Part One - Diving into Culture Amp's data lake of 34 million employee experience data points

DAMON KLOTZ:

Fresia, welcome to the Culture First Podcast. Thanks so much for joining me today.

FRESIA JACKSON:

Thanks for having me.

DAMON KLOTZ:

In order for the audience to learn a little bit more about you and the team that you work on, I've got a couple quick questions to provide that upfront context for our listeners. First question, it's the end of your day today as a lead researcher at Culture Amp, what did a typical workday consist of?

FRESIA JACKSON:

It's always so hard to answer the typical workday questions, because one of the things I love about my job is how much variety there is, but I'll try to use today just as an example. It usually consists of many, many hours in spreadsheets looking at data, which is probably not everyone's idea of a good time, but it is mine, along with a whiteboard. Also my idea of a good time. Really looking at what are the insights that are coming up? How could we visualize those insights to make them easily understandable? Working with designers to then get those insights into reports. Yesterday it also included a PR briefing to share those insights with the world. And then of course, time with my team to really help guide on what analyses we should be using for the hypotheses we're testing. So a little bit of everything.

DAMON KLOTZ:

So lots of large formatting of data and information and visualization. And you mentioned your team and I think it'd be useful to understand where does a lead researcher at Culture Amp sit? What sort of a team are you on? What are some of the other job titles of the people that you work with? What kind of backgrounds do they have?

FRESIA JACKSON:

Yeah, well, I'm very lucky to have about 50 other people scientists at Culture Amp. So my background's in organizational psychology and many of those also their backgrounds in organizational psychology or organizational behavior, organizational development, all masters or PhDs. Even some wonky ones where they're bringing a little bit of a different side of things. We have a forensic psychologist. We also have someone who has PhD in disability studies and then my team, we also have the data science component. So machine learning and really diving into the data a little deeper than we do typically on the people science side,

DAMON KLOTZ:

When people probably think of a HR tech startup, they might not think of someone with that sort of forensic background, wondering like... But at the end of the day, we're all human and humans are complex. And I think that's amazing, is we've seen Culture Amp grow over the several years that we've both been there. Is to see all the different types of people who have joined over the years.

DAMON KLOTZ:

The third question in our little get to know Fresia section is if I really knew you today, what would I know?

FRESIA JACKSON:

These open-ended questions always make me a little bit uncomfortable. So I'm just going to go with the first thing that comes to mind, which is about two hours ago, I ate half a wheel of cheese as a snack.

DAMON KLOTZ:

It's a lot of pressure to be writing questions for a lead researcher and people scientist who sits there and looks at questions all day and you're sitting there going, "These open-ended questions, I'm just not a big fan of these [inaudible 00:03:32]." So as a podcast host, it was a lot of pressure to write questions for this interview.

FRESIA JACKSON:

I need more direction, Damon.

DAMON KLOTZ:

There will be plenty of direction coming. Don't worry. All right. The final question, and hopefully you don't have too much feedback on this one, because it's the most famous intro question that I have is that if a curious 10-year-old walks up to you on the streets of Baltimore and says, "Excuse me, what do you do for a living?" How do you answer?

FRESIA JACKSON:

I like this question. It makes me think about my job differently. I would say your parents and the adults in your life go to work every day. And what I do is look at the feedback that they're giving to their company about how they feel at work, as well as the feedback they give to their colleagues. And I'm seeing not only your parents, but all the parents across the world, what is making them fulfilled so that when they come home to you, they can truly switch off and be the best parent they can be.

DAMON KLOTZ:

I've had some incredible answers on this show, but that is a profound... Like that kid's sitting there... Because other people have given answers where I'm like the kids have already walked off halfway through. And it's no offense. Kids have short-term attention span issues. You're sitting there going, "Hang on that." That kid's going to be like, "You can make my parents happier. Please do this work. We need more of you out there so that my parents can be..." I'm saying that half jokingly, but when you think about it, work borrows time from more important things like spending time as a caregiver or with your family or with a pet or whatever fills up your cup. So if our workplaces can return people in a way where they feel more themselves, where they feel safer, where they feel like they've achieved something, then that is a good thing. So profound answer-

FRESIA JACKSON:

I agree.

