can and have three teenage boys all in high school. So that keeps me busy too. And it's been a fun week for me getting this book out and seeing it's a start to ship and seeing people get it. Curtis: Let's talk a little bit about that because the book, it sounds super interesting, right? Avoiding Data Pitfalls, and there are a lot of pitfalls that people fall into. So I'm curious what you're seeing, why you decided to write the book, how difficult of a process it was and then some of the insights that you have in there as well. Ben: Yeah, so I feel like the tools that are out there now are so powerful and way more so than when I was going to school in the 90s, and it's amazing what you can do with those tools. And I think also it's amazing that it's amazing how easy it is to mislead yourself. And so I started realizing that that's sometim...
23 minutes | a year ago
Social Media and Machine Learning
How do you build a comprehensive view of a topic on social media? Jordan Breslauer would say you let a machine learning tool scan the social sphere and add information as conversations evolve, with help from humans in the loop. Ginette Methot: I’m Ginette, Curtis Seare: and I’m Curtis, Ginette: and you are listening to Data Crunch, Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. Ginette: Many of you want to gain a deeper understanding of data science and machine learning, and Brilliant.org is a great place to dig deeper into these topics. Their classes help you understand algorithms, machine learning concepts, computer science basics, probability, computer memory, and many other important concepts in data science and machine learning. The nice thing about Brilliant.org is that you can learn in bite-sized pieces at your own pace. Their courses have storytelling, code-writing, and interactive challenges, which makes them entertaining, challenging, and educational. Sign up for free and start learning by going to Brilliant.org slash Data Crunch, and also the first 200 people that go to that link will get 20% off the annual premium subscription. Let’s get into our conversation with Jordan Breslauer, senior director of data analytics and customer success at social standards. Jordan: My name is Jordan Breslauer. I'm the senior director of data analytics and customer success at social standards. I've always been a data geek as it pertains to sports. I think of Moneyball when I was younger, I always wanted to be kind of a the next Billy Bean and I, when I started working for sports franchises right after high school and early college days, I just realized that, that type of work culture is wasn't for me, but I was so, so into trying to answer questions with data that had no previously clear answer, you know? I loved answering subjective questions like, or what makes the best player or how do, how do I know who the best player is? And I thought what was always fun was to try and bring some sort of structured subjectivity to those sorts of questions through using data. And that's really what got me passionate about data in the first place. But then I just started to apply it to a number of different business questions that I always thought were quite interesting, which have a great deal of subjectivity. And that led me to Nielsen originally where my main question that I was answering on a day-to-day basis, what was, what makes a great ad? Uh, what I found though is that advertising at least, especially as it pertains to TV, is really where brands were moving away from and a lot of the real consumer analytics that people were looking for were trying to underpin people in their natural environment, particularly on social media. And I hadn't seen any company that had done it well. Uh, and I happened to meet social standards during my time at Nielsen and was truly just blown away with this ability to essentially take a large input of conversations that people were happening or happening, I should say, and bring some sort of structure to them to actually be able to analyze them and understand what people were talking about as it pertained to different types of topics. And so I think that's really what brought me here was the fascination with this huge amount of data behind the ways that people were talking about on social. And the fact that it had some structure to it, which actually allowed for real analytics to be put behind it. Curtis: It's a hard thing to do though. Right? You know, to answer this question of how do we extract real value or real insight from social media and you'd mentioned historically or up to this point, companies that that are trying to do that missed the mark.
