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We are listening to the Data Hustle podcast, where data is turned into business impact. Our guest today has been surviving and thriving on the main data streets far longer than most people have even known what a data stack is. It's Chris. He's the co-founder and chief commercial officer at Late Data. The data consultancy he'll build into one of the fastest growing in the EMA. He started out 30 years ago in the back office at Cognos, learned every product they had inside out and went independent as a BI consultant in 1998, far beyond before I even know how to speak the language. He's followed the industry through every twist since hands-on across databases, ETL, BI, big data, you name it all, before moving up to the stretch realm where he is right today.
30 years in and he has frustratingly more energy than any of us combined. We asked him to stop to give us a chance to compete. He has somewhat politely declined all of our requests so far. He's out there hosting the Data Valley show, running a data conference up a mountain, holding court at his London meetups and posting takes faster than the rest of us can dream them. He's the self-appointed Mayor of the Mean Data Streets, a proud and unapologetic data nerd and a man on a mission. Please welcome Chris Stab.
Well, that is one intro. I've actually now remembered all the things I've done, you know, I'd forgotten a lot of those things. But yeah, thank you for that intro and thanks for having on there Tony and everyone. So good to chat, good to chat as always. Yeah, I love the Mayor of the Mean Data Streets, which is a little bit of a background on where that came from, the hashtag. Because, yeah, I've had a few hashtags, bring back data modeling and stuff like that, which was annoying because there was two spellings of it, you know, one with L, one with double L. The Americans had simplified it. And yeah, but the Mean Data Streets was, I think someone was calling me out or something and saying, you don't know the Mean Data Streets I grew up on, you know, it's like, I've been around, I'm not just some sort of like, you know, marketing guy.
But yeah, so but then it was a good acronym for the modern data stack. So I thought, actually, subliminally, maybe it was, sort of, but so there's a little bit of play on that. But it's stuck and it's quite amusing. And then I think you're talking about how long I've been in this industry is seeing the same things happen over and over again, seeing the same things, simply making the same mistakes over and over again and reinventing the same thing, but with another lane over and over again, did make me a little bit angry. And I thought it was a bit mean for a little bit. So, yeah, hence the number of posts that were going out at some point, I was probably, I was a high frequency.
I slowed down a little bit recently, but I might start speeding up now. And I've stopped using AI to generate my posts, so the back misspelling mistakes and back grammar, because it seems to be better.
You know, the only thing I know about the cosmos is cognizatory is whenever somebody brings it up, you're going to get humbled. You're going to learn something.
It was I mean, I mean, yeah, it was when semantic layers are boiling, they were business objects were about then. And I didn't know. I didn't study to go into the data industry and people asking my career path. I mean, it's not a path. It was sort of just a fall in the right direction to some degree. But yeah, and it was state of the art back then. You know, you had an overlap queue for multi-dimensional that you could you could slice dice and get all sorts of analysis out of it and easy to connect everything. And it's not that I've never seen anything as good as that. I went all good products go to die when they get bought by IBM. But yeah, it was it's given the foundation of the knowledge that I built on and and still still reference these things, especially semantic layers.
Now it's like, come on, guys, we've done this before. This is not rocky. This is not new.
Yeah, fun. Let's hope quick question. And that is like you just mentioned semantic layer. And I know a lot of people listening to this might be thinking like, oh, I thought that was a new thing. You have a short list of things that data practitioners think are new, but in reality have been there forever.
Yeah, exactly. Exactly. Data modeling is always there, but people forgot to it when they went to schema read. Don't worry. Just stick it in a lake. You'll be fine. And we'll work out how we use it later. We all know how that ended up, didn't we? You know, so. But yes, yes, semantic layers. It was a big business object has something called a universe and Snowflake has something called a catalog. So, Cognos has something called a catalog and then OBI had a different version. But yeah, it was a mapping. The problem was it was a big bottleneck because someone had to go and model all that stuff and make the change that model. It was normally one person.
So very hard to scale and very hard to very disciplined to keep it always up to date. And a lot of people didn't manage to it. They haven't started a catalog. But yeah, they're nothing new. This is a go. We're going freestyle here, but I know you've got some questions for me. I think it's sometimes. But the term context layer is something that I started talking about probably a couple of years ago. That's because I was getting annoyed with everyone going, oh, we've got a graph. We've got ontology. We've got a semantic layer. We've got a vector database. We've got what we're all talking about here. What is it? Well, it's providing context to you.