DAMON KLOTZ:

... profound answer. All right.

DAMON KLOTZ:

I've let the audience know that this is part one in a multi-part series where we're going to be showcasing the breadth and depth of Culture Amp's employee experience data set. But before we do that, I thought it might be helpful to give everyone a fundamental understanding of how this data set works in the first place. So I guess my first question is, what do you want people to know by the end of this episode?

FRESIA JACKSON:

Yeah, I think it's hard to talk about something so large as our data. So today we're really going to break it down into something small. And we're going to talk about what happens when one individual responds to a company engagement survey. So we'll share how it's used by the company. Then we'll share how this response goes into the largest employee data lake with the responses of over 10 million employees. And then how Culture Amp is able to use that to provide value to our customers through things like benchmarking and algorithms, but also how we use it to really answer pressing questions or things that are on people's mind. Like what impact does engagement have on retaining customers? Or what is the best performance rating scale? These are all questions that we've answered and I'll be able to share later.

DAMON KLOTZ:

Sounds great. I think everyone needs to go on this visual journey with us where they're picturing this company that we're going to talk about and this person. And yeah, all right, sounds great. Let's dive in. Can you maybe start with, I guess this one person's journey, Harper. Who is this person and what is the experience that they're about to go on?

FRESIA JACKSON:

Harper has just received an engagement survey invite from Inner Tech, where she has been working for six months. And this really feels like the beginning for Harper, but this process has usually started much earlier. The leadership team has been thinking about what are the questions we really want to get feedback on from Harper and other employees. And then the people team has also been making sure that their employee data file is up to date so that when Harper responds, they can really know where that feedback is coming from. That it's from an early-tenured software engineer who's also a woman.

DAMON KLOTZ:

All right. I guess I'm going to be playing this clarifying question role here for the audience where I'm going to pretend I know nothing ever about employee engagement-

FRESIA JACKSON:

That's role play.

DAMON KLOTZ:

... or values. Yeah, and that I've not worked at Culture Amp for seven years. So for anyone listening who's going, "Oh my God, Damon's worked here for so long; he doesn't have a clue what we do?" We are doing role play here.

DAMON KLOTZ:

So Harper has reached that critical tenure mark of six months and they've gone through onboarding and hopefully started to feel a sense of belonging and momentum in their role. You've also mentioned that obviously the company already had some data on them from a HRIS system about when they first joined and some of their demographics. So why is it so important for them to respond to that first survey invite that they get?

FRESIA JACKSON:

This is an opportunity for Harper to not only share her experience, but also to potentially improve the experience of others like her. And I know that there are some companies out there who kind of view engagement surveys as a tick the box activity. But when a company is sending one out through Culture Amp or other providers, they've already put their money where their mouth is. So they're already putting resources towards understanding their employees experience. And they've put a stake in the ground that they're going to take action off the back of it. So by Harper responding, that action is going to be more accurate and what she wants to see happen in the organization.

DAMON KLOTZ:

Obviously what we've seen over the last few years in particular has been companies sending out many different types of surveys, trying to deeply understand the experience that their employees are having because they do want to be able to tailor their actions to it. So using this visual Harper's been there, she's responded to this survey, now where is this data going? Where is it flowing to?

FRESIA JACKSON:

I think that's a very good point that you just made. So I want to kind of double click on it that we're using an engagement survey as an example, but there is tons of other data that's here that we're looking at and connecting across. But to answer your question, Harper's response goes into a bucket with all of the other responses from Inner Tech employees. So while we at Culture Amp know who said what, Inner Tech does not know what Harper said? And that confidentiality is really important to us because we know that Harper is not going to feel comfortable being honest, unless she feels that confidentiality. And so what we're able to do to still make that response useful for the company is to tag her response with really important characteristics, whether that's her tenure, age, location, things like role, manager, so that we can combine her responses with others in each of those groups.

