Data Crunch

Today, our guest, Alain Briancon, will talk to us about how to work with Fortune 500 companies and help them get quick value from their data, how to build a roadmap of incremental value during the data collection and analysis process, how they help predict and incentivize customer purchases, and how to dial in on an idea for successful data science software companies.Alain Briancon: Adding one more question to answer is always easy. The difficult part is what question can I remove and still providing insight.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: If you’re a fortune 1000 company, and your team needs to be trained in Tableau, Statistics, Data Storytelling, or how to solve business problems with data, we’ll fly one of our expert trainers out to your site for a private group training. The most important investment a business can make is in its people, so head over to our site at datacrunchcorp.com and check out our training courses.Today, our guest, Alain Briancon, will talk to us about how to work with Fortune 500 companies and help them get quick value from their data, how to build a roadmap of incremental value during the data collection and analysis process, how they help predict and incentivize customer purchases, and how to dial in on an idea for successful data science software companies.Alain: My name is Alain Briancon. I am currently the VP of data science and chief technology officer for CEREBRI AI. CEREBRI AI is an AI company, as the name could guess. We are located in three cities: Austin, which is the corporate headquarters; Toronto, which is a hotbed of data science in North America; and Washington DC where I work. What CEREBRI AI focuses on is developing a system to help manage above the strategic component as well as the tactical component of customer experience. This is my fifth startup. This is my third startup that involves data science and machine learning. Jean Belanger, who is the CEO of CEREBRI is a friend of mine; now he's my boss. So I'm trying to work through that, and it took him about 19 years to convince me to join a startup, uh, with him. And this was the right opportunity because the kind of problems we are solving are very challenging.It has been a, an absolute blast. Besides working with a great team and building it up. But when I joined we were about 20 people. Now we're about 63 people, about 50 of them on the technical side. Half in data science, half in software. What has been fantastic is applying tricks and insight that I've gained over the years to, uh, help guide the data science side. The other thing also, which is fun, is we have a very pragmatic view of how to approach things and how to approach engagement with customers. Our customers are fortune 500 customers; they are major banks. One of them is a Central Bank. Others are car makers and we're working very hard into the telco business as well. And, uh, when you deal with such companies, first of all, a very interesting sell cycle in which data science and machine learning play a role at the right moment in time.But you have to also be humbled by the fact that you don't start on their side from a clean sheet. And I think that's one of the most interesting component of making things work is bring data science and machine learning insight to companies who cannot afford and we should not afford the, "okay, let's start from scratch. Let's share all of the data in the like," and so vis Jujitsu between the business case that machine learning brings up and the underlying machine learning technology is one of the most fun element of the work.Curtis: That's interesting. Let's, let's dig into that if we can. Can you give me a concrete example in CEREBRI AI how that works and spell out that concept for us?

