Hello, and welcome to Dataversity Talks, a podcast where we discuss with industry leaders and experts how they have built their careers around data. I'm your host, Shannon Kemp, and today we're talking to David Kowalski, principal consultant at Orteca. 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 Dataversity, 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 Dataversity.net. Hello, and welcome, my name is Shannon Kemp, and I'm the Chief Digital Officer at Dataversity, and this is my career in data, a Dataversity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to be talking with people who help make those careers a little easier. To keep up to date in the latest in data management education, go to Dataversity.net forward slash subscribe. And today we are joined by David Kowalski, principal consultant at Orteca, 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. David, hello, and welcome. Thank you, Shannon. Always a pleasure to chat with you. Likewise, you're a longtime Dataversity friend and speaker at our conferences, which we always appreciate. Always great to see you at the events and so excited that face-to-face events are up and running again, where we do get to see each other. So tell me, David, so you're the principal consultant at Orteca. What is Orteca and what is it that you do? Well, Orteca is a, we describe ourselves as a specialist consulting company. We are, our main headquarters are in London, but we also have offices in Nashville and Princeton in the US. And we're dedicated to helping companies enable and exploit their data. We're also known as, you know, widely recognized as leading experts in DCAM and CVMC. I'll tell you in a second what those are for anybody who doesn't know the acronyms, but, you know, and the other thing that distinguishes Orteca is most of our senior staff have actually had hands-on kinds of jobs. We've set in the executive seats. We've run large portions of large organizations. We're not career consultants who approach things theoretically. We've walked the walk and talked the talk and have the battle scars to prove it. I'm a principal consultant there, which basically means I, you know, the senior lead on most of the projects I'm involved with, you know, so I will go in and talk to data management executives at our clients and just look at ways that they can come up with better strategies, better policies on how to manage their data. I also act as a subject matter expert internally, especially in DCAM and CDMC. And since that's now the second time I've used those words, I will say that for anyone who's unfamiliar, those are both industry standard frameworks created by the Enterprise Data Management Council. I've been a major contributor to them. DCAM is the data management capability assessment model, which is a framework that is about really best practices in how to manage data. And then the CDMC or cloud data management capability framework is a more recent extension to that, which builds on framework and builds on the DCAM framework and then looks at the kinds of things that you need to do additionally to manage data effectively in the cloud. I like that. So, David, tell me when you were very young, is this what you dreamed of being when you grew up? I'm going to be a data management consultant. Not even close. What do you want to be? I mean, in the interest of embarrassing myself or not embarrassing myself too much, I'll skip over the parts where at the age of 10, I really wanted to be James Bond and at the age of 16 when I wanted to be the next chief Emerson. But the second one is a little closer to the truth. I actually spent most of my, well, pretty much all of my academic career and most of my early adulthood studying classical music composition. I always assumed I'd end up teaching music at some kind of prestigious institution and that I would basically make my life about teaching people about classical music. As it turns out, I got my doctorate from Princeton in 1985. And in 1985, really, nobody wanted to talk to you unless you had an MBA. It was certainly next to impossible to find teaching positions in the arts. And so at that point, I had done a lot of work with computer analysis and computer modeling of music as part of my dissertation work at Princeton. And AT&T had just been broken up a few months before I got out of Princeton. They were desperate for anybody who even knew how to turn on a computer. And I ended up looking up with another Princeton grad who was doing a lot of consulting work there. And so for nine years, almost all my time was spent rewriting old systems for AT&T. That's a pretty dramatic change. It really is. Yeah. But, you know, I have to tell you, when I first started to try to talk my way into computer positions, and I realized that academics career was really not in the cards short term. I liked the computer work. I figured, let's see how I can get my way into that. But this was also pre.com and people don't remember that. I mean, once.com hit in the early nineties, if anybody remembers the old IBM PCs, they had that big red switch to turn it on and off. And we used to say, if you knew what the big red switch did, you could get higher. But five years before that, a lot of the big companies, I mean, I'd go to places like IBM and they would tell me, you don't have a computer science degree. You can't possibly work here. So when I hooked up with this other guy from Princeton, we started talking about things. And I'll be honest, I literally did not know what a database was at that point. But then when we started to talk about it, I realized, well, what's a database?   So I liked playing around with computers. Once I made that kind of connection, it seemed even more relevant. It certainly paid a heck of a lot more than I was going to make at an entry level teaching position. And so I figured I'd try it out for a few years. And then once I bought my first house, I couldn't afford to take a teaching position anymore. So the rest is history, as they say. You know, I don't think so. A lot of people understand how much math and science is involved in music. And, you know, and and that's I love how music brought you to technology. Absolutely. And it's not just the math and science. I mean, it's the general thinking of structure. And one of the things that I like to think has always been a little bit out of the box thinking about data for me is one of the things you deal a lot with in music, especially classical music is if you know, if you know anything about fractals, patterns that repeat on larger and larger and larger scales or going in the other direction are embedded at finer and finer and finer levels, that's very much a characteristic of a lot of classical music. But it's also very much about the way a lot of data structures work. I mean, the kinds of things that you would do to define an individual data element are in many respects expandable to look at a model of how you would build a table. And then from there, how you would build a database. And from there, how you would build a system of databases. And then you start coming out of the technical or architectural part of it. And it really begins to have parallels in how you build the business side of the data management structure. So I have always said that, you know, at some points in my life, I've been closer and more involved in music than others. But even when I've not been actively doing music, a lot of musical thinking always informs the way I think. I love it. So you're at AT&T and then where do you go from there? Well, from there, you know, I start I was in there under the ages of a very small consulting firm. And because we were so small, I had to wear a lot of hats, you know. So I was I was the programmer. I was the database administrator. I was the system architect more significantly for my longer term career. I was also the person who could actually talk to the client. I was equally comfortable talking to people on the business side. And the technology side so I could bridge that gap. And that stood me in really good stead. I bounced around from one part of AT&T to another for almost 10 years. Finally, didn't actually like where my consulting company was going at that point. So left there, moved into a completely different line of business. I'm still doing, you know, system development work. But this time for Church and Dwight, who are better known as the parent company of Arm and Hammer, the baking soda people. Although these days sell a lot more than baking soda. And, you know, and there, you know, I kind of hit a glass feeling there and ended up going over to Merrill Lynch. Actually, no, I'm sorry. Before Merrill Lynch was Morgan Stanley. And, you know, I was there through a consulting company, but it was a consulting company that specialized and worked with Morgan Stanley. So I got very, very immersed, not only moving more and more on the business side, but specifically into financial services. And by the time I left there, I was kind of really very much branded as a financial services guy and also still kind of primarily on the technical side. But from there, I moved over to Merrill Lynch, which was in the process of being gobbled up by Bank of America. And shortly after starting there, I completely abandoned the technical side of things and got involved with the enterprise level group that was writing the policies, writing the strategies, writing the standards for our first data management program. Fascinating. That's interesting. Definitely a lot of data in financial institutions. Absolutely. And the thing is it's not just one of the things that I always get reminded of when I go to a data diversity event and talk to people who are not in financial services. If I sometimes forget just how heavily regulated it is, you know, so we have a different set of challenges there. I mean, in a financial environment, very often you have very stringent regulations that you have to comply with. You have federal regulators that you have to keep happy. The good side of it is because there are huge fines involved in not complying with those things, you don't very often have to justify yourself to management. They know if we don't do such and such, we're going to be paying. It finds that in some cases, a literally amount to hundreds of thousands, if not millions of dollars a day or a week, depending on the degree of infraction. And so it becomes, you know, when you start getting out there, like I say, I spent time not so much in data management when I was at church in Dwight, we didn't have any of those kinds of regulations. So it became much more the internal people skills became much more important. And it became much more a case of talking to people about why this is a good idea, you know, just from in terms of benefits to our company, you couldn't use that stick of it's going to cost us a bundle and fines if we don't do it. So it's so then leading into your consulting role, you know, what is it? How are you helping your clients with data? What's the thing that you're you're working on the most? And that's the most critical for your clients. And we we our clients tend to come in two very distinct tiers. We have a lot of regional usually banks, regional banks who are starting to grow very rapidly and just all of a sudden realizing the way we have been doing things just doesn't scale. So in those cases, we very often go in and it's like, OK, let's think strategically about this. Do you have a data management strategy? No, what's that? We have gone in. We have actually written the drafts of data management strategy for them. In other cases, we've looked at what they have is the beginnings of them and help them shape it into something that will actually give them a solid strategic direction. Beyond that, we also build out policies with them. We build out operating models. We sometimes actually get down to helping them write their initial standards. And those are all things I mean, we do, in a sense, even if they've got a chief data officer, we essentially act as a kind of chief data officer or chief data officer advocate for what kinds of things need to be put into place around the company. We also the other extreme work with a lot of really large global and probably indiscreet for me to name the names. But let's just say we've got a lot of clients that you've heard of, you know, and because we're based both in the US and London, we have a lot of our clients are based in the EU, a lot of them are here. And also, as it turns out, Australia is really kind of getting religion about data management. We're doing a lot of work in Australia right now. There's a lot of new regulations coming out there. And with those larger companies, very often what we will do is we come in, and I mentioned VCAM earlier as a framework. It's also got a scoring mechanism involved with it. It's a very effective assessment model of what are you set up to do. We are frequently brought into these large organizations to do VCAM assessments of how established various parts of their data management activities are. Very often we find gaps and there, no matter how long they've been doing data management, it turns out it's like we never really thought about that aspect of data architecture. Or we never really thought about centralizing such and such a kind of activity. I mean, there's a thousand different things that could show up in an assessment like that. And so at that point, we then start working with them. And these are usually companies that have been doing data management on a large scale for years, if not decades. So they have established strategies and policies, but we will then go in and start doing deep dives with them and look at, okay, what needs to