19 minutes | a year ago
Deep Learning, Microwaves, and Bugs
Sometimes AI and deep learning are not only overkill, but also a subpar solution. Learn when to use them and when not. Diego from Northwestern's Deep Learning Institute discusses practical AI and deep learning in industry. He covers insights on how to train models well, the difference between textbook and real AI problems, and the problem of multiple explanations. Diego Klabjan: One aspect of the problem it has to have in order to be, to be amenable to AI is complexity, right? So if you have, if you have a nice data with, I don't know, 20, 30 features that you can quote, put in a spreadsheet, right? So then, then AI is going to be an overkill and it's actually sort of not, is going to be an overkill. It's going to be a subpar solution. Ginette Methot: I’m Ginette, Curtis Seare: and I’m Curtis, Ginette: and you are listening to Data Crunch, Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. We’d like to hear what you want to learn on our future podcast episodes, and so we’re running a give away until our next podcast episode comes out. We’re giving away our book Simple Predictive Analytics. All you have to do is go on to LinkedIn and tag The Data Crunch Corporation in a post with your suggestion, and we’ll randomly pick a winner from those who submit. If you win and you’re in the US, we’ll send you a physical copy, and if you’re in another country, we’ll send you an electronic copy. Can’t wait to hear from you. Today, we chat with Professor Diego Klabjan the director of the Master of Science in Analytics and director of the Deep Learning Lab at Northwestern University. Diego: My name is Diego Klabjan. So I'm a faculty at Northwestern University in the department of industrial engineering and management sciences. I actually spend my entire career in academia. So I graduated from Georgia tech in '99, and then I spent six years at the university of Illinois Urbana-Champaign and got my tenure there. And then I was recruited here at Northwestern as a tenured faculty member a year later. So I'm at Northwestern for approximately 14 years. Yeah, so I'm the director of the master of science in analytics, actually founding director of the master of science in analytics, so I established the master's program back in 2010, and I'm directing it since then. And recently, I also became the director of the center for deep learning, which is a relatively new initiative at Northwestern. Sort of we, we are having discussions for the last year and a half, and about half a year ago, we officially kicked it off with a few founding members. So my expertise is in machine learning and deep learning. So I have, I run sort of a very big research program. So I advise more than 15 PhD students from a variety of, of departments and the vast majority of them do deep learning research. Yeah, so I started, I started deep learning what was around six, seven years ago. So I was definitely not sort of one of the, one of the early or the earliest faculty members conducting, studying, being attached to deep learning. But I wasn't that late to the game either. Right. So I still, I still remember approximately six, seven years ago attending deep learning conferences with like 50 attendees, and now, now those conferences are like 5,000 people. Just astonishing. Curtis: That's crazy. How you've seen that grow. Diego: Yup. Um, yeah, and I'm also, so the last word is ah, I'm also a founder of OPEX analytics, which is a consulting company. I no longer have much to do with the company, uh, but sort of have experience also on the business side. Curtis: Great. So this, uh, the deep learning Institute started about a year or two ago, is that right? Did I understand that right? Diego: Yeah, that's correct. I mean, so we,
26 minutes | a year ago
Potential Advantages of Blockchain for Data Scientists
Luciano Pesci is bullish on blockchain and data science. Since blockchain offers a complete historical record, no one can delete or alter prior information written into the record. He sees this characteristic as a massive advantage for data scientists. Luciano Pesci: And the key for data scientists and leaders who are gonna oversee data sciences, you've got to get a narrow enough problem to demonstrate one quick win and I mean in 90 days. If in 90 days you can't come back to the organization and show, "we have made real progress on these metrics in your understanding so that you can make these decisions," they're not going to continue to do it. Ginette Methot: I’m Ginette, Curtis Seare: and I’m Curtis, Ginette: and you are listening to Data Crunch, Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. Ginette: No matter what your position in a company is, knowing about data, how it works, and what it can do for you is vital to the success of your organization. Fortunately there are ways for you and those in your organization to learn about data. Brilliant dot org, an online educational resource, has on-demand classes in data basics that can help you understand this growing area, providing you with tools and the framework you need to break up complex concepts into bite-sized chunks. You can sign up for free, preview courses, and start learning by going to Brilliant.org/DataCrunch, and also the first 200 people that go to that link will get 20% off the annual premium subscription. Ginette: The CEO of Emperitas, Luciano Pesci, joins us today. Let’s get right into the episode. Curtis: What inspired you to get into data? What inspired you to to start the company you're working at now and how'd you get going? Luciano: All of it was a complete accident. Yeah, none of it, not the schooling, the business, none of it was intentional. Curtis: Okay, let's hear about it. Luciano: My first business was actually recording studio and a record label, and I had signed, among other acts, my own band, and we got a management deal, and we went to LA. We started to tour with national acts, and I thought that was going to be my career path without a doubt, and so I didn't take the ACT/SAT at the time, barely graduated high school, and then the band fell apart. And I was like, "well, what am I going to do?" So I went back to school, had a transformative experience, got drawn into economics, and then within economics really found data. Curtis: And what drew you to economics? Luciano: I like studying people. I think it's the most complete picture of people. So there's a lot of other disciplines that sort of dive deeper when it comes to people's psychological characteristics, their behavioral components. But economics was about the entire system and how an individual functions within that bigger system. And the reason I got to data from that was that the key assumption of modern economics is perfect information. So this is usually where critics of what is called the classical model in economics come in and say, "well, you can't have perfect information, so therefore you can't have optimizing behavior." And one of the beautiful lessons of the last 20 years, especially with data science is it might not be perfect information, but you can get really good information to make optimized choices. And so the represented that, that method of going into the real world and optimizing all these processes that we were learning about in the textbooks and at the abstract theory level. Curtis: Interesting. And that's, there's not a lot of places, if any, that I know of that teach that approach, right? Or have good coursework around that. Did you kind of figure this out on your own or how'd you, how'd you come to that?