Today is a special episode. We welcome three executive guests from different organizations to share their experiences and insights about how data science can best support executive goals. 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. There's a lot going on here at Data Crunch. Just this last week we finalized the merger of Vault Analytics and Lightpost Analytics under the new banner of the Data Crunch Corporation, which improves our capabilities to serve our clients head over to datacrunchcorp.com to check out our training and consulting offerings. For our executive panel, today we'll be talking to Simon Lee, the chief analytics officer from Waiter; Fatma Kocer, who is the vice president of data science engineering at Altair, and Rollen Roberson who is the president at Trianz. Curtis: So, welcome everyone to the executive panel. We are super excited to have you guys here. You are all executives and companies that are doing amazing things with data science. So the audience knows, again, we're talking about today, the topic is how data science, machine learning and AI can best support executive strategy and business goals. How, how does that function really work? Let's start maybe with Simon and then Fatma and then Rollen, if you could just give us a little introduction, and we'll get going from there. Simon Lee: Thanks. I'm Simon Lee. I'm actually kind of a mixed bag when it comes to data science and analytics. I've got about 20 years of experience using analytics and advanced algorithms, you know, in a whole bunch of different industries like transportation for example, airline rail, trucking, ocean carriers, printing, publishing, manufacturing, finance and delivery. Delivery is where I'm currently at. Waiter is a restaurant, food delivery company in small and mid size market. So probably a lot of people haven't heard of us because we're in the smaller communities, but, we're trying to make a big splash. So yeah, that's who I am. Curtis: Awesome. Thanks for being here. Fatma Kocer: Hi, this is Fatma Kocer from Altair engineering. I am a civil engineer by training, although I never get a chance to practice it. Um, my background is multidisciplinary design, exploration and optimization. And I was in the auto industry before I joined Altair. Um, there, I've done several things throughout the 14 years that I've been here, but always keeping, designing solution optimization as the core of my responsibilities. And Altair is a global technology company. We provide software in solutions for product development, data intelligence and high performance computing. We are located at headquarters in Michigan in Troy, Michigan where I'm speaking from and we have offices in I think 25 countries now. So that would be me. Curtis: Great. Thanks for being here, Fatma, and, ah, Rollen. Rollen Roberson: Right. Thank you. Good Morning. Rollen Roberson with Trianz. You know, for my own background, I've a similar to Simon. I'm kind of a mixed bag, I've been in the industry for 20 plus years, I'm solely in the digital transformation space. Uh, working from startups, mid-level companies through global service integrators, uh, working with Trianz currently to really expand the growth and a use within AI and IoT within the organization. And our customer base, Trianz is a company that has 1,500 plus employees, global offices mainly serving the, upper, mid-tier and enterprise level customer base, uh, solely focused on digital transformation and the use of those higher technologies for greater return on value. How Can Data Science and AI Have an Impact On Your BusinessCurtis: That's awesome.

Our guest Andrzej Wolosewicz has had years of experience helping companies define and build machine learning and analytical solutions that have a measurable impact on the business, and he shares with us his experience and expertise. He shares with us the biggest pitfalls he sees companies fall into over an over as they try to implement these initiatives.The problem was there was a lot of activity every month that they were doing, but in terms of progressing, their analytic capabilities were really kind of being able to to grow and be more effective. They weren't, they weren't able to do that. As the saying goes, they had a lot of action but not a lot of progress.Ginette Methot: I'm Ginette.Curtis Seare: 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.Andre Wolosewicz: My name is Andre Wolosewicz. I am currently the director of sales at HEXstream. We are Chicago based analytics and data consultancy. But this is kind of the, the latest step on my journey. So I actually started out coming straight out of college into a, a predictive modeling startup. And this would have been in the late nineties. Artificial intelligence at the time was, was a big buzzword as it is today. And we were looking at being able to do fairly advanced modeling of systems, but actually looking at the data as being the model. So if you were looking at uh, everything from a jet engine to the human body to complex refineries, we didn't necessarily understand all the nuances of how they ran, but we had all the data and so we would use that data to build out those models. And then I ended up going from that actually flipping into the, into the other side of the world around program management.So, not so much doing the analysis, but understanding how the analytics and designs and all of those steps fit together to actually deliver a furnished product. And so that was, that was very useful because it taught me that, hey, there's a lot more, you may find things that are interesting, but on the business side of the world you have all of the constraints that analysts may not always be aware of or or may not, you know, really want to take into consideration like budgets, schedule, things of that nature. And so I learned how to operate with that. And then another interesting twist of fate, met somebody who knew somebody who was looking for somebody that could provide that line of business experience, but actually selling a business intelligence platform. Not necessarily that you knew how the all the software worked. And you know, if you click here, this happen, if you click here, that happened, but could sit across the table from somebody who was in a line of business and say, I understand the business problem you're having.I understand how to solve it and here's how the technology can be applied. Because the, the reality is technology in and of itself will never solve a tool. It needs people, it needs processes, it needs the people to use it. My Dad used to like to look at a rake and say, well, the art's not going to rake itself, so the rake does the job, but it needs somebody to use it. After about five, six years actually selling and being involved with the bi platform, the opportunity to join HEXstream came up, and for me, this was kind of a combination of all of the past experiences because it gave me the opportunity to engage with clients and engage with our inner teens on what is it that you're trying to do. So going back to my first experience, what is the project? What is the model?What is the data that you're trying to work with and build? But then I also had to understand why that was relevant. Why would a client engage with a company like HEXstream to undertake a project? How is that project measured? There's a lot of things that over the years I've found people would love to do,

Today we’re going to see how a clever idea and the skillful use of data is starting to disrupt how people get credentials. The use case here has the potential to remove gender and racial bias in the hiring process, help companies understand specific talent gaps in their workforce, and help learners find lucrative educational pathways they can take.

