Name: From Data to Decisions: How HR Leaders Use Analytics to Influence the C-Suite
Uploaded: 2026-05-07T18:30:06.535Z
Duration: 1 h 30 min
Description: From Data to Decisions: How HR Leaders Use Analytics to Influence the C-Suite

Transcript for "From Data to Decisions: How HR Leaders Use Analytics to Influence the C-Suite": Hi, everyone. Welcome to today's webinar on HR analytics. We'll get started in just a minute. But while folks are joining, please drop in the chat where you're calling in from. Oh, wow. We are coming in from all over. I could've, started with personally, I'm calling in from San Francisco, California. I see a few other folks from California. We have Texas, Seattle, beautiful Boulder, Colorado. Amazing. Orlando. We see a lot of New York. Awesome. Well, thanks, everyone. I think we'll go ahead and get started now. My name is Lizzie Yeager, and I'm a director of HR business partnering at Ripline. And in conversations with HR leaders, I find that we all know, we all very much agree that data matters, yet we don't always know how to turn that data into strategic business decisions. And so that's what we wanna talk about here today. Before we dive in deep into the content, a few housekeeping items. First, you can use the chat feature to connect with other attendees, post your questions, comments. You all already have done so in sharing where you're calling in from. And then second, you'll see there's a q and a function on the right hand side of your screen for questions as they come up. We are going to save time at the end to go through them, so please send in your questions there. This webinar is being recorded, and I will make sure that we share it with you afterwards. Yes. Yes. Absolutely. We'll share the slides afterwards. And then finally, if you're a SHRM professional, you will receive the SHRM code in your follow-up email to receive your one PDC of credit. So a few quick intros. Again, my name is Lizzie Yeager. I have been at Rippling for four and a half years now, which still feels, quite mind boggling to say. When I first joined Rippling, I was the only HR, VP, and I think we were about five, six hundred employees at the time. We are now over 6,000 employees globally, so I've had such an incredible opportunity, to really help build and shape our HRBP team, which I'm thrilled to share with you all. I'm no longer the sole HRBP. And so what this has meant is that if I have had to figure out how to use data effectively across a really complex workforce, so that includes different countries, different employment types, different payroll setups, and as we're all very familiar with, many, many competing business priorities. And joining me is Sharon Ray. Sharon's the chief human resources officer at health health snap, a virtual care management platform with over 450 employees. Sharon brings twenty plus years of expertise in human capital management, where she's built teams from the ground up. I'm super excited. She's here with us today, and we'll bring her up on stage in just a little bit. So here's what I want you to be able to take away from today. First is how to identify the HR metrics that actually matter for your organization, not just the ones that are easy to pull. And then second, how to build a practice around actually using that data to drive decisions. In my role at Rippling, I connect with HR leaders both internally and also with other companies. And in these conversations, one thing that comes up over and over is that everyone wants to be data driven. Everyone knows that it matters, but making it a habit is really the harder part here. And so when I think about why that's the case, I've really been able to see three pretty consistent challenges. The first is that most HR teams have disconnected data. You might have your HRIS in one system, your payroll in another system, performance data probably in a separate system. And so when someone from the business comes to you with a question and it touches all three of those systems, maybe even more, you have to spend a ton of hours pulling those numbers, joining that data just to get a clear line of sight. And then this gets even messier for our global teams when you have contractors, maybe different payroll providers, EOR relationships, etcetera. And then the second challenge, I find that HR leaders struggle to translate data into business impact. So even if you've passed that first hurdle and you've joined all of your disconnected data, it can feel really hard to connect it to what your hears about. We know that they're thinking about business impact with metrics like revenue, cost, productivity, and they're asking you questions on how you can help move the needle against those metrics. And then finally, it's not always easy to turn insight into action. You might have really great data that you've joined. You might have a strong insight. But if the data just sits there in a dashboard or in a report, it's really not driving the business forward, and you need to think about the story that you're going to tell. So today, I plan to talk, about how to overcome these challenges by sharing key metrics to use, a framework that I use for turning data into action, and then I'll share a a real world example from my day to day here at Ripline. But before we get into that, I I would love to pause, and pose a question to everyone here. What analytic requests are landing on your desk right now? What questions or reports are coming up most