Name: Stop guessing your forecast: Build a scalable revenue data framework in 45 minutes
Uploaded: 2026-03-26T16:30:08.099Z
Duration: 1 h 8 s
Description: Stop guessing your forecast: Build a scalable revenue data framework in 45 minutes

Transcript for "Stop guessing your forecast: Build a scalable revenue data framework in 45 minutes": We all know messy data creates problems, but what's harder is figuring out how to fix it in a way that actually sticks. That's what today is all about. This session is focused on the how, the frameworks, governance approaches, and cleanup routines that high performing revenue teams are using to keep their data clean and their forecast reliable. I'm excited to have two seasoned experts join us today. I'll let them introduce themselves. But just as a bit of a background, Chris Douglas is a senior manager for sales at monday.com, where he helps teams maximize modern CRM platforms, scale GTM operations, and optimize sales execution. And Brendan Holland, who works as a business development manager within Procedio's clouds clouds marketplace practice, where he helps customers and internal revenue teams turn complex marketplace data into trusted, actionable insights. Today's webinars will be interactive, so feel free to respond to the poll questions and the as they go live in the chat. And q and a is also available throughout. So if you have a specific question, please submit it so our experts can answer it in real time. And now over to you, Chris. Amazing. Thanks so much, Martina. Brandon, how are doing? Doing good, Chris. How are you today? Doing really well. Really excited today and and really excited to have this conversation because when we think about kind of the state of sales or state of business today and where we're moving with things like AI and others, like, data is become so important. And making sure it's clean data is even more important as we move into this new world of business. In fact, you know, I was thinking about prior to coming today and and looking at some of the kind of industry impacts of just bad data and what's happening. And and I saw an actually interesting article that Gartner reported in 2026 report that the average cost of poor data quality is costing organizations around $15,000,000 per year. And in fact, companies lose anywhere between 10 to 12% of revenue due to bad data. And when we think about that that scale and impact, it becomes massive. And then as we move into a new era where data is gonna be helping us not just work, but make better decisions, it's really important to make sure we get it right. And so I'm excited to have our our conversation today. So, everyone, welcome again. My name is Chris Douglas. I'm the director of sales here at Monday, leading our CRM initiatives here in North America, and really excited to talk about about this topic. Before we get into it, Brandon, I'd to turn over to you to kinda add your own personal introduction of the group, who you are, and and what kind of skills we're gonna bring today. Yeah. Great. Thank you, Chris. Great great introduction. So Brandon Holland. I am a business development manager for Presidius Cloud Marketplace. Really, my role within the organization is to ensure that marketplace transactions are executed efficiently for our clients and also to work internally with our rev ops folks to make sure that the reporting is accurate and it's actually actionable, you know, to scale the business and, obviously, keep delivering additional value to our clients. Amazing. Awesome. So we're excited to jump into several different topics today. We're gonna talk about, you know, how do we go about cleaning our data? Why is it important? We're gonna talk about relationships. How do we work with marketing and RevOps partners? And we're gonna also talk about and shift and pivot in terms of AI and how we think about this and how we also leverage that to either leverage data or clean data. And really wanna make this interactive today, team, because you're we're gonna help us you're gonna help us also drive the conversation. But to get things started, we're gonna start with our first poll question. So Martina is gonna open the poll here for us. We'll see here in a second. We'd love to get it from you all. In your perspective, what takes more time up in your week than it should? Right? Is it updating your CRM? Is it fixing reports and dashboards, chasing missing information, deduplicating records? We'd love to kinda see from you all, like, where are you spending your time when it comes to data and putting data in correctly? We'll give a few seconds in terms of here. Brandon, for you, anything that you see or where do you spend your time in terms of more time than you should with inside your systems? Yeah. So that's a great question. One of the things that, in my role, that I tend to focus on with our rev ops team is chasing missing deal info. You know, occasionally, we will see sort of our CRM have records that, you know, really doesn't match what we know to be true. And so that question becomes, like, what is the true, source of the truth? And, you know, really chasing missing deal info can, you know, take up time unnecessarily where we could be focusing on selling to more clients and delivering value. Oh, absolutely. I I think there's an interesting thing I've seen is that CRM data decays around 20 to 30% throughout a year. Right? So you think about reps not adding things in or we're not enriching it or keeping it up to date, and you think about what that compounding effect is over the course of the of the year, right, or many years in that case from that standpoint. So I'm with you too. I think serum updates, missing information, and those things, and also too having know, disconnected systems, and that's something that we we're we're we're very much focused on here. So. we're getting our second or two here before we close the poll. Maybe I'll give, like, a five second countdown. Three, two, one, and we can maybe show those reports there, Martina. Let's look across here. We've got still things coming in. Looks like fixing reports and dashboards takes the number one. Closely right behind that is CRM updates from that piece and then chasing missing information. Brandon, you had mentioned that. Then deduplicating records. That's a good thing. Right? That's the least number of votes that we've seen from that standpoint. But dashboards and reporting. I'm curious, Brandon, from your experience, like, where does that fall fall into your purview in terms of utilizing that information? Yeah. So I would say that, you know, this poll I I think it's pretty standard for, you know, organizations. In in my role in particular, I'm usually in the field, more intimate with the actual deals. Whereas, like, our my rev ops team, I could see that fixing reports and dashboards could be, you know, a very common pain point, you know, where it's taking a lot more time than they need to. Yeah. For sure. And I think that leads us to our first topic in conversation here, which is clean reports. Right? And I and I and, you know, you're working with customers that have copious