Joe Kleinhenz talks about his journey from starting out in data all the way to becoming a leader in one of the largest insurance organizations in the United States. We'll learn about the importance of staying on top of technology, how to win hearts and minds of nontechnical folks, centralized versus decentralized team, pros and cons, how to hold effective conversations with stakeholders and how to go from individual contributor to leader.Joe Kleinhenz: The critical skills you bring to the table is the ability to break down complex ideas into ones that translate for nontechnical folks.Ginette Methot: I'm Ginette.Curtis Seare: And I'm Curtis.Ginette: 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 and analytics training and consulting company. One of the biggest challenges companies have in getting value from their data is finding the right talent. Good talent is scarce and building a top-tier team is hard if not impossible for some companies. If you are having this challenge try out our analytics as a service offering: we bring a fully equipped data science team to bear on your projects, on demand and with no long-term contract constraints. If you want to start seeing success for your data science efforts quickly and economically, head over to datacrunchcorp.com for more details.Today we'll be hearing about Joe Kleinhenz's journey from starting out in data all the way to becoming a leader in one of the largest insurance organizations in the United States. We'll learn about the importance of staying on top of technology, how to win hearts and minds of nontechnical folks, centralized versus decentralized team, pros and cons, how to hold effective conversations with stakeholders and how to go from individual contributor to leader. There's lots of unpack in this episode, so let's get to it.Curtis: If we could just start out just by talking about what got you interested in data in the first place, where your journey started, and we can go from there.Joe: I actually first started thinking about using math to predict future outcomes when I was a teenager. I read a book by Asimov called Foundation and whole premise of the book series was I'm using mathematics to predict the future. It's all science fiction stuff in it that point, but that's kind of what certainly got me first interested in it.Curtis: So it was a, it was a work of fiction that got you interested.Joe: Yeah, that captured my imagination. I didn't even at that point even know, it was a, you know, data science was a thing, and as I got my path into the technology, within IT, I was doing business consulting for awhile and got into data warehousing, and this was in the late nineties. From there, ended up in part of GE financial that was doing a lot of direct marketing, and they had a group called database marketing, which was essentially the precursors for data scientists. They had predictive modelers, statisticians essentially in there that were, by today's standards, relatively simplistic tools like linear regression to build, you know, models predicting who would respond to direct-drip marketing offers. I used to joke with people that I ran a team of bad people that decided to call you at dinner with an offer. You can just have the here. Um,Curtis: And you made those people very effective at, at being bad, I assume.Joe: Yes. Yes. At that point there was very few restrictions on what you could do. We were even using credit data for some of the, the algorithms cause we were with credit card companies. Credit data at the time, there wasn't the regulatory restrictions there is now, it's incredibly predictive. When you combine that with recency frequency data on purchasing behavior, you'd really kind of tune in on, you know, what someone would be interested in.

Our guest today holds a PhD in organizational psychology and has been working on data products in the health and wellness space for over a decade. We cover a lot of ground in this interview: how to create data products that work, how to avoid the unexpected consequences of poorly designed data interventions, and the importance of ethnographic thinking in data science.We'll also talk about reducing friction in data collection, the coaching data product model, and surprising things we can learn when people's routine's are broken. From today's episode, you'll come away with a better understanding of how to build contextually relevant data products that make a difference in people's lives.