change? What is it that has been working and is no longer working? We had a client last year. We were doing a lot of work. They were really starting to make a strong move into the cloud. And that was creating a lot of problems for them. And so really, it's just kind of looking at that. Like, what are all the pieces you need to get in place in order to make everything function as a unified whole? And we go in there. We do use these EVM council frameworks, DKAM and CDMC as a starting point. But as I said, most of us in Orteca have also spent a whole lot of years actually running organizations. So we have a lot of industry experience, which is further augmented by the various clients we've had around the world. And we just, we can go in and talk to people about, this isn't just a good idea. We can show you real world use cases. Even if they're anonymized, we can really show you. It's like, these are the various companies that have had problems with this. And we work with big banks that had this problem. And we work with small consumer firms that had this problem. And this is the way they dealt with it. And here's how that looks at those companies six months later, a year later, two years later. So that gives us, I like to think it gives us a lot of street cred in terms of what we bring. It's not, we don't go in there just with an academic model of how things should look. It's really a whole lot of experience in terms of what we know does. That's fantastic. So with that experience, what is your definition of data? Oh, you gave me a heads up. You were going to ask me about this one. And I don't know if I think I could probably spend a good hour talking about a really brilliant and insightful definition of it. But most fundamentally, it's like, when I think of data, it's really just any kind of measurement or description of some kind of concept or some kind of action or some kind of thing in the real world. And I think it's distinct from information. In fact, a lot of what I do as a consultant is help people turn data into useful actionable information. I mean, the data is just out there. And at a fundamental level, it's very important to make sure that you understand it. There is agreement in terms of what a given term means across the organization. And in terms of how you get to that, do you have a single source of it? Or is that same piece of data replicated in numerous places around the company? So there's a lot of things that go into just making sure that the data, whatever it's describing, whatever it's measuring, just making sure that it's consistent in terms of its appearance and what numbers and text are. But also consistent in terms of what people understand it to be. And then, of course, the next step is taking it up into coming up with the ways in which you can ensure everybody does have that common understanding, not just at the individual data element level, but also in terms of what does it mean in any given context. And that's where you start to bridge the gap into the data becoming information and the information being actionable. And your organization's leadership actually being able to look at reports and things and analytics that come out of that data and information and actually trust that it's something that they can make decisions based upon. Makes sense. It makes a lot of sense. So 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? Oh, I think it's definitely increasing. But I also think the nature of those jobs is going to be changing fairly dramatically, certainly on the technology side. I mean, and not necessarily in the ways that people immediately think. I mean, yes, there's constantly new programming languages come out. There's new kinds of paradigms for programming. But ultimately, programming is programming. I mean, yes, I mean, back in the days when I used to write in a simply language in Fortran, we had to specify a heck of a lot more detail than modern languages require. But ultimately, it boils down to being able to describe something in individual discrete steps and then describe that in a way that works for whatever kind of technical platform you're working upon. The one thing, you know, so those jobs, I think will change in details, but not necessarily fundamentally. I think there are some major kinds of ships, though often technology side, artificial intelligence and machine learning. You know, we've been talking about those terms for actually in some cases for decades, but it's now coming to a degree of fruition that most people can't even imagine. Folks who aren't in this field have no idea how often they are interacting with some kind of AI.  I mean, that's been around for ages since the earliest days of Amazon. You know, but then you've also got more subtle things. I mean, especially in the advertising world, Google is famous or perhaps infamous for the fact that they gather information about what you're doing all over the web and start to make predictions about what products are you going to buy? What kind of news stories do you want to read? And, you know, they start to try to predict things based on your past behavior. There's an awful lot happening in that space. Now, I mean, very, very recently, I mean, literally within the last couple of months, at least in the broader literature, you're starting to see a huge focus on intelligent chatbots, you know? And we've already probably all have some experience the various kinds of voice systems that we interact with when we call the airlines to get help with our reservation or, you know, when we try to get directed to the right person at a bank or at a utility company. You know, some of those have been around in very crude ways for ages where they just sort of some poor voice recognition and then they just branch down a tree, possible paths you can take. But now we're seeing true artificial intelligence in there where they really parse and try to understand what you're saying and then really don't know where they're going to next. It's all sort of determined dynamically. There's a lot of new kind of work being done there. I think just as some of those chatbots are going to be taking away a certain kind of job, you know, building them is going to take a much greater degree of education and a lot more new kinds of jobs. I think one of the things I mean, we're starting to talk about it in Orteca right now we're seeing these various kinds of AI agents out there that will write code for you, that will write articles for you, that will post your LinkedIn posts for you. And it creates not just jobs, I think around how do you train those bots so they're not just spitting out a bunch of ignorant boilerplate text, but also that it really starts to ideally you would want to see those things have an understanding of data ethics, of data privacy. A lot of these things require a degree of processing power that almost demands they sit out in the cloud, but especially if you're in a highly secure industry like healthcare or finance. These are companies that aren't going to feel too good in general about data going out into the cloud without even with all the guarantees that the cloud gives you. What do we know about some of these newer bots? I was on a meeting just earlier this morning one of my guys is doing a study of this. He now has a list of 47 different intelligent chat bots that will do everything from actually chat with you to write articles for you. And he's doing a deep dive on them from the standpoint of how much can we trust these various players. So I think there's a whole series of jobs that are going to be growing in that domain. I mean, not just in how you program them, but how you govern them and data ethics, I think becomes a big piece of that. Very often we govern things from the standpoint of what we can legally do. But what we can ethically do, there's some things that are legal, but I like to say about ethics, it's the sort of stuff, if your customer knew you were doing that, how happy would they be about that? And it's still, I think there's a lot of spotlight being shined on that lightly, but I also think that there's a lot more work that needs to be done, frankly, especially in the U.S., in integrating data ethics oversight into AI and ML in particular. And I don't want this to become a seminar on ethics in AI and ML, but I mean, on the ML side in particular, as you start to build analytical models and you look at the data that you're using to train your models, I mean, that oven in itself brings up a whole realm of things that you need to address in terms of understanding bias and what's the ethical use of what comes out of those things. Now that much said, I mean, those are things that are somewhat on the technical side, somewhat on the governance side, but I don't want people to lose track of the fact that there's still a lot of real fundamental kind of things that we will always need people in there guiding us on. We can automate a lot of things about data governance, but ultimately we have to describe how we want things to govern. We can basic processes, we can define stuff as processes and then automate them, but if we've got garbage processes defined, then all automation is going to do is give us a lot more garbage at a lot faster rate. And so I think there's also going to continue to be a role for, especially at senior management levels, the people who truly understand the ethical as well as the legal ramifications of what we are doing in any of these fields and then ensuring that any kind of automation in addition to any kind of manual processes around those things are in line with whatever are, I mean, really, everybody should have a statement of data ethics. And so they know, it's like, well, okay, we can get away with this, but do we want to get away with this? Is this who we are? Is this the face we want to present to the public? Is this what we want our customers to know us as? Ready to share your knowledge and network with your data peers? Join us in San Diego this June for the Data Governance and Information Quality Conference. Five days packed full of new perspectives, new colleagues, and new approaches are yours when you register at dgiq2023west.dativersity.net. Lock in early bird savings when you register by May 5th. We'll see you there. It's very, very appropriate and right. We've seen a lot of companies who have stood up machine learning and stood it up on data that was not prepped and did not contain quality and it went horribly all right. Exactly, exactly. Yeah, and another thing I want to say about that, I mean, it's almost why I think things like psychology become, you know, as an undergraduate, I primarily studied music. I also studied a lot of English literature and psychology and physics for that matter, which are three other fields I also continue to dabble in. But I think the psychology part of it starts to become very important as well. And in fact, it's something that I would actually think anybody who's serious about data management in the future should have at least some kind of psychology involved because you never know how people are going to react. You need to understand that. But another piece of it coming back to the question of how you're training your models is that there's always going to be bias. I mean, we talk about how do we mitigate for bias in building out an analytics model. But the truth of the matter is you can never get rid of it. All you can really do is train yourself on becoming hypersensitive to the assumptions that we make. Because really when you get right down to it, that's all bias is. But be aware of where we're making assumptions and recognize them as assumptions rather than as just the way it is. If we had had machine language or machine learning rather 50, 60 years ago, it would be typical to train certain kinds of job growth algorithms on the assumption that women are only going to do a certain kind of work and only if they have the appropriate mail oversight. Nowadays, and rightly so, we bristle at that. But that was just built into the fabric of how people thought. It wasn't even a bias. It's just the way things were. And I would posit that 50, 60 years on, it may not be that, but we have equivalent things that 50 years from now will be every bit as appalling when we look back on it. And the more that we can actually understand the human element of what goes into data management, the more we can actually start to come up with solutions that are fair, equitable, ethical. And basically, we can actually make the machine's behavior better than we as people ever did. For people just getting into data and thinking about these rules that you've been talking about that are going to become more available as time goes on, what advice would you give to those people? Is it continued just to make sure you understand the human elements? Are there classes? Are there books? Or is it what additional advice would you give? Well, I got to tell you, I have a kind of unique, well, I like to think it's not that unique, but when I read these articles and I see them everywhere from like local newspapers to the Wall Street Journal about things that basically say, these are the degrees that will make you the most money. If you want a high paying job, this is what you should study in school. It's like, no, I have always, that to me makes sense if you're going to a trade school.  