You know, it's some of them are mechanism to that. Some of the other context itself. But collectively, it's giving you content. And yeah, that was a semantic 2.0. So this has all been around a little while and it's becoming so much more relevant now. Well, because of where we are with the AI, it's not the bottleneck it used to be. He doesn't have to be that one guy with some like UI, joining up all these things, setting up the relationships and the business term. That will be automated. So a lot of the negative aspects that were around then have been solved with automation.
So talking about the many iterations that have been in the data industry, when I talked to you before, I think one of the things that I found most salient is you said not everybody should go into data consulting. So when should you not go into data consulting? I think that's a really interesting question.
When you want a life. I'm sure I think a lot of people have commented that you go around conferences all the time. You're talking here, doing that. I want to run a consultant. I want to do. But it's I suppose you see the outside. You see the fact. Do I have a holiday? No. Do I work every weekend? Yeah. Do I work most evenings? Yeah. Do I work for the night sometimes when I go get something in for a proposal? Yeah. Yeah, it is. It is a lifestyle. You've got to love what you do. And if you're sitting in your job at the moment and you're doing a nine to five or nine to six or whatever, whatever it is, you're taking your vacation. You have a couple of sick days, especially off the footballs day before.
I'm not going to soccer. You know, you're going to have a day off. You get paid when you're on holiday. You get paid even when you're sick, when something of your good benefit. And you got your job to do. You can moan about it and do what you want. And you can say, look how great everyone else's life is. But you're not the person to do be a consultant because if you're happy with that sort of life, stick with that sort of life. If you're still doing work, if you're working for a company where you feel you always wanted a bit extra and you're always doing something and actually end up doing something weekend because you love it so much, you should be a consultant because that company is getting the benefit, well, getting better, more benefit out of it than you are because you're probably getting paid the same as the guy that's doing the nine to six or nine to five.
So, yeah, it is a life. And I think that's there's two steps to this, being a consultant, an individual consultant, a contractor. I've had two permanent jobs in my life. One was for Cognos, the other one is now the company that I employ myself in. But now it's a proper job, which before I was a freelance consultant for all the years between. And that's quite a few. And why I was able to do that is because I still love what I did. And when I I would go above and beyond because I like doing it, I learn a new tool. So that always gave me. But if I just specialize in Cognos, I wouldn't be in it wouldn't be a contractor that time because that market died.
But you transferred your skills. And this is the world we're in at the moment. Everyone needs to be looking at doing that. It's OK, right. What are the skills I've got now that are foundational that I can use to become useful or have a role in the future, the future that this world looks on? You know, the days of being just an Oracle DBA. You know, you got this.
When you talk about work, are you talking more about like going to conferences and doing like side projects and learning features or like Robert Bean of all. Yeah, I mean, let's go.
I think when you're doing it and going above and beyond. So basically, if you're you know, if you're doing things in the evening or in your own time, all right, so you're working with company doing that in your own time, but you're a very into doing a job. Yeah, I do that. That's the fun part. But in between the hotel afterwards or in the lobby, whatever I'm working as well. So it's I. Yeah. And well, as you go into the conference, I get I get questions about the main data streets wherever I go. So it's it's it's well, it's not late on the evening. It gets more fun, you know, but during the day of conference, it is normally quite stressful of just trying to walk across the conference floor and while again, vendors jumping on you or people you know, you don't know, talking to you about things you've done that you realize that then nobody's seen your online.
But yeah, I'm not saying I have loads of those, you know, I'm not I'm not like Joe Reese. He's he's proper famous.
But I've heard before that we're kind of like musicians that we get paid to perform, but we still have to practice on our own time. I think it's all right.
Yeah, I think company companies, especially they always want you to have the best skills and any way of keeping on top of that now is is using in your own time. So I don't know about you guys, but since since the older ability to do vibe code is already called vibe code, it's probably probably going there. But, you know, a genteck, a genteck. I have a problem stopping. So it's OK. Oh, just one more release. Oh, another agent. I can run a run another another one of the cloud over here. And I just I just like that. The finish. Oh, I should be in bed by then. You know, but it's before there was always an easy stop factor as as OK. I don't know this bit.
I don't know Python. I'll speak to my mate tomorrow or speak to one of my team to go and progress on it. I don't have that. Don't have that hard, hard break. You know, update my agent knowledge on a bit of that. Pass that. So it's yeah, it's it's it's it's hard. It's hard to keep a balance sometimes because when you've got so many things on your backlog to things to do, especially when you're you're like me, don't stop.
