Best Practices for the CDO and Data Leaders in Times of Change
- DATAMASTERS on Demand
- Best Practices for the CDO and Data Leaders in Times of Change
Kathleen Maley and Barkha Saxena
CDOs and data experts share their perspectives about how to deal with uncertainty and change within a data organization and how they used data to lead change in their organizations. This panel includes data leaders from KeyBank and Poshmark.
DataMasters Summit 2020 presented by Tamr.
Helle everyone, thank you for joining today. I am excited to kick of this very timely discussion with our panel about how data leaders within organizations can adopt rapid change and navigate through what is extreme uncertainty for a lot of businesses today. Every day I hear from our customers and prospects about how their data goals and plans have shifted. For many, it’s now all about how data specific initiatives can deliver business value as an imperative. And this has changed the way we think about the way people and technology work together to turn data into a critical asset.
My name is Jenn Mcauliffe and I’m the Head of North American Sales at Tamr. I am passionate about data and working with customers to solve their data problems. I’ll be moderating today’s session, and it’s my pleasure to introduce our panelists. Barkha Saxena, welcome. Barkha is the Chief Data Officer at Poshmark, a leading social marketplace where she built the data team from the ground up and leads all analytics, data science, data tools and machine learning initiatives across the business.
Prior to Poshmark, Barkha spent 10 plus years in various data science and machine learning leadership roles at FICO and Comscore. She is also a Co-Founder of ElevateData, an initiative focused on scaling practical knowledge sharing among data executives in an environment that fosters open and lively discussions in order to elevate the role of data across all organizations. I’d also like to welcome Kathleen Maley. Kathleen is an Analytics Executive passionate about harnessing the power of data to directly benefit an organization’s bottom line. Kathleen was previously Head of Consumer Digital Analytics at KeyBank where she managed the centralized analytics function supporting all business units across marketing, sales, products, pricing, digital, risk and regulatory compliance.
Prior to KeyBank, Kathleen had multiple leadership roles at Bank of America as a Deposits Pricing Executive. There she successfully transformed pricing strategy from the traditional approach of grandfathering products to an innovative approach based on price elasticity and optimization models. Ladies, welcome to the panel. Let’s get started. So for both of you, I’m curious about the role of CDOs and data leaders in responding to the changing business circumstances like the one we’re all experiencing right now with the COVID pandemic. Can you share with us your experience? Barkha, let’s start with you.
Great question. So I would say, in the current times when the world is just changing so fast around us, the role of CDO definitely becomes a lot more important. And I think it’s because of the nature of the data and what it tells us, because the data is so objective, this is the best way for us to learn about the current situation. And also as you are trying to think about the different actions, what do you want to do to handle the current situation, data is the best way for you to be able to proactively measure and track what is the outcome of different actions you are taking.
And at the same time, because of this objective nature of the data, it takes the emotions out of the way from the decision making process. And I think that is really, really critical in the times where we are currently. Having the data where you can look at it and make decisions in a very objective way and just move forward. It just allows you to move fast, get consensus among the teams and even as we all have moved to the remote world, that having the data objectivity has become just even more critical because again, it’s objective. These are the numbers, numbers are just the numbers and it brings the people closer together in much more efficient way to be able to make decisions.
I would say I’m pretty lucky that I’m in an organization which is very data driven. So in lot of ways, we were fortunate that we had a lot of underlying pieces in there to be able to allow us to look at the data and make fast decision and move fast. But the couple of things which we did do, you could call them some changes to respond to the extra ordinary times we were then. There was some change in the prioritization because we had to respond to the current environment we were in because as the world was changing as being the very community focused platform, we had to listen and respond to the changing needs of the customer. So of course we had to just react to that and make sure we focus on what’s critical for our community as of today.
And the second thing was the whole timeline from the business questions coming to your mind to the data you need to the insights you get to the decision you made, that frequency just changed. I mean, despite all of us being remote, it just became a supersonic speed and we are looking at the data much more frequently and making decisions. And I think that’s the one critical role which CDOs play because they are the one who have the responsibility of connecting to the data with the decision making which happens in the business function and making sure that two things are really connected in a seamless way so that as time change, we are able to respond really quickly and arming our business functions team to be able to make quick decisions.
Great, how about you, Kathleen?
I echo much of what Barkha said. There’s a couple of threads I want to pull on. And one was this idea of emotion because we are still human beings. And when things really started to get serious, the very earliest days of this COVID-19 situation. I was working with my business leaders and the first set of questions that came around were, hey, these metrics that we track on a monthly basis, we think we need to track them daily. Can we get that done as quickly as possible? And one of them really sticks with me and that was why are clients calling? We’ve got to know. Daily monitoring of why clients are calling.
