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The biggest difference between working for the public sector and the private sector is in the public sector.
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Most people are really motivated by a mission.
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I think there is an unfair prejudice from your average person that the government worker, I doesn't work as hard or isn't as smart as someone in the private sector.
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And I have to say that was not my experience.
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I've actually been criticized a couple of times in my career that I'm too kind or I'm too nice, and I find that shocking.
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One of the best work environments I ever had was I had a team of data scientists who.
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We're all best friends and they spent their weekends together.
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They all wanted to come into the office every day'cause they were excited about the work and they were excited about seeing each other and working together.
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Welcome back to another episode of Data Analytics Chat podcast, where we discuss the world of data, ai, and the careers shaping it.
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Today I am excited to welcome Linda Powell, the former Deputy Chief Data Officer who has worked with leading companies such as BMY, Meum and Citibank.
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In today's episode, we'll explore her exciting career journey and discuss the topic of building AI without foundations.
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Delinda, welcome to the podcast.
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Thanks, Ben.
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Glad to have the conversation today.
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Brilliant.
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Pleasure to have you on.
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So do you wanna start off with sharing your career journey
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Sure.
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Mine's been not a straight path into data.
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I graduated from Rutgers University with a degree in economics.
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Knowing that I wanted to do banking and worked in internal audit for several years, and I've actually been surprised along the way how many people.
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That ended up in data, did start their careers in audit and I think that's'cause it gives you a great foundation on controls and figuring out where are the needed operational paths for various processes.
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So I started in commercial banking quite a while ago.
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Then I moved over to the Federal Reserve For the majority of my career, I worked both, both the New York Fed.
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And at the Board of Governors, and it was at the Federal Reserve that I moved from internal auditing into data management.
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Around that time I also did a degree in data database design and programming, and realized I hated writing code, but I loved the database design things.
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So that really.
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Pushed my career more towards the data management side.
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'cause I just found it so interesting.
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Spent most of my career at the Fed, spent a couple years at the Treasury Department after the financial crisis, and then at the CFPB.
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For about four years, which was an incredibly fun stint.
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We went from having to build the entire tech stack to, to the metadata and then the government governance.
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And finally, we had a great data science team by the time I left there to go to Citibank.
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So it was really fun to build the nuts and bolts and then get to do the analytics on top of it.
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Spent about three years at Citibank and then moved over to Bank of New York Mellon.
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So that's the path.
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Always a bend towards data and also more of a bend towards the business side of things than the technology side, although absolutely need to have both to be able to do data.
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Brilliant, and I guess obviously you've.
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The public and private sector, how, how does that is, is it difference for you or is it still the same problems?
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So that's the question I get the most from people'cause not everyone crosses between public and private sector.
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I have to say that there.
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Surprisingly similar.
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The biggest difference between working for the public sector and the private sector is in the public sector.
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Most people are really motivated by a mission.
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People join a government agency that has a mission that they feel passionate about.
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I, when I worked for the Fed every day, I went home from work thinking, wow I've helped the economy today.
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I.
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Whereas when you're working for the private sector, it's a much more business oriented motivation that you have on a day-to-day basis.
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You're trying to optimize the product, you're trying to optimize revenue.
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So in the private sector, you get a little bit more dispersion in the motivation.
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Because people have different passions around the same topic.
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Whereas you're more aligned in the private sector because you're really focused on optimizing your returns and the shareholder returns.
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But people are people everybody wants to do a good job wherever they work.
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People tend to.
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Be motivated by similar things.
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They want interesting work.
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They want to get paid for their work, and they wanna be appreciated.
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So in how you do the work, it's not all that different.
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And also the problems you encounter from a data point of view.
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Are relatively similar.
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The technology is similar, if not exactly the same and how you tackle those problems is pretty similar.
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I think it's fascinating because I get, every business is different, isn't it?
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Everyone has their own
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Oh yeah.
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And it is always, like obviously some businesses, like you say, you work with financial services, they tend to want people that have worked in financial services and similar with other industries.
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So how is it trying to transfer from public to.
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Do you get, do people say no when you interviewed or what is your everyone's different and there is, people have their thoughts, don't they?
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So it'd be good to get your thoughts on how you overcome this.
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Yeah, I think there is an unfair prejudice from your average person that the government worker, I doesn't work as hard or isn't as smart as someone in the private sector.
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And I have to say that was not my experience.
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I honestly the.
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Role where I worked the longest and most arduous hours was the two years that I was at the Treasury Department.
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I think I put in 14 hours a day and then one day on every weekend.
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And I left that job saying, I will never do that again.
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'cause after, after two years, it was a little grinding.
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And I know people do that on Wall Street all the time.
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But I think there are people on both sides are intelligent and working hard.
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So when I was looking to go back to the private sector.
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I did get a lot of resistance.
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I would go on interviews and I would be, one of the last couple people for the jobs.
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And I would get feedback after the interview process that they really liked you and they really thought you were the best candidate, but they were worried because you had spent so many years in government, they weren't sure that you could hack it.
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I had repeated interviews from saying that, and I just found it so surprising that, that was the common perception about the government sector to the point where it would, they would not hire me for a long time.
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And then the breakthrough role came at Citibank where they were looking for someone with some public sector experience and.
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It was a great couple of years at Citibank.
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Yeah.
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No, it's fa it's fascinating'cause it happens in, even in any industry like pri in private like moving from say like financial services into or from, say retail into financial services.
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'cause I mean that just wouldn't happen.
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But I think it's more, I guess it depends on the individual, doesn't it?
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Any expertise.
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'Cause also in the financial services, I mean they did, they've been a more open in recent years, in my opinion, because their legacy applications, they've had to get that knowledge in the business, haven't they?
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Yeah.
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Yeah, I think that's true.
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I guess it's an niche.
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Yes, there's so much resistance across businesses.
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But it's funny how people get these thoughts.
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Yeah.
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Yeah.
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And it's funny, I had a conversation the other day with a friend talking about the challenges of working in government.
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And she automatically said, oh, the worst thing about working in government is that you can't fire people.
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And this is someone who had never worked in government, and I said.
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That, that's just not true.
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That is not the hardest part about working in government.
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I I unfortunately fired several people.
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You just have to go through the process and if you've got a good, strong manager, they'll go through the process of letting people go who aren't right for the.
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The position is just work as it is in the private sector.
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You have to put people on a performance plan and evaluate them and it's a little bit harder to let people go from the government sector.
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What I found to be the absolute hardest part of working in the government sector was hiring.
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Because there's so many rules and restrictions and preferences given to different communities that you can find the perfect person who's willing to come for a lesser salary.
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And it's, it is sometimes almost impossible to, to hire them into a regular, full-time government job.
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So it's funny how there's perceptions out there about, about what the problems are.
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Yep.
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Okay.
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Everyone's had, has different experiences, isn't it?
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And so it's, yeah, it's interesting.
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See.
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Okay.
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So what if you had to pinpoint like one or two defining moments that changed your career, what would they be?
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For me, it was always education related.
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So when I first went back after my undergraduate degree, I went back for a certification in programming and system design.
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I think I mentioned that before.
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And that really changed the trajectory of what I found interesting at work.
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And I focused more and more.
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On the data side of work and that, that led me, that was really my transformation too.
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The data realm.
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And then the other big pivotal point was I went back and got my master's in quantitative finance after, after many years of working.
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And that became a pivot point also because I was then able to understand, stand and do the analysis that you need to do with to really.
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Build on the insights for data.
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So I had, many instances where I had a gut feel that something wasn't right.
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After working with some statisticians and.
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And not trusting the results that I was getting.
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And it wasn't until after I went through my master's program and learned the math and the statistics behind the various models that I could understand why the models weren't right.
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There might've been an assumption that was bad or it might've been just the wrong type of model that we were trying to use.
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And so that really.
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Pushed me into the analytics side of data management, and I find it incredibly useful.
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I don't actually use my math as often as I should, but when we're talking about AI models and AI algorithms and programs, I have the statistical foundation.
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To be able to understand how that works and why where the problems might be underlying some of the models.
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Yeah.
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And I think, that's just a common theme in today's market speaks to a lot of leaders that individuals are like lacking on that the mathematical statistical model, like the theory side.
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So I guess for your.
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Yeah, I think that's so important.
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I'll just highlight that, that you can run your analysis through a model, but if you don't understand the complexities of the model and you just trust the results you're potentially trusting bad results.
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You really need to understand how things are, the math behind it.
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So did.
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Did you just, was it more you being self-aware that you needed to get that acknowledge or did you get guided or was Yeah.
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Where did that sort of later academics come into?
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I.
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So for me it was always about curiosity.
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At the time that I went back for my degree in programming and system design, it was really I dating myself here, but that was at the early.
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Stages of personal computing.
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And I was very curious about it.
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And I'm a classroom learner.
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I'm not as good as some people at just reading the books and learning it.
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So I went back to the classroom to learn more about it.
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I had also, at the same time, I did a certification in.
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Credit analysis.
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I thought at the time I wanted to be a credit officer.
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So I, I.
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Pursued academics in the areas that I found interesting.
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Finance was a little bit of convenience, opportunity and interest.
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I had considered getting a degree in economics.
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I there was a couple of things I was considering.
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For me the, what pushed me over the edge for the quantitative finance was I was living in DC.
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George Washington University was one of the first university to offer a program in quantitative finance, and I could walk to the classrooms after I got out of work.
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I had two young kids at the time and a full-time job, so I knew if I had to go home before going to class, I probably would've not made it to class.
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So I chose a university with a program that I wanted that was.
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Super convenient.
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Yeah.
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Yeah.
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I didn't go home.
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I just went straight from work to class.
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Yeah, because it's it, obviously I know you did that, but it's like similar to after work going to the gym, isn't it?
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It's hard.
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You gotta get, it's like that extra full size.
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A lot of people do struggle and I think it's obviously fair, but it's amazing you did it'cause holding down a work or you got a job, another job with your kids and then study.
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So it's a lot.
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Yeah.
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But it was a great two years.
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It was I'll say I missed the academic stimulation, but I sat for the first part of the CFA exam afterwards thinking I'll continue with my education.
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And before I even took the exam, I was like, oh, I'm done.
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I need to go back to having a life.
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So then have you ever faced like a major career setback?
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I don't think I know anyone who hasn't yeah, of course I have.
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I'll share an early career setback that I had that it took me years to emotionally get over it.
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But I think.
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In hindsight it was a good thing to, to happen to me.
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Early on in my career I was really decided I really wanted to be a credit officer and the big banks in New York all had these credit training programs back then.
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And I had started my career in internal audit at Chemical Bank and.
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I would had applied for the credit training program and I was told I was gonna get in.
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But back then your management had to approve a transfer within the bank.
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You couldn't just get the job and move.
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And they declined letting me go into the program, and I just thought it was the end of my career, the end of my life.
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I was so sad.
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I just thought, why do I bother?
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I'll just, carry on with work and in hindsight, I'm glad I didn't go the credit training program.
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It would've been a great learning experience, but I'm really happy with the path that I took and I probably wouldn't have gone down the data path had I gotten into the.
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The credit program.
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So I try to look back now, like every time I hit a bump and there's lots of them.
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We're not human.
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If we don't hit those career bumps.
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And I try to think about, okay, what door is it opening?
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If I'm not gonna go through, through that door what's the path that I should be taking?
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Sometimes I'm successful in that attitude and sometimes I'm not.
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It's humans.
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I mean it, like I said, when you go to that element, isn't it?