In general, I would like to think that the reason why tech conferences tend to be so lively and so full of like passionate people is because if you don't have that passion, you don't put like the extra hours to like stay for long.
I very good word passion. So I think one of my posts that I didn't think you would, but it was like you have to have passion to do the job you do. If I'm hiring you or I can tell if you haven't got passion very, very quickly, you know, and I got those loads of hate stuff on that one. I go, hold on guys. Yeah, but you know, you do not everyone has the opportunity to do this or I think I'm not everyone had the the opportunity you had, Chris. I fell out of school, didn't even finish college. You know, like, what do you mean? Opportunities are, you know, I've made it myself because I have passion for something I do. But I understand that, you know, you can be a passion for a four-week-long drive.
You know, we will be one. So I think there's a combination of of having enough passion for something that is within your ability to do. And that's that's another another factor. And a taxi driver told me this and I love this or references as well, is that it was a definition of of doing something, well, doing a job you really like to do. And you should focus on that is something that you keep on doing, even though you don't have to say he was like doing stuff in the evening. He said, I wasn't clock watching. I was just carry on doing it. It wasn't like, oh, I finished now. So that if you if you're doing that, that's the career you should be going into.
If you can make it, you will you'll enjoy the journey as well as the destination. And if everyone always looks at just the destination and they are going to do this job just to get to that, you know, nice packet at the end and you'll hate your life. And, you know, maybe that destination of being that I want to be that CTO level or something like that. And if you get anything, whoa, that's not really good, is it? That was right. And that was my one and only CTO job as a contractor as well. I hated it. I was doing floor plans and people like didn't want to sit one place another going to the board meeting just being ignored because the marketing guy told him that he's going to get 20 times ROI on his flipping investment.
And I wasn't going to get 20 times, but mine was accurate. He's awesome. Don't look up what company that wants. But yeah, so he's true. It wasn't it failed that project. But yeah, I mean, I said it's a really good thing. But you do have to be careful in the P word passion word online because people like, you know,
It's also interesting that you phrase it like that, because I think what took me a couple of years to realize in my career is that every time you see and I hate this term, but I have to use it because I cannot think of a better term. Every time that you see a 10x engineer or however you want to call those people, some of them, they're stupidly smart and awesome and better human beings and they're going to succeed. But for most of them, if not all, there is always a component of a personal sacrifice in there.
They're the angry ones that mind about everything. And you can spot them a mile off. Yeah, they're the ones that say, oh, I want to be a consultant. I want to do that much easier. They realize that they were being paid way more than they should have.
Don't be a consultant if you're not willing to if you're not passionate enough to make the sacrifice.
Yeah, exactly. I mean, that that that extra money on the day rate comes comes. And of course, the people that you're paying a high day rate, they want you to be the best at it. I don't want some mediocre guy who's going to come in and just moan about the coffee machine got very good hair. Yeah, I think you're going to do the job. Interesting.
That brings us to a different platitude, right? Value. I think you've written a lot about what it is that business value actually is. I think one way you put it is business value is an evidenceable positive effect on the performance of your business.
Yes. Yeah. So this I mean, me and Matt Housley and JT Jason Taylor, I was giving New York, we had a Wednesday sort of like book club, drinking club, a bit of a bit of a regular meetup. And we and some of us go back doing a book about loads of content, we never actually run out of it. Some of the posts are related to it of, you know, that business value. And why it came there was first of all, I hated the word ROI back to that other example, I just gave a minute ago, you know, 20 times ROI. You know what, you know, what is the investment of how you quantify it? And so that's where evidence came from. Right. So you needed that term evidence in there.
The other one is is always says this is like it's about profit. Business value is about profit. No, it's not. And the reason why we came to that conclusion was depending on what mode you're in as an organization, and this led on to who you target your organization for who's going to be signing stuff off. So say, for example, if you're in physical, physical mode, your CFO is the only one that's going to sign stuff off. You need to provide value this particular year. So in that particular case, that value is going to be some cost saving or something on the profit. I use this as another example of the extreme, which is Uber, you know, 10, 11 years to return a profit.
