Value Driven Data Science

[00:00:00] Dr Genevieve Hayes: Hello and welcome to episode 100 of Value-Driven Data Science, where Data Professionals Become Strategic Experts. I'm Dr. Genevieve Hayes and to mark this milestone today we're gonna be doing something a little bit different. Today we're turning the tables. Our guest, Matt O'Mara, is going to take over the role of host and he's going to interview me about what it really takes to transform from technical implementer to strategic expert.
[00:00:32] Why I started as a statistician, got swept up in machine learning, and now champion both and lessons learned from recording 100 podcast episodes. Matt is the managing Director of Information and Insights Company Analysis paralysis, and is the founder and director of I three, which helps organizations use an information lens to realize significant value, increased productivity, and achieve business outcomes.
[00:01:04] He is also an international speaker, facilitator and strategist, and is the first and only New Zealander. To attain records and information management Practitioners Alliance Global Certified Fellow status. He also previously appeared as a guest back in episode 50. Matt, welcome back to the show and over to you.
[00:01:27] Matt O'Mara: Hey, thanks Genevieve. I really. That and look, first off, a huge congratulations. Wow. Podcast a hundred. That's just an amazing achievement. So it's a real honor to be asked to actually turn the tables and interview you today. So I. Look, what I'd like to do first is just for our listeners, some podcast fast facts that you've given me.
[00:01:52] 'cause I think these are quite fascinating actually. So I'm just gonna go over that and then maybe we'll ponder some of these fast facts. So the first episode was guest Amanda Aitken, which went live on the 16th of September. 2022. Now, since that date, you've had 77 unique guests with Dr. Prevos, who holds the record for most guest appearances with three.
[00:02:17] And this was a really impressive stat listeners in over 70 countries. So it as a truly global podcast, I'm so impressed. And the longest episode was episode number 36 with Warren Powell. And then you had the all time, most popular episode of Value Driven Data Science, which was episode nine, which was David Joyner Learning Data Science at Scale with O-M-S-C-S.
[00:02:46] So what I'd like to pause on before we consider your career journey and how you got here date was when you started your first podcast back in September, 2022. What was happening ? What was this all about?
[00:03:01] Dr Genevieve Hayes: So back in 2022, I recently started my business and I was looking for ways to boost my professional visibility. And I came across Jonathan Stark's five Day Podcast Challenge, which taught you how to start a podcast in five days.
[00:03:20] And around that time I was also talking to a lot of people in the data and analytics community to get an understanding of how they were creating business value from data. And some of these people had some really amazing stories that I thought other data professionals might also benefit from.
[00:03:39] And it seemed to me that the best way of doing this was by moving those conversations over to the podcast. So at both of those things going on simultaneously, and it just seemed logical to merge the two. And as a result, value-driven data science was born.
[00:03:57] Matt O'Mara: Fantastic. And so just because I'm interested in the changes between 2022 and what's top of. Call now, if you take your mind back to 2022 when you first started, what were some of the hot topics of the day?
[00:04:12] Dr Genevieve Hayes: When we launched, this was just before chat, GPT, so AI's always been a thing, but it wasn't the big thing that it is now. So right back then, I think cloud was the big thing. But for me, because I was launching my business at that time, I was really obsessed with this whole idea of how do you create business value from data?
[00:04:39] And you can see that reflected in the title of the podcast. And so that was what I was focusing on in those first few episodes. Just trying to answer that question for myself and for my listeners. How do you make data science productive? Because. We'd gone through that initial hype cycle of data science at that point, and a lot of people were in that.
[00:05:03] I think it's what Gartner calls the trough of disillusionment where everyone's got this thing and it's okay, how do we make it valuable? And then AI came about and you can see that reflected in a lot of my episodes that were in 2023 we've got this great new, exciting thing. How do we make that valuable?
[00:05:25] Now AI's still becoming bigger and bigger, so the topic of creating business value from data has always been there. It's just that as the environment's changed, it's morphed over the years.
[00:05:41] Matt O'Mara: Yeah, and look, I think that's a really powerful point because it doesn't matter what industry you work in or what your specialization is. There is more and more emphasis on value and outputs and linking that to what your organization is trying to achieve, whether it's strategic or operational. Yeah, I absolutely think that is a critical aspect of any industry or any organization particularly with cost pressures coming on and things like that.
[00:06:09] So really fascinating. Thank you. I'm intrigued. Your longest episode number? 36 74 minutes. You've gotta tell me more about that. Why was this so long?
