Hello, and welcome to My Career in Data, a podcast where we discuss with industry leaders and experts how they have built their careers. I'm your host, Shannon Kemp, and today we're talking to John Ladly from Summary. More and more companies are considering investing in data literacy education, but still have questions about its value, purpose, and how to get the ball rolling. We're doing the newest monthly webinar series from DataVercity, Elevating Enterprise Data Literacy, where we discuss the landscape of data literacy and answer your burning questions. Learn more about this new series and register for free at DataVercity.net. Hello, and welcome. My name is Shannon Kemp, and I'm the Chief Digital Officer at DataVercity, and this is My Career in Data, a DataVercity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to talk with people to help make those careers a little bit easier. To keep up to date in the latest in data management education, go to DataVercity.net forward slash subscribe. Today we are joined by John Ladly, the principal at Summary, and normally this is where a podcast host would read a short bio of the guest, but in this podcast, your bio is what we're here to talk about. John, hello, and welcome. Well, thank you very much for having me on your podcast. It's a delighted to be here. Delighted to be here. Well, I'm excited that you're here. I've known you for, gosh, over 10 years now, and I don't know that we've ever had that conversation of how you got started. I don't think we ever have. It's a checkered past. I hope this passes the sensors. We'll find out, I guess, you know. Are there sensors on podcasts? I don't know. Yeah. So I don't know where do you want to start? I'm... Let's start where you are currently. So you're the principal at Summary. Summary. Yeah, Summary. Summary. Yeah, it's an Irish name, right? Summary is the closest thing in Gaelic to the word data. So when I say Summary solutions, it's data solutions in Gaelic. And that's because great-granddad is from Tipperary in Ireland. And I visit Ireland quite a bit. So I like Ireland, so I go there a lot. And then I found out I was actually Irish after I started going there, which was kind of cool to find out. Very cool. And it's spelled S-O-N-R-A-I. Yeah, yeah. And that's Gaelic as a language, really has no alphabet. Right. So we mess around with the words to try to get them close. And it's an astonishingly hard language to learn, too. I've tried and given up. But the Summary is actually... The one thing for the listener is, you know, don't go looking in the Wall Street Journal for a story about, yeah, I'm semi-retired now. I've been at this data business for a very, very long time. And Summary is my LLC that people write checks to, you know. And my accountant is happy that I have that or not. That's really the only reason it exists. I quit building empires about five years ago, six years ago. I stepped away from full-time, aggressive work. And the work I do now in the data field is selective, and it's designed... And in the Spiritist Podcast, what I'm doing now is designed to further the industry, to learn different things, to try different things, to look at really hard problems, and see what can be learned from that. Before one day I wake up and just go, that's it. I'm done, right?  So... And so for those of you who cannot see currently, what's behind you? What's behind me, as you said, leaning a little bit, is a 1942 Boeing Stearman biplane. It's two wings, as you can see, but it was a trainer in World War II. A lot of people will say two wings. World War I, no. There were still biplanes in the 1940s, and the primary trainer for the United States military for World War II was that particular airplane right there. They built 9,000 of them, give or take, of them. And if you were a pilot in World War II, that's the first airplane you climbed into. And mine has been restored to its original out-of-the-factory condition. And this year, it's 83 years old this year, so... I don't know. It's an 83-year-old airplane, but it's been restored. Yeah, yeah. Very nice. Yep. So how long have you been flying? We'll get into more of your bio, but how long have you been flying? Oh, yeah, let's talk about the cool stuff first. No, I always wanted to fly from a young lad, and I couldn't do it because of some medical challenges and some life circumstances. But at some point, when I was about 30 years old, I emptied out a...  And I hadn't bought lunches for myself, and I'd quit the golf team at work and I'd quit the bowling team at work. And I saved up my money and I started to take flying lessons at the age of 30. And that's when I started, so I've been flying for 37 years. It was not military, strictly civilian, but I've always had this craving to get into these old classical machines. I mean, there's nothing digital at all about this machine. It is extremely crude. It is the best technology 1935 had to offer. And to me, it's a tremendous offset and escape from the day job. And I started to fly to get into one of these. That's why I started to fly. I love it. So, bearing back then a little bit to the data in your current role and what your profession and what you're doing. Oh, um, oh, um. Back of the mundane. Oh, no, it's not mundane at all. No, I think it's very cool things. And certainly cool things that we appreciate the community appreciates. But so tell me then what is, you know, you talked about focusing on problems and that kind of thing. So what is your work week or work month look like now? I still work hard Tuesdays, Wednesdays and Thursdays. Monday is research and writing day and Friday is don't bother me day.  And we, we all go back a long way, right? Back to when it was some other organization. I forget what the other name was with Dataversity. Sure, conferences. Yeah, yeah, Wilshire. Yeah, it was named after a sauce, was it? No, that's Worcestershire. Anyway, yeah, Wilshire. So, so that's what I do now. I do a lot of mentoring, I do a lot of coaching. I work with a lot of executives. Now it's very common for me to speak to two or three CEOs a week now. I work a lot with my peers in the industry right now. I'm right now, because no one's told me it's okay to say it or not. I can't, but in the last six months I've worked with four or five other people that have appeared on this podcast. And they are working with me or for me or we're doing research together or we're doing writing together based on research we've done together. Cause we're trying to come up with a, not a body of knowledge, but a sanity check of the profession in the last year. Or so. So that's what occupies my time. I have clients, but you know, bringing a team of five or six people. I mean, my work has always been a little different. A lot of folks in history, a lot of folks have written books like I have and things that have been one or two person operators and that's what they like to do. I built consulting firms. I brought in four or five, six people on a team and we stayed for a year or two. We did, we were a very tooth and claw competitor for the big five for when I was working in the two of my own company so that I built or helped build. And we were tooth and claw with that type of work. So, you know, lots of people training in salaries and benefits and running organizations and all of that. So my work has been a little bit different. And because of that, I bring different things to the table when there's a conversation to be had about what's working, what's not, what's practical, what's not practical and all of that. So I'm kind of trying to, the culmination of that work for the last 25 years is really what can we learn from that and add to the body of experience that we have with the other people and come up with something that works moving forward. And that's really what we're working on now. Nice. I like it. So when you say the profession, are you talking about all data practitioners or a specific subset of that? No, I'm talking about what we referred to each other as data people, all right? So, you know, there's an enterprise data world conference every year, as you well know. There's the DGIQ conferences. Now, there's other organizations that also have similar themed things. It's just no, you know, I can say TDWI, you won't edit it out, I don't think, or anything like that. There's a lot of out there. And that encompasses, to me, the profession encompasses everything now from strategic planning of saying you wanna be a digital organization or a data-driven organization. And through that leadership structure, which would then of course include chief data officers and the board of directors and CXOs, things like that. And then on down through this, the stack of the architecture, the oversight, the business intelligence part, the data quality part. Now, we've thrown in AI and machine learning, we're sprinkling that in now. And all the way through things like now, the data mesh and data fabric and all the stuff that's related to that, that's all DBAs, data administrators, database administrators, data analysts, that's all our data modelers, that's all our data people. That's the universe that I believe I work in. Like it. All right, well, let's talk about then how you got there. So tell me, John, what was the dream? Like, so I'll say you were six years old. What was the dream? Did you grow up? Did you say, I'm gonna grow up and be an astronaut?  That was the absolute, gosh, honest truth. I was gonna be an astronaut. And my grandfather sent self-addressed stand envelopes. I don't think you're old enough to remember doing that. But that's how we Googled in 1963, is you sent a self-addressed stamped envelope to NASA and they stuffed this envelope full of everything you asked for and mailed it back to you. So you said, I'd like to learn everything there is about the Mercury and the Gemini astronauts and what can I learn? I'm a young man and all this and my grandfather would do that. And I guess that triggered kind of a data research thing for me at a very, very young age. And we would sit there and we would watch the, we would look at the material that NASA had sent my grandfather and I and we would watch the Gemini mission or the Mercury mission or the early Apollo missions. And we would follow the mission cause we would have had, we would they sent us the mission profile and things like, cause it's all public domain, right? And that's why I wanted to be an astronaut. And then I found that I'm blind in one eye and they didn't want monocular astronauts, you know? I had a touch of asthma and they didn't want anyone with an inhaler orbiting the earth. So, excuse me, Houston, right? I can't do that, couldn't do that. So I ended up just going to college and coming out with a degree in accounting and a degree in economics and a minor in philosophy and a minor in music. And that's a lot of, yeah, I put myself through school. Back again, I hope you don't get tired of this cause when you talk to someone my age, Shannon, you're going to get all this back in the day conversations which could get just stupefying at some point in time, right? But when I went to a liberal arts college, it was a flat fee and you got X number of credit hours every