A.Charles Thomas, Phd, General motors
An interview with the Chief Data & Analytics Officer of General Motors
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Ali Tabibian: 00:06 Welcome, welcome, welcome everyone to this episode of Tech Cars, machines. My name is Ali Tabibian. As always, there are lots of good information and good links in the episode notes including a link to where you can find a transcript for this episode, a Charles Thomas, chief data and analytics officer of General Motors. What a pleasure and honor it is to have an executive of Charles has seniority and experience. Spend time with us. I know you. Our listeners will appreciate his time as much as we did. Charles, his entire career, as you'll hear, has been about analytics and has experienced spans industries from energy to technology companies such as HP and financial services. As an example, before coming to General Motors, he was the chief data officer and head of enterprise data and analytics at Wells Fargo, which is, you know, is one of the largest financial institutions in the world. And before that he served in a very similar capacity at the large insurer, USAA.
Ali Tabibian: 01:12 I find it interesting and important to point out for you that Charles launched his career off a Ph from Yale, focused on organizational behavior. Here's why I find this fact notable. You've noted in our episodes a, let's say with the executives from General Electric, from the tier one auto suppliers, magna and zed F, that the mandate of those executives, he's in just about selecting a particular technology or partners or making the right investments in third parties. It's also about their ability to affect change in a large complex organization, which rightfully typically have proud traditions and historically tried and tested workflows in those environments. How do you influence people and processes such that they seek the change rather than feel that those changes are being forced on them? When you survey business school graduates in a couple of decades after graduation, organizational behavior tends to be the course that they point to as the course that's been most useful to them in their careers. That's one reason why to me, Charles, his educational background was just as eye catching as his accomplishments and I invite you to listen carefully as he describes his objectives and approach. Let's get to it.
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Ali Tabibian: 02:38 Hello listeners. We're here in Los Angeles today. Downtown Los Angeles on a beautiful day, it's a bright blue sky with a wisp of cloud and I was walking over here to our interview location, just the absolute perfect weather and if I add to it that it's almost December and the weather is perfect, that kind of information, we'll you understand why it's so expensive in Los Angeles. So speaking of data leading to insights, we're here with A. Charles Thomas, chief data and analytics officer of General Motors. Charles, thank you so much for being with us today. We really appreciate it. Happy to be here. Great. Charles, your, you joined to General Motors fairly recently when compared to the, to the length of your career in the field of analytics. Tell us about your role at GM. What is it scope and what led you to accept, the current position versus a very prominent role you previously had with Wells Fargo?
A. Charles Thomas: 03:34 Well, certainly General Motors, a reparative represented an opportunity to not only do the traditional role of a chief data analytics officer, which is to make sure the data are aligned to help the enterprise leverage insights to drive actions that drive better results and the like, but to also do it within the sphere of the Internet of things are rolling Internet of things, as I call it, a, we've had connected vehicles for about 20 years now and we have not optimize the usage of those data for enhanced the customer experience or potentially even generating new revenue streams. So this was an opportunity to take what I'd learned over the past and really applied into a space I love.
Ali Tabibian: 04:13 Based on that description, is it accurate to think of the types of data that you're working with mainly in sort of the post production environment of General Motors or does it extend to the operating environment as well?
A. Charles Thomas: 04:26 It includes operating environment. We're doing quite a number of projects in the manufacturing space to help them optimize what they're doing. There’s all kinds of logistics work that can be done around inventory, around, you know, where we store parts and the likes. So this isn't just a customer facing kind of position that's dealing with customer experience only. It's really spanning the gamut of how the company generates and will potentially use data in the future across all domains.
Ali Tabibian: 04:54 Okay, excellent. What business outcomes are you targeting at General Motors? What do you expect to change as a result of your activity?
A. Charles Thomas: 05:02 Well, there's just the things that matter most to the business. We are an EBIT driven company so we want to find ways to drive positive EBITDA and cash flow. We want to delight customers and so everything from JD Power results and the like, we want to retain customers in a multigenerational framework. So that might draw new metrics around not just customer retention. Did you get your next gen vehicle, but do your kids by GM vehicles and do your grandchildren by GM vehicles. So there's quite a number of metrics that are really core to the business, but they're in there. So also some transformational ones as well. So, you know, we looked at things such as utilization of our dashboards because we wanted to transform the culture to be much more fact based and more, more insight driven. There quite a number of metrics. It's across the gamut that we're using to measure our success, but it all starts with what's success to the business.