often? Drop those in the the chat. Headcount. Yep. Employee NPS, attrition time to fill. Yeah. Wow. In the, in the chat, it looks like we're all seeing relatively similar things, Recruiting data, turnover, comp analysis, turnover, turnover. Yep. Great. Okay. Thanks, everyone, for sharing. It's really clear from from the chat that there's no shortage of questions that you get from your team or metrics that you're tracking or trying to track today. And that is certainly true about our team here at Ripley. But I find that focus is really what's most critical. You need to look at fewer metrics but tracked consistently. So I wanna share what I find to be the most useful HR analytics, and and how they fall into these five categories. So the first category being talent acquisition, which many of you mentioned, in the chat as well. Where are candidates coming from? How quickly are you hiring? Then performance. Who is thriving? Who is struggling? Where where is performance concentrated across your organization? Retention. Likely the the, piece I saw the most in the chat. Are you keeping your best people? What is losing them cost? Org health, I saw this in the chat as well. What does your employee NPS look like? How engaged is your team? How do people actually feel about working at your company? And then leadership continuity. What does mobility look like? What does leadership development? What does representation across your org look like on the leadership team? Right. But within those five buckets, I mean, we could spend hours, listing the metrics we could we could track. There's a whole host of metrics that fall into those five. So which metrics you prioritize, I think, really depend on the size and stage of your business. When I think back ages ago, that four and a half years ago when I started at Rippling, my focus looked different than it looks today, and that's because we were we were a different company. We were at a different stage at the time. I think that earlier stage companies tend to focus on speed and stability against that speed. So these are examples of of metrics that you would, track in those five buckets. So the first being time to hire, What does recruiting efficiency look like, and where are the bottlenecks? Then median tenure. Are people growing with the company? New hire retention. During Ripley's earlier stages, I hyper focused on looking at data from the ninety day mark and the six month mark, because these early exits we know are expensive, and they can represent opportunities to improve onboarding or depending on the stage you're at, maybe a lack of structured onboarding. Employee engagement score, this is definitely a leading indicator of morale and burnout risk, especially during those phases of rapid growth. And then finally, headcount growth versus plan. Are you hiring on pace with what your business actually needs? And then as you scale, the questions will naturally shift, and I think about them shifting towards sustainability. So your key metrics likely will also shift. You might start looking at key metrics like turnover rate, voluntary and involuntary. Are there patterns by department, by manager, or geography that point to structural issues? Internal mobility, promotion rate, are people growing, or are they plateauing? Are they leaving your organization? And what does representation look like by level and function? Who's getting promoted, and where are those gaps? Manager effectiveness. We all know managers have a bigger impact on retention than almost anything else. So I look closely here, usually deriving this from engagement surveys or three sixty feedback. And then training and development participation. This becomes more important as you hire globally, and compliance requirements will grow. But the principle at any company stage is really the same, that every metric should map back to a real business problem. So knowing which metrics to track is step one. And next, I wanna walk through, okay. Well, how do we put that data into action? So I'm gonna walk through an actual example from my my day to day at Ripline. But first, I wanna share a framework that I use whenever I get a data question, whether it's ad hoc, something super urgent, someone has Slack to me, or a longer term, maybe more strategic initiative. And I think about unlocking data insights in just four steps. So the first step is, what is the problem? Making sure I clearly have the problem that needs to be solved defined is step one. I think about this too as, okay, what is the question behind the question that someone is asking me? Then step two is what data do I need to solve this problem? Being super clear about this before you start can save a lot of time. It can oftentimes prevent you from pulling data that doesn't really answer that question or really solve that problem. And then the third step is an iteration. So you'll run a query or you'll pull a report. You'll see what comes back, and oftentimes, that data will lead you somewhere unexpected. Now with AI, I really think about this as a back and forth. It's become so much easier to do and almost, a natural conversation. And I find that the best insights have oftentimes come from this iteration, this follow-up, not just the very first first question. And then, likely, most important here, is the last step is so what? Like, what does this actually mean for the business? It's not just about returning a report. It's not just about returning a table