Turning Transit Performance Metrics into Action with Laura Moeini

There’s a time, a place, and an audience for every metric. Stop Requested. This is Stop Requested. by ETA Transit.

I’m Christian. And I’m Levi. These are real conversations with the innovators, operators, and advocates driving improvements in public transportation. Today, we’re talking about how transit. agencies can use data to improve service, not just report on it.

Our guest is Laura Moini, Senior Director of Performance Analysis Unit in Subways at New York City MTA. Laura leads a team focused on using data to identify opportunities for performance improvement in subway operations. We’ talk about her path from consulting to WMATA and now MTA, and how she learned to turn complex data into clear, actionable stories for operators, managers, and executives.

Here’s our conversation with Laura Moini. Welcome back to Stop Requested. Today, we have a phenomenal guest, one that Christian and I are really excited about. We have Laura Moini, who’s the Senior Director,

Performance Analysis Unit in Subways at New York City MTA. Laura, how are you? I’m doing great. Thanks. How are you, Levi? Oh, excellent.

And y- I’ve had this one marked on my calendar for a while. Really excited to get into this, and I know I’m going to have to just push Christian out of the way to be able to get my voice heard today because Christian is going to be chomping at the bit to, uh, get these questions out to you because we, y- you know, we both have a background in, in data, and I know that you’re in love with data and with public transit, so this is, this is super exciting for both of us.

Uh, but to start, can you give the audience, uh, a, you know, an introduction to yourself and what your role is at MTA? Yeah, absolutely. So as you mentioned, I’m the Senior Director of the Performance Analysis Unit within the

Department of Subways here at NYCT. We are a small but mighty team of analysts who use data to help identify opportunities for imp- performance improvements in our subway operations.

There are lots of different data analysts and data teams at the MTA. and at New York City Transit that we work very closely with. Our team is very focused on the rail operations. and service delivery side of things, so how do we move our trains more effectively in normal operations, during incidents, et cetera, so that we get people to where they need to go as on time as possible. So our team, we do performance analysis. We’re analyzing train movement and schedule and operations data in new ways and innovative ways to help really pinpoint those, um, smaller areas for improvement.

And then we also develop performance improvement pilots to help us, uh, improve that aspect of performance so that we are engaging in a continuous improvement cycle. We do not just provide the reports and go back to our desks. We wanna make sure that the data is being communicated in a way that it is effective, that is leading to action, and that we are then monitoring and turning into, uh, a positive feedback cycle. Oh, that, that’s amazing.

And I can already hear Christian in my head right now saying, “Kaizen,” because that’s the, that continuous improvement mindset where you’re, y- uh, you know, uh, constantly analyzing and, and then, uh, suggesting what those improvements are going to be, seeing how it works out in the field.

I-it’s all hypothesis driven, and, uh, it, it sounds like that’s, that’s what your bread and butter is, that that’s what you spend your time doing. I, I’m curious how you got into this, Laura, because, uh, you know, I know from our experience at APTA Emerging Leaders, your background is not in transit or, or in data, right? Uh, how, can you describe your path to be able to get into transit performance?

Yeah, absolutely. Um, no, no background in, in data and transit previously to, to these roles, both at WMATA and the MTA. Uh, never could have imagined that I would be where I am today, but, uh, very glad and happy for it. Yeah, so I studied global studies actually, uh, in college. I was very much into international affairs. I thought I wanted to become a lawyer, so very much not on the track of data analysis or transit.

After I graduated from college, I moved to Washington, DC. That seemed like an apt place for me to be to grow my career, and I started out at a small consulting firm, um, doing, uh, consulting for federal clients in the emergency management space.

And in consulting, one thing that’s really great about it is you get to wear so many different hats. You’re surrounded by a bunch of really smart people who are just tackling a variety of issues. And the projects that I was assigned to were very much in the performance management and measurement and reporting space. And as I started to do more of this work and learn about performance measurement and do more reporting,

I just grew to love the, uh, the, the storytelling and the communication that you can do with data.

I grew to love the way that you can shape data into something that is meaningful and actionable. I think I have this, I have this drive for turning, uh, uh, for turning chaos into order, and taking data and turning it to something useful and actionable that allows us to improve satisfies that itch. I also discovered that I really love the, uh, the creativity of, uh, of data visualization specifically. I think I’m a teacher at heart, and data visualization is, is an art and a science and a way to help you reach an audience that you might not have reached before because data can be so complex to understand.

So I started out in, in consulting and then moved over to WMATA, uh, a similar role in performance analysis and reporting, but not totally sure if transit would be, um, the, the, the space that I wanted to continue growing my career in. But pretty early on I was absolutely hooked.

