Listen: How Data Science, Machine Learning, and AI Can Support Executive Strategy and Business Goals
"Out our training and consulting offerings for our executive panel. Today we'll be talking to Simon Lee chief analytics officer from waiter Fatima Kosher who is the vice president of data science engineering at all tear and role in Roberson who has the president at Tryon's so welcome everyone to the executive panel. We are super excited to have you guys here. You're all executives in companies that are doing amazing things with data science so the audience knows again. We're talking about today. The topic is how data science machine learning and A._I.. Can Best Support executive strategy can business goals. How how does that function really work? Let's start maybe with Simon and then fatman enrolling if you give us a little introduction. We'll get going from their banks. I'm silently. I'm actually kind of a mixed bag. When it comes to data science Heinsohn analytics <unk> about twenty years of experience using analytics than advanced algorithms in in a whole bunch of different industries like transportation for example airline rail trucking Ocean Carriers Printing Publishing Shing Manufacturing Finance and delivery delivery is where I'm currently at waiter is a restaurant food delivery company in small and mid size market so probably a lot of people haven't heard of us because we're in <music> smaller communities but where are trying to make a big splash so yeah? That's that's awesome. Thanks for being here <hes> Hi. This is Michael from alter engineering. I am a civil engineer by training. Although I know we get a chance chance to practice it's my background is <hes> multidisciplinary design exploration and optimization and I was in the auto industry before I joined al-Tair <hes> I've done several things throughout the fourteen years that I've I being here but always keeping <hes> design expiration opposition as the core of <hes> my responsibilities and I'll tear is a global technology company we provide software and solutions for product development data intelligence origins and high performance computing on the are McCain and its headquarters in Michigan in Troy Michigan where I'm speaking and <hes> we have offices in twenty five countries now <hes> so that would be great thanks to read Padma and rolling right. Thank you good morning or Roland Roberson with Tryon's you know for my own background. I've <hes> similar to Simon. I'm kind of a mixed bag. Then in industry for twenty plus ears <hes> solely in digital transformation space the working from startups mid level companies through global service integrators working with Tryon's currently to really expand the growth in use within within a I an Iot within the organization and our customer base <hes> tries as a company of fifteen hundred plus employees a global offices and a breath mainly serving the upper mid tier and enterprise his level customer base solely focused on digital transformation in the use of those high technologies greater return on value. That's often so lots of experience here really rape panel. Were excited to hear what you guys know no and can offer to the audience so we're going to jump in right here and the first thing that I really wanted to dig into is as an executive in your specific industries. How do you approach leveraging data science machine learning and A._I.? To have an impact on your business what's your focus and how do you make that work in your companies and maybe we can start with my here and then after she's done she's talking. If other people have comments please join it. Thanks coach is <hes> I think the biggest biggest impact the data science machine learning <hes> have on our business and on our customers business is to enable and speed up ovation. Do I think about data science data signs allows us to think in the multidimensional probably dimensional way the problems works applications wrote and we no longer have to think linearly because it simplified seeing the multidimensional aspect of a problem you know this gives us relations between changes in the system and the corresponding change in system performance whether the system is in engineering system always a financial system allows us to build predictive models using machine learning and of course you know we all know that once you have predictive models all those you can do prescriptive analytics explore many alternatives to trade off you know expose new ideas and new products and that paves the way to innovation. We have many customers twelve years that has <hes> world fit to to deploy this <hes> has been mostly in engineering domain but recently with Iraq positional intelligence company. We are also leveraging these technologies for business cases for example. We have a customer in <hes> a large consumer products brands way we use data science to find optimal package designed to reduce material which is an engineering problem and at the same time indeed do optimal trait spent to increase the sales and maximize revenues which is a business problem got it. I think there's a lot more we could dig into their but let's give the other panelists that opportunity to respond as well yeah I I love Fatma's answer kind of mirrors my own experience here at waiter. What I really love about? Waiter is just the multi dimensional multi the different aspects of data science and analytics general world forecasting what the order volume will be what happens on holidays unusual events super bowl parades etc what happens to the order volume because we need to schedule that and then in terms of just optimization it just how do we how do we get an efficient schedule out because we have thousands of drivers that we need to schedule. How do we match the supply demand once we get the forecast? How do we when we're actually doing operations? Tally matched the drivers to the orders again another optimization problem and then and then in in terms of machine learning. What can we learn about the three pillars of our business <hes> the customers the drivers in the restaurants <unk>? How do we classify them what we know about churn in their lifetime value when their preferences and all that sort of stuff has tremendous business now? It's just an exciting time an exciting business where we can apply all these different aspects of analytics addicts to to make a meaningful impact. Yeah I would agree with that. You know as we look across our customer base you know data science data engineering artificial intelligence