You know what we like at TARTLE? We like talking to cool and intelligent people who are doing cool and intelligent things out there in the world. Especially if they are doing those cool and intelligent things with data. That happens to be the case with Shumin Luan, a budding young data scientist at Boston College whom Alex and Jason recently had the chance to interview for TARTLEcast. During the conversation Shumin revealed that he has always been interested in data and the way it impacts people’s lives.
Even going back to when he was very young, he had a fascination with the world of finance. He saw how data was needed to properly operate in that world. Without it, investors might as well make their decisions by throwing darts at a board. However, that isn’t what got him thinking of moving more formally into the realm of data science.
That part of Shumin’s journey began when he was working in Dubai, UAE (United Arab Emirates) as an analyst in sales and logistics. While working in that role, he was able to see how data science could help make the company he was working for more efficient and aid in making better decisions. One of the areas that the data scientist saw for improvement came from the shipping division. Shumin was able to identify that there was a lot of inefficiency in the loading dock and shipping warehouse.
The warehouse was not organized to quickly bring orders up the front in the best of conditions. Throw in a rush or any sort of computer problem and operations could be significantly disrupted. Here, the truckers picking up the products represented a golden opportunity. That inefficiency in bringing product up from where it is located in the warehouse to trucks means that the truck drivers spent an inordinate amount of time just sitting around doing nothing. What’s more, the drivers are paid by the hour. The results have been a lot of time and money saved as new efficiencies have been put in place. How did a young data scientist manage to increase the efficiency of a major company operating out of one of the busiest places in the world?
One of the most important things that he did was to comb through the data and find that certain products were more likely to be sold and shipped together than others. Shumin simply recommended storing such items together and in a way that was readily accessible. That meant there was less time spent waiting around for the truck drivers and fewer stops had to be made since the biggest sellers were going together on the same truck from the same warehouse. Otherwise, one truck driver might have been making multiple stops just to fill up his truck. Now, everything could be efficiently stored in one or two warehouses so the drivers could get on the road to delivering products to the customers much more quickly. That means happier customers because they get their products faster and a more profitable company because they aren’t paying people to sit in trucks. It also has an environmental benefit in that with fewer trips getting made, there are fewer greenhouse gas emissions to be concerned with.
Shumin also briefly touched on one of the biggest challenges confronting data scientists today – the sheer amount of data available. One of the most important things such people have to do is sift through the mountains of information out there to find the valuable data that is needed in order to conduct a meaningful analysis. The good data is definitely out there, it just takes patience and skill to find it.
That’s where TARTLE comes in. Through our data marketplace we make it possible for researchers like Shumin to find the best data of all, data that comes right from the source. Instead of trying to sift through tons of third party data, we get right to the gold they are looking for, enabling them to make better and faster decisions.
What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.
During the conversation Shumin revealed that he has always been interested in data and the way it impacts people’s lives.
Speaker 1 (00:08):
Welcome to TARTLE Cast with your hosts Alexander McCaig and Jason Rigby, where humanity steps into the future and source data defines the path.
Alexander McCaig (00:26):
Hello everyone, and good morning. Welcome back to TARTLE Cast. You'll be joining us today on an episode with [Schuman Luan 00:00:32], data scientist at Boston College. And what we want to do is talk really about Schuman's journey and how Schuman has found himself as a data scientist, why he's chosen to take that path and why it's important for others, moving into the future, to learn a little bit about his journey and how they can apply it to their own, why they should probably take that stem focus on data science.
Jason Rigby (00:54):
Well, thank you for coming on the show. We really appreciate it, Schuman.
Schuman Luan (00:58):
Sorry. I cannot hear from Jason. Oh, now it's good, just I cannot a moment ago. Right now it's good. Yeah, sure.
Jason Rigby (01:09):
Oh, okay. Perfect. So Schuman, thanks for being in the show. Really appreciate it. A question I have for you is when did this process start where you begin to thinking that you wanted to get into data science?
Schuman Luan (01:22):
I think it started from the beginning of my early career, actually, my very first job when I was working at a company in Dubai UAE. I started there as an analytical role for their sales and logistics data analysis. I found that data science could substantially increase their efficiency on sales and logistic data, strategy forecasting, and also their operational strategy decision-making. I found there is power of data science that can substantially increase their ability to make better decisions.
Alexander McCaig (02:09):
So you're saying that the work you did over in the UAE and you saw the value in decision-making around data, almost reinforced that path that you're taking currently right now, professionally, as a data scientist.
Schuman Luan (02:23):
Yeah, exactly.
Alexander McCaig (02:23):
This was one of those key drivers for you. So you had to actually go experience the use of data firsthand, that led you to be like, "Ah, I see what I need to be doing with this and I see the value of it towards the future."