I think, I think I’ve always… Public transit has just been an integral part of my life since I was 18 years old. I haven’t owned a car as an adult. Uh, I’ve been drawn to big cities in general for the, the, the diverse and energizing and fascinating places that they are, and public transit is such an integral part of that.

Um, I lived in, in Paris for a year after I graduated from college, and that system had a huge impact on my experience there. O- obviously in, in DC and New York. And when you work in transit, you feel connected to your output in a way that I have never felt in, in any other role. So you get to…

You ride the thing that you’re working for every day. Your, your friends are, your community is, your broader city is, and that’s, uh, incredibly gratifying. And then I would say more broadly, um, you know, I’ve always been in, in, in government spaces, but, uh, being a public servant, I’ve really just…

I’m incredibly motivated working in public service to help improve the way that government works for people and, and, and that’s something that, that drives me every day. I, I love that. A- and

I, I l- I… One of the, uh, things that, that I was, um… that really, you know, caught my attention of, of your experience and what you just shared is the translation of data, right? Like, I feel that data in any organization, uh, it, it could be very complex, it could be numbers, just numbers, but it is the data inference. And in, in your role, uh, is being able to communicate that inference, right? Like translating what the numbers are saying into something that is actionable.

And, and I love the piece that you said is storytelling and, like, being a teacher. Because in a way you have to be, right? Like, all those different stakeholders in the organization, you have to communicate to them what the data’s saying and how we can take action. Like, a lot of times, eh, people get caught in the reporting, and it’s like performance management is reporting. It’s showing reports that it’s like, hmm, nope, that’s not right. Like, if you’re just looking at data and you’re like, “Yeah, uh, okay, it is what it is,” eh, then, then you’re not doing it right. Like, you’re not doing it right. It’s, it’s the storytelling, and it’s the decisions that you’re making based on the data and what the data is telling you. So, um, I wanna ask you about your, uh, role at WMATA, uh, before you came to your current role and, and you’re coming from consulting, which in my opinion, consultant is a lot of analysts, right? In or- in order to consult, you have to have an understanding of what the situation is, and do it in a way that is systematically and then that is gonna help leading into actions, right? Like, what’s the point of doing consultant is not leading to making decisions and taking action. But when, when you were at, at WMATA, you went from analyst to a manager. So now, you know, you have a bigger scope.

You know, you’re still, eh, you know, hands-on working on things, but you have a bigger scope. Um, you know, what changed in how you approach problems as now you’re not an analyst but a manager? You s- you have a bigger scope.

Yeah, that, that’s a great question, and I really had to do a lot of evolving. You know, I felt so ready to be a manager, and then you get put in that position and, and you realize the, the learning curve, um, that you’re up against.

But I think where I really had to grow and where being a manager pushed me was connecting the analysis that we’re doing sometimes at such a, a very minute or discrete level to the bigger picture. And y- you know, you mentioned data storytelling and data turning into action, and sometimes we view analysis or reporting in this vacuum as this thing that’s separate. And when you’re a manager, you really want the, all of the incredible work and the brilliance of your analysts to have an impact. And so you’re constantly keeping that big picture in mind, keeping what, what leadership is focused on, what the frontline needs, um, what the news is saying, and you’re trying to bridge the gap between your analysts’ work and that bigger picture stuff and make sure that what they are producing is having that impact.

So, um, I think it, it allowed me… You know, when I was an analyst I, I was maybe h- a bit hyper-focused on the task at hand, and being a manager helped me take a step back and bring all of the pieces of all of the work that our team does, uh, together towards that bigger mission.

I, I love that because to me that, that is realizing, um, that data can empower, right? I- in a lot of the times when we’re making decisions, when we’re talking to the community, to the, the service boards, to those stakeholders, and you have to navigate change, data’s the best way to explain the, the rationale, right? Like, how, why do we have to do this?

And why do we think this is the best way moving forward? And it’s showing the data and in telling the story, look, because of all this, uh, you know, analysis that we did and what the data’s telling us, therefore, this is the best path forward.

And I tell you, uh, managers, and, and executives, eh, eh, they’re, um, 100% empowered by the work that the, the analysts are, are, you know, are doing at that level. So, um, you know, I, I, I concur with you on that. Uh, I, I wanna ask you, um, you know, more specific question. So you started working in performance management.

Is there a story or a moment where you saw the influence of that work in operations or decision-making? Like, c- could you maybe, uh, think about. a, a specific example?

You? know, like we work on, on this data, and then because of the, what the data said, then we took this action and, and also if you, saw the outcome that, you know, the, the data was telling you, uh, you know, uh, was going to come.

Yeah, absolutely. So I- I’ve got a lot of stories floating around in my head, but one I? think stands out and, and is symbolic about what maybe matters the most to me in my role, which is connecting with my audience and connecting the, the people that I’m serving internally. And at WMATA, when I’ first started, I was part of a, a strategic initiatives group that worked to, you know, bring data into special projects to help improve operations, and the big project that we were working on was optimizing our overnight right of way work.