amounts of data. I'm looking at that all the time in terms of organizations working with them that make sense of their data and how do we utilize systems to help them be more productive. But when we think about this, clean reports are are certainly important, but they're obviously not the main goal. And I'm curious, like, how have you and the organization approached data hygiene in a way that actually drives growth? Yeah. So that's a great question. Here at Presidio, one of the things that we, sort of one of our core values is accountability. And so when you have accountability as your core value, your your teams and your sales teams, they know that they have to provide, you know, the up to date CRM information so that leadership can, one, apply the resources effectively across teams. So if they know that they have, you know, a certain customer that has a lot of, requirements and a lot of that information is within the CRM, now leadership can look at the data and actually decide where to allocate resources. Maybe some customers needed, you know, a little bit of a fine touch, whereas some customers, they know exactly what they want. They communicate with us with us, and we're able to execute efficiently. So, really, the accountability thing, you know, having our individual sales reps accountable for, entering data correctly and properly and consistent with our standards is really, you know, where we shine as Presidio. Amazing. And I'm curious on on that. Like, as a sales leader, I think the accountability is is really important. We we really talk about a culture of, you know, accountability to company, to customer, to team, and to self. And I think there's a lot of different layers in there. I'm curious, like, how how do you manage that love, that culture of accountability? Right? Because it's when you think about scale and the proliferation of people and data, how are you ensuring that? Like, do you have systems in place? And, again, I'm gonna leave the witness here, but, like, what do you do to make sure that the accountability is happening? Because I think anyone on the call is kinda wondering, like, how do I do this? Yeah. Absolutely. So accountability has to be pushed down from the top down. And so, really, our sales leaders, you know, they meet one on one with their sales reps. And I would I would say from my experience, that is always usually kind of like the housekeeping rules whenever you're initiating any kind of contact with your reps and you're having a touch base is really, you know, making sure that they're up to date on their opportunities. And also, when something is wrong, you know, calling that out and keeping them accountable. So part of it is definitely gonna be with the leadership team expressing those values and then actually the day to day, sort of actions that the leadership takes as well as, you know, our sales folks, you know, really keeps everyone accountable. Yeah. That's awesome. I think that's a a really important piece. It gotta be from top down. Everyone's gonna be bought into it. And I think, you know, kinda build on that. I think the way that that we think about it here, and also to we're talking with our customers, is aligning of of of truly two things. One is system process. So I think the first one is the system and making sure the system is set up that we're asking for the right information to come in from the end endpoint and making sure we have a strong entry criteria. Right? And truly defining and creating one common language of, hey. At this point, it should enter a system. And as it enters a system, you need to have this information. Right? And that's a that's a nonnegotiable. Right? And I think for us is making sure that we enforce that standpoint. The second part is, like, what are the mile markers and just and and really kinda distilling that down to say, hey. You know, when a customer is either coming up for a renewal or we're coming into a sales process, like, which steps and stages do we move so we can actually have some good trust in terms of of that aspect? And then finally, you know, what's the exit criteria? Like, either step stages or the system, at what point do we exit? Because I think for us, like, we're we're thinking about this because it's truly gonna drive revenue for us if if we have a a unified end state in mind. Right? If we're like, hey. We're trying to move to this North Star in terms of revenue or growth or scale or or process, we need to making sure we're we're doing that. And so, like, you have there, it's, like, almost like standing daily, weekly meetings, and and I'm looking with my managers as well of saying, hey. Like, you know, the the math isn't math. Right? Like, Right. the data isn't dating, and we gotta make sure that we're we're we're holding the line in that ways because we can't make decisions if this thing is off a little bit. You know? And and I'm curious, like, from your experience, like, when you think about the downstream effect and even from your day to day, like, how does this impact you, and what are you looking at to to drive those stuff? Yeah. So in my day to day, you know, really tying back to what I earlier said about accountability, You know, when there are problems that need to be, you know, looked into, it it does create a disruption in your normal workflow. So the data has to be like you mentioned, you know, you you put those requirements ahead of time so that we know what you need to enter into your sales CRM. And and one of the things that we tend to see within our organization is that we have a written process that shows what each individual role is responsible for in entering and progressing in the sales cycle so that those roles are outlined ahead of time. It's it's it's on our company SharePoint, so we all have a reference to that. We all have that information accessible to us. The other thing we do is, you know, we make sure that we have recurring trainings because, you know, typically, what tends to happen with whenever there's training sessions, people are, you know, multitasking. They're not paying attention or maybe for whatever reason. Right? And, you know, just continuously drilling down that sort of, you know, that framework really helps, you know, individuals understand the importance of it. And then also, you know, showing, like, what happens when when you don't have consistent data, when you're not updating, you know, your CRM. Like, what are the consequences of that so that you can actually, you know, work backwards from that and really help everyone understand the importance of of good data hygiene? Yeah. I I think this the you know, you you think about that and the impact on on revenue and how it could drive growth. And I'd seen some reports, and and it's kinda like a common thing where, like, 25 to 27% of a rep's time is validating data. Right? And I think in a world that we live in today where we have such accessibility to information that can process it really quickly, you think about that lost time. Right? And and really getting the reps back to what they should truly be doing is selling. And I think that, you know, for us is, like, one