Ali Tabibian: 05:55 Absolutely. Okay. And in that case, just our listeners, the dashboard you're speaking of is the internal analytical dashboards that the, that the analysts? Yes. Okay. Yeah. Okay, great. It's interesting when I was listening to your response, you used a lot of phrases, especially as it leads to customer outcomes that we hear when we work with insurance companies, and I know in your background you have wells Fargo, which is a consumer oriented company as well as a USAA. What are your last two? I think major employers. Tell us a little bit about how, what's common and what's different maybe between General Motors and your prior position in terms of how they use data and how they think about that customer.
A. Charles Thomas: 06:35 Yeah. So in insurance and in banking, as you can imagine, they're going to be a little more advanced in terms of utilizing data largely because of risk. So imagine a major credit card issuer needs to really understand the risk that they're underwriting when they are issuing credit to a customer. So they'd been using analytics for quite awhile. Insurance is all about analytics and it's all about, you know, actuary and really assessing the risk of someone getting in an accident and the cost of that accident. So those industries are really born of needing to understand risk and understand the impact that it has on the balance sheet. I would expect that those companies would be more advanced in terms of utilizing data and analytics. However, there's a lot of data and analytics that happens within a car manufacturer. We just maybe don't think of it that way because it largely happens in engineering.
A. Charles Thomas: 07:25 And so if you think about all of the safety work, if you think about all of the work that's done to increase the performance of the vehicles went heavy. That's non traditional what we call analytics. But it's very much quantitative in nature and use as many of the same techniques and software where we have not historically used insights as much as we could. There's really been on the customer experience side. And so the similarities between the companies or a customer gets in a vehicle and they want to do something they don't want to just drive. They want to do some things and the experiences wrapped around them and you want to understand what they're trying to accomplish and how you make their lives easier. The same thing as if someone goes into a bank branch where someone calls to file a claim. You want to maximize those interactions and ensure that you're relevant and timely and that's you're speaking to them about the right things at the right time so that you can get them to use your products and be delighted by them. So there are things that kind of now, you know, are similar across that if you say a customer is a customer and if they like you, they'll buy more stuff. And if they don't, they want, then, you know, if you look at it from that perspective, very similar,
Ali Tabibian: 08:27 interesting. So the data now has us as much of a role as the product that the hardware product in terms of creating that customer affinity and, and long-term affection for the brand.
A. Charles Thomas: 08:38 It's not just about how good looking at cars are, how fast they are. Everybody makes good looking and fast cars. It's really about, you know, the safety features. It's really about ease of engaging with the technology. It's about what you can do in the vehicles, about connected experience from when you're in your home, your driveway to the car, to your office or wherever it might be. And being able to use your mobile device with simplicity across all of those things and have an experience that's replicated regardless of where you are. A simple continuation of what you're doing. I believe in the future. I'm not saying that cars won't be important, they always will be, but I think what you're able to do in your vehicle will become increasingly important. And if you have a collegiate experience in the vehicle, that doesn't overcome, you know, having an attractive car, having a good looking car, having a fast car that I think it will be less important if you don't have, you know, at least some baseline great experience in the vehicle.
A. Charles Thomas: 09:32 It's interesting in the enterprise environment, so much of the effort about it's about getting everything into a single, onto a single pane of glass. When you go to the consumer side, a lot of it is about synchronizing multiple panes of glass that the, that the consumer uses. It's, it's an interesting, seamlessly symmetry it seamlessly. That's the key. They don't care that they're doing business with one line of business now. Now they've helped across to another line of business. They want one experience and they want easy. They want value, they want speed, they want one button if possible. And if we design, you know, our experiences like did the digital world does in like mobile does a, I think we'll be in a great position moving forward. You've been with the concept of using analytics inside organizations as well as extending it to the consumer experience for as long as really most people have in this space.
A. Charles Thomas: 10:21 What can you tell us about what's changed in terms of how people use the data to things? Really, technology has made the ability to process massive amounts of data in a very short period of time. That has changed everything. You know, when I started my career, you had to go pull a sample sample. Why do you pull a sample a sample because your machine can't process a million rows. It can only process 10,000. So you have to pull a sample in a stratified sample. You write your code, you do your analysis, it takes weeks. Things now take minutes or hours and that has compressed the insight generation component of the value life cycle pretty significantly. What’s not changed is how much time it takes to go from an insight to an action to result in. If you were to look at the things that we as practitioners have not lived up to, the promise on is that we have not convinced the business to more rapidly taken insight, plug it into the operations, generate the result, was kind of like the final yard is the longest yard.