So how do we, um, how do we get, uh, how do we make the most out of this non-revenue time when the trains are not moving through the system?

And, you know, we had to work with such a diverse range of groups across signals and maintenance and capital to understand this very complex process of right of way work overnight, and then exploring new data sets and creating new metrics. And it was a lot of very intensive effort, and this was, uh, this was an initiative, uh, from our COO, so, you know, we had this mandate to do this work, but it took a lot of buy-in building, of course, because we’re working so intensely building these new metrics and showing people these new reports.

And sometimes that can feel a little discouraging. Like, am I really having an impact with my audience? Are they bought into to this work or this metric?

And I remember at, at one point, I think one of my colleagues, you know, we would go out to the, the, our field locations overnight and, and meet with people and talk about their challenges and, and how we could, how we could better help them. And one of my colleagues went out to one of our field offices overnight and, um, took a picture and sent it back to us, and this picture was the, the, the, uh, the bulletin board in this modular building, and on the bulletin board was a printout tacked up of one of our reports. Hmm. Wow. And that, that moment, you know, I could, I could cite the performance improvements and the gains that we made because of this project, which is absolutely ob- obviously incredibly important.

Um, but I think that moment on a personal level meant a lot to me ’cause it meant that our work and our product was reaching them in a way that was meaningful and valuable and useful to them. And that was the, the symbol of, okay, they, they, they do care. They are looking at this. They are taking action off of this.

Uh, that was just a, a, a really cool moment. Wow. Well, thank you for sharing that. I, I, I mean, as you were telling that story and the moment you said you, you saw your work and, and people taking, um, interest in it and, and finding it important, you know, it, it resonated with me, uh, as I was in a very similar position and, you know, putting these reports for the organization, for the community, and, and wanting people to, um, understand what they’re saying and, and why it was important for them. So, uh, you know, I think that that’s an awesome story.

Um, uh, you know, when you were at WMATA, uh, I wanna, uh, talk about more about setting up metrics, right? I think something you said is very important is connecting metrics to projects, right? And making, um, you know, projects and everything that we’re doing measurable.

And in this time right now, it’s even more important. Like, we see it more and more, uh, data, uh, being available. Uh, you have AI, you have all these different tools that, you know, helps, uh, analyze more of this data, get quicker to data inference less, uh, uh, inference, less data mining. Um, tell me a little bit of how you help, uh, develop KPIs at WMATA. In, in, in the mo- at the monthly stat meetings, uh, what makes those effective, uh, versus performative? Like, you know, tell me a little bit that story of setting up those metrics and meetings at, at WMATA. Yeah, for sure. And, and this is, this is an area too that I’ve had to go grow quite a bit in, in my personal journey and my career, which is the most important thing is listening to what your audience truly needs.

And sometimes we analysts can, can think we know what the answer is, and think we have some sense of, “Oh, these are the metrics that you should be measuring.”

But to, but, uh, uh, really our, our audience, our stakeholders, uh, whether they are our frontline executives or whether they’re our frontline or senior executives, they, they will guide you to the right metric or report or dashboard, but you have to, you have to help facilitate that process with them. But they have the answers, and you just have to listen very closely. So where I see this go wrong, and, you know, I am, um, I, I have been at fault of this too, is prescribing something or, or suggesting something and, and moving forward without a lot of fe- feedback and, um, and iterative improvement.

What works best is when you can, say you are developing a stat meeting or refining a stat meeting, is when you go to your audience and say, “Hey, what are your problems?

What keeps you up at night?” Uh, and then what we do as analysts and where our, our value comes in is how do we take all of those great ideas that they have in, in their head, all of the overwhelm, um, all of the, the desire and passion to improve, and how do we turn that into a repeatable, measurable system?

So there are key performance indicators that we are all familiar with in the transit space. Um, a lot of times those performance indicators are very high level and are difficult for various audiences to manage to. And we can get creative with our metric development. There’s, I, I, I, I believe that there’s a way to measure anything, and there’s really no bad metric if it helps you improve in a directional, directionally correct way. And so just really working with our stakeholder groups at WMATA, we did this, I worked very closely with our rail group, to every year we would revisit our list of stat metrics. Like, is, are these metrics working for you?

What, what isn’t resonating with you? You know, where are we hearing crickets in a meeting and why? Are we not looking at this metric in the right way? Do we need to break it down a couple different ways? Um, all of those things are so critically important to building a stat program and a report or a dashboard that your audience is engaged in. And then I would say also, uh, very important to have senior leadership buy-in and championing of these efforts too, to make sure that, um, your people see senior leadership engagement and involvement in these accountability mechanisms, uh, that also really helps. This episode is brought to you by ETA Transit. Legacy CAD/AVL systems were designed when on-prem servers were the only option. Today, agencies need systems that leverage modern technology that are faster to deploy, easier to use, and built for constant change. ETA Transit was built to replace the old model.