You go to college to learn how to think. And to me, one of the best ways to learn to think is study something about what you're passionate. I like to think I'm pretty good about what I do in data management. And I'm doing it all having an undergraduate and three distinct graduate degrees in music. And it was something that I have always been passionate about and still passionate about. And so I would spend hours and hours and hours digging down there, learning how things were taking stuff apart at the smallest detail until it got to the point where I had that almost automatic recognition of things and see how they replicated on larger scales. So, I mean, I don't know is advanced basket weaving great training for data management. It might be in somebody's cave. But really, I think that is the key. It's like, go to school to learn how to think. And yes, there are certain kinds of things, especially if you don't aspire to advanced graduate work. Yeah, there are some basic kind of skills you're going to need. Even if you're not a program, there's basic programming concepts you have to understand. You've got to know some basic stuff about how data is structured. You have to understand concepts like data quality and data architecture. Not necessarily well enough to do them, but even if you're going into the business side of data management, you really need to be able to talk to those people. So you do need exposure to that. But I think fundamentally study the things that will teach you to think. And I think more to the point, build the muscle. I mean, I always thought that most schools should have a course called BS 101. You want that ability. Some people, I'm appalled at what I sometimes see fundamentally intelligent people accepting a face value. It's like, you really need to start asking the questions. I had an automatic level. It's like, to the point where it's so fast and automatic, you don't even realize you're asking the questions. But it's like, who's saying this? What's their vested interest? What are their sources? Why are they saying it now in this particular form? All those kinds of contextual things. I mean, don't accept it as fact until you've really done the research. Ask the questions, push back. If you're making assumptions, be where you're making assumptions. We're back to that again. But as much as you can, don't make the assumptions. Do the research, find out what you can. Use things like AI and ML to do the heavy lifting on things. But don't believe everything that you see. I mean, it's become kind of a joke that it's like, well, it must be true. I saw it on the internet. But don't forget, not that many years ago, people used to think that about TV. It's got to be true. They said it on the news. And it's like, even more so if they said it on CBS or NBC or one of the big networks. Now, you could trust it better back in the days of Walter Cronkite, but now I'm really dating myself. But the thing is, even then, it was like, we trusted Walter Cronkite back in the 60s. And I will say, I'm just barely old enough to remember that. But he was trusted because the thing is like, even if he was saying something that seemed incredible in the moment, we knew from his track record that there was a very high probability that he was giving you an objective, fact-based report on something. You know, and I think as a population overall, we have lost a lot of that ability. We are too far too likely to latch on to the opinions that are the same as ours and to automatically reject the things we don't want to hear. And I know this, and this is an obvious conversation that goes way beyond data management. But to bring it back to that topic, I think these are the kinds of things you need to get trained in. I think college is an excellent place to do that because not only is it a place of learning through classroom activities, it's a place of learning. It's one of the reasons why I personally am very down on remote college attendance. Because to me, I think of all the arguments and the heated discussions and drinking coffee at three in the morning while trying to debate such and such a point with other graduate students. You know, whether I read with them or I didn't, I mean that kind of passionate level of inquiry was something that came out of it. And it's when you become highly immersed in that degree of inquiry, when you can be that critical, when you know the questions to ask. I think those are all things that ultimately train you to have not just an entry level position and data management. But I think those are the kinds of things that ultimately have you leading. If it's in your career path or your desired career path, you want to run a really big kind of company and manage data for some international firm. I mean, that's what gives you the kinds of skills you need to come up with. Makes sense. And just kind of paraphrase and summarize. And I'm hearing this from a lot of data practitioners is be curious, ask a lot of questions. And then you're adding to that and saying, basically build context around the data that you're receiving. Data with context, so important. And that's really great advice. I know law schools have had courses in ethics. Of course, they've had to for so many generations now, right? But maybe we do need that additional. We have a lot of it that topic on our conferences now talking about data ethics. But that maybe needs to be more prominent in a lot more education resources. Yeah, I completely agree. I mean, just given the nature of the data diversity event, I mean, specifically data ethics. Because I mean, yes, almost any large firm has mandatory behavioral ethics. You don't accept gifts over a certain value. In some companies and industries, you don't accept gifts of any value from a client or a potential client. You do the things that avoid even the appearance of conflict that interests. But there are all those behavioral ethical things. And that's very often the kind of ethics that get taught in law schools. But data ethics really boils down to it's like, what data am I collecting on you? What am I disclosing to you about how I'm using that? How am I using that? And not only just kind of like, what kind of reports am I running on your data? What kind of things am I trying to infer from your data? But how am I acting on that? Am I selling that data to other companies? Or am I only using it for my internal use? What are the internal uses? And so I think there are, so even narrowing it down specifically to data ethics, we very often deal with large firms that have a code of ethics in place. And they govern in compliance with that. But you say, okay, well, what about data ethics? And they go, okay, well, what's that? You know, they haven't really thought. I mean, in a modern society, that is every bit as critical as behavioral ethics, so much so that some of the people I talked to, it's actually hard for them to think of them differently. It's like, well, I wouldn't do that because that violates this behavioral ethics. Yeah, but let's look at the ways we actually put guardrails in place to prevent people who aren't thinking as carefully about it from accidentally stumbling into these kinds of things. Absolutely, and then, and the other thing that I heard you say is be passionate about what you're doing. And that's a common theme as well. So don't just do it for the sake of doing it, but be passionate about it. Love what you're doing. Exactly, yeah, yeah. So important, yeah. Years ago, I did, I was on a consulting job with somebody from another firm who told me, I've forgotten exactly how she put it, but she, prior to becoming a consultant, she had worked in a company where she had a small team reporting to her. And she told me, I hated passionate people. All they did was argue. And I get it, you know.  I had a boss there for most of the time I was there, who we, a lot of what we did was write standards and policies and internal white papers. And we would review each other's work. And then like debate, like every single thing in there that we disagreed with. But we would never stop the discussion until the other person legitimately came around to the way of thinking. And the thing is, even though he was my boss, you know, he never once said, shut up, this is the way it is. It's like it was either he was convinced of my point or I became convinced of his point. And it happened about 50-50, you know, and they were always really, really fruitful kinds of discussions that not only led us to produce higher quality work, but I think really gave us both broader understandings of the environment in which we worked. As the other piece of your advice, keep learning, right? Keep thinking, learn how to think and keep thinking. And yeah. Yeah. So which you can't do that without debate. And learning from others. Yeah. And I think and that loving to learn is, I think that's something that's critical because we are in a time when so much of our industry is changing at, I mean, sometimes almost. I mean, it doesn't happen every day, but it's not uncommon every year, a couple of years for something to happen that so drastically alters it. Who, I mean, what was Zoom in January of 2000? Our 25th anniversary. You know, it's like, yeah, people knew about it. It was just like, it was an alternative to Skype. I mean, when your friends moved to London, it was how you called them because it was cheaper than using the phone. Who would have thought that it would become so part and partial to how we operate as a business? And then as data management people and data governance people, it's like, how long did it take us then to start to realize it's like, oh, wait a minute, this is essentially data sharing. How do we control what gets said on a Zoom meeting? How do we know if somebody is recording a transcript or recording the whole thing? That's when we started to see the various kinds of warnings pop up, not just in Zoom, but in the other video conferencing tools. So there are these game changers that come about at an ever increasingly rapid rate. And those of us who aren't willing to learn about the paradigm shifts and to learn about, you don't have to learn about the new technologies to the level that you can program them, but you've got to be aware of what they offer and what the ramifications abuse of them are. And so that lifelong passion for learning, I think stands anybody in very good stead for a career in data management. That's great advice, David. Ah, I love it. So important, such important concepts. And I'm hearing that a lot from a lot of different people. And it's, which is really good. It's fun to see the patterns of all from these interviews because I do love data as well. So if somebody were to want to reach out to you or get involved with Orteca, how would they go about doing that? Um, yeah, um, well, I don't expect anybody to write it right down and I don't have it printed out to show you, but david.coalski at Orteca.com will reach me. You can also find me on LinkedIn. There are a bunch of other David Koalski's there, but I'm the one, I never remember the format of that personalized address they give you on LinkedIn, but it's whatever it is, LinkedIn.com slash in, I think. And then, but the last part of it is David Alcoalski. And we'll get that posted to our page as well. So make sure people can, can reach you if they, if they want to solicit your services. Yeah. And I'm always interested in talking to people about this, you know, whether it's somebody who wants to work with Orteca, work for Orteca, or, you know, just chat about new concepts. You know, I, you know, A, because I love learning about it, and B, it is part of my job, but I just am always interested to hear about emerging trends. You know, and not just trend, I'm interested in the technology trends, but you know, I'm also very interested in what kind of business drivers are emerging. You know, we are seeing a lot of businesses that are now being forced to align, even if it's not regulatory pressures.  I mean, the U.S., I'm embarrassed to say, is woefully behind the curve in terms of having, you know, a good data protection act at the international level. And unfortunately, that means we're now getting into this situation where, you know, one by one, the states are writing their own. And so far, it's, well, this one's a little bit stricter than this, and this one, you know, lets you get away with such and such, and these two are almost the same except for this. But eventually, if that continues, we will get to the point where this one says do X, and this one says do Y, and you can't do them both because they're mutually contradictory. And at that point, it literally becomes impossible for certain companies to operate in different parts of the United States. And I know there have been efforts to come up with a national standard and still not going anywhere really substantive yet, but, you know, those are kinds of things. I mean, to some respect, I always find people who are tied directly into federal government or state governments, for that matter, but especially at federal government levels. You know, I'm always very interested to find out what kinds of things are happening there because ultimately that impacts the businesses and the businesses, you know, are what it's really driving how data gets managed in any particular firm and even across any particular industry. Absolutely. Very, very important. And I agree that I hope it unifies a little bit here soon. It would make my life a lot easier. Yeah. Well, David, thank you so much for joining us today. I really appreciate the time. It's really been an enjoyable conversation. And for all of our listeners out there, if you'd like to keep up to date on the latest podcast and the latest in data management education, you may go to dataversity.net forward slash subscribe. Until next time. Thank you for listening to Dataversity Talks brought to you by Dataversity. Subscribe to our newsletter for podcast updates and information about our free educational articles, blogs, and webinars at dataversity.net forward slash subscribe.