[00:06:20] Dr Genevieve Hayes: So Warren Powell is a professor emeritus from Princeton and is probably the world expert in strategic decision analysis, and he has been. Working in this field for over 40 years and had a lot to say on the topic.
[00:06:42] He's written a textbook on the topic that's, I think something like a thousand pages long. So he actually had even more to say than 74 minutes. But he's just an incredible man and was very generous in sharing his knowledge with our listeners.
[00:06:57] Matt O'Mara: So I've gotta ask, that leads to my next question. How do you go about finding these speakers?
[00:07:04] Dr Genevieve Hayes: It's a combination of different things. So some of them are just friends of mine or people who I've worked with over the years. So one of our recent guests professor Steve Stern, who is the custodian of the Duckworth Lewis Stern Cricket Scoring System. He was one of the supervisors on my PhD, so I was lucky in that respect.
[00:07:27] But I don't have an infinite supply of friends unfortunately. So I do have to find other people and some of them are people who I encounter on LinkedIn, for example. So I'll get into a conversation with someone on LinkedIn and they'll say something interesting and one thing will lead to another, and.
[00:07:47] We'll have a podcast episode. I've been approached by podcast agents to have people appear on my podcast who they represent. And also sometimes I'll read a book that I really love and I'll just reach out to the author, and more often than not, the author will agree to appear on the podcast.
[00:08:12] Matt O'Mara: That's fantastic. I wasn't actually aware there was such a thing as podcast agents just fascinating the world of podcasting. Oh, that's fantastic.
[00:08:21] Dr Genevieve Hayes: I didn't actually realize they existed either until they started reaching out to me.
[00:08:26] Matt O'Mara: Just amazing. Now given your longest episode question I have to ask what made episode nine? David Joyner your all time most popular episode.
[00:08:37] Dr Genevieve Hayes: David Joyner was the Executive Director of Georgia Tech's Online Master of Science in Computer Science Program, which is the program that I undertook when I did my Master's. And this is the world's largest computer science program. So there are. Thousands of students in that program at any one time, and they are very engaged online.
[00:09:03] So there was a large number of people who were interested in hearing David Joyner speak. He's one of the academics who teaches in that program. I had him as a lecture three times while I was undertaking the program. And the man is amazing.
[00:09:20] Matt O'Mara: Fantastic. Oh, great. What I'd like to do now is flip to your. Career journey. We've all got career journeys and it's really fascinating to hear your one, particularly given this is Podcast a hundred. So you started your career as an actuary and statistician, but then you moved into machine learning and data science about the time when everyone was started to become excited about them.
[00:09:45] So I'm interested in what drove that shift.
[00:09:49] Dr Genevieve Hayes: So around the time everyone started becoming excited about machine learning and data science that would've been around. 2015, I'd say. I attended several conferences where people were talking about machine learning and how machine learning and data science were the way of the future. I remember a futurist coming on stage and talking about this, and just hearing these people speak made me feel really excited about this.
[00:10:18] I remember going away from those conferences and going back to work and googling what machine learning was and what data science was, and going down that whole rabbit hole to explore what was involved. And I came across this article about the, then recently established. New York City Mayor's office for data analytics and how, if I remember correctly they'd used data science to help identify locations where cooking oil was illegally being dumped in New York sewers
[00:10:52] Matt O'Mara: Wow.
[00:10:53] Dr Genevieve Hayes: And how they were using data science to help coordinate emergency services responses following, I think it was Hurricane Sandy and.
[00:11:03] It was just so practical how they were using data science to solve these major problems in New York City, and I remember I desperately wanted to be a part of that and getting on the ground floor, and in fact, at that time, working at the mayor's office for data analytics, that became my dream job. And I was trying to figure out how to do it and in the end I concluded, okay, I probably don't wanna move halfway around the world to work in this mayor's office of data analytics.
[00:11:35] But it did get me thinking about, yeah, how can I do this sort of thing where I was? And it led me to proposing that I turn the actuarial and business intelligence team that I was managing at that time into a smaller version of the same, and to my surprise, my boss and the senior executive in my organization approved that. I actually remember speaking to one of the executives about a year or so later, and he told me the reason why they approved it was because I came into the executive meeting and I was so excited about this whole data thing that they all got excited by it and just wanted to be a part of it too.
[00:12:19] Matt O'Mara: Fantastic story.
