Data Driven Leadership
How Data Is Enhancing the Fan Experience with Machelle Noel of the Texas Rangers Baseball Organization
Guest: Machelle Noel, Manager of Analytics Systems, Texas Rangers Baseball Club
In 2018, Major League Baseball recorded its lowest attendance in 15 years. Unfortunately, the years that followed haven’t looked much better. But ballparks may be unlocking a strategy that can turn these trends around: data analytics.
In 2018, Major League Baseball recorded its lowest attendance in 15 years. Unfortunately, the years that followed haven’t looked much better. But ballparks may be unlocking a strategy that can turn these trends around: data analytics.
Data analytics isn’t new to the baseball world, but the amount of data teams can now collect—and how fast they can collect it—opens up a host of new possibilities. Scaling their data analytics starts with building a meaningful foundation.
To kick off this episode, Brittany Goodwin, data modeler at Resultant, shares how to start your data strategy on the right foot. Through Solution on the Spot, she explains the importance of defining the problem you want to solve with your data.
Machelle Noel, manager of analytic systems at Texas Rangers Baseball Club, shares how these concepts have played out for her team. She explains how they leveraged data to enhance the fan experience. By starting with a strategic data assessment framework, the team efficiently scaled their analytics program.
In this episode, you will learn:
In this podcast:
Machelle was called up to the big leagues in May 2006 and has been with the Texas Rangers in a variety of roles ever since.
Her formal education includes degrees in accounting, programming, and business management, but what really set her up for success in what she does now was her 5-year tenure as director of finance for a minor league baseball club in Pennsylvania, affiliated with the Pirates. As a member of a minor league front office, she truly did everything—including pulling tarp.
Her current responsibilities include management of all data for the Texas Rangers business analytics team, analytic reporting for the REV Entertainment division, as well as analytic support for just about every department in the organization (except baseball operations). She specializes in business intelligence for operations—that is, everything behind the scenes, from HR and accounting to concessions, security, parking, guest services, and then some.
She gets the biggest joy in her career by helping others find better, more efficient ways of doing things. She's always thinking about where her organization can change things up, and what information her team can provide to help make better, faster decisions.
Jess Carter: The power of data is undeniable and unharnessed it's nothing but chaos.
Speaker 2: The amount of data was crazy.
Speaker 7: Can I trust it?
Speaker 8: You will waste money.
Speaker 9: Held together with duct tape.
Speaker 10: Doomed to failure.
Jess Carter: This season, we're solving problems in real time to reveal the art of the possible. Making data your ally, using it to lead with confidence and clarity, helping communities and people thrive. This is Data Driven Leadership, a show by Resultant.
Hi, I'm your host, Jess Carter, and on this episode of Data Driven Leadership, we're diving into scaling data and analytics. Specifically what we're going to look at is where should you get started if you want to build a meaningful foundation around your data and analytics. To help me solution on the spot is Brittany Goodwin, our data modeler at Resultant. Hey Brittany, how are you?
Brittany Goodwin: I'm doing well, Jess. How are you doing today?
Jess Carter: I am good. You've had a really fun, interesting career in data. Could you just walk us through who is Brittany and what is the role that you're playing now and sort of what other things have you done around data?
Brittany Goodwin: So, I've been at Resultant for about two and a half years now, and the first part of my journey at Resultant, I was working with project excellence and I was doing business analysis type work. Primarily working on some application development projects that were pretty data focused. It was around supply chain risk analysis and aggregating data from all different types of places. But I was really on more the BA, business analysis, side, working with the client directly, figuring out what the application needed to do, what we were trying to achieve with it, and how we could use all those data sources to actually do that.
Recently I transitioned over to working more on the tactical side with the data architecture team. So, I am doing some BI development, working directly with data there, as well as doing data modeling work primarily for Ideally.
Jess Carter: That is so cool. What's the part of working with data that you're the most passionate about? Where do you get lost in working with data?