semester and I put myself through school. I scraped and saved and worked and I got 19 credit hours a semester and for all eight semesters, I took 19 credit hours. And then in the summertime towards the end of my college the pit, I was some from Pittsburgh and the Pittsburgh economy, the steel industry collapsed in the late seventies, right? So I couldn't get a summer job. So I went to community college in the summertime and I got another 9, 12, 15 credit hours at community college. So I ended up with two majors and two minors. And they couldn't put that on a diploma. They didn't know how to, but that's what I came out of it with. So then I go into just working and everyone, my first job had nothing to do with that. My first job was a disc jockey because I got involved with the school radio station and that's probably honest, just between you and I, let's not tell anyone else's. That's the most fun I've ever had getting paid for anything and if I could do it again, I'd probably do it again in a heartbeat. But I made a dollar and a quarter an hour and they were giving me a whole eight hours a week. So do the math. I couldn't put gas in the car. Anyway, I ended up, it's a long story, don't have time for it. I ended up doing IT stuff, mostly because a friend called me and said, John, my company needs computer programmers. If you can spell IBM, we'll hire you. And I said, I have no desire to be a programmer. And my friend said, but you get $12,000 a year. And I went, did you just say $1,000 a month? And she said, yes, $1,000 a month. Well, in 1979, that's a princely sum. And I said, where do I sign up? And that is no lie how I got into IT, all right? Wow. And then for the next 10 years, give or take, I was a developer and I'm program manager, low level coder, I've coded, I've coded in Assembler and PO1 and Fortran and Coball and all of that. Now, I learned stuff that, I learned that there was a mystery out there. There was a mysterious force we weren't addressing in applications development that was messing everything up. And eventually I figured out it was data. And at some point, I found myself in a small company in St. Louis where it was a defense contractor and they collected data, analyzed data and sold the results of their data back to the appropriate defense agencies, Army, Navy, Air Force, et cetera. So in the late 80s, I worked for a company that monetized data. Now, you're in this business. You know, you've heard that term in the last five years. 1989, I was the CIO of an organization that monetized data. And at that point, without knowing it, I became a data person, without knowing it. And we had all the problems everyone has discovered in the last 10, 15 years. We had data quality issues. We had no consistent models. We didn't know what a data model was, but we didn't have any, right? And so we invented everything from scratch. We had to do everything. There was nothing we could use and we created a big wave bunch of people that, and I guess the privilege of age or something is in 1989, our little business, our beautiful company where we're having fun, we went out of business cause peace broke out. The Berlin Wall came down. A big historical event affected my career. I, you know, like many human beings, the wave of history pushed me in a different direction. So I had to go into the big five to make a living. And in the big five, I learned how the consulting drill and all of that kind of stuff. And I ended up being a data person in the big five. And then I ended up being a meta group, which became Gartner Group. So I was the industry analyst type in that industry. And then I went off on my own at least 25 years. I've been self-unemployed, I tell people. But in that data, but it was about in the late 80s when I became a full-time data person, which was a lot sooner than a lot of other people in our industry. But I can't claim I invented it because we were clueless. We didn't know what we were doing. We were just meeting our customer's demands as the best we could. And we didn't put a label on it. We just did our jobs. We just showed up and did our jobs.  I just people, in the big five, the partners began to ask me to speak and do speeches at executive breakfast and stuff because I'd done all this stuff. And to me it was like falling out of bed. But to everyone else it was like, oh my gosh, this is nuts. This is the future. So here I am, out of custom as I was to public speaking. How do you imagine now? Well, luckily the DJ thing really helped because I was really a heads down, leave me alone programmer guy. I mean, look, as long as the checks were clearing, I mean, I was in there at eight o'clock and at five o' one when the whistle blew and I actually did work somewhere where we did have a whistle that blew. And at five o' one, I was headed out to the parking lot. There was no hard charging entrepreneurial executive here. I was just a coder guy. But this data stuff, you know, and again, we intimated on this at the beginning. This is society changing. This is an anthropological wave for humanity, this data business, which is why I'm still in it. It's exciting. It's exciting. But we had no idea back then. Other than to me, this was really cool. It was really different and I wanted to run it down. And then the only other thing career-wise really is I was always in the wrong doorway at the right time when someone said, John, we have a terabyte of data to store and we only have 200 gig to do with it. How do we go about doing that?  So, you know, we wrote algorithms to do data compression in 1989, which disappeared into history. The company went under and everything disappeared. But we were doing stuff like that way before anyone else was doing stuff like that. Anyway, that's a nutshell. A lot of stories along the way. I will stop here. And- Well, let me, I would say, you know, that you got into data even earlier. I mean, with a degree in accounting. Yes, yeah.  But we never worried about whether the data was accurate or not when I was an accountant. I mean, one of my first corporate jobs was a plant accountant. I used my programming skills one week a month to hard code COBOL programs to do adjusting entries. I didn't say I wrote good code, but we would change hard code and routines to adjust the books and stuff like that. But the data part, this is an interesting thing for the current generation to understand. If this is the way you want your podcast to go, this is something I'd like to put out there. In 1985, 86, 87, whenever I was in my earlier corporate jobs, or even in the early 80s, we didn't quibble about data accuracy or data quality because the accountant's had controls in place and we had data control clerks in every department. And if we did a batch of data, and that batch of data, the control numbers didn't add out, my beeper went off. Yes, a beeper in the middle of the night, a pager, Google it, young people. My pager would go off in the middle of the night and I would be at work at three in the morning till we found that data problem and we fixed it. And we didn't have, we didn't have to, the way we worry about data now, we didn't have to worry about it. So we had that issue, but we also weren't using data the way we use data now, all right?  It has to do with the fact that everyone still believes I can take all of our transactional data and drop it into some place. Warehouse, Mart, Lakehouse, outhouse, I don't care. And easily, easily get what I want to out of that data. And the fact is we knew that in 1980, it doesn't work. It does not work, it'll never work. Data quality makes it even worse, but the fact is just the context of operational data makes what we wanna do with data now almost impossible. That's why I find AI scary. Okay, we think we have something that we can do AI with and I don't think we have it. So, and I'm sorry, I went on a soapbox there. I'll get off of it now. With a robust catalog of courses offered on demand and industry leading live online sessions throughout the year, the Dataversity Training Center is your launch pad for career success. Browse the complete catalog at training.dataversity.net and use code DVTALKS for 20% off your purchase. Oh, no, the soapbox is good. It's very, very good. So, but you know, and along that line, so tell me what has been your biggest lesson so far in your career? I've had several epiphanies. I mean, cause at one point, so I can share a story here. I was, when I left the big five, I got the job as the senior director or VP of data at a Blue Cross company. And we had to build what became known as like one of the first big, large-scale data warehouses. This is the early nineties. And I remember going to my boss asking for two terabytes of storage and he almost fired me on the spot. And he said, nobody on God's earth will ever, in the history of everything, till the sun goes nova, need two terabytes of data. And he said, by the way, how much money do they want? And he said, IBM wants $10 million for two terabyte server. And he was like, ah, I just had, you know, running around in circles and smoke coming out of his ears. I guess I'm not gonna get my two terabytes of storage. Anyway, so I was in this healthcare company and I had the playbook for data with me and I was do a data model, agree on the definitions, draw all this stuff. And I went up to a blank white board and I brought everyone in on a Saturday morning cause we were busy and said, let's do a data model like Mickey and Judy, let's do a show, right? And I got thrown out of, you know, but just about got thrown out of town, hard and feathered. I learned really quick that an awful lot of stuff we had been taught was good in theory but had not had any contact with reality yet. What I, my first epiphany was a lot of what we do in, and by the way, I'm not gonna, this is not just in data for a short time because I was a developer. I got involved with software engineering and development standards and the, I was involved with a very early capability maturity model out of, you know, Carnegie Mellon. I was part of that process many, many years ago with software engineering tools, things like that. And I learned from that one too and I learned from the client server a bit and the object oriented a bit and all this kind of stuff. We get these great ideas in this world. And my first epiphany was most of the great theories don't survive first contact with reality. So I became my first epiphany was in the, mid 90s was be very practical. And I guess if I have a trademark, if I have a brand characterization, that's me is practical, all right? That was my first big epiphany in the industry. The second one was coming out of my metagroup slash Gartner group days and going into my own work and still being hung up on installing tools and stuff. The second big epiphany was a lot of vendors really don't care about the success of their technology. They just wanna make money and get acquired. And that was sad, that was sad. Now, we're in a different era now and I think to an extent that's changed but you know, during the dot com boom things, were pretty wild west.  