U.S. quadrupling number of troops in Taiwan. Washington is planning to send between 100 and 200 troops to Taiwan in the coming months. The Wall Street Journal reported citing anonymous officials. The personnel will be tasked with training the Taiwanese military against what was described as a rising threat from China. The deployment will more than quadruple the U.S. military presence on the island. According to the Pentagon's Defense Manpower Data Center, there were 23 American soldiers in Taiwan as of autumn 2022. These Americans have been acting as advisors and trainers for the U.S. weapons and equipment sent to Taipei. The new troops will also train the Taiwanese in tactics to protect against a potential Chinese offensive, according to unnamed U.S. officials. There were also Taiwanese troops in the U.S. with a contingent training alongside the Michigan National Guard in the north of that state. When reached for comment, the Pentagon said only that the U.S. commitment to Taiwan is rock solid. We don't have a comment on specific operations, engagements or training, but I would highlight that our support for and defense relationship with Taiwan remains aligned against the current threat posed by the People's Republic of China. Army Lieutenant Colonel Marty Meeners, a Pentagon spokesman, told. The expanded training is intended to thwart a possible invasion by China and has been planned for months, long before this month's incident with the Chinese spy balloon officials who spoke to the journal said. They also don't believe it amounts to anywhere close to a tipping point for Beijing. One of the difficult things to determine is what really is objectionable to China, one official said. China has repeatedly warned the U.S. against arming Taiwan and warned Washington to abide by the treaties and agreements governing their relationship. The U.S. used to recognize Taipei as the Republic of China, but switched that recognition to Beijing in 1978, adopting a one-China policy.


Barter, in trade, barter derived from barator is a system of exchange where participants in a transaction directly exchange goods or services for other goods or services without using the medium of exchange, such as money. Economists distinguish barter from gift economies in many ways. Barter, for example, features immediate reciprocal exchange, not delayed in time. Barter usually takes place on a bilateral basis, but may be multilateral i.e., mediated through a trade exchange. In most developed countries, barter usually only exists parallel to monetary systems to a very limited extent. It's actors use barter as replacement for money as the method of exchange in times of monetary crisis, such as when currency becomes unstable e.g., hyperinflation or a deflationary spiral are simply unavailable for conducting commerce. Economists since the times of Adam Smith 1723-1790 looking at non-specific pre-modern societies as examples, have used the inefficiency of barter to explain the emergence of money, of the economy, and hence of the discipline of economics itself. However, ethnographic studies have shown that no present or past society has used barter without any other medium of exchange or measurement. Barter anthropologists found evidence that money emerged from barter, instead finding that gift giving credit extended on a personal basis with an inter-personal balance maintained over the long term was the most usual means of exchange of goods and services.


Now, as I've mentioned in the past, this is any time I access any node. So what happens if instead of doing an insertion, I came in and I did a lookup on, I don't know, 50. So, just like a normal binary search tree, I would navigate sort of my tree, and once I saw 50, ah-ha-ha-ha, it needs to now become the new root. Well, we happen to go through those same rules. We see if it is a zig, a zig-zig, or a zig-zag. Again, zig-zig, or zig has no grandparent, and that's clearly not the case, because I see that I have an x, a y, and a z, so I have a grandparent. It's not a zig-zig because the relationship between the grandparent and parent, and the relationship between the parent and our kind of accessed node, are not the same. In this case, y is the right child of z, and x is the left child of y. So we know it's not this one, we know it's not this one, so that only leaves us with zig-zag. Now, if we are kind of going through those rules of what I do, since I've seen I got a zig-zag, that means I do my trinode restructuring, restructuring. And so, as always, we have to find out my a, my b, and my c. As always, once again that is in order, which means I look at my left child first, then I get to look at myself, and finally I get to look at my right child. So in our case, I need to access my left child first, but it's not being kind of classified if I'm looking at sort of only these three. I don't care about 25 right now, it's not in the three I'm dealing with. So if I skip over my left node in our trinode restructuring, that's fine. That means z happens to become my a. As always, I don't go immediately to y, because we see that y has a left child. As always, the in-order traversal is that I go to my left child, then I get to go to myself. So I see I've gone to myself, I went to my right child, I see that that right child has a left, so I go to that left child. I see I have no left child, so I get to go to myself, so b becomes x. There is no right child, so I can kind of come up the tree, and I see that, well, now that I'm done dealing with my left child or children, potential children, I can finally be myself. As always, once again, b is going to become the new parent with c and a being the children. So if we were to take this approach, we were to take that look up 50, what we should see happen here is that, again, my x becomes my new root, so 50 still for here. So what about my z? Well my z is going to become my new a, or my new left node, and my y is going to become my new right node. So what happens to sort of 25? Well, as we've said in the past, that's classified as t1, and we have seen in previous videos that t1 is going to become the left child of whatever a was. And so, now that we've done our look up on 50, we happen to notice that we are now set up in a, we've moved it to our root.


