Subscribe
Share
Share
Embed
A Ropes & Gray (RopesTalk) podcast series from the R&G Insights Lab that is a curiosity-driven hunt for good ideas and better ways to tackle organizational challenges.
[Better Way?] Curiosity & Empathy: A Better Way to Approach Data? – podcast transcript
Zach Coseglia: Welcome back to the Better Way? podcast, brought to you by R&G Insights Lab. This is a curiosity podcast, where we ask, “There has to be a better way, right?” There just has to be. I’m Zach Coseglia, the co-founder of R&G Insights Lab, and I am joined, as always, by the one and only Hui Chen. Hi, Hui.
Hui Chen: Hi, Zach. I’m pretty excited about yet another wonderful guest that we’re about to have a conversation with.
Zach Coseglia: Me too. Our guest today is one of my favorite people in the world, and so, it is a treat to actually have a conversation with two of my favorite people in the world at one time. We are joined today by Tara Palesh from Pfizer, who leads up the compliance analytics program over there. Hi, Tara—welcome.
Tara Palesh: Hi—it’s such a pleasure. I love flattery, so thank you.
Zach Coseglia: I know you do—that’s why I started there.
Tara Palesh: You’re also one of my favorites.
Zach Coseglia: We are very excited to talk to you today. I know you, Hui knows you, but we want you to introduce yourself to our listeners: Who is Tara?
Tara Palesh: I’m, at my base, a data-driven, high-inquisitive individual that thrives on pursuing what seems a bit impossible, and so, that’s taken me through life in a couple different paths. But just to ground people here to show what I might be an expert at for this conversation, I studied engineering. Then, from there, I went into strategy consulting and pursued lots of different ideas. I loved it so much, and I loved working with Pfizer so much, that I became a strategy consultant in-house. That allowed me to pursue other areas with data and analytics, so I not only looked at commercial analytics, I went into business development and loved this idea of predicting the future. I did a lot of forecasting and effectiveness, and I just explored data in many different ways. And now, here I am in the compliance division.
Zach Coseglia: When we first met, when you were interviewing for a role in Pfizer’s compliance department, you said you had a passion for industrial engineering, which I don’t think anyone had ever said that to me before. So, I love that about you. Do you want to talk a little bit about your passion in that way, and your origin story?
Tara Palesh: I am passionate about industrial engineering. I grew up in a manufacturing environment. My dad owned a small shop in the Midwest where we manufactured things like lathes and milling machines. Even at a young age, I started being interested in order and process, and how things got done, and so, I started doing time studies. Unfortunately, I wasn’t aware of compliance and such at the age of 12 or 11, and I started doing time studies of people using the restroom. And so, I did have to get called into the office and have conversations about the inappropriateness of trying to see how long people were taking restroom breaks, and that wasn’t where the efficiency could lie. What it did identify is that I just loved the idea of being able to maximize time and think about the order in which people could do things in a more efficient way. I also loved science. So, I went to college, I was going to be a chemical engineer, and discovered there’s this entire major around making things better, solving problems, and making it more efficient. And it was just like love at first sight—I switched.
Hui Chen: I’m always very, very excited when I meet women who are in STEM. And not only you are in STEM, but everything you’ve said so far has displayed that passion and that curiosity that we value so much. You’re the dream girl.
Tara Palesh: Thank you so much. I can relate, because I am also very passionate about seeing other girls growing into women in the STEM fields. We have a lot to contribute.
Zach Coseglia: Absolutely. The other thing that you shared with me when we first met was I asked you what were you inspired by, what were you focused on, or what was a differentiator for you? Your answer was “empathy,” which, I think, is such a huge part of the work that we do and the conversation that we’re going to have, both about analytics and about compliance. Talk to me about where your empathy comes into your approach to this work.
Tara Palesh: Empathy is a grounding principle for me, because, I think it actually helps solve a lot of the things that seem like challenges to people. If you really put yourself in the position of others, depending on what you’re looking at—it’s maybe who you’re delivering to, who you’re trying to do analytics for, who you’re trying to find or what you’re trying to find, the behaviors you’re trying to find—you try and sit in the position and understand what they’re truly worried about, what they’re truly trying to identify, and how that can manifest through data. A lot of times, I think, it really ties to understanding how someone might be processing the information you’re putting forward, and I have discovered in life that it is often not the same way I am processing it. And that’s totally fine—in fact, it’s kind of exciting. But then, it’s my job to help bridge so that we’re on the same page.