The third epiphany has been pretty recent. I would call that and the final one here is in the last couple of years, we've been banging away, doing things a certain way. Just look at all the presentations at EDW for the last 10 years, try to sum them up into some certain processes. You'll see some distinct patterns, right? A lot of those patterns, a lot of those protocols that people have tried to use aren't working. They sound like they should work. They've worked once or twice. Our good friend, Tom Redman calls them points of light, right? But on the whole, we're finding out that a lot of what we've been doing is not delivering on the promises we've made. It works, all right? I'm not dismaying our industry. It works. We're on the right track. Data's super important. You need data people to understand that. But a lot of what we have done hasn't really delivered the mail. So my third epiphany is how I've been doing the work I'm doing now, which is what does work? And is there something new we need to do? And do we have to re-skill our profession? And that's my third epiphany, really. Those are the three bellwether things in the last 35 years for me. That's fantastic. And it definitely sets up the your soapbox. Yeah.  Everyone was in the union in the family, except me. I was the first person in... No, I was the second person in the four generations since the immigration in the mid-19th century. I failed me to get there. I failed immigration in the mid-19th century. I failed me to go to college. So we're talking 50, 60 offspring and all the other offsprings and stuff. And there's only two of us complete at college. And I'm one of them. But what I came out of here was a fierce, probably early on in my, when I was less mature, almost an unrealistic and frankly annoying sense of justice. I'm here tempered now by, you know, the fact that the world's not perfect and the world's just by definition going to aggravate you and you might as well just laugh at it. So, but I do find along the way that I do lean in on this stuff because I just don't like to see people waste their time. Does that make sense? You see someone appointed to do a data governance program or a data architect or something and they bust their tail for two years and do this and do that. And they follow all the protocols we've given them over the last 20, 30 years. And at the end of it, nobody looks at it. Everyone ignores it. And then someone else comes along in two years and does the exact same thing. And you see it again and again and again. And to me, I just, it gets me viscerally. You know, it's someone's wasted their time. Life is precious. You know, if we're going to waste that much time, I think we all just go fishing, okay? And just tell corporate America to stuff it, all right? But there's a job that needs to be done and we've got to figure out how to do that job. And that's what, I'm working on my third book right now. My third book is starting to form and it's going to be around that theme that we've got to learn how to do better. And by the way, it's not just the data, folks. It's also our peers in the business and our constituents in and out of the companies and organizations we work with. All of humanity has to up its game around data. We are in an era here where data is much more profoundly affecting our life than we ever imagined it would. And it's no longer just a cool thing and a way to make money, et cetera, et cetera, et cetera. We are infantile in this world as to what the accumulation of our activity, which is data. Data represents human activity now. It documents human activity real time 24 by seven, 365. And we don't know how to manage that, deal with it, handle it or anything. I think our generation, our data people, we're the first generation to actually confront this and go, ooh, ooh, this is hard. This is not easy. But we go to what talk and say, well, the vendor says all you gotta do is buy this tool and do this thing and get some buy-in. And then the heavens open and there's skittles and unicorns flying all over the place. And that's just all hoo-ha, okay? It doesn't mean it's wrong. It just means we're, I don't know how many other things in human endeavors have every people started optimistic. Hey, you and I were supposed to be having anti-gravity pants right now, right? And we were supposed to be the Jetsons, right? We were supposed to just fly to school and every push a button and we were gonna supposed to have when I was a kid, my grandfather said it's amazing that someday I'll take a pill and it'll give me all the nutrition I need for the day.   And all of that, people oversell everything. We've oversold data maybe, I don't know. Anyway, that's part of what's forming this conversation here in where I am, I am where I am. I like it. So I'm still waiting for a teleportation, by the way. Oh, hey, the beaming thing, sign me up day one. Right. That would be so nice. I don't know. You were at our event in Washington, DC, right? In December, you were there. I saw you there. Yeah, I mean, I had the going home nightmare was at the airport plenty of time and didn't get home till nine hours later. On a one-hour flight. Oh gosh. Sign me up, Scotty. I'm ready. Yeah, absolutely. All right, gentlemen, with such a distinguished career in data, what is your definition of data and how do you, so what's your definition of data? What's changing, all right? I mean, in the 70s or 80s, data was row and column representations of operational events, right? And then data became rows and columns and also collections of bits and bytes without structure, like a document that represent events and contracts and agreements and expanded. And then we got into this, there's data and then there's information and then there's knowledge and we rode that bus for a while. And I think that's malarkey, by the way. That's total malarkey now. Okay, I mean, I've had CFOs lean across the table and bang on a table and say, John, I don't want to hear about a data strategy. I don't need data, I need information. And I said to one, I said, well, here's the problem in this 20th century. And by the way, before I go on in the story, the CEO had given me full carte blanche to be candid with his team.  All right, I might be an annoying person. I just don't want you to think it. So, and I took to the CFO and I said, here's the problem with that. If I gave you all the information you think you need, you don't know what to do with it. You can't handle the truth, okay? All right. And I said, I want to hire Harry Potter. Harry Potter is going to come in and go twink, boom. There it is. Lean your head against its terminal.  What's different tomorrow? What do you improve tomorrow? Oh, well, I got to think about it. No, no, then you haven't thought it through, right? They're like, no, I haven't. I go, yeah. I said, forget data and information. Now I'm on this third thing. What's data? Data is, as I intimated earlier, it is the record of our human existence in all of its forms. But that means it comes with an enormous amount of baggage. You know, what are the ethics? Who can see my data? I'm sorry, Facebook. So I can have an enhanced user experience. Forget that now. I want to sell it. I don't want, I mean, the minute I can sell my data to Facebook for 80 bucks a year is when I sign up for that. And I drop all of my apps on this kind of thing. I mean, we are, you know, so data is really, to me, it's the recording of our human existence in all the shapes, sizes, forms, and permutations. So that's data now. Yeah, indeed. So tell me, do you see the importance, as the definition is changing, do you see the importance of data management and the number of jobs working with data increasing or decreasing over the next 10 years? And why? Well, I certainly see data being more important. But I also see data becoming more and more worked into the everyday language of business. So to be maybe scarily prescient, I see the data jobs, the data profession. I see shrinking, all right? A, because the promises haven't been met. And corporate America has no mercy with that. All right, let's just be kind, let's be candid. You only get so many failures, and then they go something else, all right? Second is data is so ubiquitous that eventually, and this is starting to happen, you're seeing this a lot of places, that the whole thing of data mesh and data fabric and having business-empowered business people learning to be that product manager out on the mesh or the fabric or whatever syntax you want to use or something like that.  No, it's like, oh, you're in marketing. You understand marketing. I want you to represent the marketing data, right? And the AI and the metadata and the technology are taking care of all that nuts and bolts stuff. So I see our profession shrinking. I see the need to be smarter about data and the need to be data aware and the need to educate more people about it. And at the end of the day, I see the need for data skill sets in, you know, expanding exponentially. But I don't think that means our profession grows exponentially because this is just like anything else. Everything starts out specialized and then it commoditizes. And there is no universal rule that says the data business is going to be any different than taking care of your car, making food, electrical.   Right, right.  It's commoditized, right? Data's not immune from that. Oh, we must have a normalized database. Ah, the AI will do it. Forget it, right? Yeah, it's gonna do it. I mean, I asked chat GPT two years ago when I was first starting to rumble, I asked chat GPT to do a data model for a specific client of mine. And I had their data model in front of me and it matched about 90%. And it did it in 30 seconds. So, no, it's gonna change a lot. It's gonna change a lot, but it's not a bad change. It's an evolution. It's we're gonna get a handle on it. I think the skills we have, the fact that our data profession gets it and got it a lot before other people puts us ahead. We have an obligation to discern what is gobbledygook to our customers and don't make them endure that. I've had on a client meeting, I had someone all of a sudden thought they had the ear of the CEO and they started talking about a very complicated snowflake schema that they were working on. And the CEO was like, excuse me, how did we get here? I think if we learn to talk right to leadership and be more a part of the business and we lose some of the hubris we have accumulated and we have on there, I think we are in the driver seat. But I don't think our profession's gonna grow exponentially. It's gonna get absorbed. Okay, it's gonna get absorbed into the fabric of the organizations. That might be 20 years from now, third in renews, but that's, I mean, take finance. Okay, yeah, there's the financial people they have the controls. They make sure everything adds up. They make sure that the financial picture represents the organization legally.  