of things that I'm thinking about constantly is, you know, when you said that the enablement, I think of also that too as the enforcement that sometimes the subjectivity of a seller comes into play, and the information comes in there of, like, I I think this is what I have to do, or I think I need this information for my pipeline because it's gonna accomplish this OKR or it's gonna it's what my it's what my leader wants to see versus the objectivity that we really need to see so we're not relying on lag metrics, but we can also be more predictive in terms of there. And. I had a conversation with someone recently. They and they were putting all these these deals and information in there, and we went through and that it was real. Right? And it was, like, kind of the analogy of they're like, well, I got a pipeline. I'm like, yeah. It's great. It's like kinda having rotten food in your refrigerator. Your refrigerator is full of food, but you can't eat any of it. Right. Right? So so what what are we doing here, and how are we ensuring that? And I think also to to your point of enablement is to to establish the why, but also too, I think also creating that culture of psychological safety that, like, hey. Things might not be working out. Right? Like, what do we do to help with the inputs to get you to those outputs that may be misaligned versus I need to have a a arbitrary three x pipeline or whatever that may be to cover in order for us to to show success. I'm curious, like, how do you go about that thought. Right? Because there's that pressure to add things in, right, as a seller or a system and and. not to put things in. Yeah. So I think the important thing here is, you know, our sales reps, you know, they have a lot going on. We have clients that have varying demands, varying requests. And so when we're delivering value, I think it's important to to set our reps up for success. And what does that look like? Really standardizing your fields, making sure that they have either a drop down so that it limits their options so they know exactly what they can put. Whereas if you have open ended fields that they they're required to input, you know, it it could vary from rep to rep and really just making their life easier. Right? You want them to focus on selling. And so by putting, you know, options in front of them, you know, that eliminates friction. So I think that's probably one of the key things that I think about in in terms of enabling our reps to do what they need to do. And then, also, you know, it makes it easier for the rev ops team, know, to have those standardized fields. So now that there's there's no sort of variation between opportunities, you can kind of standardize on whatever you set ahead of time. Huge. Right? I think that's a great way. Right? I think it's, like, simplifying what comes in and then standardizing operationally to. ensure that we're all kinda working off of the the same rule book there. I think that's a it's a really, really strong point in terms of that standpoint. When we think about that, you know, I think that, like, making sure that we're we're putting those that right information in there, and you talk about best practice with RevOps. You know, I think I think that's a a good little pivot point in there. I'm curious, like, what is your relationship with RevOps? Because I know they're they're they're vital to my business, and they allow me to see from the future. But we think about a system and operation, but then we have a whole group in there. And I could see shout out to all the RevOps folks inside the chat. Let me see a little thumbs up from all my RevOps who definitely support the day in, day out. But, Brandon, like, how do you have a what's your relationship like RevOps? Yeah. So, primarily, my relationship with RevOps is, you know, I rely on their analysis of where we are in the business. Because in my role as a business development manager is to interact with different vendors and partners and really understand where we are today, what is our historic, sort of, you know, transactional history, what kind of marketing events have we done to this point? Because that allows me to see, you know, the potential in different vendors and and, really, you know, where do I need to focus in more is relying on the data and what has worked in the past and what doesn't work. Right? So relying on the data to see if there's marketing or go to market campaigns. You know, we need that data to be very tight and accurate and and actually meaningful. So that's really where I lean on my rev ops. And on the opposite end, they rely on my team to to ensure that the data is accurate, to ensure that we're collecting data that is actually relevant and actionable to them. Huge. I I for me, like, that's a great call out. Right? And I think for any any aspiring sales leader, sales leader, even RevOps partner, I think, like, they're they're the most important person to me in the business. I think, you know, it's the it's where the the art meets the science in many ways. Right? And, you know, you have these gut feels of, like, I think this is where we're we're winning, or I think this is where our opportunity is because this is where I'm hearing from customers. And to have that valid the validity that comes through from them is is the only way I can operate. And I think that that's something that, you know, anyone on that team is is making sure that you have a really strong relationship. I am pro I actually sit with my Red Box partner at least almost daily just to check-in and help me of, like, hey. How are we getting in front of something? Or, hey. Are we on course? Like, are we seeing either certain unique trends or or or am I off? Right? And I think, like, that is something that is so so important. And and for anyone, even even a seller too, is kinda understanding what the back end systems are fed so that we can have the the right information. And we'll talk about this a little bit later in in this webinar because we think about, like, data and leadership. You know, sometimes a leader can say, like, hey. I know from experience is where I need to go, but if I don't have my rev ops partner, right, we're just we're gonna playing with with chance. And so. we'll talk a little bit more about that in the future. So really great stuff. So, folks, what we're gonna do, we're gonna pivot to our next poll question. We'd love to get your feedback here. When it comes to this, on a scale one to five, how strong are your cross functional relationships between sales and marketing in the company? So we think about the top of funnel impact. We think about what that relationship is. We're both trying to accomplish the same exact thing. But love to understand, like, what's that relationship look like? One to five. Five being, we had a super strong relationship. One, I know they have a function in the organization, but we hardly talk. So we'll give a few minutes in terms of here. Even for my RevOps partner too, what does that look like? Because you guys are vital in terms of signals and strengths of the business. But, Brandon, while we're waiting people to