I'm sure that business value for them, I've seen the film as well, but it was more about market share. It was about, you know, getting to new cities. It was about the experience. It wasn't about making money and that they weren't making money. So business value to them was not about profit. It was about future gain. And I think the other the other one, which is in between, which is I think a future growth, I think I call it or something like that, which is, okay, now I'm funding future growth with savings. So I will be able to fix this problem here that saves up money that I can use that money to invest in something that actually provides a use of some force multiply.
So you reuse many times, provide additional value. So and yeah, so that that's where it came from. You know, business value is an evidence will positive effect on your company's objectives. Company objective is is going to be set at the highest level of your CEO. That is what your that is what you should be measured against. And I think it should roll up to that. It never does because the Conway's law and all the rest of it is all the organizations don't really work in the structure that they are. And then you need to work around the structure, which comes on to my friction. So friction comes in three forms as well. So you've got friction being delivery friction.
Friction is the time complex in the effort to form an activity by the frequency. So you've worked a lot of your STLC process, which is stuff that we're shameful plug. It's just stuff that we're working on the moment, which you may have seen my post recently. But I wasn't meant to do. But the the the other sort of friction is business friction. Business friction is OK, right. I want to do this activity, but it's really complicated. I've got to talk to this person, this person. I've got to request someone over here. Friction that. And then we're due to Conway's law and the fact that no organization matches how the systems work. You've got operational friction, which is like friction on friction.
So because of all these things I'm working around, I've got these. So we're focused on the moment is helping companies identify these types of friction, quantify these types of friction and then providing them with solutions to fix that friction that we've already evidence. So it becomes the idea is it becomes a no brainer is that we've seen this problem. We can evidence that problem. Here's a solution. We know that is going to do that. It's going to save you money. Would you like it? Yes, you know, that's the idea. Now, everyone's been queuing up yet, but we're just really going to work with it. But but it's and this is not something that's new.
This is this is bringing lots of things we've had. You know, we've had maturity assessments. We've had, you know, SDLC or CICD or DevOps automation. We've always put things in to address these problems. But I think now we're in a situation where it's very easy to join these things together. And again, the post that I guess they was to talk about friction, it was, you know, we've put methodologies, you know, we've had agile. We've had, you know, a scrum. We've had scaled agile. We've we've had meet, sorry, different cadence of different meetings. And then we've moved on to having, OK, now we need Jira boards, we can ban boards, we need comfort and still, still we had friction, you know, and probably as slow as waterfall was, but it gave the impression it wasn't a slick.
So, yeah, so all these all these all these challenges we have before, this is again being here for a while. Looking back and say, OK, well, let's not just throw, let's look at what worked well, what didn't work well and from the past and then try and use that to define what the future is. And I think if you don't do that, you know, that's a definition of stupidity, isn't it? You just keep making the same mistake over and over again. So we do this, the centralized, decentralized, we're always swinging, you know, from one side to another and gradually I went that pendulum is becoming less, less, less. Yeah, less, less movement in it as we should be learning from each star.
We've gone the wrong way too far and OK, and you're just thinking but it's only because of Bill Inman said about, you know, he still shows all the things I've forgotten about. And then I'll try my best for as long as I can to, you know, call out these things will mean data streets.
Hey, you know, it's kind of funny because like every time that there is a lot of that friction built in, that's when Shadow IT happens.
That's yet is that's the operation. Exactly. Exactly. People work around problems. And I think there's a whole like organization hierarchy and everyone thinks it, you know, CEO comes up. Actually, it's, you know, Suzanne that sleeping with so and so that going out with friends with this person, if you need someone to get done, speak to her because you'll be able to arrange it all for you. You know, it's like you worked around the whole favor system or that old actually I've got a count we can go to that. We know again, we may have even had some shadow IT in our own company. Just say it's not anymore because I've admitted to it.
So would you say would you say now that people have sort of tasted productivity with AI that their patience for friction has reduced significantly?
I don't think so. I think so. I think AI was AI enabled. AI is enabled. Confluence page be created faster than anyone can ever read them. And before people don't bother writing this stuff up, now we've got too much. And everyone wanting to do this stuff because they can all do it. And sometimes it's surprising. It looks good until you start reading the data and no one reads the data. So then that gets reused again. So we've seen it and it's how do you scale this stuff? Everyone's got their agents and the rest of it. They're all working in silos doing stuff and nothing to join it up. And we have a solution for that as well. So we've been working on some SDLC process that allows to join up with yourself.