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It's like you are in sinking sand.
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Oh,
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the worst thing ever.
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But obviously now looking back, you probably think it was nothing.
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yeah.
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Yeah.
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I'm very happy with the path my career took so I have no complaints.
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I think it's just, you've gotta just obviously real obviously realize that, and then you gotta snap at it as quick as possible.
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And again, see, focus on the opportunities as opposed to like the negativity of not getting what you want at that period.
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Yeah.
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Yeah, absolutely.
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Obviously the market's highly competitive.
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What do you feel is giving you a.
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I'm gonna go back to my curiosity.
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I think part of it is the luck of timing.
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I got curious about database design and technology and data at a time that.
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It was growing.
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When I first started at work, they called me the metadata maven for a little while.
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And that was at a time when no one knew what metadata was.
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So I think it's been helpful that I've been on the frontier of the data management and data analytics trend.
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But it's also the curiosity and the willingness to go back and get further educated on a lot of the topics.
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If I were 20 years younger, I'd be doing a data science program right now and beefing up my math and modeling skills even more.
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Yeah.
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No, I think it is.
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It's, there's a lot out there, isn't there?
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It's, you've got the tech out there to learn.
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You've got the, and then I think a lot of people do forget about the maths is the, I think it's probably the more, more important bit.
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That's not gonna change.
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I.
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Yeah.
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And I think the other thing that's important is to not be afraid of not being good at things.
00:20:19.837 --> 00:20:30.843
And I guess that's a theme for better or worse in much of my life that I will try something and I'll work towards something even if I'm not good at it right away.
00:20:31.202 --> 00:20:34.022
I took up playing soccer in.
00:20:34.913 --> 00:20:44.603
In my forties because a friend invited me and I was terrible at first, but I was reasonably fast for a middle aged woman.
00:20:45.143 --> 00:20:52.192
So I was valuable to the team in a different way, even though my, my, my foot skills were terrible.
00:20:52.673 --> 00:21:01.103
So it's also, don't be afraid of something, just'cause you're not good at it doesn't mean you won't get better and it doesn't mean you won't learn from it.
00:21:01.133 --> 00:21:03.992
'cause the key is the learning.
00:21:05.567 --> 00:21:06.288
It's funny you say that.
00:21:06.317 --> 00:21:07.488
My mom did the similar thing.
00:21:07.488 --> 00:21:12.363
She started playing soccer, football later in her life as well, so
00:21:13.022 --> 00:21:14.732
Oh, it's such a fun game.
00:21:14.823 --> 00:21:15.728
So much fun.
00:21:16.113 --> 00:21:18.202
Obviously you touching on yeah, no, obviously bit.
00:21:18.282 --> 00:21:26.022
Did you also, you touched on getting over that fear factor and I guess you would've been like the more of like technical expert in your career.
00:21:26.022 --> 00:21:28.873
How did you push yourself into more leadership type work?
00:21:29.202 --> 00:21:33.117
Was it, did you get, because that's a big challenge for individuals I can speak to is.
00:21:34.853 --> 00:21:41.682
Should they just fear they, they've got enough experience, they've got the right knowledge whether they can do it.
00:21:41.923 --> 00:21:49.563
How did you overcome that sort of hurdle to get some knowledge, to get like mentors or how did get, is there anything in particular that's stood out for you?
00:21:50.663 --> 00:21:56.722
I think with most things in my life, if I don't know how to do it, I start by reading about it.
00:21:56.772 --> 00:22:03.133
So when I started playing soccer, I got a couple of kids' books out to learn all the rules in an easy way.
00:22:03.758 --> 00:22:08.048
When I had to do a marketing campaign at work, I never took any marketing classes.
00:22:08.048 --> 00:22:10.988
I went to the library and read every book about the marketing.
00:22:11.347 --> 00:22:13.718
And so with leadership, I did the same thing.
00:22:13.748 --> 00:22:20.607
I read a lot of the leadership books and quite honestly, it's about being authentic.
00:22:21.057 --> 00:22:22.438
And treating people.
00:22:22.988 --> 00:22:35.363
It's like the golden rules you learned as a back in grade school you treat people the way that you wanna be treated and we're all motivated by similar things but not exactly the same things.
00:22:35.393 --> 00:22:40.103
And so asking people what do they want rather than assuming, what do they want?
00:22:40.613 --> 00:22:43.823
Treating people with respect and kindness.
00:22:43.873 --> 00:22:52.093
I've actually been criticized a couple of times in my career that I'm too kind or I'm too nice, and I find that shocking.
00:22:52.423 --> 00:23:03.583
I think it's important that we recognize that we're all humans bringing ourselves to work and we all have lives that we're dealing with.
00:23:04.063 --> 00:23:06.772
And the more you find kindness.
00:23:07.143 --> 00:23:12.133
At work the more motivated you're gonna be to go into the office and work.
00:23:12.163 --> 00:23:18.403
One of the best work environments I ever had was I had a team of data scientists who.
00:23:19.337 --> 00:23:24.468
We're all best friends and they spent their weekends together.
00:23:24.498 --> 00:23:33.347
They all wanted to come into the office every day'cause they were excited about the work and they were excited about seeing each other and working together.
00:23:33.798 --> 00:23:37.218
And we moved mountains for a couple of years.
00:23:37.538 --> 00:23:47.383
Just because this team was so motivated, and I had a boss at one point who said, I can't believe you've got this team of data scientists working for a government agency.
00:23:47.383 --> 00:23:52.903
They could make so much money, more money if they went into the private sector.
00:23:53.563 --> 00:24:00.492
And the honest answer was this was where they wanted to be because they wanted to be together and they had interesting work.
00:24:00.492 --> 00:24:03.853
So I think leadership is about.
00:24:04.853 --> 00:24:06.803
I lead by example.
00:24:07.252 --> 00:24:13.413
And so if I am, I'm honest with people and I have their best interests at heart.
00:24:13.952 --> 00:24:19.653
And if I don't know something, I'm honest about it and we'll figure it out together.
00:24:20.932 --> 00:24:21.153
It.
00:24:21.222 --> 00:24:28.218
It's fascinating saying that because then on the flip side, you get this perception that leaders should be this like tough.
00:24:28.623 --> 00:24:29.643
Horrible person.
00:24:29.643 --> 00:24:31.593
Not say I'm horrible, but they're like strict.
00:24:31.593 --> 00:24:36.032
But I think in day, if you get, you want respect from your team, don't you?
00:24:36.093 --> 00:24:38.432
And trust, that's the biggest bit.
00:24:38.432 --> 00:24:45.153
And they want, if,'cause in day they're gonna be more junior in their career and they're gonna have more questions.
00:24:45.153 --> 00:24:50.222
They wanna be confident to come to you to ask questions and get an answer on.
00:24:51.182 --> 00:24:58.113
Yeah, and you can be firm and tough and demanding, but still be kind.
00:24:58.502 --> 00:25:09.232
One, one of the leadership roles I had I was told after the fact that people joked about if they heard the phrase, I don't expect to have this conversation again.
00:25:10.002 --> 00:25:12.593
They all knew oh, that was really bad.
00:25:12.742 --> 00:25:14.212
That better not happen again.
00:25:14.603 --> 00:25:16.613
But I didn't have to yell at someone.
00:25:16.833 --> 00:25:22.383
We'd ha we, we had a level of trust where I said, I don't expect to have to say this again.
00:25:23.192 --> 00:25:26.512
Was enough that they knew that it was serious.
00:25:28.393 --> 00:25:31.847
Again, I think it's, again, it's I guess articulating it in the right manner, isn't it?
00:25:31.897 --> 00:25:32.198
Okay.
00:25:32.347 --> 00:25:32.738
Brilliant.
00:25:32.857 --> 00:25:33.798
No good.
00:25:33.917 --> 00:25:38.357
Okay, so let's move on to the data topic, and I guess.
00:25:38.988 --> 00:25:41.597
It's one that gets overlooked.
00:25:41.698 --> 00:25:43.867
The foundations in ai or data.
00:25:44.238 --> 00:25:48.383
But I feel, I do feel that it's getting more, it's getting more momentum.
00:25:48.383 --> 00:25:50.637
There's more happening in this space, I think.
00:25:51.258 --> 00:26:00.452
And I think stakeholders are starting to realize that more needs to be done to get the, or to get better results, really which gets businesses want.
00:26:01.083 --> 00:26:05.583
So are, in your opinion, are businesses chasing AI for innovation?
00:26:06.317 --> 00:26:09.377
Or are they just fearing being left behind?
00:26:11.268 --> 00:26:13.548
I am gonna say it's probably both.
00:26:13.738 --> 00:26:18.232
There's a lot of hype about, AI first, and you don't wanna get left behind.
00:26:19.982 --> 00:26:21.992
A lot of that is probably true.
00:26:22.232 --> 00:26:32.282
It's with any great innovation, if you're not figuring out how to use these new really powerful tools, you probably will get left behind.
00:26:32.962 --> 00:26:38.542
And the people who are being the most successful with it, I think are starting with strategy.
00:26:38.873 --> 00:26:39.202
Okay.
00:26:39.202 --> 00:26:40.883
What is my biggest problem?
00:26:40.948 --> 00:26:49.948
That, that I need to resolve and how can I use these tools to really help me be more efficient and more effective?
00:26:50.258 --> 00:27:00.488
Somebody who's just saying we need to do AI for the sake of AI is not gonna be as, as successful as someone who says, Hey, I've got people who are spending too much time.
00:27:00.998 --> 00:27:11.248
Make, taking notes in meetings or having meetings or doing these manual calculations this is where I should focus my attention.
00:27:11.758 --> 00:27:13.823
So it's the folks that are really.
00:27:14.758 --> 00:27:20.278
Trying to drive the strategy with a new powerful tool that I think are being helpful.
00:27:20.998 --> 00:27:26.877
And I think with ai the key is it's so much faster.
00:27:27.147 --> 00:27:33.387
I back during the era of big data, people talked about the three Vs.
00:27:33.387 --> 00:27:37.048
Which are volume, velocity, and variety.
00:27:37.708 --> 00:27:39.988
And during big data it was all about.
00:27:40.843 --> 00:27:47.103
Volume, the volumes just skyrocketed overnight, and we needed the tooling to deal with the volumes.
00:27:47.432 --> 00:27:57.782
Now, with ai, I think it's about velocity because we're doing everything so much faster, and if you're calculating.
00:27:59.298 --> 00:28:03.198
Data faster and you're running things through your models faster.
00:28:03.557 --> 00:28:14.597
As humans, it's harder for us to keep up and if there's errors or problems in it, we could be really far down the road before we identify those problems.
00:28:14.837 --> 00:28:16.667
So I think it's the velocity.
00:28:17.107 --> 00:28:22.298
Is what we need to figure out how to manage in the AI era.
00:28:22.478 --> 00:28:24.788
And it also might be part of the variety.
00:28:24.788 --> 00:28:30.877
'cause now we can do analysis on unstructured data that we could never do before.
00:28:30.907 --> 00:28:39.728
And it's fascinating and it's awesome, but it's also a little bit scary'cause we're doing it so fast.
00:28:40.877 --> 00:28:43.657
Yeah, I mean it's, yeah, so much happening.
00:28:43.728 --> 00:28:46.758
If you just look at the last couple of years, things are just, it's rapid.
00:28:46.857 --> 00:28:50.508
And I think sometimes said, there's, there's so much talk, isn't it?