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Yeah, I, I concur with that. Senior leadership support is critical. If they’re not showing everybody else that they’re paying interest, uh, to the metrics, to the exercise that we’re doing with, with analyzing what we care about and taking that data inference into actions.

Uh, and, and I really wanna go back to what you said, just to kind of like highlight it again, um, you know, going to the source, right? Like, that’s part of that Kaizen exercise, is you go to the source, you observe what’s happening, and then you ask questions, right? Like, why do you do this? Why is this important to you? And then get that feedback to actually, uh, be measuring the right things because we, you know, I see transit agencies we, you know, on time performance, you know, passenger per revenue hour, you know,

10,000, uh, uh, preventable accidents per, you know, 100,000 miles. Like, there’s, there’s all these numbers that are just kind of like numbers i- and then sometimes we’re just measuring it because it’s the, the standard measure that everybody’s tracking. But is it really something that we’re tracking for the purpose of taking action and solving a problem, right? And you manage what you measured. So i- if you go to the source, like you were describing, and then you get from them, like, “This is very important for me. This is what’s keeping me up at night,” and even though it’s not the standard measured that, that, that might be in a report and, and being tracked, that’s the one that you should be focusing on because that’s the pain point that you need to resolve.

So I, I think that was, that was just fantastic. Uh, thank you for sharing that with us. I- In the same sense, and I guess it’s tied with, with what I just said and, and the story you told us, what are other common mistakes agencies make when defining KPIs?

Yeah, I, I kind of, you know, similar to what, what I was describing, and actually I, I kind of wanna build on what you just said, which is that we, we view these top line that are very critical, important health metrics of a transit agency, but we view those at th- as things that we, that, you know, everyone in the organization needs to measure too. And I think what, what we can do in an exercise that is incredibly important that analysts can facilitate is to define and develop, um, uh, if she listens to this, she’ll laugh, one of my, my boss at WMATA, who is-

… just a brilliant performance, um, measurement expert. Her name is Jordan Holt. She’s incredible, and she’s taught me nearly everything I know about this space. And, you know, we can, um, view this as a metric tree of sorts, that we’ve got these health metrics, but what are all of… H- how does that flow into the drivers and the sub-drivers and the sub-sub-drivers of performance?

And where does each person fit in that big tree? Almost if we think about an organizational hierarchy, our metrics are kind of like that as well.

And where does each person fit to align with those various sub-drivers and sub-metrics? That’s what we should be building out for them because it is all related. We are trying to find the levers that matter to an individual person that they can manage to, but everything contributes to those top line health metrics. And when we just look at the top line health metrics, we, we, um, we risk inaction because it’s just too big and it’s too overwhelming. But one bite at a time.

I, I, I like that. And, and the fact that the metrics, um, a lot of them are, are connected, right? Like, I would say most of them could be connected. And sometimes to have an impact in one area or one metric, it comes from, you know, improving a different one. So, a- and, and in the connection between the employees and the performance, a- and the performance metrics, right? To, to what you just said, like the different hierarchy and the different individuals and understanding how they can have an impact. Uh, because if you don’t make that connection, then everybody is, starts thinking, “I’m not it. Like, that doesn’t talk to me. That’s probably operations or somebody else. Like, it, you know, doesn’t talk to me at the individual level.” So just making them, um, you know, helping them make that connection i- is critical. And, uh, the last thing I, I wanted to ask you, uh, on a, you know, setting up metrics is goal setting. So h- how, how, how have you gone about, uh, setting goals for those different metrics, uh, you know, targets, goals, and getting the buy-in from individuals that they feel like, okay, yeah, that, that’s attainable, or that’s kind of like a stretch goal, but, you know, I’m, I’m gonna commit to it. Like, I’m,

I’m gonna, you know, get involved with, with driving those numbers Yeah, this is so incredibly important.

Um, and it’s, it’s really a balance, and balance is, is a tough thing to find of, of pushing ourselves to improve, but also being realistic. Because if we are so wildly unrealistic, there isn’t gonna be any buy-in, and we’re not gonna care, uh, because there’s nothing that we feel that we can do about it. Um, and so

I think it’s, it’s balancing, uh, another phrase that a dear mentor of mine has shared with me and taught me is balancing grace versus grit. So how do we push ourselves, but how do we also do so in a way that is sustainable and that works for us and that actually does encourage us to improve?