fill this out, what would you say for you? How would you rate this? Yeah. So at Presidio, I would definitely rate that as a five. The the communication between our sale our sales marketing team and, you know, the rev ops team is is is really strong. And I I think it's important, one, to have that communication, you know, having that back and forth, that constant, you know, back and forth of of, hey. What's important to my business? What's important to your business? And how do those values align for the greater business of Presidio? You know, I can't stress that the importance of that because when when there is when you have two silos within an organization, I've seen, you know, nothing but bad things happen. You know, Yeah. you have your marketing team going this way. Your sales team, you know, they may have a good relationship with a partner. And so when there's a disconnect there, you you really do not get, you know, momentum where where the organization is trying to go. For sure. For sure. It's like one of the it's like tail old is time. We're gonna give, like, another minute or or about them a couple few seconds here. Maybe, like, five seconds, three seconds, two, and one. Martina, maybe we can just show the poll and show some of these poll results. Alright. So on a scale one to five, let's see what we've got here. I like it. About a four out of five. Right? So it's not terrible. Right? And it's also not the best. Right? So we have some some working relationship. And really in a close second is about twenty percent of folks are are in that that two and three. And it it's definitely all there. Right? And I think this is something that that we see across organization. We'll talk a little bit about that in a second. But before I go over there, there was a question in the in the chat here, a great little question, is and, Brian, I'm throw this to you as well. Is how do you keep reps motivated to follow the new data rules when the broader organization changes, like, or systems that are moving very slowly. So how do you keep reps motivated to follow new data rules when the broader org changes like processes or systems that are moving very slowly? Yeah. That's a great question. So from my experience, I would say that, you know, changes, they they occur at times. You know, reps themselves, they may not understand why that change is happening. I think it's important for the leadership to really just dive deep into why they're making those changes. Right? Full transparency. Like, why are we making these changes? Why why are you changing the way I've been working for ten, fifteen, twenty years? And really just being fully transparent with the whole organization and, you know, asking questions back and forth to make sure that, you know, nothing is unclear on why it's required and and how to motivate them. You need to show why this impacts their business. Why why why do they have a stake a vested interest in following these systems if they can't see what the value is? And and you you definitely have to communicate that and and really just provide it for them so it's a, you know, clear as day why they need to make those changes. Yeah. Well, a great call out. Right? And I and I and and great question, Jenny. I think to to Brandon, to your point, I totally agree. Right? I think what what you have to do is we've gotta establish the why. Why why the change? Right? Because there's probably some, you know, broader thing that we may not have light insight into and why that changed. And really as a leader is understanding the why and then understanding the what. So what's the impact? So why are we making a change, and then what are we gonna do to impact it and make the change? And the question of, like, how do we keep them motivated, I think once you align people on the why and then understanding the what we need to do, that helps also provide clarity. And the the other part of it too is is also to opening up the conversation to provide an area where there is some psychological safety, where people can come back and say, like, hey. I'm gonna I disagree with this change. And I think, you you know, what we also look at is I kind of adopt to the Amazon principle is I'm gonna disagree and we're gonna move on. Right? So, hey. I disagree with it. I don't really like it, but, hey. I understand the why, and let's move on and move off of it because we don't have the change or they don't have the ability to change it. Unless it's something like, I would say, Right? And and and maybe that's a way as a leader, I can also bubble that up if it is gonna create some maybe log jams or challenges to also provide that up to say, like, hey. Like, just so you know, this is what we're seeing from the field and bubble that from the bottom up. And then how do we work together and collaborate on that? So really great question. Great question. More questions you have, throw them on in there. Keep them keep them coming. We love to see them. So let's bring it back. We're going back to marketing. We think about this in that cross functional relationship. Right? And, you know, think about this. Revenue is truly a cross functional KPI. You know? And what shifts when marketing and sales and operations are truly not aligned on the same datas and definitions. And and you think about, like, what breaks when they're not. Right? So when we think about this, and I know this from my world, you know, one of our challenges that we have at times is we have an unbelievable marketing team. They are the absolute greatest partners. But sometimes our definitions aren't aligned. And where they're going to focus on particular, you know, ICPs, identified customer profiles, may not align with us. Or how they're determining what should be a lead that we focus on is not necessarily the same language that our reps are following. And so we kinda have this misalignment, and we're losing opportunity. Give you a case in point. One of the things that we kinda challenge with is, hey. This is a MQL, marketing qualified lead that came through a system, and a rep may not prioritize it over other leads that they're receiving, although they're still extremely valuable. And I think for us, and one of the things that I've been working on across our org is making sure that there's a understanding going back to Jenny's question originally is, like, there's there's a common language. Right? And and this is what we define and why we define it and why it's important and how did it get here so that reps prioritize marketing leads over self source or maybe things that have come through other channels. And I think that when we do this, like, it's such a big miss as an organization because it's truly lost revenue that we invested, whether it be for our click to capture or our content or things like this that we work to promote. And if reps don't prioritize it, we're we're missing in hot leads. And I'm curious, like, from from you, Brandon, like, you think about either cross functional partners, sales operations, and what breaks, like, what do you your day to day? Yeah. I think we we've touched on a little bit and hinted a little bit early