machine learning earning all the technologies dealing with today helps us take and make the difficult easy for our customers we find many of our customers now are quite frankly struggling just to keep up with the amount of data the different types Oh to changes in the environment that they're dealing with on a day to day business and what we do is actually make those type of challenges easier to overcome we look at the Sonam e of data that they're now getting presented with in we take that in presented to to them in a way that is not only easier to use but consumable and thus they can actually make better business decisions with the type of data now the right information is presented to them so they can make a better day to day choices. That's a great overview. I'm curious now. We've talked about these various use cases across all of your industries. Actually what are some maybe specific concrete examples that you guys have seen where you can help help our audience really get a grasp on here's a specific use case that we saw and the result from it and in kind of how we built that out so they can see this process sure we happy too so as an example we recently been working with a large global logistics company and part of their transformation internally is to actually migrate from Human Driven Vehicles Do automated vehicles vehicles for types of shipping logistics now part one of transformation is just getting their arms around the telemetry and the telematics. That's happening within vehicles on how a human driver interacts day a two day now part of that transformation. It's actually not only the sensors that have been upgraded around the vehicles to collect the information but actually the collection then and distribution of that data to understand what's happening so real time use case is that while we might understand on how to move accussed driver from aid be what you're really seeing though is and the intent was maybe I can move them to location see if a storm is in a way to maintain he might logistics schedule but with some of that censor data we're now collecting weather information across all the different vehicles nationally and so with that we understood that now we have a better weather collection database of the National Weather Service at the time that is even more real time in just having that data is valuable into itself in his actually can be considered revenue stream so we identified new revenue streams for business at really had denied even have that in mind when I first went down this journey and now we started opened up new ideas and new ways to collect revenue that they never knew that they had before so it's not just attacking the problems at that. They're interacting with today and trying to move forward forward but it's it's identifying new sources of revenue that they never knew that it was even capable of having got. I love that yeah because revenue and profit are what what keeps us in business right. That's really interesting other examples that people have I didn't more generic <hes> response to it as you know. What makes success? Is You know I didn't find the right case in one that has business value but technologically we didn't reach and has data and I think the example that role engage it is a perfect example of how all those pieces are aligned and and I find one of the most rewarding things in terms of the work that we do is is as as human beings we tend to be fairly proud of our ability to make good decisions and I think that as people working in online or manufacturing evening executives we tend to be very proud and then when somebody comes along and says you know there may be an algorithm or an approach or a data driven method that can help make a better decision. The immediate response is usually skepticism <hes> <hes> you know you. You can't figure out all the all the different factors I've been doing this for a long long time good luck and when you do deliver something that actually far exceeds needs what human can do. Sometimes you get. This incredible change of attitude and perception is yeah that is actually better than what I can actually very rewarding to see that transformation to say you know we can help make better decisions with this massive information that we're collecting and we've seen it in the airline industry you seen him printing and publishing where in printing publishing week there's a schedule of how to how to construct the British could material which is a fairly complicated thing but we represented graphically for for one of the experts in the field when I showed what he did at what what the Algorithm did he's he actually stopped. Oh my goodness that's absolutely beautiful and to to get that success which translates the dollars is quite rewarding from a personal level because it's you can you can actually see yes. We were making a difference here even the experts in the area see that at acknowledged and I think that's really really awards lots of use cases here and lots of transformation as we're talking about this Simon. You brought this up just now that there's sort of a hesitancy for people to accept maybe that an algorithm can do better than I can do right when once you show them that there's a change of mind. What other areas are you guys? Seeing that are big. Hurdles are big challenges that an executive needs to be aware of when they're trying to take advantage of of what the capabilities are nowadays when they need to be thinking about what are the pitfalls. I'll give you an example you know in many cases what we find with our customers is that they already have a significant amount of data over the last decade. They've been struggling quite a bit with how to leverage that data effectively for day to day use and you can acquainted more with actually the customers really and firefighting mode because they are actually taking bits and pieces and trying to stitch relevant story together. Gather as to what that data really means and ultimately find that they don't have a complete view of what that data is so they have to go find out more so time and time again they're fighting struggling trying to make use coherency z. of the data and bring meaning to it now with allow the more recent advances. I mean you're getting more and more data now. That's being moved not only from customer data. That's being brought in thew larger scale the European systems but you're also getting IOT enabled devices so this wave of data is now going to completely swap you know the customers enter their"