Zach Coseglia: You’ve got this passion for engineering—you are about the math of it all. How did you wind up in compliance?
Tara Palesh: I think, like many things in life, there’s a bit of chance. I happened to be coming off of a very large-scale strategy project. I happened to be in a role that was business development, and the company happened to be working on a massive, large-scale reorganization, which is rare (I say sarcastically). But in that, business development isn’t very busy when you’re reorganizing, and I am not very good at being idle, so I went out and pursued some body of work that would still be moving forward that might need some assistance. I was pointed at something that was tied to enterprise product risk. They didn’t have anyone who was available to work on it, because of this reorganization, and people didn’t know what they could commit to. I just was very passionate about looking at something new, so, I went in to help, and I learned about risk in a whole new light. I had had an entire career around risk, but it was always about financial risk, revenue, and the interchange between those. And so, first, there was this grounding I realized I needed, which was: What do you mean by “risk” if it’s not money? I had this new introduction to the same data I’d been using my whole life, but in a whole different way. I got this exposure to risk in a different way from the compliance division about how we would think about corporate risk that could manifest in different ways using the same data I had all along. It also put me in touch with new colleagues that I hadn’t been interacting with on a robust manner, and they saw in me this, “Wow, you get things done. We’ve been struggling to use data in this way, and to really dig in deep.” I said, “This is great. I want to work with you.” Zach, I think you know the rest of that tale: I was introduced to you, and you had a role opening. I went for it, full kilter.
Zach Coseglia: Why don’t we pick up there. Now, you were very wisely, if I do say so, hired into the compliance department. Tell us about your team.
Tara Palesh: My team exists to enable compliance. Our main purpose is to help compliance colleagues do what they do in a more productive and focused way. So, if we’re successful, they’re more successful. It’s a really symbiotic relationship. But if you want to get down to literally also what we do, we find data that can be used by legal colleagues to help focus their attention. We transform that data, we analyze it, we often score it, and then we package it up for an end user, which can be the business as well as, predominantly, the compliance division.
Zach Coseglia: To do compliance right, we need skills beyond what lawyers provide, and the department that you’re in is dominated by lawyers. So, describe the skills that you’ve embedded on your team, and how they’re different from maybe what is just elsewhere in the compliance division.
Tara Palesh: You do need a wide range of skills, and so, I have at its essence, a team that has data scientists, what I would traditionally then call “analysts”—which is separate from “data scientists,” I would like to clarify—and visualization experts. And I’ve really instilled a great foundation of quality control and standards. Those are the high-level skill sets, but really it was also about creating a team that could be more fluid across what they were doing, and really make sure that they all had this really challenging skill set, which is, “I’m not just going to do what I’m told.” That’s not what an analyst or a data scientist is meant to do. We’re meant to help support and think in new ways that only we are trained to do with data, to help answer real problems and questions. If we are just doing what we’re told, we are not doing our job. So, it’s to question and really get to the foundation of what problem or risk you’re trying to identify, and work through as thought partners with the business and the lawyers, to identify ways that data can inform that risk. But you cannot do that if you want to stay separate from compliance. You need to learn what compliance is trying to achieve, and why, and how it pulls itself through data, so you need those people who aren’t going to just, on the surface, do what they’re told.
Hui Chen: Let’s say, the compliance team as a whole says to you and your team, that, “We’re interested in knowing more about X, and we think the following data set would be helpful.” And you and your team come back with, “We have a different answer to your question. It’s not exactly the data set that you had in mind, or it’s not the way to approach it that you had in mind, but here’s what we got.” Would you be able to give an example of something like that so that folks can get a more concrete idea of what that conversation may be like?
Tara Palesh: Sure. I think there are a couple different ways this can manifest. One of them often is scalability—so, while they may have identified the right data attribute, the ability to scale it up. Adverse event reporting is one of those data points that can be often useful in trying to identify potential for risk in a product, and there is an external source, or multiple sources, that you would have to go to and piece together, and it’s really hard to automate. In fact, you can’t fully automate it—you have to go get it and bring it in, and then you can automate from there. In a company the size of ours, especially—we are not the only ones who want to know this, need to know this, and use it in a meaningful way—I was able, querying through my network, to identify almost the same data source internally that had already been scrubbed and cleaned, and at its essence, had all of the risk elements in it that we were requiring for our assessment. So, we can grab it directly from a system.
Hui Chen: I’m going to pause you for one second. Can you explain what “adverse event” is?
