Podcasts about Analytica

Claudio Caprarulo @ClauCaprarulo (Economista, Director en Analytica Consultora @AnalyticaArg ) Unas Cuantas Verdades @marianoobarrio

Ricardo Delgado, economista y presidente de la consultora Analytica @delgado_rr @AnalyticaARG @rosca_casta 27-5-2025.mp3

Claudio Caprarulo Economista - Director de @analytica (@ClauCaprarulo) @ASTERISCOSTV 14-5-2025

Le big tech non sono più solo aziende che vendono telefoni o servizi. Sono diventate imperi digitali che controllano le nostre vite. Decidono come passiamo il tempo, come lavoriamo e come parliamo tra noi. Il problema? Nessuno ha chiesto loro di farlo. L'hanno fatto per guadagnare sempre di più. E quando qualcuno prova a mettergli dei limiti, fanno resistenza.Tutti i miei link: https://linktr.ee/br1brownHedge fund activism: some cases studies | Semantic ScholarThe New Fascist International: Technocratic Oligarchy and the Threat to DemocracyBig Tech, sarà dura battaglia: ecco i principali trend 2024 - Agenda DigitaleLe società di consulenza che influenzano le istituzioni europee per favorire le Big TechIl fenomeno del Big Tech: una sfida agli equilibri tradizionali della finanzaTELEGRAM - INSTAGRAMSe ti va supportami https://it.tipeee.com/br1brown

Ricardo Delgado, economista y presidente de la consultora Analytica @delgado_rr @AnalyticaARG @JoaquinMugica @MarianoBoettner 14-4-2025

Ricardo Delgado Economista - Director de la Consultora Analytica @AnalyticaARG (@delgado_rr) @asteriscostv 26-3-2025

Claudio Caprarulo @ClauCaprarulo (Economista, Director en Analytica Consultora @AnalyticaArg ) Unas Cuantas Verdades

Ricardo Delgado @delgado_rr (Economista, Presidente de la Consultora Analytica @AnalyticaARG ) Hora 15 @Hora15ECO