17 minutes | a year ago
How to Predict World Events with Predata
There have been some spectacular fails when it comes to looking at Internet traffic, think Google Flu Trends; however, Predata, a company that helps people understand global events and market moves by interpreting signals in Internet traffic, has honed human-in-the-loop machine learning to get to the bottom of geopolitical risk and price movement. Predata uncovers predictive behavior by applying machine learning techniques to online activity. The company has built the most comprehensive predictive analytics platform for geopolitical risk, enabling customers to discover, quantify and act on dynamic shifts in online behavior. The Predata platform provides users with quantitative measurements of digital concern and predictive indicators for different types of risk events for any given country or topic. Dakota Killpack: Over the past few years, we’ve have collected a very large annotated data set about human judgment for how relevant many, many pieces of web content are to various tasks. Ginette Methot: I’m Ginette, Curtis Seare: and I’m Curtis, Ginette: and you are listening to Data Crunch, Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. Let’s jump into our episode today with the director of Machine Learning at Predata. Dakota: My name is Dakota Killpack and I'm the director of machine learning at Predata, and Predata is a company that using machine learning to look at the, the spectrum of human behavior online organizes it into useful signals about people's attention and we use those to influence how people make decisions by giving them a factor of what people are paying attention to. Because attention is a scarce cognitive resource. People tend to pay attention only to very important things, If they're about to act in a way that might cause problems for our potential clients, they'll, they'll spend a lot of time online doing research, making preparations, and by unlocking this attention dimension to web traffic, we're able to give some unique insights to our clients. Curtis: Can we jump into maybe a concrete use case into what you're talking about just to frame and put some details around how someone might use that service? Dakota: Absolutely. So one example that I find particularly useful for revealing how attention works online is looking at what soybean farmers did in response to a tariffs earlier this year. So knowing that the, they weren't going to get a very good price on soybeans at that particular moment. A lot of them were looking up how to store their grain online and purchasing these very long grain storage bags, purchasing some obscure scientific equipment needed to insert big needles into the bags to get a sample for testing the soybeans and moisture testing devices to make sure they wouldn't grow mold. And all of these webpages are things that tend to get very little traffic. And when we see an increase in traffic to all of them, at the same time, we know that a, a very influential group of individuals, namely farmers, is paying attention to this topic. Using that we're able to give early warning to our clients. Curtis: Sounds like looking for needles in a haystack of data. Right? So how do you determine what is a useful bit of information in the context of what your clients are looking for? Do they kind of have an idea of what you're looking for and then you'd go out and search for that or, or does your algorithm find anomalies in the data and then characterize those anomalies so that you can then report that back? How does it work? Dakota: It’s a mix of both. Because the, the Internet is such a rich and complex domain. It's, it's very dangerous to just look for anomalies at scale. There there've been some high profile failures, most notably the Google Flu Trends
16 minutes | a year ago
Structuring Your Data Science Dream Team
The way you organize your data science team will greatly affect your business’s outcome. This episode discusses different structures for a data science team, as well as top down versus bottom up approaches, how to get data science solutions into production organically, and how to be part of the business while remaining in contact with other data scientists on the team. Mark Lowe: Having lived through small scale, two people working, to large scale, thousands of people in your organization, the way that you organize the data science team has dramatic effect on its productivity. Ginette Methot: I’m Ginette, and I’m Curtis, and you are listening to Data Crunch, a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. Building effective data science processes is tough. Mode, the data science platform, has compiled three tips to make it a bit easier: don’t over plan, there’s no one process that fits everyone, and waste time. That’s right. Waste time. Read more at mode.com/dsp M O D E.com/D S P. Today we’re going to talk about effective ways you can organize your data science team, and we’ll hear lots of great insights from our guest. Let’s get to it. Mark: My name is Mark Lowe. I’m currently the senior principal data scientist here at Valassis. Curtis Seare: Describe just a little bit about what Valassis does. Mark: So we work with pretty much every major manufacturer retailer in the U.S. Our work kind of runs the gamut in terms of solving problems for them in terms of how do I influence customers. And so we manage a lot of print products that go reach every household, every week and of course a lot of digital products. So everything from display advertising, campaign, search campaign, social. Pretty much any distribution mechanism that can influence customers, we try to use those channels. Curtis: And in working on these problems we talked a little bit about earlier what the approaches for data science. Some people try to bin it in a software development kind of a role, an agile role, and how that usually