Cream City Calculation

Behind the Mic: Sal Fadel

• Cream City Calculations

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In this episode, Colleen Hayes and Frankie Chalupsky interview their cohost, Sal Fadel. This series is designed for listeners to get to know us better as well as learn our best practices.

The Coding Train: https://www.youtube.com/channel/UCvjgXvBlbQiydffZU7m1_aw

Rajistics - data science, AI, and machine learning: https://www.youtube.com/c/Rajistics

Welcome to the Cream City Calculations Podcast. We're three colleagues and friends that love data and to talk about how data is impacting our lives. I'm Colleen. I'm Frankie. And I'm Sal.

Cream:

Hello everybody. Welcome to Cream City Calculations. We're doing a special series to get to know your podcast hosts. We thought it would be kind of fun to interview each other and in turn, let you get to know us better so that you know our backgrounds and how we got And with data. So today we're going to start with interviewing Sal. Sal, would you like to introduce yourself?

Salim:

Yeah I think I introduced myself in the beginning one a little bit, but I'll go a little bit more into detail. Southdale worked in data for, know, like 15 years or something like that,

Frankie:

Yeah.

Salim:

all the way into financial consulting into working for a financial institute. So I've been in the industry for a while have experience ton of tools, and backgrounded a bunch of tools really on the business side, so the BI or business intelligence side and data science side and really love data in general. It's been great.

Cream:

Cool. Do you want to tell us a bit about what you were like as a kid?

Salim:

Yeah, I'm weird. Just kidding. Yeah. So my parents always said that I had like a engineering mind, so I would of come at solutions and different like problems with the engineering mind. So I'd break it down and kind of solution it in my head which then later. of came into working in the industry. It was like, it works out really well. But I've always been a tinker. So I used to take apart everything. So I take apart computers, I take apart just random thing, controllers, remote controllers, and stuff like that. I've also built hovercrafts as a kid. And so like, I've always been a tinker and just wanting to like build and see what I can, you know, make. Can can kind of put together then I'd say like in high school and college. I didn't know how to code and I didn't know where to start and I was like, kind of at the beginning of YouTube and stuff like that and so I didn't know, but I was like, interested in like, Hey, how do you like we're hackers? And like, again, am not a hacker. I'm nowhere near a hacker, but that's the kind of first introduction to coding I saw. And I was like, Oh, wow. Is this how they do this? I wonder. I want to learn more. And then I ended up starting to get into in college analytics with applied economics and then, so that's kind of sparked my, my interest. So it was kind of. Always been a tinker and it was kind of was the way to go. But one of the main things that I think I'm, why I'm good at data is, or at least gives me, I can't say that I'm like amazing at it, but as I do have dyslexia and so I've always figured out a different way to read or kind of take in information and I wonder if that actually helped me. Look at data, look at patterns in a different way. I've always felt like I had a slight advantage in that way.

Cream:

That's interesting.

Frankie:

cool. So, what was the 1st major project where you realized you have this data ability.

Salim:

There's kind of two And again, it sounds like a superhero data, but it, it. Honestly, all three of us have this trait of like, we can identify patterns better than probably most people we can see it through data and we can see where things might be an outlier. Right? Like, we all can do that really well.

Frankie:

my husband crazy because like anytime we do a puzzle or anything he's he like can't keep up and he hates it.

Cream:

Oh, yeah, I'm good at Google too.