A. Charles Thomas: 11:28 And so, you know, that still takes a lot of collaboration, a lot of partnership. And so I mentioned two things. The first one is technology. The second is people when I came out of school, there's no such thing as a degree in data sciences and so I think the professionalization of the space is helping, but there's still that final yard piece where we, we've gotta help business people get more comfortable with using this information and taking risks, calculated risks using, you know, multivariate experiments in market tests and things like that that allow you to test ideas without betting the farm on it. Um, we've got to get those people a much more comfortable doing. So. One of the things that I recommend and if I were to coach young people in the space as aspire to move out of the data sciences and become a marketing manager or product manager or move into one of these decision maker roles where you know, what you need from a data perspective and you can move more quickly to get to the answer and drive results. So that's a, a, that would be kind of my aspiration for people coming behind me is to think more broadly, not just about being data and analytics professionals, but being professionals who use data and analytics and if you round out your skills, you can be very successful and drive measurable results that you can take credit for.
Ali Tabibian: 12:50 Very interesting. Thank you. And I think one thing I noticed in your bio was you actually use the term storytelling in one of the things you try to teach. Uh, I believe it's at Cal that you have an advisory role that you try to do. Do you ask them to focus on the role of storytelling?
A. Charles Thomas: 13:06 It's critical. I mean, you're dealing with very smart people who, you know, your, your business partner is the person you're trying to influence, very intelligent and got great degrees from great places. They've been doing their job, you know, for 30 years, they know their space, but they've just never used this stuff before. And so, you know, if you present it as well, that's the fact. And why aren't you taking action? Good luck, which you really need to do is kind of walk a mile in their shoes, helped them use language that will appeal to them, help them understand. Not that they're not intelligent, it's just that they've never used this stuff. So the burden is on the teacher. Think back to your favorite professors in college. Were they the most brilliant professors are the ones that really helped you understand and you walked away. So I got it. That person spoke to me in a way that reached me and used, you know, analogies use symbolism that now I totally get it. Or the person who went up to the board and you know, they wrote out a bunch of equations that figured it out. Right. Those, those people might be brilliant, but they are not very effective. I'd rather be effective than brilliant.
Ali Tabibian: 14:10 Understood. Maybe making that a little bit even more specific if you care to. How do you then specifically line up the roles and responsibilities between the office of the data and analytics officer and the business units and the R and d organizations who presumably have some people who were kind of doing maybe less sophisticated versions of the same things.
A. Charles Thomas: 14:33 Yeah. So, you know, there are some recent articles about a couple of firms that really focus on how do you bring in this coalition of peers. Um, you know, so you need to have the CIO at the table because you need to leverage the technology in the data that they're generating. A, you need to have a business person who owns the action. And then as the analytics professional, you need to come to the table with, you know, you asked me for this very basic thing, right? And so I'm going to answer the mail on that, but then I'm going to earn your trust so that now I can counter propose the next time you ask me for something, so yeah, I'll be happy to deliver that, but let's take a step back and see what you're trying to accomplish and what would it not be more effective if we tried this approach that gives you what you asked for plus, but that's really about relationships and that's about trust, but it's having the right people at the right level and how to engage with them.
A. Charles Thomas: 15:23 Now that doesn't always mean it's the senior most person of the group is great when you have a mandate from the top, but very seldom are they actually in the details, so you really need to understand is where the leverage points are in the organization. Who owns the actions, and then how do you reach out to them and then influence them. Sometimes you're not the right person to do the influencing a. sometimes you need to get their chief lieutenant on board and then have that person influenced. And so no matter how much data, how much technology, how much tools, and all this other stuff, it's all about people because people still make the decisions and until people don't make the decisions anymore and we run everything on algorithms, analysts are going to have to learn how to influence. I'm in the influencing business. I don't own the means of production, nor do I own the business outcome. In this particular case, I own the influencing of those things and holding people accountable wants to influencing has happened. But that is the extent of my role. And in people need to understand that there are certain skills that you must have if you're going to be effective in doing that.
Ali Tabibian: 16:24 That's a great answer. How do you calibrate for the rate of change? There's people who want you to make that change go faster, but then there's also the reality of the organization and some of that immune system response that you might get if you try to push too hard. How do you calibrate that?