If you don't know those things, you don't get your job, right? So I think that's where we're headed. I don't know, what do you think? Can I interview the interviewer? No. You know, I- I haven't interviewed, but I- No, I really do think, I don't know if anyone else has said that to you when you've asked me that question, I think the growth of data skills is exponential. The growth of our profession is not so much, not so much, the bottom line it. Would you, what advice then would you give to people looking to get into a career in data? Well, I was thinking that there was a, that that question might come up. And I actually in a flash of uncharacteristic organization wrote a few things down. And I actually do a slide on this for a lot of my talks now. Tom Redmond and I just did an article and it's in the Dataversity content right now. It's about two months ago now. And it's, things aren't working as well as we want them to, what can we do about it? And we stepped back, Tom and I threw everything out. We did a thought experiment, right? Can you do this philosophically or logically? You can take a premise and you can say, well, what if my premise is wrong? Let me posit the incorrect premises or the premise that's different. And we posited that and we used that the way to run down all kinds of scenarios that happened in our industry. And what we came up with, was some common things that we see whether you accept the premise that data management works or you accept a premise, which is not true, but you're thinking this stuff doesn't work. We got to try something entirely different. The one thing that came up a lot was that there are innate skills required in any human endeavor to make that endeavor work. And those innate skills are missing in data folks. Now, Tom's new book about people and data is wonderful. And he touches on this, but just to recap, because this is something where Kim and I have been doing for a couple of years, okay? First of all, our profession needs to learn how to communicate better, okay? I would give, on average, every team I've worked with that are just pure data people who are running the data governance program or setting up data management or working on it, I would give them, at best, a B-minus in communication skills, right? Where we've been successful is there's been that business sponsor come in and they've done the communication, okay? I would say there's essential business skills missing. When you do a meeting, you take minutes and you send the meetings out. You send the minutes out. I do a lot of firefighting. I do a lot of coming in when things have gone off the rails right now. That's kind of most of my work is we've tried this four times, John. What are we doing, Ro? And one thing I go and I go, let me see the minutes. Let me see the notes, the meetings. Let me see the admin flow of this now. There aren't any minutes. Oh, are you kidding me? All right, learn how to be a business person, okay? Oh, we don't have a tool to record the workflow. Oh, well, too bad.  Okay. Learn logic. Janet, did you ever take a symbolic logic or philosophy or logic or the math of logic in school? Yeah, that's a fun course. I've never talked to anyone who didn't like that course. Oh yeah, absolutely, yeah. Yeah, well, I hear a lot of people express things as fact in an architecture or something that if you would mathematically reduce their argument, it's not a valid argument, right? It's a lot like listening to the heavily tilted news media, whether it's right or left in our world right now, the divisive news media, it is the fact that you will hear them say something and you go, wait a second, I studied logic. And when they just said they would get a C or a D on our logic test, okay? So that's the next thing. And the last thing is just learn to talk about your business or your organization. Don't sit back there on your ivory tower and saying, well, you obviously don't understand, but I have the magic keys to the kingdom. And if you just do what I'm going to tell you to do, it will all work out well because you don't have the right context there. Okay, you're separating yourself from your peers, you're separating yourself from the practical. And the result you're gonna get is a textbook generic philosophically correct result that may not work. And that's, so those are things I actually tell everybody all the time. So that's a great advice. I try. Very good advice. I try. John, it has been such a pleasure. I'd be remiss if I didn't ask how people would get in touch with you if they wanted to reach out. I have a website after a lot of research and investment in marketing of smart people, people smarter than me, we decided to call it johnlandley.com. There you go. I have a website called johnlandley.com. So it's not hard to find. It's got some good content on it. It's not up to date, but that's a way to get ahold of me. You can also email me at johnatladley.biz. I'm no longer on Twitter. I don't do Instagram. Also LinkedIn, I'm on LinkedIn. So that's the ways. Or just go down to an airport in the St. Louis area and hang out around. I'll show up sooner or later in that blue and yellow beastie that I fly. So. Oh, I love it. Well, John, thank you so much. And we'll get those links posted to the podcast site as well. Sure, sure. We'll have those to that. Thank you so much for chatting with us today. Thank you, Shannon. And thank you, DataVersity. You guys are awesome. And we've had a good 20, 25 year run since we'll share and I'm hoping for many, many years of it. Likewise. All right. Well, and to all of our listeners out there, if you'd like to keep up to date on the latest podcast and in the latest in data management education, go to dataversity.net forward slash subscribe. Until next time. Thank you for listening to DataVersity Talks, a podcast brought to you by DataVersity. Subscribe to our newsletter for podcast updates and information about our free educational webinars at dataversity.net forward slash subscribe.


Hey everybody Angel Abbey here and I'm the volunteer coordinator here at Columbia Animal Services. On behalf of the entire CAS team I want to extend a warm welcome and to thank you also for your interest in our program. Although Columbia Animal Services has always had an active volunteer program it has grown exponentially in the last 12 months. This orientation will get you acquainted with our shelter and show you how we go about doing things. If you have any questions feel free to let us know. Thank you. Welcome to the Doggie Day Out digital orientation. Thank you so much for your interest in Columbia Animal Services Doggie Day Out program. In this orientation we will briefly go over the field trip process as well as the do's and don'ts to guide you on a successful trip with the adoptable dogs at Columbia Animal Services. What is Doggie Day Out? Doggie Day Out is a field trip program for our adoptable dogs here at Columbia Animal Services. The goal is to get our longer term dogs out of the kennel in order to enrich their lives, showcase them to the community and get more information about them while they are out and about how to get a dog ready for a field trip. Step one sign up. Only one dog is allowed per individual slash group and only six dogs can be signed out daily. Please make sure that you reserve your day and slot on signup.com. If you're already at the shelter and slots still need to be filled please go online and add yourself to the calendar so that other volunteers know. All dogs must be back by 4 p.m. Step two sign into the volunteer book. Helps us keep track of your volunteer hours by signing into your personal volunteer sheet. The volunteer binder is located on the desk in the volunteer room. Step three pick a dog. All dogs are color coded. The animal cage cards with the check mark and matching lanyard color dot are the animal volunteers can take for walks, doggy day out and slumber buddies. Step four complete foster contract. Every dog that goes on doggy day out must have a completed foster form even if they're just going out for an hour. Step five go get your adventure partner. Grab a slip lead in a doggy day out sign out of the volunteer room. Go get the dog and hang the sign on their kennel. Please note it is very important that you put a sign on their kennel so that we do not fill it. It's generally suggested that you take the dog out to our play yard and let them get some of the sillies and any pee out before putting on the collar and harness lead. Step six collect sign out doggy day out gear. All gear must be signed out. Sign out sign-in sheet is located on the doggy day out and slumber buddies cabinet. Every dog that goes on doggy day out or slumber buddies must have a collar with the tag and harness lead on. If you need assistance please ask a staff member. We are happy to help. Last but not least have fun. Fit check. Every field trip dog gets an escape proof kit. Doggy bag.  Info cards. We send these cards out so that someone is interested in adopting your doggy day out partner. You can write the name and animal identification number of the dog you have with you. The code on the back takes them to our pet finder which has all of our adoptable dogs. How to return a dog after a field trip. Step one take the dog back to their kennel. Sometimes the dogs don't like to go back in so be sure to take a treat. Remove bandana collar and harness lead. Take down the field trip sign from their kennel and return it to the volunteer room. Step two return sign-in doggy day out gear. Please place the collar harness lead and bandana in the doggy day out bag put it in the white hamper beside the cabinet and sign the gear back in. If you dress them up please return the costume to the rack. Step three complete field trip report card assessment. When you fill out the report card be sure and be honest and be detailed. We pass this information on to potential adopters. Step four email photos to the social media coordinator. We love photos and we will upload them to the dog's pet finder profiles which can help encourage adopters. The more photos the better. Step five sign out from the volunteer book. Help us track your volunteer hours by signing out. Field trip dues make note of your dog's name and animal ID number. Make sure to sign all paperwork before taking a dog out. Fit dogs safely with both a harness lead and a collar. Make sure you're able to handle the dog before you take them off the premises. Ask for help if you need it. Check bag for water, a bowl, treats, waste bags and cards. Always clean up any messes left behind. Take lots of pictures and email them to the volunteer coordinator.  Field trip don'ts. Don't take dogs to off leash dog parks. Don't take the leash off anywhere outside of your own secure yard. Don't leave the dogs anywhere unsupervised. Don't hand the leash to anyone under 18 or that has not done the orientation. Don't take dogs to overly crowded overwhelming areas. Don't allow dogs to greet other unknown animals. Don't allow the dogs to pull or bark excessively. And remember it's important that each dog that goes on a field trip represents themselves as positively as possible. Not only are we advocating for the specific dogs that goes out to be adopted, we're advocating for all of the shelter dogs as a whole. Keeping dogs quiet, cleaning up after them, making sure that they are in their best behavior and generally following the guidelines laid out here is in the best interest of the individual dog in the entire doggy day out program. Thank you for your interest and please email angelic.abby at columbiasc.gov with any questions.