on. But, really, when we see all the organizations truly aligned, you see a synergy, you see momentum, there's lower friction. And I think the important way to to reach that point is you have to have that shared responsibility or accountability model. You know, AWS has a saying as a shared responsibility model. You know, whenever a client is building on the cloud, there are things that AWS is responsible for, and there are also things that the customer is responsible for who is leveraging AWS. And so I think you can apply that to your organization as well, you know, making sure that each department is accountable for certain things. And when they have accountability, I I think individuals themselves are more likely to take responsibility for those outcomes. And and so when you have accountability and individuals are taking responsibility for actions, they're more likely to work together because they all share that same vision. And and so, really, I think the biggest thing is, one, increasing productivity, but, two, reducing friction to get your job done. And so when everyone's working together, I think that's kind of, like, the magic sauce of reducing friction within an organization because you're able to solve problems more efficiently for your client base. And then on the opposite end, misalignment. When there's misalignment between all of these departments, you're you're gonna have inaccurate forecast. You're gonna have, you know, debates on what dashboard is actually the the the source of the truth for what's actually happening. You know, if there's definitions that are lacking within the data, you know, it it's hard to hold people accountable unless we clearly outline that. And and then there's also this other thing called, like, the bystander effect. When when it's not clear who's responsible for what, it's it's almost like things don't get done unless somebody's taking accountability and and really defining that ahead of time. Yeah. It's it's so true in in that piece. That bystander effect is has major impacts. And I think, like, you know, I I'd seen a a report that that, you know, this misalignment, and you you talked to it really well here. Right? Is this this is gonna impact revenue downstream. And I think as RevOps partners and sellers, you know, this could have as as great as 10% of a reduction annually when you're not aligned, right, from marketing and sales. And in fact, I've seen a couple of different things in in my background. I'd I'd worked at organizations like HubSpot as well from a marketing standpoint. And what we see is what's interesting is in that top of funnel that comes downstream is anywhere between 60 to 70% of marketing leads go unworked by sales. And when we think about the impact that is to an organization, you know, this is demand that's been generated by marketers, but it's never monetized. And we just think about we're just throwing things out there. And and for us is making sure, again, I think alignment and collaboration are so important because, you know, like you said, the bystander effect is like, hey. If we don't define the same things as equal value, We're gonna have that impact, and it's just gonna sit there, and it's gonna say, well, I thought that was your job, not my job. And the second thing is we have that information that's going out, and we kinda get this conflict of marketers are like, hey. We're driving really good leads, And sales are going, no. They're not because they're not converting. In reality, Right. like, we're both in that bystander. Right? No one's actually taking action on it. And as a result, we're we're just we're just spending money to spend money. And you think about, you know, what that what that impact would be and how we can be able to create that greater alignment to to drive that. And and also with that too is the signals back to marketing is, hey. Like, this isn't the right fit, and here's why. You know? And I think that's something that, like, creates that that that this alignment. I'm curious, Brandon. Like, with your relationship with marketers and the business development, like, how do you guys work day to day? And and what advice you'd give the team here as a tool to make sure that we're we're having the same exact expectations in terms of driving revenue for the business? Yeah. So great question. So when my team is working with the marketing team in the field, really, what we do is, you know, sometimes we we will try new things. Right? Like so we'll we'll actually do a new go to market campaign with a partner. We'll we'll clearly record what the outcome of that is, and we'll also, you know, collect data on how much engagement we're getting so that we can analyze that data. And, really, I I I'm gonna use Amazon again, but, you know, there's a concept there, like, failing fast. So when you try something new, track that data and use the data to inform your decision. But if it doesn't work, you can scrap that, or you can you can try revamping that. And if the revamp doesn't work, you go a different path. So I I would say you can try different things, see what works, see what doesn't work. And, really, the concept here from Amazon is really failing fast just so you're not spending your time just spinning your wheels on something that doesn't deliver any impact. The other thing I would say about, like, working with our marketing team and, you know, our partners is that, you know, when we're aligned on the same goal, you know, and we know what metrics are actually delivering value, that makes it easier for us to to look at the data holistically and make a better decision based on that data. I love that. Yeah. I think there's a a really great piece there. And how I would do it every day is I I'm actually tracking all of our leads for all of our reps, and I wanna make sure that, like, the funnel is coming down, but also to the conversion rate. And then this be signaling either one or two parts, and I kinda become the intermediary in many ways to say, like, hey. Either downstream, we're not working this, and we need to we need to fix that. And then upstream to say, hey. Maybe this isn't the right thing, and the systems may be may be off. And I think the biggest part of all of that too, you have a failing fast, but I think in terms of that is also bringing some constructive feedback back to the teams to make sure that we're we're having good collaboration. Because I think one thing I have learned in my my career is you can't have collaboration without conflict because we both want the same goals. And so how do we be able to align to make sure that we're doing it correctly? So that's really helpful. So we're gonna pivot to the next part here, and we're gonna talk about that leadership philosophy and clean data. So we have a poll coming up here. Right? Which matters more, clean data or strong leadership? Kind of an interesting take here. So clean data or strong leadership, or they're both equally important. I'll have to get it in there. Brandon, while we have people filling this out, I'm curious from you. What's your take here? What what do you think? Yes. So initially, when I thought