Schuman Luan (02:32):
Yeah, exactly. Especially for some traditional companies, they didn't use a lot of data analytics to make decisions before. But recently they are doing the digital transformation. They are transferring from traditional decision-making ways to some decision-making based on data analytics. That's a very common transition for most businesses right now. Also, from that experience, I've found a very good example is a project I did here, because the company there is a very traditional international trading company for engineering equipment. And they were having a business problem that they had had a very high logistics cost for their shipments because it has multiple warehouses for the storage of their products. They usually import those products to store at their warehouse, and then make shipments to some other countries in Middle East or Europe or Africa.
Schuman Luan (03:46):
But they were having a problem that they pay a lot of actual payments to their container trucks, who would load those products from the warehouse and make shipments, because every time they make a shipment, the container truck has to go to multiple warehouses to load different goods. And they get paid for their actual waiting time they spent at the warehouse. So we were intended to reduce the amount of time they waited there so that we can reduce logistics cost. But the key is how to reduce that, their waiting time. So there, it comes to some data analytical problems. So I made a proposal that we can reorganize the inventory distribution so that we need to keep those products that are most likely shipped together, to store together, so that the container trucks literally just go to one place to load the products without spending extra time to go to multiple warehouses to load different products. So the key is how to find those products that are most likely shipped together, correctly.
Alexander McCaig (05:12):
So you're saying that instead of shipping a whole slew of different products, what you guys did is use data to analyze which products should be moving with one another to decrease the amount of trips, and therefore the amount of payments that that logistics warehouse actually had to go through. So by using data to find commonalities between products and shipping routes that was able to decrease the cost for that firm altogether, economically.
Schuman Luan (05:37):
Yeah, exactly. That's absolutely true. It is the case. So there, the challenging part is how to find those products that are most likely shipped together correctly. So that once we get this idea we can just reorganize the inventory distribution to keep those products that are most likely shipped together. So this can substantially reduce the time that the container trucks spend at any of our warehouses, and hence reduce our logistics cost.
Alexander McCaig (06:09):
Now, from a business standpoint, that makes a lot of sense. So you're saying, when you look at data and you're combining these things together, what happens when we step outside of economics? What happens when we're analyzing human beings? You're saying that there's benefit if we can find things that are commonalities between us and using data analysis, through this big data or machine learning, affords us the ability to figure out what these commonalities are between people.
Schuman Luan (06:30):
Yeah. So in terms of... I found sometimes that any science that can be gleaned from data analytics are sometimes counter-intuitive to our common sense. So I think that's also the power of data science, of data analytics, because that helps make us make better decisions. So usually we human beings, we make decisions simply based on our own knowledge or our own experience but we are limited to that. While data science gives us another approach, which can expand our understanding of what we are doing, and give some insights that we are not even aware of, or we don't even know if we don't do such analysis. This can substantially just expand our intelligence, and hence make banter decisions in our business.
Alexander McCaig (07:27):
I think that's a good point. Jason, you and I talk about this all the time.
Jason Rigby (07:30):
Yeah, I know. And I want to get back to your past, Schuman, if that's fine. When was the point, I know when you were in the Middle East, but was there a time when you were, before that, high school, that if somebody is listening to this podcast right now, and they're kind of wanting to get into data science because this field is growing like crazy, was it before as a teenager, what was the idea in your head? What were you thinking about wanting to do? Were you wanting to do something else? I'm talking like 16, 17, 18, what were you wanting to do then?
Schuman Luan (08:07):
Yeah. Actually, that's a great question. Actually, when I was very young, my first dream career is in finance. I was [inaudible 00:08:19] investment to get profits from simply doing some decisions about choosing the best financial product, best stock to get financially rewarded. But later, actually, how my mind was changed was due to, I did an internship, which is also related to finance, but it was quantitative finance. We did a lot of data analytics on stock, on different types of data, to gain some actual insights on how to make better decisions in investment. And there, I started to know the power of data science, how those data analytics can give us a much better understanding than our own simple experience or our own thinking, and how that would expand our intelligence. So, actually from that point, I started to know that, that data science could make a huge impact to our, not only business, but also our economy and our society. This would definitely be the future that we can explore it from data and to make our human intelligence advance in to our next step in the future.
Alexander McCaig (09:57):
Oh. I think that's what it's about. Yeah. It's all about elevating humanity with that data, right? And if we can use data to challenge the biases that we've had through our prior experiences, like as what you were talking about before, then we can be, "Wow, we've actually been making the wrong choice." And now that we can see clearly with all this data, through this analysis, this can help move us onto the next step that we need to be going to.
Schuman Luan (10:21):
Yeah, exactly.
Alexander McCaig (10:23):
So if we have tens of thousands or millions of Schumans across the world, people saying, "Hey. I've looked at the data, I've analyzed this. This is the path that we really should be looking to take. And this is what we should be taking action on." I think that's going to be a very powerful future. So just to keep this brief, Schuman, what is it that if you were to tell someone that was like a budding engineer or a budding data scientist or someone that is even considering it, what about it from your own mind and your own heart regarding this and how you see the future, what message would you want to deliver to them and tell them right now?