Ladies and gentlemen, this is Jimmy Wallington. Owing to the illness of Mr. Dick Powell, the motion picture story Mrs. Mike will not be heard on Screen Directors Playhouse tonight as scheduled. However, we are proud to bring you two of Hollywood's most brilliant stars who were to have been heard November 30th. Screen Directors Playhouse, stars Dorothy McGuire, Charles Boyer, production Clooney Brown, director Ernst Lubitsch. This is the Screen Directors Playhouse, the Thursday night feature on NBC's five show festival of comedy, music, mystery and drama. Brought to you by RCA Victor, world leader in radio, first in recorded music, first in television. The makers of Anison for fast relief from pain of headache, neuritis and neuralgia. And by your local Ford dealer, who is displaying the new 1951 Ford, the car that's built for the years ahead. Ladies and gentlemen, before we present Clooney Brown, here's a word from RCA Victor. For my money, the most amazing creatures on earth today are 10-year-olds with television sets at their command. Presumably, while lying in wait for their favorite desperadoes, they casually become authorities on what bills are now before Congress, who won the Nobel prizes, where the UN forces are today and precisely what the electron finds so attractive in the proton. All let's face it, the television child at 10 knows more about how the world wags than his father knew at 20. Seriously, it's mighty important to give your children the incomparable advantage of education by television, not just someday, but now. Naturally, you'll want the incomparable set, America's favorite, already proven in over a million homes, an RCA Victor. See your RCA Victor dealer tomorrow and choose from 18 beautiful new million-proof models, the RCA Victor masterpiece, which will be not only the greatest amusement center any man ever knew, but the greatest classroom any man's children ever attended. Now, for the first act of Clooney Brown, but may I add that at the end of the program, there will be an announcement of importance to Hollywood and to motion picture audiences everywhere. Here now is Clooney Brown, starring Charles Boyer in his original role and Dorothy McGuire as Clooney. Hello. Is this Mr. Porritt or the plumber? Oh, I beg your pardoners. Is Mr. Porritt there? Yes, I know it's Sunday. My dear young lady, it's Sunday for me too. I like to rest on Sundays. Everybody likes to rest on Sundays, but it's the sink. It won't drain, and I've got 30 people coming for cocktails. No, no, I don't plan to serve them in the sink, but I'd like to have a glass washed when the party is over. Look, I've tried every plumber in London. One of them promised to come right over. That was two hours ago. But look here, I can't call up 30 guests and tell them my sink stinks. I certainly can't. I certainly can't, and I don't have to. Run along, Miss. Have a nice punt on the Thames. The plumber's at the door now. Congratulations. So long. Come in. Come in. Never been so happy to see anyone in my whole life. Right this way. Oh, thank you. I hate to spoil your Sunday like this, but with me, well, it's sink or swim. Come along. Thank you. Well, there it is. Oh, yes. Oh, yes, it is. Interesting. Very interesting. It is. Could there be anything more fascinating than a sink out of order? I've never thought of it in quite that way. In ordinary? Every day, commonplace kitchen sink. And yet the height of human frustration. Look at it. Look at it without prejudice, my dear sir. You start the day with an infinite capacity for living. Then somewhere along the line, your social and personal plumbing goes wrong. And the best of you ends in the sink. The best of your youth, the best of your hopes, the best of your efforts, pure waste, an ocean of defeated humanity. Believe me, I know a lot about sinks. Well, I should hope so. And your guests are arriving in a minute? Right.  Right. Well, then what you need is a plumber. Right. A what? Aren't you, I mean... Oh, no, I'm not now. No matter what you mean. No, I came here to see my old friend, Professor Lee. Professor Lee is in Scotland. I've sublet this apartment. Oh, what a mess. Well, I must say that's a mess for me, too. But what am I going to do?  What? Of course you are. Well, you don't even know me. And already you're not interested in me. Why don't you ask me why I want to see Professor Lee? Why don't you ask me about my landlady? Is she humane or does she want a rent? Do you know? Do you care? No. Have you even said a flick for my guests? My dear sir, is there something I can do for you? Is there? Oh, thank heaven I misjudged you. Do you know Professor Lee Ames? Not very well. Ah, magnificent fellow. He would have said, Is there anything wrong, Billinsky? And of course I would have said no. No, nothing. But he would not have believed me. He had the most charming way of forcing twenty pounds on one. Made you feel you were doing him a favor. Remarkable fellow. Obviously. Well, I'm not precisely in the habit of forcing things on people, but... Oh, my dear Ames, this is kind of you. Twenty will be quite enough. Not at all. You can stay for cocktails. Oh. Thank the Lord. I mean, thank the Lord if that isn't a guest. I'm sure that's the plumber. Excuse me. Hello. Hello. Well, shall we have a go at it? Are you sure you have the right apartment? Well, it's the right number. Your Mr. Ames, all right.  You're the gentleman who phoned, all right? You, a plumber? Oh, no, sir. But I've been around pipes and sewers and faucets and things ever since I came to live with my uncle on. Mr. Porat, that is. You called him. And I talked to you? Yes, sir. But I've watched him work a lot, and he's a good plumber, too. But if you ask me, he's much too conservative. Conservative? Yes, although he votes labor. But when it comes to pipes, he takes the long road. He fiddles and fiddles, and it turns a knot, gets a drop here and a drip there when one good bang might turn the trick and a jiffy. Yes, and might smash the pipe to smithereens, too. Now, why don't you let me take a whack at it? Oh, no, you don't. No amateur is going to put my house underwater. Oh, my dear Ames. Where is the gypsy in you? Where is your sense of adventure? Are you the sort of man who puts on his pants before he answers the telephone? What if it does go wrong? What if the whole place gets flooded and there is no party?  Is that bad? But if this girl succeeds... Please do let me try. No, come on, Lene, it's in here. My! Isn't it a beauty? What a congestion. It is stuffed up, isn't it? I never thought it would be as good as this. Oh, I can't thank you enough, Mr. Ames. Don't mention it. Well, enough talk. Let's get to work. Good day, gentlemen. It's been a very pleasant chat. I'll see you when I come up. Here we go. Who's promising? Did you say tea at the rich? Yes, I have a system, you know. And all at once I said to myself, don't note me a stocking. Why don't you have tea at the rich? And so I did. Come over me. She sacks oranges. Tones up the system. Was it a good tea? What? I said, was it a good tea? Oh, it wasn't a tea. But to hear them say it this way, Miss. Please, Miss. Crumpets, Miss. You can hold in my chair for me. You never would have thought for a minute I was out of place. Miss... Clooney. Clooney, tell me. What made you think you were out of place at the rich? I didn't. It was Uncle Arn. He's always in forever telling me, Clooney Brown, you don't know your place. Clooney Brown, you ought to learn your place. And where does Uncle Arn think your place is? He didn't say. Because he doesn't know. Who can tell you where your place is? Where's my place? Where's anybody's place? I'll tell you where it is. Wherever you're happy, that's your place. And happiness is something that only you can identify. You're the sole judge. Now, look, Clooney. In Hyde Park, for instance, some people like to feed nuts to the squirrels. But if it makes you happy to feed squirrels to the nuts, who am I to say nuts to the squirrels? You might have said that all over again. I say in Hyde... Look, you two, I don't like to intrude, but do you realize what time it is? You see, my dear Ames, faith moves mountains and sinks. Wonderful. But I think a toast is in order, Mr. Ames. A toast to the new master plumber. And he as good as new sink. Perfect. We'll test the drinks for the guests. Come on, Miss Brown. Today this has been for me. My first sink and my first cocktail. A martini cocktail with an olive. Have some more. Should she? Definitely. Oh, yes. Thank you. I feel absolutely lovely. I can't quite describe it. I feel, uh, chirripy. Chirripy? I don't recall ever feeling chirripy. I'm afraid you never will, Mr. Ames. I'm sure there isn't a chirrap in you. No, isn't that funny? Now I feel entirely different. I know what it is. It's coming over me. That Persian cat feeling. I, I, I'm sorry. I, I, I don't mean to be inhospitable, but it's getting late. It's never too late for a cat. You know what, Bill? To be a cat? I can tell you. Your lies stretched out in bed, reading the wonderful travel log and the daily mail. And wanting to go places and wondering if you ever will.   It is so wonderful to be a cat and to read the daily mail. I knew it. I knew it. There they are. Would you mind, Miss Brown? Yes, what is it? Clooney Brown. What are you doing here? Uncle on. What does this mean? What are you doing here on that couch? I've been plumbing, Uncle on. There's something happening here I ought to know about. Well, I don't think so. Lucky I found this address written down or I might never have looked you in the face again. And I assure you, Uncle on... Name's Porritt. Mr. Porritt. Including you got liquor on your breath. Strong liquor. You don't know your place. You never will know your place. That's your thing. But Uncle on, what is my place? What's anybody's place? If you want to feed nuts to the squirrels, whom are I to say, do you? That settles it. You're going to service. And right now. Come along. I don't like to bring it up, Mr. Porritt, but I haven't paid your niece.  