FRESIA JACKSON:

And so this makes it possible for, let's say her department leader, for example, to be able to see the responses in that bucket that are just relevant for them, just within the department. So that they can see what employees are most satisfied with, where there's room for opportunities. If this isn't their first time serving, they can see how things have changed, where things have increased, where they've decreased, as well as hopefully if it's not their first time, they've also taken action. So did those actions have the impact that they expected? And they can even drill down to see particular types of employees. So they could see women engineers, specifically if they're having a different experience, but that's only if there are at least five people within that group. So we really try to keep that data sacred and hide those pieces so that people feel comfortable.

FRESIA JACKSON:

And then at the company level, leaders can see if those opportunities in Harper's department are unique to that department, or if they're shared across the organization and need to be acted on at a higher level.

DAMON KLOTZ:

I think for everyone listening right now, we've got Harper, we've got Inner Tech. Inner Tech is this made up organization we are using to bring this story to life. And we've just talked about this bucket, this bucket of data and that like this is when we start to tag it and it's getting more interesting. And we're starting to see how these individual data sets of just one person responding to one survey, ends up painting a much bigger picture. You mentioned the breadth and depth of, I guess, Culture Amp data and how much has been collected over a long period of time. How does that past data actually have an impact on this current survey that Harper has responded to?

FRESIA JACKSON:

Yeah, a lot of impact. Over the last decade, there have been 34 million surveys run through Culture Amp, which is really... I had to triple check that because it was such a high number that I didn't trust it. Think of that as 34 million buckets of data. And what we do is copy and paste, duplicate that bucket, tag it with the really important context, like what industry was this company from? What is the company's size and what region is this coming from? And we pour it into the data lake. And so with this data, we're able to create benchmarks, which really helps companies understand what's normal.

FRESIA JACKSON:

For example, let's say that Inner Tech found that across the company, employees were unsatisfied with their compensation. So about a third were disagreeing with the question, "I believe that my total compensation is fair relative to similar roles at other companies." So by using the benchmark, they're able to find out that actually they score higher than other tech companies on this question. So the benchmark really tells them, is that something they should be concerned about, or is that an expected result? And that can help them figure out where to focus and take action.

DAMON KLOTZ:

I think that's something... If I remove my hat of not knowing anything about this and reflect on the customers that I've worked with over the past seven years, I think that's always been one of those really critical moments where they're like, they see this red flashing light of, "Oh my God, people are responding to this. We need to go do something about it." And they need that contextual reinforcement within this industry like that's okay. That's actually a very expected number, or within this tenure range, that's a very expected response.

FRESIA JACKSON:

That is the first thing companies do. They go and they sort by lowest and they're like, "Oh no."

DAMON KLOTZ:

Yeah.

FRESIA JACKSON:

But there's additional context that can help them with those concerns they feel immediately.

DAMON KLOTZ:

Yeah. I love that we're now talking about benchmarks and that being able to kind of... If people are staying with this visual, that data has been pulled into its lake and we're starting to get this really interesting contextual information. So I feel like the topic of benchmarks has been fascinating for a long time. It was some of Culture Amp's first research that we ever wrote about, was like our new tech benchmark, like what's happening inside of tech companies. Our chief scientist, Dr. Jason McPherson, that was some of the first blogs he ever wrote about. So outside of being something that his Culture Amp can write about, how else are benchmarks being used at Culture Amp?

FRESIA JACKSON:

I actually love that you bring that up because when I was interviewing at Culture Amp, I saw the 2016 benchmark report and that it was open source. And that was one of the reasons that I decided to come, because I love that we are so open with what we're learning. So benchmarks are also useful for us at Culture Amp because they really help us guide our customers and keep a pulse on what's going on for them, what's top of mind. For example, when we crunched the 2020 data, we saw that while there was a huge increase in employees saying that they were able to use flexible working arrangements, we also saw a decrease in the ability to really switch off from work and also to take time off from work when they needed to.