Genevieve Hayes Consulting Episode 53: A Wake-Up Call from 3 Tech Leaders on Why You're Failing as a Data Scientist Are your data science projects failing to deliver real business value?What if the problem isn’t the technology or the organization, but your approach as a data scientist?With only 11% of data science models making it to deployment and close to 85% of big data projects failing, something clearly isn’t working.In this episode, three globally recognised analytics leaders, Bill Schmarzo, Mark Stouse and John Thompson, join Dr Genevieve Hayes to deliver a tough love wake-up call on why data scientists struggle to create business impact, and more importantly, how to fix it.This episode reveals:Why focusing purely on technical metrics like accuracy and precision is sabotaging your success — and what metrics actually matter to business leaders. [04:18]The critical mindset shift needed to transform from a back-room technical specialist into a valued business partner. [30:33]How to present data science insights in ways that drive action — and why your fancy graphs might be hurting rather than helping. [25:08]Why “data driven” isn’t enough, and how to adopt a “data informed” approach that delivers real business outcomes. [54:08] Guest Bio Bill Schmarzo, also known as “The Dean of Big Data,” is the AI and Data Customer Innovation Strategist for Dell Technologies' AI SPEAR team, and is the author of six books on blending data science, design thinking, and data economics from a value creation and delivery perspective. He is an avid blogger and is ranked as the #4 influencer worldwide in data science and big data by Onalytica and is also an adjunct professor at Iowa State University, where he teaches the “AI-Driven Innovation” class.Mark Stouse is the CEO of ProofAnalytics.ai, a causal AI company that helps companies understand and optimize their operational investments in light of their targeted objectives, time lag, and external factors. Known for his ability to bridge multiple business disciplines, he has successfully operationalized data science at scale across large enterprises, driven by his belief that data science’s primary purpose is enabling better business decisions.John Thompson is EY's Global Head of AI and is the author of four books on AI, data and analytics teams. He was named one of dataIQ's 100 most influential people in data in 2023 and is also an Adjunct Professor at the University of Michigan, where he teaches a course based on his book “Building Analytics Teams”. Links Connect with Bill on LinkedInConnect with Mark on LinkedInConnect with John on LinkedIn Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE Read Full Transcript [00:00:00] Dr Genevieve Hayes: Hello, and welcome to Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and today I’m joined by three globally recognized innovators and leaders in AI, analytics, and data science.[00:00:24] Bill Schmarzo, Mark Stouse, and John Thompson. Bill? Also known as the Dean of Big Data, is the AI and Data Customer Innovation Strategist for Dell Technologies AI Spear Team, and is the author of six books on blending data science, design thinking, and data economics from a value creation and delivery perspective.[00:00:49] He is an avid blogger and is ranked as the number four influencer worldwide in data science and big data Analytica. And he’s also an adjunct professor at Iowa State University, where he teaches AI driven innovation. Mark is the CEO of proofanalytics. ai, a causal AI company that helps organizations understand and optimize their operational investments in light of their targeted objectives, time lag and external factors.[00:01:23] Known for his ability to bridge multiple business disciplines, he has successfully operationalized data science at scale across large enterprises. Driven by his belief that data science’s primary purpose is enabling better business decisions. And John is EY’s global head of AI and is the author of four books on AI data and analytics teams.[00:01:49] He was named one of DataIQ’s 100 most influential people in data in 2023. and is also an adjunct professor at the University of Michigan, where he teaches a course based on his book, Building Analytics Teams. Today’s episode will be a tough love wake up call for data scientists on why you are failing to deliver real business value and more importantly, what you can do about it.[00:02:17] So get ready to boost your impact. Earn what you’re worth and rewrite your career algorithm. Bill, Mark, John, welcome to the show.[00:02:25] Mark Stouse: Thank[00:02:26] Bill Schmarzo: Thanks for having us.[00:02:27] John Thompson: to be here.[00:02:28] Dr Genevieve Hayes: Only 11 percent of data scientists say their models always deploy. Only 10 percent of companies obtain significant financial benefits from AI technologies and close to 85 percent of big data projects fail. These statistics, taken from research conducted by Rexa Analytics, the Boston Consulting Group and Gartner respectively, paint a grim view of what it’s like working as a data scientist.[00:02:57] The reality is, you’re probably going to fail. And when that reality occurs, it’s not uncommon for data scientists to blame either the executive for not understanding the brilliance of their work, or the corporate culture for not being ready for data science. And maybe this is true for some organizations.[00:03:20] Particularly those relatively new to the AI adoption path. But it’s now been almost 25 years since William Cleveland first coined the term data science. And as the explosive uptake of generative AI tools, such as chat GPT demonstrate with the right use case. People are very willing to take on AI technologies.[00:03:42] So perhaps it’s finally time to look in the mirror and face the truth. Perhaps the problem is you, the data scientist. But if this is the case, then don’t despair. In many organizations, the leadership just don’t have the time to provide data scientists with the feedback necessary to improve. But today, I’m sitting here with three of the world’s best to provide that advice just for you.[00:04:09] So, let’s cut to the chase what are the biggest mistakes you see data scientists making when it comes to demonstrating their value?[00:04:18] Mark Stouse: I think that you have to start with the fact that they’re not demonstrating their value, right? I mean, if you’re a CEO, a CFO, head of sales really doesn’t matter if you’re trying to make better business decisions over and over and over again. As Bill talks about a lot, the whole idea here is economic,[00:04:39] and it is. About engaging, triggering the laws of compounding you’ve got to be able to do stuff that makes that happen. Data management, for example, even though we all agree that it’s really necessary, particularly if you’re launching, you know, big data solutions. You can’t do this sequentially and be successful.[00:05:04] You’re going to have to find some areas probably using, you know, old fashioned math around causal analytics, multivariable linear regression, things like that, to at least get the ball rolling. In terms of delivering better value, the kind of value that business leaders actually see as valuable[00:05:29] I mean, one of the things that I feel like I say a lot is, you have to have an understanding of your mission, the mission of data science. As somebody who, as a business leader champions it. Is to help people make those better and better and better decisions. And if you’re not doing that, you’re not creating value.[00:05:52] Full stop.[00:05:53] Bill Schmarzo: Totally agree with Mark. I think you’re going to find that all three of us are in violent agreement on a lot of this stuff. What I find interesting is it isn’t just a data scientist fault. Genevieve, you made a comment that leadership lacks the time to provide guidance to data scientists. So if leadership Is it treating data and analytics as an economics conversation if they think it’s a technology conversation is something that should be handled by the CIO, you’ve already lost, you’ve already failed, you already know you failed,[00:06:24] Mark mentioned the fact that this requires the blending of both sides of the aisle. It requires a data scientist to have the right mindset to ask questions like what it is that we’re trying to achieve. How do we create value? What are our desired outcomes? What are the KPIs metrics around which are going to make your success?[00:06:39] Who are our key stakeholders? There’s a series of questions that the data scientist must be empowered to ask and the business Leadership needs to provide the time and people and resources to understand what we’re trying to accomplish. It means we can go back old school with Stephen Covey, begin with an end in mind.[00:07:01] What is it we’re trying to do? Are we trying to improve customer retention? We try to do, you know, reduce unplanned operational downtime or improve patient outcomes. What is it we’re trying to accomplish? The conversation must, must start there. And it has to start with business leadership, setting the direction, setting the charter, putting the posts out where we want to go, and then the data science team collaborating with the stakeholders to unleash that organizational tribal knowledge to actually solve[00:07:32] Dr Genevieve Hayes: think a lot of the problem comes with the fact that many business leaders see data science as being like an IT project. So, if you’ve got your Windows upgrade, the leadership It gives the financing to IT, IT goes along and does it. And then one morning you’re told, when you come into work, your computer will magically upgrade to the latest version of Windows.[00:07:55] So no one really gets bothered by it. And I think many business leaders treat data science as just another IT project like that. They think they can just Give the funding, the data scientists will go away and then they’ll come in one morning and the data science will magically be on their computer.[00:08:15] Bill Schmarzo: Yeah, magic happens, right? No, no, magic doesn’t happen, it doesn’t happen. There has to be that leadership commitment to be at the forefront, not just on the boat, but at the front of the boat saying this is the direction we’re going to go.[00:08:29] John Thompson: That’s the whole reason this book was written. The whole point is that, analytics projects are not tech projects. Analytics projects are cultural transformation projects, is what they are. And if you’re expecting the CEO, CFO, CIO, COO, whoever it is, to go out there and set the vision.[00:08:50] That’s never going to happen because they don’t understand technology, and they don’t understand data. They’d rather be working on building the next factory or buying another company or something like that. What really has to happen is the analytics team has to provide leadership to the leadership for them to understand what they’re going to do.[00:09:12] So when I have a project that we’re trying to do, my team is trying to do, and if we’re working for, let’s say, marketing, I go to the CMO and I say, look, you have to dedicate and commit. that your subject matter experts are going to be in all the meetings. Not just the kickoff meetings, not just the quarterly business review, the weekly meetings.[00:09:36] Because when we go off as an analytics professionals and do things on our own, we have no idea what the business runs like. , we did analytics at one company that I work for. We brought it back and we showed it to the they said, the numbers are wildly wrong. And we said, well, why? And they said, well, you probably don’t understand that what we do is illegal in 10 US states.[00:10:00] So you probably have the data from all those 10 states in the analysis. And we did. So, we took it all out and they look down there and go, you got it right. It’s kind of surprising. You didn’t know what you were doing and you got it right. So, it has to be a marriage of the subject matter experts in the business.[00:10:17] And the data scientists, you can’t go to the leadership and say, tell us what you want. They don’t know what they want. They’d want another horse in Henry Ford’s time, or they glue a, a Walkman onto a radio or something in Steve Jobs time. They don’t know what they want. So you have to come together.[00:10:36] And define it together and you have to work through the entire project together.[00:10:42] Mark Stouse: Yeah, I would add to that, okay, that a lot of times the SMEs also have major holes in their knowledge that the analytics are going to challenge and give them new information. And so I totally agree. I mean, this is an iterative learning exchange. That has profound cultural implications.[00:11:11] One of the things that AI is doing right now is it is introducing a level of transparency and accountability into operations, corporate operations, my operations, your operations, that honestly, none of us are really prepared for. None of us are really prepared for the level of learning that we’re going to have to do.[00:11:36] And very few of us are aware of how polymathic. Most of our challenges, our problems, our objectives really are one of the things that I love to talk about in this regard is analytics made me a much better person. That I once was because it showed me the extent of my ignorance.[00:12:01] And when I kind of came to grips with that and I started to use really the modicum of knowledge that I have as a way of curating my ignorance. And I got humble about it made a big difference[00:12:16] John Thompson: Well, that’s the same when I was working shoulder to shoulder with Bill, I just realized how stupid I was. So, then I just, really had to, come back and, say, oh, God nowhere near the summit, I have a long way to go.[00:12:31] Bill Schmarzo: Hey, hey, Genevie. Let me throw something out there at you and it builds on what John has said and really takes off on what Mark is talking about is that there is a cultural preparation. It needs to take place across organizations in order to learn to master the economies of learning,[00:12:48] the economies of learning, because you could argue in knowledge based industries that what you are learning is more important than what you know. And so if what you know has declining value, and what you’re learning has increasing value, then what Mark talked about, and John as well, both city presenting data and people saying, I didn’t know that was going on, right?[00:13:09] They had a certain impression. And if they have the wrong cultural mindset. They’re going to fight that knowledge. They’re going to fight that learning, oh, I’m going to get fired. I’m going to get punished. No, we need to create cultures that says that we are trying to master the economies and learning and you can’t learn if you’re not willing to fail.[00:13:29] And that is what is powerful about what AI can do for us. And I like to talk about how I’m a big fan of design thinking. I integrate design thinking into all my workshops and all my training because it’s designed to. Cultivate that human learning aspect. AI models are great at cultivating algorithmic learning.[00:13:50] And when you bring those two things together around a learning culture that says you’re going to try things, you’re going to fail, you’re going to learn, those are the organizations that are going to win.[00:13:59] John Thompson: Yeah, you know, to tie together what Mark and Bill are saying there is that, you need people to understand that they’re working from an outmoded view of the business. Now, it’s hard for them to hear that. It’s hard for them to realize it. And what I ask data scientists to do that work for me is when we get a project and we have an operational area, sales, marketing, logistics, finance, manufacturing, whatever it is.[00:14:26] They agreed that they’re going to go on the journey with us. We do something really simple. We do an exploratory data analysis. We look at means and modes and distributions and things like that. And we come back and we say, this is what the business looks like today. And most of the time they go, I had no idea.[00:14:44] You know, I didn’t know that our customers were all, for the most part, between 70 and 50. I had no idea that our price point was really 299. I thought it was 3, 299. So you then end up coming together. You end up with a shared understanding of the business. Now one of two things is generally going to happen.[00:15:05] The business is going to freak out and leave the project and say, I don’t want anything to do with this, or they’re going to lean into it and say, I was working from something that was, as Bill said, declining value. Okay. Now, if they’re open, like a AI model that’s being trained, if they’re open to learning, they can learn what the business looks like today, and we can help them predict what the business should look like tomorrow.[00:15:31] So we have a real issue here that the three of us have talked about it from three different perspectives. We’ve all seen it. We’ve all experienced it. It’s a real issue, we know how people can come together. The question is, will they?[00:15:46] Dr Genevieve Hayes: think part of the issue is that, particularly in the area of data science, there’s a marked lack of leadership because I think a lot of people don’t understand how to lead these projects. So you’ve got Many data scientists who are trained heavily in the whole technical aspect of data science, and one thing I’ve come across is, you know, data scientists who’ll say to me, my job is to do the technical work, tell me what to do.[00:16:23] I’ll go away and do it. Give it to you. And then you manager can go and do whatever you like with it.[00:16:29] Mark Stouse: Model fitment.[00:16:31] Dr Genevieve Hayes: Yeah. And then one thing I’ve experienced is many managers in data science are, you know, It’s often the area that they find difficult to find managers for, so we’ll often get people who have no data science experience whatsoever[00:16:46] and so I think part of the solution is teaching the data scientists that they have to start managing up because they’re the ones who understand what they’re doing the best, but no one’s telling them that because the people above them often don’t know that they should be telling the data[00:17:08] John Thompson: Well, if that’s the situation, they should just fire everybody and save the money. Because it’s never going to go anywhere. But Bill, you were going to say something. Go ahead.[00:17:16] Bill Schmarzo: Yeah, I was going to say, what’s interesting about Genevieve, what you’re saying is that I see this a lot in not just data scientists, but in a lot of people who are scared to show their ignorance in new situations. I think Mark talked about this, is it because they’re, you think about if you’re a data scientist, you probably have a math background. And in math, there’s always a right answer. In data science, there isn’t. There’s all kinds of potential answers, depending on the situation and the circumstances. I see this all the time, by the way, with our sales folks. Who are afraid we’re selling technology. We’re afraid to talk to the line of business because I don’t understand their business Well, you don’t need to understand their business, but you do need to become like socrates and start asking questions What are you trying to accomplish?[00:18:04] What are your goals? What are your desired outcomes? How do you measure success? Who are your stakeholders ? You have to be genuinely interested In their success and ask those kind of questions if you’re doing it to just kind of check a box off Then just get chad gpt to rattle it off But if you’re genuinely trying to understand what they’re trying to accomplish And then thinking about all these marvelous different tools you have because they’re only tools And how you can weave them together to help solve that now you’ve got That collaboration that john’s book talks about about bringing these teams together Yeah[00:18:39] Mark Stouse: is, famously paraphrased probably did actually say something like this, . But he’s famously paraphrased as saying that he would rather have a really smart question than the best answer in the world. And. I actually experienced that two days ago,[00:18:57] in a conversation with a prospect where I literally, I mean, totally knew nothing about their business. Zero, but I asked evidently really good questions. And so his impression of me at the end of the meeting was, golly, you know, so much about our business. And I wanted to say, yeah, cause you just educated me.[00:19:21] Right. You know, I do now. And so I think there’s actually a pattern here that’s really worth elevating. So what we are seeing right now with regard to data science teams is scary similar to what happened with it after Y2K, the business turned around and looked at him and said, seriously, we spend all that money,[00:19:45] I mean, what the heck? And so what happened? The CIO got, demoted organizationally pretty far down in the company wasn’t a true C suite member anymore. Typically the whole thing reported up into finance. The issue was not. Finance, believing that they knew it better than the it people,[00:20:09] it was, we are going to transform this profession from being a technology first profession to a business outcomes. First profession, a money first profession, an economics organization, that has more oftentimes than not been the outcome in the last 25 years. But I think that that’s exactly what’s going on right now with a lot of data science teams.[00:20:39] You know, I used to sit in technology briefing rooms, listening to CIOs and other people talk about their problems. And. This one CIO said, you know, what I did is I asked every single person in my organization around the world to go take a finance for non financial managers course at their local university.[00:21:06] They want credit for it. We’ll pay the bill. If they just want to audit it, they can do that. And they started really cross pollinating. These teams to give them more perspective about the business. I totally ripped that off because it just struck me as a CMO as being like, so many of these problems, you could just do a search and replace and get to marketing.[00:21:32] And so I started doing the same thing and I’ve made that suggestion to different CDOs, some of whom have actually done it. So it’s just kind of one of those things where you have to say, I need to know more. So this whole culture of being a specialist is changing from.[00:21:53] This, which, this is enough, this is okay , I’m making a vertical sign with my hand, to a T shaped thing, where the T is all about context. It’s all about everything. That’s not part of your. Profession[00:22:09] John Thompson: Yeah, well, I’m going to say that here’s another book that you should have your hands on. This is Aristotle. We can forget about Socrates. Aristotle’s the name. But you know. But , Bill’s always talking about Socrates. I’m an Aristotle guy myself. So, you[00:22:23] Bill Schmarzo: Okay, well I Socrates had a better jump shot. I’m sorry. He could really nail that[00:22:28] John Thompson: true. It’s true. Absolutely. Well, getting back , to the theme of the discussion, in 1 of the teams that I had at CSL bearing, which is an Australian company there in Melbourne, I took my data science team and I brought in speech coaches.[00:22:45] Presentation coaches people who understand business, people who understood how to talk about different things. And I ran them through a battery of classes. And I told them, you’re going to be in front of the CEO, you’re going to be in front of the EVP of finance, you’re going to be in front of all these different people, and you need to have the confidence to speak their language.[00:23:07] Whenever we had meetings, we talk data science talk, we talk data and integration and vectors and, algorithms and all that kind of stuff. But when we were in the finance meeting, we talked finance. That’s all we talked. And whenever we talked to anybody, we denominated all our conversations in money.[00:23:25] Whether it was drachma, yen, euros, pounds, whatever it was, we never talked about speeds and feeds and accuracy and results. We always talked about money. And if it didn’t make money, we didn’t do it. So, the other thing that we did that really made a difference was that when the data scientists and data scientists hate this, When they went into a meeting, and I was there, and even if I wasn’t there, they were giving the end users and executives recommendations.[00:23:57] They weren’t going in and showing a model and a result and walking out the door and go, well, you’re smart enough to interpret it. No, they’re not smart enough to interpret it. They actually told the marketing people. These are the 3 things you should do. And if your data scientists are not being predictive and recommending actions, they’re not doing their job.[00:24:18] Dr Genevieve Hayes: What’s the, so what test At the end of everything, you have to be able to say, so what does this mean to whoever your audience is?[00:24:25] Mark Stouse: That’s right. I mean, you have to be able to say well, if the business team can’t look at your output, your data science output, and know what to do with it, and know how to make a better decision, it’s like everything else that you did didn’t happen. I mean it, early in proof, we were working on. UX, because it became really clear that what was good for a data scientist wasn’t working. For like everybody else. And so we did a lot of research into it. Would you believe that business teams are okay with charts? Most of them, if they see a graph, they just totally freeze and it’s not because they’re stupid.[00:25:08] It’s because so many people had a bad experience in school with math. This is a psychological, this is an intellectual and they freeze. So in causal analytics, one of the challenges is that, I mean, this is pretty much functioning most of the time anyway, on time series data, so there is a graph,[00:25:31] this is kind of like a non negotiable, but we had a customer that was feeding data so fast into proof that the automatic recalc of the model was happening like lickety split. And that graph all of a sudden looked exactly like a GPS. It worked like a GPS. In fact, it really is a GPS. And so as soon as we stylized.[00:26:01] That graph to look more like a GPS track, all of a sudden everybody went, Oh,[00:26:10] Dr Genevieve Hayes: So I got rid of all the PTSD from high school maths and made it something familiar.[00:26:16] Mark Stouse: right. And so it’s very interesting. Totally,[00:26:21] Bill Schmarzo: very much mirrors what mark talked about So when I was the new vice president of advertiser analytics at yahoo we were trying to solve a problem to help our advertisers optimize their spend across the yahoo ad network and because I didn’t know anything about that industry We went out and my team went out and interviewed all these advertisers and their agencies.[00:26:41] And I was given two UEX people and zero data. Well, I did have one data scientist. But I had mostly UX people on this project. My boss there said, you’re going to want UX people. I was like, no, no, I need analytics. He said, trust me in UX people and the process we went through and I could spend an hour talking about the grand failure of the start and the reclamation of how it was saved at a bar after too many drinks at the Waldorf there in New York.[00:27:07] But what we’ve realized is that. For us to be effective for our target audience was which was media planners and buyers and campaign managers. That was our stakeholders. It wasn’t the analysts, it was our stakeholders. Like Mark said, the last thing they wanted to see was a chart. And like John said, what they wanted the application to do was to tell them what to do.[00:27:27] So we designed this user interface that on one side, think of it as a newspaper, said, this is what’s going on with your campaign. This audience is responding. These sites are this, these keywords are doing this. And the right hand side gave recommendations. We think you should move spend from this to this.[00:27:42] We think you should do this. And it had three buttons on this thing. You could accept it and it would kick into our advertising network and kick in. And we’d measure how effective that was. They could reject it. They didn’t think I was confident and we’d measure effectiveness or they could change it. And we found through our research by putting that change button in there that they had control, that adoption went through the roof.[00:28:08] When it was either yes or no, adoption was really hard, they hardly ever used it. Give them a chance to actually change it. That adoption went through the roof of the technology. So what John was saying about, you have to be able to really deliver recommendations, but you can’t have the system feel like it’s your overlord.[00:28:27] You’ve got to be like it’s your Yoda on your shoulder whispering to your saying, Hey, I think you should do this. And you’re going, eh, I like that. No, I don’t like this. I want to do that instead. And when you give them control, then the adoption process happens much smoother. But for us to deliver those kinds of results, we had to know in detail, what decisions are they trying to make?[00:28:45] How are they going to measure success? We had to really understand their business. And then the data and the analytics stuff was really easy because we knew what we had to do, but we also knew what we didn’t have to do. We didn’t have to boil the ocean. We were trying to answer basically 21 questions.[00:29:01] The media planners and buyers and the campaign managers had 21 decisions to make and we built analytics and recommendations for each Of those 21[00:29:10] John Thompson: We did the same thing, you know, it blends the two stories from Mark and Bill, we were working at CSL and we were trying to give the people tools to find the best next location for plasma donation centers. And, like you said, there were 50, 60 different salient factors they had, and when we presented to them in charts and graphs, Information overload.[00:29:34] They melted down. You can just see their brains coming out of their ears. But once we put it on a map and hit it all and put little dials that they could fiddle with, they ran with it.[00:29:49] Bill Schmarzo: brilliant[00:29:50] Mark Stouse: totally, totally agree with that. 100% you have to know what to give people and you have to know how to give them, control over some of it, nobody wants to be an automaton. And yet also they will totally lock up if you just give them the keys to the kingdom. Yeah.[00:30:09] Dr Genevieve Hayes: on what you’ve been saying in the discussion so far, what I’m hearing is that the critical difference between what data scientists think their role is and what business leaders actually need is the data scientists is. Well, the ones who aren’t performing well think their role is to just sit there in a back room and do technical work like they would have done in their university assignments.[00:30:33] What the business leaders need is someone who can work with them, ask the right questions in order to understand the needs of the business. make recommendations that answer those questions. But in answering those questions, we’re taking a data informed approach rather than a data driven approach. So you need to deliver the answers to those questions in such a way that you’re informing the business leaders and you’re delivering it in a way that Delivers the right user experience for them, rather than the user experience that the data scientists might want, which would be your high school maths graphs.[00:31:17] Is that a good summary?[00:31:20] John Thompson: Yeah, I think that’s a really good summary. You know, one of the things that Bill and I, and I believe Mark understands is we’re all working to change, you know, Bill and I are teaching at universities in the United States. I’m on the advisory board of about five. Major universities. And whenever I go in and talk to these universities and they say, Oh, well, we teach them, these algorithms and these mathematical techniques and these data science and this statistics.[00:31:48] And I’m like, you are setting these people up for failure. You need to have them have presentation skills, communication skills, collaboration. You need to take about a third of these credits out and change them out for soft skills because you said it Genevieve, the way we train people, young people in undergraduate and graduate is that they have a belief that they’re going to go sit in a room and fiddle with numbers.[00:32:13] That’s not going to be successful.[00:32:16] Mark Stouse: I would give one more point of dimensionality to this, which is a little more human, in some respects, and that is that I think that a lot of data scientists love the fact that they are seen as Merlin’s as shamans. And the problem that I personally witnessed this about two years ago is when you let business leaders persist in seeing you in those terms.[00:32:46] And when all of a sudden there was a major meltdown of some kind, in this case, it was interest rates, and they turn around and they say, as this one CEO said in this meeting Hey, I know you’ve been doing all kinds of really cool stuff back there with AI and everything else. And now I need help.[00:33:08] Okay. And the clear expectation was. I need it now, I need some brilliant insight now. And the answer that he got was, we’re not ready yet. We’re still doing the data management piece. And this CEO dropped the loudest F bomb. That I think I have ever heard from anybody in almost any situation,[00:33:36] and that guy, that data science leader was gone the very next day. Now, was that fair? No. Was it stupid? For the data science leader to say what he said. Yeah, it was really dumb.[00:33:52] Bill Schmarzo: Don’t you call that the tyranny of perfection mark? Is that your term that you always use? is that There’s this idea that I gotta get the data all right first before I can start doing analysis And I think it’s you I hear you say the tyranny of perfection is what hurts You Progress over perfection, learning over absolutes, and that’s part of the challenge is it’s never going to be perfect.[00:34:13] Your data is never going to be perfect, you got to use good enough data[00:34:17] Mark Stouse: It’s like the ultimate negative version of the waterfall.[00:34:22] John Thompson: Yeah,[00:34:23] Mark Stouse: yet we’re all supposedly living in agile paradise. And yet very few people actually operate[00:34:30] John Thompson: that’s 1 thing. I want to make sure that we get in the recording is that I’ve been on record for years and I’ve gone in front of audiences and said this over and over again. Agile and analytics don’t mix that is. There’s no way that those 2 go together. Agile is a babysitting methodology. Data scientists don’t do well with it.[00:34:50] So, you know, I’ll get hate mail for that, but I will die on that hill. But, the 1 thing that, Mark, I agree with 100 percent of what you said, but the answer itself or the clue itself is in the title. We’ve been talking about. It’s data science. It’s not magic. I get people coming and asking me to do magical things all the time.[00:35:11] And I’m like. Well, have you chipped all the people? Do you have all their brain waves? If you have that data set, I can probably analyze it. But, given that you don’t understand what’s going on inside their cranium, that’s magic. I can’t do that. We had the same situation when COVID hit, people weren’t leaving their house.[00:35:29] So they’re not donating plasma. It’s kind of obvious, so, people came to us and said, Hey, the world’s gone to hell in a handbasket in the last two weeks. The models aren’t working and I’m like, yeah, the world’s changed, give us four weeks to get a little bit of data.[00:35:43] We’ll start to give you a glimmer of what this world’s going to look like two months later. We had the models working back in single digit error terms, but when the world goes haywire, you’re not going to have any data, and then when the executives are yelling at you, you just have to say, look, this is modeling.[00:36:01] This is analytics. We have no precedent here.[00:36:05] Bill Schmarzo: to build on what John was just saying that the challenge that I’ve always seen with data science organizations is if they’re led by somebody with a software development background, getting back to the agile analytics thing, the problem with software development. is that software development defines the requirements for success.[00:36:23] Data science discovers them. It’s hard to make that a linear process. And so, if you came to me and said, Hey, Schmarz, you got a big, giant data science team. I had a great data science team at Hitachi. Holy cow, they were great. You said, hey, we need to solve this problem. When can you have it done?[00:36:38] I would say, I need to look at the problem. I need to start exploring it. I can’t give you a hard date. And that drove software development folks nuts. I need a date for when I, I don’t know, cause I’ve got to explore. I’m going to try lots of things. I’m going to fail a lot.[00:36:51] I’m going to try things that I know are going to fail because I can learn when I fail. And so, when you have an organization that has a software development mindset, , like John was talking about, they don’t understand the discovery and learning process that the data science process has to go through to discover the criteria for success.[00:37:09] Mark Stouse: right. It’s the difference between science and engineering.[00:37:13] John Thompson: Yes, exactly. And 1 of the things, 1 of the things that I’ve created, it’s, you know, everybody does it, but I have a term for it. It’s a personal project portfolio for data scientists. And every time I’ve done this and every team. Every data scientist has come to me individually and said, this is too much work.[00:37:32] It’s too hard. I can’t[00:37:34] Bill Schmarzo: Ha, ha, ha,[00:37:35] John Thompson: three months later, they go, this is the only way I want to work. And what you do is you give them enough work so when they run into roadblocks, they can stop working on that project. They can go out and take a swim or work on something else or go walk their dog or whatever.[00:37:53] It’s not the end of the world because the only project they’re working on can’t go forward. if they’ve got a bunch of projects to time slice on. And this happens all the time. You’re in, team meetings and you’re talking and all of a sudden the data scientist isn’t talking about that forecasting problem.[00:38:09] It’s like they ran into a roadblock. They hit a wall. Then a week later, they come in and they’re like, Oh, my God, when I was in the shower, I figured it out. You have to make time for cogitation, introspection, and eureka moments. That has to happen in data science.[00:38:28] Bill Schmarzo: That is great, John. I love that. That is wonderful.[00:38:30] Mark Stouse: And of course the problem is. Yeah. Is that you can’t predict any of that, that’s the part of this. There’s so much we can predict. Can’t predict that.[00:38:42] Bill Schmarzo: you know what you could do though? You could do Mark, you could prescribe that your data science team takes multiple showers every day to have more of those shower moments. See, that’s the problem. I see a correlation. If showers drive eureka moments, dang it.[00:38:54] Let’s give him more showers.[00:38:56] John Thompson: Yep. Just like firemen cause fires[00:38:59] Mark Stouse: Yeah, that’s an interesting correlation there, man.[00:39:05] Dr Genevieve Hayes: So, if businesses need something different from what the data scientists are offering, why don’t they just articulate that in the data scientist’s role description?[00:39:16] John Thompson: because they don’t know they need it.[00:39:17] Mark Stouse: Yeah. And I think also you gotta really remember who you’re dealing with here. I mean, the background of the average C suite member is not highly intellectual. That’s not an insult, that’s just they’re not deep thinkers. They don’t think a lot. They don’t[00:39:37] John Thompson: that with tech phobia.[00:39:38] Mark Stouse: tech phobia and a short termism perspective.[00:39:43] That arguably is kind of the worst of all the pieces.[00:39:48] John Thompson: storm. It’s a[00:39:49] Mark Stouse: It is, it is a[00:39:50] John Thompson: know, I, I had, I’ve had CEOs come to me and say, we’re in a real crisis here and you guys aren’t helping. I was like, well, how do you know we’re not helping? You never talked to us. And, in this situation, we had to actually analyze the entire problem and we’re a week away from making recommendations.[00:40:08] And I said that I said, we have an answer in 7 days. He goes, I need an answer today. I said, well, then you should go talk to someone else because in 7 days, I’ll have it. But now I don’t. So, I met with him a week later. I showed them all the data, all the analytics, all the recommendations. And they said to me, we don’t really think you understand the business well enough.[00:40:27] We in the C suite have looked at it and we don’t think that this will solve it. And I’m like, okay, fine, cool. No problem. So I left, and 2 weeks later, they called me in and said, well, we don’t have a better idea. So, what was that you said? And I said, well, we’ve coded it all into the operational systems.[00:40:43] All you have to do is say yes. And we’ll turn it on and it was 1 of the 1st times and only times in my life when the chart was going like this, we made all the changes and it went like that. It was a perfect fit. It worked like a charm and then, a month later, I guess it was about 6 months later, the CEO came around and said, wow, you guys really knew your stuff.[00:41:07] You really were able to help us. Turn this around and make it a benefit and we turned it around faster than any of the competitors did. And then he said, well, what would you like to do next? And I said, well, I resigned last week. So, , I’m going to go do it somewhere else.[00:41:22] And he’s like, what? You just made a huge difference in the business. And I said, yeah, you didn’t pay me anymore. You didn’t recognize me. And I’ve been here for nearly 4 years, and I’ve had to fight you tooth and nail for everything. I’m tired of it.[00:41:34] Mark Stouse: Yeah. That’s what’s called knowing your value. One of the things that I think is so ironic about this entire conversation is that if any function has the skillsets necessary to forecast and demonstrate their value as multipliers. Of business decisions, decision quality, decision outcomes it’s data science.[00:42:05] And yet they just kind of. It’s like not there. And when you say that to them, they kind of look at you kind of like, did you really just say that, and so it is, one of the things that I’ve learned from analytics is that in the average corporation, you have linear functions that are by definition, linear value creators.[00:42:32] Sales would be a great example. And then you have others that are non linear multipliers. Marketing is one, data science is another, the list is long, it’s always the non linear multipliers that get into trouble because they don’t know how to show their value. In the same way that a linear creator can show it[00:42:55] John Thompson: And I think that’s absolutely true, Mark. And what I’ve been saying, and Bill’s heard this until he’s sick of it. Is that, , data science always has to be denominated in currency. Always, if you can’t tell them in 6 months, you’re going to double the sales or in 3 months, you’re going to cut cost or in, , 5 months, you’re going to have double the customers.[00:43:17] If you’re not denominating that in currency and whatever currency they care about, you’re wasting your time.[00:43:23] Dr Genevieve Hayes: The problem is, every single data science book tells you that the metrics to evaluate models by are, precision, recall, accuracy, et[00:43:31] John Thompson: Yeah, but that’s technology. That’s not business.[00:43:34] Dr Genevieve Hayes: exactly. I’ve only ever seen one textbook where they say, those are technical metrics, but the metrics that really count are the business metrics, which are basically dollars and cents.[00:43:44] John Thompson: well, here’s the second one that says it.[00:43:46] Dr Genevieve Hayes: I will read that. For the audience it’s Business Analytics Teams by John Thompson.[00:43:51] John Thompson: building analytics[00:43:52] Dr Genevieve Hayes: Oh, sorry, Building[00:43:54] Mark Stouse: But, but I got to tell you seriously, the book that John wrote that everybody needs to read in business. Okay. Not just data scientists, but pretty much everybody. Is about causal AI. And it’s because almost all of the questions. In business are about, why did that happen? How did it happen? How long did it take for that to happen?[00:44:20] It’s causal. And so, I mean, when you really look at it that way and you start to say, well, what effects am I causing? What effects is my function causing, all of a sudden the scales kind of have a way of falling away from your eyes and you see things. Differently.[00:44:43] John Thompson: of you to say that about that book. I appreciate that.[00:44:46] Mark Stouse: That kick ass book, kick[00:44:48] John Thompson: Well, thank you. But, most people don’t understand that we’ve had analytical or foundational AI for 70 years. We’ve had generative AI for two, and we’ve had causal for a while, but only people understand it are the people on this call and Judea Pearl and maybe 10 others in the world, but we’re moving in a direction where those 3 families of AI are going to be working together in what I’m calling composite AI, which is the path to artificial, or as Bill says, average general intelligence or AGI.[00:45:24] But there are lots of eight eyes people talk about it as if it’s one thing and it’s[00:45:29] Mark Stouse: Yeah, correct. That’s right.[00:45:31] Dr Genevieve Hayes: I think part of the problem with causal AI is it’s just not taught in data science courses.[00:45:37] John Thompson: it was not taught anywhere. The only place it’s taught is UCLA.[00:45:40] Mark Stouse: But the other problem, which I think is where you’re going with it Genevieve is even 10 years ago, they weren’t even teaching multivariable linear regression as a cornerstone element of a data science program. So , they basically over rotated and again, I’m not knocking it.[00:46:01] I’m not knocking machine learning or anything like that. Okay. But they over rotated it and they turned it into some sort of Omni tool, that could do it all. And it can’t do it all.[00:46:15] Dr Genevieve Hayes: think part of the problem is the technical side of data science is the amalgamation of statistics and computer science . But many data science university courses arose out of the computer science departments. So they focused on the machine learning courses whereas many of those things like.[00:46:34] multivariable linear analysis and hypothesis testing, which leads to things like causal AI. They’re taught in the statistics courses that just don’t pop up in the data science programs.[00:46:46] Mark Stouse: Well, that’s certainly my experience. I teach at USC in the grad school and that’s the problem in a nutshell right there. In fact, we’re getting ready to have kind of a little convocation in LA about this very thing in a couple of months because it’s not sustainable.[00:47:05] Bill Schmarzo: Well, if you don’t mind, I’m going to go back a second. We talked about, measuring success as currency. I’m going to challenge that a little bit. We certainly need to think about how we create value, and value isn’t just currency. John held up a book earlier, and I’m going to hold up one now, Wealth of Nations,[00:47:23] John Thompson: Oh yeah.[00:47:25] Bill Schmarzo: Page 28, Adam Smith talks about value he talks about value creation, and it isn’t just about ROI or net present value. Value is a broad category. You got customer value, employee value, a partner stakeholder. You have society value, community value of environmental value.[00:47:43] We have ethical value. And as we look at the models that we are building, that were guided or data science teams to build, we need to broaden the definition of value. It isn’t sufficient if we can drive ROI, if it’s destroying our environment and putting people out of work. We need to think more holistically.[00:48:04] Adam Smith talks about this. Yeah, 1776. Good year, by the way, it’s ultimate old school, but it’s important when we are As a data science team working with the business that we’re broadening their discussions, I’ve had conversations with hospitals and banks recently. We run these workshops and one of the things I always do, I end up pausing about halfway through the workshop and say, what are your desired outcomes from a community perspective?[00:48:27] You sit inside a community or hospital. You have a community around you, a bank, you have a community around you. What are your desired outcomes for that community? How are you going to measure success? What are those KPIs and metrics? And they look at me like I got lobsters crawling out of my ears.[00:48:40] The thing is is that it’s critical if we’re going to Be in champion data science, especially with these tools like these new ai tools causal predictive generative autonomous, these tools allow us to deliver a much broader range of what value is And so I really rail against when somebody says, you know, and not trying to really somebody here but You know, we gotta deliver a better ROI.[00:49:05] How do you codify environmental and community impact into an ROI? Because ROI and a lot of financial metrics tend to be lagging indicators. And if you’re going to build AI models, you want to build them on leading indicators.[00:49:22] Mark Stouse: It’s a lagging efficiency metric,[00:49:24] Bill Schmarzo: Yeah, exactly. And AI doesn’t do a very good job of optimizing what’s already happened.[00:49:29] That’s not what it does.[00:49:30] John Thompson: sure.[00:49:31] Bill Schmarzo: I think part of the challenge, you’re going to hear this from John and from Mark as well, is that we broaden this conversation. We open our eyes because AI doesn’t need to just deliver on what’s happened in the past, looks at the historical data and just replicates that going forward.[00:49:45] That leads to confirmation bias of other things. We have a chance in AI through the AI utility function to define what it is we want our AI models to do. from environmental, society, community, ethical perspective. That is the huge opportunity, and Adam Smith says that so.[00:50:03] John Thompson: There you go. Adam Smith. I love it. Socrates, Aristotle, Adam[00:50:08] Bill Schmarzo: By the way, Adam Smith motivated this book that I wrote called The Economics of Data Analytics and Digital Transformation I wrote this book because I got sick and tired of walking into a business conversation and saying, Data, that’s technology. No, data, that’s economics.[00:50:25] Mark Stouse: and I’ll tell you what, you know what, Genevieve, I’m so cognizant of the fact in this conversation that the summer can’t come fast enough when I too will have a book,[00:50:39] John Thompson: yay.[00:50:41] Mark Stouse: yeah, I will say this, One of the things that if you use proof, you’ll see this, is that there’s a place where you can monetize in and out of a model, but money itself is not causal. It’s what you spend it on. That’s either causal or in some cases, not[00:51:01] That’s a really, really important nuance. It’s not in conflict with what John was saying about monetizing it. And it’s also not in conflict with what. My friend Schmarrs was saying about, ROI is so misused as a term in business. It’s just kind of nuts.[00:51:25] It’s more like a shorthand way of conveying, did we get value[00:51:31] John Thompson: yeah. And the reason I say that we denominated everything in currency is that’s generally one of the only ways. to get executives interested. If you go in and say, Oh, we’re going to improve this. We’re going to improve that. They’re like, I don’t care. If I say this project is going to take 6 months and it’s going to give you 42 million and it’s going to cost you nothing, then they’re like, tell me more, and going back to what Bill had said earlier, we need to open our aperture on what we do with these projects when we were at Dell or Bill and I swapped our times at Dell, we actually did a project with a hospital system in the United States and over 2 years.[00:52:11] We knocked down the incidence of post surgical sepsis by 72%. We saved a number of lives. We saved a lot of money, too, but we saves people’s lives. So analytics can do a lot. Most of the people are focused on. Oh, how fast can we optimize the search engine algorithm? Or, how can we get the advertisers more yield or more money?[00:52:32] There’s a lot of things we can do to make this world better. We just have to do it.[00:52:36] Mark Stouse: The fastest way to be more efficient is to be more effective, right? I mean, and so when I hear. CEOs and CFOs, because those are the people who use this language a lot. Talk about efficiency. I say, whoa, whoa, hold on. You’re not really talking about efficiency. You’re talking about cost cutting.[00:52:58] Those two things are very different. And it’s not that you shouldn’t cut costs if you need to, but it’s not efficiency. And ultimately you’re not going to cut your way into better effectiveness. It’s just not the way things go.[00:53:14] John Thompson: Amen.[00:53:15] Mark Stouse: And so, this is kind of like the old statement about physicists,[00:53:18] if they’re physicists long enough, they turn into philosophers. I think all three of us, have that going on. Because we have seen reality through a analytical lens for so long that you do actually get a philosophy of things.[00:53:38] Dr Genevieve Hayes: So what I’m hearing from all of you is that for data scientists to create value for the businesses that they’re working for, they need to start shifting their approach to basically look at how can we make the businesses needs. And how can we do that in a way that can be expressed in the business’s language, which is dollars and cents, but also, as Bill pointed out value in terms of the community environment.[00:54:08] So less financially tangible points of view.[00:54:11] Bill Schmarzo: And if I could just slightly add to that, I would say first thing that they need to do is to understand how does our organization create value for our constituents and stakeholders.[00:54:22] Start there. Great conversation. What are our desired outcomes? What are the key decisions? How do we measure success? If we have that conversation, by the way, it isn’t unusual to have that conversation with the business stakeholders and they go I’m not exactly sure.[00:54:37] John Thompson: I don’t know how that works.[00:54:38] Bill Schmarzo: Yeah. So you need to find what are you trying to improve customer retention? You’re trying to increase market share. What are you trying to accomplish and why and how are you going to measure success? So the fact that the data science team is asking that question, because like John said, data science can solve a whole myriad of problems.[00:54:54] It isn’t that it can’t solve. It can solve all kinds. That’s kind of the challenge. So understanding what problems we want to solve starts by understanding how does your organization create value. If you’re a hospital, like John said, reducing hospital acquired infections, reducing long term stay, whatever it might be.[00:55:09] There are some clear goals. Processes initiatives around which organizations are trying to create value[00:55:18] Dr Genevieve Hayes: So on that note, what is the single most important change our listeners could make tomorrow to accelerate their data science impact and results?[00:55:28] John Thompson: I’ll go first. And it’s to take your data science teams and not merge them into operational teams, but to introduce the executives that are in charge of these areas and have them have an agreement that they’re going to work together. Start there.[00:55:46] Bill Schmarzo: Start with how do you how does the organization create value? I mean understand that fundamentally ask those questions and keep asking until you find somebody in the organization who can say we’re trying to do this[00:55:57] Mark Stouse: to which I would just only add, don’t forget the people are people and they all have egos and they all want to appear smarter and smarter and smarter. And so if you help them do that, you will be forever in there must have list, it’s a great truth that I have found if you want to kind of leverage bills construct, it’s the economies of ego.[00:56:24] Bill Schmarzo: I like[00:56:24] John Thompson: right, Mark, wrap this up. When’s your book coming out? What’s the title?[00:56:28] Mark Stouse: It’s in July and I’ll be shot at dawn. But if I tell you the title, but so I interviewed several hundred fortune, 2000 CEOs and CFOs about how they see go to market. The changes that need to be made in go to market. The accountability for it all that kind of stuff. And so the purpose of this book really in 150, 160 pages is to say, Hey, they’re not all correct, but this is why they’re talking to you the way that they’re talking to you, and this is why they’re firing.[00:57:05] People in go to market and particularly in B2B at an unprecedented rate. And you could, without too much deviation, do a search and replace on marketing and sales and replace it with data science and you’d get largely the same stuff. LinkedIn,[00:57:25] Dr Genevieve Hayes: for listeners who want to get in contact with each of you, what can they do?[00:57:29] John Thompson: LinkedIn. John Thompson. That’s where I’m at.[00:57:32] Mark Stouse: Mark Stouse,[00:57:34] Bill Schmarzo: And not only connect there, but we have conversations all the time. The three of us are part of an amazing community of people who have really bright by diverse perspectives. And we get into some really great conversations. So not only connect with us, but participate, jump in. Don’t be afraid.[00:57:51] Dr Genevieve Hayes: And there you have it, another value packed episode to help you turn your data skills into serious clout, cash, and career freedom. If you found today’s episode useful and think others could benefit, please leave us a rating and review on your podcast platform of choice. That way we’ll be able to reach more data scientists just like you.[00:58:11] Thanks for joining me today, Bill, Mark, and John.[00:58:16] Mark Stouse: Great being with[00:58:16] John Thompson: was fun.[00:58:18] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been value driven data science. The post Episode 53: A Wake-Up Call from 3 Tech Leaders on Why You're Failing as a Data Scientist first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Claudio Caprarulo @ClauCaprarulo (Economista, Director en Analytica Consultora @AnalyticaArg ) Unas Cuantas Verdades @marianoobarrio