Keep it. Come on now, Clooney Brown. Goodbye, gentlemen. I've had a very pleasant stock. And thank you for everything. Meow. Today, many thousands of people are thankful to their physicians or dentists for first having introduced them to that remarkable preparation called anison, which brings such incredibly fast and effective relief from the pains of headaches, neuritis, and neuralgia. Anison is like a doctor's prescription. That is, anison contains not just one, but a combination of medically proven active ingredients in easy-to-take tablet form. Thus, in using anison, you are following sound principles. So ask for anison at your drug counter next time you suffer pains from headaches, neuritis, or neuralgia. Try these tablets on this guarantee. If you don't feel anison gives you all the relief you want, as fast as you want it, your money will be refunded. Easy-to-take anison tablets are available everywhere in handy boxes of 12 and 30 and economical family size bottles of 50 and 100. I'll repeat the name for you. Anison. A-N-A-C-I-N. Now back to the second act of the Screen Director's Playhouse production of Clooney Brown, starring Dorothy McGuire and Charles Boyer. I stayed for Mr. Aime's cocktail party, at least part of it, but when the noise rose to a tumult and the smoke began to form in clouds, I slipped quietly into a back bedroom, down on the bed and dosed on it. I don't know how long I'd been sleeping when I heard Kosh's voices whispering. I thought I heard my name. I tell you, it's Balinsky. Not Adam Balinsky. Yes, Adam Balinsky, the Czech. Oh, he's a great man. He's famous. Of course he is. He's a writer, Professor of Prague. That's why the Nazis are after him. He's probably just a jump ahead of them right now. Wonder how he got to London. The underground, no doubt. Oh, hello. Hello. We know who you are. You can trust us. I'm Andrew Carmel. I'm John Fruin. Oh, how do you do? We don't mean to pry, but you are in trouble, aren't you? Well, yes. As a matter of fact, unless a miracle happens, I'm a man without a home. Oh, that beast. That terrible beast of a Hitler. Well, I wonder if I've made myself clear. Oh, perfectly. But the time for talk is past. We must do something. I'm afraid you are a little confused. We know how busy you must be. But believe me, we wouldn't interfere. We would just provide you with a safe place to work. Out at Fryer's Carmel. With my mother and father. Sir Henry and Lady Carmel. Oh, well, don't you think you should know a little more about me? You are very kind, but I think you are being too impulsive. These are impulsive days, Professor. Someday the world will thank us for it. When I hope so, Andrew, I shall be happy to come with you. With my deepest gratitude. After all, as you say, my safe day is most important to me, too. When Lauren told me I was going into domestic service, he wasn't fooling. He found me a job at once. And before I knew what I was doing, I found myself getting instructed on how to be a parlor maid at the home of Lord and Lady Carmel. On my first night, I was sent into the dining room, where I have mutton. Professor, it's a pleasure to have you here. Oh, how very hospitable you are. But I don't feel I should accept before you know more about me. But Andrew has told us all about you. I'm glad you're, son Andrew. Oh, yes, yes, I'm rather fond of him myself. Sits his horse well. Then there is only one thing I can say. This land of such dear souls. This dear, dear land. This blessed plot. This hearth. This realm. This England. Well, a toast to Shakespeare. Oh, how charming are you, Professor. And how well you speak English. Rolled right out of him.  Come now, take some of the roast, dear. Brown. Brown, the roast.  Hmm? Well, this is a likely cut. What? Don't take that piece, sir. This one on the right is much better. What? Well, it hasn't so much fat. And it's browner and bigger. You won't regret it, sir. Oh, really? All right, all right. Thank you, sir. The roast, sir. Why? Thank you. Oh, no. Not you. Not you. Nuts to the squirrels. Go to the kitchen, Brown. At once. I am very sorry, my lady. Oh, gracious. It's preposterous. But it strikes me absolutely speechless. A maid choosing my mutton for me. She will be dismissed immediately, sir. No, no, no. One moment, sir, Henry. You took the piece, she suggested. May I ask why? Why? Because the other piece had a blob of fat on it. And this one is browner, leaner, and bigger. And you liked it better. Look, hang it all. It just isn't done. What a pity. It should have been done long ago. Does it occur to you that for generations, the lords of Carmel have probably eaten the wrong piece of mutton? That's a very interesting way of looking at it. Well, she needed to drop the platter and insulted my guest. What did she say to you? I remember very well, sir. It was if I may take the liberty of repeating it, nuts to the squirrels. Doesn't make sense. No, it doesn't. It should have been squirrels to the nuts. But I have an open mind. And if someone says to me, nuts to the squirrels, I accept it. You may be inclined to say it to me yourself someday when you know me better. And I'm not so sure you'll include the squirrels. Oh, that's too deep for me, Balinsky. If I may say so, your ladyship, the sooner the young woman is dismissed, the better. Please, please, Monsieur Syrette. I know that in the policies of the kitchen, the balance of power rests with you. I also know that as a guardian of English customs and traditions, these young women has offended your sensibilities. But permit me to quote someone to whom everything English was all so dear. The quality of mercy is not strained. It droppeth as the gentle rain from heaven. Well, another toast to Shakespeare. You've made a charming evening for us, Professor. I've never seen my husband more stimulated. Well, he is a very stimulating man himself. Yes, yes, he is. I hope you'll be comfortable. Syrette will valid you. Oh, I'm sorry, but dear Lady Carmel, I have so little to offer a valid. Oh, well, would you mind letting Syrette do it anyway so as not to hurt his feelings? Oh, oh, very well. I have this suit and Andrew's dinner jacket which you have offered to lend me. They are both at Syrette's disposal. Oh, thank you. I hope you sleep well, Professor. Oh, by the way, there's a nightingale under your window. Oh, you shouldn't have gone so much trouble. Good night, Professor. Good night, Lady Carmel. It's me, Clooney. Let me in. Oh, Clooney. Oh, I'm so sorry I upset you. Well, how do you do, Mr. Belensky? Oh, you shouldn't be here. But for heaven's sake, how did you get here? You friars carmel of all places. Tell me, what happened? Well, it's all Uncle Art. Oh, Mr. Belensky. Oh, no, no, Clooney. Look at me. I'm here too. And I haven't even gotten, Uncle. And after all, you're at least a maid. I'm only a guest. I don't want to be a maid. I'll go on and on, dropping platters, putting hot water bottles in the cold beds, and having Wednesday afternoon off in a village where the cinema opens only at night. So what about me, Clooney? I'm a city man. I like crowds and traffic and lights, smoke in my lungs. What have I got?  Oh, it's so good to talk to someone who's out of place too. Yes, yes, Clooney. Talk to me at any time. Open your heart to me. Ditto. Oh, Mr. Belensky. No, no, Clooney. Come into my arms. Oh, Mr. Belensky. Oh, I didn't mean... Oh, please forgive me, Mr. Belensky. I don't know what came over me. It isn't as if you were my type. Believe me, you aren't. Oh, I'm sure I'm not. I understand perfectly. You were just happy to find a friend here. And so am I. We must go on being friends. And as we are not our types, that should be easy. You know, we're just like two people on a desert island waiting for a ship to rescue us. That's right, Clooney. That's right. But you know how it is on a desert island? You wait and wait, then you don't wait anymore. Clooney Brown? Let's admit it. We're in danger. Today, we are not our types. But, you know, as time passes, we might not look so bad to each other. If we are in Fryer's carmel long enough, who knows? You might even find me possible. And I might find you the most beautiful creature in the whole county. It's not much of a county, but that's all we have. Mr. Belensky, this must never happen. You must never become a victim of my circumstances. And if you should ever seem romantic to me, don't hesitate. Just kick me. Yes, that's it. Let's kick it shorter. It's a pact. Oh, I feel so safe. Good. Well, now I'd better go. Yes, it would be terrible if they heard us. Who? Well, anybody, but especially Mr. Serretten, Mrs. Miley. Thank you for everything. Oh, I feel so good. So do I. I wish I were back in that apartment where we were. Oh, I wish I could roll up my sleeves and roll down my stockings and unloosen the joint. Bang, bang, bang. I know how you feel. Well, I'll go to my room now and let the nightingale bang me to sleep. Good night, Mrs. Miley. Good night. Well, Mrs. Miley. I agree with you, Mr. Serretten. What can one expect? A maid with no references and a foreigner who isn't even in the diplomatic service. Good night, Mr. Serretten. Good night, Mrs. Miley. Tony Brown. Oh, hello, Mr. Balanski. Well, look at you.  What's the occasion? Oh, don't you know? It's your birthday. It's my day off from three to seven. Oh, of course. Perfect. Perfect. No wonder I've always loved Wednesday. From three to seven. Four hours. All to ourselves. Two hundred and forty minutes. And if you think of it in seconds, I'll cancel all my engagements. In fact, I'd ignore them. Cluny, the village is ours. Mr. Balanski, it's awfully sweet of you, but I think I should tell you, something has happened. What? Well, you know Mrs. Miley suffers from rheumatism. You haven't caught it, have you? Oh, no. But you see, if Mrs. Miley hadn't sent me to the chemist shop for Petrie's liniment, I might never have met Mr. Wilson, the chemist. Oh, well, that's the way things happen. Think of it, Mrs. Miley's swollen knee might change my whole life. Is it that bad, Cluny? Well, I don't know. But what would you think of a gentleman in vited utility and a meter's mother, too? I wouldn't go. I've already accepted. And I'm certain I did the right thing. I'm sure you did. You know, Mr. Wilson's the only chemist round here for miles and miles. Oh, it's so exciting to meet a man who's surrounded by hundreds of bottles. And every one of them life and death. Mr. Wilson hinted that when we get better acquainted, he'd let me watch and make up a prescription. But this is confidential. Your secret would be buried with me. Well, Cluny, it looks as if your shave has come in. The bottle of beer I was going to offer you seems awfully flat beside all those bottles filled with magic. Well, I hope the magic would make you happy, Cluny. Very happy. Ladies and gentlemen, tomorrow morning, your Ford dealer will display the finest Ford ever produced. It's the new 1951 Ford that's built for the years ahead. Your Ford dealer wants you to see this great new car. When you do, you'll find that it offers 43 new look-ahead features.  