And I thought about this and I thought, we know why clients are calling. They’re calling because they want a fee returned. They’re calling because they’re worried they’re not going to be able to make their payment. They’re calling for all of the reasons we would expect. So let’s back off of this knee jerk reaction to go from monthly monitoring to daily monitoring. And let’s take a step back and really think about what do we need to know of our data right now. We don’t need it to confirm things we already know are happening. We need to redirect it differently. So there’s different types of emotions that sometimes a data leader needs to manage. To say, “Okay, what I’m hearing is an urgency, a sense of urgency, but let’s make sure we actually put that energy into a productive direction.”
That can be a difficult conversation to have at times, but it’s a really critical responsibility of a data leader. I also think about the fact that business is always changing. And so this role as an analytics leader or a data leader is really about consulting. Just like Barkha said, everything, everything, everything I do should be directly tied to a business initiative. In fact, I go as far as to say, there’s no such thing as an analytics initiative, there’s only a business initiative.
And so I have to be constantly consulting with the business leaders I support. And in quieter times, we take a longer view. Where do we want to be 18 months from now? What’s some of that foundational work we need to do? And in times like this or even a couple of years ago when we experienced a government shutdown, it’s a matter of hours and dates. But that nature of being a consultant to the business so that I can enable the decisions they need to make through data, that never changes.
Yeah, so you ladies touched on two things, supersonic speed and the rate at which now the data became so important to have and to use, and the urgency that now was present because of everything that’s going on around us. How do you think that will help make organizations like those themes? How will those help make organizations more data-driven for the longterm and not just during a changing time, a pandemic, et cetera?
I think necessity is the mother of invention. And in times like this and I saw the same thing in 2006, 2007, 2008, organizations that were already somewhat familiar with analytics leaned in heart because they knew that those people who were studying the data could find leakage in business process that others wouldn’t be able to find. They’re like these little hidden gems that can be found when one studies data critically. Well, in times like this when revenues are impacted and every organization has to become more efficient, those are really easy activities to get behind. Those are really easy activities to measure.
And then what happens of course is an organization says, “Wow, that amount of work was worth a lot of money. Let’s keep doing that.” And so depending on where an organization is, and I think depending on where an industry is, finance, all in with analytics. Financial services, the hiring increases in times like this. That may be the case in retail. Healthcare, I think was just, just really beginning to explore everything they can do with analytics and the value of analytics. I think, I don’t know, I haven’t done a study, but anecdotally, I get the sense that healthcare has pulled back a little bit. It’s not an investment they’ve made. They haven’t felt that huge return on investment that’s possible. And so I think there is now a reluctance at this specific time to actually make that leap when arguably they need to, even more.
Yeah, more than ever. Barkha, how about you?
Yeah, I 100% agree. Everything you’ve said, Kathleen. And the way I look at it, if there are organizations who are still kind of becoming a data driven, but not all the way there, I think if anything, the last few months I’ve proven the value of the data. Again, if I take a step back from they’re like why some of these organizations are not as data driven as we will want to be. Because this is a topic I’m very passionate about and the idea behind the whole ElevateData. I do think that the each and everyone do want to make decisions based on the data. And where we data leaders can help, I think there is still a gap between what business wants to do and where the technology process and the people and how we are thinking about it.
And that’s why these days, there is a gap. And as a data leader, if we can work on filling the gap between the business needs and the business questions and how the technology, people and processes are going to support it and not in the support manner, the month that I have used at Poshmark and even my previous career, which is what you said it in a really beautiful way, Kathleen, “There are no analytics initiatives, there are only business initiatives.” And that’s how I look at it. It’s a partnership. It’s not the support. It’s not a service. And at Poshmark, the way I have organized my team, it’s a centralized team. But then there are these verticals where each of the vertical data team is partners with the business team in such a way that they look more like the part of that business team as opposed to the my centralized team. And I’d love it. That’s how I wanted it to be.
They are in there, they understand their business, they feel their pain and they are working on initiatives which answered their questions. And not just the CMOs question for example, but the question of everyone in the team at every level so that they are enabling the decisions. And when the business team sees this data teams are really focused on solving their needs, they are very open in sharing where is it where they’re struggling. And as team works on building these, the different data products, which can range from honestly just a simple analysis or a one dimensional report to a sophisticated statistical analysis to a machine learning model, it always starts with the top level business question. And we are designing it with the business person’s mind in every single meeting that someone from the business team is along with us.