about this question, I would say strong leadership. However, with the recent advancements in AI and getting hands on experience using AI agents, clean data, you know, I think the the scales are tipping a little bit in in the clean data side of the house just because AI is just so data dependent. But there's also a leadership component to that. Right? Like, AI can can do amazing things, but it needs someone to direct it and orchestrate it to do exactly what you want. So that's where leadership is important. They need to set the vision. So you know what? I would just say they're equally important because they they actually build upon one another, and they're dependent on one another in order to be successful. I love it. I love it. Martina, let's see what the poll looks like. So equally important. I agree. I think they're I think they're both of the equally important and and also do the group. Right? Larger group, equally important. There's also a good balance between clean data and strong leadership. And I think to your point, Brandon, right, like, that's that's an interesting part of this conversation because, you know, we kinda pivot to the next piece here. Some leaders argue that great leadership can overcome messy data, while others believe that poor hygiene makes scale impossible. And you kinda talked a little bit about this, right, and and where you stand on it. But also too, I'd love to see from your perspective, like, where do you see this breakdown? Yes. So, really, when it comes to leadership, right, leaders, they typically have a great intuition on where the business is heading. However, even those leaders who have great intuition, you know, you can't fix what you can't measure. So if there's any problems in the field, whether you're a great leader or not, you need to understand some sort of baseline of of of what that feeling is based on. So, again, the data is actually there to help provide insights into what's actually happening. And so I think there's a mix between a little bit of intuition as well as using the data to validate that intuition and really prove, you know, why a leader believes something is true or not. Leadership, it's important for them to set expectations, really, the data needs to be there to make sure that whatever they're saying is actually the the truth. And so if data is missing or incomplete, you know, they really can't justify their feelings, you know, about where the business is heading or any efforts that they're putting forward. You need that data to validate it and really provide a baseline. Yeah. I I I totally agree. I think there's, like there's this really equal balance inside of there. You know? I I think, you know, we were we were talking about this, and and it kinda got me thinking, you know, in terms of, like, the data can help me provide the directions of decisions. Right? And I think that that intuition can help me in terms of there. And and and, you know, we think about we hear the analogy. Right? I'm flying blind or, hey. We're flying by the seat of our pants. You know, I I was thinking about this question, and my dad's a pilot. And he talks about different types of of of flying. Right? And there's there's visual navigation, and then there's instrument led. And, you know, when you're flying as a pilot, you can use your eyes, you can use landmarks and sights to track your course to make sure you're on it. But when you don't have that and you're truly flying blind, you have to fly with your instruments. And I think data is such an interesting part of that because you need to have a system in place that allow me to fly with my instruments. And more importantly, I have to trust those instruments that's also going to allow me to test my intuition to make sure we're on the right course. Because, again, like, we can be so guided and and and and led to one or another, but making sure I have that is there. And I think in terms of that is, like, understanding as an organization what our true north metrics. And I wouldn't say true north. I'll say, like, kind of our our our magnetic north. What's kinda guiding us to where we need to be and and utilizing that information to help us make decisions. And I think about this in a couple ways. Number one, I think about this in terms of how we rate and rank our customers and our accounts. Right? We're thinking about lead scoring. What are the key things that actually matter that's going to lead us to an area that, like, we want reps to prioritize on? Right? What is the thing that says, like, there should be something here our data tells us, and then having my reps directionally to go and suss out the no. Right? The second part of it is, you know, is understanding from a retention aspect and really understanding where where are we losing people in this. Right? And I think about our our lifetime customer, our lifetime value, and kinda, like, understanding what steps and stages are we losing customers, or where are we keeping them? This is something outside of my day to day. I'm I'm a business owner, and I look at my members. Right? And I and I look at someone that maybe churned with inside the first ninety days. Well, that data to me, that data tells me I have an onboarding issue, and I need to make sure that I address the experience of someone. Because if I lose someone in the first ninety days, number one, it's a big loss to me in the business because I spent money to get them in there, and I didn't be able to really realize that revenue long. term. Second part of it, that's only gonna be continue to happen. So I use that data that guides me to areas where I'd be blind because I'm thinking, hey. Everyone's having a great experience. Right? Everyone loves our product. Everyone loves our service. But if I'm losing in those steps and stages, that's alerting me to something that I may not have purview to so I can be able to take that to do it. And and and I I I think about the leadership aspect is also the vulnerability to to say, like, I'm wrong. Right? The data. says this. You know, as a RevOps partner, like, I love that to test my my my assumptions and, like, my biases and say, I think it's this. And then they're like, no, Chris. Like, you're you're way off. You know? And I'm curious like that too, Brandon. Like, how do you use data, like, kinda test your assumption or your biases when you're thinking about where to focus and who to focus on? Yeah. So so one of the things that I use data for in my day to day as a business development manager in the marketplace is really looking at different metrics of where has the business been historically, which customers are engaging with our practice, and and how can we uncover more of those opportunities where we can deliver value. So within the marketplace space itself, you know, to give people who are unfamiliar with it a quick rundown of what that is, is we help clients essentially purchase third party solutions like monday.com, you know, like different security vendors who are on the marketplace or, you know, workflow SaaS, And and we help them transact and navigate the cloud space and and particularly transact the purchasing