[00:12:20] And so following on from that, you obviously started in that traditional statistical methods versus machine learning, and that's evolved. How has your thinking changed, if anything, core evolved over that time?
[00:12:36] Dr Genevieve Hayes: When I first started my career because my background was in classical statistics and the roles I was taking on, specifically required someone with a statistical or actuarial background. I believe that statistical methods were the answer to everything and. In the world that I inhabited, that was actually true.
[00:12:56] And then when machine learning became the next big thing and I became a part of that world, I believed that machine learning had superseded statistics and had made, in many ways my statistical skills obsolete, or at least machine learning was like the next level of evolution beyond what I had. And again.
[00:13:21] Within the world that I inhabited at that time. That was also true. So my last office job before launching my business was machine learning focused, and several for the last several years, I've also worked part-time as a university lecturer and I taught a class on machine learning. That it was through my work in my business that I came to recognize the value of traditional statistics once again.
[00:13:50] So if you look at my website, which may have changed by the time you're listening to this episode, but at this point in time, if you look at it I advertise the fact that I can help businesses with machine learning or with statistical type work, and. What I found really interesting was over that time, the thing that people have tended to approach me about isn't the machine learning side of my business, but the statistical side .
[00:14:24] I've had people come to me and say, we want these particular jobs done, which require statistical analysis, and they say, and we don't know anyone else who can do this sort of work. And it got me really interested because it made me look at things from their point of view. What they wanted, it wasn't about automating processes, using machine learning in order to eliminate people from.
[00:14:54] Whatever they were doing, it was about making big decisions where they needed data to justify those decisions. And they might be just a one-off decision and the data might be really sparse and there was nothing else you can use other than traditional statistical analysis. And that made me reevaluate my statistical skills.
[00:15:21] It made me realize there is a place for machine learning and there is a place for statistics and machine learning isn't the next level of evolution beyond statistics. It's just a different tool in the toolkit, which is suited to one particular type of problem. And. Statistics are another tool that are suited to another type of problem.
[00:15:47] And actually now that we're getting into this whole era of ai, I think in many ways for data scientists, that statistical toolkit is becoming more powerful because a lot of the machine learning automation. Can now be handled by AI tools, auto ML type things or you could get something like Claude Code to write your code for fitting machine learning models, but that's. Old bespoke statistical analysis for one-off high stakes decision making that requires a lot of human input. You can't automate that as easily, and that's actually become now where I'm starting to shift my focus to again.
[00:16:33] Matt O'Mara: Gosh, that's fascinating. And there's a lot to unpack there in a way. I love the point you made about when you talk to these clients. They provided you with their own insights. I talk myself about this concept of discipline disconnect, where you have, say a finance team or an IT team, an HR team, and none of them understand what each other does.
[00:16:54] The attraction to your. Consulting workers that you get this amazing insight in terms of what these folk are trying to achieve. And yeah, for several years it's been, hey, how do we make evidence-based decisions, particularly, for example, if we're working for a public organization and we're spending public money.
[00:17:14] So yeah, I find that absolutely fascinating. And really invaluable. In terms of what you add to that process, do you think there is a risk of skills being lost that you've gained over the years that now.
[00:17:30] Just not gonna be there because it could be AI taking jobs or that entry level jobs into statistics or machine learning and associated areas are just, those entry level jobs are just gone, so people aren't getting some basic experience. Do you see that as a risk to the profession?
[00:17:50] Dr Genevieve Hayes: I think, can I answer a different question from what you've actually
[00:17:54] Matt O'Mara: Absolutely. Absolutely.
[00:17:55] Dr Genevieve Hayes: I've actually seen already, this was pre ai. When I first started working as a data professional, one of the main components of my job was performing these statistical analysis.
[00:18:10] And I managed the team that did this sort of work. And we did do some BI reporting, but there was also a lot of statistical analysis involved. And then a number of years back, all these dashboarding tools suddenly came onto the scene. So before that, we were literally hand coding dashboard type things using SaaS.
[00:18:32] But then you had things like, Tableau, power BI and all that, and. The role of the data analyst morphed from being someone who is doing that sort of bespoke statistical analysis to being someone who builds these dashboards using point and click tools like Tableau. And then the data scientist role emerged and that became a lot of machine learning automation, and that's.