FRESIA JACKSON:

And so really knowing these two insights, our customer success managers and our people scientists, they knew that they were going to need to talk to their customers about employee wellbeing and have recommendations for how to improve that as employees made this huge switch. And so we also share those key trends in blog posts, because I think, again, that's something I love. We want people to use Culture Amp. Yes, of course, that's great. But we also want to share our research, even if you aren't using Culture Amp to really enlighten the community on what we're seeing with our big data set.

DAMON KLOTZ:

I think what you brought up is, when you described who else is on your team, that combination of people science and data science, it's so important I think for the industry as a whole to have companies and to have people like this, sitting there going like, "What's actually happening? What's changing?" I feel like I try to do this at a very micro level by having individual conversations with people about putting culture first and listening and doing that storytelling, but what you are talking about is like the biggest macro trends. What are we seeing in huge data sets? What are we seeing in what types of questions are being asked? How is this data changing and what can we do to actually respond to that? I think by aggregating this data together, it provides this greater context about how unique or similar an experience actually is, which I know I mentioned helps a lot of leaders understand not only their data, but how are they performing? Where do they focus?

FRESIA JACKSON:

Well, I just want to say, I don't think one of those is better than the other. I'm looking at the big data set. You're talking to leaders about what they're experiencing, and I think we have to combine those to create a story because data in itself is not inspiring. It's understanding what's the anecdote that comes with that. Right?

DAMON KLOTZ:

Very true. I just feel very validated with my micro sets of data, talking to one person at a time. So let's stick on this topic of benchmarks for a bit because I think Harper has given this one survey response at this moment in time, and this data is still helping us better understand, I guess, some of this context that we were talking about. Are there any other ways that benchmarks are being used at Culture Amp outside of that first research part and helping companies understand their own experience? What are other ways are benchmarks useful?

FRESIA JACKSON:

We also do research to see, what truths can we uncover about the employee experience? A common, common question we hear from customers is, "Why are our employees leaving?" And so to answer that question, we actually undertook a very extensive research study, analyzing the survey responses of over 300,000 employees who had voluntarily exited. And we found those who went on to leave within one year were much lower on believing that there were career opportunities for them in the company, there were not as happy with their role, and they didn't feel like they belonged. And so those were three things that were really important for why an employee chooses to leave.

FRESIA JACKSON:

But also unsurprisingly, the most predictive question is just asking people if they're planning to leave. So we found an employee's response to, "I see myself still working at the company in two years time," is the most predictive. Those who strongly disagreed were almost three times more likely to leave than those who selected other responses. So, if Harper tells Inner Tech that she doesn't see herself there in the future, Inner Tech should really take that seriously.

DAMON KLOTZ:

I think that's this interesting balance of like there's all this research that we can do and we can try, combine all these different things together. Then there's sometimes just this hardly predictive question where you literally ask your employees like, "Do you still see yourself working here?" And it's not like they're saying, "No, I'm leaving tomorrow." It's saying, "I'm questioning this. I'm not as strong about this as I used to be." And I think that is definitely something that companies should be taking seriously.

DAMON KLOTZ:

Earlier this week, I was presenting at an event where I was talking to an audience in Australia about trends I was seeing in employee experience. And when I was sharing some of those stories, I told the audience that there's this lens framework that I use when I think about employee experience, which is seeing the EX through the individual lens, the team lens, the leader lens, and the company lens. And I think it's because we experience our work firstly as an individual. You are an individual. I'm an individual. We experience our work through our own lens of the role that we were hired for. Then we experience it as a member of a team. So that team is a container within that team. We start to understand who we are in this larger environment. How do we operate within that team? Most likely, people also are working underneath a leader. So having some form of management or leadership that then impacts their employee experience. And then finally, you do all that with the name of a company that you are sort of saying, "This is who I work for."

DAMON KLOTZ:

I think when I talk about that to people, it's because each lens gives us this different viewpoint when we talk about the employee experience, because the employee experience as a macro is just a gigantic term, employee experience. It encapsulates so much and I bring that up because I think it's useful to help to understand this data set that you're talking about, to answer the question, "Why are my employees leaving?" You look at the answers that are focused on the individual who doesn't feel like they're happy with their current role scope. You can think about it through the lens of the team member who's questioning their sense of belonging right now on that team. Or the leader who isn't doing a good job, maybe at providing clarity on career opportunities, not only for themself, but for their team.