So having that collection of backlogs that has become the spine of what everyone can join up to. So the only way of getting the real value out of AI and not spending thousands and thousands of tokens is some structure. Which not everyone's nailed yet. I think everyone's going, "Oh, don't worry. We've just rolled out Claude to all of our developers." How's that work? Well, we haven't got any reduction. Well, there's been a few. Unless, if that's the root of doing it, they're all going to be working even quicker. They're going to be like creating loads of stuff that's on top of each other. You have code that's generated that people won't actually understand why it's been generated.
Because you haven't started with, "This is what we're building. This is the structure. Let's do some old school architecture designs at the beginning. Let's maintain the designs." And then everything comes off that. But yeah, we're having a little get there. But it's just how much that was spent on tokens in the meantime. Yeah, it's a shameful amount that people blast on stuff that you don't need to use the sort of models that you're doing. I've got some stuff. The demo will go out. It's not even one cent I've spent to do a whole thing. Because I'm using 4-mini or some 4-0 or something like that. It costs so many million tokens. It used 40,000 tokens the whole run.
But it did what it was meant to do. Because it was tuned for that purpose. With the right context, given the right time. But to give it to someone who's going to stick it in Claude. Yeah, they may get there as well. But yeah, it's a what price. And it's probably it's probably morphed into something that wasn't exactly what they wanted.
I feel that if you learn how to work with scale before AI, like I think most of us have worked in very large projects. We still remember the first time, like, all things are now working and you need to take more steps. If you kind of develop that mindset, then dealing with AI should feel a bit simpler because a lot of it is like problems that come up with scale. But for a lot of people that never had that experience of working on large data deployment, it's the first time that they're seeing big scale and they're like crazy.
I never thought I'd ever say is we admit this. This is the first time. Toe gaff. I never thought that all of the toe gaff sort of toe gaff concept, it was a OK. It's an architectural architectural design framework, but it's like no one goes for it's like so many different. You've got B.R.D. You've got this one. You've got a HLD. You've got a solution design. You've got just all of these different artifacts, just so many of them. And very large scale projects. You use some of these styles, go and use all of them and then you whittle it down to the ones it needs. A canonical model being in there, you know, it was a nightmare to maintain. But knowing all those artifacts are the purpose of them or what they're doing is basically what I've used to do this stuff.
It's all of those relationships. And this is the thing that was before it was so hard to maintain. Can you keep a word document updated with the right references to some spreadsheet that's over here that someone else is working on? And you've got to go through an architectural review board to go and get your document signed off. What version of that document signed off against for this one that's running the APIs? Just a nightmare. You could do it now. Because you need to know all those things that lead that were pivotal to very hard to do. And that's why the project took so long. But it was this point. This point you were in here.
There is one question in there that I would love to talk about in this. For most people that start as consultants freelancing, I think the most common outcome is after a certain walk, give up and then go back to being a regular employee. But some people actually go on and start their own companies. Can you walk us through how that looks like?
Yeah. So and this is at least every consultant's dream is like, you know, get a few of us together, you know, we can build a company. You know, it's it's it's yeah, it's harder. It's harder to do than I think. And to do that, you need to consultancy can't be you can't be we do everything. We specialize in everything because you're never going to be able to scale up anything like that because you'll be trying to do everything and you're never going to have been known for something. So you choose what that something is. And I think we were quite lucky that I'd worked for I did some I saw snowflake. We first came over here. I put in a larger record delivery anyway.
So I was working with not not delivering the food, but they're doing a migration from Redshift to it was the first European customer of snowflake. And then so I knew the original crew that were there and then opportunity came up to me. It's part of it. You know, we don't know a lot of people. So that that was our we still ask no flex special, although we do a few other things outside of it now. But that was our core thing that we were focused on. So it timing is I think having having that I think this may be an opportunity with all of these new technologies out there becoming a anthropic partner or we've just become a partner of one or can't name yet.
You know, so we had to run very lean for a long time to build up enough work in capital. To get an employee, you know, and do that. And then next book employee. Say it's someone's giving you some investment or something like that. You've got a lot of money to start organically. It's it's slow and you can't go from zero to 100. And there's plenty of things you'll learn on that journey because when we set out there was there was three of us. There's two of us now. But I'm. They. We've learned the hard way the whole way of what what did well what didn't do well. I know the marketing now in the before probably blew a lot of money beginning on stuff I shouldn't have.