Brittany Goodwin: I think it's all the possibilities of what is actually in the data. So, I think a lot of times understanding what we're trying to do first and foremost is most important, but then you start looking into data and you're like, "Yeah, we can definitely achieve that, but also here's all this other stuff that maybe you guys didn't realize is actually in your data and how that can be tied to your original use case or how we can use those additional data points to help solve some of the other use cases that clients have."
Jess Carter: That's awesome. Very cool. So, we're going to do solution on the spot and we're in this together. So, the scenario for us today is we've walked into a potential client meeting and they've sat us down to say, "We need your help." They're at a enterprise level, large organization with, we'll say more than seven large enterprise systems that don't integrate easily. And they've been down a path to try to scale their data and analytics and essentially they're just not getting where they need to. So, we're walking in stuck between they wanted some quick wins and they've now not gotten those, but we also need to make sure they get the foundation right. And so as we step into that, where do you begin the conversation with them?
Brittany Goodwin: I think that scenario is probably more common than people actually realize if they don't work with that stuff every day, as you think these big organizations have the most developed data strategy ever when most of them don't. I think in that scenario, first place to start, I think most companies will look at what tools are out there, what software can we implement, what databases do we need, what visualizations are we going to create?
But the first step is really what are we trying to achieve going one step behind that and what is the strategy? What are we trying to create? Who is going to be using this data? Who are we creating these visuals for? How is this data going to actually help them make decisions in their everyday job? So, I think taking a back a step back before we get to what tools we want to use and actually analyzing the strategy of how they're trying to do this and why they're trying to do it.
Jess Carter: Even that, right? How many times do we walk into and they have an opinion about the tool. "Hey, we're halfway through building it out in Tableau, Power BI. We've got a half built warehouse, we really don't want to give up on this path." Is it painful for you to see when a client is immediately entrenched in some assumptions about tools or are you okay with that and largely it plays out? What are some of your thoughts on that?
Brittany Goodwin: It depends on what tool they're attached to and why they're attached to it. I think as consultants, if they've invested in this tool and they need to use it, obviously we're going to try to do our best to take that into consideration, but it's not always the best path forward. So, I think always it's pros and cons of maybe this isn't the best tool for you guys to be using, but if we can back up a step and figure out, let's look at your strategy first and then see from there what can we salvage from what you guys have already done and what are the pros and cons of stripping it out and starting over?
Jess Carter: If we're in this situation where they do want some progress, you can tell they're feeling the burn on, "We wanted some results." We kind of do our best to figure out are there some quick wins that we can get them one way or another? Even if they're sunk cost and we're like, "Listen, bad news. It just is the wrong platform." Even if that's the case and we can explain that, walk them through why, because we understand the pressure to keep things that they've already spent money on. And if we really think that's not going to serve you for your scaled analytics in four years, two years, we really feel the responsibility to say that out loud and still create opportunities to say, "We can iterate quickly over here and get you the three biggest pain points and push those out quick and figure out are those visualizations or three data sources that you need to pipe into their data warehouse?"
Brittany Goodwin: I think we do that, a lot of iterations. And okay, our first iteration, we're going to target whatever your biggest pain point is or what's a quick win for you guys? Can we prioritize, out of all these different dashboards you want to make, is there one dashboard or a specific section of that that's really important? And let's focus on that, use what we can to get that out the door and get that to the people who need it.
And I don't think that, even if we know maybe that's not the right tool, it's not a waste. I know that I've been on projects before where we'll do one iteration knowing that's not going to be the final step and we know that we're going to iterate, we're going to change later on, but the things that we learn from doing that first iteration always make the second iteration better. So, it's definitely not a waste because we are getting people what they need quickly and because we're learning a lot before we move on to the next step.
Jess Carter: One of the things I want to pick your brain about here though is when it comes to iteratively building out a scaled data analytics platform, I as a non-data modeler might think, "How do you know the way you're iterating isn't sunk cost? How do you know you're building this dashboard in a way that is truly modular and that we can move the right things around at the right time?" And so, I don't know, how do you address some of that?