So first of all, we build all the data products with the guidance from the business team and then we train them so that we are using it. We have the processes in place to make sure whatever we build it gets used because when it gets used, we get the feedback and then we change it because there is no data product which can be stationary. You build it once and you are done. You have to keep evolving as the business needs are evolving. And by building those relationships and not just at the higher level that, hey, the team you build, going with the Poshmark, all the people and the love thing, we actually spend time in actually getting people to be personally very close to each other.
And the last thing which I think really helps is the success or the failure of each of the vertical teams in my team is aligned with the success and the failure of the team which they are partnering with. And when the two teams have the same goal, they are bound to work together and be there for each other. And I think if we can take that approach in general and turn the data into the operating tool for the company, and by definition, when you’re talking about the operating tool, it has to be in the hands of the people who are running the operations. Then I think that the data can get it’s rightful place in an organization to drive decisions.
I love the centralized, but fully dedicated model that you were talking about. One of the things we talk about as for data and for insights to be actionable, the analyst has to understand the business. Well, there’s only one way for that to happen and that is spend time with and in the business. The reporting structure is such a small part of it. It’s that collaborative nature. It’s making sure that the goals of the business are my goals. I don’t have an independent set of goals. My team report to me, they work for the business. I care for their careers. I care for their career progression, but they don’t work for me. Nothing they do is for me. If there’s a team meeting with one of their business partners or my own team meeting, they know without asking, you go to your business partners team meeting, that’s the more important meeting. The value comes through implementation and adoption, not through build. And so that relationship with the business partner, the working with is so critical. That is everything.
So can I ask on that theme. And you guys obviously share the common understanding that the business is everything. What advice would you have for data organizations? With COVID, they’ve now been, it’s supersonic. I love your word, Barkha. Supersonic for the need for data results for data. There’s probably big gaps in terms of how they actually do that. They may not have these partnerships in place that you guys are advocating for. What would be your advice to those that are in organizations that maybe that’s not as mature as it needs to be, how do you get started with making the business a partner?
I think data literacy plays a huge role in this. But I’m going to talk about data literacy a bit more holistically than I’ve heard it discussed in many places. I think there is the obvious aspect of data literacy that is what is predictive modeling? What is machine learning? When are these different techniques applicable? But I think an aspect of it that often goes unaddressed, but is really critical to that question you just asked Jenn, how do business leaders work with their analysts? What should they expect of their analysts? What does a successful engagement look like? What does the analyst need from the business leader to be successful?
And for that initiative, for any initiative to be successful, I need my business leader to talk to me about her strategy. I need my business leader to talk to me about the problem she’s trying to solve, about the hypothesis she has, the risk that’s keeping her up at night. What I don’t need is for her to tell me what data to pull over, what time period, how to cut it. There could be some of those sorts of requirements sort of at the back end, but what we’re really talking about is how we work together. And I think that piece has been largely unaddressed and it’s absolutely critical.
I as an analyst have a role, the business leader I support has a role and her primary responsibility is to open up to engage in dialogue, to invite me to the table, not to answer a specific data question, but to really engage with her and her business and the problems she’s trying to solve. I think when we come at it from both sides, that we have a shared objective and that is for the business to win. My objective is for the business to win. And we talk about it in those terms as opposed to what are the specifications of the report. We can do a lot more and go a lot further.
Yep, I would 100% agree with that. And the keywords in what you use Kathleen, if we use them, it’s the seat on the table. I think having to spent my first 10 years at financial services, honestly now that I’m in the technology, what I do tell people, whatever we are trying to do here, financial services has been doing it for many more years. So[inaudible 00:18:37] having been on… Honestly, it’s funny the number of things I took for granted from coming from financial services. I have seen some of the organization where there is a struggle for the data leaders to get started because they don’t have access to the people who will share with them the strategy or they don’t trust them that, why would I share it with you? And that’s where the word you use, the data literacy, and you have to smell somewhere.
And I’m a big believer in just the resilience. Don’t give up, just keep working on it. If you are a passionate data leader, first of all, you just have to make sure sometimes… Again, going back to every business leader I’ve talked to, they do want to hear. It’s just that sometimes they don’t understand the language data leaders are speaking. So as I have built my team or even the way, I mean, my own career, starting from the analyst, I always gravitated towards the business questions and the business knowledge.