Um, and so I think when it comes to setting targets, this is also very much a hands-on process that you do with your stakeholders. So for example, with our stat meetings at LaMotta, every year we would have a target-setting process with our stakeholders that they were very familiar with. Uh, after several years of doing this, it was, it was target-setting time, and we would take a second look at targets. And we would approach target setting, uh, w- with a sort of a scientific methodology, I guess, to apply some analytical rigor to it. But we also kept in mind the human element of it, like, hey, this metric, you know, I know that you can only control really a few aspects of this particular metric. Maybe we can set a target for those specific aspects.

Or, hey, let’s look at the last several years and see how we’ve done and base our target off of that. So it’s, it’s really that, that, that give and that take with your, with your stakeholder audiences so that you come to, uh, a target that, that everyone agrees on and feels invested in, and that you can track progress towards. So Laura, I know your forte is really in data visualization, um, and I think you, you mentioned that earlier. Uh, can you describe to us what a good dashboard or a good report looks like? You know, what’s, what sets it apart from just being a dashboard to one that people are actually exercising, they’re using, they’re referencing in meetings?

How do you get there? Oh, yes. This is, this is my favorite topic. Um, dashboard design is, is incredibly, uh, challenging, and to develop a truly valuable dashboard, it is, it is a bit of an elusive task, but it is possible. And this goes back to sometimes I feel like a, a, a broken record in these conversations ’cause I repeat the same tenets over and over again, but they’re so powerful and so true, that it goes back to really listening to what your audience’s concerns are and challenges are, and designing a dashboard that fits into their workflow in a way where taking action off of the dashboard doesn’t feel like… It, I mean, obviously it’s work, but it is a natural progression. They look at the dashboard, and th- after they process the information, they know what action they need to take. And so any time you’re designing a dashboard, you really want to almost put yourself in the shoes of your… N- not almost, to absolutely put yourself in the shoes of your audience member.

Interview them, talk to them. Ask them how their, how their, um, thought processes go. What’s the first thing… You know, what’s their top-line concern? D- dig a little bit. Pull at that thread more. What’s their next question they ask? What’s their, what’s the next question they ask? What tools do they have to improve? And then you design the dashboard in a way that flows with their thinking. So with any element that I’m putting on a dashboard, number one, the information should be hierarchical in a sense. So most important information, top line, first question, uh, that they ask should be that first thing that they see, and then it should flow into the, “Okay, so tell me more, tell me more, tell me more. Great, now I have what I need to take a very discreet action.” And so any element that I’m putting on a dashboard page, uh, it’s question-driven. Uh, really sometimes, sometimes I even suggest to my team, instead of, uh, just a classic chart header, write a question.

Uh, write a question that your, your, your audience member has. Like, “What was my XYZ performance metric last month?

Where were my biggest problem areas?” Like, including that language can help make a dashboard feel so much more intuitive and integrated into their work, uh, ’cause I, I have, I have been responsible, and I see this a lot, of, of many dashboards that, oh man, we worked so hard, we do su- such a great job on them, and they just don’t get used, and it really goes back to listening to our audience.

I love how you treat it like an investigation. Uh, you know, I haven’t really thought about that, and I don’t think you used those words, but the, the more you spoke about it, it, it sounds like you are, you, you have that rigor and want to place yourself in the other person’s shoes. You, you have to almo- almost work with them side by side and, and live with them for a little bit in their workflow to be able to understand what they care about. Um, and then- 100%. Yeah, and then it’s not a chore, right? Uh, to your point that, you know, it is work, but it shouldn’t feel like an obligation. It’s a, should feel like something that they are attracted to. There, there should be this sort of magnetic attraction to this dashboard because this is where I can un- better understand, uh, the, the issues and then try to solve those problems. Yeah. Yeah. And there are a lot of, you know, reports and dashboards just, just in general that

I’ll look at for the first time and, and think, “Okay, wait. What am, what am I looking at exactly, and what is this, and what…” And, and I’m a data person. And so if I’m, if I’m scratching my head, um, at a lot of these tools, and, and this is not just in the transit industry, this is everywhere, this is a pervasive problem, especially as we get more data, and it’s so exciting to get more data, and what do we do with it, and we turn it into these quick tools.

Um, but if I’m scratching my head, I can, I can bet that my audience is scratching their heads too. So a follow-up question there, Laura, is how you think about the granularity of the data.

Like when you’re developing the, the dashboard or the report for that particular audience, it needs to be translated to, to the world, right? To, uh, maybe to your community, uh, to your CEO or those in the C-suite.

How do you get from this very finite granular level and communicate the message out of that? Because if you’re not communicating it, then, you know, it, it, it’s kind of like science, right? You, you can have great scientists, but if they’re not telling the world about what they’re doing and what the results are, then it just falls on deaf ears. So I, I’m curious how you take it from, uh, you know, that very local level to spreading and distributing that message. Yeah.