Dans cet épisode Analytica 029 en compagnie de Karine, on passe en revue l'épisode S02E02 de Battlestar Galactica intitulé Les Centurions De Caprica en VF et Valley Of Darkness en VO. Nous vous faisons part de nos remarques et analyses, sans oublier de nombreuses anecdotes sur les coulisses de la production et du tournage. Quelques liens en rapport avec l'épisode : Le morceau Metamorphosis Five de Philip Glass qui figure dans cet épisode : https://www.youtube.com/watch?v=Rebr_F53db8 Article "Les études liées aux relations au travail se comptent par centaines" : https://emploi.lefigaro.fr/vie-bureau/les-etudes-dediees-aux-relations-amoureuses-au-travail-se-comptent-par-centaines-voila-ce-qu-il-faut-retenir-20240213 Article "L'amour au travail : 14% des couples se forment au bureau" : https://www.francetvinfo.fr/replay-radio/c-est-mon-boulot/l-amour-au-travail-14-des-couples-se-forment-au-bureau_2798741.html Article "Résultats de l'Étude sur la Romance du Travail par le Groupe Technologia" : https://topmanagement.fr/communiques/resultats-de-letude-sur-la-romance-du-travail-par-le-groupe-technologia/ L'échange tendu entre Dualla et Billy en début d'épisode : https://youtu.be/_wMlTKJCfUw?si=VBr4S3EVPAyZIh64 Adama noie le "bébé" de Gaius Baltar : https://youtu.be/noVXDnXBSRc?si=tTw4Kpgy9PNc9yiO Les gradés du Galactica se rendent compte que des Cylons sont à bord : https://youtu.be/LfDRH1YV-3Q?si=hSrLj-AGEF5ylDr_ Les choses ne se finissent pas bien pour Socinus sur Kobol : https://youtu.be/L9JhVPnGU44?si=bFHYMnRLEC5cURS8 Starbuck traite Helo d'idiot sur Caprica : https://youtu.be/wjqQOhzQ02I?si=ZDkNNtQ2DvpOIEgU Apollo motive ses troupes avant la confrontation finale avec les centurions Cylons : https://youtu.be/lgF-u2fiuis?si=X_iYzXpzxtTdKw6h Le formulaire pour poser vos questions pour notre épisode 100 : https://forms.gle/tUTrdihcCdiRkcXWA GalactiFrak fait partie du label PodShows et est disponible sur podCloud, ainsi que sur Apple Podcasts, Spotify, Deezer, Google, ou Amazon ainsi que YouTube. Retrouvez les notes de l'émission sur galactifrak.lepodcast.frRetrouvez GalactiFrak sur Facebook, Twitter, Bluesky, Instagram et Discord. Suivez Draven et Karine sur Twitter ou Bluesky