DAMON KLOTZ:

So hopefully when people are listening to this and they're looking at all these data sets, it's like, yes, use the benchmarks in the context, but also we can provide our own storytelling lens. We can all provide these lenses to give us extra data, extra context to help us. Someone might be listening and going, "Okay, I have to do a lot of work. I have to really go out there and help my employees and their employee experience, and Damon just asked me to think of four additional lenses to look at this through." So how do you suggest a company Inner Tech uses some of this data, uses some of these storytelling techniques to improve the employee experience?

FRESIA JACKSON:

Well, we're trying to help with that. So we're using those same lenses as well and we're always trying to basically either share it or add it back into the product. So in that turnover research that I just shared, that was actually an input for an algorithm in the product that's called the turnover forecast. And that combines several data points. One is the relationship between certain questions and turnover. I just described that I see myself here in two years time, as well as career opportunities, belonging. We have an odds ratio for every single question and how likely it is that an employee is going to leave based on how they're responded. But then also the other is the relationship between demographics and turnover. So through our research, we know that employees with three to six months tenure are 19% more likely to churn. And that 18 to 24-year olds are 40% more likely to leave.

FRESIA JACKSON:

So by bringing these two things together, we're able to combine the probability from questions as well as the probability from demographics from our bigger data set and provide that to the company. So if Inner Tech has low scores on the career growth questions compared to the tech benchmark, and if they have a larger proportions of young employees, they would have a higher turnover forecast as an example. And then we can then tell them which groups are most at risk and dive into their data to let them know what are the likely causes for that and what ideas for action could they take. So this shows how we can combine what we learn from the data across all of our customers to help a single customer and give them insights that they won't have otherwise.

FRESIA JACKSON:

Another example is we've undertaken some research to guide customers around performance rating scales. Customers were constantly asking what's the best performance rating scale. And so we looked at what are the scales that our customers are using? How are those ratings distributed? There are a lot of people being considered high performers, lots of low performers. What does that distribution look like? And ultimately, how did that differ by the labels that they were using? And we found that the best scale to really discern top performers is a four-point scale. And so the first is underperformers, the second is solid performers, the third for good performers, and four being high performers. And people listening might wonder why are there three buckets for good and one for bad? But this is because we found it really combats a problem that organizations have, which is leniency bias, where they try to put everyone in the top bucket. So if you have several different options, you're able to differentiate those top performers a little more.

FRESIA JACKSON:

And we also found that the way you define those buckets is quite important. So including the word average meant that it became very positively skewed, because average means that the majority of employees should be there, but we found the opposite because no one wants to be told their average, no one wants to tell someone they are average. And so we found that actually it's much better to use a label that's based on expectations. So did they meet their expectations? Did they hit their goals? And through this research, we were able to update our recommended templates so that our customers have that automatically, but also share it in a blog for the broader community.

DAMON KLOTZ:

I love that. What you've touched on so far is, I guess, there is the very tactical things that the product helps a customer do, which is understand some of this data better and give them that context. But also at a much higher level, it's also looking at the actual process of the data collection, like is there things that our company should be changing that would actually have a dramatic impact on the entire... I think if I said to any head of people, "You need to change your performance management process," like nervous sweat runs down their back and they're like, "Okay, how much do I need to change? What do I need change?"

FRESIA JACKSON:

[inaudible 00:28:23].

DAMON KLOTZ:

Yeah. And then it's actually, if you change to a four-point scale, and if you change some of the language, this is actually the impact that it can have. And that is a gigantic shift in how a company operates when you remove the word average and you actually get something that is removing something like leniency bias. Now, that sounds like a gigantic thing to try to do. How do we remove leniency bias? It can be as simple as looking at the words that you're using. And I think that's what's really important here, is that companies don't just turn on a product like Culture Amp, and then go like, "All right, we're just going to turn it on and not change anything. Let's just keep going." It's always iterating based on what we are seeing and I love those examples that you've shared.