mechanism, and there are incentives for them. Right? So we help them navigate that. And so when I look at the data of which customers are purchasing in the marketplace, I can then provide some of that intel with our some of our other business practices who who maybe they offer services around AWS or, you know, some other cloud like Azure or Google and really, you know, connect the dots and see, hey. There's more opportunity here. This customer uses the marketplace today. What other opportunities can we do to expand and deliver more value to those customers? Huge. Huge in that sense. I I think that's, like, a really important part, especially the I mean, the the marketplace. I mean, it's so huge big. Right? And there's so much that goes into it, you know, Right. from that point. You know, I think that's there's so much to be learned from it. Like, I have a friend who does is in in that business so well and, like, understanding, like, where to go, who to go, and what to do, and where do you make those small little tweaks to have that impact. Right. Exactly. Kinda pivot here, and and this is, like, also too as we start moving into some of the playbooks and and and tasks for those on the call. Thinking about your if you're leading an organization, Brandon, like, if you were to start over today and you're responsible for building a source of truth, like, what would be the first thing that you would prioritize, and and why would you do it? Yeah. So that's a that's a really great question. So if we were to start over today, you know, really just determining where you want to be in the future and really working backwards or reverse engineering that outcome to see what data is actually needed, you know, to make to make that possible. I think I mentioned earlier friction. Right? Like, what data do we need to reduce friction? You had mentioned it earlier. If you're looking at the data of, you know, how are leads, you know, converting and also, you know, if you're seeing that your customers are you know, they're not having a good experience in the first ninety days, Really, metrics like that are important to implement from the very beginning, so you're not adding that kind of data after the fact. Because if you collect that data upfront, now you have a historic timeline of how that's been, you know, occurring within your organization, and you can align that to whatever changes or new processes you implemented within your business. So you you have clear visibility into, hey. I changed this process on this date. The data shows that, hey. After this change, I'm starting to see, you know, greater retention with my customers, repeat buyers. You know, all those metrics are important to set ahead of time and and really just getting ahead. You know, collecting data is really important from the very onset because the the thing with, and I know we're gonna talk a little bit later, AI. Yeah. AI is essentially a predictive model. It's a statistical representation of what it believes to be true. And so there's a thing that happens within that kind of framework that the longer data that you have, the greater insights AI and statistical models are able to generate. So it's important to get that kind of data ahead of time. So I I would get to answer your question, I would get fields like that that actually predict that that can be used to plug into AI to predict greater outcomes. Yeah. I I love that, and that's that's a really good thing. And I I'm excited to talk about that. And I think for me, like, if I were start over today, I'm thinking about my outcomes. Like, I'm thinking about what do we wanna achieve and what does that look like and then kinda work the process backwards. I I've seen it kinda both ways of, like, we start from one to the other, but I think, like, what's the overall outcome? And I and I and I kind of advise a lot of customers when they're thinking from you know, we we talk it from from chaos to first CRM is thinking about, hey. What's the outcome? Like, what do we want to, and, like, how do we work backwards? And thinking about each of the different parts of the process that touch the revenue stream and cycle, and what does that look like as we define that moving forward. Because it it'll it's gonna lead to me a couple things. One, it's gonna it's gonna lead me to make sure what what information is valuable, what I wanna collect. It's also gonna guide me into, like, what information do I wanna integrate. Right? Do. I wanna integrate notetakers? And then what do I want from the notetakers? Do I wanna integrate external third party enrichment platforms? What matters? What's what's gonna be giving me real information? And also to, like, at what points did it enter? And I think that's something that from a marketing standpoint, how do I make sure I'm enriching it? How much make sure it's accurate? Because, again, the downstream effect in today's world, the amplification is gonna be massive. But I think about that process. If I had to reengineer it, what's my outcome? And then work backwards. Mhmm. And then this is, like, how do we get there the fastest? Right? So not allowing the rep to have to do so many manual aspects, but also questioning those manual aspects. And you're kinda almost running an exercise in Lean Six Sigma in terms of variabilities and inputs and making sure that, you know, the less variability as I have, the less likely that I have for disruption or or just chaos. Right? I I wanna go from chaos to chaos in that way. You know? But on the topic of AI because I think this is the one of the biggest things. Right? And and I'd love to kinda pivot to to you on this on on practical tips and tools for the audience here because we have so many people that are that are here from RevOps and others thinking about AI. Because everyone is. Right? What's my strategy? Is my use case? But data is one of them. Talk to me a little bit more about that about that and the tooling and how you're thinking about this in in this new world. Yeah. Absolutely. So it it there it's no secret. Every SaaS company or platform is integrating AI in within their existing tools. And and so, really, you know, the key here is, you know, making that decision up front may not be apparent because there's just so much out there, and every organization advertise their product as the hot new thing. And and so, really, I think at a foundational level, I think it's important for your organization to understand what AI is capable of and what it's not capable of and really just overlaying that into your existing workflows. Obviously, with CRM and RevOps, the advancement in AI, there really are a lot of options out there. I think what's really important is to compare and contrast the tooling for your specific industry and what matters to your business and which tools have that sort of value add that you can easily implement. I would say that one of the things that I've noticed is that not everyone understands AI overall, like, what it does. At the very base level, people are used to using AI as a chat interface. Right? So, Yeah. a, you know, how do I, you know, update this Excel file to