Tara Palesh: I think I have a very easy way. If you’ve ever taken anything, or you might have someone you know who has taken something, and then they start talking about all the things they think that product might have caused them—a headache, a bruise on their arm, whatever it is—we are supposed to report that. Anyone in our company, absolutely, or anyone who’s trained in this area, we report it. But a lot of people in the public forum, they also know to report or go through their doctors. It gets tied to the product they took, and that’s called an “adverse” or a “bad thing” that has happened that may or may not be tied to whatever medical procedure or product they’re taking. And so, that data is heavily scrutinized to make sure even though product is out there and used, they want to continue monitoring to see if some new type of event is occurring that they would want to revisit whether the product is healthy or safe. So, it’s constantly monitored.
Another example would often be more around how you want to score or think about the data you’re looking at. You find this a lot as people have been moving into effectiveness measurements—there was always this tendency to want to scale your data, think about the worst and the best, relative to each other. It’s very easy at the foundation when you start talking about why it doesn’t work, but the point of effectiveness is there should be a world where everybody is effective—there is a threshold by which you should be thinking of this. Your goal is to be effective across the board, which means the way you look at the data has to allow for the opportunity for everyone to be in that bucket. If you are constantly scoring against each other, you always have a loser and you always have a winner. So, when you’re pivoting to different types of analyses, you really have to step back and think about how you’re going to score and measure that data for what you’re trying to achieve.
Zach Coseglia: I really like that. Also, Hui, it very much builds off of something that you often say in the context of benchmarking, which is if you benchmark against your peers, you very well may find that you feel comfortable where you are in comparison to them, but everyone might be bad. And so, this idea that everyone might be bad, everyone might be good, or there is a world in which there’s a combination of those just makes perfect sense when you’re taking a more data-driven approach.
Hui Chen: I also want to make that connection about adverse events to people who are not in the pharmaceutical industry, because a lot of people would say, “Well, that’s something specific to pharma.” Every industry has its equivalent of adverse events.
Tara Palesh: Think of the car industry: crash data.
Hui Chen: Exactly—it’s feedback on your product. I also think it’s interesting that pharmaceutical companies are required to collect adverse event records, but I also oftentimes wonder how often they systematically collect not adverse, but interesting side events connected with their products. Pfizer’s blockbuster drug Viagra was originally developed for angina, hypertension, and chest pain, basically. It turned out, as they were experimenting during the trials, they discovered an interesting side effect, which now became its primary usage. But it’s a lot of drugs—if you look into the history of medication and scientific discovery, a lot of these discoveries are accidental.
Tara Palesh: I think a lot of companies do look at that data. I think adverse event reporting is heavily monitored also by the FDA, which I appreciate. But that particular side effect that you speak of, I think also manifests when doctors use things that we call “off-label”—that’ll still come through data. And still, a lot of companies do monitor and observe to make sure that we continue to feel comfortable with how the product is being used, especially if that is being used for something that it wasn’t necessarily originally intended. So, I think there is pretty heavy monitoring from a health and safety perspective, which is not my space, but always happy to hear they’re looking at it.
Zach Coseglia: It just all comes back to the power of curiosity—being curious about data and exploring data. Let’s talk about what areas of compliance you touch. When we talk to people about compliance analytics, sometimes, folks conflate or equate analytics with monitoring, either as a tool used for monitoring or as one and the same with monitoring. I know that your team does more than just analytics for purposes of monitoring, so talk to us about the breadth of ways in which you are embedding analytics into the compliance program.
Tara Palesh: To me, the only areas of compliance that are not touched by analytics are those we haven’t found yet—that’s how broad and useful I think it is. Everyone globally, in every industry, can benefit from trying to identify data that is relevant to them, and using that data. In this world of generative AI, this is only getting more prolific. We, as a team, work very closely with investigations—of course, monitoring is involved, but we also have risk awareness-type analytics that help us focus people’s time and energy. Whether it be markets or whether it be products, from a compliance perspective, where is risk sitting that we want to try and address? In a world where you want to be more effective and efficient, analytics is the way you get there. And so, that’s why I talked about that symbiotic relationship before, because everyone can benefit from this. We work with training. Data can be used to improve how they’re training in the compliance space. But their data is also valuable to think about: What are we seeing in different places? How are these scores in places where we’re seeing events occurring (their correlation)? Or is this an indicator of a systemic problem if people are scoring poorly? I wouldn’t put absolute rules ever on this—you have to think about the dynamic of every culture you’re working in from a training perspective—but that data, it’s meaningful. You want to work with everyone, not just monitoring.