It was back in 2017 when we first saw GL Break Techngology introduced into the epic driver. I own that driver and I've got to say to this day I still think a relevance of the limitations and restrictions that are put on driver technology. I still believe it was one of the longest drivers that I've ever hit. Still to this day. The problem that I had with it, is it was long, but every was inconsistent in terms of why I couldn't get it to find fairways. Mae'r cyfnod 4 oed, GEL break technology mae'r cyfnod ac mae'r cyfnod yma yn ymlaen i'r AI technology ac mae'r cyfnod o'r effeithio yn ymlaen i gyfnod Calloway. Ac mae'n rhaid i'r cyfnod i'r cyfnod. Felly mae'r cyfnod o'r cyfnod o'r cyfnod? Ac mae'r cyfnod, mae'n gweithio. Mae'n gweithio'r cyfnod o'r cyflogol yma a'r cyfnod o'r cyflogol yn ymwneud yn y ddysgwch yn ysgol. Mae'n gweithio. Mae'n gweithio'r cyflogol, mae'n gweithio'r cyflogol mae'r cyflogol yn ysgol. Mae'n gweithio'r cyflogolio'n gweithio. A chi'n fawr o'r gweithio yma yw eich gweithio i rhan i gael yn ymlaen i gyflogol. Yna ddy'n gwybciamo oherwydd mae'n gweithio'r cyflogol rysyn i gyflogolol. Daen ni'n gweithio'r cyfnod sy'n gweithio'r cyflogol. ac yn ôl yn rhan o ein bodai, gallwn gwneud yn cael ei tynnu o'r omosiaeth gwyrdd fel cael eu caratteryst i'r bodai yn enw i'n aelod o'i wneud. Felly, nid i wneud, gyda fwy o arbennod, hwnnw i'r gwaelio enwach yn ôl yn amlwg o'r hanfod chegaith yn olygu. Felly, oherwydd, yr oedd y gwein iawnon er mwyn, yna enw i'n gwneud o fynd i maen nhw yng Nghaerffael Aei, o ar môl drwy deitwys a phobl�ithau i chi. A mae'n gwertho'r dweud yn ymgyrchawwyd ar gyfer. Felly bywyd yn ddi'r cyfestau.不對. Rwydd, drwy'r hefyd, drwy'r chweithio. Mae'n gwybod i'r ddweud, meantime, y pwynydd y bydd y rhannu. Hefyd, mae'n ddweud. A ddweud mor fod yn mynd yn ei ddоб i. Mae'n ddod doedd rwy'n mynd yn holl. Dych chi'n mynd gwneudhu'r gelf efo ar gyfer gweithio gyffredigol gwheithio eu gwirio ddim. i'n dda i fod y gallwn i'r rai gynnwys i'r ddechrau'n gweithio, a'r ddweud i ddim yn i-byd yn ymarfer. Rhaid, wrth gwrs, yn y cael ei gweithio ar y boli sydd. Rydym yn y bwrdd gyrfa'n gwneud, ond byddwn yn cyfweld yn y cwysig o'r ddweud o'r pwysig. Fy fyddwn i gyrwch yn ymrydau y ffordd ac yn cyfodol o'r ddweud o'r ddweud. Efallai os yw'n gweithio'n ddweud. And I don't mean just aesthetically because I think they're a great looking driver but I always say that's a very subjective thing but more than that I think that the the driver that you're buying nowadays and this will be no different it's going to be £400 plus and I think what you want to see is that you're getting some value in terms of at least the components that are used and I think for me this is a massive step forward for Callaway I think this is it looks expensive it looks like it's got quality components. eiscredu yn y wneud o'r heb yn fwy o'r ysgol i gynnwys. Felly, edrych yn ddechrau'n teimlo fydd o'r mafric ar y dyfodol a y bydd yn ei ffordd yn gynyddu o'r mafric ar y pob oherwydd o'r cymdeithas i ddull. Yn yn mynd i erbyn beth oedd, oedd o'n mynd i enwedig, Ac, oherwydd, mae'n badh o'r prydau erbyn twf, ac mae'n bryd trafod yw yn ddiwrnod mewn ysgol. I blwyddurio mae'n bryd yn bwysig ystafell ac'na ei ei cyfnod ymell, Fel gwneud o'i ddim eisiau mwyaf, yn gallu eich llen nhw'n cymdeith erm ddim eithaf, hwn nhaid i ddim i fod yn ôl. Mae'r sylfaen i'r cymdeithas i Llooks a ddim eto, nid wedi bod yn ysgrifennu. Mae'r cymdeithas sy'n gwneud, rydyn ni i'r glwb. Mae'r hynny ydw i'w ei gwaith eich bod yn ddigon nhw wedi'i rwynt llei i'r cyffredig o'r popeth teimlo'r ysgrifennu i ydynt eich cyfrei'r fod yn ysgrifennu, fel yna yw'r iawn gwneud. Mae'r gwaith yma yn ei bodai. Mae'n gweinwch o'r ffordd. Mae'n ymddangos i'w wneud ffordd o'r ffordd. Rwy'n cael ei wneud. Rwy'n cwrddol am gweinwch. Gawd i'r gweinwch. Mae'n gweinwch o fy nghylch ar y dyn nhw'n keyf dechrau ymlaen. Mae'n gweinwch ar y dyn nhw'n gweinwch ar y producyn o'ch gwerthoedd yn ardal Ogw. Mae'n ddod o'r gwerthoedd o'r Ggulfer. Mae nhw'n gweinwch. Ac mae'n dwydiadau ei wedi wirio hwyl i iddyf yn ymrywodraeth chi'n holl maen nhw'n ddweud? Rwy'n meddwl o safiat, mae'n ddweud fel ei bod yn dod, rydyn ni'n ni'n dod. Rwy'n meddwl i ceisio'r ddweud e'n un yous, fydd ni'n meddwl o'i ffordd wedi'i fewn o'r busrif? Rwy'n meddwl gweithio'n meddwl e'n meddwl i'r busrif. A'u'r peiriau teknol a'r cael ei ddweud yn dweud. Mae'r teimlo i'r holl yw beth. yna. Dyna'r ystod yw'rull yma ar y cyfnodau. Aeitechnaf amser yw'r dweud yn 3 angen o dweud yma. Mae'r dweud. Rwy'n cael ei ddweud yna, gallwn i'r ddweud yna, dyna'n dweud drwy'n gwneud am aeitau yn ddweud yr ysgol fwylo'r dweud, dyna'r dweud yma yng Nghymru. Rwy'n cael ei ddweud, mae'r dweud ar y dweud ar y dweud yma, ac rwy'n cael eu ddweud yma. Rwy'n cael eu bod yn bwysig, JL brake that has got this sort of new speed frame as they call it built in and it's just a strengthening of that structure again I think that's fairly self-explanatory we know this works it attaches or strengthens the crown to the soul so we've got that we know that much and then it's a three different sort of styles or shapes so starting with the speed this is all about ball speeds it's all about that golfer that wants to hit the driver as far as possible it's a slightly more I'd say traditional style of shape of driver head it hasn't got any adjustability other than the hosel so there's no shifting of weight next up it would be the max and this is all about maximum forgiveness and maximum ball speed and weight has been distributed in this head to do exactly that and it's all about forgiveness and high launch as well as something and again that will bear out when we see the data very very shortly the other thing is there is adjustability in the backside of this I think it's 19 grams of weight which again can dial in in terms of custom fit and finally is the epic max ls and this is the new one that slightly confuses me that maximum forgiveness low spinning version of a driver is quite an interesting mix and we'll see how it performs and whether what I'm about to read out to your bears out in data but what Callaway says it's designed for the mid to low handicap player who want more speed, neutral ball flight and forgiveness in a lower spinning high moi package that's a means a really interesting combination and like I say if that bears out could be a real interesting addition for a lot of golfers out there right but like I said how will they all perform and will those differences been noticeable and the only way we can find that out is get back to four golf and start recording some dry ball data I'm naming this this morning you know out of interest the the club you've seen me hit on camera has been the the max product but you've heard the again the details between each three which I find is incredibly interesting but I just want to talk about before we go back in and look at some numbers is the sound and feel which even the releases we've seen so far has been something I've noticed you picked up on in the comments I thought it was only a big deal to me but I think it means quite a bit to others they've done a fantastic job with this but I like the sound of Callaway drivers because I think they get that balance between you want to feel like something is really fired out off that club face so you want to hear that kind of I don't know what I don't know how to sort of describe that feeling but you want to hear that there's power in there somehow but at the same time you want it to be that little bit more muted and soft and it's that combination which I'm really struggling to describe not very well at all but that's what they do with this as drivers go that's what I want it to sound like it's a terrible explanation and you know what's also incredible is that we often question technology and stories has been hype and marketing all those other things you've got three drivers here and without seeing dry ball data you can visually see the difference in the performance between each of those three and I'm talking about in the main ball flight uh these are all lofted at 10 and a half degrees in terms of their heads but they all come out the club completely different in terms of their ball flight so once again to me it says screams custom fit right we've had it out in the course we've now got dry ball data you've heard what I think it looks like what it feels like but how did it perform which is ultimately I think what any golf club is going to be measured on and we'll start off we'll get straight into numbers and we'll start with the speed product just for clarification my club head speed remained around 96 mile an hour 2795 spin 237 carry 12.4 launch 146 ball speed 86 peak height the things that stand out there is that 146 ball speed in relation to the club head speed is a really good number spinning well decent carry distance decent launch i'm going to move quickly into the max LS product um not too different in terms of the spin 274 2 234 carry slightly down on launch at 12.2 ball speed very slightly different these are averages by the way don't forget an 84 peak height so they were almost difficult to split and I sort of found that out on the course to be quite honest with you the one notable difference