00:28:50.567 --> 00:28:52.758
People chasing shiny toys.
00:28:53.157 --> 00:28:57.718
When really you should be looking at where you are as a business.
00:28:57.897 --> 00:28:58.617
What's what?
00:28:58.617 --> 00:29:00.147
And Dave, you want, what's your current problem?
00:29:00.147 --> 00:29:02.518
You need to obviously go back to business concepts.
00:29:02.518 --> 00:29:02.788
Don't you
00:29:02.942 --> 00:29:04.377
Yeah, exactly.
00:29:04.587 --> 00:29:10.738
Because Yeah, who knows what's gonna be happening in two years time, four years time, 10 years time?
00:29:11.067 --> 00:29:12.087
You need to just end there.
00:29:12.087 --> 00:29:22.948
You wanna be focusing on what is your key problem now and how can you solve it really, when it's not the other way around where people just go try and look at tech and then trying to solve a problem.
00:29:23.952 --> 00:29:28.452
Yeah, and I think starting with the solution is the wrong answer.
00:29:28.452 --> 00:29:30.343
It's always should be starting with the problem.
00:29:30.343 --> 00:29:33.643
Like I talk, hear people talking about, oh, you can use AI for data quality.
00:29:33.643 --> 00:29:33.762
Great.
00:29:34.982 --> 00:29:38.133
But what's the problem that you're having with data quality?
00:29:38.643 --> 00:29:42.633
Is AI really the answer for that or is it something simpler?
00:29:43.123 --> 00:29:49.843
And I fear there's a little bit of a propensity to over-engineer things with ai.
00:29:49.942 --> 00:29:54.772
Many years ago I was involved in a machine learning project where.
00:29:55.563 --> 00:30:02.702
This was when machine learning first really became big and people were, oh, we gotta do a machine learning project.
00:30:03.063 --> 00:30:10.123
And they figured out how to analyze customer complaints about things using machine learning.
00:30:11.077 --> 00:30:32.188
What we found was I could have a couple of data scientists write a few algorithms in a matter of hours that caught 99% of what the machine learning was catching, and the machine learning was a multimillion dollar project versus, a few hours of a data scientist's time.
00:30:32.188 --> 00:30:38.968
I suspect we will have some over-engineering with some of the AI solving problems that could be solved more easily.
00:30:39.538 --> 00:30:41.938
But that's also part of the learning curve, I think.
00:30:42.603 --> 00:30:47.738
So can can you leverage AR without understanding the data foundations that power it?
00:30:48.738 --> 00:30:50.873
I think that's dangerous.
00:30:50.972 --> 00:30:51.782
Because you don't.
00:30:52.738 --> 00:30:56.397
Know how the AI is interpreting the data.
00:30:56.817 --> 00:31:08.012
So I'm a fundamentalist with data, I think you have to have good data that's well-defined in order to be able to use it at scale.
00:31:08.528 --> 00:31:16.327
If you're analyzing for the first time and you've got disjointed data, it can help you make sense of the data.
00:31:16.748 --> 00:31:30.468
But just like a human brain will make assumptions, the artificial intelligence will make assumptions, and those assumptions may or may not be better than the human brain's assumption.
00:31:30.807 --> 00:31:32.488
But there's still assumptions.
00:31:32.488 --> 00:31:33.387
So if I've got.
00:31:33.923 --> 00:31:36.442
A data set with 10 different dates in it.
00:31:37.073 --> 00:31:46.133
I can probably, I can definitely tell either as a human or as artificial intelligence that they're dates and you can make.
00:31:47.153 --> 00:31:56.212
You can discern what the dates probably mean, but you may not be able to discern, discern exactly what those dates mean.
00:31:56.613 --> 00:32:00.903
Just from looking at the data, you would need to know the processes that go into it.
00:32:00.962 --> 00:32:10.147
So if you don't understand the implications of your data, then you're bound to interpret it incorrectly.
00:32:11.147 --> 00:32:11.657
Yeah.
00:32:11.657 --> 00:32:11.748
And.
00:32:12.748 --> 00:32:18.553
In that regards, is there, do leaders misunderstand the relationship between AI and data then?
00:32:19.553 --> 00:32:22.133
That's a great question.
00:32:23.133 --> 00:32:24.002
I don't know.
00:32:25.002 --> 00:32:34.202
I don't know if it is misunderstand the relationship or I think a lot of people don't really know.
00:32:35.807 --> 00:32:35.928
I.
00:32:37.008 --> 00:32:41.278
How messy data foundations can be.
00:32:41.577 --> 00:32:45.357
When we tend to think of things on human terms.
00:32:45.417 --> 00:32:49.117
And so I think of I often use the analogy of a library.
00:32:49.742 --> 00:32:52.173
My bookshelves at home, I can find any book.
00:32:52.532 --> 00:32:53.613
Doesn't really matter.
00:32:53.673 --> 00:32:54.752
I don't have that many.
00:32:55.173 --> 00:32:58.982
If you go to the local public library, there's a lot more books.
00:32:58.982 --> 00:33:05.823
It would take you a lot longer if they weren't organized with good metadata telling you exactly what shelf the book is on.
00:33:06.242 --> 00:33:15.133
But if you go to the Library of Congress you could look at a lifetime and never find the book if it's not well organized and well documented.
00:33:15.373 --> 00:33:17.143
And so I think we think.
00:33:17.428 --> 00:33:28.107
Of data as we encounter it in our daily lives, and it's not as overwhelming as our business data truly is.
00:33:28.167 --> 00:33:33.567
So I think it's just a matter of realizing the.
00:33:34.063 --> 00:33:38.232
How complex our data can truly be.
00:33:38.442 --> 00:33:47.053
And it's not until you, you start trying to dig into a database that you can find out quickly how overwhelming it can be.
00:33:49.002 --> 00:33:52.153
Yeah, no, I think it's, yeah, it is definitely complex.
00:33:52.992 --> 00:33:57.613
It's, I think it's, yeah, it's so much to go that going on, isn't it really?
00:33:57.673 --> 00:34:01.732
And it's obviously, it's challenging for businesses'cause there's so much there.
00:34:02.482 --> 00:34:05.553
So what, what is the danger in skipping, like the fundamentals of AI then?
00:34:07.407 --> 00:34:14.557
I think the dangers are you are doing analysis on potentially undefined data.
00:34:15.588 --> 00:34:20.202
And what we're learning about AI is if it can't.
00:34:21.132 --> 00:34:25.543
Find the facts that it's looking for, it will make them up.
00:34:26.563 --> 00:34:39.552
There's been lots of cases where ai ha it's trying to optimize for something and so it will do what it needs to do to be it able to optimize.
00:34:39.603 --> 00:34:41.563
One of my favorite cases.
00:34:42.387 --> 00:34:43.257
There's two of them.
00:34:43.257 --> 00:35:06.512
One was there's a company out there, I forget the name of the company that was doing software development and it, the AI either accidentally or intentionally deleted an entire database from this company and then figured out how to cover up its tracks and lie about it, saying that it hadn't deleted the entire database.
00:35:06.632 --> 00:35:08.463
That seems a little crazy to me.
00:35:08.922 --> 00:35:10.663
But it's optimizing.
00:35:11.213 --> 00:35:16.822
So if you don't understand what could becau what could cause different results?
00:35:17.282 --> 00:35:19.742
You're gonna end up with some crazy stuff.
00:35:20.012 --> 00:35:32.833
There was also a legal case where they ran some AI to do some of the legal analysis, and it turned out that it took a bunch of pieces, I think from different.
00:35:33.913 --> 00:35:47.043
Case files and merged it to create a case that gave it the results they needed to optimize for that particular problem they were trying to solve.
00:35:47.313 --> 00:35:51.213
And then when someone dug into it, they found out that wasn't a real case.
00:35:52.592 --> 00:35:58.422
So I think the challenge is you have to understand the underlying.
00:35:58.478 --> 00:36:03.427
Data to ensure that you're getting real results.
00:36:03.478 --> 00:36:05.157
That it's not hallucinating.
00:36:05.987 --> 00:36:12.538
Yeah, and I think, you've worked in financial services and that's a important sector.
00:36:12.777 --> 00:36:20.458
And even in I guess if you look at other industries like pharma, the data there is so much more sensitive, isn't it?
00:36:20.487 --> 00:36:26.853
As opposed to, and obviously we obviously want the correct data across the industries, but different industries, we're gonna have different impacts, aren't there?
00:36:27.818 --> 00:36:28.237
Yeah.
00:36:28.237 --> 00:36:31.422
In the pharmaceutical industry it impacts your health.
00:36:31.422 --> 00:36:35.717
And personally, I think your health is if you don't have your health, you don't have anything.
00:36:36.108 --> 00:36:37.668
I'd rather lose my money than my health.
00:36:39.302 --> 00:36:39.693
yeah.
00:36:40.682 --> 00:36:45.768
But yeah again, if you, there's massive, obviously, like that's, it could be the extreme kind of that industry.
00:36:46.112 --> 00:36:47.523
Oh, absolutely.
00:36:47.583 --> 00:36:48.543
Absolutely.
00:36:48.932 --> 00:36:55.402
And there was an interesting article that came out recently about doctors using ai.
00:36:55.452 --> 00:37:05.262
And somebody did a study where someone had broken their hand, and depending on how you prompted the ai, it.
00:37:06.507 --> 00:37:17.708
It may or may not ha they, they did a test of prompting the AI in five or six different ways, and a couple of the cases it found the fracture and in a couple of cases it didn't.
00:37:18.108 --> 00:37:25.878
But the simplest prompt was, evaluate this x-ray or this scan, and it came back with nothing.
00:37:25.907 --> 00:37:29.927
And so they found that the doctors who were starting to rely on ai.
00:37:30.447 --> 00:37:41.137
Are also not finding things when they're evaluating scans, so it's a, an interesting chicken and egg.
00:37:42.867 --> 00:37:43.168
Yeah.
00:37:43.768 --> 00:37:46.637
Again, it's like I say, it's learning, isn't it?
00:37:46.637 --> 00:37:49.547
End of the day we still it's gonna get better and better.
00:37:49.608 --> 00:37:56.057
But again, you want that, again, the right foundations in place so you can overcome this.
00:37:56.237 --> 00:38:03.288
These these can be, some of them could be big problems for people, but yeah, no, I think it's things will get better and better.
00:38:04.097 --> 00:38:08.418
Who should be responsible for ensuring AI is built on the right foundations?
00:38:10.052 --> 00:38:13.483
It really needs to be a group effort.
00:38:13.592 --> 00:38:17.762
The business leaders need to ensure that they're getting good answers.
00:38:18.152 --> 00:38:23.043
And in order to get good answers, you need to have a good foundation.
00:38:23.463 --> 00:38:28.873
I think of it like if you're gonna build a skyscraper in Manhattan you don't wanna build it.
00:38:29.253 --> 00:38:37.253
On shifting sands you wanna make sure you have deep pilings, you wanna make sure you have a good foundation, otherwise, disaster happens.
00:38:37.523 --> 00:38:40.512
It's the same thing with data analytics.
00:38:40.753 --> 00:38:52.592
If you're building on poor quality or undocumented data, you're more likely to have pro structural problems with your analytics.
00:38:53.103 --> 00:39:02.753
The business leaders need to ensure it, but they need to partner with their CIOs and their chief data officers to make sure that they have good foundations.
00:39:04.387 --> 00:39:04.717
Brilliant.