Dans cet épisode Analytica 028 en compagnie de Karine, on passe en revue l'épisode S02E01 de Battlestar Galactica intitulé Le Tout Pour Le Tout en VF et Scattered en VO. Nous vous faisons part de nos remarques et analyses, sans oublier de nombreuses anecdotes sur les coulisses de la production et du tournage. Quelques liens en rapport avec l'épisode : Extrait d'un livre qui aborde l'impact de la confiance en lui du dirigeant sur la réussite d'une entreprise : https://shs.cairn.info/revue-questions-de-management-2017-3-page-47?lang=fr Article sur l'influence de l'empathie : https://www.nature.com/articles/s41398-017-0082-6 Article sur la générosité du cerveau des femmes : https://www.cerveauetpsycho.fr/sd/neurosciences/le-cerveau-des-femmes-est-plus-genereux-12715.php#:~:text=Des%20diff%C3%A9rences%20entre%20hommes%20et%20femmes&text=Les%20chercheurs%20ont%20d%C3%A9nombr%C3%A9%20les,contre%2039%20%25%20pour%20les%20hommes Article sur les avantages du daltonisme pour les chasseurs : https://lucperino.com/595/avantages-pour-les-daltoniens-chasseurs.html Starbuck et Helo ne sont pas d'accord à propos de Sharon sur Caprica : https://www.youtube.com/watch?v=iB8hNWYixGk Tyrol, Cally et Tarn se font attaquer par surprise sur Kobol : https://www.youtube.com/watch?v=iB8hNWYixGk Felix Gaeta laisse un virus Cylon s'infiltrer dans le Galactica pour tenter de retrouver la flotte : https://www.youtube.com/watch?v=P0BD4jmTS_M Le colonel Tigh a un flashback de son passé : https://www.youtube.com/watch?v=YpmwEdgQc2E GalactiFrak fait partie du label PodShows et est disponible sur podCloud, ainsi que sur Apple Podcasts, Spotify, Deezer, Google, ou Amazon ainsi que YouTube. Retrouvez les notes de l'émission sur galactifrak.lepodcast.frRetrouvez GalactiFrak sur Facebook, Twitter, Bluesky, Instagram et Discord. Suivez Draven et Karine sur Twitter ou Bluesky

Ricardo Delgado Economista - Director de Analytica (@AnalyticaARG) (@delgado_rr) @asteriscostv 20-11-2024

Today, we're visiting Old Blighty again to chat with Mr. Timothy Hughes, our social guru extraordinaire. Tim resides in London and is co-founder and CEO of the global social selling company DLA Ignite. In 2021, LinkedIn listed him as one of the top eight sales experts to follow globally, and Analytica ranked him as one of the most influential people in social selling.  His book of the same name, Social Selling Techniques to Influence Buyers and Changemakers is what we spend a large portion of our time discussing. From defining changemakers through to providing key tips and suggestions on what you can be doing to ensure that your social community is being served with quality resources that bring value and thereafter the potential to engage with new business. And as Tim says, do you want to be part of the noise or want to be heard? Here's an extract from a recent post by Tim. What's changed in 2024 from 1980? It would seem very little, but buyers now expect to live in a zero interruption world. And we still hate being sold to. In fact, buyers have become skilled at hiding away from suppliers. Data shows that buyers hide away from the suppliers until there's 75% of the way through the buying process. That statement alone team is reason enough for you to join us through this conversation. Visit our website to access and download the full transcript, the guest links, and episode notes - Coaching 4 Companies

Ricardo Delgado, economista y presidente de la consultora Analytica, conversó con Leandro Gabin sobre la situación de la macroeconomía.

In this episode Mark and Shashank delve into the intricacies of AI-driven decision systems with Lonnie Chrisman, CTO of Lumina Decision Systems. They explore the innovative product Analytica and its new AI extension, Assista, designed to enhance decision-making processes. Chrisman, an industry veteran, shares his journey from being the top electrical engineering student to spearheading technology that influences significant governmental and corporate decisions globally. This episode provides a profound understanding of the potential and challenges of integrating AI into complex decision environments.   Checkout Lonnie's talk as part of the Oct 10 Risk Awareness Week https://2024.riskawarenessweek.com/speakers/lonnie-chrisman/

Claudio Caprarulo Economista - Director de Analytica Arg (@ClauCaprarulo) @ASTERISCOSTV 28-8-2024

To raise awareness about genetic makeup and the critical importance of blood donations, the Blood Bank Department of the Ezra Long Laboratory has partnered with GenTech Analytica's Summer Genetics Program for students. This collaboration is a major step in the Blood Bank Department's mission to educate and encourage citizens about the value of blood donations, providing students with a thorough understanding of their genetic profiles and blood group significance.

Welcome back to the Grey Dynamics Podcast! This week we are talking to Dr Andreas Krieg, director of MENA Analytica and associate professor at King's College London. He is also the author of Subversion: The Strategic Weaponization of Narratives. Andreas has a wealth of experience in the private sector, academia and supporting the UK armed forces training and doctrine. We discussed learning how to spot opportunity, getting exposure outside of academic circles, ways forward for the situation in Gaza and much more.Find Dr Andreas Krieg:King's College London LinkedIn X Facebook YouTube Subversion: The Strategic Weaponization of NarrativesWe Spoke About:0:44 - Andreas' background 6:15 - Hunting for opportunities 11:10 - Legacy, economy of effort in academia and exposure outside of academia 15:00 - Academic publications and their relationship to the public 19:48 - Getting exposure outside of academic circles 25:29 - The situation in Gaza and possible ways forward 49:00 - Andreas discusses his work surrounding subversion, and how Israel uses it 54:56 - Career advice 1:06:28 - Cultural recommendationsAdvance Your Intelligence Career Today!We are the first fully online intelligence school helping professionals to achieve their long term goals. Our school with tons of new material is currently under construction and will be out there very soon. Meanwhile, you can sign up and be the first to know when we launch, plus get exclusive tips and offers.Get access to exclusive Grey Dynamics ReportsWith security clearance, you can take a crucial role in our intelligence community. As a cleared member, you get access to secret & top secret grade publications. If you are a Top Secret holder, you also get access to our community area, where you can interact with other members and with our analysts! Subscribe today!The Grey Dynamics Podcast is available on all major platforms!Grey Dynamics YouTube Spotify Apple Podcast Google Podcast Amazon Podcast Hosted on Acast. See acast.com/privacy for more information.