FRESIA JACKSON:

That's an interesting thing for me and my team, because I love that we allow so much customization, but then it also makes it difficult on the research side because we have to do a lot of standardizing of the data to make it useful for us. It's a double-edged sword, so I hope everyone enjoys it.

DAMON KLOTZ:

Culture Amp also has a mission of creating a bed of world of work. And obviously that in itself requires some data and some metrics around it in terms of how does Culture Amp have that impact? So how are you using, I guess, the research and the data set that we have to understand our impact?

FRESIA JACKSON:

Yeah, it's such a lofty goal. And I think a lot of companies have that mission and they aren't actually assessing if they're meeting that. So it's really exciting whenever we can do that. Recently we actually filtered down to customers who use both Culture Amp performance as well as engagement. And through that, we were able to look at after one round of performance reviews, how did their engagement survey responses change? And specifically we found that after just one cycle of performance reviews, the employee's perceptions of the fairness of that performance review process improved on average 5%. So that was already like, yay, that's incredible.

FRESIA JACKSON:

But what's more is that we found there's often a four to 7% gender gap on some key questions around workload recognition, career opportunities. Unfortunately with women scoring lower than men. And we found that after just one cycle, we were able to completely eradicate that gender gap. So it seems like using a structured performance review process, allowing a self-reflection as part of it, creating an opportunity for peers to provide feedback was able to make it a more equitable experience. And so this is exactly what we hoped we'd find, which is really nice to know that we're on the right path, but sometimes we don't find the impact we're expecting. And that's really an input for us to know that we need to make changes to the product.

DAMON KLOTZ:

Yeah. No, it's obviously such... To change things around, I guess, the experience that people of different genders are having in the workplace, especially around, like you said, some of the perception, questions around things like workload and whether they're being recognized and obviously massive things like career opportunities. To have some of those things eradicated after one cycle is really amazing and I think it shows... Also, when you think about 12-plus years ago, when Culture Amp was first created, it was just to replace this annual engagement survey that companies were doing on paper. To now see how far the industry has come and the data sets that we're getting and the fact that we can combine these things together, it really is incredible.

FRESIA JACKSON:

And that's a good point that we tried to look at nonbinary as well, but not that many companies are including that in their dataset. So we can do more inclusive research, the more inclusive our customers are. So please use inclusive demographics so that we can do even more expansive research, especially when we look at gender.

DAMON KLOTZ:

All right. Everyone, Fresia has told you, go have a look at your demographic data. She wants to be doing more research on this. So if we go back, I guess, for the visual learners listening at home, who they might be out there walking their dog or listening to this podcast while they're working... Hello, we're still here, and we want you to of see whether this metaphor, this story that we've been telling makes sense. So for the visual learners at home, you would've heard us discuss that Harper's data went into a bucket and then it enters this gigantic data lake that Culture Amp has. So we've got that bucket to lake water metaphor here. Now, we don't just stop at the millions of data points that we have access to. Like you said, 34 million plus buckets have existed in the Culture Amp ecosystem. I guess what can make it even more interesting is when we can overlay our data lake with a different data lake from outside of Culture Amp. So lake on lake, what does that look like? Can you explain that for everyone listening.

FRESIA JACKSON:

Double lake, yes. All of the research that I've shared so far is looking at only our own data, but we definitely recognize that we can come up with even more unique insights by combining our data with others, other lakes, if you will. For example, in the past we did a research project with Zendesk to really understand the relationship between employee engagement and customer satisfaction. And it wasn't a big surprise that we found that higher engaged companies had happier customers overall, as well as higher engaged employees specifically had the highest customer satisfaction scores. But what was even more interesting was the relationship we found between ticket volume and employee engagement. Employees with the greatest job demands were the least engaged. So that's something that helps companies know actually what to do. That could be one of the reasons.

FRESIA JACKSON:

And we're always looking for data sets that we can tap into for those unique insights. So if you're listening and you think there could be an interesting partnership with your company, please don't hesitate to reach out. Or if you're already doing academic research and it could be built upon with a real world data set with 10 million employees, please, we're always excited to partner.