00:39:04.807 --> 00:39:13.737
And yeah I think business and data tech, I mean they are starting to integrate more from my expertise dealing with businesses as well.
00:39:13.907 --> 00:39:30.708
Yeah, I see it happening more and more, and especially AI, I think is driving it like people are excited about AI and wanna be able to use it and are hearing some of the horror stories.
00:39:30.818 --> 00:39:33.547
So they're figuring out how can we do this safely?
00:39:34.547 --> 00:39:41.827
So then how would we shift the culture from like quick wins AI to like long-term capability building?
00:39:42.827 --> 00:39:51.418
I dunno that you have to, I think in business you're always gonna have the desire for quick wins.
00:39:51.838 --> 00:40:07.998
And part of is, and as long as those quick wins come with a strategic plan on how to improve the foundations, I think that I don't think you need to get rid of the quick wins.
00:40:08.478 --> 00:40:19.237
What I'm seeing in the AI world is that the companies that had good data foundations are able to leverage that to get quick wins.
00:40:20.137 --> 00:40:21.967
But if you don't have a.
00:40:22.907 --> 00:40:32.958
The Sound Data Foundation right now, starting with a strategic plan, what's gonna be most impactful for your company?
00:40:33.688 --> 00:40:45.498
Build the foundations in that particular area to do a quick win in ai and then build the foundations elsewhere so that you can have quick wins later on.
00:40:45.557 --> 00:40:47.927
But I think in the, in.
00:40:49.608 --> 00:40:52.177
As human beings we like our quick wins.
00:40:52.237 --> 00:40:56.378
We do not have a lot of patience for, this'll be great in five years.
00:40:57.233 --> 00:40:57.682
Yeah.
00:40:58.373 --> 00:40:58.702
Okay.
00:40:59.523 --> 00:41:00.123
No I agree.
00:41:00.172 --> 00:41:04.163
I think, again, it obviously depends on the project and where you're at.
00:41:04.282 --> 00:41:04.503
Yep.
00:41:04.853 --> 00:41:16.597
What, so what happen if companies wanted to focus more on their sort of data foundations, pause their AI initiatives and focused on becoming more DA data mature?
00:41:17.597 --> 00:41:19.172
Could they do that or.
00:41:19.547 --> 00:41:19.938
Oh.
00:41:19.938 --> 00:41:20.447
Obvious.
00:41:20.538 --> 00:41:24.947
Yeah, that would in, in a textbook world, that would be great.
00:41:25.007 --> 00:41:28.847
You get your foundations set and in good order.
00:41:29.128 --> 00:41:30.088
But I don't think you need.
00:41:31.213 --> 00:41:36.213
To reset, I think you can do them both at the same time.
00:41:36.213 --> 00:41:38.342
As long as you're strategic about it.
00:41:38.402 --> 00:41:54.853
You can't, the old phrase about you can't boil the ocean, but you can figure out what's the most important data that you have that you can leverage AI with and get that organized and in order, and then.
00:41:55.427 --> 00:41:58.608
Run, get your quick wins with AI there.
00:41:59.268 --> 00:42:06.498
I should also say there's a lot to be said for using AI to help you organize your data.
00:42:06.978 --> 00:42:14.418
It can, you can use it to profile and make suggestions and organize data.
00:42:14.628 --> 00:42:15.557
You just have to be.
00:42:16.422 --> 00:42:18.463
You just have to be sure that it's right.
00:42:18.492 --> 00:42:21.193
It's trust put but verify.
00:42:21.773 --> 00:42:27.432
When it's making suggestions on how to organize things or when it's coming out with its analysis,
00:42:28.827 --> 00:42:29.068
Yeah.
00:42:29.547 --> 00:42:35.577
And then so how, how would, what would be a good starting point for someone to build their AI foundations?
00:42:35.762 --> 00:42:35.882
I.
00:42:37.728 --> 00:42:37.847
I.
00:42:38.777 --> 00:42:41.867
Again, it goes back to what's the problem you're trying to solve.
00:42:41.867 --> 00:42:47.248
But the key steps are, one is it in the technology that you need it to be in?
00:42:47.588 --> 00:42:51.157
If you're dealing with unstructured data, do you have easy access to it?
00:42:51.157 --> 00:42:51.818
Is it res?
00:42:52.463 --> 00:42:53.902
Machine readable.
00:42:54.083 --> 00:42:56.123
Is it in the right technology?
00:42:56.123 --> 00:42:57.293
Has it been ocr?
00:42:57.503 --> 00:43:04.313
If it's structured data, is it in a structured environment that is, is easy to use?
00:43:04.313 --> 00:43:10.322
I'm gonna say as much as I love Excel as a finance person, it's a terrible database.
00:43:11.818 --> 00:43:21.788
And so folks who are keeping their data, stored in Excel are gonna run into challenges with trying to run AI on top of that.
00:43:21.788 --> 00:43:31.867
So the first step is make sure it's in the appropriate technology and storage facilities to really be able to enable your ai.
00:43:32.407 --> 00:43:37.478
And the second is to make sure that it's properly.
00:43:38.092 --> 00:43:43.672
Labeled and defined whether you're dealing with structured or unstructured.
00:43:44.103 --> 00:43:51.663
Unstructured is a little bit harder, but you can set the parameters around what the data are and what the data aren't.
00:43:52.032 --> 00:44:04.702
And if it's structured data, you can tell me exactly what's every date mean so that I, AI doesn't have to interpret it or hypothesize what these dates could be.
00:44:04.733 --> 00:44:05.572
So it's your.
00:44:05.978 --> 00:44:09.818
Technical structure in your metadata, I think are the two key foundations.
00:44:11.188 --> 00:44:11.608
Brilliant.
00:44:11.887 --> 00:44:13.682
Some great advice shared today.
00:44:13.782 --> 00:44:18.342
I really appreciate you being on the podcast and sharing career journey.
00:44:18.742 --> 00:44:20.398
And yeah, obviously discussing the.
00:44:21.543 --> 00:44:24.873
Foundations, which is growing in popular.
00:44:25.097 --> 00:44:25.478
I'd say.
00:44:26.478 --> 00:44:26.697
Yes.
00:44:28.402 --> 00:44:29.572
Thanks so much for having me.
00:44:29.572 --> 00:44:29.998
This was fun.
00:44:30.998 --> 00:44:31.217
Cheers.
00:00:00.000 --> 00:00:06.815
The biggest difference between working for the public sector and the private sector is in the public sector.
00:00:06.875 --> 00:00:11.118
Most people are really motivated by a mission.
00:00:11.148 --> 00:00:24.599
I think there is an unfair prejudice from your average person that the government worker, I doesn't work as hard or isn't as smart as someone in the private sector.
00:00:24.838 --> 00:00:27.689
And I have to say that was not my experience.
00:00:27.719 --> 00:00:35.939
I've actually been criticized a couple of times in my career that I'm too kind or I'm too nice, and I find that shocking.
00:00:36.219 --> 00:00:42.459
One of the best work environments I ever had was I had a team of data scientists who.
00:00:43.393 --> 00:00:48.524
We're all best friends and they spent their weekends together.
00:00:48.554 --> 00:00:57.404
They all wanted to come into the office every day'cause they were excited about the work and they were excited about seeing each other and working together.
00:01:10.307 --> 00:01:21.016
Welcome back to another episode of Data Analytics Chat podcast, where we discuss the world of data, ai, and the careers shaping it.
00:01:21.647 --> 00:01:33.257
Today I am excited to welcome Linda Powell, the former Deputy Chief Data Officer who has worked with leading companies such as BMY, Meum and Citibank.
00:01:34.052 --> 00:01:42.992
In today's episode, we'll explore her exciting career journey and discuss the topic of building AI without foundations.
00:01:43.802 --> 00:01:45.751
Delinda, welcome to the podcast.
00:01:46.841 --> 00:01:47.492
Thanks, Ben.
00:01:47.581 --> 00:01:49.441
Glad to have the conversation today.
00:01:50.072 --> 00:01:50.462
Brilliant.
00:01:50.492 --> 00:01:51.361
Pleasure to have you on.
00:01:52.022 --> 00:01:56.371
So do you wanna start off with sharing your career journey
00:01:56.798 --> 00:01:57.397
Sure.
00:01:57.498 --> 00:02:00.927
Mine's been not a straight path into data.
00:02:01.347 --> 00:02:05.608
I graduated from Rutgers University with a degree in economics.
00:02:05.617 --> 00:02:14.978
Knowing that I wanted to do banking and worked in internal audit for several years, and I've actually been surprised along the way how many people.
00:02:15.758 --> 00:02:31.448
That ended up in data, did start their careers in audit and I think that's'cause it gives you a great foundation on controls and figuring out where are the needed operational paths for various processes.
00:02:31.807 --> 00:02:35.078
So I started in commercial banking quite a while ago.
00:02:35.467 --> 00:02:41.978
Then I moved over to the Federal Reserve For the majority of my career, I worked both, both the New York Fed.
00:02:42.888 --> 00:02:51.048
And at the Board of Governors, and it was at the Federal Reserve that I moved from internal auditing into data management.
00:02:51.277 --> 00:03:06.687
Around that time I also did a degree in data database design and programming, and realized I hated writing code, but I loved the database design things.
00:03:06.788 --> 00:03:08.108
So that really.
00:03:08.557 --> 00:03:13.027
Pushed my career more towards the data management side.
00:03:13.027 --> 00:03:14.948
'cause I just found it so interesting.
00:03:15.427 --> 00:03:23.889
Spent most of my career at the Fed, spent a couple years at the Treasury Department after the financial crisis, and then at the CFPB.
00:03:24.638 --> 00:03:28.598
For about four years, which was an incredibly fun stint.
00:03:28.627 --> 00:03:40.568
We went from having to build the entire tech stack to, to the metadata and then the government governance.
00:03:40.627 --> 00:03:46.687
And finally, we had a great data science team by the time I left there to go to Citibank.
00:03:46.687 --> 00:03:52.298
So it was really fun to build the nuts and bolts and then get to do the analytics on top of it.
00:03:53.377 --> 00:03:59.318
Spent about three years at Citibank and then moved over to Bank of New York Mellon.
00:03:59.467 --> 00:04:00.818
So that's the path.
00:04:00.867 --> 00:04:15.888
Always a bend towards data and also more of a bend towards the business side of things than the technology side, although absolutely need to have both to be able to do data.
00:04:17.668 --> 00:04:20.283
Brilliant, and I guess obviously you've.
00:04:21.283 --> 00:04:29.463
The public and private sector, how, how does that is, is it difference for you or is it still the same problems?
00:04:30.632 --> 00:04:38.322
So that's the question I get the most from people'cause not everyone crosses between public and private sector.
00:04:38.713 --> 00:04:41.413
I have to say that there.
00:04:41.788 --> 00:04:43.677
Surprisingly similar.
00:04:44.098 --> 00:04:51.668
The biggest difference between working for the public sector and the private sector is in the public sector.
00:04:51.728 --> 00:04:56.228
Most people are really motivated by a mission.
00:04:56.257 --> 00:05:01.687
People join a government agency that has a mission that they feel passionate about.
00:05:01.718 --> 00:05:07.817
I, when I worked for the Fed every day, I went home from work thinking, wow I've helped the economy today.
00:05:08.398 --> 00:05:08.408
I.
00:05:09.132 --> 00:05:18.742
Whereas when you're working for the private sector, it's a much more business oriented motivation that you have on a day-to-day basis.