showing the data, but it's about what decision it enables or what action it leads to from here. So now I want to bring you, through a little bit of my, excuse me, my day to day with an example. How often does an exec come to you with a feeling or an observation, like alarm bells are going off? This is an example of a question I have received, from our exec team before. It seems like a lot of people are leaving. Who are our highest flight risk employees right now? Okay. So I pause, and using the framework I shared, here's how I first think about it. So the first step is to identify the problem. James is asking why people are leaving. Right? So he's asking about attrition, but he he he's telling me it seems like he's observing that people are leaving. But I wanna understand, has there actually been an uptick in attrition? What is the data showing? Do we really have an attrition problem, or did James just happen to be in the office and he saw someone drop off their laptop? And so now alarm bells are going off to James. So the first thing I would do is actually validate, by checking with Ripley AI. Is attrition a problem? So I'm gonna share here, a prompt. We'll zoom in a little bit, a prompt that I shared with Ripley AI. What does attrition look like for the past twelve months? I do wanna caveat. This is demo data, not sharing any any real data here. But over the past twelve months, we can see that we've had five separations, two voluntary, three involuntary. And you'll even see Ripley AI has called out for me. This is a notably low attrition rate reflecting strong employee retention across the organization. But I don't need to just take that line and move on. I can actually see the breakdown. I can see at which month who left. Was it voluntary? Was it involuntary? And pull that, okay, the, the highest driver here were involuntary separations in engineering. Interesting. I will I I will share with James that I am not overly concerned by a change in attrition. The data isn't showing that we have a a change or or a obvious attrition problem. But before I just return with that insight, I also think his second point was really valid. Whether or not we have a high attrition, rate, James still wants to understand who is at risk. And so, to look into that, I will also go to Rippling AI, and I wanna understand and find which employees are at risk. So instead of pulling a report and spending hours parsing through data, I'm actually gonna ask Ripley and AI who are our highest flight risk employees based on tenure, performance reviews, and comp ratio. And so, again, I don't have to sit here and parse through tons of data. I actually get, returned to me a prioritized list of employees. And the, Ripley has broken it down for me based on high, medium, low, and then called out the top 15 highest flight risk employees, and really calling out that, these employees are high performers that are on the lower end of our compensation framework. Their comp ratios are lower, compared to their peers even though their performance is higher. This is incredibly actionable for me and James, especially with the culture of high performing team that we are, building and have built here at Rippling. And then Rippling AI will even give me actionable recommendations here. So we can have a, immediate compensation review focusing on the high risk employees that have a high tenure, high performance, but are paid low, compared to their peers. It also has called out for me, where to focus. So which departments are the most at risk. So starting here with customer support and then calling out some some quick wins. And then what I also really appreciate, about what Ripley and AI has returned to me is it's telling me the exact assumptions it made. I actually didn't even think to exclude this when I first wrote the prompt, but this included only active noncotton tractor employees. So it's focusing specifically on the FTEs, here at Ripley. So now what I can go back to James and I can say is, hey. I'm not overly concerned about a change in attrition, but let's talk about the framework that I used that I built with Rippling AI, to categorize who may be most at risk at Ripley. And together, we can come up with a plan on what we wanna do. Does it look like a compensation review? Does it look like taking immediate action in the departments that have the highest concentration of these at risk employees? But we can now move towards an actual conversation together. And that's really just one example, of how how I've turned data into a decision or how I've turned data into an actual conversation. But for any of this to be possible, I've really found that you need three things to be in place. So the very first piece is you need integration. You need systems that actually talk to each other. So I was able to pull that because I had compensation, payroll, performance, HRIS data all in one place. And if you're managing a global workforce, your global payroll, EOR provider, contract management should also be in that same system. Otherwise, you're missing a huge chunk of your workforce. So for us, of course, this all lives within Ripleyn. It's our system of record for all of the above, and that means my team can spend way less time wrangling, reconciling, and joining data. And instead, we spend our time interpreting the data and then getting to strategically partner with the business on actioning. The second piece here is you really need to be able to self serve. You