Salim:

so, and bet you all three of us are super competitive and want to do this. That's probably why we, Came together here. But, like, my 1st project, like, where I got the introduction to data itself was I was doing a senior level class that it was about executive summaries. And every week we had to report on data right from scratch. So we had to learn Excel, learn matplotlib and really kind of build out charts and graphs and then write an executive summary on it. And it was like, oh my God, like this is really overwhelming. Cause there was so much information, but it sparked an interest in me that it was one of the hardest, but also most fascinating classes that I've ever had. And so, but I really liked kind of learning that process. I didn't really like the report writing obviously, but I really liked the of just diving into the numbers and finding patterns and finding relationships and meaning out of it. Which was really awesome. And then as my career grew, I was a Northwestern mutual rep. And then at that point, I really liked again, diving into the numbers for people, right, and diving into their metrics looking at their retirement and and their life insurance policies just based on that. But I, again I really focused on the numbers and I really enjoyed that. And then randomly got into a healthcare consulting firm. it was kind of a long road from there, but I was a salesperson for a year, did really good numbers and I ended up having, and like during that sales time, I actually use analytics to drive and get Better insight into who I was going to target. And I had a really good, call to sale ratio because of that. And so you can see that analytics is always there. I just never matured it. and then at this company I got into the consulting side and I got to really dive into Alteryx Snowflake, Databricks and all these other Uh, and then within there, I got. introduced to two Harvard fellows when we got bought out by, it's called a company called Decision Resources Group. They had two Harvard fellows that I had the opportunity to work with and I worked with them for about two years, became really good friends with them but also learned a absolute ton because they took me under their wings and Developed my data science skills. And so I grew a ton from there, got to learn about what OLAPs are, you know, there's just so much that I took on at that project. And it was really greenfield in that case, like where we could do whatever we wanted. So we could explore a lot of different things learn how to do network analysis which was really cool doing edge and node set. And then. That led me into working at Continuous. And at that point, I was pretty honed in on my skill sets, but I wanted to amp them up even more. And Continuous gave me that flexibility just to upscale so much and teach people how to think through analytics and think through data. And then finally, now I'm at a financial institute and senior data scientist there.

Cream:

That's very cool. Can I just ask what was your degree in?

Salim:

Applied economics.

Cream:

Applied Economics. I always find it really interesting how people get into the analytics and data space.

Salim:

yeah, I didn't like pure economics or the macro and microeconomics where it's all theory based And but I really liked it applied So like how does it actually work in the real world do real world examples and Madison had that? Degree where it was like, all right do real world examples as part of the agricultural school. So what we got to do is like, how do you maximize yield for farms? Right? But it's so much more tangible for me. And I think in general, getting into analytics, it's really hard to be conceptual. It's hard to learn, at least for me, hard to learn analytics, how to learn data science when it's just pure conception. Yeah. It's more around like diving into let's actually get some use cases so that you understand where the benefits are and you're like, I always felt like I had a business mindset so I could think like a business person in that aspect. So it helped me get there.

Cream:

that's cool. That's interesting.

Frankie:

So can you tell us about an example of how you're using data in your everyday life, outside of your career?

Salim:

Yeah. I use it Danielle, my wife and I She's a want to budget all the time kind of person.

Cream:

I don't know.

Salim:

I don't mean to bash her in that way, but it's a really good thing. But so just in, in that, like we use these apps it's called Monarch. It helps us track all of our data, but we ended up pulling a lot of it out and then categorizing it and understanding how we spend our money. And what we spend it on. Uh, So I do a lot of that. Obviously there's a lot of fun little side projects that I do. I've using open CV as we all know, my dad was the fencing coach our last episodes and part of that was building, I wanted to see if I could build out a body tracking thing so

Cream:

Um,

Salim:

Quickly, like throw it together a tableau dashboard that kind of can track something that I'm interested in. I, I really love.

Cream:

I was going to say you kind of tease your wife about wanting to analyze those things. But I mean, what a great tool to show you sort of where your money is really going.

Salim:

Absolutely. Absolutely. And I think that's a big thing. Maybe it's, I don't have a ton of at home projects because it has probably both you. Can attest to you do so much of it at work. You're like, it's kind of like a chef doesn't want to come home and cook. Right. And so a little bit of that, it's Oh, I've been doing it all day. I don't need to continue doing it at night all the time. But that is actually where you do develop a lot of your additional skills. like doing Coursera and looking at YouTube and honestly, Instagram Reels or TikTok or whatever to find out new trending things within analytics. That's where I start to add a little bit more knowledge and then I go and do the research. But that's more of what I do at home with analytics.