How do you whittle the murky business of creating a data-driven culture down to a proven process? Today we talk to a guest who has done this time and time again, helping companies transform their operations. He points out the small nuances and details about the process, like questions to ask to start on the right foot, critical feedback loops to put in place along the way, and how to overcome some of the most common problems that make people give up.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.Now, let's jump into our interview with Ryan Deeds, VP of technology and data management at Assurex Global.Ginette Methot:            How do you whittle the murky business of creating a data driven culture down to proven process? Today we talk to a guest who has done this time and time again helping companies transform their operations. He points out the small nuances and details about the process, like questions to ask to start on the right foot, critical feedback loops to put in place along the way and how to overcome some of the most common problems that make people give up. 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, a vault analytics production. Let's jump into our interview with Ryan deeds that VP of technology and data management at Assurex global.Ryan Deeds:                  Uh, I think it's an interesting time in the whole a data experience because I think so many people failed. You know, in the last like decade that this next couple of years everybody's now trying to look at root cause. And so culture actually is becoming important now, you know? And so that's kind of a cool thing.Curtis Seare:                 What do you mean by that? In terms of a lot of people have failed.Ryan Deeds:                  I think when you look at bi projects from 2003 to 2013, they were just, companies went through litany of failures and trying to get data to a place that what made sense was easily accessible, had had a good quality. Um, but they didn't address that. They just put the visualizations on top of kind of crappy data and they did that over and over and over again. Um, and then finally it seems like, you know, in the last year or two years, we start really having a conversation about what has to happen inside an organization to make data usable. I mean, it's just like water, right? You can't just take water from a stream and start drinking it. You got to process it and clean it and make it and make it valuable and make it worthy of consumption. And that's exactly the thing we got to do with data.Curtis Seare:                 Sure. Maybe we can dive into that as well, because you've had this experience taking a lot of companies through those steps, right? So what do you see as the major roadblocks? How do you start this process of helping people get their hands around? How do I get value from my data?Ryan Deeds:                  So it's interesting. I kind of have, uh, you know, I've done this a lot and so I have, uh, organizations that come to me and they say, hey, you know, we want to, we were ready to start leveraging data. Um, and the, the typical thing is there's just a lack of expectation of the time it takes. Um, and so I threw together like a timeline to try to help, uh, educate individuals on that, you know, and kind of like the steps that it would take to get to usable data, um, in, and the first is really a recognition that today we don't, you know, the organization that we're in is not effectively using data, um, as a, as a strategic advantage.

Beginning: Statistics are misused and abused, sometimes even unintentionally, in both scientific and business settings. Alex Reinhart, author of the book "Statistics Done Wrong: The Woefully Complete Guide" talks about the most common errors people make when trying to figure things out using statistics, and what happens as a result. He shares practical insights into how both scientists and business analysts can make sure their statistical tests have high enough power, how they can avoid “truth inflation,” and how to overcome multiple comparisons problems.Ginette: In 2009, neuroscientist Craig Bennett undertook a landmark experiment in a Dartmouth lab. A high tech fMRI machine was used on test subjects, who were “shown a series of photographs depicting human individuals in social situations with a specified emotional valence” and asked “to determine what emotion the individual in the photo must have been experiencing.” Would it be found that different parts of the brain were associated with different emotional associations? In fact, it was. The experiment was a success. The results came in showing brain activity changes for the different tasks, and the p-value came out to 0.001, indicating a significant result.The problem? The only participant was a 3.8 pound 18-inch mature Atlantic salmon, who was “not alive at the time of scanning.”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.Ginette: This study was real. It was real data, robust analysis, and an actual dead fish. It even has an official sounding scientific study name—”Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon”.Craig Bennett did the experiment to show that statistics can be dangerous territory. They can be abused and misleading—whether or not the experimenter has nefarious intentions. Still, statistics are a legitimate and powerful tool to discover actual truths and find important insights, so they cannot be ignored. It becomes our task to wield them correctly, and to be careful when accepting or rejecting statistical assertions we come across.Today we talk to Alex Reinhart, author of the book “Statistics done wrong—The Woefully complete guide”. Alex is an expert on how to do statistics wrong. And incidentally, how to do them right.Alex: We end up using statistical methods in science and in business to answer questions, often very simple questions, of just “does this intervention or this treatment or this change that I made, does it have an effect?” Often in a difficult situation, because there are many things going on, you know, if you're doing a medical treatment there’s many different reasons that people recover in different times, and there's a lot of variation, and it’s hard to predict these things. If you’re doing an A-B test on a website, your visitors are all different. Some of them will want to buy your product or whatever it is, and some of them won’t, and so there’s a lot of variation that happens naturally, and we’re always in the position of having to ask, “This thing/change I made or invention I did, does it have an effect, and can I distinguish that effect from all the other things that are going on.” And this leads to a lot of problems, so statistical methods exist to help you answer that questions by seeing how much variation is there naturally, and this effect I saw, is it more than I would have expected had my intervention not worked or not done anything, but it doesn’t give you certainty. It gives us nice words, which is like “statistically significant,” which sounds important, but it doesn't give you certainty. You're often asking the question, “Is this effect that I’m seeing from my experim...