for me between the max LS and the speed was the sound of the two and they were quite different uh acoustic wise that's something to bear in mind and then you go into the final product which was the max um 2849 spin so again the spin number remains very similar for the three products but 242 carry and that was largely down to this combination of a 12.9 launch 147 ball speed uh which proves 94 peak height and I think the interesting number there again is that 147 ball speed which tells me that the max product is performing perhaps a little bit better all across that club face which is what it was certainly intended to do because we know I'm not going to be at this ball consistently out the middle so to produce 147 ball speeds off 96 swing speed really really good number and that combination of all then put together was really impressive and I think for me out on the course I would have chose the max products hands down for me and I thought the difference in terms of data would it would have been even more noticeable I was a little surprised there wasn't bigger gaps but it just suited me like I said the one thing that's interesting for me is you've got three very different products and I could certainly tell that out on the course and I would have jumped at the max product straight away before I'd even seen data but then you go into dispersion I think this is the bigger tale of this whole thing and if you look now and it's going to get these on screen for reminding me as to what colour these dots relate to we'll start off with that speed product and look at the dispersion there it's fairly tightly packed where everything was sort of leaking out to the right hand side couldn't do a great deal about that and then I'm going to put in the max LS and again fairly decent numbers but once again sort of over towards that right hand grouping and then the final set of numbers was from the max product and again pretty much what I've seen out on the course much tighter grouping it seemed to suit my swing and I started finding down the middle fairly consistently and again a noticeable difference between the three so the thing that I would take from the test is this that it's very clear to me to see that there are three very different products here and once again I like that from Callaway and I like the idea that you go and get custom fit and you find out which one of those models head shapes is best to suit your requirements and your swing type and I think that's what they've done with this whole thing this whole line up of drivers so overall I'm I'm impressed I'm impressed with the other big deal for me is the way the quality of the product in the way it appears visually it looks like it's been well put together it's priced at a point where I said earlier wasn't sure I've seen the list price at £499 which indicates to me it'll sell for about £449 British pound so it's steep again it's very heavily priced but at least in this model I think it stands head and shoulders above some other things that are out there right now in terms of the quality of its build but that again very subjective and just my opinion right as ever I'm done thank you for watching if you like what you've seen don't forget this is testing on behalf of an average golf with a clubhead speed and driver wise of 96 97 miles an hour so if you swing it like that if you don't quite hit the centre quite as often as you should do then maybe you consider subscribing hit that like button and come back and watch us a little bit more often in the future but for this one thank you for watching and I'll see you all very soon


No, that's right. I think this one is our way this far north. It's nice to nibble on. That's it, yeah. She was the other day. It was a couple combinations of things.


Okay, see me me me sema, but me me skia kama kuna be meru sa zinia ni paka umkumbu shea meze skuma. Nyukuli, please let me know. Tua at 5-4 Facebooka, tua at 2-5-4 channel, or Twitter, hashtag. So why in the morning my name is Valentine, or at Kalamival, and we're rapping this Monday up real nice with a bow. It's man crush time. Na leo topikiye tu ni kukua mjanja. Amato kua mjanja. We need to see how Boy Child is surviving in these hair streets. Na skia skuizi wa sta na neza kua gumu, ana kufa ni leki tu. Abu tela, tua at 5-4 on Facebooka, tua at 2-5-4 channel, Twitter. Or you can use our text sign 2-0-1-5-4 and start at 5-2-5-4. Meanwhile, let's come and say hello to our panelists. Good morning guys. Good morning. I want more energy. Good morning guys. Good morning. Yay, there we go. Let's start over here. What's your good name sir? Okay, me na ma Fredrik Palsai. Kamajana name student. I want you to have it a bit closer and I want you to guru malekaliya. Okay, my name is Fredrik Palsai. There we go. I'm a journalism student at the University of Nairobi. Yeah, pledge. Welcome Fred. Do you have social media handles? Yeah, I do. Please give them to us. Okay, we can follow me on Instagram at Fredrik Palsai. Also on Twitter at Fredrik Palsai, Facebook Fredrik Palsai. Are you afraid of me? Hi, there you go. Here we make eye contact. Sasarela. Dadin me kumis. I know. Karego sana. Asante. Naka poa. Atawa naka poa daddy. Kwanzaat ni katuli yunga. Nani ali wa dress. Maze? Please do introduce yourself. I'm Ray Laupanj. Nalumna of Mount Kenyo University. Teacher, youth governor. Ya, Pissambasada. Hai yote, on Facebook, Twitter, Instagram, but Ray Laupanj. Kruz. Ya, thank you. I'm happy to be here once again. With Miss Chu, Karego sana. Miss Chu sana. Awww. And Kwanzaat, what's your good name? That's funny. It's okay. Tell me what your name is. Sharon Deritu. Who?  I'm Sharon Deritu. I'm a student at Nibs Technical College. A journalism student. So, for future reference, you already have a lapel so we can let the gentlemen. Watumie rungu, wana jokui tumie. Sindhia. Chika, are you ready? Sorry, sorry, sorry, so sorry. So, lila oniku kwaumjanga. What do you understand by kwaumjanga? Niske kuna asja na na sumwa wa no me down. Nukweli? Kwaumjanga, ina manisha, umetuka nyumbani, umeku jana Nairobi, hawishik ni kama umetuka kitanleten. Otherwise, they are telling me there are some behaviors and some of them we have categorized and put them in a couple of slides. Sijiniwa ibe to siri, siri. To moja. We start with one, Sindhia. So, what happens if you just met Ashore two weeks ago? Let's see, say na kamrembo. Okay, you meet her two weeks ago, you've been talking, hei, kidoku doga naza pigi asimum and say, I urgently need a favor, please ni tumie hei pesa. Tumie. No, see where is he. Kwanini sasa. See, she's beautiful. See, at least two weeks mejwa namba kamaji ina na ploti namba nine. Ni ni tumie hei kumtumia do? Okay, see, it's not a matter of ploti, it's not a matter of kujwa na, but, okay, unaisam tumia, kulingan na shida. Akinisai tumi na tumie ju you are dating, ama kuna there is something between the two of you. Bimi na niza kukusahidi, nimi saide wa tuengi, no na. And I feel good. And na niza kukutumia, kama wei ni demuangu, kama wei ni demuangu, no na. But he designed, umekutuna na demuangu, after two days, ni heiti siji ni meishiwa gas, kwa ni wei ni mzazi, kumtumia na kujwa na Nairobi peki hake. Kwele utrapambevo. Ya. Akuna wu jazutu. Akuna msi na sikwizi heiti shi pesa. Oh. Akuna. Kwele. Kupata oni, labda mwenya li tokawa ni ni, machako zyuzi, ni ni kidui zyuzi. Ya, na itisha ni ni maji. Ah, labda na itisha maji akunia. Si pesa. Would you do such a thing? Would you like umekutuna na manu ume juzi? You can see he is clearly hitting on you. He likes what he says. Na una jyambia. Okay, we can make some money out of this. Have you ever just simply wanted to make money out of someone? Okay, no. I've never wanted to make money out of somebody. But ekifika situation ni nini kwa at the end, and I have nobody else to help me. Na umekutuna na tshajuana for two weeks. So, okay, a friend ni mtumshajuana. Lakin na two weeks are as in zidi, so I can depend on him. Anis idea, but see jwea benefits zingine, either go into a relationship or maa. Anis idea ni rudisha yu do, na another favor. Ya. So, we, for example, demunakatiata, adiangi a box badu, but asha anzokwe itisha pesa? Okay, it simply depends on how she prioritizes you. Okay, if she doesn't make your priority, why should you make her priority? How do you want to be made a priority? Ashineto ameku text, ameku ona? You know, being prioritized is very speculative. It doesn't necessarily mean texting or what. At least meeting, showing some concerns. You know, there are some ladies who can even text, they don't reply, or rather they can even take two days to reply. So, how do you expect to prioritize such a woman? A kumbe umbe kredit atumie yote. I love for a couple, please call me umbe inkili. No, I really can do that. I buy a kredi, unless I buy a kredi. He calls me, I confirm that it's over. That's all. You know, these streets are not friendly. It's true. These times I had all. You know, these streets are not friendly. And as they say that, the city is shambalamavit. So, you can't simply be such. You can't allow someone to use you like a stepping stone. No, you can't allow that personally. Ya. What if you want love? There is no love. There is no love. Udayi changaniki wunam se maze. Ya, umbe wunanza tukupumuna, shkwashkwatu na joto kimfkiri atu. What are you jamuna? A meze ma, iti you can... iti akuprioritize. Aposendia medanganyu. Yuhu, isahi watu anakampeni kibra na kumbe ya ataraila na enda wuko. Aja after campaign. Tumayo pese wina takikana. Ataraila na bluti. That's not the case. Wajupi aniim kwebo. Kukuna mimflani na se ma, bona machale wukwangatu na pese. That first week they meet you, during campaign week. Ukshape wakazi. Nihivo. Talk to me, talk to me. You know, at the current generation of the current world, it's a matter of exploitation So if you don't become clever, you'll be exploited by someone. But when her she becomes clever to just extract some monies from you. Why is it being a good digger or some strange name that you want to put on girls? Bona una kubalia kumjanjana ustana wa how is it? You know, there are different motives for spending money. You can't spend money on someone who on someone whom you don't have any chemistry with. Okay, assuming it's a lady you met two weeks ago. And then a week later she starts requesting for money. How many men does she request money from? And now it depends on how she prioritizes it. Okay, it's not a bad thing giving a lady money.  