need to have the right permissions in place and use systems that are actually easy to use. Your leaders, managers, HRBPs should be able to pull insights and reports without waiting days to hear back from from an analyst, from from a support agent, from someone, that would be getting this data for you. And then finally, of course, the third piece is AI. This is an incredibly helpful tool in making sense of your data and doing that, synthesis at scale. And having data or having AI sit on top of your people data like it does in Rippling with all of our existing permissions and approvals in place has been a game changer for me and my team. You you could see it it makes us, it it enables us to build reports quickly and take action, and it enables us to do so in a very natural conversational way where I'm asking Ripley and AI to help me, solve the problem that an exec has brought forward. It's a super cool tool. And if you wanna see it up close, you can grab time with our team after today's session. Okay. Now I recognize that not all of you have those three components in place. So I wanna talk through a practical way to assess where your organization is at today. We call this the HR analytics excuse me. We call this the HR analytics maturity curve, but I think of it really as just a diagnostic. There are three stages, facts, insights, and business decisions. And some of you, may sit in stage one, which is facts. This is where the data exists, but it's really siloed. It's hard to get to. And so I think about this stage as generating anything useful requires a significant amount of manual work. You may actually even have an analyst or someone dedicated to doing this manual work where you're exporting, you're building spreadsheets, hours, spending hours cleaning up, and you're reporting what happened usually after the fact. I think about being in this fact stage. You'll know you're there when it when you know when it takes you days to get to an answer. And oftentimes, because of that delay, it can mean that the answer takes, that you have the answer, unfortunately, after a decision has already been made. The second stage is insights. This is when systems are really starting to connect. You have better access, and you're starting to tell stories with that data rather than just share numbers. So you can start actually answering the why questions, and not just the what questions. And then finally, stage three is business decisions. You have your data integrated across your core systems. The people who need answers can get them without waiting on a manual pull, and you can synthesize across your full workforce quickly and ask the kinds of questions I just walked through. At this stage, your HR team isn't just a source of information, but your HR team is really contributing to making strategic decisions that move the business. I think that most teams I talk to are somewhere between stage one and two, but moving forward doesn't always mean buying a new system. Sometimes it's getting two existing systems to talk to each other, or sometimes it's simply changing how your team thinks about requests from what report do I need to build to thinking more deeply, like, what question am I actually trying to answer? Knowing where you are on this curve, I think, is a really useful tool before you walk into a conversation about systems or budgets, and I think it can tell you where you actually need to invest in next. So how can you start building an HR analytics practice today? Three three tactical steps here. One, whiteboard the next step to integrate your systems. And you don't have to solve everything at once, but pick the one disconnection that causes the most pain to you and your team and map out what it would take to fix it. Oftentimes, this answer is already within close reach. Then the second, piece here is get really clear on your top five metrics. Not 20 metrics, but really five. The ones that connect the questions your CEO or your CFO are already asking, where if you had a faster, more confident answer, you it would really change how you and and your team show up in those conversations. You could start with the five metrics I shared earlier, for both scaling companies or larger companies. And then the third piece here is pick one business challenge to solve. I recommend using the framework, I shared earlier. Start by identifying a problem, find the right data, iterate, and then state the so what with your exec team. Now I'm very excited, to bring Sharon Ray up on stage. She is an amazing leader who has transformed the HR function at HealthSnap through leveraging the power of data. Hello? Hi, Sharon. I'm so excited to have you chat on this stage, here with me. Thank you so much for joining us. Thank you for having me. Sharon, to get started, can you just quickly tell us about your role in HealthSnap? Yes. So my role is, chief HR officer. I do have a talent acquisition team, an HR business partner team, and a leadership and development team as well. We are about, I know we said four fifty earlier, but we're about 550 people now, which is. great because we grow really, really fast. And we are in, I would call, the health care field. We. are a remote patient monitoring company, which is so cool. Amazing. And congratulations on that growth. That's a since the last time we chatted, it'll amazing. So, Sharon, earlier, I walked through how I think about five key business metrics for HR. Curious for. you to share what are