doesn’t work for data science cause it’s more of an experimental type of a thing. Can you comment on its similarities and differences and how you should be approaching data sites? Mark: I think that’s a great question. Honestly, if you, if you asked me 10 years ago if this was an interesting question, I would have found it very boring. But having, having lived through small-scale, two people working, to large scale, thousands of people in your organization, the way that you organize the data science team has dramatic effect on its productivity, and there’s no one size that fits all. Honestly, you kind of have to cater the organization of the data science team to where the company is. For example, the two common models that are deployed and, and we’ve, we’ve lived in both of them is kinda thinking about data science as an internal consulting group. So I have a a pool of data scientists. Stakeholders throughout the company come to me and ask, they say, “I have this problem. I think it needs data science” and then the data science lead or team. Yes, we do need a data scientist working on that. Here’s a person with that specialty. So kind of farming out individuals on the team to solve particular problems. So it’s a fairly centralized organization and that, you know, there’s a lot of benefits to that. One, you’ve got strong sense of community as a team. Oftentimes you’re very tightly organized together. You function as a data science unit. You can try to make sure that you’re putting the right skillset for the right problem. As you know, as you’ve talked to that, there’s, there is no one definition of data science, there’s no one skillset. So oftentimes the data science team has a mixture of skills across the team,
19 minutes | a year ago
The Hidden World of Data Science in Utilities
David Millar is a man bringing analytical solutions to an industry that historically has had little data. But with the explosion of smart devices, that is all changing, and the way utilities operate is as well. David Millar: The way that electricity markets work is that you have what's called the day ahead market. And so the day before, let's say one o'clock tomorrow, markets run, and this is a big optimization problem. Ginette Methot: I'm Ginette Curtis Seare: And I'm Curtis Ginette: And you are listening to Data Crunch, Curtis: A podcast about how applied data science, machine learning and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation and analytics training and consulting company. Ginette: The father of lean startup methodology once said “There are no facts inside the building so get the heck outside.” The utilities industry is no different. Sometimes the facts that’ll make your machine learning career are waiting just outside your office. Read more at mode.com/MLutilities. m o d e dot com slash M L utilities. Ginette: David Millar is a man bringing analytical solutions to an industry that historically has had little data. But with the explosion of smart devices, that's all changing, and the way utilities operate is as well. Let's get into it. David: I'm, ah, Dave Millar. I am the director of resource planning consulting at Ascend Analytics where I lead the research client consulting team. And so my team and I work with utilities primarily to help them make decisions using analytics, regarding their longterm power portfolio. So primarily I read looking at we'll say we're retiring coal plants or retired, retired gas plant. What would we replace it with? Renewable energy. We need batteries. How do we approach these questions using analytics in order to help us come up with the best solution going forward. Curtis: You had talked a little bit about, you sent me some notes about how the, the sector that you're in, the power sector, you know, is kind of slow moving, right? It's not known for these quick changes and innovations, but you are starting to see some things that, that's gonna change this fundamentally. And so if we could jump into that and, and then get your perspective, I'd love to hear about it. David: Yeah, the power sector basically didn't change from the time of once they figured out that we're going to use alternating current that it didn't really change much in the past hundred years, that the model is essentially the same. You have big power stations that are far away from the load centers and then you have this transition network and flow of electricity is really one direction, right, from, from the big power plants to your home. And technology is rapidly changing that and it creates a space to becoming both more digital and more decentralized. So, on the digital front, we, we actually have generation technologies, that don't use anything, any spinning parts, right? so you have solar, solar power, and you have, now we're seeing more and more batteries being connected to solar. And so those are both digital technologies that are increasingly becoming this default, energy source, wind or solar and batteries and and just because the cost of the signals is have, dramatically over the past 10, 10. It's really happened over the past 10 years. And so now renewables are at parity with the more conventional sources of electricity. So gas, power and natural gas power, coal power. Curtis: Is that in terms of like how much energy they're currently producing parity or just effectiveness or efficiency. What is that parity? David: Parity in terms of costs. So, you know, as renewables drop in costs, especially as batteries drop in costs, that means that when, when I look at a problem with my clients, we're comparing, technologies that essentially have the ability, similar attributes,
22 minutes | a year ago
The Good Fight against Shadow IT