Brittany Goodwin: So, doing that kind of on one project currently, and we're going to do this in iterations, right, but we know that the underlying data model, we don't want to have to rip out the data model and start over because that takes a lot of time. That's part of your base, part of your foundation. So, we put a lot of thought into how do we design a model for the data that can feed not only the application or the dashboards we're doing now, but also what are some other use cases that this client has and can we build the model in a way that we're also not going to back ourselves into a corner where when we do the next iteration of whatever we're doing, where we have to start over on the data model and create a new one?
So, doing data models that are modular, that sometimes we bring in data sets or data fields that aren't 100% required for the first iteration of this, but we know bringing it in now is going to save us time later on in the road. Then if it's easier to do it now, we'll go ahead and do it now.
Jess Carter: You've gotten to work on the platform, the backend, and the front end. In this kind of scenario where they do want some quick wins, but maybe they're in a mess and they're halfway done and need us to step in. What's usually the hardest challenge in your opinion?
Brittany Goodwin: I think one of the hardest things we come to is that, in this huge amount of data that we have, actually pinpointing what people are trying to understand from their data, which is why we try to make that step number one. What are you actually trying to understand better? What problem are you trying to solve? And chances are there's data to support whatever decision you're trying to make. But if we don't understand that underlying, what issue are you looking at, then we end up chasing the wrong data sets or we don't build the model correctly. So, that's really important is understanding exactly what problem they're trying to solve.
Jess Carter: I don't know if it's hard, but step number two maybe or three, I'm not sure on your list, is with that many source systems and that many enterprise solutions, there's probably a ton of noise. There's probably people who do things differently depending on their standard operating procedures and their compliance measures around those. So, people use it differently and the quality of data might be awful where they really thought they were relying on good data, but there's really good data tucked somewhere else where sales is actually putting really good data in their sales pipeline, but no one was pulling that into the warehouse to use it for some sort of forecasting. And so imagine the noise is like, "Okay, what are we aiming at? And then let's go get all the noise out so we can look at what the critical data is to be our signal." Is that fair?
Brittany Goodwin: Oh yeah, for sure. There is a lot of noise when it comes to data and a lot of data that gets collected that people will think is important but ends up not being important or the way they've collected maybe is not conducive to proper standards and so you can't really use it for anything.
Jess Carter: So, if I'm hearing you correctly, you're saying, hey, get in, understand what their intent is, understand the kinds of triggers or levers they're trying to pull with their data, understand and analyze what is some of the noise and what are the signals they're relying on? Maybe evaluate those signals for feedback on if they're strong enough signals to trust to make these kinds of decisions in the first place. And then, yeah, quick wins, sure. We can make some cool visualizations or tuck in a pipeline or two and do our best to salvage what's already been built when and if we think it's possible. Is that right?
Brittany Goodwin: Yep, absolutely.
Jess Carter: Awesome. I think you just gave us a lecture in five minutes flat, so awesome job. Thank you for solutioning on the spot with me.
Now for the deep dive on scaling your data analytics. The two experts you'll hear from are Brian Vincent, client success leader at Resultant, and Machelle Noel from the Texas Rangers Baseball Organization. In their conversation, Machelle shares the power of starting with strategy, which is what Brittany just alluded to, and how the Texas Rangers operations team leverage the SDA framework to scale their data and analytics program. I think you're going to see a ton of similarities between what Brittany just said and what Brian and Machelle are going to share. Let's jump in on the conversation where Machelle gives a little background on the Texas Rangers and her role with the organization.
Machelle Noel: So, Texas Rangers of course, professional baseball team, but we are also a business, just like any other business. We have revenue, expenses, operations, those kind of things that we have to pay attention to. I've been here for 16 years and I am on the business analytics side. So, there is a whole other department that deals with all the stuff on the field, and that's baseball analytics. But I'm in a department that deals with the business side and our business analytics, we got started, I want to say, about five years ago. Pretty much is a grassroots kind of a start. At the time I was in IT because I have an accounting and a programming background and I came from a minor league team that knew the operations side of things. So, we started out, I was in IT, there was someone from CRM, someone from marketing, and then someone from the ticket operations.