Zach Coseglia: Why don’t you talk to us about a project that you’re particularly proud of?
Tara Palesh: I spoke a bit about effectiveness earlier, and I think that is really one of the projects that stands out to me. We were embarking on an effectiveness project to think about how effective we are as a compliance division globally. The thing I really love about this is not only to check ourselves, which I’m always a fan of—the output of it is very directionally oriented—if you’re good, there still are probably areas you could focus on. It goes around different areas of risk: You’ll have investigations data, training data, your typical corporate survey-type data for culture and stuff like that, and you bring it all together. So, you could say, “If you’re performing poorly, or even if you’re okay, how does that look across the board?” Even the best countries, in this case, probably have an area that they can focus on. And some of the other countries that may not be performing as well as one would like, they know exactly where to hone in and work on mitigations again. It’s very directionally oriented—it’s not just an answer—it actually helps people take the next step. The thing that I love most about it is that it has now bridged over to facilitating the conversation with the business, because at the end of the day, it is often not the compliance colleague driving the colleagues who are performing the behaviors. We’ve gone all the way down the path now that we can have that conversation and say, “Look at the data. This is where we want to focus. These are the things we’re going to embark on this year together to improve.” I love that it has gone that far—to the degree that we’ve gotten feedback from the business thanking us.
Hui Chen: Zach and I have opportunities to talk with a lot of companies who are really more at the beginning of their data-driven journey. Would you be able to, let’s say, point to three datasets or three steps that they can take to get started on this journey, assuming they’re not as well resourced and at the very beginning of it?
Tara Palesh: I think the dataset perspective is extraordinarily challenging because I think it would vary heavily, based on the industry you’re in. Generally, one of the first things is you do chase money a bit, because money makes the world go round. So, I do think having a good perspective of where the company believes their revenue will be coming from is where the company often focuses their activities, and those activities are often ones that can manifest risk.
Hui Chen: That seems so instinctive to us, but you’d be surprised, perhaps, at how many people that we’ve said this to, that this clearly was something that never occurred to them. I agree—that’s where I would start.
Tara Palesh: I think it ties back to actually what Zach said before, this empathy concept really. Empathy there is you don’t have to focus so much on your companies. What works in your personal life? What do we all have governing over us? And if one were ever to make a poor choice in their life, where would it manifest? I think of people and this empathy as some people steal to feed their kids. If my kid was starving and I was watching that, I think I would probably make some poor choices because my kids are my life to me. So, you try and have this empathy. It also helps you trace the data of how you can see these things happening. Money is just top of mind for everyone, so you follow it further. I would say next from the top-line finance data, can you look at transactional data? Another dollar sign involved, but with people who are transacting in ways that might not be similar to their colleagues or cohorts. Think of people in buckets. They don’t actually have to have the same job. It’s: Who do you expect to act and perform in the same way? Put them in a bucket, and then look. Do you have anyone that stands out? That can be from how much they’re spending, and you look into it. It doesn’t mean something bad is happening—it’s just a great way to direct people’s attention. I would go have a conversation with these five people because they look a little bit different, and I just want to make sure we’re comfortable with why they are different.
Zach Coseglia: It’s not just about using the data to find that someone did something wrong, which is the message that a lot of business folks are used to getting from compliance. As you said, it’s empowering you to ask better questions, to follow up, to understand why that potentially peculiar behavior happened. And it very well may be that there’s nothing wrong with it. You’ve got to be curious. I love it. How do you get lawyers—who are, in large part, your peers and your stakeholders immediately, before you get to the business—and other traditional compliance personnel comfortable with a more data-driven approach? We hear a lot, “I’m not a numbers person,” from those folks. How do you get them excited about what you’re doing, and how do you get them to understand what you’re doing so that they can actually use it to its full potential?