I don't know what sales are still working. I've got a good guy. So yeah, it's a it's a journey that. Yeah, I again, I wouldn't suggest it for everyone as well. It's it's it's a it's a hard work. But first we'll take that consultant consultant. And you do that. And I think the other thing is if you want to consult on your own one person to consult to a company. I don't know how you do it. You do. You need you need to. You need to join forces and choosing the right people to join forces is critical because you're going to you're going to need someone you can they can lean on you. You can lean on them when you need to. If it's just the one of you, it's it's it's it's too lonely.
I think that's it.
So I guess for some people listening in just to give some perspective, I mean, there's consultancies that run in various different sizes. But I think, Chris, you're the person that we spoke to of the largest one that we've seen so far a couple dozen of people. So obviously there's challenges at different stages. Right. You just mentioned one. If you're on your own, you've got a partner. If you're going to work for a big client, we will be on the other end. Yeah. If you're if you're getting bigger, what are some of the things that you might not expect run into but are going to be hard.
Hey, our issues, my God, nightmare and legal issues and all that sort of stuff. So we went up to about 100 people at one point. A lot of offshore. I think we we we scaled to we went too far. And again, I don't think we should have done the offshore thing. But we took the opportunity. Well, probably wasn't where we probably would have we probably would have better focusing on time into that. What we didn't have into into other parts of business instead. And I think we talked about is it's about quality and margins on the stack and high sell them cheap with all the hassle of it or have appropriate size shells and appropriate prices based on the value you provide.
And it's hard challenging or it's hard forecasting bench forecasting, you know, new new clients coming on when they come on when they sign. They all want the best people making sure we put the right best people in there, making sure you can line up in time. It's it's tough to get all that time in place. And the more that coordination you've got, the more risk of bench time you've got, things like that. So, yeah, it's it's a game we learn on that one as well. I think we've got a good good balance on it now that we can we can keep track when we act quick enough. So that's that's that's the key thing.
Yeah, the the chart seems like the scariest thing because I know one thing is, for example, if Tony and I create our companies, they will be more like, oh, yeah, we're friends. But the moment that you start scaling to like those people are like dependent on me, that's that's another story.
Yeah. Yeah. Yeah. But it's yeah. Just don't just don't think about it. Just one last.
So I don't think about it. We'll just go away by itself. Right. Don't think about it. And that's why you need someone else because you can't go on your own. You throw this interview, you have mentioned at least a couple of times that you you look for people that you know somebody has passion. It seems to me that you already have some standards of what you look for in less college generically a data person. You mentioned consultants, you mentioned partners.
You've got some good senses. They're always each of the ones that are being and this is the script and as well. We have a script. I think we've gone off it. So we're doing a look at the focus of what we think the shape of the teams look like going forward and what sort of people we need for that. But I'll go back to some of the still with us now. A couple of the grads that we hired right in the beginning. And what I loved about them was it was how they presented back what they did. And back then it was like a juke to notebook and they tried to do. And what one was was on correlation of Elon Musk tweets, Bitcoin price. I think I won't be thinking about that.
He's going to have done it. But how he talks about how he got a data, how he tackled the problem and you know, critiquing it on what his technology skills were. I found a problem. I've solved the problem. This is how I solved the problem. And he could just talk about it. And it was like, I like that. And I think people, for example, come along and look at the job. You got a delivery code. Well, you've not built a vent, done a vent you want to show or. So it's yeah. So that's. Yeah, I can't go through all the criteria. You know, I guess the HR stuff. But yeah, I do look for that sort of passion. I'm going to go back to do look for people are solving problems.
No, not not not like listing out, you know, if they know where a having clause goes in an SQL statement or, you know, how you do a date conversion or what Python library doesn't matter. That's even less and less important. Especially with what's now is you need architecture thinking, design thinking and creativity and passion.
I guess that comes back to when you see a portfolio that actually solves real problems. I think I did a job because I created a tool that would scrape our website's book. Sorry, a website about books. And he would send me an email every time the books I want to wear in a discount and people like that. And it was called. It was probably awful back then.
Talking like you've got to say about it. Maybe just talk about it. Oh, this website. You've got to. OK, I'm going to solve it. And that's that's that's that problem problem solving skill that we need. So, OK.
I suppose you mentioned earlier when when people are just thinking about clocking in and out from nine to five and they're just outcome oriented, right? They're just focused on that's where I want to be, but not about the process. How do you suss that out when you interview somebody? How do you like they're like that?