00:05:18.742 --> 00:05:22.223
You're trying to optimize the product, you're trying to optimize revenue.
00:05:22.673 --> 00:05:29.822
So in the private sector, you get a little bit more dispersion in the motivation.
00:05:29.932 --> 00:05:33.992
Because people have different passions around the same topic.
00:05:34.423 --> 00:05:46.012
Whereas you're more aligned in the private sector because you're really focused on optimizing your returns and the shareholder returns.
00:05:46.432 --> 00:05:50.853
But people are people everybody wants to do a good job wherever they work.
00:05:51.333 --> 00:05:52.713
People tend to.
00:05:53.713 --> 00:05:55.783
Be motivated by similar things.
00:05:55.783 --> 00:05:57.192
They want interesting work.
00:05:57.583 --> 00:06:02.023
They want to get paid for their work, and they wanna be appreciated.
00:06:02.023 --> 00:06:06.822
So in how you do the work, it's not all that different.
00:06:06.992 --> 00:06:10.413
And also the problems you encounter from a data point of view.
00:06:10.843 --> 00:06:12.853
Are relatively similar.
00:06:12.942 --> 00:06:21.213
The technology is similar, if not exactly the same and how you tackle those problems is pretty similar.
00:06:22.598 --> 00:06:25.762
I think it's fascinating because I get, every business is different, isn't it?
00:06:25.762 --> 00:06:26.452
Everyone has their own
00:06:26.583 --> 00:06:27.543
Oh yeah.
00:06:28.733 --> 00:06:36.252
And it is always, like obviously some businesses, like you say, you work with financial services, they tend to want people that have worked in financial services and similar with other industries.
00:06:36.882 --> 00:06:41.773
So how is it trying to transfer from public to.
00:06:43.072 --> 00:06:52.132
Do you get, do people say no when you interviewed or what is your everyone's different and there is, people have their thoughts, don't they?
00:06:52.163 --> 00:06:54.497
So it'd be good to get your thoughts on how you overcome this.
00:06:55.473 --> 00:07:09.372
Yeah, I think there is an unfair prejudice from your average person that the government worker, I doesn't work as hard or isn't as smart as someone in the private sector.
00:07:09.612 --> 00:07:12.463
And I have to say that was not my experience.
00:07:12.533 --> 00:07:14.283
I honestly the.
00:07:15.148 --> 00:07:23.427
Role where I worked the longest and most arduous hours was the two years that I was at the Treasury Department.
00:07:23.427 --> 00:07:28.718
I think I put in 14 hours a day and then one day on every weekend.
00:07:29.077 --> 00:07:31.507
And I left that job saying, I will never do that again.
00:07:32.862 --> 00:07:35.767
'cause after, after two years, it was a little grinding.
00:07:35.958 --> 00:07:38.267
And I know people do that on Wall Street all the time.
00:07:39.278 --> 00:07:47.048
But I think there are people on both sides are intelligent and working hard.
00:07:47.557 --> 00:07:50.557
So when I was looking to go back to the private sector.
00:07:52.148 --> 00:07:54.697
I did get a lot of resistance.
00:07:54.728 --> 00:08:00.637
I would go on interviews and I would be, one of the last couple people for the jobs.
00:08:00.697 --> 00:08:14.677
And I would get feedback after the interview process that they really liked you and they really thought you were the best candidate, but they were worried because you had spent so many years in government, they weren't sure that you could hack it.
00:08:15.677 --> 00:08:30.627
I had repeated interviews from saying that, and I just found it so surprising that, that was the common perception about the government sector to the point where it would, they would not hire me for a long time.
00:08:30.947 --> 00:08:40.447
And then the breakthrough role came at Citibank where they were looking for someone with some public sector experience and.
00:08:41.107 --> 00:08:44.028
It was a great couple of years at Citibank.
00:08:45.168 --> 00:08:45.408
Yeah.
00:08:45.408 --> 00:08:57.852
No, it's fa it's fascinating'cause it happens in, even in any industry like pri in private like moving from say like financial services into or from, say retail into financial services.
00:08:57.857 --> 00:08:59.268
'cause I mean that just wouldn't happen.
00:08:59.268 --> 00:09:04.307
But I think it's more, I guess it depends on the individual, doesn't it?
00:09:04.307 --> 00:09:05.388
Any expertise.
00:09:05.488 --> 00:09:16.092
'Cause also in the financial services, I mean they did, they've been a more open in recent years, in my opinion, because their legacy applications, they've had to get that knowledge in the business, haven't they?
00:09:17.038 --> 00:09:17.607
Yeah.
00:09:17.727 --> 00:09:19.258
Yeah, I think that's true.
00:09:20.623 --> 00:09:21.403
I guess it's an niche.
00:09:21.972 --> 00:09:24.613
Yes, there's so much resistance across businesses.
00:09:24.952 --> 00:09:26.488
But it's funny how people get these thoughts.
00:09:27.758 --> 00:09:28.447
Yeah.
00:09:28.508 --> 00:09:28.927
Yeah.
00:09:28.927 --> 00:09:35.947
And it's funny, I had a conversation the other day with a friend talking about the challenges of working in government.
00:09:36.337 --> 00:09:42.548
And she automatically said, oh, the worst thing about working in government is that you can't fire people.
00:09:42.597 --> 00:09:45.327
And this is someone who had never worked in government, and I said.
00:09:46.173 --> 00:09:47.763
That, that's just not true.
00:09:48.332 --> 00:09:51.092
That is not the hardest part about working in government.
00:09:51.123 --> 00:09:54.143
I I unfortunately fired several people.
00:09:54.493 --> 00:10:03.003
You just have to go through the process and if you've got a good, strong manager, they'll go through the process of letting people go who aren't right for the.
00:10:03.663 --> 00:10:07.013
The position is just work as it is in the private sector.
00:10:07.013 --> 00:10:15.033
You have to put people on a performance plan and evaluate them and it's a little bit harder to let people go from the government sector.
00:10:15.273 --> 00:10:21.482
What I found to be the absolute hardest part of working in the government sector was hiring.
00:10:22.043 --> 00:10:34.613
Because there's so many rules and restrictions and preferences given to different communities that you can find the perfect person who's willing to come for a lesser salary.
00:10:35.182 --> 00:10:41.712
And it's, it is sometimes almost impossible to, to hire them into a regular, full-time government job.
00:10:41.743 --> 00:10:48.643
So it's funny how there's perceptions out there about, about what the problems are.
00:10:49.212 --> 00:10:49.572
Yep.
00:10:49.962 --> 00:10:50.232
Okay.
00:10:50.232 --> 00:10:52.283
Everyone's had, has different experiences, isn't it?
00:10:52.462 --> 00:10:55.493
And so it's, yeah, it's interesting.
00:10:55.493 --> 00:10:55.653
See.
00:10:56.423 --> 00:10:57.052
Okay.
00:10:57.232 --> 00:11:02.572
So what if you had to pinpoint like one or two defining moments that changed your career, what would they be?
00:11:04.312 --> 00:11:07.393
For me, it was always education related.
00:11:07.663 --> 00:11:18.322
So when I first went back after my undergraduate degree, I went back for a certification in programming and system design.
00:11:18.322 --> 00:11:19.913
I think I mentioned that before.
00:11:20.423 --> 00:11:26.092
And that really changed the trajectory of what I found interesting at work.
00:11:26.393 --> 00:11:28.312
And I focused more and more.
00:11:28.852 --> 00:11:37.192
On the data side of work and that, that led me, that was really my transformation too.
00:11:38.062 --> 00:11:39.082
The data realm.
00:11:39.413 --> 00:11:48.722
And then the other big pivotal point was I went back and got my master's in quantitative finance after, after many years of working.
00:11:49.263 --> 00:12:01.793
And that became a pivot point also because I was then able to understand, stand and do the analysis that you need to do with to really.
00:12:02.812 --> 00:12:06.143
Build on the insights for data.
00:12:06.413 --> 00:12:12.302
So I had, many instances where I had a gut feel that something wasn't right.
00:12:12.363 --> 00:12:15.572
After working with some statisticians and.
00:12:16.232 --> 00:12:19.143
And not trusting the results that I was getting.
00:12:19.202 --> 00:12:34.202
And it wasn't until after I went through my master's program and learned the math and the statistics behind the various models that I could understand why the models weren't right.
00:12:34.253 --> 00:12:41.092
There might've been an assumption that was bad or it might've been just the wrong type of model that we were trying to use.
00:12:41.543 --> 00:12:43.072
And so that really.
00:12:44.067 --> 00:12:51.087
Pushed me into the analytics side of data management, and I find it incredibly useful.
00:12:51.138 --> 00:13:03.668
I don't actually use my math as often as I should, but when we're talking about AI models and AI algorithms and programs, I have the statistical foundation.
00:13:04.133 --> 00:13:13.452
To be able to understand how that works and why where the problems might be underlying some of the models.
00:13:14.457 --> 00:13:14.668
Yeah.
00:13:14.668 --> 00:13:27.092
And I think, that's just a common theme in today's market speaks to a lot of leaders that individuals are like lacking on that the mathematical statistical model, like the theory side.
00:13:27.722 --> 00:13:28.618
So I guess for your.
00:13:28.773 --> 00:13:31.202
Yeah, I think that's so important.
00:13:31.232 --> 00:13:46.982
I'll just highlight that, that you can run your analysis through a model, but if you don't understand the complexities of the model and you just trust the results you're potentially trusting bad results.
00:13:47.013 --> 00:13:52.413
You really need to understand how things are, the math behind it.
00:13:53.413 --> 00:13:53.913
So did.
00:13:55.908 --> 00:14:04.908
Did you just, was it more you being self-aware that you needed to get that acknowledge or did you get guided or was Yeah.
00:14:04.967 --> 00:14:07.717
Where did that sort of later academics come into?
00:14:08.732 --> 00:14:08.852
I.
00:14:09.232 --> 00:14:12.472
So for me it was always about curiosity.
00:14:12.682 --> 00:14:24.118
At the time that I went back for my degree in programming and system design, it was really I dating myself here, but that was at the early.
00:14:24.982 --> 00:14:27.173
Stages of personal computing.
00:14:27.682 --> 00:14:29.753
And I was very curious about it.
00:14:29.842 --> 00:14:32.153
And I'm a classroom learner.
00:14:32.153 --> 00:14:36.452
I'm not as good as some people at just reading the books and learning it.
00:14:36.452 --> 00:14:39.842
So I went back to the classroom to learn more about it.
00:14:40.273 --> 00:14:44.442
I had also, at the same time, I did a certification in.
00:14:45.638 --> 00:14:47.018
Credit analysis.
00:14:47.067 --> 00:14:50.087
I thought at the time I wanted to be a credit officer.
00:14:50.427 --> 00:14:51.327
So I, I.
00:14:52.582 --> 00:14:56.513
Pursued academics in the areas that I found interesting.
00:14:56.812 --> 00:15:03.352
Finance was a little bit of convenience, opportunity and interest.
00:15:03.452 --> 00:15:07.003
I had considered getting a degree in economics.
00:15:07.293 --> 00:15:10.202
I there was a couple of things I was considering.
00:15:10.582 --> 00:15:16.673
For me the, what pushed me over the edge for the quantitative finance was I was living in DC.
00:15:17.173 --> 00:15:28.993
George Washington University was one of the first university to offer a program in quantitative finance, and I could walk to the classrooms after I got out of work.