And we just started playing around with Tableau and visualizations. And because we knew all the different parts of the business, we just started putting things together and then we would present it to executives and leadership and like, "Hey, what do you think about this?" And that led into during committee meetings, and rabbit holes, and lots of questions. And eventually, I think it was about two years ago, we actually created and we were knighted, appointed our own department and there's currently just three of us on the business side of the analytics for the Rangers.
Brian Vincent: Awesome. Well, thanks for that. Let's dive into the idea of starting strategy. Were there some challenges that you saw in the organization that made you guys step back? What did that look like?
Machelle Noel: So, we were looking down the road at a brand new ballpark and it was a very short road. So, they were building a brand new ballpark across the street with state of the art technology and with the ability to have data that we had never had before. So, it's not like we didn't want it, the systems across the street were just old. We just couldn't get the data that we wanted.
So, a bunch of new data from state of the art technology and we were like, :Wait a minute, this is our opportunity to have a clean slate and start out fresh." So, we wanted to make sure that we were going to put the foundation in place that was going to allow us to move forward with all the data needs that we needed so we that needed the bigger picture, if you will, because we knew we had all this stuff that we hadn't had before.
Brian Vincent: Awesome. Yeah, I think one of my favorite meetings was with some of your IT staff where they started talking about how in the new ballpark that even the restrooms were going to have all this internet of things stuff, like understanding how many paper towels are left and ...
Machelle Noel: Oh, yeah.
Brian Vincent: That's a lot of data.
Machelle Noel: Yeah, it was crazy. Like I said, state of the art technology and it was crazy the amount of data that we were going to be able to have. It was almost a little overwhelming. So, we were like, "Oh wow, we got to make sure that we are putting the right foundation in place for all of this stuff." And I'm sure there was stuff that we didn't even realize that was going to happen and that we were going to get.
Brian Vincent: So, instead of digging in and figuring all this stuff out yourself, you elected to partner. Can you talk about some of the factors that helped the Rangers come to that decision?
Machelle Noel: Sure. Actually, that was a no brainer for us. So, we knew that you had a much more expanding knowledge base. We're baseball people, granted we're in a business, but we're still baseball people. And we know that you probably had ideas or processes that could help us instead of us trying to reinvent the wheel. And also too, I said we were a department of three and with everything else going on and all the things that we had, all the new sources of data and just trying to get into the new ballpark, we knew that if we tried to do that ourselves, it would probably never get done.
Brian Vincent: Well, so we did the strategic data assessment. So, the five areas that we focus on when we do that are people in process, data models, data structure, data architecture, and the platforms that are being used. Like visual analytics and advanced analytics. What were one or two of those areas that the Rangers thought that you might need the most help in?
Machelle Noel: On the tail end, the visual analytics, we have that pretty well wrapped up. Not that we're the greatest at it, but we were confident that we were fine in that. And the predictive analytics stuff, we do some of that already. So, we were satisfied for now with where we are with that. Predictive is more on the field kind of stuff too. So, the other categories is where we really knew that we really needed the help and to lay the foundation. Again, with all of this data coming in, we knew that we had to have a really good platform and data structure, formation, warehouse. We just needed guidance in thinking about the future and we had never really done that as an organization before. So it was helpful for, again, someone to come in and help us think about what that looks like.
Brian Vincent: What were one or two takeaways from all of those interviews that we did? We talked to marketing, we talked to accounting, HR, sponsorships. What are one or two takeaways that you were surprised with that came out of that?
Machelle Noel: So, one of the first ones that really was an eye opener, I was like, "Wow," was the amount of manual work that we've been doing across the organization and that there were so many departments that touched the same kind of data, but it was in Excel spreadsheets and it was in local drives and it was in all this stuff.
So, it brought up a big red flag that, oh my gosh, they're using the same set of data, but it's in all these different places. So, who knows if it's the most recent version? We really needed a way to put everything in a central place, a system of record, if you will, and have that for everyone. Not that we would, as analytics department, have control over that, but we could make sure that they were getting the right data and making the right conclusions, which was even more important.