Simeon Schwarz has been walking the data management tightrope for years. In this episode, he helps us see the hidden organizational and economic impacts that come from leading a data management initiative, and how to understand and overcome the inertia, fears, and status quo that hold good data management back. Simeon Schwarz: Fighting against shadow IT . . . you have to find a way to adopt it, you have to find a way to incorporate it, and you have to find a way to leverage it. You will never be able to completely eliminate it. Ginette Methot: I'm Ginette. Curtis Seare: And I'm Curtis. Ginette: And you are listening to Data Crunch, Curtis: A podcast about how applied data science, machine learning and artificial intelligence are changing the world. Ginette: This might come as a surprise to some, but......tools won’t build a data-driven culture. The right people will. Read more at mode.com/datadrivenculture. m o d e dot com slash data driven culture. Ginette: Today we speak with Simeon Schwarz. He’s been working in data management for over twenty years and owns his own consultancy, Data Management Solutions. Simeon: Being in the data management function, you're de facto seeing the life blood of how the business flows, how the uh, where the information goes, how the decision are made. Curtis: So have you been focused mainly in a, in a specific industry or have you spend a lot in your career? Simeon: I've started in telecom. I've built first cell phone carrier back in my home country. I worked in academia, in a retail, ecommerce, and then 10 years in financial services, most recently, and now I do insurance. So a lot of different fields. Curtis: So you've run the gamut. That's interesting. And now that you've done this in several different fields, do you find that the principles and your approach is basically the same or or is it different depending on the problems that you're trying to solve? Simeon: The approach is the same, and there are two parts to this. We'll talk about what's difficult in this role a little bit further in this conversation. The second part is you really need to understand the domain you're dealing with because, one, if we, if we're talking about data management in general, one of the key functions, one of the key challenges that you're going to be facing is establishing and building your credibility. Without knowledge of the domain. B insurance or financial services or manufacturing or any other field, you simply can't have intelligent conversations with your stakeholders in a way that would lead to good conclusions. So you will absolutely have to know the domain, which is large portion, of your value. Curtis: So as you've gotten into a domain that maybe you weren't as familiar with in a data role, how did you overcome this need to understand the domain better? Simeon: Let's step back and talk about what a data genuinely is right now and specifically talk about data management. You are running a data function or sometimes called data services because what used to be DBA teams or data analysts or various forms is really becoming a practice and looking at it as a practice. You have a certain set of clients, the are paying you for the services, you have certain amount of resources and you trying to optimize those resources to serve your clients better. So what are the challenges that you're going to face in any data management role? So you're in this interesting balance between moving forward very rapidly as well as not destroying what already exists, not destroying the services that are already provided. People have to breath, people have to be able to, to leave. You can't disrupt too much the services that already exist, your reports, your, you know, our auditing work your work with, you know, regulatory agencies. Anything else that the business needs to produce has to continue to happen. The people who are doing their jobs in the current way simil...
19 minutes | 2 years ago
Using Data to Design Tests People Don’t Hate
David Saben is on a mission to make taking tests less painful, and he’s using data to do it. In this episode, he’ll discuss reviving methods developed in 1979 to shorten tests and make them more effective, as well as how to use psychometrics to aid in the design and crafting of an effective test. David Saben: When I see my son who's 11 years old, spending three days and testing when I know there's absolutely no reason for it that you can do that in an hour. Ginette Methot: I'm Ginette Curtis Seare: And I'm Curtis Ginette: And you are listening to Data Crunch Curtis: A podcast about how applied data science, machine learning and artificial intelligence are changing the world. The father of lean startup methodology once said “There are no facts inside the building so get the heck outside.” The education industry is no different. Sometimes the facts that’ll make your machine learning career are waiting just outside your office. Read more at mode.com/mledu m o d e dot com slash M L e d u Ginette: Today we chat with David Saben, the CEO and president of Assessment Systems, an organization innovating psychometrics (the science of assessment) Dave: I originally started my career in telecommunications, uh, bringing voice and data services into institutions and to learning institutions. And then when I realized is, is that connecting universities and for profit schools, you know, connecting them online really created a huge opportunity for learning and really crossing barriers to learn and really meeting learners on their terms with online learning courses. And that kind of brought me through this, this journey with using technology to, to really make better decisions in learning and knowledge and how we do that effectively. And that has started a about a 16 year career focused on that using using data, using e tools to make a better learning environment for everybody and make us more effective in the way that we, we gather information and retain information. And that that's left. Let brought me, um, into several areas. One is in the learning sciences is how do you, how