00:15:29.043 --> 00:15:38.763
I had two young kids at the time and a full-time job, so I knew if I had to go home before going to class, I probably would've not made it to class.
00:15:38.822 --> 00:15:44.043
So I chose a university with a program that I wanted that was.
00:15:44.488 --> 00:15:46.227
Super convenient.
00:15:47.227 --> 00:15:47.857
Yeah.
00:15:48.097 --> 00:15:48.788
Yeah.
00:15:48.878 --> 00:15:49.748
I didn't go home.
00:15:49.748 --> 00:15:51.847
I just went straight from work to class.
00:15:52.222 --> 00:15:56.587
Yeah, because it's it, obviously I know you did that, but it's like similar to after work going to the gym, isn't it?
00:15:56.587 --> 00:15:57.187
It's hard.
00:15:57.248 --> 00:15:59.977
You gotta get, it's like that extra full size.
00:15:59.977 --> 00:16:09.097
A lot of people do struggle and I think it's obviously fair, but it's amazing you did it'cause holding down a work or you got a job, another job with your kids and then study.
00:16:09.097 --> 00:16:09.668
So it's a lot.
00:16:10.798 --> 00:16:11.398
Yeah.
00:16:11.548 --> 00:16:13.018
But it was a great two years.
00:16:13.018 --> 00:16:23.557
It was I'll say I missed the academic stimulation, but I sat for the first part of the CFA exam afterwards thinking I'll continue with my education.
00:16:24.008 --> 00:16:27.548
And before I even took the exam, I was like, oh, I'm done.
00:16:29.357 --> 00:16:31.067
I need to go back to having a life.
00:16:32.067 --> 00:16:35.128
So then have you ever faced like a major career setback?
00:16:36.128 --> 00:16:39.658
I don't think I know anyone who hasn't yeah, of course I have.
00:16:39.807 --> 00:16:47.317
I'll share an early career setback that I had that it took me years to emotionally get over it.
00:16:47.868 --> 00:16:48.648
But I think.
00:16:49.217 --> 00:16:52.878
In hindsight it was a good thing to, to happen to me.
00:16:52.977 --> 00:17:03.758
Early on in my career I was really decided I really wanted to be a credit officer and the big banks in New York all had these credit training programs back then.
00:17:04.147 --> 00:17:08.907
And I had started my career in internal audit at Chemical Bank and.
00:17:09.643 --> 00:17:14.292
I would had applied for the credit training program and I was told I was gonna get in.
00:17:14.633 --> 00:17:19.673
But back then your management had to approve a transfer within the bank.
00:17:19.913 --> 00:17:22.313
You couldn't just get the job and move.
00:17:22.563 --> 00:17:30.393
And they declined letting me go into the program, and I just thought it was the end of my career, the end of my life.
00:17:30.393 --> 00:17:31.742
I was so sad.
00:17:32.232 --> 00:17:34.532
I just thought, why do I bother?
00:17:34.742 --> 00:17:41.803
I'll just, carry on with work and in hindsight, I'm glad I didn't go the credit training program.
00:17:41.803 --> 00:17:53.212
It would've been a great learning experience, but I'm really happy with the path that I took and I probably wouldn't have gone down the data path had I gotten into the.
00:17:53.323 --> 00:17:54.732
The credit program.
00:17:54.732 --> 00:18:01.633
So I try to look back now, like every time I hit a bump and there's lots of them.
00:18:02.653 --> 00:18:03.373
We're not human.
00:18:03.373 --> 00:18:05.272
If we don't hit those career bumps.
00:18:05.452 --> 00:18:09.393
And I try to think about, okay, what door is it opening?
00:18:09.792 --> 00:18:15.123
If I'm not gonna go through, through that door what's the path that I should be taking?
00:18:15.363 --> 00:18:18.722
Sometimes I'm successful in that attitude and sometimes I'm not.
00:18:19.998 --> 00:18:20.643
It's humans.
00:18:20.643 --> 00:18:23.282
I mean it, like I said, when you go to that element, isn't it?
00:18:23.432 --> 00:18:24.962
It's like you are in sinking sand.
00:18:25.923 --> 00:18:26.252
Oh,
00:18:26.367 --> 00:18:27.048
the worst thing ever.
00:18:27.278 --> 00:18:30.577
But obviously now looking back, you probably think it was nothing.
00:18:31.268 --> 00:18:31.837
yeah.
00:18:31.837 --> 00:18:32.678
Yeah.
00:18:32.837 --> 00:18:37.567
I'm very happy with the path my career took so I have no complaints.
00:18:38.542 --> 00:18:44.752
I think it's just, you've gotta just obviously real obviously realize that, and then you gotta snap at it as quick as possible.
00:18:44.752 --> 00:18:52.272
And again, see, focus on the opportunities as opposed to like the negativity of not getting what you want at that period.
00:18:53.083 --> 00:18:53.623
Yeah.
00:18:53.833 --> 00:18:56.053
Yeah, absolutely.
00:18:57.053 --> 00:18:59.032
Obviously the market's highly competitive.
00:18:59.212 --> 00:19:01.883
What do you feel is giving you a.
00:19:02.883 --> 00:19:05.883
I'm gonna go back to my curiosity.
00:19:05.883 --> 00:19:08.222
I think part of it is the luck of timing.
00:19:08.282 --> 00:19:18.532
I got curious about database design and technology and data at a time that.
00:19:19.327 --> 00:19:20.377
It was growing.
00:19:20.688 --> 00:19:25.198
When I first started at work, they called me the metadata maven for a little while.
00:19:25.478 --> 00:19:28.448
And that was at a time when no one knew what metadata was.
00:19:29.498 --> 00:19:40.167
So I think it's been helpful that I've been on the frontier of the data management and data analytics trend.
00:19:40.617 --> 00:19:48.998
But it's also the curiosity and the willingness to go back and get further educated on a lot of the topics.
00:19:49.097 --> 00:19:57.327
If I were 20 years younger, I'd be doing a data science program right now and beefing up my math and modeling skills even more.
00:19:58.327 --> 00:19:58.657
Yeah.
00:19:58.657 --> 00:19:59.557
No, I think it is.
00:20:00.678 --> 00:20:01.938
It's, there's a lot out there, isn't there?
00:20:02.038 --> 00:20:04.482
It's, you've got the tech out there to learn.
00:20:04.542 --> 00:20:10.323
You've got the, and then I think a lot of people do forget about the maths is the, I think it's probably the more, more important bit.
00:20:10.323 --> 00:20:11.343
That's not gonna change.
00:20:11.343 --> 00:20:11.403
I.
00:20:12.258 --> 00:20:12.738
Yeah.
00:20:12.768 --> 00:20:19.417
And I think the other thing that's important is to not be afraid of not being good at things.
00:20:19.837 --> 00:20:30.843
And I guess that's a theme for better or worse in much of my life that I will try something and I'll work towards something even if I'm not good at it right away.
00:20:31.202 --> 00:20:34.022
I took up playing soccer in.
00:20:34.913 --> 00:20:44.603
In my forties because a friend invited me and I was terrible at first, but I was reasonably fast for a middle aged woman.
00:20:45.143 --> 00:20:52.192
So I was valuable to the team in a different way, even though my, my, my foot skills were terrible.
00:20:52.673 --> 00:21:01.103
So it's also, don't be afraid of something, just'cause you're not good at it doesn't mean you won't get better and it doesn't mean you won't learn from it.
00:21:01.133 --> 00:21:03.992
'cause the key is the learning.
00:21:05.567 --> 00:21:06.288
It's funny you say that.
00:21:06.317 --> 00:21:07.488
My mom did the similar thing.
00:21:07.488 --> 00:21:12.363
She started playing soccer, football later in her life as well, so
00:21:13.022 --> 00:21:14.732
Oh, it's such a fun game.
00:21:14.823 --> 00:21:15.728
So much fun.
00:21:16.113 --> 00:21:18.202
Obviously you touching on yeah, no, obviously bit.
00:21:18.282 --> 00:21:26.022
Did you also, you touched on getting over that fear factor and I guess you would've been like the more of like technical expert in your career.
00:21:26.022 --> 00:21:28.873
How did you push yourself into more leadership type work?
00:21:29.202 --> 00:21:33.117
Was it, did you get, because that's a big challenge for individuals I can speak to is.
00:21:34.853 --> 00:21:41.682
Should they just fear they, they've got enough experience, they've got the right knowledge whether they can do it.
00:21:41.923 --> 00:21:49.563
How did you overcome that sort of hurdle to get some knowledge, to get like mentors or how did get, is there anything in particular that's stood out for you?
00:21:50.663 --> 00:21:56.722
I think with most things in my life, if I don't know how to do it, I start by reading about it.
00:21:56.772 --> 00:22:03.133
So when I started playing soccer, I got a couple of kids' books out to learn all the rules in an easy way.
00:22:03.758 --> 00:22:08.048
When I had to do a marketing campaign at work, I never took any marketing classes.
00:22:08.048 --> 00:22:10.988
I went to the library and read every book about the marketing.
00:22:11.347 --> 00:22:13.718
And so with leadership, I did the same thing.
00:22:13.748 --> 00:22:20.607
I read a lot of the leadership books and quite honestly, it's about being authentic.
00:22:21.057 --> 00:22:22.438
And treating people.
00:22:22.988 --> 00:22:35.363
It's like the golden rules you learned as a back in grade school you treat people the way that you wanna be treated and we're all motivated by similar things but not exactly the same things.
00:22:35.393 --> 00:22:40.103
And so asking people what do they want rather than assuming, what do they want?
00:22:40.613 --> 00:22:43.823
Treating people with respect and kindness.
00:22:43.873 --> 00:22:52.093
I've actually been criticized a couple of times in my career that I'm too kind or I'm too nice, and I find that shocking.
00:22:52.423 --> 00:23:03.583
I think it's important that we recognize that we're all humans bringing ourselves to work and we all have lives that we're dealing with.
00:23:04.063 --> 00:23:06.772
And the more you find kindness.
00:23:07.143 --> 00:23:12.133
At work the more motivated you're gonna be to go into the office and work.
00:23:12.163 --> 00:23:18.403
One of the best work environments I ever had was I had a team of data scientists who.
00:23:19.337 --> 00:23:24.468
We're all best friends and they spent their weekends together.
00:23:24.498 --> 00:23:33.347
They all wanted to come into the office every day'cause they were excited about the work and they were excited about seeing each other and working together.
00:23:33.798 --> 00:23:37.218
And we moved mountains for a couple of years.
00:23:37.538 --> 00:23:47.383
Just because this team was so motivated, and I had a boss at one point who said, I can't believe you've got this team of data scientists working for a government agency.
00:23:47.383 --> 00:23:52.903
They could make so much money, more money if they went into the private sector.
00:23:53.563 --> 00:24:00.492
And the honest answer was this was where they wanted to be because they wanted to be together and they had interesting work.
00:24:00.492 --> 00:24:03.853
So I think leadership is about.
00:24:04.853 --> 00:24:06.803
I lead by example.
00:24:07.252 --> 00:24:13.413
And so if I am, I'm honest with people and I have their best interests at heart.
00:24:13.952 --> 00:24:19.653
And if I don't know something, I'm honest about it and we'll figure it out together.
00:24:20.932 --> 00:24:21.153
It.
00:24:21.222 --> 00:24:28.218
It's fascinating saying that because then on the flip side, you get this perception that leaders should be this like tough.
00:24:28.623 --> 00:24:29.643
Horrible person.
00:24:29.643 --> 00:24:31.593
Not say I'm horrible, but they're like strict.