[00:18:59] Statistical analysis piece in the middle that sort of just evaporated. And I think that's why, as I said, these clients were coming to me saying, we don't know people who have this skillset. I think already that skillset has started to evaporate because it's fallen into this crack between business intelligence dashboarding and data science, machine learning automation.
[00:19:25] So I think. That there is value to be had in that little gap in the middle, because looking back on my career. I think that's where a lot of the value that I've created, has come from. And to answer your second question about what's gonna happen now that we have AI automation, I could imagine that new cracks will be created and further skills will fall into those gaps.
[00:19:54] And I think there will be a time when people with those skill sets will become very valuable because if you are trying to control, say, AI agents who are building machine learning models, for example, or dashboards, let's say, and you don't understand the technical capabilities beneath that, you're setting up a house of cards and it's gonna collapse at some point.
[00:20:20] Matt O'Mara: Yes, and I guess it goes to AI governance and explainable AI and all of that, because if you haven't got those basics. Then how are you checking on the work of ai, for lack of a better word? Fascinating.
[00:20:35] Dr Genevieve Hayes: I know you are very much into ai, Matt you've recommended several books to me on that topic. What's your view on this?
[00:20:43] Matt O'Mara: I think from a societal viewpoint, what concerns me is that governments of the day are not tracking. What's changing? I was only in Australia this week on the Gold Coast for a mper board meeting. And there were lots of announcements in the Australian media about job losses. I think it was isolation or some other, yeah, thank you.
[00:21:06] What was it? 1600 people. And so what concerns me is. Are we monitoring what is disappearing and are we monitoring what it could be replaced with? Or do we need to revisit these constructs about things like universal benefits? And also, I think from a societal point of view, there's a significant mental health load on our youth who are leaving school and thinking, is this thing I'm going to potentially be doing at university for the next several years and a big investment on behalf of me or my family or whatever, is that going to still be valid?
[00:21:41] And so I don't think we are doing enough strategic thinking about this. I think we've got our heads buried in the sand. And I think there needs to be more hard data on, Hey, where are these jobs disappearing? Are there opportunities to think differently? What are these people gonna do? And if we think, Hey, I don't need to worry about that.
[00:22:06] I'm okay. It's going to affect everybody because less money in the economy, no work. And I just don't think we have considered this enough. My strong point would be from a societal governmental point of view, we actually need to do some solid work space to start tracking it and then inform our thinking about how we address some of these key issues, not withstanding things like the environmental impact and stuff like that.
[00:22:33] Here in New Zealand, there's a new data center being built in the south island, and one of the things that struck me was that it will be the second biggest power user in the country. And what are all the other considerations around that, water usage. We've seen anecdotally in America that where they've set up these data centers or where water sources have been purchased for this kind of use case that people are suddenly paying more for their water and things like that. So again, I think we need to do on those effects. If you are happy, Genevieve, we will circle back to interviewing you Now I've got a fascinating question here. I'm really interested in your response.
[00:23:15] So you've talked about your target audience now. Your target audience is what you've called working data scientists who want to transform from being technical implementers into valued business partners or strategic experts. And around this, you have a central theme of your workers. That technical ability alone isn't enough, which I absolutely concur with.
[00:23:41] You talk about this concept of data professionals needing to create strategic business value. Now you've alluded to this in some of your responses, but what led you to this realization? Was it one thing or was it several things?
[00:23:54] Dr Genevieve Hayes: It was several things, basically when I started my business, I took a lot of courses and read a lot of books about the fundamentals of running a business, and one of the messages that kept coming up again and again was for a business to be successful, it needs to create value for its customers.
[00:24:12] So I thought, great. I have all these technical skills that I've been told are highly valuable. I'll go out and offer them to prospective clients and they'll jump at the chance to use them. Spoiler alert. They didn't, you can't just go to people and say, hi, I'm a data scientist. Are there any models you'd like me to fit?
[00:24:32] Trust me, it doesn't work. But it did get me reflecting back on my career to date and thinking about how I'd created value as an employee. And I realized that. On the occasions when I had effectively gone to the business and said, I have these skills.
[00:24:53] How do you want me to use them? It hadn't led to anything meaningfully being created, but when I'd actually gone to some senior manager and just started saying, you've got this problem.
[00:25:07] This is how I'd solve it. You really should do something about it. That had usually resulted in them saying, good go do it. We approve this project. And that's where the real value had been created. And by comparing those two scenarios, it made me realize it wasn't actually. The technical component that was creating the value, although it was necessary in order to build the solution.