the core metrics you and your team care most about right now? Perfect. Great question. Hiring. So since we are in health care, we hire every single week. And, we we pair our hiring with our business coming in. So it's a crazy process that we go through, but it's really cool. It makes sure that we're not spending money that we don't need to spend, before the business actually comes in. So we're always working to be on track. So hiring's one, and then termination, is the next one. You know, we actually want people to term early because it's better for us if they're not gonna be here for the long tall the long haul. The reason for that is we really have to train people up. Right? Mhmm. And it's a new most people have not heard of remote patient monitoring. And let me just add this. We're a 100% remote. So that works for some people and doesn't work for some people. So, terminations, we track new hire retention, headcount growth, and employee satisfaction. Okay. Great. Sounds like we, have a lot a lot of overlap there in what's. most important on, my side here at Ripleyn. Have you introduced using AI to your HR team? Yes. I have, and we actually love it. We're so excited. And, what I would say is to your point earlier, Lizzie, everything has to be connected. And I would say we're a proud user of Rippling, and it's our everything payroll. Every system is in Rippling because we wanted to make sure we had that connection and the ability to pull data quickly. I'm so glad to hear it. You may have kind of organically answered this, but, what what has made it easy for your team to use AI for reporting? I for for me specifically, I can say I'd love to say that I'm the best data person ever, but we all have our weaknesses. So I'm not the best data person. But what's been great for me is I will be in a senior leadership meeting, and they. ask a very specific data point that has nothing to do with what I'm tracking, and I'm able to type it into AI, get the answer quickly, and answer it in that moment. Amazing. While you're in the meeting. You don't have to follow-up. Correct. Correct. Very powerful. It's wonderful, and they were shocked, by the way. They were shocked. I love that. My exec team less so because we they've been using Ripleyn AI, but but love to hear it. And has your your HR team been using Ripleyn AI as well? Yes. The the whole team has been using it. I've, sent it out to a couple other people who have not outside of HR who haven't delved into it yet, but we use it all the time every day. Great. Sharon, have you heard any noise from the HR team or or maybe more broadly, noise like, will AI take my job? I have not heard that noise. And specifically because when I came to HealthSnap, I said from the beginning before we ever had Rippling that I wanted to scale by technology and not necessarily by people. And if you think about it, we have back in the day, because I've been in been in HR for a long time, there were multiple people working to get information. And I think it's best if we can all do it and use the tools that we have. So we started out with that mantra. We're gonna scale with technology and not necessarily with people. So it's become, is it fair to say it's very much a celebrated and not a not a shocking change to your team? That is correct. And I think the other thing I would say, if I can, is. that everybody on my team has a different skill set. Right? And so it's not that we're going to hire somebody for one specific thing. We kind of collaborate together and draw on everybody's strengths Mhmm. so that, you know, even if it's not their job, they can do something that's not because they like doing it, if that makes any sense. Yeah. Yeah. Totally. I think about it as, the way that I speak to my team, I think, sounds similar where it's about figuring out how to use AI to leverage your strengths Yes. more so than anything. I think AI can be really overwhelming. Everything is changing so rapidly around us. How do you recommend getting started? I would say just get started. Start with a question, any question. And, and I'm probably one of those people that I just go in and say, what is this? Check check but verify. Right? That's what. I say. Start there. Make sure it's getting you what you want. But if it's not, you know, just ask the question over and over and over again and add little different caveats that you want. to. And that's how I've used, Rippling AI, but I also use AI outside of Rippling personally, to get information that I need. It is truly a really good tool. I can just remember being back in the day, I'll call it, you know, where you have to go to five different people to get the answer that you wanted. Right. And now you can have it definitely more quickly. Yeah. Absolutely. Like, back to having that that connected data, I think. And then the AI layered on top of that, it feels like we're unstoppable now. Yes. Can you share some tactical advice, Sharon, for the HR leaders here about how to go from pulling data, to making strategic people decisions? Love that. And, Lizzie, I actually am gonna draw on what you said earlier. You have to identify the problem first. You can't just go in and say, I need all this data. What do you need the data for? And it has to be to solve a problem. So I always start from back. What's the problem I'm trying to solve, and what's the data that I need to do that? And that will also help you with Rippling AI as well because. you're just going out kind