So, um, w- this goes back to the, the interconnectedness of these metrics too, that I really think every, every metric in the space, every transit metric is connected to every other metric and sub-metric and sub-sub-metric.

And so knowing that and having faith in that means that we can really find and focus on, again, what is most important to our audience and what resonates with them the most. So what decision are they responsible for, or what information means the most to them that they can use?

Um, of course, we wanna be mindful of, for that, for that level of audience, of, uh, making sure that the risks and constraints and the pros and cons are communicated at the right level for them. But that looks very different from an executive than from a frontline employee or from a member of the public. So even if all three audiences are essentially looking, I don’t know, say, at the same metric, on time performance, they’re looking at it in different ways, and we need to be mindful of that. To the customer, we need to, to, to tell them what the impact to them is and what that looks like ’cause they care about understanding the impact. To a senior leader, they have certain levers and can make certain decisions and need a level of information about OTP to, to make those sorts of decisions. And for someone on the frontline who’s really involved in the granular aspects, the nitty-gritty, we need to make sure that they have the most detailed information to be able to make those small, discrete, but valuable, incredibly important actions to improve.

Oh, yeah, that makes a lot of sense. I, I appreciate that explanation because some- sometimes it’s just, you know, it’s not always clear as to what you should communicate and to whom, but again, it, it– you just have to dig a little bit deeper, find out what is important to them.

A- and, you know, speaking what is important to the audience, I, I, I know we spent a, a good amount of time on WMATA because you, you had a num- number of years with that organization.

Uh, but one of the things that I, I, um, know you were responsible for there, or at least played a part in, was WMATA’s equity toolkit. Can you describe to us what that was trying to solve, what you were trying to get out of that, and who you were communicating that message to? Yeah, for sure. So this project, uh, gosh, it was about five years ago that we started it. Um, so, you know, first and foremost, transit, I think, has an inherent equity component of it. Uh, we, we all know this, but transit allows people of all income levels and backgrounds access to jobs and education and services and economic mobility that they might not have.

So transit is the great equalizer. We, we all know that. Um, with the equity toolkit, this was an opportunity for us to standardize and to make more accessible ways of incorporating equity and equity lenses, or looking at, uh, performance through an equity lens, and language surrounded that, um, into a one-stop shop for analysts and policy people and leaders at our organization.

So we already had an equity framework. We already had, uh, our policy folks who were really, who were thinking about these things. We were already making, um, operational decisions that were equity driven. But this allowed us to bring all of these great practices that were happening in different parts of the organization into a centralized toolkit that anyone could access.

So it involved, uh, definitions and guidance, um, so we brought our policy people in to help us with that, and it also involved us, uh, bringing in and making demographics of census and ridership data more easy to integrate and to match up against our existing data sets so that we could start doing more analysis through an equity lens. Yeah, that, that, and that’s very important. Uh, and a lot of the, hmm, service changes or any modifications, we’re looking at, you know, Title VI, we’re looking at demographics and census data and how that’s impacting the community and making sure, uh, you know, it, it, some of these changes are equitable.

Um, let, let, let us go now into your role at MTA. So you’re at MTA Subways. Uh, what’s the difference about performance management at, at that scale, you know, in such a large agency?

Oh, yeah. So in my early days here, I, you know, my mind was constantly comparing at WMATA it’s like this, and at the MTA it’s like this.

Um, obviously the MTA is a significantly larger system. It is incredibly complex. That has been the biggest learning curve for me. It’s a four-track system.

It’s very old. Um, and so there are, there are, it, it, there are so many… It’s almost like, um, there’s so many different variations of challenges here that I maybe just wasn’t as exposed to or aware of at WMATA. I will say though that so many of the tenants that we’ve been discussing about performance measurement and performance improvement, and connecting with people, and continuous improvement translate from one job to another. And so in a lot of ways, the, the scale of the agency and just the number, the sheer volume of problems that, um, or challenges that we have to address feels like a lot, but, uh, these, these sorts of patterned processes and ways of addressing them, uh, is the same. And so it, it, it, it, it all feels…

It, in so many ways it, it’s a, it’s a different job, and in a lot of ways it, it feels just the same. Hmm. Okay. Good. That, that makes sense, right? And, and you’re always gonna, when you go from one place to another one, you know, bring some best practices, and that’s kinda like an easy way to drive improvement, uh, particularly in a performance program when you’re bringing some best practices.

But also you get to learn, right? Like, what are the things that you actually were not working on or, or you were not, uh, involved with in that previous agency?

Uh, uh, let me ask you this question. Uh, I don’t know if you’re also involved with the, um, you know, the bus, uh, system performance metrics, but, you know, how do subways differ from bus systems in how performance is measured and managed?

Yeah. So at the MTA, I’m very much in the subway space. At WMATA, I managed a team of both rail analysts and bus analysts, and also paratransit analysts.