[00:25:30] It was that piece where I actually identified that problem and pitched that project to the senior leaders that made all the difference. And I think it's because non-technical stakeholders just don't understand how technical capabilities can be used to solve problems. They need people to guide them to a solution.
[00:25:53] So that ultimately led me to draw that conclusion that although your technical skills are what enable you to build solutions, unless you have the ability to diagnose the problem in the first place, you're not gonna have many problems to solve.
[00:26:09] Matt O'Mara: Fantastic. So in terms of that, you talk about this concept of a. Valued business partner. And so I'm really fascinated what are the hallmarks of a valued business partner? And on that, if I'm a chief executive or a business owner, why would I need this role? And just before you think about a responding to that, what would you say to an executive who says, Hey, I can just use ai, or what I refer to as my drunken uncle?
[00:26:43] Dr Genevieve Hayes: When you're looking at the idea of having a data professional who's a valued business partner versus having an AI or your drunken uncle as the business partner. It's like the difference between a medical professional.
[00:27:02] And a person who's taking orders at McDonald's, so a person who's taking orders at McDonald's, they aren't there to try and help guide you to the best meal to satisfy your hunger needs they're just there to take instructions tell me what you want me to build and I'll build it.
[00:27:25] And that's great if you know that you want a Big Mac or if you know that you want a machine learning model that does exactly this and data professionals who have some tool like Claude Code or whatever you happen to use, they can use that tool to create that model. That's great. But if you think about.
[00:27:50] A medical professional. Imagine if you went to a doctor and the doctor behaved like a McDonald's order taker. You went to the doctor and said I feel this weird pain in my chest. I'd really like you to give me a heart bypass. And they say, sure. Just jump up on the bed over there and I'll go and scrub off and get my scalpel and be with you in a minute.
[00:28:18] It'll be a disaster. And that's why medical professionals, they go through the whole diagnostic process. Diagnose, prescribe, treat the patient, et cetera. If you've got something serious going on, like. You're wondering if that weird pain in your chest is a heart problem or just that you eat too much spicy food, then you want someone to place a diagnostic filter over things.
[00:28:46] So in that case, you want to have a valued business partner who is capable of doing that. That might not be what you require in every situation, but if that is the situation you have where you've got a serious decision to make high stakes decision, you gotta ask yourself, do you want that decision to be made by someone who just takes orders like a ai or do you want it to be taken by someone who understands your specific situation and is capable of.
[00:29:19] Creating that diagnostic layer, even if they happen to use AI to do the technical work under the hood.
[00:29:26] Matt O'Mara: Yeah, look, and I take your point. I love your point about hey, high stakes situation. I think that actually answers it in about three words. So just following on from that question and quite related to it is. In my experience, anyone can write a strategy. It doesn't mean it's a good strategy.
[00:29:47] And there is a lots of strategic experts. So from your own insights and obviously those derived from, all the interviews you've done over the a hundred podcasts, I have to ask, what is a strategic expert and what are the hallmarks of a strategic expert, and how do you become effective in this role?
[00:30:08] Dr Genevieve Hayes: I divide data science work into tactical or technical versus strategic. So you've got the tech. Clinical stuff, which is just doing the maths, building the models, doing all the stuff that data scientists love to do, getting your hands dirty with all the code , but the strategic work at the end of the day, what that is, is identifying business problems and helping your stakeholders who are generally senior leaders to solve those problems because.
[00:30:41] That's what business is all about. It's about senior executives trying to solve business problems and you supporting it and. The way you become a strategic expert is by well, having that technical knowledge underpinning all your work because otherwise you're not an expert.
[00:31:05] But also adding that extra layer where you connect your technical work back to the business problems, and that's through diagnosing those problems. Prescribing solutions to those problems and effectively communicating those solutions to those problems in ways that your potentially non-technical stakeholders can understand and which drives them to act and support the business in whatever its objectives may be.
[00:31:39] Matt O'Mara: And I think you've hit a key point there in terms of, we do have non-technical stakeholders, typically you wouldn't. Really expect deep expertise in the space on a board or on a executive leadership team. So that communication in layperson's terms is absolutely vital. So yeah, I take that point.
[00:31:57] Dr Genevieve Hayes: Yeah, usually the most technical people on the executive are gonna be the CTO and the CFO. And even then, they're still not going to understand often the. Details of a lot of this data science work.