of blind. You're saying, okay. This is the problem. Let me get this information and see where the story takes you from there. Yeah. I love it. And AI is just changing so rapidly. It's changing the role. I I think of HR. You know, I shared I like to think of it as just providing myself and my team with leverage. But I'm curious since you've been in the HR space for now more than fifteen years, How are you thinking about, being an HR leader in the age of a of in the age of AI? I think it's cool. It makes to me, it makes my job so much easier, because I'm not relying on other people. And I think with this, you get to focus on other things that are equally as important as the data. Once you get the data, you solve the problem, then you have to go implement something. Right? Yeah. So the time save is, like, amazing. The time to problem solve and then get to what you need to do is really the most important part of it. And then how do you just operationalize it going forward? I love it. Mhmm. Totally agree. And I think getting the data is one thing. I found I now can get the data so much faster with a lot more ease. But acting on it, I still find is our role. So how do you move from just reporting on key metrics, hopefully, with more ease to making those strategic business decisions at your organization. Yeah. I would say we are a really tight knit, leadership team. So we are talking about the issues daily, weekly, monthly, and. so all connected in terms of what the priority needs to be at this point in time. So if I think about just hiring, we talk about hiring literally every single day with the senior leaders on the call to ensure that we're hiring, at the right time, the right people so that we can make sure that our business runs efficiently. So communication is the key to everything. Yeah. So it sounds like the key metrics that you're tracking, is it, fair to say that they're widely adopted? Meaning, you have. buy in, the exec team wants to be tracking the same metrics. Yes. In some respects, I feel like we track a lot. We do. We track a lot across every business or every business department. Mhmm. So we always have it if we need it. And depending on the month, you know, we may focus on one more than we focus on some others. It just depends on how the business is moving. Yep. Makes total sense. I think about the same thing over, the multiple years I've been at Ripline. There's definitely I think both seasonality and then you you adapt based on how your company is growing. Yes. Agreed. Sharon, how has the use of AI changed your talent strategy, if at all? So it has. Again, we hire early and often. And so what it does for us specifically is it gets all of our qualified candidates in one place. And, so that's amazing for us. I have three people on the team, and they are interviewing literally every single day and almost every single minute of the day. So when that data can be pulled to have all the candidates come to the top that we've that matched the job description, Mhmm. then that gets us into the queue a lot faster. And I don't think we could hire as much as we do if we didn't have that in place. So it's making the it's I I guess, like, time to hire And to hire I'm hearing is expedited. and Yes. the right more of the right candidates as well. Love that. Hiring the right people faster. Yes. Great. Well, thank you so much, Sharon. I, I think we now wanna open it up and, hear from hear from the wonderful folks joining us, what questions they have, for either Sharon or me. We can tackle them together. But if you wanna drop your questions in the q and a tab, we Sharon and I will take a look here. Okay. Sharon, this may, we'll see if you have an example on the spot or let me know if you need a minute, but would love, to see a prompt you're actually using in Rippling AI or AI in general, if you have an example. I do. So the other day, what was I looking for? I was looking for a report of people who have left the company in the last three years because I wanted to compare how many people we've hired in the last three years and. how many people left the company in the last three years. Mhmm. And it took me a minute to get there. Again, AI is so easy with with Rippling. You can just put in what you wanna put in, and it brings up the data. And then in addition to that, it'll give you the assumptions. And then I can go back what I go back and do okay. No. Not that. This part is right. This part, I just need a little more detail. Yep. So that works out really nicely. Yeah. I think that is really goes back to my point of that iteration. And I and I love that you can actually have a it feels just like you're speaking with a human, the back and forth to get ultimately to what what you're looking for. I I I could share an example too that I recently was looking for, which was I had a very urgent request about making an offer, that a recruiter reached out to me for, and they wanted to make an offer above our compensation band. And I rather than simply responding yes or no, I wasn't looking to just approve or deny the request. I actually, sent a prompt that asked for everyone in that, level and that specific role of Rippling AI. I asked for their compa ratios. And the reason I did that was I wanted to understand if we deviated from our process, what would that look like as it relates to internal parity at Rippling. And so I was able to return to the hiring manager and the recruiter not just with a yes or no, but