A. Charles Thomas: 16:40 Yeah. You have to think of yourself as being an internal consultancy that comes in and diagnoses not only the business challenge that your partner is facing, but also their level of adoption slash maturity. And so we have certain business partners where there's really not much we do other than provide tools and maybe some training because they know what they want to do and they're ready to go fast. And so we're just wanting to be a key enabler. There are others who were like, I believe that there's something there. There's some there there regarding this data stuff, but you know, I really need someone to escort me all the way through. And so our service offering, you know, and the skillset of the people will vary. So someone who's got great bedside manner would be assigned to the ladder. Someone who's efficient and gets things done might be assigned to the former and they both get what they need.
A. Charles Thomas: 17:30 But you get to, you know, mix up your service offerings so that you're giving the organization where it needs and your, if you peanut butter and say, well, everybody gets the handholding escorting within. You've just grown into place to a halt a, but if you just simply want to provide tools and training everybody, people will say, okay, what am I supposed to do with that? So you really got to balance it and understand who you're talking to and understand their ability to digest and the speed with which they can and then how do you operate in leverage channels that they have already established in order to make things happen when trying to bring this sort of change to an industrial organization. Frequently, when I ask people in your position, they say that for a period of time, if you kill the gestation period, really the initiative for this type of thing has to come right from the top.
A. Charles Thomas: 18:16 The people at the top nurture your effort until it's had a chance for people to draw in what you're offering them as opposed to you having to explain what you have to offer them. Is that your view and your experience as well? Yes, and it. If the senior most person says, we're going to do this, but not just that, we're going to do this, but I'm going to periodically check in and make sure that we're doing this. That's where you have the greatest degree of success. Where it doesn't work is when someone says, we're going to do this, and then they come back as, okay, so where's the results? And I'm like, well, it fell apart three layers down because they had the 1000 reasons why they can't do this and you can mandate it from the top, but you're going to have to set up some sort of cadence around when do you check in as a senior leader and say, okay, we said we're going to do this.
A. Charles Thomas: 19:04 Did we do it? If we didn't what standing in a way. And then my expectation is that we're going to do it in next time. I'm going to see some results and when you have that or you're much more likely to be successful, outstanding. Sort of the corollary question to what's the right way to start is how do you know you've been successful? And when I was preparing for this interview, you gave an answer to this question that I always ask and some of the public collateral that I saw that was probably the most illuminating one I've heard. And you said, I know I'm being successful when the business unit starts stealing my people essentially. I'm paraphrasing.
A. Charles Thomas: 19:40 Yeah. Well, so they're kind of operational metrics that we want to establish regarding service level agreements. And did we help the company make money or save money or whatever the objective of activity was, is that, that's kind of price of admission that's not transformational, is very transactional. Their transformational piece is, you know, do you see senior leaders interrogating data, having robust debates and having conversations about what to do next from what they see in front of them as opposed to staged, you know, presentations were coming to. Someone comes in and says, here's what we're going to do. And everybody nods and they move forward, or they really questioning and seeking truth, seeking knowledge and interrogating information in order to come up with that. Another way that I'll know that I'm successful is if we can, you know, move away from a lot of these paper binders full of, you know, information that lots of senior people walk around with and if we make information easy for them to engage on their iPad and we coach them and teach them so that they can interrogate the themselves and not have to flip through a thousand pages.
A. Charles Thomas: 20:50 The more educated the consumer, the easier it is for me to do my job and focus on the next big thing. The stealing of people is a, is a good thing because people will see that hey, the quickest way for me to be insight driven, it's to go take someone who knows how to do this by second nature that I love because one, it creates great opportunities for our people. And I think I should be an exporter of talent too, is that it makes my job so easy because it's just pitch and catch because I don't have to influence. I don't have to beg or I don't have to do anything to get people to use my stuff. They know exactly what to do with it and the quickest way for it to catch fire is they're sitting around their staff and you know, we have one example of they're just killing it with results and the boss like, well, where's, where's everybody else's results? You know, why aren't you using this stuff and that person, if they're wise and they're mature system, I can help you with that and I think there's a lot of opportunity for it to catch fire when you have a, you know, someone who is exported from an analytics team who goes into a business function and then creates change.
Ali Tabibian: 21:55 Outstanding. Charles, in a lot of what you've described here, there's a running theme of you getting all parts of the organization that the people you wouldn't necessarily expect to be involved with. The kinds of things that you are, that you're working with. You really get them involved, get them engaged. And I know if you don't mind, I'm transitioning a bit into the personal side of the history. I know your own background in your own personal history involves sort of a history with. You're probably starting with your father of him being drawn into and being groundbreaking in terms of what he did at his own career and in some ways, at least in terms of a role model setting the stage for you. Are you okay? Giving us a little bit of that background in terms of what that, what that meant to you, to you?