[00:32:11] Matt O'Mara: Exactly. And I think that, part of your proposition is, no one person can be absolutely across everything. So totally agree. Now I have to ask, there is lots of competition for people's attention, lots of competition. We are time poor. What's the secret sauce for a winning podcast?
[00:32:31] Dr Genevieve Hayes: I'm not trying to be Joe Rogan. That's, it's the end of the day.
[00:32:36] Matt O'Mara: Love it.
[00:32:37] Dr Genevieve Hayes: I'm just trying to help data professionals who wanna make that transition from technical implementer to strategic expert and create value from their work. And my audience numbers aren't those of Joe Rogans, but. I make a difference to the people who do listen to this.
[00:33:02] So the secret sauce is understand who your audience is and deliver something that will help them achieve whatever their objective is.
[00:33:14] Matt O'Mara: I absolutely love that response. Genevieve, I would say if I was to sum it up in one word, I'd say authenticity. Which can be very rare these days. A fantastic response. So just to start wrapping things up, so a hundred episodes, that is a lot of preparation and knowledge capture. A bit of a tricky question, but what if you had to say are the three best nuggets you have learned and how have they shaped your thinking over those 100 amazing episodes?
[00:33:49] Dr Genevieve Hayes: The things that stand out for me one particular nugget which came from Mark Stouse, who's been on two episodes of this show he's a CEO and the nugget of wisdom that he came up with was. Many data scientists don't understand what their role is within an organization.
[00:34:12] The role of the data scientist is to enable executives or senior managers to make better decisions. So that really crystallized for me what it meant for a data scientist to create value within an organization, so it's making better decisions. A second guest, which helped me to further my understanding of this was professor Jeff Camm, who is actually a decision scientist, and we were talking about how decision science skills can help augment data science skills and make them more effective.
[00:35:01] And one of the things that he was pointing out to me was the importance of the piece at the beginning and the end of the project. So understanding what decisions are to be made and then connecting the technical work back to those decisions. And it made me realize that those are the pieces that are largely missing from a lot of data scientists, skill sets.
[00:35:33] And I remember one particular piece of wisdom that he shared, which was that, it's not just about helping your stakeholders to make better decisions, it's helping them to make decisions with regard to levers that they actually have the power to pull. So if it's something that they don't have any influence over, it doesn't matter what decision they make, it's just a pointless decision.
[00:36:01] So that connection really struck me. And a third piece of advice that really struck me this is something that came from Gregory Lewandowski, the five executive priorities. So five things that Executives Care most about, which were increasing revenue, reducing costs. Reducing risk or uncertainty.
[00:36:25] Increasing customer satisfaction and increasing employee satisfaction. So if you can tie your work to one of those executive priorities, then it is going to resonate more strongly with. Executives, and that's become a framework through, which I've looked at all my work ever since.
[00:36:49] Matt O'Mara: That is absolutely insightful and real nuggets there. I was taking some notes for myself 'cause I'm so impressed. And it's funny because I often ask people, what are the outcomes you're trying to achieve? Because sometimes. It's just not clear. So back to your problem definition point of view. So what I'd like to say is just a big thank you for giving me the honor to interview you on your a hundred podcast.
[00:37:16] I hope that wasn't too stressful having the. Tables turned. I think you did an amazing job as always, Genevieve. And it's been as I say, my absolute pleasure and just so insightful and I'm looking forward to number 200 when you get round to it.
[00:37:33] Dr Genevieve Hayes: Oh yes, I was talking to my parents the other night, and that's what they're saying. Now that you've reached a 100, what's your next goal going to be? And my answer was to make it to episode 200.
[00:37:43] Matt O'Mara: Brilliant. I love it. So thanks so much again.
[00:37:47] Dr Genevieve Hayes: And thank you for taking over the role of host Matt.
[00:37:50] Matt O'Mara: My absolute pleasure.
[00:37:52] Dr Genevieve Hayes: It was difficult putting well, my baby into the hands of someone else. But thank you for doing justice to the show, so thank you. And for those in the audience, thank you for being a part of this journey over the last three and a half years.
[00:38:10] I hope you've enjoyed listening to the program as much as I've enjoyed producing it. And please consider taking this moment to leave us a rating and review on your favorite podcast platform. Next episode, I'll be back in the host chair with another exceptional guest. Make sure you're subscribed so you don't miss it.
[00:38:29] I'm Dr. Genevieve Hayes, and this has been episode 100 of Value-Driven Data Science.