What is that something that you expect in return? Biology. You know, the first thing is also, it depends on what you want from the lady. Okay, if you want love, it's fine. If you want time, it's fine. Men want very many from ladies. You can even give her money for conjugal benefits. It's very speculative. But what I can say personally, I can't just give money to a lady. Someone we've not bonded with. Someone we've not created some chemistry with. So I can't do that. You know, these streets are not free. But there's that kind of time you want to impress the girls. You want to tell me girls that you don't want to impress and how do you impress a girl? You know, you can impress someone. You are supposed to impress someone but not to oppress yourself. Okay, now you can impress a lady. Maybe you have a budget line. Maybe spending something like 5,000 or 7,000 in a week. It happens. You give that lady the money you are supposed to spend for that week.   So you're supposed to weigh things and see if you're bonding. Okay. I want us to start. But before we do, there's one question I want to ask. He made a point that was not clear to me before. Yani uki peya you expect something in return. That means you're making an investment. You're not giving out. You're not helping your investing. You're putting in and you're expecting it to come out. Na, what happens if she breaks your heart? You don't expect her to prioritize you. She does not prioritize you the way you expect her to. Apu na jamu na kuja na shoka? Ya. Okay. For me, what I can tell men is that you look at that lady and then you give her a time when we give in as fast as possible in Afanya. Waname, waname ya. But who forced you? I did come with a gun to give me all your money. Okay. Waname ya. I'm not sure. I'm not sure. So, I told you that I was going to sell you. You said that you'll sell me? Ya. You said that you'll sell me. I said that you'll sell me. You said that you'll sell me. Raila b. Raila b. I'll sell you. You want that? So basically, it's about ladies. It's their problem. And the same problem comes in with men. Waname, waname, mimi kama sayi if I can give in to the money that was yana wana takakutoka kuanku it's not that I'm dating them on ini in a month in Africa 20k unona it's not that because I'm dating them on ini but because was yana wana taka every time wana wana wei tunibeshitake for 2 days wana taka pesa in case problemi ngini lihape na liyona raila likuja TV raila timani tuni mimi kama sayi na I've learnt to say no I don't have and that is how you can survive in Nairobi otherwise uta fungiwa njumba uta inunu atra socks na boxa ita kwa yo mao cha unajwa metihimu hoi tisha doko zasya wana wana wana kuna interest kuangu kasi itu me mitu sayi sezi kwa miha nipeti wk lakini tunibeshitake nipeti wk nipeti wk nipeti wk nipeti wk nipeti wk nipeti tisha doko nipeti wk I still can't match up a single smile that we did so I need to shift our confidence from that relationship What he's likes better What's personal No matter how many people are from New Jaffa How? Yes, I am in & Machakon... another one... a doctor Samu We so Monday to Friday akuna weu ananda kwa kilna nebera ila na trakwen yangami shi Akshia ni yangami shi nomu aja weekend na travel usiku na feka uku asubu saizuna na travel asubu ena feka uku Jionia weu ni kumuwa mei shi ako kumfanya Shope X Igasi ya ki Misha shi jininini weya na kata for that it's not that because she doesn't have money. She is a mess. At night we need money. Na kumu runa we mo takupi pesa. You have to try the means especially come... ah kuna how many gays? How many now are you? She just needed two. iskani na ma dekta adi? As long as ona pata pesa. It's better. Atwezi, I say ma niku hasu i ya ya na kafu hasu won bando See what you on that side has to do with kids. Kuna jiyo? Kwa zi problemu zi? Sasi e. Uwa ni zanempawad uman. Oh, kila pesa na kazi. That's a good question. That's a good question. Do you feel if ladies ask for money, that means they're not empowered? Now, where we're in?  Uliwa na iyo katini ni ka kafotia dhim, ala kuna sema. If I need to pay my bills and pay, niki takama na ume. It will just be for that one. Yes. Zatati zanempawad uman. Kwa iwo. Kwa buna inge binguni na wibu. No. No. No. Kwa iwo se kwa getre. You know, there is always this complex mental representation that ladies are inferior. And these ladies now who need money from men are the one who have taken that mentality. I like what my colleague has said about empowered women. You know, a woman who is empowered, a woman who is empowered simply means at first she is class. She doesn't need men in her life. She doesn't need men to be used as stepping stones. You know, there are these two kinds of ladies. There is this lady. Okay, maybe you have moved in together. When you argue in the house, she will tell you, I am moving to another apartment. I will buy another leather seat. And in that house maybe you own these normal seats. But now there is this other lady. Whenever you argue in the house, give me my money, give me my clothes. No. Those are two different categories of ladies. So what is the difference between those two ladies? The first one, she is empowered. She is classy. Okay, she needs men in her life. But not meant to be used as stepping stones. But now this other, the second one lady, she has that complexity, that for me to move from this step to the other step, I need a man. And that is what you are supposed to get rid of. I am glad you said that. Because our first slide of the day involves not one lady, but several. So I gave you a couple of scenarios. Nkyo wa numemta ni ambiyave nyemta kuawa janja mtoki kwae scenario. So you call her on a date. Are you ready? So you call her on a date. And then anakuja nambogi. Ya kimo nyoski. Radha, what do you do? She comes to the battalion. And you called her on a date. And you're in this restaurant. That's it. Maybe we may save Menda some place fancy. What do you do now? Okay, and that's the situation. You can't take the bull by its own. You just say, in the kuwita, I pick up. If you now come with your friends, give me time with your friends. Haya. Yeah. You can't do that. That is what you're supposed to get rid of. Okay, so you don't get rid of her. No, no, no. Let me finish. You made a phone call. Hey, hey. Let's meet at this place. I did it in the phone call. You didn't specify to be accompanied by friends. But you didn't ask me to come alone. You just said, come to end it. Namini kapata na atu. Kwae nda kakucha? Okay, no.  In this essence, a lady should not get money from you in all possible means. Okay, umemuita death. It doesn't guarantee even in her mind. She knows that. Okay. This guy needs my time. Okay, maybe I'm afraid of him or I'm afraid of the other circumstances. Let me accompany myself with my friend. Okay, that's fine. But now kujana mbogi. Doesn't make sense. Your budget line, it was maybe 2 to 3 people. I kujana up to 7. Oh, thank you. Kujana mboga na ume. Kujana mboga na ume. I know, akiija na mbogi apuni uta pata plan B. Uu na zitafta wa B. Because when you want to kujana nwarembu kumbilik, you have to be in the middle. Bil mara mboga. Now considering this, this is your girlfriend. You booked a space in a very pressures hotel, these pressures hotel in town. Okay, obviously that space will be reserved for 2 people. Now, consider the situation. Okay. You said you're living here alone. Maybe you'll do it too. Kwa kwa? Surapa solo. What will you do with that?  It's too interesting to have her in your room. So whether it's personal or not I'm happy that you're with me. As a man, ru loma trained, in Treasure era, uta f Aunata k Hugo Chaka, a p Kahudiyumu nandaka edits bu kwa be bff,��asiesti중 a Корita You can find comfort here... Ptuh!  