A. Charles Thomas: 22:38 Yeah. So we're from Baton Rouge, Louisiana, and my parents or children or the fifties and sixties, and so integration particularly professionally was starting to happen at that time. And my dad in 1968 was the first black professional hired by a Exxon refinery there, which was at the time when the largest in North America. And he was the only one for a little while and the years before my grandfather was a janitor there, but unfortunately passed away before he saw my dad do that. But you know, he being a first has you take a lot of poison darts. So I learned a lot of lessons about being a first in my own career. I'm first chief data and analytics officer. Three, three major companies. And you know, how to win people over, how to understand their business challenges and how to help them and how to solve them, how to break down some of the.
A. Charles Thomas: 23:37 Maybe there aren't the same racial barriers, but there are certainly barriers of while I'm an egg head and so, you know, how do I fit into a environment and, and, and that sort of thing. So how do you, you know adapt to your environment. So those are lessons that ultimately you want to be effective and you want to be successful. And so the burden is often on you to make the adjustments once you understand what you're walking into. And so that's allowed me to, I think, be able to, to move through various industries, various environments, and to pioneer things and not have any fear of failure because without risk there's no reward. So I learned that a long, long time ago and didn't have carried that into my professional career.
Ali Tabibian: 24:22 Excellent. You know, in my own case, we used to live in Baltimore in the seventies and my dad who has a little bit of a darker complexion that I do, he would be in the hospital in an elevator with a surgical unit on. I could see he was a. He was a surgeon and if he was the only person in the elevator and people wouldn't get in with them, but I was always struck by how little that he didn't really let that bother him. He just didn't let them define it. It was just an observation that he. That he made and then he just kind of moved on from there and part of his reaction was to be always well groomed, well dressed, just present himself a little bit more sharply than you otherwise would need to. And it's hard to do that in scrubs, but still trying to put the logo designer glasses. You know, you've talked about in some of the material I've seen about the analytics being about really cool stuff versus Roi. How do you measure the Roi of, of what you do, what structures have you found out to be useful and successful?
A. Charles Thomas: 25:16 The first person you need to get to meet in the whole process is the chief financial officer that is the number one person because you need them to certify the benefits that you claim to the company so, so that's understanding, helping walk them through methodologies as you're going to use the going to drive incremental performance that you can now take credit for. Getting them on your side is critical. That way you don't have to extend your own credibility and oftentimes someone in my role is relatively new and so you don't have much credibility and so and in you know, success has many fathers as the saying goes and failure is a bastard according to President Kennedy. And so as a result, lots of people will want to take credit when the results are there. So you've got to establish the methodological side that says we're going to use an in market, A-B test or multivariate test against.
A. Charles Thomas: 26:12 And the challenge there is going to be business as usual. Here's how we're going to construct it in any incremental lift will attribute to the change in stimulus we put into the environment and now can we get her agreement across all of the people who were involved before we kick off on this? Okay, yes. Alright, great. CFO, you're going to certify it, right? If it works, great. If it doesn't, fine, you know, I think it'll work, but let's try it. But having those processes that not only define the methodology but the venues through which you will get agreement that, okay, once we find this is the answer and then this person is going to certify the answer and they're in charge of that. Does everybody agree? Once you have that, if a much greater likelihood of being able to claim benefits above and beyond what you normally would when you don't have that agreement. Because once again, market forces or you know, or, or marketing brilliance or executive vision will always take credit for great results, but when you can say, yeah, but business as usual means that you would have done it exactly the way you've always done it and this is something new. And the new trump, the old by five percent, that five percent multiplied by a thousand dollars a vehicle equals blah, and you know, CFO, do you certify that gives you your greatest chance of having claimed and recognize the benefits.
Ali Tabibian: 27:29 About three years ago in one of our conferences, the chief executive of AAA Insurance, the CIO of Dignity Health, which is a large healthcare provider, a couple other people, CIO of Chevron basically said, listen, we don't pay for discovery when it comes to data analytics. We define a problem and we and we try to solve it. What's interesting is today, or during the course of this year, when I've talked to those same people both privately as far as this podcast series is concerned. That's changed where people now will pay for discovery. In other words, there's a little bit of an r and d mentality when it comes to data analytics. Have you noticed that change? Is that something that you would say has been the case over the last few years or do you have a different view on?