And as I have built my team at Poshmark or even before Poshmark, I always look to hire people, do call them [unicorn 00:19:46], but you do find them. People who are technically savvy, but they are also have a very high business acumen. And as much as they are passionate about using the [niggle net 00:19:56], they also know niggle net is not the answer to everything. I think that’s what data leaders need to adopt. Sometimes I feel that people get caught up in trying to explain how sophisticated it is, who cares? That’s the thing. What they care about it, is this going to help me hit my this month DMV target or not? If you can just talk about that, you will get people to start hating you.
Fortunately, I think I have generally had good experience, but because I do talk to a lot of people and I mentor a lot of people, I have seen this strategy to be working. Change what you are talking. Your end goal is the same. You don’t have to tell how you are doing it. I seriously do not explain. We have built a recommendation engine. I always talk about what it’s doing. Nobody knows what’s going on because nobody cares really. I mean, they love listening to people care, and I will go and talk about that in those places, either technical consensus, but business people are not the world.
I think business people really need to have the transparency in what you’re trying to do, otherwise it becomes a black box. So even if you have built a black box, demystify it. Find a way to simplify it. They need to have the trust because business people have the gut and they know their business and know what they’re trying to do. And if the way you are building your data solution can match with their gut feeling, you will get that option. And start with one problem for one business function somewhere. And once you see the success in one place, the other organizations will ask for it and for the same support. And that’s how you slowly built the role of the data in a company.
I agree. I love that concept of almost a grass roots movement. Where can I affect change? Where can I build a different relationship with my business partner? Where can I take a different approach to a problem or to communicating back an answer? And it can be very, very simple things, instead of talking about the additional lift, percentage lift of a model version A versus version B, just translate that into dollars, translated into dollars. That’s going to be a much more meaningful conversation. Who cares if the model is 54% effective versus 58% effective, whatever that even means. How many more dollars am I going to get if I convert from this model to that model? How expensive is it going to be to implement the new model? And so what ultimately is my return on investment? And that’s the bottom line.
But the idea that yes, there absolutely needs to be support from the top, nothing really changes if senior leaders aren’t also leaning into analytics. But the boots on the ground, that’s where the change really happens. And so it is possible to make incremental improvements, one, two, three people at a time because those three people becomes three organizations become an enterprise wide culture shift. And then it is sustainable. It is systemic transformation. It is the gift that keeps on giving. People are doing their jobs differently from then on.
So a lot of times when we talk to the business, I hear from the business side the need for analytics. The analytic assets, the analytic outcomes, the analytic ready dashboards. Giving both of your experiences, how critical is having that tangible piece of whatever that is to communicate to the business in the theme of, you’ve got to sit with the business at the table, we’ve got to make sure that they’re partner in this thing, how do you communicate with them? And how critical is that component of the analytics to your operation?
Kathleen, do you want to go first?
Sure, so I think, first and this is one of the reasons why this is such a difficult topic is because there’s no standard set of definitions. We mean so many different things when we say analytics. We say so many different things when we… It means so many different things when we say reporting or self-serve or all of these things. But I do think, well, I think about reporting as visibility into my portfolio as a business leader. So when I was running deposits pricing, I had a lot of reporting. Now my team studied that reporting. We looked at competitive intelligence, we looked at growth, we looked at it by state, we looked at competitor pricing, we looked at so many different things that was absolutely critical to doing the job.
I think there has been, it’s some mixing of concepts. So I need visibility into my portfolio to manage my portfolio. But not every question I ask is a question that I will ask over and over and over. Not every question I ask is something that I’ll need to study on an ongoing basis. That’s the benefit of a report. So if I’m seeing a new problem, because I’ve been studying my reporting, I might ask what sounds like a data question. If my analytics team then goes and builds a new report and I look at that report and I go, okay, well, now I have another question. Now I have another question, another question, another question.
What that signals to me is that I’m trying to use a dashboard to answer a question that is really best served through exploratory analytics. So again, it comes back to what is the purpose of the question? Is this something I’m going to ask over and over? I’m going to need to study it because I need visibility in my portfolio. Is this something that is related to a hypothesis, a risk, an unknown, something that’s changing? I need to explore that differently. I do think business leaders are often going to have those exploratory questions. I think that there are new tools that do a much better job of helping them get to some of those, I just need the data questions much more quickly.