So the two spaces are very different. I have much more significant, or I have much more experience in the subway space, but subways are,

I, I mean, the, the, the biggest difference is gonna be traffic and how that, and how that impacts just how we, um, approach challenges with bus versus subways, which, uh- Right … obviously is a fixed- Mixed traffic versus having, you know, your own track per se. Yeah, absolutely. Yeah. So, so bus systems, you know, w- we manage them as, as flexible, adaptive networks, and subways there’s a, there’s an element of precision and infrastructure focus, and consistency. And, um, there are, you know, there are those key performance indicators that are just cross-cutting.

Like, really we have the same goal of getting people to where they need to go on time and safely, and, um, and, and hopefully in a way where they enjoy their ride. And so there are a lot of similar aspects across both modes in that sense, but how we manage and how we improve, and the levers that we have to improve each of those different areas, uh, differs. So, uh, uh, where do you see the biggest opportunity for improvement in subway performance, particularly at the MTA today? Oh, man.

I mean, modernizing our signaling. Uh, so that’s, that’s, that’s the, that’s the standard answer, but it is so true. So- … moving from a fixed block signaling system to a communication-based train control system, CBTC, which we are actively doing at this agency. But, um, you know, any, any opportunity that we have to move to that more advanced signaling system that allows us to, um, you know, manage our, manage our service at a higher precision and, um, you know, improve real-time control, uh, will absolutely have, have a benefit.

Excellent. Uh, I will say though, I’m gonna, I’m gonna add in a little people element to that answer, which is I think that there is in both, there’s incredible talent within this industry already and there’s incredible talent that wants to be in this industry.

Um, transit isn’t going away. I only see excitement about it, and, you know, I, we are, we actually, our team just had the opportunity to hire two new analysts, they’ll be starting with us in a couple months, and I am blown away at their skill and talent, and so honored and excited that they wanna join this cause.

And I, you know, I’m just really excited for the future of transit with all of this great talent that we have coming in. Excellent. That, that’s very exciting to hear. And, and you need those folks to be interested, uh, to be, you know, have the skills, uh, for doing the work because it is a lot of work, especially when you get at the granular level. And, and you mention it, you know, uh, people when you see it big scale is like, “Let’s improve a lot.” And it’s like, okay, you know. But it, it, it doesn’t work like that. It, it, it works in all the, those, uh, small improvements. And when you talk about precision, it precision is also a small improvements. Like, how can you get closer to perfect? You know, get, get those inches of improvement and, um, you know, the, it requires a team effort and, and more people contributing to it.

Um, so thank you for, for all those comments and for the stories you shared with us. And we’re coming close to the end of our podcast, and we have a couple of segments, uh, to go through to finalize our conversation today. The first one is gonna be, uh, rapid fire. So we’re gonna ask you some, uh, kinda like short questions, uh, short answers, almost like first thing that comes to mind. Uh, so are you ready for them?

I’m ready Excellent. So the first one, favorite transit system? I, I’m gonna say, I’m gonna say

Paris. Um, I spent a year there. Uh, love that system. With enough transfers, you can get, uh, anywhere in the city within walking distance of your destination. Uh, it’s, it’s fast, it’s frequent.

Um, so many great memories of that system. I must say, I know this is a rapid-fire response section, and I’m going on and on. But I’ve had the, the opportunity and honor of traveling and experiencing a lot of different systems. The fact that the MTA is a 24-hour system is pretty incredible, that the, to know at any hour of the day or night that this is available to you is, is a mindset shift that I’ve not yet experienced and, and that’s awesome. So, so kudos to the system I work for. Yes. It’s, it’s phenomenal. I, I’ve had the opportunity to, uh, use both, uh, the system in Paris and, and, and also in New York, and they’re just incredible. I mean, the volume of people and, and the level of service, right? Like as a, a user, you just need to know where you wanna go. You, you don’t have to really check schedules or anything. You just, you know, ride the system and, and you get from, you know, point A to point B is, is incredible.

Second, uh, question, biggest misconception about transit performance? Ooh.

Um, that it’s only just a bunch of top-line metrics when it’s so vast and complex, and that’s what makes it challenging but also fun, and it makes you feel like there are ways that we can improve. But man, uh, when you look under the hood of a metric like on-time performance or mean distance between failure, there’s a lot going on and a lot that contributes to it. Oh, yeah. It’s a lot of fun. I agree with that.

Uh, most underrated metric or overrated? Oh, man. I, I refuse to throw any metric under the bus.

I’m gonna- I, I’m gonna say there is a time and place for every metric. There’s a time, a place, and an audience for every metric. Actually, since you did not specify transit, I will say the 10,000 steps metric that my fitness tracker holds me to and guilts me into meeting, that is the most overrated metric. I love that answer. I, I love my metrics and, and because, you know, from each one of them, you get some insight, so I, I, I agree with your answer.