What does it take to become a data scientist? We speak with three people who have become data scientists in the last three years and find out what it takes, in their opinions, to land a data science job and to be prepared for a career in the field.Curtis: We’ve talked a lot in our recent episodes about all the interesting things you can do with data science, and we’ve only talked a little bit recently about what it actually takes to get into the field, which is a topic that a lot of you have reached out to us and asked us to cover in a more thorough way. So today, we’re taking a broader approach on this topic by talking to three data scientists who have become data scientists in the last three years. You’re going to be able to hear all the details of each of their three journeys, how they got started, how they landed their jobs, and what their best advice is for getting into the field, and this will give you a broad view about how to get into data science from three people who have actually done it.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: A Vault Analytics production.Ginette: Here at Data Crunch we’ve been hard at work developing a technology that allows executives and business leaders to gain insight from their data instantly—simply by talking to the air. We hook up your data to an Alexa device with custom skills built in to understand the questions you have about your business - and give you answers. Figure out sales forecasts, marketing performance, operational compliance, progress on KPIs, and more by just talking to Alexa. We are officially launching the product this week and have room for three initial customers—if you're interested, head over to datacrunchcorp.com/alexa or datacrunchpodcast.com/alexa (both work), and book some time to chat with us. We’ll assess if your company is a good fit, and if so, we look forward to working with you!Tyler Folkman: My name’s Tyler Folkman. I've gotten into data science in kind of a strange route to be honest. I did my undergrad in economics, actually originally thinking to get into computer science, but for some reason, I had this thought that computer science was going to get outsourced; I don't know if that was a thing, but I think people back in the early 2000s were talking about computer science getting outsourced, so I thought about business, which ended up begin economics, which I really liked, and then ended up doing economic consulting, which is, basically in usually large litigation cases, lawyers hire economists to value damages, so for example, when Samsung and Apple were suing each other, I worked on the Samsung side to help value how much they might sue Apple for, for patent infringement, and a lot of that involves statistical analyses, data analytics, econometrics as economists would call it. And I got really interested in just this idea of data being a really powerful tool for making decisions and coming to conclusions, and so I started hearing about machine learning on the Internet, kind of dabbling with Python, which at the time, I was a Windows user, and it was a huge pain to get Python installed, but I kind of got it up and running, played around with things like SciKit learn, read some blogs, and really got into machine learning and found that it was really housed more in the computer science department at that time, and just kind of decided to apply to some computer science departments and was lucky to get in at University of Texas at Austin and do some studies there, join a machine learning lab and got to do some work at Amazon. Really got a really good set of experiences to kind of help me learn how to be both a programmer and a machine learning person, a little bit of statistics, and jumped straight from there over here to Ancestry and was luc...

Would you rather take a year to develop a proprietary algorithm for your company that has an accuracy of 95% or use an open source platform that takes a day to develop an algorithm that has nearly the same accuracy? In most business cases, you'd choose the latter. In this episode, we talk to Till Bergmann who works on a team that developed TransmogriAI, an open source project that helps you build models quickly.