DAMON KLOTZ:

I think for people who don't know what Zendesk is, Zendesk is a customer support, customer success software. And we're looking at things like support tickets and correlation to engagement and customer satisfaction. So you might be thinking, "Oh, I work at a company. I don't think our data would match." But no, we're looking at things like number of support tickets per employee matching that with our own data lake and the impact. And I think what you touched on there was the people with the greatest job demands were the least engaged. That's screaming to me like wellbeing, burnout. Like some of these things when you're just like, "Oh, we've got burnt out employees." It's not just about this idea of, "Oh, I'm so sorry that you are not well right now. Here's one day off." It's like no, structural things, job demand, role scope.

DAMON KLOTZ:

Has there been role creep that has come in? Has this person accidentally taken on two jobs over the past six months because someone else left during the pandemic and they've just taken on all their roles? There's all these other things that we can be thinking about when we overlay these data lakes together. So yeah, the invite is there for you. If you're working at an organization that has an interesting data... Maybe you didn't even know you had a data lake and now you've heard the term data lake so many times, you're like, "I think I've got a data lake." Reach out to us-

FRESIA JACKSON:

Now you want to go, "We're swimming together."

DAMON KLOTZ:

Exactly. We are here. We are here. Invite your lake and send it over to us and we can do some research together. I think that's the exciting part of where Culture Amp is going. Is the size and the scale of this organization being 900 plus employees now, from the times that we joined, when it was a very small company. We're now reaching out to some of the bigger institutions in the world and saying like, "Let's do some research together." So very, very exciting. I guess, to round this episode out, before I let you go, it would be remiss of me to not thank Harper, our fictional employee who has helped us understand the data bucket and the data lake metaphors, what types of research are we doing with benchmark data and how this helps organizations take action to create a better word of work. Is there anything I've missed in terms of what was critical information for anyone listening about the world of employee data and employee experience?

FRESIA JACKSON:

I don't think so. I mean, we really covered all the ways we're able to use data and not only learn from it, but bring those learnings back to companies so that they can improve the world of work for their employees, for all those parents out there.

DAMON KLOTZ:

Exactly, exactly. Going home and creating happier families. Who would've thought that a lead researcher at Culture Amp was having that impact, but these are the things that we're doing. So I also teased at the start that this is going to be the first in a multi-part series. I thought it was before we just dived in and just did a whole episode where we were just talking about all this research and you were like, "Where the hell did this come from?" It was important to lay the foundations of actually, how do we collect this? And I guess we wanted everyone to have that same level of context. So hopefully doing this little role play has helped. For those who were saying, "That's cool. I already knew all this stuff. What's coming? I want to see the next episode." Can you give us a teaser of what's to come?

FRESIA JACKSON:

Yeah, I really hope I haven't scared anyone off because we only scratched the surface today. So the next time I'm on, we'll do a really true deep dive into a research project we're working on currently. We focus on the foundation now, so that next time we can go into the nitty-gritty details, all the insights and get deep. So I'm excited and I hope everyone else is too.

DAMON KLOTZ:

Definitely. Yeah. And if you've got a burning question you want answered, let us know, send me a note. If you're listening, leave a review wherever you're listening and send me your question. Is there something that you want to us to focus on?

FRESIA JACKSON:

Send me your research questions.

DAMON KLOTZ:

Yeah.

FRESIA JACKSON:

I can add them to the roadmap.

DAMON KLOTZ:

Exactly. No promises of roadmap research were made today-

FRESIA JACKSON:

No, no.

DAMON KLOTZ:

... on this episode, but we will definitely like to hear from you. Fresia, a big thank you for coming on the Culture First Podcast today, this has been enlightening. I've certainly learned a lot and I'm excited to have you back on soon.

FRESIA JACKSON:

Thanks for having me. A long time listener, first time caller. So it's fun to be here.

DAMON KLOTZ:

Thank you for listening. Thank you for calling. This has been the Culture First Podcast. I've been your host, Damon Klotz. And until next time, thanks so much for listening.