Cream:

Do you have any other career or data related goals?

Salim:

At some point, I would love to be continue to grow, help others grow, but also be more well rounded in the, maybe like a chief data officer at some point or have those skill sets around that. So where I'm running more of a, an enterprise data or orchestration and how data comes in and kind of thinking at the higher level or the 30, 000 foot view strategic level. I like getting into the weeds, but. As both, you know, the weeds sometimes can burn you out.

Cream:

Yeah.

Salim:

and so it's one of those things that I'm like, I would like to get into the business side and maybe at some point come back into the weeds, but my focus, I think overall, and my career focus is growing into a a senior level person.

Cream:

Yeah. It's interesting stuff. You talked about your role, your current role, being that of a data scientist. Can you tell me, some things that you enjoy about the data science side of things that maybe you didn't get to experience

Frankie:

be

Cream:

on the data analytics side?

Frankie:

minute

Salim:

Yeah, absolutely. So

Frankie:

little

Salim:

is data science Watch it.

Frankie:

the

Salim:

is

Frankie:

very much.

Salim:

that are probably going to yell at me and be like, no, we do so much more.

Cream:

Yeah,

Salim:

the 80 percent of it, I think you've both heard that stat before, 80 percent of it is data science. Data engineering and data analysis. So having those skill sets is extremely important. And you're doing more, most of the time doing some of those just data analysis, right? Then when you get into more of that fun modeling side it's the what if, and that's a really cool part is you're trying to figure out like. Hey, this business leader wants us to figure out how to identify what I'm making this up, what influencers had the biggest impact on on their sales. Right. And so You could just straight up do that by just doing a analysis, or you could go above and beyond and build out maybe a network analysis and figuring out who's in their networks, who they have the highest influence on. And then going from there and saying, what are the touch points to those additional people? And then how much, so If I'm going to invest a thousand dollars into an influencer or a million dollars into an influencer, what could be my expected return based on their network and their influence on those network, that's where you start to get that green space of you're testing out things, you're testing out your hypothesis. And running models and running really experiments, and that's the data science part of it. And then running that and then looking at that from a statistical way of saying, Hey, we should approach it this way. I think that's really fun to do. In the financial realm, there's so much,

Frankie:

Okay.

Salim:

predict how the trends of certain securities or portfolios will end up.

Frankie:

much.

Salim:

modeling, or also there's other ones, like within the financial services firms that business has a marketing group, right. Or a human capital group is they have data science projects that they, that maybe they want to do that they're like, Hey, I want to know, I want to make it a little bit more, a little easier to target new hires. Right. And

Cream:

yeah.

Salim:

who are the ideal people. So those are all things that are exciting about data science that I've had an opportunity both to work in and to. out in,

Cream:

That's cool.

Frankie:

Yeah. So how have you seen the analytics industry evolve since you've started? How have you seen the analytics Since you've

Salim:

even from when I came in, it's evolved exponentially. When I started Tableau was like, I think they maybe had a hundred companies that they were working with, if that. Like you think of that, it was at the birth of Tableau. It was at the birth of Alteryx. And so I think the hardest part for it was the benefits, right? And so we spent probably more time selling the benefits. Internally to our company to say, Hey, we should move in this direction because there's great insights that we're getting at it. We're not using any of the data that we're collecting leaders were that old school mentality no, I don't need that. I don't need that. Why are we spending more money to get, to, to look at this data? And then as you saw it, leaders become more and more. Aware of it and the benefits of it. And then they started asking you, and now we're at a point of them coming to us really, and saying, Hey, can you tell me insights on this or that or this? Right. And then we are building out that. And so it's a lot less hand holding or requesting or asking more of Hey, We all now see the benefits. And then one thing I do see coming from like new grads coming out of college and everything, they have already so much more skill set. They're coming out with Python knowledge or at least basic level Python, basic level Tableau. They've worked in these tools that you're like, Oh my God, like we're not teaching this anymore. We're not like looking at the first YouTube videos of Tableau to try to figure out how to a chart in Tableau, right? It's evolved so far that Tableau is now very simple. And now it's like, all right, how do we add the more complexity and then add an AI into all this that is sparking so much.