Among them, you'll discover the new automatic ride control that automatically adjusts the ride to the road to give you a level ride, an easy ride. You'll find the automatic mileage maker that saves gas by squeezing the last mile out of every drop. You'll see new luxury lounge interiors with a wide choice of rich Ford craft upholstery fabrics in harmonizing colors. Visit your Ford dealer tomorrow and check all 43 look-ahead features. And you'll agree that you can pay more, but you can't buy better than the 51 Ford. You are listening to the Screen Directors Playhouse, one of five great radio shows that are brought to you by RCA Victor, where a leader in radio, first in recorded music, first in television, the Whitehall Pharmacal Company makers of Anison, Collinus, Bicidal, and other fine drug products, and by your local Ford dealer, who is displaying the new 1951 Ford, the car that's built for the years ahead. The Screen Directors Playhouse presentation of Clooney Brown, starring Charles Boyer and Dorothy McGuire, will continue after a short pause for station identification. Stay tuned to your local station on NBC. Now for the third act of the Screen Directors Playhouse production in Brown, starring Charles Boyer and Dorothy McGuire. I stood on the village square looking after Clooney. Her step was light, her very feet were singing a song as she disappeared of the little door of Mr. Wilson's chemist shop. And now, let's repeat it again, Mrs. Watkins.  Will his nose stop running, Mrs. Watkins? It may interest you to know that after the use of one bottle of my nasal bath, the Marquis of Rockamere, a distinguished speaker, was for the first time clearly understood when he addressed the House of Lords last week. The charge will be two shillings. Good day. Good afternoon, Miss Brown. Good afternoon, Mr. Wilson. Well, Miss Brown, I could relish a crumpet or two. And you? Did oh. Then shall we step into the parlour? Oh, Mr. Wilson, didn't you notice anything? Notice what? Oh, the way I look. Well, I remarked about it the last time I saw you. I said you looked intelligent. Oh, no, that's not what I mean. I mean here, the garden on my head. Oh, well, I don't object to it myself, Miss Brown, but my mother might think it a little frivolous. Oh, well, then I had better take it off. Thank you. I hope you understand. Oh, I do. I should have been satisfied to look intelligent. Shall we go? It's right through this door. Oh, what an elegant room. Oh, well, it's not backing in palace, but it's Wilson's little castle. You might enjoy looking at this picture, Miss Brown. It's painted by hand.  It hasn't much future, has it? Just mutton. And where would England be without it? Now, if I were a sheep, I'd be proud to serve the empire. And now, Miss Brown, would you like to know where we are? Oh, yes, I would. Well, then let's have a little glance at the map of our valley. Well, just look at that. Are those battle flags? Not exactly, Miss Brown, but a victory, nevertheless. This flag marks where I was born. Yes. And this flag here is where we are at this very moment. Oh. And this is where I intend to remain for the rest of my life. In this, a very house. Oh, you have it all so perfectly planned. But what if the house burns down? I have considered that too. It won't. I've taken heavy precaution. You may have noticed the lightning rod on the roof. That's Moulton's imperial pin point, the very best. And if I should ever be blessed with a little Wilson's, I should expect a Mrs. Wilson to keep matches away from them. That isn't asking too much now, is it? Oh, no. I think that's the least Mr. Wilson could expect from Mrs. Wilson. Yes. However, if in spite of all my provisions a slight blaze should occur, it may reassure you to know that I am chief of the Friars Carmel Volunteer Affire Department. Mr. Wilson, you aren't. I am. Oh, it would be almost worth a fire to see you in action. Thank you, Mrs. Brown. Now, I shall call my mother. Mother? Mother? Mother has been arresting. Yes, indeed. Well, mother, here is our guest. Mrs. Brown, my mother. How do you do, Mrs. Wilson? Well, I have tea already, mother. Will you pour for us? No, Mrs. Brown. If you have no objection, we'll play something on the harmonium. Me too. Is there anything you don't do, Mr. Wilson? Well, you have your choice.  They're both so beautiful. I wish you would decide for me, Mr. Wilson. Well, shall we say a sweet Alice and perhaps a sweet Afton as an encore. Are you ready, Mrs. Brown? Oh, yes. And mother has taken a great liking to you. How do you know? She didn't say anything. That's the point. Mother doesn't waste words on flattery. If she speaks, it's to correct faults.  Once or twice, I thought she was going to smile at me. Clooney! Oh, it's Mr. Belensky. Hello, Mr. Belensky. This is Mr. Wilson, the chemist. How do you do? Mr. Belensky is staying at Fryer's Carmel. Oh, you're a friend or relative of Mrs. Miley or Mr. Syrit, I suppose, Mr. Belensky? Oh, no. He's a guest of Sir Henry and Lady Carmel. A guest? I understand, Mr. Wilson, surprise. A guest is not usually aware of the existence of a maid. That's what troubled you, Mr. Wilson, isn't it? Oh, exactly, sir. Well, you see, we at Fryer's Carmel don't regard Mrs. Brown merely as a maid. We are very proud of our Clooney and interested in her welfare. And above all, in her friends. Oh, naturally. I understand, sir. Well, it's seven o'clock, Mrs. Brown. Yes. Oh, how time flies. Yes, it does, but we must learn to fly with it. Oh, I know. I'll loop along now. Goodbye, Mr. Wilson. Goodbye, Mrs. Brown. Thanks for a wonderful afternoon. And for a sweet Alice Bend bolt. Oh, don't mention it. Yes, a very worthy young lady. Yes.  I'm entirely at your disposal, sir.  Yes, sir. Certified? Oh, yes, sir. My diploma is at your disposal. I may have a look at it sometime. Thank you, sir. I was first in my class, if I may say so. How many were there in your class? Twenty-three, sir. A small class. Mr. Wilson, I presume that you have weighed your intentions toward Mrs. Brown as carefully as you weigh the contents of a pill. Oh, I assure you, sir, I am not the sort of man who would invite a young lady for tea merely to a while away in afternoon. I'm glad to hear it, Wilson. I have no use for a light-minded man. Nor, I, sir. May it be presumptuous to ask you to say a word in my behalf to Mrs. Maley? What? Have you intentions toward Mrs. Maley, too? Oh, heaven for fins! Well, I must admit I was worried about Clooney, but you have relieved my fears. Mr. Wilson, you could not have prescribed a better sedative than yourself. Oh, thank you, sir. Not at all. Good night, Mr. Wilson. Good night, sir. Oh, Mr. Wilson. Oh, yes? Yes, sir. You don't drink, do you? Oh, no, sir. Good. That we couldn't stand. It took me a big whisking and soda to get over Mr. Wilson. I sent a note to Clooney telling her to bring a hot water bottle or something to my room that I had to talk to her. I began composing my speech. This romance was fantastic. How could she stand the man? I started to speak to an empty chair. Now, sit down, Clooney. Now, look here. I know we have a pact. Now, let me tell you something about pacts. Pacts are made for two reasons. One, to be kept. Two, to be broken. Now, no, I don't say let's break our pact. But on the other hand, I'm not suggesting that we keep it. I came as soon as I could.  How do you like, Mr. Wilson? Do you still think my ship has arrived? Now, look here, Clooney. Out there is an ocean. And on the ocean is a boat braving the storm and battling the billows. That's not Mr. Wilson. But in a quiet harbor, there is a freighter. Each engine is turned off. No smoke comes out of its funnel. Its anchor rests deep on the bottom. It's moored to the pier with a hundred ropes. Nothing could ever budge it. Neither wind nor wave. Well, that's Mr. Wilson.   And then his mother started to snore. Oh, you're like that. Well, not just the snoring, but because she was his mother. You see, I'm an orphan, and I've never heard my mother snore. Well, you're happy now? That's all that matters. Mr. Belensky, it's so selfish of me to talk only about myself. But I'm sure your ship will come in someday, too. Don't worry about me, Gruny. If it doesn't come, I'm a good swimmer. Good night, Gruny. I worked very hard from then on. I didn't drop any trays, and I was most respectful to Mrs. Maley and every Wednesday Mr. Wilson would stop by just at the stoke of three. I hoped I was making a good impression on him, although sometimes I felt that I wasn't quite being myself. One Wednesday, Mr. Wilson told me he was giving his mother a birthday party at night, and he invited me. I didn't know how I was to get the evening off, so I went to Mr. Belensky for help. He didn't even ask any question. He just went to work. Well, the evening is yours, Gruny. You see, it's not only Mrs. Wilson's birthday, but, well, things have sort of been happening. Why, Gruny? Yes, they have. Mr. Wilson has spoken to his mother about me. I'm sure she approved of you. Well, anyhow, she didn't say no. Oh, that's very encouraging. And then Mr. Wilson asked his aunt and Mr. Latham, his solicitor, and everybody he possibly could ask. Did he ask you? Oh, no, that's just it. But he might tonight. Or he might not. That's the suspense. And so romantic, Gruny. Mr. Belensky, I... No, no, I can't tell you. Well, you're not keeping secrets from me, are you? Oh, it isn't a secret, but I shouldn't tell you anyway. But I'm going to. I had a dream last night, but you won't tell Mr. Wilson. Why, did he forbid you to dream? Well, how could he? Oh, no. But Mr. Wilson's so sensible, and I don't think he'd object to dreams as long as they were sensible, I dreamed about you. Gruny, you did? Hmm, you don't know how wonderful you look in affairs. And how you rode that black Arabian stand.  Why did we sit that horse well? Tell me, Gruny, tell me, did I take you to my tent? Well... You were taking me somewhere, but I remembered our pact just in time, and I kicked myself. Oh, and took the kick right out of the dream. Mr. Belensky, do you wish I had gone to your tent? Hmm? No, no, Gruny. You did the right thing. I have no tent. Not on the desert or anywhere. Better well along now, good luck. Same to you. And thanks for getting me off of the birthday party. Where is the Roman so aptly put it? A tempura, a mutanta. That is to say, a time's a change. 