That's quite tricky, but she loves you. Okay, she loves me. Very much. Okay. You know, at this current situation, at this current generation, okay, sponya kuna pesa sindew. Okay, it's fine. Go and have fun with him. Later pesa to gav. Aye. Let me tell you. You have misled. Okay. You have misled. In case, wu pate demwakwa na cheat, ama your wife, automatically you'll know that that lady doesn't love you. A woman will never cheat in a relationship for a person he loves. No. A woman will never. Are you sure? Manumka atafanya single no yes. Kamaa ma penzi shafed kutu kakua ke.  No, because anapenda awasiyana. But because anata katu ninja tu kitu kidogo ama, ama manumela zima anunue karanga. Onona yatenbo. Siti apendi mkwa ke, but because man it's natural. Onona. But for a woman, wu kipara anachift on you, automatically haku peni. How do you know? A woman will never sleep with you. How do you know? How do you know? You know, you too, that's about men to kwa kuskiza, because we are not men. Because this is an experience. Manumka. A woman will never cheat for love. Uski anahasu. No. Honestly I have heard people comment on our Facebook page at White Fair 4 by the way. Na na samaya. Mini kwa hasponyo and my boyfriend knows. Infact akini baya vitu. Saz nina tana itisha vitu na joa chaliangu atapenda. Doni mpele ke. That's not love. Okay that is not love. Ui manumaya kupendi. Yoni business. So you don't know what the lady feels. One woman will pay. Just imagine. Nimi, mikute demwangu. Do you know it took me time. Kumengina box. No na. You took me time. I left it in my bag. That is the last date. I don't know what she's got. I don't know what benefits kwa ima kwa sani nyuwata wa wili. So I am asking you. Okay, where is it? Let me tell you. Everybody has a story. Kilam tu kunamali alianziya. No na. You don't expect kula pesea sponsor na wipi utafika you place. Muwazi naumziya na kutembe apa, alafum trafika pale, namzuyu umaskini. After all, ukikubali vo at the end of the day atapata estiai na takuletia wewe. Sambili na nakuletia pesea na una kula jipo Wai wai when you na kula juni natural where is the letter? No. Can you be asking these questions madame? No. No. No. No. No. No. No. No. No. No. No. No. No. No. No. No. OK, yunna wena they antopika wohangia ya. If you're financially constrained, you have a lady, she has a sponyu somewhere, when this lady hangs up the sponyu, as a man, you feel bad, but now you're supposed to go for another option. But keep this lady as a business. Let that be a man-londing business. So only 8 a.m. A man-londing, ya. Okay, for a man and a sponyu, she was given maybe something like 50,000. Let's share it, 25,000, 25,000. Then that 25,000 you spend with the other lady. Now it becomes a man-londing business. The money comes and it's done to this guy, it goes to another lady. Let her be supposed to be. Lord your boy children, I don't know. I don't know what's happening, Lord.  Lord, Lord hear them. All right, another one. She keeps insinuating she may be pregnant. Like kilasa, kilasa. Beb, beb. I think nikona bol nani ya ako. Do you think you're being trapped? Okay, in such a thing you can't narrate down and say it's a trap. You'll first take some measures. Nye, we see kevwa mabrayo. I like that. I like that first response. Good job. Continue. See what you're looking at? Okay, let me just finish what I'm saying. Now that's scenario. You can't just narrate down and believe whatever she's saying. You know there are several measures that they are physical. Other measures are physical. Okay, the first thing, obviously when a lady is pregnant, you're not through some mood swings, emotional changes. So that is the first approach. In case you detect such things, okay, if you came early, you're highly suspicious that this lady is pregnant. But now the pregnancy is not, my hand, you're not 50-50. So okay, in case it happens that this lady is pregnant, you confirm that the baby is yours, now things will become dramatic there. But now if you come pro-easy work or no. No. That is now where you're supposed to bring a man who then say, apapa, and at re-easy hands are meti kama nia, one zero. Let's start it, zero zero.  Nama na muku na nini. At re-easy hands are, we can't start a match. You know, normal, your match cannot start when the home team is. It is true. Is, when one of the teams is trailing. Okay, if it happens this lady, she has a baby, when the baby is not yours. No. Let's close the business there. Meti, yes, the hands are one zero. It's supposed to start zero zero. Yeah, that's what I can say. I don't think I agree with that trend. But I would talk to me. Do you agree with that? But do you know ladies are different? Kwanzao kisimati, kwanzao mga jeo wanae signs kama kunao ball. Kuna, okay, I've never experienced, but it may take time. Kuna signs to the day, at ya kunao ball. So, okay, say kwa again is to girl.  No. Tess kawde mga kunao. Mimba, kwa zunajia. Aneza, aneza cities are for two months nikunao ball. Uwani signs are lafana. Shindatua kikutisha dobe buntatua lewa nataka omena. Keshom truaga. Uwa, sabu janbe. Refi. Uwena, chita, omena. Sufu hana. Sozi, sozi, sozi, sozi. All right, so I have, my gosh, we gotta leave, we gotta go, guys. But I want to ask this last question, and I think it's very special. So, if she withholds intimacy in the relationship and only is affectionate when she wants something, what to do. Like item yote, I am not coming after anyone, but umonaizoma mimza, it's the answer to her. I like, yeah, heavy, heavy. Yeah, so she's like that most of the relationship, but akiyona nataka tokaki tundio. Maze, kwanza yusiko, anaku jinji akarini, kakuuku. Anything you want, baby. How do you deal with that one? Okay, actually, it will give you nikija. He's doing a mima. Okay, ambi wa hivu. Obvious, you know, you need to pay, when it comes to me, when I'm in a club, when I'm in a... For one thing, then... For one thing, then... Ah, ah, goja. First, he said, make sure it's correct information that indeed, actually she said that, that akona both. Because someone does not sit and have pregnant thoughts. We sit, they want you to come there. I'm not biologizing yet. So, Kenza kusema atini, minipasa kuhana itisha na maybe, actually, ni bolia ukweli. Nani ako, siya ti, nikwana practice na mimi. Up to honestly, yango. Yes. Okay, fukuna. Okay, okay, okay. Inakuma, kawu na jo, the impact after that. Itakuma honestly. Pash tabiduzoe. Itabiduzoe. You know, nizima kama ruen. Kusima kama ruen, kwa sumbu, kusini. Ah, toriya, kuhidru intimacy, ina depend. Iu automatikali, you know that, upendui, wana, siya somebody else she sing. So, manome ukiyambiwa, ukiyaw na, like, bivei miyaza kuchen, yuna wambia kujasili powana, wambia nanda kuna, enti yango, ali jifungua. Obiasili, you know that. Na ushatu mafea. Ya, ushatu mafea. Na umeo siya numbat. Na umeo gav once the whole weekend. Neli kwa tujuanakam. Lemitiliw, this is a real story. Demkali toka kiyu, nini Pakland, kiyu, kufi kajuja, kali pandagari kakarudi na fea angu. And that is the last time nidiongi anai. So easy for it to happen. So when they happen, maze kwa ground. Yung wikiya kinuzia. Yung wikiya kinuzia. And I thank God. Thank you. Thank you. Mikiya nini kwa da ni. So easy for it to happen. So easy for it to happen. Unatuma fea, lufuna hambiwa. Sifiki kwa numbat. Na how? Kwa ni mami kujaho many times. E kwa ni nini kwa na exam. Kwa na iwa student. A kwa na juwa. A kwa na juwa nini kwa na exama kiki. A kwa na mi nini kwa na iya exam. Kwa za kwa na zasili. Hai ya. Hai ya. We get to the summer too. Niki ni 20s wemo. Kwa niki ni 20s wemo. Niki ni 20s wemo. Niki ni 20s wemo. Niki ni 20s wemo. Niki ni 20s wemo. Niki ni 20s wemo. Niki ni 20s wemo. Niki ni 20s wemo. Niki ni 20s wemo. Niki ni 20s wemo. I'm so sorry. Apana, I'm so sorry. I'm so sorry. I'm so sorry. Oh my God. We've been talking about kwa mdanda. I hope you get to catch this episode of My Gosh Madeira Zandapo at 254 Channel on YouTube. But we gotta leave. We gotta go. I hope you had a great time as we have. Shokuna kesho. Niki ni 20s wemo. You don't wanna miss it. In the meantime have an amazing week and amazing day. So we wave now.


Hey shit-ass, you wanna do a manhunt? Go-go-go-go-go Oh yes! Yeah, get back, get back Yeah! Go-go-go-go-go, faster Yeah! Go-go-go-go-go-go What the? BOOM!


In at number 5 we have a clip sent in by BobPlays on PS, Wenzir decides to trap you in because he wants to tickle you with his tentacles. What happened here? This is a little weird. Uh huh. I'm sure glad I was recording this, I want to know what happened. Hey Zor, what's up? Yeah, I hope you're not claustrophobic. Yeah, yeah good job. Good job with the door. In at number 4 we have a clip sent in by SLStryker9.  In at number 3 we have a clip sent in by ElectricRoon when you just fall through the map for no apparent reason. In at number 2 we have a clip sent in by Night Ninja. Now I have never really liked Blink and I do think it's a little OP so this kind of makes me happy. In at number 1 we have a clip sent in by SteveGHShadow so it seems the infinite shadow shot glitch is back. This time worse than ever.  If you enjoyed the video if you could leave a like I would appreciate the support. Don't forget any badass clip you've got. Send it into the email or just a link in the top of the video description. Thanks for stopping by as always and I'll catch you on that next one. Peace out until next time. Peace! Up below is gonna have you a hype starter in the UK now he's worldwide. DPJ go and subscribe!