Uh, one thing every agency should measure but doesn’t? Th- see, that’s a, that’s also a tough one too because we, we do measure and each a- agency measures so much, and even

I don’t know everything that the MTA measures. I would say one thing every agency should measure but doesn’t, um,

I would like to encourage and empower small teams in any organization, not just transit, to develop small performance metrics for themselves, that performance can start very small, and we don’t need to, uh, like I, like that, that second question that you had about the biggest miscons- conception about transit performance, there’s an opportunity to improve. There’s s- there’s something within our control, and we can challenge ourselves even at the micro level to measure it. I, I love that. Yeah, I agree with that. A lot of agencies, uh, stick with that, you know, kinda like executive dashboard that have, like, on time performance, and great. You know, you, you have a number there, but it’s not until you look at the, like, the, the group level and you break them down and create those more, like, team specific that you can have an impact to a, such a big metric like OTP. So, uh,

I think that’s, that’s an awesome answer. And, um, my last rapid-fire question, tool or habit you rely on most in your work? I’m gonna say the, the humble Microsoft Excel, and not just for data analysis. It’s where I, I take the chaos of my mind and I turn it into order on a page. You can do a lot with a table. I, I agree. I, uh, uh, a lot of my dashboards and visualizations and work I, I also completed just in Excel, you know? Sometimes, um, you know, the, the system-generated reports, they don’t, uh, give you exactly what you want, so you, you have to do some of that on your own on something as simple as

Excel, but end up being very powerful. Yeah. Uh, so thank you for those. Now, we’re gonna go to the next segment before we close. This is our rapid fire, I mean, our, our, uh, key takeaway segment. Apologies.

Key takeaways. And I’m gonna go over some key takeaways that I took, uh, wrote down from our conversation, and then you let me know if you wanna add anything else to it. Uh, the first one I’m gonna say, uh, you know, performance improvement at our organization is about continuous small improvements. It’s not like something you just do, you know, snapping your fingers. It’s constantly finding those inches to advance and, you know, uh, uh, keep the organization moving forward.

Uh, data should translate into action. Data should not just be numbers that you’re presenting, but, you know, how are you making decisions? And also the, the power of storytelling with data, uh, to make it meaningful for the stakeholders, uh, that they’re, uh, you know, absorbing what the data say and, and hopefully translate it into action. Um, another one that I wrote down is, is connecting metrics to projects. So sometimes when we want to do things, uh, and, and we feel that’s the right thing for the organization, you know, are you setting up metrics that is helping support those projects and helping translate it to the stakeholders why it’s important?

Um, another key, key takeaway that I wrote down is listening to what your audience really needs when it comes to, uh, establishing performance metrics, right? Like, sometimes we just wanna have numbers for people to consume and, like, this is the data you should look at, but you should start with going to your audience and asking them what’s keeping them up at night. Like, you know, what are the things that you care about? And then we can start, uh, creating, uh, metrics and, and measurements that are meaningful to them.

Um, and also senior leadership support is critical to move forward any performance management, uh, program. If the folks at the top, they don’t care about the metrics and making improvement and taking seriously, uh, what the data is saying, then nobody down, uh, the, the hierarchy i- is going to care about. So I think that’s, that’s critical. And also have your stakeholders involved in target setting.

Uh, eh, that’s gonna create that, uh, ownership of the, of, you know, of the performance of the organization and, and, you know, helping and contributing to make strides towards improvement.

Is there any other key takeaways that I, that I lost or that I missed? No, I think you did a fantastic job. Excellent. Uh, Levi, you have any additional key takeaways you wanna add? Uh, chaos into order. That’s, that’s what I wrote down. I- Mm. I love that line. That’s a quotable for us.

Absolutely. I should, I should print that on a big poster and hang that in my wall. You should sell it. Sell the poster. I would buy it. Uh, Christian, I also really like the, the other phrase, um, the… that’s along the lines of, um, you manage what you measure, or you can’t manage what you don’t measure. I, I really like that one, too. That’s gonna make it into a shirt as well. All right. Well, Laura, thank you so much for the conversation today. It’s really been delightful, uh, you know, just to hear about all your experiences and the great work that you’re doing at

MTA. How can our audience connect with you or learn more about the organization if they’re so inclined? Yeah. I would say the, the best way to connect with me is to follow or add me on LinkedIn. You can find me at Laura Mawhinney. I think I’m the only one in existence, so it shouldn’t be too difficult to find me. Um, but yes, would, would love to, to keep the conversation going with anyone passionate about these topics. Excellent. Well, again, thank you so much. It’s really been a great conversation. And thank you to our listeners for tuning in. We’ll be back next Monday with another episode of Stop Requested.