65 years ago, mother wasn't even here. And today, she's been here 65 years. Tempora certainly do, mutanta. Thank you. But before we examine those 65, a well-spent years. Let me thank you all who came here to celebrate this joyous occasion. You, Mr. Snapple. Mrs. Snapple. Mrs. Snapple. Master Snapple. Oh, Master Snapple is out of the room temporarily. Mr. Tappam. Mrs. Tappam and Latham Esquire. Perhaps you notice that I am guilty of an omission. But when you hear later what I have to say, or better, to announce, concerning a young lady not too far away, then I'm sure that you'll agree that sometimes an omission is an head mission. I have a... The plumbing! I just turned on the faucet. We'll be quiet. It's the plumbing, ma'am. Don't say that. But it was. It was. Well, sit down and be quiet. Let us go on, shall we? Well, that... Miss Miss Clooney. I'm here. As I was saying, a sixty-five years of faithful service to the community. All I did was turn the faucet and run. It's probably the joint. Sixty-five years of unblemished reputation. I beg your pardon. Some of you might not know it, but I'm a plumber's niece. Just give me a hammer and a wrench and I'll show you. I'll get it, Miss Clooney. Oh, please, Miss Clooney, I wish you wouldn't. There's nothing to it. It won't take more than five minutes. I'll interrupt your announcement. Well, I might not be the best cook in England. I'll try for an onion, but whoever gets me doesn't have to worry about it. Tools for me, Wilbur, while I roll up my sleeves and roll down my stockings. I wasn't dressed for plumbing tonight. There. There you are, Miss Clooney. If it's the joint, a couple of bangs might do it. If not, we'll try something else. Birthday gift to your mother, Mr. Wilson. Mrs. Wilson, I hope that... Well, where is she?  Oh, what's the matter? Is she ill? My Mr. Wilson, it is getting late. Yes, isn't it? We'd better be going. I hope it wasn't too much for your mother, my boy. I hope not. Good night, Mr. Wilson. It was a lovely party. Good night. Good night, Mr. Wilson. Good night. Good night, my boy. Thanks for a charming evening. Good night. Thanks for letting me watch, Clooney. Oh, you were a great help. Wilbur? All right, I'm coming. Well, good night, Mr. Wilson. Good night. I've never seen one so bold. Did I do something wrong? I wish I had never seen what I saw. But I only wanted to help. I would rather not discuss anything until you have made yourself presentable. Oh, I am sorry, Mr. Wilson. I'll roll down my sleeves. Good morning, Mrs. Milly. Good morning, Mr. Saret. I did not see Brown as I passed through the kitchen. Your tea, Mr. Saret. Oh, thank you. I'm afraid Brown is indisposed this morning, Mr. Saret. I should not have permitted her to have had last night off. It was entirely my fault, Mr. Saret. Not at all, Mrs. Milly. It was a direct request of Sir Henry through the intervention of his foreign guest. Good morning. Oh, good morning, sir. Sir, please, please don't get up. Go on with your breakfast. Sir Henry's at breakfast on the terrace, sir. Oh, I've had mine. I've been in the village doing some shopping. I must go back to London today, and I wanted to say goodbye to you both. Oh, that's very kind of you, sir.  And so will my one suit. Oh, this is for you. Thank you, sir. Shall I, uh, peck your thing, sir? Oh, yes, that would be very kind. At once, sir. And this is for you, Mrs. Milly. Thank you. Thank you, sir. Where's Clooney? I'm afraid the birthday party was a bit too much for her. Oh, I see.  It's not often a person in her place attracts a man like Mr. Wilson. Yes, indeed. A man like Mr. Wilson. Shall I call her? I'm sure it's all right. No, no, no, no. Please don't. It's better this way. Uh, will you be good enough to give her this for me? And should she ever feel unhappy, tell her just to close her eyes and say, squirrels to the nuts. You remember this, Mrs. Milly, won't you? Oh, very good, sir. Goodbye, Mrs. Milly. Goodbye, sir. I will be on the terrace with Sir Henry. Very good, sir. Good morning, will it scheme? Or I'll brighten early? Yes, and sadly, I found out that I must go back to London.   I can't tell you what it has meant to me to be here. Oh, what's the matter with you anyway? Good morning. Oh, good morning, Lydia Carmel. Oh, good morning, dear. What do you think of this fellow? He's leaving. Leaving? Oh, that makes me very sad, Mr. Belinsky. You're very sorry to see you go. So am I, believe me, Lydia Carmel. Oh, you're coming back, you know. Oh, I hope so. Oh, that's absurd. You've got to. Oh, no, Henry. We must leave that to the professor. Don't hang it off. I can only tell you both that you've made me very happy. I'm very grateful. Your bags, sir. Oh, thank you. And I have taken the liberty of ordering a car to take you into the village. It is at the door, sir. Thank you, Sirette. Did you come here? Let me say goodbye to you here. Goodbye, Mr. Belinsky. Oh, look here. We're going to write to each other, Adam. Oh, certainly. Oh, what's your address, Adam? Oh, just Belinsky, London. Oh, you might add general delivery. They know me there. Well, goodbye. Goodbye. Thank you. Good chat, there. A fine man. So appreciative of everything. Oh, how he loved that nightingale under his window. Hmm. Belinsky. London. General delivery. Where's Mr. Belinsky? He's just left, Cluny. Oh, I didn't have a chance to say good-bye to him. And look what he gave me. Block stock and silk stock. And Sofina silk, too. And so are the tops. Oh, Mr. Belinsky. Mr. Belinsky. Thank you. They're beautiful. Why didn't you say goodbye to me? You know, we might never see each other again. Yes, I know, Cluny. Oh, it's kind of awful to think of. Yes. Yes, awful, Cluny, but how is Mr. Wilson? Oh, he's better. Was he sick? No, he was upset about his mother. Mr. Belinsky, I disgraced myself last night. What did you do, Cluny? Well, you know what Plumman does to me. I just can't keep my hands off of it. And last night, I didn't either. Oh, I don't blame Mr. Wilson. He said, with his standing in this community, he can't afford to have a wife who's subject to impulses either to pipes or to himself. And that was when he banged on the table. He's going to ask his mother to give me another chance. That's very kind. Now, isn't it? I'm certainly going to watch myself.  Can one? Come on, get in. Oh, where, Mr. Belinsky? The train. Get in. Get in? Oh, get in. All right. I have no tickets. That's all right. We can pay at the other end. I haven't any money. I have. Oh, where are we going, Mr. Belinsky? General delivery. Are you expecting a lever? Always. That's what's so wonderful about general delivery. Let us pour into it millions of them. Greetings from all over the world. Oh, I've passed it many times and I've never thought of that. You do make one see things. And among all those millions of letters, there might be one letter for us, Clooney. Might be very disappointing, but it might be good news. Might be from America. Mr. Belinsky, you sound as if you like me. Ah, Clooney. Clooney, if I were rich, I would build you the most beautiful mansion with the most exquisite and complicated plumbing. And right in the middle of the most elegant housewarming party, I would hand you a hammer and say, ladies and gentlemen, Madame Clooney Belinsky is about to put the pipes in their place. Madame Belinsky. Oh, that's as good as Mrs. Belinsky, isn't it? Take off that apron. All right. Now take off that slit cut. Here it is. Ah, watch, Clooney. Out the window with them, you see. That means just one thing. You will never again have to serve three meals a day. On the other hand, you might not have three meals a day. Sometimes maybe only one. And sometimes maybe only none. I don't care as long as we eat together, Mr. Belinsky. Ah, just for that, we're going to have three meals with hors d'oeuvres and champagne and snacks between. You know what you've done to me? I'm going to write a book. The Economic Causes of World War III. Well, with luck, I might have made just enough money for myself. But now, I'm going to write a bestseller. A murder mystery. A murder mystery? What's it going to be about? A murder. A man gets murdered. Who killed him? Who did it? For 365 pages, I won't do myself. When on page 366 it finally comes out, will I be surprised? And so will millions of others. Clooney, this book will make enough money for both of us. Oh, but Mr. Belinsky, what if there should be three of us? Then I'll write a sequel. But why lay me to ourselves? I'll write a serial. Oh, Mr. Belinsky. I don't think I'll have much time for plumbing. And so ends our screen director's playhouse presentation of Clooney Brown with two fine performances by Dorothy McGuire and Charles Boyer. Ladies and gentlemen, at the beginning of the program, we promised you an important announcement. It comes from the president of the Screen Director's Guild of America. And so it's my pleasure to introduce him to you now. The Academy Award-winning writer-director of the 20th Century Fox production, Letter to Three Wives and All About Eve, Mr. Joseph Mankewitz. Thank you very much. The talk about an announcement seems to indicate a secret. That's exactly what it is up to this moment. It concerns the winner of the Screen Director's Guild's second quarterly award for directorial achievement. That means the Hollywood film directors have scanned each other's work with a critical and appreciative eye. They have voted. And they have decided that the most brilliantly directed picture of the quarter is Sunset Boulevard. And that the quarterly award belongs to its director Billy Wildo. I take particular pleasure in presenting it to you, Billy, on this program because a director of Clooney Brown was a close friend to us both. We were both his protégés. The late and great Ernst Lubitsch. Here it is, Billy. The silver plaque of the Screen Director's Quarterly Award. Put it with that Academy Award you earned for the last weekend. Thank you, Joe, and all my fellow Guild members. I know all directors are so intent on their own pictures that while when they applaud the works of a colleague, it's the finest compliment a director can receive. Thank you so much. Good night. Next week, the Screen Director's Playhouse brings you one of Hollywood's most delightful couples. Our play, Mrs. Mike. Our stars, June Allison and Dick Powell, with Screen Director Lewis King. Clooney Brown was presented through the courtesy of 20th Century Fox, whose latest release is the Technicolor production, Halls of Montezuma. Charles Boyer may soon be seen in the 20th Century Fox production, The Secret Pen. Dorothy McGuire currently may be seen in the 20th Century Fox production, Mr. 880. Clooney Brown was adapted by Nat Wolfe. The Screen Director's Playhouse is produced by Howard Wiley, with direction by Bill Karn. Portions were transcribed. This is Jimmy Wallington speaking and inviting you to listen again next Thursday to Dick Powell and June Allison with Screen Director Lewis King. Archie invites you to visit Duffy's Tavern tomorrow night on NBC.