explaining, what impact this decision would have on the team and how, what internal parity would look like if they if they deviated from our typical compensation philosophy. Okay. Another one. Sharon, for you again, what is your advice for aligning your leadership to the Rippling system, and how do you help your leadership team gain trust in the data that you're pulling? Wonderful. So I am gonna put a plug here for my team. I have an amazing data driven HR team. So when they came on board, people were actually going to them to pull the data out of Rippling. And so I think we got a lot of alignment from the beginning. What we have done is changed it a little bit along the way. As long as people are wanting or leaders are wanting more information, we align it for them. So we pull data for a number of different departments. But what's even more cool about that is that once in Rippling AI, and I'm not sure if we I'm supposed to go this far, but I'm gonna go this far. One of the things that Rippling AI does for us, not only does it pull the data, you can then have it go into your reporting module is what I would say. So you don't have to re pull it again. Once you get it where you want it, it's like, do you wanna save it? And you're like, yes. So you never have to rebuild that. Yeah. Right. And that's what's really cool for us so that we know we're getting the same information over and over and over again with different with different data points, if that makes any sense. Totally. I think it also I think what you just called out too is really important that you brought up earlier the the ask but verify. Because. you're asking Ripley and AI, but then you are able to see the the raw data Yes. that Ripley and AI used to provide you with that answer. Yes. Right? Which they got there. It's the whole process of how they got there. And I think that's the verify part. It's it's amazing. It's super helpful. right. Great. And not having to repeat yourself. We we just love that. Time more time saved. Okay. Another question. You sort of answered this already, so we can kinda both take this one, Sharon. But the question is, were your businesses always data driven from the beginning? If not, how did you build this muscle? We Oh, please. Go for it. no. You go, please. Look. I will say when I first joined, was the business always data driven? I you know, four and a half years ago, when I joined, I it was definitely I was in a much more reactive role, and really working with the leadership team to build out, first a more strategic HR function and then really build all aspects of rippling. And so I think as we, have grown over the past multiple years, we've shifted from reactive to strategic. And I think how we built that muscle was very much, building while the plane was flying. So we couldn't stop reacting. And, of course, at the the rapid rate we have been growing, there's always been a reactive element. But in parallel, started to build the mechanisms, across the board to become more strategic. So that looked like having the exec team and then strategic partners across finance, compensation, the people team get ahead of the cyclical mechanisms to better plan, forecast, and over, multiple quarters and iterations, we've, I think, shifted into a a healthy balance of continuing to react, whether that's, react to the the macro environment we're in, react to whatever is changing, and then also to have a healthy balance of forward strategic thinking in terms of being data driven. But we certainly use data for both both pieces, the reactionary and then the strategic forward looking. How about you, Sharon? I'm gonna say ditto, Lizzie. I actually feel like HealthSnap grew up with Rippling. So when I started with Rippling with, HealthSnap, we did not have Rippling. And. I think we've grown with Rippling over time, and that's been incredible to see. Yeah. That's what I would say. So ditto to everything you just said. When you have the information, then you use it, and then you push it to what other data can I get to help move the business forward? I love that. Okay. Next question. Which tools are you enabling your team to learn how to use AI smartly and safely for data and analytics? I would say for us, we I don't really have any other tools other than Rippling AI that we have in the company that Okay. or for the HR team. Right. But what we do is we work together. So anything that's new for us, we learn together. We're such a small team. And then. if we're you know, just the HR team, there's just three of us in the HR team. And so we're probably the people that use the data the most. Yeah. So if somebody learned something, they're teaching the other. We do meet all of the time. And so it's, it's very nice to see that happen. In addition to that, leadership will ask us for something specifically, and that's a really nice way to go in and use the information a little bit differently. I I love that. I think small team or large team, what you said is important about learning together. I think right now what I've observed on my team is there can be this fear of missing out or this fear of I'm already too far behind. And so what I've started to do is something similar where I bring the team together to either share, hey. What have what have you learned? What have you been using? What time have you saved using AI? And just making it a very known and safe space that everything is changing rapidly, and it may have changed yesterday. So you're