Decoding Data Dynamics: A Conversation with Jafej Faya, Supply Chain Data Analyst
For the Data Chat assignment, I had the opportunity to interview Jafej Faya, an accomplished professional in the realm of data analytics. Jafej, a graduate of Florida International University, completed his bachelor’s in Business Administration and swiftly pursued an accelerated program to attain his master’s degree in Supply Chain in 2021. Presently, he serves as a supply chain data analyst at Southern Glazer’s Wine & Spirits, the largest wine distributor in North America. During our discussion, Jafej shared insights from a fascinating side project. He presented a scatter plot graph similar to Airbnb’s concept, analyzing factors such as customer ratings, room numbers, and amenities to study their impact on revenue generation. The graph vividly portrays diverse relationships and data distributions.
In this interview, Jafej delves deeper into his work and the impact of data analytics in the supply chain sector. His insights and practical examples, like the Airbnb-inspired project, illustrate the real-world application of data analytics.
listen here:
Simon: We can start again with your name and just your profession.
Jafej: Yeah, of course, my name is Jafej Faya. I’m a supply chain data analyst for Southern Glazer’s Wine &Spirits. We’re the largest North American distributor of liquor and wines.
S: You tell me about like, the background like how you got the job? Yeah, of
J: course. So I got my bachelor’s in Business Administration, got my Master’s in supply chain. And then they have a supply chain department that they have a group of five different analysts, data analyst. And then I started there about a year and a half ago. And I’ve been there ever since. And so far so good. I work with you know, some of the famous hacker companies that you know, have you know, Tito’s more Hennessey’s already, etc. On
S: what was your major supply chain, and you graduated when?
J: So I got my bachelor’s in 2020. And then my masters was a a accelerated program. It was during COVID. And it was a year. So I got my masters in December 2021.
S: Also at FIU?
J: Yeah.
S: So yeah. Now we can talk about the graph, if you have a graph with you
J: It’s just one I did. So basically, here, here are a few scatterplot graphs that I constructed for a little project I did on the side, is that a website for like, Airbnb, same, same company similar to that. And then And then basically, we’re trying to see which factors can help generate more revenue. And then obviously, like a review score location, basically a rating by the customers. This is the number of bedrooms that are amenities, or the location of the actual listing of the apartment, condo or house that you’re renting. And then what these graphs did is, it kind of showed the different relationships and distribution in the data, for example, the revenue versus the reviews, core location sites that receives higher review scores on a scale from one to 10. As you can tell, generated more revenue. That’s the why you said Correct. Yeah, yeah. So basically, here’s the x, which is the ratings received by the consumers. And then the revenue here is the y axis. So as you can tell, in this relation, sites are received higher ratings, generated more revenue in general, so
S: and the y will be the prices I see. And the
J: y is in revenue. So basically, how much each site generates. And as you can tell, the better the rating, the more revenue they were able to generate.
S: Oh, and you did that graph now? Like you created it based on Correct, yeah,
J: I’m gonna show you. So basically, here is a set of data with the different variables that is offered by a site for like, for example, this house is in Chicago, Illinois has, you know, one and a half bathrooms, three bedrooms, and it shows obviously, just different details in regards to that listing.
S: Okay, so now based on doing like a data graph, and you get so much information, like do you not lose the overview?
J: Yeah,of course. So, basically, I start first, just revising my data, just making sure it’s, you know, works for myself, but also, anybody else who’s going to obviously, look into this data, for example, give me you know, your boss, or co worker, or just anybody who’s viewing it in general. And then I would say, our start kind of find any discrepancies in the data. So if anything is, let’s say, doesn’t have a value, or is just not set up correctly, just overall, clean the data and preparing it for the analysis. And then once it’s cleansed, is ready to to be analyzed. That’s when I would pivot the data and then convert it into a graph, and then obviously, set the graph to resemble to display the relationship for the variables that I want to show. So in this case, this graph obviously showed the amount of revenue generated versus the ratings given by the consumers or the people that you know, obviously rented the apartment, condo or house, like I said, it’s just very similar to Airbnb, obviously, like, if you were to stay somewhere, like three days, usually after your stay there, they ask you to rate it. And then obviously, if you give it a good rating, it’s because you know, of different factors, of course, so it’s just one of them. And and obviously, these are just different factors that are taken into consideration for a site that that is doing well.
S: What platforms do you normally use? Like? What tools are very common in data analyst?