A. Charles Thomas: 28:12 Yes. I think you've got to, in and out all of those people you, you mentioned you've got to answer the mail on their immediate needs to earn the trust. If you come in with this, you know, fancy, strategic visionary, it's going to take us five years thing. You won't be around to see that actually executed. You've got to, you've got to execute on their immediate needs, earn their trust. Then they come and say, okay, so that's pretty good. What else can you do? And then then you say, well, you didn't know this, but we were working on this other thing on the side for you for just this moment. And here's what the possibilities really local, like let's try and in, you're much more likely to, to have when, when you, when you answer the mail on the immediate need, that doesn't mean you don't start the transformational thing until you get agreement. You should finance that yourself, right? Carve off a little bit of your capacity to work on the next big thing. You might not necessarily tell them because they'll say, I want all the resource working on my immediate problem. So you solve their immediate problem. And then if they're good business people, they'll say, well, what else can you do? And he said, well, I'm glad you asked that question. Here's what we've been waiting for. Here's what we can do.
Ali Tabibian: 29:32 Excellent. Let me turn the conversation a little bit toward the technology architect or some of the things that's sort of the vendor ecosystem might be interested in hearing.
A. Charles Thomas: 29:42 What's your view on what, what is the right architecture for, for analytics within the organization? There's the typical, when you talk about IoT, analytics, etc., one of the big pushes and pulls his edge versus core. How much of it do you bring back and consolidate and, and run, you know, in one fell swoop versus kind of enabling the edge, whether it be the machinery itself or the people themselves to do a little bit of the work. Is that an interesting question for you? Or we can move on to something and I think, you know, it just depends on the application, right? I mean, you know what I mean, the technical application, I mean the business application, if you're wanting to solve for an immediate need that's happening in a vehicle while a customer is driving in real time, that would have a certain set of parameters around how the data needs to be captured in process and utilized.
A. Charles Thomas: 30:30 You might collect something that generation algorithm that you know, warns them about, you know, lane departures or warns them about speeding or you know, if you have an insurance product that warns them about reckless driving behavior that you know, that brings them back into line that now processes things you know right there on the vehicle with an algorithm that now, you know, is, is very informational. There are other things that require that you blend those data points with other things about the customer or about whatever it might be that you now need to bring it back to your larger data lake into blend that together to generate an insight that now might be pushed and executed in real time once you generate the algorithm. But in order to generate the insight, you might need to pull it all back into, into one place.
A. Charles Thomas: 31:19 So I think the key is to understand and discern which business application you are trying to accomplish and then design your architecture to meet that very specific business. The companies will often one size fits all it approach and then they are very sadly disappointed when you're looking for something to happen in real time as close to the customer as possible. But you've created an environment that requires you to pull it into, process it, to merge it, to do some other things and then push it back out again. It can do it much faster than it could have when I started my career. But it's certainly not real time. Is there something you're surprised that we didn't talk about today or you wish we had talked about today? And the second question is when people talk about machine learning, Ai, visualization, etc. What do all those, all those mean to you?
A. Charles Thomas: 32:11 Are they, are these really dramatic breakthroughs or they just increasing refinements and how data's always been used? So the first one I would have said, I thought we would talk more about AI and ML so and so. So now to me technology is always obsolete in a few months anyway. So to me it's just the better tool. It is transformational in the right hands. The company has to be mature that people who are leveraging and need to be mature, they need to know what to do with it. Um, but for me, these are just the latest of what people think are new tools, but they're really not. I mean, when I was an analyst we were building neural network models and you know, which is a form of artificial intelligence and many cases they outperform traditional regression based techniques. In many cases they didn't.
A. Charles Thomas: 33:03 I think, you know, these new evolution is now are because they happen a lot faster, they don't need an analyst to actually execute the work. They are in many cases self-learning and they also happen in real time or near real time. That's the biggest difference. But the math hasn't really changed. It has changed, but it's still based upon many of the same principles. My question is, you know, who's using it and if it's just the digital companies once again within, that's interesting. But you know, there's a, there's a great book out that I wrote. Um, I was able to, to write a little, not a forward but the jacket comments and um, it was really just about how do you, how do you actually put the stuff into play and, and use it. And that's still the great beyond. So, you know, until companies do that these are all interesting science projects and you know, what happened to your fourth grade erupting volcano is somewhere in the trash heap and nobody is talking about it anymore. So, um, if we don't get companies use this stuff is all just going to be interesting and interesting is the kiss of death in this business because ultimately you want to be effective. Interesting. That's nice. Ineffective but nice. Right? Charles, thank you so much for spending the time with us. Really appreciate it. Alright, thanks. Thank you.
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