Cream:

Yeah, they're definitely starting with a step or two up from where we did.

Salim:

They're also coming out with like different types of degrees. Like when I went to school, they didn't have. data science degree per se, they, you could go into like sociology or something like that, and then you get your master's in sociology and then that could lead you into data science. I know so many data scientists that are actually like, they have a PhD in psychology or sociology or some kind of other study where it's heavy stats based, but it's really They're focused on like a healthcare realm, right. Or in some aspect of social realm, not so much of like peer data analytics. And you've you've gone to school and you have been going, you're getting your master, but to like, how has it changed for you? I mean, you probably saw it like from the different science platforms that you've been working with too.

Frankie:

just from going from my undergrad degree to my master's degree I have seen drastic change and I've only at one school for both of these programs, but they've just evolved so much into just really focusing in on analytics and that's my emphasis in my MBA is analytics. That didn't exist probably 3 years ago. I feel like that's a very recent trend because they want business leaders to be to utilize analytics and drive via analytics. So, yeah,

Salim:

it applied economics. Just kidding. When we're. all of us do, we help facilitate data analytics at our companies that we work for. It's becomes, it's a lot easier to talk to people now than it was 15 years ago when they, when this was coming out and not that it was not already out, but it was exploding, I guess.

Cream:

Yeah.

Frankie:

Okay, and I can speak to that a little bit to from my undergrad degree, anybody that had a bachelors of science degree, I think they had to take a Python course. So that's kind

Cream:

Really.

Frankie:

that changed already by the time that I was in my undergrad. It was very

Salim:

Wow.

Frankie:

introductory like they gave you all the code but we learned what it meant introductory course but we were at least exposed.

Salim:

And I honestly think it's extremely important. I really wish I was exposed earlier. And again, that's just timing wise, but feel like I would even be so much farther and just have the fundamentals. A lot of my stuff is obviously self taught and just through courses that I picked up and it's not so much this what you have, like probably some kids now that are in elementary school. I know this for a fact that are in elementary school that are coding. I wish I had that skillset

Cream:

Okay. Okay, I'm going to explain to you in a short example how old I am, but I had an opportunity to do something like that when I was in grade school, but it was in a programming language Okay, I'm going to explain to you in a short example how old I am, but I had an opportunity to do something like that when I was in grade school, but it was in a programming language called Logo. And essentially the, the screen was split into two. There was the upper half and the lower half. And the lower half was where you would type your commands. And the top half, there was a little carrot, like a little triangle. It was called the turtle. And you would, you would type in your commands, RT for right turn, 90 for 90 degrees. And then it would turn the little triangle. Then you, I don't remember the command for like move forward, but you'd give it a measurement. And you'd say draw a line going forward, 20 spaces or whatever and then you do left turn So if you were going to draw a square you'd have to give the turtle Commands to make it draw a square and I remember going through the process to draw a house And I don't remember. I mean I went to a private school as a kid. So that may have had something to do with it Like maybe there was some parent that made donations to the school of these computers. I don't know but I loved it and it You're, you're right. It probably did put in my head that, you know, this isn't an overwhelming concept to tell a computer what to do. And I guess I never really thought about that before, that that may have kind of helped me understand things in a way that was really helpful to me later.

Salim:

It's funny that you, remember that so vividly

Cream:

I know.

Salim:

you can tell that we're, we all got into this industry because, I mean, I don't know if those are like your moments that, that kind of, we'll find that out in the next one, right?

Cream:

Yes, I guess so.