Also too, that there were departments that didn't even realize that some data was out there. It was really a way to say, "Hey, guess what we can do for you?" One of the ones that I talked about, this one sponsorship, when we were talking about the concessions data that was going to come in and sponsors like, "Wait a minute, you can get concessions data? We can get access to that?" So, it was really enlightening for us and for them.
Brian Vincent: Well, let's switch gears a little bit and talk about how the Rangers took the output of that strategic data assessment and started letting the rubber hit the road. If I remember right, you're pretty methodical how you wanted to execute, making sure you got everything and get some early wins. Give us some thoughts on how you laid out that plan and a little bit of the why behind it.
Machelle Noel: The why was, like I said, we had a very manual process that, in my opinion, was held together with duct tape. Now, not that duct tape isn't good stuff, but it was always a challenge. Like, "Well geez, is this the day this stuff is going to break?"
And because of that, that didn't really do us any favors. So, reliability was something that was not really an issue here, but we had to make sure that, because things had failed so much in the past or it took so long, we had a very limited opportunity to really prove ourselves and say, "Look, we know we got it right this time." And this time we had a plan, we had action steps, we had deadlines, we knew what the expenditures were going to be. So, it wasn't just being reactive to stuff and saying, "Oh no, this isn't working. Now we have to spend X amount of dollars."
We knew exactly what was going to happen and when it was going to be done. And again, because we were building a new ballpark, we were like, "Okay, we have to have this done by now and this is going to happen then." And our biggest opportunity was the real time game day reporting. So, that was really our first opportunity to have some really, really good results because we knew that the real time data expectations were going to be there. Because everything else was state of the art, our reporting and our analytics and our conclusions needed to be state of art as well.
Brian Vincent: What were some of the data points that you're able to pull in real time now instead of having to wait until the next day?
Machelle Noel: Parking. We kind of had parking before, but now we could tie it all together. The biggest one is concessions because we never had access, real time, to the concessions data. And it's much more robust now. The ticketing and the scanning, so as people are coming in the gates. So, we can tie them, we know where they're parking, we know when they can come into a gate and which gate they're coming into. We know which concession stands they're going to, all of that stuff we can now put together.
We also have real time information on our operations part of things. Whenever you come into the ballpark, you might not realize all the things that are happening behind the scenes. If somebody needs a wheelchair access or if someone says, "Hey, there's a spill on the concourse." Or, "Hey, we need a medic here."
All of that stuff gets recorded in a system and now we can report on that real time. We know how fast it takes for us to resolve an issue. We know where hotspots are, or if we have an area that has bunch of broken cup holders or broken seats. It's just much more easier now to see all that kind of stuff.
We had all these grand plans for 2020, right? Brand new ballpark. All the data. And then we got hit with, a lot of other businesses, with this struggle and this challenge of COVID and what this did. The good news out of all that is, number one, we as a business kind of just pivoted a little bit. So, we started bringing in, we were doing tours of the new ballpark. So, because the information was already out there in a place that was easily accessible for us as a department, we were able to quickly ... For the tours analysis we did, I could show the effect of the retail sales on people that were coming in for tours. We sold, you might have remembered when we started having games, even though we couldn't have fans, we were selling the Doppel-Rangers. So we were selling those and I was able to quickly pull together an analysis of, okay, here's all of our sales for the day.
But then I was able to tie that back to here are people who have previously contributed to the foundation because all those proceeds went to the foundation. Here are the people that have bought a Doppel-Ranger that have also contributed in other ways. Or here are the people that were Doppel-Rangers that are already season ticket holders. So, we could service those people. We could have our sales rep call and say, "Hey, you've bought a Doppel, that's awesome. Here's where your Doppel-Ranger's going to sit." They were able to reach out to those. And we were also able to identify people who currently don't have any kind of a ticket plan.
Brian Vincent: So, having prepped and followed the roadmap to get that data lake in place, you were able to, when COVID hit and you weren't opening a new stadium with fans, you were able to pivot and still provide more of that real time analysis for your sales team and other people. But you guys just recently had a really big event there. Can you tell me more about that?