00:24:31.593 --> 00:24:36.032
But I think in day, if you get, you want respect from your team, don't you?
00:24:36.093 --> 00:24:38.432
And trust, that's the biggest bit.
00:24:38.432 --> 00:24:45.153
And they want, if,'cause in day they're gonna be more junior in their career and they're gonna have more questions.
00:24:45.153 --> 00:24:50.222
They wanna be confident to come to you to ask questions and get an answer on.
00:24:51.182 --> 00:24:58.113
Yeah, and you can be firm and tough and demanding, but still be kind.
00:24:58.502 --> 00:25:09.232
One, one of the leadership roles I had I was told after the fact that people joked about if they heard the phrase, I don't expect to have this conversation again.
00:25:10.002 --> 00:25:12.593
They all knew oh, that was really bad.
00:25:12.742 --> 00:25:14.212
That better not happen again.
00:25:14.603 --> 00:25:16.613
But I didn't have to yell at someone.
00:25:16.833 --> 00:25:22.383
We'd ha we, we had a level of trust where I said, I don't expect to have to say this again.
00:25:23.192 --> 00:25:26.512
Was enough that they knew that it was serious.
00:25:28.393 --> 00:25:31.847
Again, I think it's, again, it's I guess articulating it in the right manner, isn't it?
00:25:31.897 --> 00:25:32.198
Okay.
00:25:32.347 --> 00:25:32.738
Brilliant.
00:25:32.857 --> 00:25:33.798
No good.
00:25:33.917 --> 00:25:38.357
Okay, so let's move on to the data topic, and I guess.
00:25:38.988 --> 00:25:41.597
It's one that gets overlooked.
00:25:41.698 --> 00:25:43.867
The foundations in ai or data.
00:25:44.238 --> 00:25:48.383
But I feel, I do feel that it's getting more, it's getting more momentum.
00:25:48.383 --> 00:25:50.637
There's more happening in this space, I think.
00:25:51.258 --> 00:26:00.452
And I think stakeholders are starting to realize that more needs to be done to get the, or to get better results, really which gets businesses want.
00:26:01.083 --> 00:26:05.583
So are, in your opinion, are businesses chasing AI for innovation?
00:26:06.317 --> 00:26:09.377
Or are they just fearing being left behind?
00:26:11.268 --> 00:26:13.548
I am gonna say it's probably both.
00:26:13.738 --> 00:26:18.232
There's a lot of hype about, AI first, and you don't wanna get left behind.
00:26:19.982 --> 00:26:21.992
A lot of that is probably true.
00:26:22.232 --> 00:26:32.282
It's with any great innovation, if you're not figuring out how to use these new really powerful tools, you probably will get left behind.
00:26:32.962 --> 00:26:38.542
And the people who are being the most successful with it, I think are starting with strategy.
00:26:38.873 --> 00:26:39.202
Okay.
00:26:39.202 --> 00:26:40.883
What is my biggest problem?
00:26:40.948 --> 00:26:49.948
That, that I need to resolve and how can I use these tools to really help me be more efficient and more effective?
00:26:50.258 --> 00:27:00.488
Somebody who's just saying we need to do AI for the sake of AI is not gonna be as, as successful as someone who says, Hey, I've got people who are spending too much time.
00:27:00.998 --> 00:27:11.248
Make, taking notes in meetings or having meetings or doing these manual calculations this is where I should focus my attention.
00:27:11.758 --> 00:27:13.823
So it's the folks that are really.
00:27:14.758 --> 00:27:20.278
Trying to drive the strategy with a new powerful tool that I think are being helpful.
00:27:20.998 --> 00:27:26.877
And I think with ai the key is it's so much faster.
00:27:27.147 --> 00:27:33.387
I back during the era of big data, people talked about the three Vs.
00:27:33.387 --> 00:27:37.048
Which are volume, velocity, and variety.
00:27:37.708 --> 00:27:39.988
And during big data it was all about.
00:27:40.843 --> 00:27:47.103
Volume, the volumes just skyrocketed overnight, and we needed the tooling to deal with the volumes.
00:27:47.432 --> 00:27:57.782
Now, with ai, I think it's about velocity because we're doing everything so much faster, and if you're calculating.
00:27:59.298 --> 00:28:03.198
Data faster and you're running things through your models faster.
00:28:03.557 --> 00:28:14.597
As humans, it's harder for us to keep up and if there's errors or problems in it, we could be really far down the road before we identify those problems.
00:28:14.837 --> 00:28:16.667
So I think it's the velocity.
00:28:17.107 --> 00:28:22.298
Is what we need to figure out how to manage in the AI era.
00:28:22.478 --> 00:28:24.788
And it also might be part of the variety.
00:28:24.788 --> 00:28:30.877
'cause now we can do analysis on unstructured data that we could never do before.
00:28:30.907 --> 00:28:39.728
And it's fascinating and it's awesome, but it's also a little bit scary'cause we're doing it so fast.
00:28:40.877 --> 00:28:43.657
Yeah, I mean it's, yeah, so much happening.
00:28:43.728 --> 00:28:46.758
If you just look at the last couple of years, things are just, it's rapid.
00:28:46.857 --> 00:28:50.508
And I think sometimes said, there's, there's so much talk, isn't it?
00:28:50.567 --> 00:28:52.758
People chasing shiny toys.
00:28:53.157 --> 00:28:57.718
When really you should be looking at where you are as a business.
00:28:57.897 --> 00:28:58.617
What's what?
00:28:58.617 --> 00:29:00.147
And Dave, you want, what's your current problem?
00:29:00.147 --> 00:29:02.518
You need to obviously go back to business concepts.
00:29:02.518 --> 00:29:02.788
Don't you
00:29:02.942 --> 00:29:04.377
Yeah, exactly.
00:29:04.587 --> 00:29:10.738
Because Yeah, who knows what's gonna be happening in two years time, four years time, 10 years time?
00:29:11.067 --> 00:29:12.087
You need to just end there.
00:29:12.087 --> 00:29:22.948
You wanna be focusing on what is your key problem now and how can you solve it really, when it's not the other way around where people just go try and look at tech and then trying to solve a problem.
00:29:23.952 --> 00:29:28.452
Yeah, and I think starting with the solution is the wrong answer.
00:29:28.452 --> 00:29:30.343
It's always should be starting with the problem.
00:29:30.343 --> 00:29:33.643
Like I talk, hear people talking about, oh, you can use AI for data quality.
00:29:33.643 --> 00:29:33.762
Great.
00:29:34.982 --> 00:29:38.133
But what's the problem that you're having with data quality?
00:29:38.643 --> 00:29:42.633
Is AI really the answer for that or is it something simpler?
00:29:43.123 --> 00:29:49.843
And I fear there's a little bit of a propensity to over-engineer things with ai.
00:29:49.942 --> 00:29:54.772
Many years ago I was involved in a machine learning project where.
00:29:55.563 --> 00:30:02.702
This was when machine learning first really became big and people were, oh, we gotta do a machine learning project.
00:30:03.063 --> 00:30:10.123
And they figured out how to analyze customer complaints about things using machine learning.
00:30:11.077 --> 00:30:32.188
What we found was I could have a couple of data scientists write a few algorithms in a matter of hours that caught 99% of what the machine learning was catching, and the machine learning was a multimillion dollar project versus, a few hours of a data scientist's time.
00:30:32.188 --> 00:30:38.968
I suspect we will have some over-engineering with some of the AI solving problems that could be solved more easily.
00:30:39.538 --> 00:30:41.938
But that's also part of the learning curve, I think.
00:30:42.603 --> 00:30:47.738
So can can you leverage AR without understanding the data foundations that power it?
00:30:48.738 --> 00:30:50.873
I think that's dangerous.
00:30:50.972 --> 00:30:51.782
Because you don't.
00:30:52.738 --> 00:30:56.397
Know how the AI is interpreting the data.
00:30:56.817 --> 00:31:08.012
So I'm a fundamentalist with data, I think you have to have good data that's well-defined in order to be able to use it at scale.
00:31:08.528 --> 00:31:16.327
If you're analyzing for the first time and you've got disjointed data, it can help you make sense of the data.
00:31:16.748 --> 00:31:30.468
But just like a human brain will make assumptions, the artificial intelligence will make assumptions, and those assumptions may or may not be better than the human brain's assumption.
00:31:30.807 --> 00:31:32.488
But there's still assumptions.
00:31:32.488 --> 00:31:33.387
So if I've got.
00:31:33.923 --> 00:31:36.442
A data set with 10 different dates in it.
00:31:37.073 --> 00:31:46.133
I can probably, I can definitely tell either as a human or as artificial intelligence that they're dates and you can make.
00:31:47.153 --> 00:31:56.212
You can discern what the dates probably mean, but you may not be able to discern, discern exactly what those dates mean.
00:31:56.613 --> 00:32:00.903
Just from looking at the data, you would need to know the processes that go into it.
00:32:00.962 --> 00:32:10.147
So if you don't understand the implications of your data, then you're bound to interpret it incorrectly.
00:32:11.147 --> 00:32:11.657
Yeah.
00:32:11.657 --> 00:32:11.748
And.
00:32:12.748 --> 00:32:18.553
In that regards, is there, do leaders misunderstand the relationship between AI and data then?
00:32:19.553 --> 00:32:22.133
That's a great question.
00:32:23.133 --> 00:32:24.002
I don't know.
00:32:25.002 --> 00:32:34.202
I don't know if it is misunderstand the relationship or I think a lot of people don't really know.
00:32:35.807 --> 00:32:35.928
I.
00:32:37.008 --> 00:32:41.278
How messy data foundations can be.
00:32:41.577 --> 00:32:45.357
When we tend to think of things on human terms.
00:32:45.417 --> 00:32:49.117
And so I think of I often use the analogy of a library.
00:32:49.742 --> 00:32:52.173
My bookshelves at home, I can find any book.
00:32:52.532 --> 00:32:53.613
Doesn't really matter.
00:32:53.673 --> 00:32:54.752
I don't have that many.
00:32:55.173 --> 00:32:58.982
If you go to the local public library, there's a lot more books.
00:32:58.982 --> 00:33:05.823
It would take you a lot longer if they weren't organized with good metadata telling you exactly what shelf the book is on.
00:33:06.242 --> 00:33:15.133
But if you go to the Library of Congress you could look at a lifetime and never find the book if it's not well organized and well documented.
00:33:15.373 --> 00:33:17.143
And so I think we think.
00:33:17.428 --> 00:33:28.107
Of data as we encounter it in our daily lives, and it's not as overwhelming as our business data truly is.
00:33:28.167 --> 00:33:33.567
So I think it's just a matter of realizing the.
00:33:34.063 --> 00:33:38.232
How complex our data can truly be.
00:33:38.442 --> 00:33:47.053
And it's not until you, you start trying to dig into a database that you can find out quickly how overwhelming it can be.
00:33:49.002 --> 00:33:52.153
Yeah, no, I think it's, yeah, it is definitely complex.
00:33:52.992 --> 00:33:57.613
It's, I think it's, yeah, it's so much to go that going on, isn't it really?
00:33:57.673 --> 00:34:01.732
And it's obviously, it's challenging for businesses'cause there's so much there.
00:34:02.482 --> 00:34:05.553
So what, what is the danger in skipping, like the fundamentals of AI then?
00:34:07.407 --> 00:34:14.557
I think the dangers are you are doing analysis on potentially undefined data.