Salim:

But all have these like moments where I'm almost fascinated by computers and out of hell what to do and then understand it. I. I love that. And I, one of the things that I was always fascinated by, like hackers, I think I might've said that in less earlier, but, I was always fascinated by hackers and what they did. And I don't know if you've ever watched movie hackers as a little kid, I was like, wow, how do you do that? It almost seemed like a superpower. And now that you're like in it, you're like, wow, that I can hack, but not so farfetched and it's not such a superpower

Cream:

It's not a superhuman thing to learn those skills.

Salim:

yeah, it's not an intangible thing that you can't ever reach.

Cream:

Yeah. For me, it was the 80s when I was learning this, and I think computers seemed very, like, fancy and very futuristic. It was before people would normally have a computer in their home, so just to get to play with one or to work on one seemed really cool, and I think that was part of the draw for me, too.

Salim:

I just remember a kid in high school with me. He was a wiz at computers, but you never really couldn't really understand what like he was doing, but then he built this, you remember like the TI 85,

Cream:

Oh, yeah. The calculator.

Salim:

calculators, is before computers being on every kid, but he built programs using TI 85 that would do our math class questions for us. So all you can do is put it in the, inputs and they would answer it for us with, depending on what it was like the biggest cheat, but it was also like amazing thing. You're like, Oh my God, this is, you could actually like program it.

Cream:

Yeah, I remember that, too. I don't think it was a download because, the internet wasn't, very prolific then at all, but it was some way that you could unlock different things into your TI 85 to make it do more of your calculus homework for you.

Salim:

exactly.

Cream:

that out.

Frankie:

That's so

Cream:

So,

Frankie:

Okay.

Cream:

of AI. Do you have any thoughts about where AI will take us next in the next one to five years?

Frankie:

Okay.

Salim:

have been around for a long time. AI has always been a talked about thing, but it's not true. Artificial intelligence. It's not coming up with its own thoughts. A lot of these things, maybe we'll get to a point of that, but it's really using training models of understanding how humans think to think right, or to

Cream:

It's like a more

Salim:

it.

Cream:

way we've told computers what to do, right?

Salim:

Yes, exactly. And we're just building out those end points. And I guess the human brain is very similar to that way, but I think what we're going to get to is a much more complex version of AI or of what we're having of these LLMs where it's actually. Taking in so many data points that it's almost conceptually thinking, or it's almost having that artificial intelligence based on that because we just can't conceptually understand how many data points is pulling in right and the information it's putting. So I think we're gonna involve a ton there. I don't think that there ever will not be human influence into it.

Cream:

Yeah.

Salim:

And then when I was saying machine learning has been around forever, like we've Like I've done projects well before open AI and all that to do natural language processing and do vectorizations and SVM. So that was something that were already doing it. We just didn't have both the amount of data. First of all, second is like building out these vector databases that. Work at this scale, we couldn't do that or have funds or need to do it, I guess. And now that open AI, it just made it so much more accessible to build that out.

Cream:

right? I

Salim:

And I, so I think we're going to go, we're going to build things that we can't even understand now with AI and it's going to help us. I truly think it's an industrial revolution, or whatever you want to call it, an AI revolution.

Cream:

hope.

Salim:

yeah. Yeah. But I do think, just like the industrial revolution, it is a revolution. Needs human impact. It needs the human to understand how to use the tools the best ability. So you, I think it's very important to understand how to prompt right. For everyone to understand how to prompt when you're using these things. and then for AI engineers and AI data scientists, which is a new title out there, like to build these and how to understand bias and how to understand what the output is going to provide is going to be really important.

Frankie:

Absolutely. And I think it's funny, like what you said, probably five years from now in the history books, they'll have the AI revolution right there.

Salim:

Yeah. I just hope it's not called that because it just, it has to be a better name than that.

Frankie:

Somebody asked for a name,

Cream:

Let's make it. Let's come up with a term and make it a thing, Sal.

Salim:

Yeah. machine learning revolution or something. The AI just seems so Terminator ish.

Cream:

You think so? Just kidding.

Frankie:

so with everything advancing so quickly and, how much learned just in the last couple of years around AI and how we've been able to things. How do you stay up to date with these industry changes and advancements?