Machelle Noel: So, we were able to host the post season. So, we hosted some divisional and then the championship. And for the championship series we were able to have fans in the stadium. Now granted, it was only about 20% of our capacity, but still. It was the first chance for us to have fans in this new ballpark. So, we had the championship series and then we had the World Series. So, I knew that I had a very limited window, a very narrow window because technically we really might have only have had eight games at the most.
But I had a very narrow window of, "Okay, real time data from systems that we have never had before with a structure that we had never had before. And what does this look like?" And I'm very pleased with how it all works. We have lots of opportunities now to go forward. But it was a really, really good dress rehearsal, I want to call it. I don't want to call the World Series a dress rehearsal, but it was our first opportunity to have fans in the stands and to see how all of this worked together.
Our management team, our operations department, were all very happy with that. MLB was really happy with how everything went. So, it was awesome. It was like Christmas to me. I was so excited to have this chance to put all this data that we now have together.
Brian Vincent: You know you're a data nerd when having all that data feels like Christmas, right?
Machelle Noel: Yes.
Brian Vincent: I'm right there with you.
Machelle Noel: You know what? I'm okay with that. I'm okay with that.
Brian Vincent: Yeah, no, it's been great. I know that organizations measure success in different ways. Can you share with our audience a couple of successes that resulted from the strategic data assessment? In addition to being able to pivot for COVID, any other successes that your whole organization recognize and not just us data nerds on a Christmas with all that data?
Machelle Noel: It was just really awesome to finally have that opportunity to put some of those things together. We had so much grand plans, but even for right now, we have so many other departments that are reaching out for our help and departments that we had never serviced before. So, we're doing stuff now with our legal team, with the foundation, and whenever we had the World Series, they were able to do the 50/50. So we had real time data on how their 50/50 sales were going.
Partnership, like we mentioned before, now that we have concessions, they can see, the partners that we have, how their sales are doing. We talked about tours and events because the organization has pivoted. And so we're doing a lot more events. Even not just that kind of stuff, but on the back end, where the people don't see. I talked about the legal department. Or on the operation side, we're tracking the phone calls that our receptionist gets and we're helping out with all the lost and found stuff. So, whenever people were here for that post season. It's just been so exciting and we just cannot wait to see what else we can do.
Brian Vincent: Now, so you took the data from parking, from retail, from concessions, you've landed all that real time instead of the next day. So, for the World Series, for that post-season stuff with fans, you were able to see that and did you have a dashboard that other people are looking at, or is this just for the data nerd on Christmas in you?
Machelle Noel: It even got some high profile, actually. So, I had been building stuff all along, but it's so hard to actually see what it looks like when you don't even have ... I couldn't even use historical data because we had never had this data before.
Brian Vincent: It's brand new systems.
Machelle Noel: Brand new. So, the first night was very interesting because I had built everything, so I had some hiccups that first night obviously. But I had a summary landing page with all of the big numbers, all of our revenue sources. And then I would have some detailed stuff about the concessions and even down to how much income or how much was being brought in per stand. What food item was the most popular? In the retail store, same thing. What were the top 10 items and how were sales going in each department? Which gate people were coming in? What was our busiest gate? And at what time within 15 minute increments? What was our busiest time of bringing fans into the stadium?
So, all of that was being published on a Tableau dashboard. And then I was emailing that out, some parts of it, every 15 minutes to our managers or people that were working the gates or on the frontline.
And then the summary dashboard, I was emailing that every half hour to our executives and our management team. So, that was shared, the day after the first night, that was shared in an executive meeting. And our ownership said, "We want that too. We want it."
Brian Vincent: So great.
Machelle Noel: "Can you please send that to us too?"
So, the managers and the executives just got it during the game. At the end of every night, whenever the game ended or about a half an hour after the game, because you never know when the game's going to end, that night, I would send an email that had that summary of everything that happened during the game and that went to executives and to ownership every night. And we had never had that before.
Jess Carter: Thank you for listening. I'm your host, Jess Carter. Don't forget to follow the data driven leadership wherever you get your podcasts, and rate and review letting us know how these data topics are transforming your business. We can't wait to see you in our next episode
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