Herzlich willkommen zu unserem Podcast, in dem wir uns eingehend mit dem Film "Harry Potter und die Kammer des Schreckens" auseinandersetzen und uns an völlig unwichtigen Dingen aufhängen

Du swing, deux mariages, un enterrement et deux enfants, de la soupe et des sandwiches, du R'n'B à la sauce Motown ou à la sauce à la menthe, de la synth pop, de la musique de hippie, trois vagues de ska, de l'electro, du post punk, du Shakira, de la folk avec l'accent du Sud-Ouest, de la pop rennaise, de la cold wave et du punk à roulettes, il y a tout ça dans le troisième épisode de Super Love Songs Battle ! SUPER LOVE SONGS BATTLE #3 - This is not a Digital Tainted Love & Marriage Song to Die for on the Beach Sommaire de l'épisode : 00:00:00 Un bref message du Dr. Samuel Draven 00:00:57 Générique Vous aurez surement remarqué (ou pas !) que nous avons remplacé Made To Love You II des Hard-Ons (qui figure sur l'album So I Could Have Them Destroyed) par Are You My Woman? (Tell Me So) des Chi-Lites (qui a donné le sample principal de Crazy in Love de Beyoncé qui a été classé dans l'épisode précédent). Vous noterez que, contrairement au morceau des Chi-Lites, le morceau des Hard-Ons est éligible pour notre classement. Ça m'amusait assez d'avoir glisser une sorte d'easter egg dans le générique. Voilà. Ça ne vous empêche pas du tout de le proposer. Je sais, ça peut ressembler à un appel du pied assez lourdingue ce que je fais. J'en suis conscient. Mais bon, on est entre nous. Quasiment personne ne lit les notes des épisode. Je suis sûr que Draven non plus ne les lit pas. En tout cas, pas en entier. Je vous propose un test... Draven ? T'es là ? Draaaveeen ??? (silence) Vous voyez, il ne répond même pas ! 00:01:14 Introduction et rappel des règles Pour participer, vous pouvez envoyer vos listes de chansons à l'adresse suivante corneliusandzirapodcast[arobase]gmail[point]com 00:06:39 Petit changement éditorial 00:09:28 Et pourquoi Wet Wet Wet ? 00:10:23 Rappel du Top Ten 00:12:02 Frank Sinatra - Love & Marriage Proposition de AK Dallas Contient du contenu sponsorisé par Campbell's Soup Company 00:50:36 Soft Cell - Tainted Love Proposition de Deviant Prod, Monkape, Faye Fanel et YoDassin Retrouvez les différents podcasts de Faye dans son linktree Allez également faire un tour sur la chaine YouTube de Deviant Prod Extraits utilisés dans ce passage : Gloria Jones - Tainted Love T-Rex – Teenage Dream The Standells - Sometimes Good Guys Don't Wear White Minor Threat - Good Guys (Don't Wear White) Ruth Swann - Tainted Love Si jamais vous avez envie de proposer Feeling Called Love de Wire, allez-y ! Balancez vos e-mails et vos messages sur les réseaux sociaux !!! 01:19:58 Jefferson Airplane - Somebody to Love Proposition de Monkape et YoDassin Extraits utilisés dans ce passage : Starship – We Built this City The Great Society - Somebody to Love On parle également de l'épisode de Cornelius & Zira consacré à Attack of the Flying Donuts, un film que personne n'a jamais vu (il paraitrait que même les gens qui ont participé à cet épisode ne l'ont pas vu...) ! 01:48:56 Bob Marley & The Wailers - One Love Proposition de Blast On vous invite à écouter Les Sondiers Extraits utilisés dans ce passage : Bob Marley & The Wailers - One Love/People Get Ready Madness - One Step Beyond No Doubt - Just A Girl Vladimir Cosma – Inspecteur La Bavure Byron Lee & the Dragonaires - Jamaican Ska The Specials - A Message To You Rudy (qui est, je ne l'ai pas dit dans l'épisode, une reprise de Rudy, a Message to You de Dandy Livingston) Rancid - Time Bomb 02:13:27 George Duke - I Love You More Comment ça ce n'est pas le bon morceau ? Bon, bah si quelqu'un veut le proposer officiellement, c'est possible ! 02:16:37 Daft Punk - Digital Love Propositon de Bibounette de Chez Bibou et Bibounette Extrait utilisé dans ce passage : Space – Magic Fly Par contre, Surrender de Chemical Brothers est sorti en 1999 et non en 1998... Ca va, j'étais pas super loin non plus... 03:05:39 Public Image Ltd. - This is not a Love Song Morceau proposé par YoDassin et Bengir de La Diagonale du Vide (sur une proposition de Séverine) Extraits utilisés dans ce passage : Sex Pistols – God Save the Queen MC5 – Kick Out the Jams Faces – Ooh La La Devo - Uncontrollable Urge 03:27:33 Francis Cabrel – Je l'aime à mourir Proposition de XP On en a pas parlé pendant l'épisode mais on vous invite à écouter l'épisode de C'Tout Comme consacré à Francis Cabrel et qui avait été enregistré en live durant l'édition 2022 de PodRennes Extrait utilisé dans ce passage : Shakira - Je l'aime à mourir 03:45:25 Niagara - L'Amour à la Plage Proposition de Grincheux de Mon Premier album à moi et (anciennement) du Cyclocast On vous invite à écouter l'épisode de Mon Premier album à moi consacré au premier album de Led Zeppelin Extrait utilisé dans ce passage : L'épisode de Jura'Zik Park consacré aux origines de Niagara. Merci encore à Debrophy d'être aussi cool et on vous invite à écouter la bAnale 03:59:05 The Gathering - In Power We Entrust the Love Advocated Proposition de Maxime de Recoversion On parlait déjà de The Gathering dans l'épisode 13 de la Ape List Extraits utilisés dans ce passage : The Gathering - Strange Machines Dead Can Dance - In Power We Entrust the Love Advocated 04:41:21 The Descendents - She Loves Me Proposition de Mini Zaius que vous pouvez entendre dans Docteur Zaius & Fils Extraits utilisés dans ce passage : Middle Class – Out of Vogue Black Flag – Rise Above Descendents - Hope X – Los Angeles All – She's my ex 05:06:23 Presque la fin de l'épisode Extrait utilisé dans ce passage : Pete Yorn – Ever Fallen in Love 05:09:00 Le Courrier des auditrices et des auditeurs 05:12:17 Conclusion Allez écouter l'épisode Analytica 25 de Galactifrak pour comprendre la private joke en introduction de cette conclusion On rappelle qu'on a repompé sans la moindre vergogne le concept de Super Cover Battle, l'émission de Maxime de Recoversion, le Podcast des Meilleures Reprises et Dam d'Écoute Ça ! Le podcast des analyses musicales! Retrouvez Draven dans 24FPS, le podcast ciné avec ou sans spoilers, dans Galactifrak, le podcast francophone dédié à Battlestar Galactica, dans The Masters of Horror Show, dans Stranger Films et dans C'Tout Comme. Le classement avant cet épisode : Au-dessus de tout le monde : Type-O-Negative – Love you to Death Roger Glover feat. Ronnie James Dio – Love is All Buzzcoks - Ever Fallen in Love (With Someone You Shouldn't've) Ella Fitzgerald – Sunshine of your Love Cream – Sunshine of your Love 10cc – I'm not in Love Beyoncé feat. Jay-Z – Crazy in Love Aerosmith - Falling in Love (Is Hard on the Knees) The Ramones - Oh oh I Love Her so The Sonics - Have Love, Will Travel Sonic Youth - I Love You Golden Blue Golden Earring – Radar Love The B52's - Love Shack Dire Straits – Tunnel of Love Huey Lewis & the News – The Power of Love Second Rate - You don't Deserve my Love The Damned - Love Song The Meteors - Psycho for your love (version 2007) deLillos - Jeg elsker du Skid Row - Psycho Love AC/DC – Little Lover Danko Jones – Lovercall Strapping Young Lad – Love ? Joy Division / Warsaw - No Love Lost Megadeth - Last Rites/Loved to Deth Counting Crows - Accidentally in Love Vangelis - Love Theme (Blade Runner) Téléphone - Un peu de ton amour Birds in Row - Love is Political Cat Stevens / Yusuf - Last Love Song Haddaway – What is Love Bonne écoute et à bientôt ! podCloud | Apple Podcast | YouTube | Spotify | BlueSky | Mastodon | Twitter | Facebook | Instagram | TikTok

Dans cet épisode Analytica 027 en compagnie de Karine, on passe en revue l'épisode S01E13 de Battlestar Galactica intitulé À La recherche De La Terre 2/2 en VF et Kobol's Last Gleaming en VO. Nous vous faisons part de nos remarques et analyses, sans oublier de nombreuses anecdotes sur les coulisses de la production et du tournage. Quelques liens en rapport avec l'épisode : Adama ordonne l'arrestation de la présidente Roslin : https://www.youtube.com/watch?v=nRGyfYh0NJ4 Starbuck arrive sur Caprica, trouve la flèche d'Apollo (notez la présence d'un caméraman à l'image, derrière la verrière avec ses manches orange, à 1:25), se bat avec Numéro 6 et est retrouvée par Helo : https://www.youtube.com/watch?v=NElYp4eVOnk Adama explique à Boomer le plan qu'elle doit accomplir sur le vaisseau Cylon : https://www.youtube.com/watch?v=fZQuTSPVHBk Boomer découvre sa vraie nature, en parallèle des événements sur Caprica et dans la flotte : https://www.youtube.com/watch?v=UJQReHQx-OE La fin de l'épisode, où Numéro 6 montre son futur à Gaius et où il se passe un événement particulièrement choquant sur le Galactica au même moment : https://www.youtube.com/watch?v=KpoCLAJTfDQ Avertissement : contrairement à ce qui est dit dans l'émission, la réponse à la question posée par Karine ne se trouve pas dans l'épisode. Donc ne vous inquiétez pas si vous n'avez pas la solution ! ;) GalactiFrak fait partie du label PodShows et est disponible sur podCloud, ainsi que sur Apple Podcasts, Spotify, Deezer, Google, ou Amazon ainsi que YouTube. Retrouvez les notes de l'émission sur galactifrak.lepodcast.frRetrouvez GalactiFrak sur Facebook, Twitter, Bluesky, Instagram et Discord. Suivez Draven et Karine sur Twitter ou Bluesky

Según el Banco Central de la Republica Dominicana, la economía terminará creciendoeste año 2.5 % muy por debajo de lo pronosticado a principio de año que fue de 4.5 %.Hoy vamos a hacer un repaso economía del año 2023. Y primero queremos comenzarpor el ámbito internacional, cuáles son esos factores que nos afectaron a nivelinternacional y nos seguirán afectan en el 2024.Tambien hablaremos sobre la sostenibilidad de la deuda dominicana y de la tannecesaria reforma fiscal. Vamos a dar la bienvenida a Germania Montas, economista,Miguel Collado, economista senior del Centro Regional de Estrategias EconómicasSostenibles (CREES) y Raúl Ovalles socio-director de Analytica.Temas a tratas:Contexto internacional que afecta a la economía domincianaSectores de la economía dominicana que se estancaronCuánto afectó el conflicto con HaitíSostenibilidad de la deudaCuánto es la deuda con relación al PIBReforma FiscalQué tipo de reforma fiscal necesita el paísPronósticos para el año 2024

Dans cet épisode Analytica 026 en compagnie de Karine, nous décortiquons l'épisode S01E12 de Battlestar Galactica intitulé À La recherche De La Terre 1/2 en VF et Kobol's Last Gleaming en VO. Nous vous faisons part de nos remarques et analyses, sans oublier de nombreuses anecdotes sur les coulisses de la production et du tournage. Quelques liens en rapport avec l'épisode : La scène où Baltar éméché s'en prend à Starbuck pendant une partie de cartes : https://youtu.be/JFmPPvs6hrg La scène où Boomer et Crashdown découvrent la planète Kobol : https://youtu.be/1XJ4-82JJ00 La scène où Laura Roslin a des hallucinations en regardant les plans de la planète kobol : https://youtu.be/JFmPPvs6hrg La scène où Baltar pousse Boomer à se suicider : https://youtu.be/S1dBn5cCQog La scène du crash du Raptor sur la planète Kobol : https://youtu.be/n75Qed-wUvc La fin de l'épisode où Starbuck décide de faire un bond PRL non prévu avec son raider Cylon : https://youtu.be/KBx6Q1wfmJY Le podcast Sismique n°121 intitulé La Technique Comme Solution : https://www.sismique.fr/post/121-la-technique-comme-solution-aur%C3%A9lien-barrau-pablo-servigne Le podcast Venus s'Épilait-Elle La Chatte consacré à l'esthétisation des femmes mortes : https://www.venuslepodcast.com/episodes/esth%C3%A9tiser-les-femmes-mortes La page du site de Bear McCreary où il est possible de télécharger les 3 versions de la musique de fin de l'épisode : https://bearmccreary.com/bg-season-1-mx-mix-ups/ GalactiFrak fait partie du label PodShows et est disponible sur podCloud, ainsi que sur Apple Podcasts, Spotify, Deezer, Google, ou Amazon ainsi que YouTube. Retrouvez les notes de l'émission sur galactifrak.lepodcast.frRetrouvez GalactiFrak sur Facebook, Twitter, Bluesky, Instagram et Discord. Suivez Draven et Karine sur Twitter ou Bluesky

Santiago Pont Lezica y Gisela Larsen dialogaron con Ricardo Delgado, presidente de Analytica ARG. - Economista UBA

Economista y Presidente de Analytica charló sobre las propuestas económicas de los candidatos

Santiago Pont Lezica y Evangelina Barone hablaron con Ricardo Delgado, economista y Presidente de Analytica, sobre las medidas de Massa.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: A Model-based Approach to AI Existential Risk, published by Samuel Dylan Martin on August 25, 2023 on The AI Alignment Forum. Introduction Polarisation hampers cooperation and progress towards understanding whether future AI poses an existential risk to humanity and how to reduce the risks of catastrophic outcomes. It is exceptionally challenging to pin down what these risks are and what decisions are best. We believe that a model-based approach offers many advantages for improving our understanding of risks from AI, estimating the value of mitigation policies, and fostering communication between people on different sides of AI risk arguments. We also believe that a large percentage of practitioners in the AI safety and alignment communities have appropriate skill sets to successfully use model-based approaches. In this article, we will lead you through an example application of a model-based approach for the risk of an existential catastrophe from unaligned AI: a probabilistic model based on Carlsmith's Is Power-seeking AI an Existential Risk? You will interact with our model, explore your own assumptions, and (we hope) develop your own ideas for how this type of approach might be relevant in your own work. You can find a link to the model here. In many poorly understood areas, people gravitate to advocacy positions. We see this with AI risk, where it is common to see writers dismissively call someone an "AI doomer", or "AI accelerationist". People on each side of this debate are unable to communicate their ideas to the other side, since advocacy often includes biases and evidence interpreted within a framework not shared by the other side. In other domains, we have witnessed first-hand that model-based approaches are a constructive way to cut through advocacy like this. For example, by leveraging a model-based approach, the Rigs-to-Reefs project reached near consensus among 22 diverse organisations on the contentious problem of how to decommission the huge oil platforms off the Santa Barbara coast. For decades, environmental groups, oil companies, marine biologists, commercial and recreational fishermen, shipping interests, legal defence funds, the State of California, and federal agencies were stuck in an impasse on this issue. The introduction of a model refocused the dialog on specific assumptions, objectives and options, and led to 20 out of the 22 organisations agreeing on the same plan. The California legislature encoded this plan into law with bill AB 2503, which passed almost unanimously. There is a lot of uncertainty around existential risks from AI, and the stakes are extremely high. In situations like this, we advocate quantifying uncertainty explicitly using probability distributions. Sadly, this is not as common as it should be, even in domains where such techniques would be most useful. A recent paper on the risks of unaligned AI by Joe Carlsmith (2022) is a powerful illustration of how probabilistic methods can help assess whether advanced AI poses an existential risk to humanity. In this article, we review Carlsmith's argument and incorporate his problem decomposition into our own Analytica model. We then expand on this starting point in several ways to demonstrate elementary ways to approach each of the distinctive challenges in the x-risk domain. We take you on a tour of the live model to learn about its elements and enable you to dive deeper on your own. Challenges Predicting the long-term future is always challenging. The difficulty is amplified when there is no historical precedent. But this challenge is not unique; we lack historical precedent in many other areas, for example when considering a novel government program or a fundamentally new business initiative. We also lack precedent when world conditions change due to changes in technology, ...