00:34:15.588 --> 00:34:20.202
And what we're learning about AI is if it can't.
00:34:21.132 --> 00:34:25.543
Find the facts that it's looking for, it will make them up.
00:34:26.563 --> 00:34:39.552
There's been lots of cases where ai ha it's trying to optimize for something and so it will do what it needs to do to be it able to optimize.
00:34:39.603 --> 00:34:41.563
One of my favorite cases.
00:34:42.387 --> 00:34:43.257
There's two of them.
00:34:43.257 --> 00:35:06.512
One was there's a company out there, I forget the name of the company that was doing software development and it, the AI either accidentally or intentionally deleted an entire database from this company and then figured out how to cover up its tracks and lie about it, saying that it hadn't deleted the entire database.
00:35:06.632 --> 00:35:08.463
That seems a little crazy to me.
00:35:08.922 --> 00:35:10.663
But it's optimizing.
00:35:11.213 --> 00:35:16.822
So if you don't understand what could becau what could cause different results?
00:35:17.282 --> 00:35:19.742
You're gonna end up with some crazy stuff.
00:35:20.012 --> 00:35:32.833
There was also a legal case where they ran some AI to do some of the legal analysis, and it turned out that it took a bunch of pieces, I think from different.
00:35:33.913 --> 00:35:47.043
Case files and merged it to create a case that gave it the results they needed to optimize for that particular problem they were trying to solve.
00:35:47.313 --> 00:35:51.213
And then when someone dug into it, they found out that wasn't a real case.
00:35:52.592 --> 00:35:58.422
So I think the challenge is you have to understand the underlying.
00:35:58.478 --> 00:36:03.427
Data to ensure that you're getting real results.
00:36:03.478 --> 00:36:05.157
That it's not hallucinating.
00:36:05.987 --> 00:36:12.538
Yeah, and I think, you've worked in financial services and that's a important sector.
00:36:12.777 --> 00:36:20.458
And even in I guess if you look at other industries like pharma, the data there is so much more sensitive, isn't it?
00:36:20.487 --> 00:36:26.853
As opposed to, and obviously we obviously want the correct data across the industries, but different industries, we're gonna have different impacts, aren't there?
00:36:27.818 --> 00:36:28.237
Yeah.
00:36:28.237 --> 00:36:31.422
In the pharmaceutical industry it impacts your health.
00:36:31.422 --> 00:36:35.717
And personally, I think your health is if you don't have your health, you don't have anything.
00:36:36.108 --> 00:36:37.668
I'd rather lose my money than my health.
00:36:39.302 --> 00:36:39.693
yeah.
00:36:40.682 --> 00:36:45.768
But yeah again, if you, there's massive, obviously, like that's, it could be the extreme kind of that industry.
00:36:46.112 --> 00:36:47.523
Oh, absolutely.
00:36:47.583 --> 00:36:48.543
Absolutely.
00:36:48.932 --> 00:36:55.402
And there was an interesting article that came out recently about doctors using ai.
00:36:55.452 --> 00:37:05.262
And somebody did a study where someone had broken their hand, and depending on how you prompted the ai, it.
00:37:06.507 --> 00:37:17.708
It may or may not ha they, they did a test of prompting the AI in five or six different ways, and a couple of the cases it found the fracture and in a couple of cases it didn't.
00:37:18.108 --> 00:37:25.878
But the simplest prompt was, evaluate this x-ray or this scan, and it came back with nothing.
00:37:25.907 --> 00:37:29.927
And so they found that the doctors who were starting to rely on ai.
00:37:30.447 --> 00:37:41.137
Are also not finding things when they're evaluating scans, so it's a, an interesting chicken and egg.
00:37:42.867 --> 00:37:43.168
Yeah.
00:37:43.768 --> 00:37:46.637
Again, it's like I say, it's learning, isn't it?
00:37:46.637 --> 00:37:49.547
End of the day we still it's gonna get better and better.
00:37:49.608 --> 00:37:56.057
But again, you want that, again, the right foundations in place so you can overcome this.
00:37:56.237 --> 00:38:03.288
These these can be, some of them could be big problems for people, but yeah, no, I think it's things will get better and better.
00:38:04.097 --> 00:38:08.418
Who should be responsible for ensuring AI is built on the right foundations?
00:38:10.052 --> 00:38:13.483
It really needs to be a group effort.
00:38:13.592 --> 00:38:17.762
The business leaders need to ensure that they're getting good answers.
00:38:18.152 --> 00:38:23.043
And in order to get good answers, you need to have a good foundation.
00:38:23.463 --> 00:38:28.873
I think of it like if you're gonna build a skyscraper in Manhattan you don't wanna build it.
00:38:29.253 --> 00:38:37.253
On shifting sands you wanna make sure you have deep pilings, you wanna make sure you have a good foundation, otherwise, disaster happens.
00:38:37.523 --> 00:38:40.512
It's the same thing with data analytics.
00:38:40.753 --> 00:38:52.592
If you're building on poor quality or undocumented data, you're more likely to have pro structural problems with your analytics.
00:38:53.103 --> 00:39:02.753
The business leaders need to ensure it, but they need to partner with their CIOs and their chief data officers to make sure that they have good foundations.
00:39:04.387 --> 00:39:04.717
Brilliant.
00:39:04.807 --> 00:39:13.737
And yeah I think business and data tech, I mean they are starting to integrate more from my expertise dealing with businesses as well.
00:39:13.907 --> 00:39:30.708
Yeah, I see it happening more and more, and especially AI, I think is driving it like people are excited about AI and wanna be able to use it and are hearing some of the horror stories.
00:39:30.818 --> 00:39:33.547
So they're figuring out how can we do this safely?
00:39:34.547 --> 00:39:41.827
So then how would we shift the culture from like quick wins AI to like long-term capability building?
00:39:42.827 --> 00:39:51.418
I dunno that you have to, I think in business you're always gonna have the desire for quick wins.
00:39:51.838 --> 00:40:07.998
And part of is, and as long as those quick wins come with a strategic plan on how to improve the foundations, I think that I don't think you need to get rid of the quick wins.
00:40:08.478 --> 00:40:19.237
What I'm seeing in the AI world is that the companies that had good data foundations are able to leverage that to get quick wins.
00:40:20.137 --> 00:40:21.967
But if you don't have a.
00:40:22.907 --> 00:40:32.958
The Sound Data Foundation right now, starting with a strategic plan, what's gonna be most impactful for your company?
00:40:33.688 --> 00:40:45.498
Build the foundations in that particular area to do a quick win in ai and then build the foundations elsewhere so that you can have quick wins later on.
00:40:45.557 --> 00:40:47.927
But I think in the, in.
00:40:49.608 --> 00:40:52.177
As human beings we like our quick wins.
00:40:52.237 --> 00:40:56.378
We do not have a lot of patience for, this'll be great in five years.
00:40:57.233 --> 00:40:57.682
Yeah.
00:40:58.373 --> 00:40:58.702
Okay.
00:40:59.523 --> 00:41:00.123
No I agree.
00:41:00.172 --> 00:41:04.163
I think, again, it obviously depends on the project and where you're at.
00:41:04.282 --> 00:41:04.503
Yep.
00:41:04.853 --> 00:41:16.597
What, so what happen if companies wanted to focus more on their sort of data foundations, pause their AI initiatives and focused on becoming more DA data mature?
00:41:17.597 --> 00:41:19.172
Could they do that or.
00:41:19.547 --> 00:41:19.938
Oh.
00:41:19.938 --> 00:41:20.447
Obvious.
00:41:20.538 --> 00:41:24.947
Yeah, that would in, in a textbook world, that would be great.
00:41:25.007 --> 00:41:28.847
You get your foundations set and in good order.
00:41:29.128 --> 00:41:30.088
But I don't think you need.
00:41:31.213 --> 00:41:36.213
To reset, I think you can do them both at the same time.
00:41:36.213 --> 00:41:38.342
As long as you're strategic about it.
00:41:38.402 --> 00:41:54.853
You can't, the old phrase about you can't boil the ocean, but you can figure out what's the most important data that you have that you can leverage AI with and get that organized and in order, and then.
00:41:55.427 --> 00:41:58.608
Run, get your quick wins with AI there.
00:41:59.268 --> 00:42:06.498
I should also say there's a lot to be said for using AI to help you organize your data.
00:42:06.978 --> 00:42:14.418
It can, you can use it to profile and make suggestions and organize data.
00:42:14.628 --> 00:42:15.557
You just have to be.
00:42:16.422 --> 00:42:18.463
You just have to be sure that it's right.
00:42:18.492 --> 00:42:21.193
It's trust put but verify.
00:42:21.773 --> 00:42:27.432
When it's making suggestions on how to organize things or when it's coming out with its analysis,
00:42:28.827 --> 00:42:29.068
Yeah.
00:42:29.547 --> 00:42:35.577
And then so how, how would, what would be a good starting point for someone to build their AI foundations?
00:42:35.762 --> 00:42:35.882
I.
00:42:37.728 --> 00:42:37.847
I.
00:42:38.777 --> 00:42:41.867
Again, it goes back to what's the problem you're trying to solve.
00:42:41.867 --> 00:42:47.248
But the key steps are, one is it in the technology that you need it to be in?
00:42:47.588 --> 00:42:51.157
If you're dealing with unstructured data, do you have easy access to it?
00:42:51.157 --> 00:42:51.818
Is it res?
00:42:52.463 --> 00:42:53.902
Machine readable.
00:42:54.083 --> 00:42:56.123
Is it in the right technology?
00:42:56.123 --> 00:42:57.293
Has it been ocr?
00:42:57.503 --> 00:43:04.313
If it's structured data, is it in a structured environment that is, is easy to use?
00:43:04.313 --> 00:43:10.322
I'm gonna say as much as I love Excel as a finance person, it's a terrible database.
00:43:11.818 --> 00:43:21.788
And so folks who are keeping their data, stored in Excel are gonna run into challenges with trying to run AI on top of that.
00:43:21.788 --> 00:43:31.867
So the first step is make sure it's in the appropriate technology and storage facilities to really be able to enable your ai.
00:43:32.407 --> 00:43:37.478
And the second is to make sure that it's properly.
00:43:38.092 --> 00:43:43.672
Labeled and defined whether you're dealing with structured or unstructured.
00:43:44.103 --> 00:43:51.663
Unstructured is a little bit harder, but you can set the parameters around what the data are and what the data aren't.
00:43:52.032 --> 00:44:04.702
And if it's structured data, you can tell me exactly what's every date mean so that I, AI doesn't have to interpret it or hypothesize what these dates could be.
00:44:04.733 --> 00:44:05.572
So it's your.
00:44:05.978 --> 00:44:09.818
Technical structure in your metadata, I think are the two key foundations.
00:44:11.188 --> 00:44:11.608
Brilliant.
00:44:11.887 --> 00:44:13.682
Some great advice shared today.
00:44:13.782 --> 00:44:18.342
I really appreciate you being on the podcast and sharing career journey.
00:44:18.742 --> 00:44:20.398
And yeah, obviously discussing the.
00:44:21.543 --> 00:44:24.873
Foundations, which is growing in popular.
00:44:25.097 --> 00:44:25.478
I'd say.
00:44:26.478 --> 00:44:26.697
Yes.
00:44:28.402 --> 00:44:29.572
Thanks so much for having me.
00:44:29.572 --> 00:44:29.998
This was fun.
00:44:30.998 --> 00:44:31.217
Cheers.