J: So number one, I would definitely say Excel, any data, analytical role, whether you’re a data analyst, data scientist, data engineer, Excel is very, very driven. And I think in most roles in general, so obviously, it’s widely known and very, very common and then allows you to obviously store lots of data. And also Google Sheets is there is something to it, but I would say Excel definitely is one that I’m more familiar with in common with. I started also embarking into, you know, different programming languages like you know, SQL Python.
S: Python is something I read or like SQL, yeah, that’s something you guys also used in the company you’re getting into now.
J: So my company just started getting to it. And I started learning it about six months ago. And so far, so good. You know, it’s what it is basically, it’s a language to store database. And and obviously, the code, what it does is, allows you to manipulate data, like in this case, to show you know, different parts of the data, which you want to see different relationships, combined different data’s, etc. So same process, but obviously just, you know, in a different format, like
S: all these tools are similar, in a way, it’s like Apple and Samsung, like phones, for example.
J: Well, I would say, like, for example, SQL is more for like database management. And I think it’s one of the most common ones. And then once you start getting into Python are, those are not databases. But those are languages that allow you to manipulate the data in a certain way, that sense of like a family, they all work in unison. So if you can use all of them, and obviously, for a set of data, it allows variables to your assignment on hand. Is
S: there a specific reason why you chose this? Or what is the point for you to decide, okay, I see my information, this is the graph I’m going to do.
J: So I would say a scatterplot, in general allows me to see more of a distribution of the data enrolled in relation to the two variables, like in this case, like the first one right here, the reviews core location, and then the y axis, obviously, the sum of revenue allows me to see exactly distribution of data is for those two variables in the dataset. And it gives me a visual of what the relationship is between those two variables. And usually, um, for example, in my current role, we have already like developed templates and models that we use for you know, every supplier, whatever, they want to exactly see people for them as well.
S: So do you have any tips of what you learned now, from your experiences or your journey to people that might be interested in common data analyst,
J: I would say definitely, in general, you know, whatever bachelor’s you get, you know, you can get at least something similar to it, or, again, to industry that just uses data very heavily. If you do want, again, to being a data analyst, I definitely say you know, focus on Excel be very dependent on that, you know, improve your skills on those. And other languages, for example, like, I would say, first focus on SQL for sure. And that will give you a good foundation. And then learning wise, I mean, nowadays, there is several resources everywhere, if you choose your best friend, as well, as I do know, FIU offer some, you know, some like boot camps or certifications where you can learn as well. So, I mean, in terms of finding the resources, that’s not hard, the hard part, the hard part is just, you know, obviously being dedicated and going along with it.
S: Since like, the graphs, or like, the information you get, you have to be very exact, can you even mess up? Like, can you even look at the data graph, and then you have an error tool, or something that you could use to, like, not deal with it too long?
J: In general, just making sure you know, it’s formatted in a certain way, it’s legible to all eyes, and then obviously, you know, after you know, whatever type of analysis you’ve done, or data visualization, you know, obviously, just double check everything, you know, make sure you know, numbers are lining. And then once you do, I mean, if you have a coworker, I would say, you know, have them review it real quick. That’s pretty much it, I would say just, you know, make sure the numbers align the your data clean up beforehand, and then you should be okay.
S: Do you have like current trends that you use, for example, or bars are currently very trending, because when I go on websites, and I look like for graphs, I see a lot of bar graphs and can that be, because it’s the easiest to like, for people to understand, or because it’s like, the most popular, I
J: would just say, kind of just depends on what message you’re trying to get across. And also, when you’re talking to, let’s say, you’re submitting this to the CEO of your company, I mean, he’s not going to be very, very analytical. So if you’re having a lot of just database words, generic terminology, he’s just not gonna understand it. But if you show him a graph, and explain it verbally, you know, obviously concise and straight to the point that’s gonna allow him to, you know, obviously, make better informed decisions of my analysis and, you know, very integral aspects of it. So,
S: if you want to share something from your graph that you feel like it for us to know, you have any inputs?
J: Yeah, of course, I would say in general, I’m just formatting making sure it’s legible, like I said, and make sure it looks nice, I haven’t been you know, if I’m trying to display this to, you know, a higher up executive or let’s say, a supplier or whatever, or whoever it is, obviously, we want to present them data so they can make the best informed decision and then obviously presented them something like this, like a graph. That’s, you know, obviously showing the data but also very legible, very, you know, well formatted can help them in a sense, purse, persuade them to a point to obviously take your, your, your analysis seriously in a sense, so Okay,
S: no, all right. All thank you for your time for so far and yeah hope you have a good day and thank you so much bye bye
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