A lot of new capabilities are based on natural language processing. So instead of building for example, a Tableau dashboard and then refining it and adding in more layers and it becomes so complicated. Now I have to build a summary dashboard for all of my dashboards. Now I have all of my underlying data tied to this capability that allows me to ask questions the way I would of Google. This I think means that leaders can get that data point that they want much more quickly. Can start to develop hypotheses much more quickly. But I don’t think that necessarily replaces. In fact, I’m sure it doesn’t replace some of that more sophisticated work that we’d need to do, what it does is open up analyst’s time for the more sophisticated work.
Yep. No, 100% agree with what you mentioned, Kathleen. So what we have done very similar to. So at Poshmark, I have had the fortune of going with the company when we were a small 35 people company to where we are today. So the added benefit of that was I was so deep into each of all parts of the business from the product to marketing to operations that working with each of the business leader, I had a pretty good sense of the KPIs, which you need to look at to understand the small disruptions which happened here then there. And those are not a small set of KPIs. Each of the business function has decent number of KPIs. But in today’s world, they also have that decent number of, I mean, the teams have the decent size as well.
And as you mentioned that the analyst or the data scientists, they don’t necessarily want to be pulling the same data again and again. They want to do the more deeper projects. So we actually did identify what are the core set of the metrics, which are important for each of the business function to keep track of. And not just to track of. At Poshmark, my responsibility is also, I’m responsible for the overall business health and I can’t do it by myself. First of all, it just too many KPIs. Plus just looking at the data is not going to give me the answer. The answer is going to come from the people who actually took the actions.
So we actually just build out all those KPIs. We got some input. We provided some input that we think based on we have been helping you for this long, I think you should be looking at this thesis as well. And then we define the methodology for which you should be reviewing the data because when you have a lot of data, they can be discrepancy on how the person A is leading the trend and the person B is leading the trend. So we just created a standard around that as well so that when few people get in the room, there is no discussion on whose trend is the right one or the not. You all have the same.
So we basically build the basic infrastructure, the terminology and how you should be reviewing the data. So basically, train the business people also some work like what the analysts do to bring that consistency. And of course, the whole data governance and data management plays a key role in this. Because for example, GMV gets looked by every single business function, needs to have the same definition everywhere. The numbers have to be the same way. But once you have those KPIs assigned, and then I started this process where we actually do go to each of the… Myself and my CEO, we sit with each of the exec of the business function and we go to the data and make sure the top level KPIs which you can of, there are no surprises there. And if there are surprises, that’s where we formed that what are the deeper analytics projects teams should be doing and we take it from there.
But the two part was first we build out this, just the basic infrastructure saying that, and it could not be built in any of the standard Tableau or the local, because it’s a massive database. So we ended up just building internal system to have that. And again, you need the data daily, weekly, monthly, et cetera because different sets of decisions. But then I democratize who is looking at it and making sure it gets looked at and use at. And then based on that, we identify what are the next set of the thing which team is doing. So that has, I would say allowed us to start using a lot of data, removed that dependency of the business user waiting for the simple data, because every time you’re reporting any, it gets the analyst also more excited and keeps the…
And my team is decent size, but it’s still pretty small for the different functions we are supporting. But because you are not asking the day to day questions, it just gets them to focus on some of the larger problems. So I think that the decentralizing of the keeping track of the metrics really helped. And in the process, we ended up building some dashboards, but we didn’t build them in one at a time. We at the top level, we decided, hey, this is what we are going to build. We got the sign off with the business user to make sure we take care of it. But then once we build it, and I would say that whole thing was a really big thing. Since we built it, it has been a big level for us to stay on top of the business and not have the simple questions on day to day basis. And it has really made the decisioning a lot more efficient.
Did the pandemic have any impact on accelerating that or changing what your team does on a day to day basis or how they relate to the business?
I would say, all that was actually already done. So it turned out to be just super useful for us. But let’s say our review process on that used to be every two weeks. Data team was looking at, I mean, I was looking at every day, but then a lot of it started to actually looking at every day. We did ended up, I mean, I would say we had all that build more the daily, weekly, monthly for some of the pieces we picked more than the operational side to make sure that the packages are getting shared. And if customers are reaching to us, we are responding them. Some of those we just needed to know to keep track on the hourly basis. So there were some change, but no change at the higher level in terms of the process, et cetera we had built out to stay on top of the metrics. But I think the usage of it probably increased a lot during that time.
So both of you have touched on speed being a key component of this whole operation. What are your current perspectives on, Kathleen, you mentioned AI and machine learning. How do you think those types of technologies will either apply themselves now or apply themselves as you guys move forward with your data initiatives?