Schuman Luan (11:04):
Thank you for the question. I think there are a couple of things that I would like to share to someone while they are in their early stage of thinking about a career in data scientist. I think the first thing is, is your role as a data scientist a more technical role? So first thing is to have a thorough understanding of those techniques, like machine learning, statistics, and mathematics, that can learn how those things could be combined with our own knowledge and make a greater impact. I think the second thing is, besides this, as the basics of data scientists, I think there are a couple of other things that I would like to share, based on my previous experience as a data scientists. Because these are some things that's not directly related to technical things, but also some things that are essential as a data scientist nowadays in professional settings.
Schuman Luan (12:10):
The first is to know and understand the business better. Because a data scientist is usually like an internal consultant at the company, its mission is to serve the business. So the better you understand the business, how the business goes, the better decisions you can make. So although data scientist is a more technical role, but in a company, you have to contribute to the business values to make an impact on businesses. So understanding the business is also the key of a data scientist that's working at a professional setting. So on top of that, I think, another is to have the ability to well-communicate with people that have no technical background.
Schuman Luan (13:18):
So usually in a professional setting, as a data scientist, you do all your own business. You do all your own analysis, it's very technical. But you usually need to present this to either decision-makers or some business partners, and they would implement your findings, for example, your machine learning models or your algorithms for some advanced analytics. Those decision-makers or business partners, they would implement your analysis. So it's necessary to have the ability to communicate with people your proposal in very general words, rather than simply using those very technical words. So communication with those people in a non-technical background is also the key as a data scientist. So just to recap, the first is the basic stuff of techniques, and the second is to understand the business well, the third is to communicate with the people with the non-technical background.
Alexander McCaig (14:30):
All right. That makes sense. So if you are a budding person that's looking to get into this field, and thank you for saying this, Schuman, you want to enhance your background in whatever, mathematics, statistics, computer science. You also have to act as that business consultant. So understanding many different types of businesses and how they work. And then third, learning how to communicate things that are very technical into very simple forms and not to insult anybody, but when things are very technical, the more you simplify it, essentially the more intelligent you can be in that situation and the better people are going to receive whatever you're trying to give them. Is that correct?
Schuman Luan (15:11):
Yeah, exactly.
Alexander McCaig (15:11):
I don't know, I think that's fantastic.
Jason Rigby (15:13):
I want to ask one last question before we go, Schuman, and I appreciate you coming on the podcast today. How do you think data science is going to help humanity, help the globe?
Schuman Luan (15:24):
Yeah. I think there are a couple of things. I think, here, I would like to use TARTLE as an example. I think data nowadays is a very valuable asset. It's a very scarce source, especially from a data scientist perspective. So that's why when I first heard the idea of TARTLE, I was truly fascinated by it because from a data science perspective, valuable data is always a scarce resource. Even nowadays where you have too much data around us, but valuable data is always the most scarce source for it nowadays, whatever business or any organizations.
Schuman Luan (16:23):
I think, for example, as TARTLE is a platform for data sharing and data trading. I think it's pretty much like the bank in the financial system or economic system. I think the invention of TARTLE is like the invention of a banking system in our economic history. Because, several hundreds of years before, what limited the development of a business or a government is the lack of capital. While banking plays a great role, it can gather from assets, get assets from single individuals to gather all of those assets and then make loans to either companies or organizations or even the government. This could enhance the economic activities and boost economic growth. But nowadays actually what limits the development of a company or organization is no longer the lack of capital because, with a mature financial system, they can always get sufficient capital either from loans or different ways of investments.
Schuman Luan (17:43):
Yeah. So, data actually is nowadays a scarce resource for companies and organizations to make better decisions because they need to forecast either short-term or long-term, of how their environment would be, how their business would be. They need sufficient data to support their decision-making. So that's why I think, not only data science, but also the sufficient data that we can access are also the key for nowadays businesses and organizations. I think TARTLE is like the bank of data that, every single individual, they can contribute their own data, then we can gather all of those data, and then those companies or organizations or governments will purchase their data to support their decision-making and enhance their strategy moving forward to make a better impact. That's why I think data science is playing an increasingly important role in nowadays businesses and organizations for their decision-making, while valuable data is being increasingly important for any of the entities in our society to move forward.
Alexander McCaig (19:05):
Well, listen. You didn't hear it from us, you heard it from Schuman. And the fact is that you can have all the data you want in the world, but unless it's quality data coming from the person, you're going to have a hard time making the right decisions.
Jason Rigby (19:15):
Well, Yeah. Thanks, Schuman, for being on Tcast. We really appreciate you being a guest and taking the time out of your day to come on.
Schuman Luan (19:23):
Thank you. I really appreciate this. I really enjoyed this as well.
Speaker 1 (19:34):
Thank you for listening to TARTLE Cast, with your hosts Alexander McCaig and Jason Rigby, where humanity steps into the future and the source data defines the path. What's your data worth?