Entrevista de Leandro Gabin al economista Ricardo Delgado, presidente de la consultora Analytica, para analizar qué impacto puede tener el nuevo desembolso del FMI.

Dans cet épisode Analytica 025 en compagnie de Karine, nous décortiquons l'épisode S01E11 de Battlestar Galactica intitulé La Fête Coloniale en VF et Colonial Day en VO. Nous vous faisons part de nos remarques et analyses, sans oublier de nombreuses anecdotes sur les coulisses de la production et du tournage. Quelques liens en rapport avec l'épisode : Les premières minutes de l'épisode durant lesquelles Tom Zarek (Richard Hatch) demande à Laura Roslin (Mary McDonnell) qu'on procède à l'élection du nouveau vice-président, une proposition soutenue par Gaius Baltar (James Callis) : https://youtu.be/dolSTE_DFpg Les résultats de l'élection du nouveau vice-président et la célébration qui s'en suit : https://youtu.be/qvdU9SgGbcI GalactiFrak fait partie du label PodShows et est disponible sur podCloud, ainsi que sur Apple Podcasts, Spotify, Deezer, Google, ou Amazon ainsi que YouTube. Retrouvez les notes de l'émission sur galactifrak.lepodcast.frRetrouvez GalactiFrak sur Facebook, Twitter, Instagram et Discord. Suivez Draven et Karine sur Twitter

El economista y PResidente de Analytica habló con Eduardo sobre los acuerdos de Massa con el Fondo Monetario

Muchos hablan del famoso "Big Data" pero muy pocos realmente saben y conjugan bien este termino. Jaqueline Mora, fundadora de Analytica y Vice-Ministra de Turismo es de las pocas personas que no solo lo domina, si no que lo vive con pasión. La data es un elemento primordial para la toma de decisiones pero no hacemos nada con la data si no sabemos interpretarla y entender la historia que cuenta. Conoce la trayectoria de esta economista que ha logrado romper esquemas y marcar la diferencia tomando la información y convirtiéndola en su ventaja competitiva en todos los sectores profesionales donde ha colaborado. ------Ya puedes ver todo el detalle de las  BECAS PESADAS que estaremos otorgando junto al TEP de la PUCMM en nuestra pagina web www.pesospesados.do y en nuestras redes sociales. ¡Aplica ya!Recuerda seguirnos en nuestras redes en @pesospesadosrd, suscribirse + activar las notificaciones en #Youtube, darnos follow en #Spotify, #ApplePodcast, #GooglePodcast para ver a nuestros invitados y enterarse cada vez que salga un nuevo episodio.

Dans cet épisode Analytica 024 en compagnie de Karine, nous décortiquons l'épisode S01E10 de Battlestar Galactica intitulé Le Minerai De Tylium en VF et The Hand Of God en VO. Nous vous faisons part de nos remarques et analyses, sans oublier de nombreuses anecdotes sur les coulisses de la production et du tournage. Quelques liens en rapport avec l'épisode : Laura Roslin partage ses visions avec la prêtresse Elosha qui y voit une prophétie de la Pythie : https://youtu.be/g5UDp4HnroM Adama encourage Apollo avant la mission et lui confie le briquet familial : https://youtu.be/eMJR_w2ivh0 Apollo et ses pilotes interviennent lors d'une manoeuvre surprise concoctée par Starbuck qui observe l'opération à distance : https://youtu.be/uZfU-jrT28w Des détails sur le briquet de Joseph Adama de marque Regens : https://sellgeek.com/products/joseph-adama-working-lighter-prop-replica-battlestar-galactica-caprica La recherche à efffectuer pour se procurer le même briquet Regens que celui de la famille Adama sur eBay : https://www.ebay.fr/sch/i.html?_from=R40&_trksid=p2380057.m570.l1313&_nkw=regens+lighter&_sacat=0 GalactiFrak fait partie du label PodShows et est disponible sur podCloud, ainsi que sur Apple Podcasts, Spotify, Deezer, Google, ou Amazon ainsi que YouTube. Retrouvez les notes de l'émission sur galactifrak.lepodcast.frRetrouvez GalactiFrak sur Facebook, Twitter, Instagram et Discord. Suivez Draven et Karine sur Twitter

Who is Mark?Mark is a leadership professional who has developed a unique tool. TeamLytica is a web-based team analytics tool that:* Analyses and identifies a team's strength indicators allowing focus to raise their performance through immediate actionable insights* Is web-based, intuitive, simple and quick to complete with zero tech integration and instantly deployable – results in just twenty minutes* Has recently launched but whose benefits are already being realised by companies such as Boeing, Lockheed Martin, and NatWest* Has impactful and unique insights to improve team cohesion and actions to reduce stress and increase overall employee wellbeing* Is purposely different and complementary to other solutions like staff surveys and psychometric profiling tools* Allows for benchmarking and Team360 retests to help prove ROI on training spendKey TakeawaysHow do you offer value to your team? 2:28Mark's approach to measuring team cohesion. 4:12Launching a self-serve platform. 5:30How to get free stuff from the website? 6:50Inspiration: Scouting. 9:07Toxic masculinity and the core of gold. 10:47Leaders need to be committed to their teams. 11:56The importance of building trust in your team. 14:35Valuable Free Resource or Actionhttps://www.teamlytica.com/solution/free/A video version of this podcast is available on YouTube :https://youtube.com/live/tkshPCTe-Uo_________________________________________________________________________________________________Subscribe to our newsletter and get details of when we are doing these interviews live at https://TCA.fyi/newsletterFind out more about being a guest at : link.thecompleteapproach.co.uk/beaguestSubscribe to the podcast at https://link.thecompleteapproach.co.uk/podcastHelp us get this podcast in front of as many people as possible. Leave a nice five-star review at apple podcasts : https://link.thecompleteapproach.co.uk/apple-podcasts and on YouTube : https://link.thecompleteapproach.co.uk/Itsnotrocketscienceatyt!Here's how you can bring your business to THE next level:If you are a business owner currently turning over £/$10K - £/$50K per month and want to grow to £/$100K - £/$500k per month download my free resource on everything you need to grow your business on a single page :It's a detailed breakdown of how you can grow your business to 7-figures in a smart and sustainable way————————————————————————————————————————————-TranscriptNote, this was transcribed using a transcription software and may not reflect the exact words used in the podcast)SUMMARY KEYWORDSteam, people, mark, coach, litical, trainers, longer term, business, problem, psychometric, building, high performing teams, tool, spending, customers, report, leila, question, coffee, stressSPEAKERSMark Hide, Stuart WebbStuart Webb 00:08Hi again and welcome back to it's not rocket science five questions over coffee I have, I've just admitted to mark actually, my coffee is going cold, I've got a fresh one in front of me. But that's another story that we will refresh later on. Mark, I know you got a nice fresh cup of coffee in front of you. So, welcome to the podcast. Let's enjoy 1520 minutes talking about team dynamics, team cohesion, over a cup of coffee mark from Team litical. Welcome to the podcast.Mark Hide 00:37Thank you, oh, my coffee is warm and gorgeous.Stuart Webb 00:41As it should be, as it should be. So Mark, sort of talk us through the problem that you are looking to help businesses with and and the sort of the problems that you've seen that they get the get wrong, which you're trying to help them to correct.Mark Hide 00:59Short when I came into corporate life and said, set up a leadership and team development company 20 odd years ago seems like yesterday. And over the years I've struggled, I struggled to differentiate myself from the competitors in a crowded marketplace, all doing very similar things. And I decided to do something about it. And I created a web based app that analyses a team and how it works and functions together, my marketplace, our business coaches, trainers and HR professionals that I understand because that was me that 20 odd years. And I think the biggest challenge they have is to for new business development getting in that funnel getting inconsistent business. And the challenge is to be different. And there's an awful lot of business coaches out there all doing very similar things. So my platform helps them differentiate that helps them stand out from the crowd and creates a new way of talking to their customers and adding value to those teams and those leaders that they deal with.Stuart Webb 01:56That's an interesting problem anyway, what sort of things have you seen business leaders doing, which gets them into the problem of not having a cohesive team before you help to try and solve that problem.Mark Hide 02:10So the business coach or trainer, they, they tend to have come out of corporate life, they've gone on a course they bought in to an accreditation process, and they love being qualified in something, the majority of them, there's those like me that wanted to become a coach, but I can't because I ended up just telling rather than listening, because I think I know the answer is probably don't. So without that consistent approach, and how they're going to help their customers, they rely on referrals. And so therefore, they get challenged how they continue a long term business with a with a customer. Whereas what my platform loves team, the ticket enables a team to measure sorry, coach will try to measure a team here, then we've their professional magic, whatever that magic is, and then come back and really importantly, retest three 612 months later. And that gives them this brilliant thing of return on investment, which is like the Holy Grail for for coaches and trainers, it's very hard to quantify and to measure how much a team has improved. I believe I've achieved that. And I can do that with my platform.Stuart Webb 03:13Brilliant. So tell us, how do you offer value to your team litterer customers, both the coaches and our guests the end customer of that coach or, or your client question. SoMark Hide 03:27I do have a number of ways. First is I'm really passionate about this is this is a 20 year journey for me. And I want to help every team realise its potential every manager, give them an easier life and be more productive. So first thing I'm doing is building a community. These are good people, they're nice people, they're good. The people you want to do business with coaches and trainers are very passionate about what they do. So that community is important to me, and then giving them the resources to help them stand out in that credit marketplace. And they will probably use one of a plethora of psychometric profiling tools that range from free to hundreds of pounds. And they will do a very similar thing, which is allow the individual to assess themselves in their career. This report goes, Hey, yeah, that's me, Wow, this psychometric profile is so accurate. Well, of course it is because you've done it on yourself, then show it to their boss, show it to their partner home and put it in a drawer and it gathers dust, vast majority 10s hundreds of millions of pounds wasted. Whereas what I've built is something that I will analyse a team measures cohesion, stress morale, and 54 other colour coded matrix, I spent a lot of money making it very accessible. And it allows the coach to go in and work with that team and that manager, do some immediate quick wins, which makes them stand out and gain trust with that team. And then over a period of time implement longer term, either structural or behavioural changes to that team to get them from either stressed or apathy into what I call optimal performance, the top of that performance curve, and building that relationship investing in that team. Might be small things might be coaching might be training might be skills, and then coming back, as I say, and retesting, and enables that coach or trainer to have a longer term relationship with their customers, and hopefully see them good and improve their productivity and measure it along the way. So that's what I do.Stuart Webb 05:15So Mark, have you got any, any metrics, you could point to some success stories, you could just briefly tell that sort of give us an indication of that sort of longer term success of the cohesion of the team changing because of the interventions?Mark Hide 05:29That's a great question. So I originally launched this as a self serve platform directly into teams, and but my passion is with business coaches, and trainers and a consulting firm pick this up last summer. And they compared my report with another report, they were judging to see which one might be more appropriate, as part of their three initial tools they use with very senior leadership teams to consult with. And the the gentleman phoned me up, Matt phoned me up, and he said, we tested your platform. And we tested this other one on our same zoom call. He said, yours we introduced, and I sent the team off with the questionnaire and a packet of biscuits on a virtual call. He said, within 20 minutes, we were making positive changes to how our team operated, which is not quite as you can see, I'm very proud of that. And he said, and then with the other team, the guy took two and a half hours to explain how the software worked and how to utilise the results. And of course, we never picked it up. Because I think managers are time poor and they want quick wins. So we go straight in there, urgent actions, get it done, start moving the dial forward. He then implemented it with two of his clients at very senior leadership levels, with airline manufacturers, which are household names. And sadly, I can't tell you, but they are very big companies. And so yes, I'm really proud of the I got quite emotional, actually, because he asked me to actually analyse a team with having never met them. And we sort of nailed this team and that their main challenges and the opportunities for for them to go in and help that team progress. So it was good.Stuart Webb 07:00Love the story. Mark, there must be a great way that we can sort of get some free, free stuff from you to help us I'm gonna just put the website now scrolling across the bottom of the of the screen here, which is Team Politiker. Which is you know, it's not Analytica, it's team politica. So don't get don't get confused. Team litical.com. Tell us what can we get when we get onto that website? What what what great offers and, and stuff? Can we learn about team litical from that website,Mark Hide 07:31or I do like you, Stuart, great approach. So there's a number of things. Firstly, it's packed full of case studies. And so we try and give away lots of information about how that can be used in different industries and other culture training can use it was starting to build up a whole series of blog articles around the 54 different metrics, which is a problem, each of those metrics is a problem in a team somewhere. And we're going to be building up that bank. If you want to access the report, and you don't have a budget, or you just want to try it out, we do a free version, which will give you just cohesion, stress and morale and the nine categories scores. And that's free, you can just come in and access that and have a look through. And then you can just pick up the phone and talk to me because I'm quite generous, and I tend to give away too much. And I'm really happy to help a team and get stuck in and help them with their problems.Stuart Webb 08:20Mark, I love it. Because as you know, as I said, you know, the one thing that we believe in here on this particular podcast, if if we give massive pre sales value, it'll come back. So I love the approach. Yeah, good free stuff, love the love the the offer of picking up the phone and just talking to people. Because that if if anything for somebody who's cash strapped and then wants to move a bit forward, that'll might just give them enough momentum that they come back later and go now I really need to engage. So, Mark, thank you so much for that. Listen, we're getting to the real meat of this now, which is there must be a book or a course or a programme or something which has sort of inspired you which you want to sort of pass on to the to the audience listening, what is that book or course that you think would really add some value to the way that they would bring about some sort of cohesion in their team.Mark Hide 09:07My first inspiration and my first guide for people I spent my entire life in scouting, and it's been a an amazing family resource, motivator, whatever it might be. And I was taught Scout leadership and team working from seven or eight years old. And in fact, next week and a half term week. I'm up to Scotland with 22 young people will teach you ice climbing skills and slow holding and doing stuff teaching leadership to young adults. So that's that's really important to me. I'd love to stress that a couple of things. I'm I'm following a guy in America called Jocko Willink. And he's an ex US Navy SEAL he's he's a bear of a man he's but you know what if you take away the WHO raw American stuff underneath is a core of really good solid, valuable leadership and team working knowledge Insights and skills. And I really like what he does. There's a badge he had, which was, nobody's coming. It's down to you. And I just I have this little badge and I stick it up on the wall, because it's, it's not us, isn't it, we've got to work hard and get out there and knock on doors and make it happen. And the other person I really admire her approach is lady called Helen Pritchard. Now, Helen Tudor. She's a LinkedIn specialist. And she has his very down to earth, very simple, replicable, scalable way of using LinkedIn to develop your business. And if you're a coach or trainer out there wondering how to get further contacts, then I would check out her details. And it's very accessible. And she's good at what she does No, no rubbish, either. Just straightforward advice.Stuart Webb 10:47Mark, thanks. Yeah, no, I must admit the, there's an awful lot of we could talk for many hours, I suspect about people who are now beginning to form opinions of the way they should form team through some of this toxic masculinity. And, you know, there's, there's there are people that are in the news, we won't talk too much about them that are out in the sort of the eastern part of Europe that have recently been arrested, because they sort of spout a particular type of toxic masculinity. And so one thing I've learned over my years is that most of those people that have that sort of, they may be gruff on the outside, but the core Heart of Gold are the people that you look at and go, they'll make a great team, no matter what's going on around them, because they are focused on not only what they've got to do to move something forward, but how they bring everybody else with them, not forced them, not drag them, not push them ahead, but turn around, you know, you're coming with me. And I'm a great fan of the fact that, you know, most of the people that I've been working with in the past have those that will turn around and say my foot will be the first out of the helicopter when we attack the hill and the last foot off off the hill when we when we finish taking it. And that is what truly inspires me that you as a leader, you have to be committed as much as you'd expect anybody else to be committed, don't you?Mark Hide 12:05100%. And we're all people. And there's a really bit of old theory called Hertzberg as motivation theories, why we go to work, and we go to work, because we're tribal, we want to be with other people, we want to interact with other people and enjoy what we do. And if we get to work, and we will like a bit of pressure pressures, okay? Stress is okay, but not too much. And if you've got the wrong manager, or the wrong people in that role, then you come to work, and it's not enjoyable. So you're not likely to put your all into it. And when that team is cohesive and working well, and it's fired up, and it's aligned, it's got shared goals, and it's got energy and passion, and then it's going to drive forward and deliver whatever the role is, whatever the job is, they will do that, and they will deliver it at a high standard. So that's what I'm trying to aim for reallyStuart Webb 12:51had a great comment from Leila. I do know Leila, pretty well. And, you know, she sort of talked about, it's a great tool. So, Leila, I hope you check it out. Because it is really quite an interesting tool. I've seen it it is simple to use. So thank you, Mark, where we're, I've been sort of asking you questions for the last 1015 minutes. And you must be fed up with me asking you all the questions that you didn't want to be asked. So what's the question you would like me to ask you? And then once obviously, you ask the question, you need to answer it. Otherwise, I should just be forced to make you answer it. So what is the question you would have liked me to have asked you.Mark Hide 13:26So the Holy Grail that people buy? Have you ever been in a high performing team? And because we all strive for it, but very few of us ever get there. And it's a joy when you're in a high performing team. It is pleasurable, it's exciting. You solve problems you didn't know you could. There's a high and you're waiting for that bubble to burst, but it's Pairing your Phone. It's an amazing feeling. And I've only been privileged to be in two high performing teams in my life, one in my work life, and one in my scouting life, and I was climbing a big mountain. So how about you, Stuart? Have you ever been in a high performing team IStuart Webb 14:05have been lately, yes, you really should connect with Mark. He's an interesting character. And lately, you're an interesting character, say the very least, as well. So Mark, I will find a way of connecting the two of you together. Yes, I've been in a high performing team, one that I joined, and was an inspirational leader who, you know, was very single minded, but at the same time, you know, was prepared to get there by committing themselves more than anybody else, and one that I was fortunate enough to actually end up leading. And that was largely because I was able to pick the people that I needed around me who the trust between us was so was it already somewhat established, but through the circumstances it built and that some of those people, you know, this is now some 20 years later, we're still in contact because we still value each other's advice and that's the sort of the thing that comes out of He's high performing teams, isn't it? It's not just do you solve that problem? But do you want to solve other problems into the future and continue to support each other long after the project or the company or whatever has moved on.Mark Hide 15:12It's so true. And in fact, that high performing I mentioned in my corporate life was 20 years ago, and I'm still in touch with the guy that was my boss. And we're still trying to do work together because I enjoy His company. And I know what we can do when we work well together and achieve this amazing thing.Stuart Webb 15:29Mark, this has been an inspirational and thank you so much for spending a few minutes with us. I do really do. I'd like people to go and check out Team liquid.com. That's T am litical l y t i c a.com. It's well worth having a look at even the free stuff that Marc's got up there. It's quite an interesting tool. As I said, I've seen it in depth, and sort of the the report that he produces is really well worth having a look at. So please check out what Mark is doing with Tim Nitika. Mark, thank you so much for spending a few minutes with us. I'm just going to tell everyone, if you would like to get onto our mailing list and get an email each week telling you who will be on this week's podcast to be able to join us Layla did to either put questions or comments during the recording, or please join the mailing list, which is TCA dot FYI, that's TCA dot F. Well, why I forward slash subscribe. I will put that into the show notes. I'll also put into the show notes, the email, sorry, the web address of Team let's get in case you weren't able to capture it, so that you can connect with Mark and further discussions. Mark, thanks so much for spending a few minutes with us really appreciate you spending the time. It's been a fascinating discussion. I just love the love the energy that you bring to your and the passion you bring to your business. And I know that team litical will will will be a tool most people won't be to get into.Mark Hide 16:51Great Alison Stewart. Thank you for your time. I love your question set. It's different and it's it sparks conversation and thought and debate which is what it's all about. So appreciate it.Stuart Webb 17:01That's exactly what we're trying to solve and debate is absolutely it. Thank you, Mark. I really appreciate it. Speak to you again in a very short time. Get full access to It's Not Rocket Science! at thecompleteapproach.substack.com/subscribe