There's no magic. I've been called out on this before with someone's nailed an interview and that is not being what they were in the interview. I've done it is when I was at the CTO doing psychometric tests. They're quite interesting to. I mean, there's a there's a cost to them. But when it's senior role, it's quite true because you actually you you get to see that you guys have that feeling of they just say what I want to hear. And then you go back to where they realize that actually the conflicting in what they're saying is that it's yeah, each story is independent on their answer. There's no correlation or link between consistently good story.
You know, I am quite interesting. I am trained 11 labs 11. So I gave you all of my old podcast and train was trying to train its voice on me. So my demo is now that recording my voice and I have to listen to it. I have to play it back. Make sure it's okay. And I think God, I have my poor staff and everyone has to listen to this all the time. No, I feel I feel relate to them more and more now. But yet it's scary. It's so it's even picked someone's work. So I do. You know, my little herbs every now and then. I think it's like shit. You get my bank.
I think I've even heard of companies running commercials where they train like a very famous British popcaster. They use their voice to train the A.I. and then use that as the broadcast voice and it picks up all the quirks. Yeah. Yeah. No, it's shocking. Very good. But yeah, this
is some fun times, fun times. You get so much more done nowadays with with with with not as long as with the time I have.
I mean, at least if anything, we are just increasingly. Increasingly faster sharing all of our private information with a bunch of big companies overseas. So I think that's good in and of itself. Right.
And the old scammer as well. So they seem to know where I was. I said message my staff asking to buy them by a less vouchers. I think they know a bit of conference.
My God. So I mean, obviously, Chris, you've been you've been a very, very active part of the data community. I mean, you're you're everywhere and you're a very lively environment person. What does community mean to you?
I think it's a place we can go where it's not just a bunch of salespeople that are trying to sell to you. And you talk about the winter data conference and I think what why why that was why we tried to keep it different. The others was there was it was a story to it. It was curated. You know, it's like, OK, we're going to have a different lot of sessions that all tell a story collectively, you know, gave them a bit of vendors work to pay for some sort of some of it. But, you know, but it it was people came to because they got to see your other friends. I mean, they say Texas. That's another another good example of community. It's practitioners that know practitioners that like talking about this stuff.
And if you if you're just going there to be paid to go to speak or something or something like that, you're not going there just because you like you like the you like sharing. You like sharing ideas. You like sharing collaborating. Yeah, that's what the community. I think I could serendipity serendipity and synchronicity. Two words I can never spell. But it's doing things that you may you help someone else that creates just future things that can happen. And I've done things that I didn't know I would be useful to someone. And they were, you know, I didn't think it was so such a big thing for them. I helped them out. But later on, those things have created opportunities.
And I think that's what that community thing is. You're going there with no objective to get something else. You're going there. But things could. You know, and I think if you start going there thinking things, you're good. In fact, you've already wrote it also. So you have to do it subconsciously.
Yeah. So maybe can you can you tell us a little bit more about the conferences that you're currently organizing?
So I didn't have maybe Wednesday conference next year. It was about four or five of them. And that was born straight after Covid because everyone wants to have a conference. It was easy then to go and get vendors to sponsor it. I think the modern data stack or ecosystem there is, you know, it's only snowflake or Databricks now. So it's hard to go and get those independence to sponsor. But it's the community thing we'll still probably do that will be a big data in London. It'll be like a little talk there. Any of the meetups once a month in London as well. So, yeah, that's enough for now. I think I've seen Linbenders doing a data event in October.
So I'm going to go to that. Yeah, we'll
definitely try to make it out there. I guess that's a very natural segue to. Yeah, like what's next? Where can people find you? Obviously, you mentioned the conference. I know you organized a low key meetup as well in London.
Yeah. So yeah, we'll check the websites. The best place. Well, follow on LinkedIn. You'll get to see where I am at that point. And the website www.leit-data.com. You'll get to see podcasts, historical data value shows and when the next meetup is. And a bit more about what we do. So the milestone needs to be updated. So we've got meetings tomorrow to get some more content up there. But yeah, follow me on LinkedIn is the best thing to do.
Absolutely. No better place to get hot ticks on the mean data stream. Exactly. Chris, we'll give everyone all the links in the comments and the descriptions down below. Thank you for joining us on the podcast. We had a lovely conversation and I wish you all the best with surviving many, many more years on the mean data stream.
Thank you very much, Tony. Thank you. I love the questions. It's been a good chat.