do you deliver learning content more effectively, but also in the assessment side as well, where, how do you measure what folks are learning effectively and painlessly in that that's brought me on this, uh, this journey into the assessment industry and really making sure that every exam that's delivered in classrooms or whether it's a licensure exam is as fast and as fair as possible and using data to be able to do that. So really mitigating the risk of human bias when it comes to measuring a human's abilities, uh, which is, uh, which is a troublesome area, right? Curtis: Yeah. And now you say a effective and, and painless. And I know most people hate taking tests, so, so tell me how you approach that. Dave: Yeah. Well, I think there's a lot of ways. I mean, I think one of the, one of the most important ways is that you make the test faster, right? You make, you know, in 1979, I was the chairman of assessment systems help create a technology called computerized adaptive testing. What that uses, it uses algorithms to gauge what you know and what you don't know and then basically tailoring the content that you see, the next item you see gets more progressively difficult or progressively easier depending on your, your ability. And what that does is that reduces test time by about 50%. We see that with the ASVAB exam that's given to our service men and women to make their testing experience faster and fair and really, and we're starting to see that really across the world with measurements. So really making those exams tailored to the person's ability, uh, which is really, really important. You know, what you don't want to do is you don't want to give one test that doesn't change to everyone cause that's really, really inefficient. You know, if I'm going through the test and I know I know the content really well,
14 minutes | 2 years ago
Activating Analytics in Business and Government
Todd Jones: My name is Todd Jones. I'm the chief analytics officer here at WebbMason analytics. We are a professional services firm helping our clients accelerate their analytic evolution. So I think my journey started about 10 years ago. Uh, I graduated from Princeton with a degree in operations research and financial engineering. So I could have basically taken f two paths. One, I could have went into the financial space or the second path I could have taken was going into the analytics space and I, and I chose the, the analytics space. I joined a very early company called Spry. When I joined. It was about four months old and primarily started off doing a lot of DOD contracting specific to analytics and data. And we eventually built that company to a pretty nice size. We expanded past the DOD space, got into commercial, started consulting with some large, uh, pharmaceutical companies, transportation companies, and really built that company up and then sold that in 2015. Curtis: When you fill that is Webb Mason, the company that then bought Spry? Todd: Correct. So Spry was again, another professional services firm specializing in data and analytics. WebbMason historically has been a marketing a firm and so they specialize in all aspects of marketing. And as you can imagine, analytics is definitely a big area of focus for them and their clients. And so they brought us in and about 20% of our revenue comes from marketing related activities through WebbMason and then 80% of our revenue still comes working with it and analytic groups outside of the WebbMason portfolio. Curtis: Interesting. Okay. So there was some crossover there, but not as much as you might expect. Todd: Yeah, definitely some crossover without a doubt. So that was definitely beneficial. But you know, as, as I'm sure you can imagine with any acquisition, you learn a lot. And so we're in a great spot right now, and we're able to generate very healthy stream of business independently, but then also find those synergies with WebbMason as it relates to the marketing activities. Curtis: Sure. That's awesome. So when you got started at Spry, ah what, what was your role? What did, what did that look like? Todd: Yeah, so when I got started, most of my role at that time was consulting. So I was working directly with our stakeholders who at the time were within the Department of Defense. So I split my time between Crystal City, Virginia and the Pentagon. And really what we were trying to do was help them build a solution that gave them a enterprise view across the four military groups, specifically related to human resources. So if you think about it, when we, you know, when we fought world war two, you had, you know, one division, the Marines and the navy out in the Pacific and then you had the army in Europe and they, for the most part fought separate campaigns.And then we started to get into Iraq and Afghanistan and all of a sudden all of these individuals started to really come together. And so you might look at a city block and you have the air force there, army there, you know, navy seals in the area. And so all of these groups now have to work very closely together. And one of the things that the DOD was trying to accomplish at that time was to start to get a better view of people across the different military branches. So, for example, rather if I need a particular skillset within a particular city block, can I get that skillset from the navy? Can I get that skillset from the army? Maybe the Marine Corps has that skillset. And so they needed a very, they needed a large enterprise view so that they could very easily and quickly start to develop these blended teams. And so that was definitely a combination of technology solutions as well as analytics solutions. And so we were consulting with individuals within the Pentagon to help them build that technology solution. Curtis: That's really interesting.
Terms of Service
Do Not Sell My Personal Information
© Stitcher 2020