Check out the Greensboro College Analytics Apprenticeship program here: https://learn.silvertoneanalytics.com/apprenticeship/Check out the Silvertone Analytics Career Services Program here: https://learn.silvertoneanalytics.com/career-services/Learn more about Sol Analytica here: https://solanalytica.com/In this podcast episode Charles Baldridge who is an analytics industry veteran and founder of Sol Analytica conducts a mock interview with our Greensboro College analytics apprentice Amanda Bell. After the mock interview Amanda flips the script and asks Charles questions about his interview process with the analysts he hires and breaks down his career path.Welcome to the How to Get an Analytics Job channel. Discover how you fit into the analytics marketplace, what skills you should build, and how to land your analytics dream job. Analytics agency owner John David Ariansen and his team will give you tips and tricks to land your dream job and level up your analytics career.Check Out Our PlaylistsHow to Get an Analytics Job Podcast:https://lnkd.in/dMv987nmGreensboro College Analytics Lecture Series: https://lnkd.in/dH66tJixLooking to land an analytics job? Sounds like you need a solid resume... Sign up for our email list to get a free analytics resume guide: https://lnkd.in/dSqBSwQgFollow us on LinkedIn:John David Ariansenhttps://lnkd.in/dXFrGHqhHunter Brownhttps://lnkd.in/gqGwk2Rk

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: [Cross-post] Is the Fermi Paradox due to the Flaw of Averages?, published by Aryeh Englander on January 18, 2023 on LessWrong. [This article is copy-pasted from the Lumina blog, very lightly edited for LessWrong.] Where is everybody?— Enrico Fermi The omnipresence of uncertainty is part of why making predictions and decisions is so hard. We at Lumina advocate treating uncertainty explicitly in our models using probability distributions. Sadly this is not yet as common as it should be. A recent paper “Dissolving the Fermi Paradox” (2018) is a powerful illustration of how including uncertainty can transform conclusions on the fascinating question of whether our Earth is the only place in the Universe harboring intelligent life. The authors, Anders Sandberg, Eric Drexler and Toby Ord (whom we shall refer to as SDO), show elegantly that the apparent paradox is simply the result of the mistake of ignoring uncertainty, what Sam L. Savage calls the Flaw of Averages. In this article, we review their article and link to an Analytica version of their model that you can explore. The Fermi Paradox Enrico Fermi. From Wikimedia commons. One day in 1950, Enrico Fermi, the Nobel prize-winning builder of the first nuclear reactor, was having lunch with a few friends in Los Alamos. They were looking at a New Yorker cartoon of cheerful aliens emerging from a flying saucer. Fermi famously asked “Where is everybody?”. Given the vast number of stars in the Milky Way Galaxy and the likely development of life and extraterrestrial intelligence, how come no ETs have come to visit or at least been detected? This question came to be called the “Fermi Paradox”. Ever since, it has bothered those interested in the existence of extraterrestrial intelligence and whether we are alone in the Universe. The Flaw of Averages on Steroids Dr. Sam Savage who coined the term “Flaw of Averages” Sam L. Savage, in his book, The Flaw of Averages, shows how ignoring uncertainty and just working with a single mean or “most likely” value for each uncertain quantity can lead to misleading results. To illustrate how dramatically this approach can distort your conclusions, SDO offer a toy example. Suppose there are nine factors that multiplied together give the probability of extraterrestrial intelligence (ETI) arising on any given star. If you use a point estimate of 0.1 for each factors, you could infer that there is a 10−9probability of any given star harboring ETI. There are about 1011 stars in the Milky Way, so the probability that no star other than our own has a planet harboring intelligent life would be extremely small, (1−10−9)100B≈3.7×10−44. On the other hand, suppose that, based on what we know, each factor could be anywhere between 0 and 0.2, and assign a uniform uncertainty over this interval, using the probability distribution, Uniform(0, 0.2). If you combine these distributions probabilistically, using Monte Carlo simulation for example, the mean of the result is 0.21 – over 5,000,000,000,000,000,000,000,000,000,000,000,000,000,000 times more likely! The Drake Equation Frank Drake, a radio astronomer who worked on the search for extraterrestrial intelligence (SETI), tried to formalize Fermi's estimate of the number of ETIs. He suggested that we can estimate N, the number of detectable, intelligent civilizations in the Milky Way galaxy from what is now called the “Drake equation”. It is sometimes referred to as the “second most-famous equation in science (after E= mc2)”: Frank Drake (1930-2022). N=R∗×fp×ne×fl×fi×fc×L Where R∗ is the average rate of formation of stars in our galaxy,fp is the fraction of stars with planets.ne is the average number of those planets that could potentially support life.fl is the fraction of those on which life had actually developed;fi is the fraction of those with life that ...

I'm happy to introduce you to Connor Slayton of Futures Analytica. He's only 20 years old and has a fascinating story. It all started when he heard about the existence of quantitative systems at a school fair. It planted a seed in his mind. So after withdrawing 8k from his earn2trade funded account, using a basic MACD strategy, he started to implement his current system...Listen to this podcast to discover the entire journey!HERE'S WHAT WE COVER:01:55 Introduction to Connor Slayton03:30 Connor's Backstory05:34 Getting started in trading08:46 Transitioning to Futures12:09 Starting on YouTube18:10 Trading Strategy20:25 Other activities outside trading22:36 Separating impulse to do more28:00 Futures Analytica's next stage of development29:10 Future plansWant to work privately with me? Book a 15-min Discovery Call: https://go.tradacc.com/discoveryGET FUNDED TO TRADE FUTURES

Hablamos sobre la pérdida de valor adquisitivo y salarios con Ricardo Delgado, economista y presidente de la consultora Analytica. --- Send in a voice message: https://anchor.fm/urbanaplayfm/message

Luke Yingling founded his legal tech startup, Analytica Legalis, about a year ago during his second year in law school. As you'll hear from Luke, his company has already raised its pre-revenue seed-stage financing and is looking forward to launching its beta version (with law firm beta testers already lined up and eager to go) this coming August. Hear Luke (a) chart his path from law student to legal tech startup founder, (b) relate how, as a student entrepreneur, he was able to take advantage of programs at his law school and elsewhere aimed at assisting very early-stage student-founded startups, (c) describe what Analytica Legalis does and how it distinguishes itself from other tools for litigators that analyze judges' opinions, (d) explain the preparations his company undertook to ready itself for its beta testing program, (d) also explain how he attracted investors that were interested in funding a pre-revenue legal tech startup, (e) discuss the importance of data visualization techniques for making his company's UI intuitive and easy-to-use and (f) tell listeners of the pride he and his team have taken in generating both commercial and academic excitement for the results available from the built "from-the-ground-up" version of Analytica Legalis' machine learning software and in innovating in the judge analytics space.

Facing Our Own “Nagumo Dilemma” With Patrick Marrinan of Marketing Scenario Analytica With Host Richard Levick of LEVICK: Patrick Marrinan, Managing Principal and Co-Founder of Marketing Scenario Analytica (MSA) speaks with host Richard Levick of LEVICK about the intelligence required to evaluate risks and the information needed to effectively employ change management now, before the risks have matured into crises. At the World War II battle of Midway, Japanese Vice Admiral Chuichi Nagumo did not have the necessary information to guess correctly if the battle would come from air or sea and as a result made the wrong choice during a crucial half hour that turned the tide of the war. How do we gather the information we need to recognize risks and plan appropriately? Patrick provides an MSA Social Risk data sample as a way for business leaders to see the changing trends and specific industry and company vulnerabilities.