I love this topic because in some ways AI and machine learning are not at all new. So a lot of the machine learning techniques are actually, as I was taught 20 years ago in Graduate School, maybe not quite that long. Clustering algorithms and random forest and all these different things, in many ways, they’re not new. I think what is new, how they’re applied and the fact that we have such a massive data that can be processed so quickly. So I think about, well, not related to COVID, but think about ways. Now I don’t know exactly what ways is built on. but I absolutely know that if MapQuest is your typical optimization model, ways is machine learning and AI.
It’s that two way input, it’s immediate updates. Constant, you’re just constantly refreshing and rerunning the models. The models aren’t really learning. They’re just ingesting new data and providing updated output very, very rapidly. This is incredibly relevant for applications like pricing, which is why I changed the pricing strategy while I was at Bank of America. I was looking at, and we all thought at the time, a rising rate environment. And so I built my models on three years of history.
And the second rates started to rise. Those models would be totally irrelevant.What could I do to maintain the stability of those predictions? And that’s where the machine learning came in. Being able to take every months, being able to take new data, run it through my algorithm, update my coefficients, decide if I wanted to replace my existing coefficients with the new coefficient so that I would get a more accurate prediction. It allowed me to build stability into my model.
Ladies, great discussion. As a wrap up, I’d like to hear from each of you. So given COVID and the changing times, are there any learnings or realizations that you’ve come to that may have changed your perspectives on something related to data, whether it’s the technology, people, the business?
I think our biggest challenge is, and maybe always will be people. People have preconceived notions. People have individual and personal perspective. People have a very, very hard time being objective. Data can help us get so much closer. But I think not only in terms of what I experienced professionally, but what I see out in the world right now, as we talk about how we’re measuring or how we’re counting or how well the models are performing, they are always, always interpreted and understood through the lens of the human condition. And I think we have to continue to put as much effort and energy into managing the truth through that human experience as we’ve always had to.
Yeah, I would say my answer is also on the people side. A little bit different take on it. As I think about this question and reflect back on the last few months and we went through a lot of changes in our personal lives, professional lives, working from home, field, walking in the middle of the meeting and your team going through its own experiences. But we did really well, the Poshmark team as well as the data team in terms of how we rose to the occasion and we responded. And I think that it goes back to, again, this team is, I’m very emotionally attached to this team because this is the first place where I got to build it from… I was the first data person. Got to build it from the very first person and then say, build this team.
It was a very personal and the emotional experience. Every single person I hired in the team, I looked for the team fit. I looked for the technical and the business acumen, and I looked for how passionate you are for the Poshmark vision, what Poshmark is trying to do, which is all about serving our community. And as we hired, whether it’s individual contributive of the leaders. There was so many, I would say almost 50 plus percentage of the interview and more for the leaders is focused on, are you a team fit? Are you someone who for whom this team will be not just a place where you work, but this is the place where you belong?
And I think, again, I wouldn’t know, it’s not that I went and asked, but I think the way I saw the whole team coming together and the shifting priorities, and as much as I had the vertical lanes. In last few months, people got close to staff on the projects because some parties were changing and people just need to work on whatever. And people were just taking it with the fullest smiles and supporting each other and joining me at every level in the company from the leaders to the analyst who just graduated from the school a few months ago in coming up with a team initiatives like how do we get together, taking time to talk to each other.
So the team is still stick together. That was the one thing which I had in my mind that the team was so important for me. When I used to walk on the trove and my team said, it used to be emotionally, the heartwarming experience. I was like, I don’t know what’s going to happen because Poshmark did not have a remote locking culture. We were like really in the office on the face company and they tried to stay intact. And I feel that, and the work quality has been the A-plus and everyone is so excited and the partnering. And I feel that over investment I had on looking at and thinking about the people side, hopefully paid off.
And I think that’s the one thing I’m just taking that to, as I continue just think about the building the data team, that people aspect is just as so much. But sometimes the technical word we forget. So much becomes about the technical things that we forget that whole, the building the team and all the pieces fit together makes a bigger thing. It’s just so much critical. And I would say, it just makes me very happy that how the team has continued to function so nicely and team has the state, the team where they all belong.
Very cool. And that’s where our passion comes from? At the end of the day, that’s where the passion comes from. So ladies, thank you so much for your participation today. We really appreciate the energy and the time that you’ve given to Tamr DataMasters. For all those folks attending the session, if you’d like to learn more about Tamr and how we can help you navigate your data challenges, please let’s start a conversation. Join us at tamr.com.