Tara Palesh: It comes down to trust. Now, there are branches of this trust, so you have to demonstrate a lot. I don’t think you come into this type of space, and you don’t start with trust. In some spaces, you’re like, “Until you do something wrong, we have some trust.” But in analytics, a lot of people across the globe demonstrate poor analytics, so no matter who you are, if you haven’t worked with someone, you want to start with this foundation of getting them to believe what you’re doing is going to be done in the best way with quality, with the right intent, and there will be a lot of transparency. I think there is a comfort with coming in and saying, “I am going to want to share with you, just at the base line, what I’m doing and how I’m approaching it. I’m going to have some questions because I’m going to want to know your input and your very valuable experience to what I’m doing. And I’m going to walk you through the analytics that I’m going to conduct.” Put machine learning and AI aside—start with the simple analytics that really drive a lot of what is still done, it’s basic math, and everyone actually is comfortable with basic math. All these lawyers, they’re highly intelligent people—they definitely understand the math, the basic math, that I’m going to lay out. So, that’s how I work in this symbiotic relationship, where I’m going to be transparent and I’m going to make sure they understand why I’m doing what I’m doing and how I’m doing it.
Zach Coseglia: So, you’ve done the math, you’ve collected and collated the data, you’ve put it into a dashboard or a presentation, and they say, “So what?” How do you combat the ‘so what’ factor that sometimes plagues data-driven compliance teams?
Tara Palesh: First of all, get ahead of it. You have to say what the value is here before you do the work. And if you can demonstrate that value, thus, getting the right resources to actually build it—which you should think of as money that you are now paying to do the work—you will already have the answer to the ‘so what’: the value. Everything should be built off of a value proposition or what it can bring, what risk it’s identifying, and what you would do once you knew that risk exists. If you can’t take it to the next step, you should already be saying, “We shouldn’t do this in isolation. We would need to do that, and then, after identifying those people who spent more than we wanted, or who looked different than their colleagues, we want to see these three things. Where were they before? Has this followed them in their career? Have they done well in training?” You should already have been building all these things on so that you actually have a direction, an answer, or a mitigation strategy you can develop. If that’s not part of the build, then you’re making a mistake. You should never actually land on, “So what?” You already have the answer before you build it.
Zach Coseglia: What if the ‘so what’ or the answer to the ‘so what’ is not, as it historically had been, somebody needs to get fired, somebody needs to get a warning, or there has to be some other form of punitive measure or corrective measure? When the ‘so what,’ when the action is, “I just want you to know this. I need you to know this,” how have you successfully (or not), shifted expectations around what action actually means?
Tara Palesh: If you have these instances or things you’ve identified of, “I want you to know,” you tie it to, “I want you to know this because what we’ve been finding is when we see this and as it builds, down the road, we see more of this, and we end up having to fire people. We don’t want to do that—it’s not fun. You don’t want to do that—it’s not fun. It’s gone too far. We want to get ahead of it, and so, if we tell you this now, here are a few things we believe could change that course. We have the chance to not land in that uncomfortable, poor, crappy place, so let’s work together to never get there.” I think everyone’s motivated to not be in that situation, so it changes the way people see compliance colleagues too. We want to help.
Hui Chen: Tara, I think I would strongly echo this because when you’re saying, “I need you to know this,” there’s a reason why you’re saying that. And I think getting ahead of it is being clear about what that reason is. Sometimes, that reason is not necessarily to avoid trouble, it’s also, “I need you to know this so that we can just perform better.” One of the things that I am trying to perhaps unsuccessfully shift the mindset of companies is, “Don’t be driven by fear of disaster. Be driven by the desire for excellence.” My goal is not for everybody to have the gold standard compliance program. I think every compliance program needs to want a gold standard company—that’s what you’re after.
Tara Palesh: Yes, I agree completely. I think it depends on your perspective, but I am fully in that camp of realizing there’s a really strong correlation between wanting to be the best, striving to be the best, and ending up being the best. There’s a strong correlation.
Zach Coseglia: You mentioned generative AI before, so I’d be remiss not to bite on that bait. Talk to us a little bit about either how you’re using AI or what you see as the future continued evolution of compliance analytics.
Tara Palesh: Thankfully, Pfizer is a very big company, and so, we do have a lot of resources. As a company, there are multiple ways that I am exploring with the team to use generative AI. First, I want to say, it’s not something you can jump into. If you haven’t done the base line work of getting your data together, if you haven’t done the base line work of putting it somewhere where you can point it to the tool you’re trying to use, you will still get nowhere. But generative AI is opening some new areas, in terms of thinking about how we can more quickly analyze our data, how we can more quickly create some of the matchings we have to do between datasets (you can just do it faster and more accurately), and how you write your code. These are all things that are being tested, I want to be clear, but this is what it’s opening for us and what we’re working on, as well as being able to analyze some data that historically would have been maybe put to the side because it’s unstructured. And so, that answers the second part of your question of, “How does this truly transform the compliance space?” In my mind, because it is a bunch of lawyers, as you pointed out, a lot of the output “data” is words. And for people like me, often times, it has been too hard to be able to tap into that because the resources aren’t there—the natural language processing skills were not as elevated as they are today. What I see is a dramatic shift in being able to tap into what is a majority of compliance data because I will now be calling all those words “data.” There’s that future. That’s, to me, what is revolutionary.