Salim:

That is the hardest thing. Luckily, I was in the industry, or as in working, when I was working at Continuous, it helped a lot, because you were hearing stuff, you were hearing like, oh, we're partnering with Snowflake, and Snowflake coming out with Updates and understanding it. And then we also had Gardner reports. So understanding like what are the trending things that are going on, but honestly, it's moving so fast that those of knowledge, unless you want to go deep diving into things, which is. That is, you should do that once you find out like something that you are, you want to focus in. But to stay on top of what the industry is doing and all that, like it's moving so fast. I think social media and YouTube and all these like platforms are the only way to take in this amount of information. Or this is I think schools are 10 years behind

Cream:

Yeah,

Salim:

teaching it again. They are exposing people to the tools that we are using in the industry, but I don't think we're going to be using them in 10 years when they get out or four years. And when they get out of school, I think there'll be more advanced things.

Cream:

yeah, you know, I've talked to some professors and to some colleagues that are helping professors write curriculum currently, and they said that's their biggest problem is that it takes nine months to a year to go through all the different steps to get a curriculum approved by whatever university. And by the time they do that, everything has changed.

Frankie:

Yeah, I've seen that.

Cream:

It's like, how do you keep it broad enough that everything's applicable, but at the same time make it specific enough to be usable?

Salim:

I think this goes against my my reason why I went for applied economics, but I think at some point schools need to come, focus on machine learning or data science from a theory perspective, and understand conceptual. Approach to it. If like why are we doing these things? How do you think through data science and less around like the technologies that, that they're just going to be so fast and it's going to be moving so fast. But yeah, some of the best things that I like, it is I used, I think I still do. I take a ton of information off of YouTube. And really don't know if you've ever heard of the coding train, I know it's really, it's like a basic thing, but he is phenomenal at explaining, Machine learning concepts or how to use Python in, in certain projects. So he's So I would highly recommend him from a, like a coding perspective. And then there's, I probably butchering his name is Rajiv I believe he's a blogger and YouTuber. I think I, I found him on Instagram. I'll be honest. And he talks about machine learning and AI, and he is a PhD level, but he talks about it in a really easily conceptual way that I can take in the information and learn. And he's fast enough or more in the trend. So he will come up with stuff and showcase stuff that I haven't heard of yet. And then I'll go and do additional research from there. But both of those are fantastic.

Cream:

That's great. You'll have to provide us with some links that we can partner with. Yeah, we can put links in the show notes.

Frankie:

If you could solve any global problem using analytics, what would you pick?

Salim:

That is a tough question because there's so many things that you need to do. Cause I, I don't know if analytics and maybe I think analytics will play parts in hoping or hopefully solving some of these things, but obviously global warming, right. And understanding that you're, we're using technically analytics to track and understand that data when, of what degree that we're increasing. But also I would be curious if maybe AI, when we talk about that could start to problem solve for reducing our CO2 emissions. I think that would be really helpful. I don't know. I don't think AI or ML, Will ever help us create this utopia of a place, but I think it's going to have insights into, this is where we are messing up and maybe we can catch it before it gets bad.

Cream:

Yeah. I mean, again, it's a tool, right? So you,

Salim:

Yep.

Cream:

you might use it to identify here's the peaks, here's the outliers, and maybe can we do something about these large values, the biggest impact to the climate crisis?

Salim:

Yeah. I like, I always wonder if had open AI. Would the world have been any different or is it like what additional ads, what do you have had. Or any of the. Smartest people in the world. Like I can not even understand like what they could even think through and how they could bounce ideas off an AI. But what would be amazing is coming from 10 years from now, when we have our next Einstein, or there is one. Is seeing what they use to help them of figure out information. And then what solutions they come from it. Hopefully it's good ones. Not bad ones.

Cream:

That's a great question, Frankie.

Salim:

Yeah. Way to make it difficult.

Cream:

Make it real deep at the end.