not behind. Like, we're we're all on this together and making sure that people don't get bogged down by that overwhelming feeling of not knowing, and then they stay quiet and they don't ask the questions. So I think the learning together is really, really important and something that, we certainly do on my team here at Rippling and I think Rippling as a whole. And then the other the other piece to your question, or to the question about smartly and safely, what's interesting is, Sharon, you probably have all the the admin privileges, but if someone were to engage with Rippling AI and ask a question they shouldn't have the answer to, of course, luckily, Rippling AI will will not allow them to access that information. So if someone, you know, has Rippling AI and they are they ask, how much is Sharon paid? Rippling. will not return that, unless unless they should have access to that information, which helps just some of the out of the box, smart and safe uses of Rippling AI. And, Lindsay, I think you said something, like, so important, which is the permissions. The permissions have to be set up. So if you haven't set that up, that is a key to setting up who has access to what in Rippling. Yeah. Yeah. I I think, kinda similar to another question I'm seeing, which is there's a few things around how like, what is needed to use Rippling AI effectively. And I think about two big buckets, permissions, which you just called out, Sharon, and then also just understanding the data, infrastructure that you have set in place with Rippling. So understanding, like, what does your employee graph look like in terms of departments, job, families will only enable you to to use Rippling AI, I think, faster and and more effectively is understanding that data model. And if you don't, you can have conversations with Ripley and I to eventually unpack what that looks like. Yes. Okay. Another question. Being new to Ripley and my current organization after starting about four weeks ago. Oh, congratulations. Welcome to your your new role. What advice do you have in getting a better understanding of, oh, reporting an AI in Rippling? So, Sharon, maybe you could talk to us about what did it look like when you first use Rippling AI? How did you learn? To me, it was really easy. We just went in and started asking a bunch of questions. And if it didn't give me what I wanted, we just ask it differently. And we were on the front end. So we we were right when it came out, actually, before it came out, and we tested it. a bit and gave feedback. But I did not find it difficult to use at all. So we just kinda went in and did it. But I do think from a reporting standpoint, it's we I'll go back to what reporting do you want and why. Right? Are you using the data that you are requesting? And then the setup piece is you've gotta have to your point, Lizzie, earlier, the data has to be set up the way you want it to be set up. So even if you're new and you don't know what the setup is because everybody does it differently, you need to find that out as well. Yeah. I totally agree. I think if I, and this is very genuinely how I first use Rippling AI was I had a question or a problem to solve. I didn't I didn't approach Rippling AI or using Rippling reporting as, okay. I wanna make sure I understand the tool end to end. I just dove in with, okay. I have to get this answer, for my CRO. Ripley and I, like, we're in it together. And then I, you know, worked through asking the questions, and I think just jumping in with a business problem to solve is how I became familiar with the platform. Sharon, another one for you here. How do you get your team to transition to using Ripline? My current org hesitates because it impacts both HR and finance. It does. But if you have permission set up, you know, those people can only get an an amount of information. The permissions really guide all of this. Mhmm. So when when we got it, we knew what we wanted to do, with Rippling. So when we started with Rippling, we started with a basic package, and then we evolved to, getting devices from them. That was actually one of the main reasons they said, okay, the devices. But. then after the devices, we permissions was, like, next for us. Like, I had set it up in the beginning, and then my tech guy was like, we're gonna put some more lockdown on this, and, which was great. So our permissions are set up, which does not give anybody information in HR or finance that they cannot get. Yeah. Yeah. I think a fun way if you're, seeking to build that trust or if or if someone is hesitating is first, like Sharon said, have your permission set up. And then second, have someone test them because you can see that, the data won't be shared with anyone it it shouldn't be. And I think that will go a long way in terms of addressing some of that hesitation across the org. Okay. Well, everyone, I think we are we're getting close to time here. I wanna thank you all so much for joining. A special thank you, Sharon, for taking time out of your day to be with us. And our team is going to share a quick survey now. Would really appreciate if you filled it out. The goal of the survey is is really just to hear from you. We wanna make sure that these sessions, only continue to get better, in the future. We review each and every response, and that's really to make sure we're bringing in the right speakers and that we're speaking to what is top of mind for everyone here today. So thank you all so much.