Zach Coseglia: Couldn’t agree more. Alright, Tara, it is time to get to know you, to subject you to the Better Way? questionnaire—our version of the Proust Questionnaire, inspired by Inside the Actors Studio. Question number one—you can choose one of two questions. (A) If you could wake up tomorrow having gained any one quality or ability, what would it be? Or you can answer (B) Is there a quality about yourself that you’re currently working to improve? If so, what?
Tara Palesh: What I’m trying to improve is to fundamentally understand everyone—not agree, but understand. It’s to further that empathy.
Hui Chen: Again, you get to choose from one of two questions: Who is your favorite mentor? Or: Who do you wish you could be mentored by?
Tara Palesh: My favorite mentor is a professor I had in college. His name is Dr. Dave Gustafson, and he works on some really critical research. He’s just one of those amazing examples of someone who just wants to make the world a better place and has found a way to use his unique mental abilities to do that. I think it’s inspiring, and being able to spend even a moment of my life with him has been extraordinarily meaningful for me.
Zach Coseglia: Love that. Question number three: What is the best job, paid or unpaid, that you’ve ever had?
Tara Palesh: It’s actually tied to him. I worked with Dr. Dave Gustafson on research during my master’s that was looking to improve addiction treatment using technology. It just had been very poignant and meaningful, and it was really cool.
Hui Chen: That’s great. The next question is: What is your favorite thing to do?
Tara Palesh: Cuddles. I love to cuddle with my kids. Anyone who listens to this podcast and has had kids or has had a life with children that are young, it’s that stage where they just lean into you wholeheartedly and they trust you and you can kiss their head. I am soaking that up so much right now because I know it is just a finite part of my life, but it is my favorite thing to do.
Zach Coseglia: I think that is our first cuddles answer, but not our last perhaps. Alright, the next question is: What is your favorite place?
Tara Palesh: My brain is always going very rapidly in many directions—that’s just the brain I have. So, my favorite place is when something is so intense it can take all of my attention, and I can be in that moment and only that moment. That’s my favorite place. It’s so passionate that it takes over all parts of you and you’re just living that moment. It’s a pretty cool feeling.
Hui Chen: You give some amazing answers. What makes you proud?
Tara Palesh: When I’ve helped someone in a meaningful way. It doesn’t have to be just in the compliance space. I really try and find areas where I might uniquely be able to help make someone’s life better and more meaningful.
Zach Coseglia: That’s deep. Now, I’m going to go right to the very shallow: What email sign-off do you use most frequently?
Tara Palesh: I have a shallow answer: “XxOo” is my sign-off that I use the most—not professionally, to be clear, as it is totally inappropriate. At work: “Best.”
Hui Chen: What trend in your field is most overrated?
Tara Palesh: Wanting everything. The trend that more is better. Everyone, I think, in the beginning, was just so excited to have any data, and now, in this world, where data is becoming more accessible and you can bring it together more, there’s this wave of just thinking that you should grab it all. I think that’s an unhealthy and totally overrated trend.
Zach Coseglia: Great answer. And finally: What word would you use to describe your day so far?
Tara Palesh: “Promising.”
Zach Coseglia: We’ll take it—great. Tara, thank you so much for joining us. Hopefully, you will come back. I’m wondering if you have any just final words for our listeners?
Tara Palesh: Don’t be afraid of analytics, or if you are in the analytics space, don’t be afraid of those who seem to not understand you—that’s on us. So, just embrace it, love it, be passionate about it, and people usually go along with you.
Zach Coseglia: Terrific—thank you, Tara.
Hui Chen: Thank you.
Zach Coseglia: And thank you all for tuning in to the Better Way? podcast and exploring all of these Better Ways with us. For more information about this or anything else that’s happening with R&G Insights Lab, please visit our website at www.ropesgray.com/rginsightslab. You can also subscribe to this series wherever you regularly listen to podcasts, including on Apple and Spotify. And, if you have thoughts about what we talked about today, the work the Lab does, or just have ideas for Better Ways we should explore, please don’t hesitate to reach out—we’d love to hear from you. Thanks again for listening.