Frankie:

a difficult question, but I was trying to get what you're passionate about

Salim:

Yeah. Yeah. to be honest there, I felt like I had to say something bigger than, you know, something I mean, I'm super passionate about, but it felt like, yeah, I hope analytics would solve those things. Something that maybe I'm passionate about is I don't even know what I'm passionate about.

Cream:

You have little kids. You don't know what you're passionate about anymore.

Salim:

Yeah, I'm just like trying to survive.

Frankie:

Survival.

Salim:

yeah, survival. It is just get through day to day

Cream:

mean, but think about the applications for that, even, you know, different resources for parents to make juggling childcare more sustainable or, I don't know,

Salim:

that's

Cream:

I, I just think. about how many people were impacted by the pandemic when we all had to shift to this model where we were working from home. You know, in what ways could you use data, technology, AI, all of it together to provide solutions to some of those things?

Salim:

Yeah. I mean, I think just like from a daycare perspective like ways to reduce the cost. Right. And so like a project that I helped with at our daycare, he, the daycare owner is Hey, I waste. I think it's 500 pounds a year of extra food because students might not be there that week or we overbought or any of that cause they feed the kids too. and just imagine just reducing, first of all, excess waste of food,

Cream:

Yeah,

Salim:

super important. But also the amount of costs that happens, right. And that could be reflected down hopefully into the parents paying it. There's a lot of things that you could just be On a micro level, help out and build out to,

Cream:

absolutely.

Salim:

be more efficient.

Cream:

When Frankie asked that question, that's immediately where my mind went. I think that so many of the world's problems could be solved or attempted to be solved on a, on a micro scale, on that very local scale. And I think sometimes it's a matter of logistics in that case of your daycare provider, having excess food, even if they just wanted to find somebody who could make use of that food before it did go bad. You know, that's almost so difficult to figure that out. But what if there was a way that you could use data to say, hey, this person at this shelter or this restaurant, whatever it is, however many blocks away, could use this exact thing today. Well, at least it wouldn't be going to waste, right?

Salim:

Yeah, absolutely.

Frankie:

our next nonprofit idea just bubbling right here, developing some sort of food relocation.

Cream:

And, and honestly, I believe I stole that. I saw a presentation, I think it was a Tableau presentation of somebody who did something very similar to that. And I don't remember in what area or even what country it was that they did this, but they were able to do it successfully on a very local level. I don't know how long that was sustainable for, but. Makes you think that you could, you know, that there's real power in being able to use data for these things.

Salim:

Yeah, I mean, just even coming up with the The idea of doing it, I think that's really important. Yeah, maybe there might be some regulations that can't drink and foods across or

Cream:

Right.

Salim:

But I think coming up with the ideas and saying, Hey, all right, maybe there is like a larger platform that we can start to do so that we can. this up to make it so that can pass this, right. And we can share the food for all like 10 restaurants, right. Or that have a community that does it. So, yeah.

Cream:

Interesting ideas. Anything else you want to add, Sal, that we didn't ask you about?

Salim:

just that I'm glad everyone got to know me a little bit and I'm excited to get to know my, or just share a little bit more of my two other colleagues, on this podcast. But yeah, just if anybody's ever interested in. Data science or data analytics. free to email me or call me up and I will, I'm happy to discuss about it. It's a fun topic.

Frankie:

Do it quick before we use a big shot CD, or

Salim:

CEO. Is

Cream:

I can attest to the fact that Sal will go on, you know, deep dives into nerdy conversations any chance he gets,

Frankie:

Oh for sure.

Salim:

that a backhanded way of calling me a nerd?

Cream:

it's a pretty front handed way, I don't know,

Salim:

good.

Cream:

but it also applies always in these conversations with you as well, so

Salim:

true.

Cream:

no shade.

Frankie:

well that's a wrap on today's episode if you loved today's episode, make sure to subscribe and stay up to date on other topics related to data. Thanks for listening to Cream City Calculations, and until next time, keep calculating!