@kmens5 and @daochemist review Whale Analytica, an AI-driven data analytics platform focused on NFT and introducing the first Metaverse property appraisal. We look into how the platform works and who is it meant for.

Find Conspiracy Analytica: On BitChute https://www.bitchute.com/channel/LLrgzJoLMNpX/ On Rumble https://rumble.com/c/c-735873 On Podbean https://conspiracyanalytica.podbean.com/ On Telegram and Instagram: TG: https://t.me/ConspiracyAnalytica IG: https://www.instagram.com/conspiracyanalytica/ Website: http://conspiracyanalytica.com/ My Main Website: https://www.jordansather.com/ Donate via Crypto: https://cointr.ee/jordansather Support on SubscribeStar: https://www.subscribestar.com/jordansather

1-Iran – Stati Uniti : il disastro aereo e le nuove sanzioni rischiano di acuire la grave crisi...Aggiornamenti e analisi. (Serena Tarabini)..2-Haiti 12 gennaio. Dieci anni fa il terremoto che ha raso al suolo Port au prince, 230 mila era stati le vittime. L' Intervista di esteri. (Luisa Nannipieri, Fiammetta Cappellini – resp AVSI Haiti)..3-Francia. Nonostante 37 giorni di sciopero e 4 manifestazioni il governo non cede sulla riforma delle pensioni. (Luisa Nannipieri)..4-Quando le nuove tecnologie diventano uno strumento per violare i diritti umani...In Brasile I nuovi documenti di cambridge Analytica rivelano le manipolazioni sui social che hanno permesso a Bolsonaro di vincere le presidenziali. (Marco Schiaffino – doppio click)..5- “ Errore di sistema “ Edward Snowden racconta i meccanismi della sorveglianza globale. (Vincenzo Mantovani)

Yay, we have just released a new beekeeping Q & A show. We hope you will enjoy this one. We enjoyed bringing it to you. In this show we answer questions about finding Bees, stopping wasps and Winter Preparations. Links / Resources mentioned this week Swarm Trapping bees with a Mobile Swarm Trap – Michael Jordan https://kiwimana.co.nz/swarm-trapping-bees-with-a-mobile-swarm-trap-km104/ Bee Swarm Collectors https://kiwimana.co.nz/bee-swarm-collectors/ Swarm Commander https://swarmcommander.com/ How to Prepare a Beehive for Winter In New Zealand https://kiwimana.co.nz/how-to-prepare-a-beehive-for-winter-in-new-zealand/ Vespex - Wasp Control https://www.merchento.com/vespex.html Hill Laboratories https://www.hill-laboratories.com/testing/honey-testing/ Analytica https://www.analytica.co.nz/ What's Your Number One Beekeeping Problem? https://kiwimana.co.nz/whats-your-number-one-beekeeping-problem/ What is in the Show Finding Bees - How Find Bees For Free - 00:01:04 Getting Ready for Winter - 00:03:41 Wasps - How to stop them killing your bees - 00:06:48 Aggressive bees - What do I do? - 00:12:04 Tutin in Kawerau - 00:15:33 Government Madness in Florida - 00:16:46 End of the Show - 00:19:40 Full show notes These notes are a summary of the full show notes. The full show notes include pictures and more detail information about the show. The full shows are locked for non-supporters until the shows have been released to the public. The full show notes are here:- http://kiwi.bz/143 If you have a question feel free to visit:- http://kiwi.bz/problem Thanks Gary and Margaret kiwimana buzz Beekeeping Show Follow us on Social Media Facebook - http://kiwi.bz/facebook Pinterest - http://kiwi.bz/pinterest Twitter - http://kiwi.bz/twitter Want to get the next episode the minute it is released, Learn ways to subscribe HERE https://kiwimana.co.nz/how-to-subscribe-to-the-kiwimana-buzz-show/ Please Support this podcast on Patreon

This week, Amazon & Apple have some secret projects in the works, T-Mobile buys Sprint, Facebook's F8 developers conference, DNA catches a killer... and much, much more. What We're Playing With Dwayne: MoviePass - Repeat viewing is gone, but unlimited is back again. Andy: Westworld S2, Violent Delights Headlines Apple's working on a powerful, wireless headset for both AR and VR Amazon to Increase U.S. Prime Annual Fee to $119, Up From $99 Amazon Has a Top-Secret Plan to Build Home Robots Amazon Key In-Car delivers packages right to your car's trunk Audible Book of the Week All Systems Red By Martha Wells Sign up at AudibleTrial.com/TheDrillDown Music Break: Westworld (theme) by Ramin Djawadi Hot Topics T-Mobile to Buy Sprint for $26.5 Billion in Bet on Networks Cambridge Analytica Closing Operations Following Facebook Data Controversy The power players behind Cambridge Analytica have set up a mysterious new data company The 5 biggest announcements from Facebook's F8 developer conference keynote Music Break: Psycho Killer by Talking Heads Final Word GEDmatch, a tiny DNA analysis firm, was key for Golden State Killer case The Drill Down Video of the Week We Sent Garlic Bread to the Edge of Space, Then Ate It Subscribe! The Drill Down on iTunes (Subscribe now!) Add us on Stitcher! The Drill Down on Facebook The Drill Down on Twitter Geeks Of Doom's The Drill Down is a roundtable-style audio podcast where we discuss the most important issues of the week, in tech and on the web and how they affect us all. Hosts are Geeks of Doom contributor Andrew Sorcini (Mr. BabyMan), marketing research analyst Dwayne De Freitas, and Box product manager Tosin Onafowokan.

This week, Leaked information from Apple about leaks, plenty of leaking at Amazon warehouses as well, Tesla stops & starts... and much, much more. What We're Playing With Andy: Hulu + Spotify bundle Headlines In a Leaked Memo, Apple Warns Employees to Stop Leaking Information Facebook Container Extension: Take control of how you're being tracked Cambridge Analytica may have used other quizzes to gather Facebook data Audible Book of the Week Future Presence: How Virtual Reality Is Changing Human Connection, Intimacy, and the Limits of Ordinary Life by Peter Rubin Sign up at AudibleTrial.com/TheDrillDown Music Break: Under Pressure by Queen Hot Topic: Amazon Troubles Jeff Bezos reveals Amazon has 100 million Prime members in letter to shareholders Undercover author finds Amazon warehouse workers in UK 'peed in bottles' over fears of being punished for taking a break Amazon warehouse culture "was like a prison" Amazon and Best Buy team up to sell TVs, but it's a risky move for Best Buy Roku stock dives after Best Buy and Amazon partner to sell smart TVs Music Break: One Piece at a Time by Johnny Cash Final Word Tesla temporarily stops Model 3 production line Tesla could benefit from looser restrictions in China, says auto analyst Subscribe! The Drill Down on iTunes (Subscribe now!) Add us on Stitcher! The Drill Down on Facebook The Drill Down on Twitter Geeks Of Doom's The Drill Down is a roundtable-style audio podcast where we discuss the most important issues of the week, in tech and on the web and how they affect us all. Hosts are Geeks of Doom contributor Andrew Sorcini (Mr. BabyMan), marketing research analyst Dwayne De Freitas, and Box product manager Tosin Onafowokan.

This week, Facebook's CEO Mark Zuckerberg answers to US Congress, and we've got the details... Hot Topic: US Congress Hearings With Mark Zuckerberg The 5 biggest takeaways from Mark Zuckerberg's appearance before the Senate 7 takeaways from Mark Zuckerberg's appearance before the House Zuckerberg's notes (IMAGE) Mark Zuckerberg's Facebook hearing was an utter sham Facebook is offering a $40,000 bounty if you find the next Cambridge Analytica Audible Book of the Week The Accidental Billionaires: The Founding of Facebook by Ben Mezrich Sign up at AudibleTrial.com/TheDrillDown Music Break: Intriguing Possibilities by Trent Reznor and Atticus Ross Final Word The EU's General Data Protection Regulation, explained: Everything you need to know about GDPR, Europe's new data privacy law. The Drill Down Videos of the Week Weekend Update: Mark Zuckerberg on Cambridge Analytica Subscribe! The Drill Down on iTunes (Subscribe now!) Add us on Stitcher! The Drill Down on Facebook The Drill Down on Twitter Geeks Of Doom's The Drill Down is a roundtable-style audio podcast where we discuss the most important issues of the week, in tech and on the web and how they affect us all. Hosts are Geeks of Doom contributor Andrew Sorcini (Mr. BabyMan), marketing research analyst Dwayne De Freitas, and Box product manager Tosin Onafowokan.

This week, a tragedy at YouTube HQ, Facebook's woes get worse, Spotify goes public, an app publicly reveals users health status, a clone army of news readers... and much, much more.   What We're Playing With Andy: Keurig K475 Headlines Tesla confirms Autopilot was activated during fatal crash under investigation after reviewing data logs This is what may have happened in the recent Tesla Autopilot Crash Grindr Is Sharing The HIV Status Of Its Users With Other Companies Apple Plans to Use Its Own Chips in Macs From 2020, Replacing Intel Spotify IPO: How Will It Affect Your Music Listening Habits? Audible Book of the Week Infinite by Jeremy Robinson Sign up at AudibleTrial.com/TheDrillDown Music Break Hot Topic Facebook says Cambridge Analytica may have had data from as many as 87 million people Facebook limits data available from Pages API, Events API, Groups API, and Facebook Login, will now need to approve every app that uses them Shooting at YouTube headquarters leaves suspect dead and several injured YouTube shooter IDed as woman angry at site's “age-restricted” policies Music Break: Lies by Thompson Twins Final Word How America's Largest Local TV Owner Turned Its News Anchors Into Soldiers In Trump's War On The Media The Drill Down Videos of the Week Subscribe! The Drill Down on iTunes (Subscribe now!) Add us on Stitcher! The Drill Down on Facebook The Drill Down on Twitter Geeks Of Doom's The Drill Down is a roundtable-style audio podcast where we discuss the most important issues of the week, in tech and on the web and how they affect us all. Hosts are Geeks of Doom contributor Andrew Sorcini (Mr. BabyMan), marketing research analyst Dwayne De Freitas, and Box product manager Tosin Onafowokan.

This week, Apple gets educational, What will Facebook do after Cambridge Analytica, gas-powered cars feel the threat from electric, a possible cure for blindness ... and much, much more.  What We're Playing With Andy: Amazon Echo Spot Dwayne: Samson C01u Pro Mic Headlines Mark Zuckerberg says he's ‘open' to testifying to Congress, fixes will cost ‘many millions' and he ‘feels really bad' Why Nothing Is Going To Happen To Facebook Or Mark Zuckerberg Facebook scraped call, text message data for years from Android phones Tim Cook speaks out on the Cambridge Analytica scandal, says Facebook's collection of user data 'shouldn't exist' Facebook is rushing out a new design for privacy settings Craigslist Is Shutting Down Its Personals Section Mega-hit ‘Fortnite' game has wiped out $6 billion in market value from industry leader Activision Blizzard Audible Book of the Week Ready Player One by Ernest Cline Sign up at AudibleTrial.com/TheDrillDown Music Break: Pure Imagination by Gene Wilder Hot Topic: Apple Educational Event The 5 biggest announcements from the Apple education event Tablet market down 7% year-over-year, though iPad business is up 3% year-over-year Hot Topic: Electric Cars Tesla shares drop to the lowest since April on fatal crash investigation, bearish analyst note NTSB is investigating Tesla Model X that caught fire after fatal crash in Silicon Valley Nvidia plunges after suspending self-driving tests Hyundai union chief warns of job crisis, says electric cars are 'evil' Music Break: ABC by The Jackson 5 Final Word Raytheon's laser and microwave buggy test brought down 45 drones Doctors Have Restored The Sight of Two People in a Monumental World First The Drill Down Videos of the Week Range Rover P400e: Eco friendly with no compromises Subscribe! The Drill Down on iTunes (Subscribe now!) Add us on Stitcher! The Drill Down on Facebook The Drill Down on Twitter Geeks Of Doom's The Drill Down is a roundtable-style audio podcast where we discuss the most important issues of the week, in tech and on the web and how they affect us all. Hosts are Geeks of Doom contributor Andrew Sorcini (Mr. BabyMan), marketing research analyst Dwayne De Freitas, and Box product manager Tosin Onafowokan.

This week, a self-driving car kills a pedestrian, a burger robot is fired, Does space travel alter our DNA? And experts Tom Cheredar & Gina Lee De Freitas help us decide if it's time to delete Facebook... and much, much more. Headlines Theranos CEO Holmes and former president Balwani charged with massive fraud Self-Driving Uber Car Kills Arizona Pedestrian Tempe police chief says early probe shows no fault by Uber Humans Couldn't Keep Up with This Burger-Flipping Robot, So They Fired It Audible Book of the Week A Brief History of Time by Stephen Hawking Sign up at AudibleTrial.com/TheDrillDown Music Break: Face the Face by Pete Townshend Hot Topic: Facebook & Cambridge Analytica FAQ: Interview with IMM President Gina Lee De Freitas Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach How Trump Consultants Exploited the Facebook Data of Millions Cambridge Analytica caught on tape proposing blackmail, propaganda #deletefacebook Facebook's Stock Is Plummeting Amid the Company's Latest Crisis. Here's What's Going On Facebook's value destruct-o-meter: $50 billion and counting #DeleteFacebook movement gains steam after 50 million users have data leaked Mark Zuckerberg responds to the Cambridge Analytica scandal Wrapup: Interview with Tom Cheredar Music Break: Life on Mars? by David Bowie Final Word: Space Exploration Scott Kelly Spent a Year in Space, and Now He Has Different DNA Than His Identical Twin Brother What Trump's 'Space Force' might look like – and when it would be ready Visionary physicist Stephen Hawking dies The Drill Down Videos of the Week Elon Musk Surprises ‘Westworld' Panel With SpaceX Falcon Heavy Launch Trailer Robot flipping Robot Subscribe! The Drill Down on iTunes (Subscribe now!) Add us on Stitcher! The Drill Down on Facebook The Drill Down on Twitter Geeks Of Doom's The Drill Down is a roundtable-style audio podcast where we discuss the most important issues of the week, in tech and on the web and how they affect us all. Hosts are Geeks of Doom contributor Andrew Sorcini (Mr. BabyMan), marketing research analyst Dwayne De Freitas, and Box product manager Tosin Onafowokan.