do this x, y, and z? Right? Whereas people who are getting more strategic, they're letting their platforms handle and abstract a lot of those functions. And so it's important to look at tools that sort of fit to the lowest common denominator. And so if you can make AI very useful and frictionless to the the average user, I think that's what you need to look at because you may have people in your organization who are very tech savvy, who know how to use agents and orchestrate them. But then, you know, the average person, you know, they they think of AI as a chatbot. So having a tool that really simplifies things for the end user, that's really gonna be important what you should look out for. You're right on. Because because AI AI models, they amplify bad data. Like, they don't fix it. And. I think that's a really important thing to be thinking about, whether I'm a rev ops leader or I'm a a sales leader, because that's gonna have massive impacts to how we make decisions. In fact, I I saw something that, like, more than more than 60% of organizations are worried that their data in their organizations is not AI ready. And, you know, to compound that and and you mentioned this, right, is is how are we thinking about beyond chat, right, and using it as, like, search engine versus an actual tool. Right? Whether it's agentic, whether it's automation, whether it's analysis, and how are we using it beyond that? And I think that one of the practical tips I'm thinking about and is to kinda step back and understand what the use cases are. And the hard part is is if we don't really have a good grasp of AI, it's hard to understand what the use cases are versus rewrite this, go do that for me, and thinking about how you can get into a little bit complexity. I would say a practical tip for everybody on the call, and this is something that I'm encouraging and we're doing as an organization and and pushing our reps and pushing our leaders to do this, is to really jump in fully on in AI and and and and really kinda ask ourselves, like, what don't I know? And to make it as simple as possible and as friendly as possible. And then also to to really kinda be in that world where we're asking each other and and just really sharing what are potential use cases that can can be utilized here for AI. And then what is the what is the impact? I'll give you an example from data. I have a a friend that talked to me about their need for a data analyst, and he said, listen. I can get a data analyst. It's very expensive for our organization, or we can make sure we have really reliable data, we can run it through several different AI models to provide us really kind of trends and and and proactive issues in the business rather than relying on lag metrics and going back to a customer thirty, forty five days later after it's been analyzed. And that has had a major impact for them. So in real time, they can be able to understand where there are certain trends in the business to take action. And and that's been hugely impactful for their business. But the key is making sure the data is is accurate. And the other practical tip that that I've I've that we're taking as well is using one model. Right? Getting one point where all data is so that the data can work off of that that centralized area. And I think a challenge is the proliferation of of agents and usage throughout an organization where everyone is doing their own thing, and I'm all gonna get different types of responses. So I'm curious, Brandon, how are you thinking about standardizing this to bring it into in house? Or what tips would you give that you're doing to making sure that we're all kinda using the same pool and the same language models to get the same results as an organization? Yeah. Absolutely. So so there's this concept of of data lakes. Right? So when different organizations, sales, marketing, you name it, rev ops. Right? Like, we we all have data living in different areas, and so it's important that you're thinking holistically as your business, you know, scales to really put all of that data in one common place. The reason for that is because AI is very powerful in in leveraging the data it has access to. But if you have a data lake and all of that data is there for it to access, you can then, you know, use that data to its maximum potential. There are some problems with that, obviously. You know, if garbage in, garbage out is is is a term usually used. And and so if some data is contaminated or or it's not formatted properly, you could expect some kind of, hallucinations or, improper reporting by the AI. But, really, like you said, standardizing on a link large language model. The the the clog models today are amazing. Right? But who knows, you know, a year from now, who's gonna be the leading AI, foundational model at that time? We don't know. So I think, yes, it it it's important to kinda standardize on one, foundational model, but it's important to look in the future. You know, there's gonna be more efficient, agents to have more reasoning capabilities and and really just having an open mind to what could be possible in the future. But building your data around that to be, sort of module so you can plug and play plug and play your different foundational models as needed and as the technology improves. The other thing that I've I recently had a conversation with the sales leader about AI. And and, really, I think the hesitation within organizations is that AI is not accountable. And so there always has to be a human in the loop in that process. And so identifying I I think going forward in the future is gonna be important to see, you know, to have sort of, like, an AI center of excellence or, you know, someone who you can point to within the organization that can be accountable for your AI systems. I think that's where we're heading in the future. I totally agree. I think it's, you know, it's it's it's gotta be policed in a way. Right? I think you gotta have, you know, some some barriers in there because to your point, it can amplify the data. If it's not good data, it's gonna amplify the poll results, and then you're not gonna be able to make decisions. Right? If you. got multiple systems and multiple datas in multiple different lakes, you get disconnected decisions. Right? Versus, you know, as a leader, going back to our first thing, is I'm able to make a decision by being guided by looking at things really quickly. And I think that's the really exciting part. And I think that helps us make data and data framework easier. And I I know and where where I'm excited about this, and I'd love to ask you this question before we kinda wrap up, is where do you see AI starting to make these data frameworks easier, like, from your perspective? Yeah. So so I think AI is really good at doing what you instructed to do. So when you have a framework, it can actually help you identify when there is, you know, there's a field that's not within parameters. AI is easily able to, you know, scrub through data and and make those decisions and call it out to you. And then also with the with the advent of AI agents, you can actually instruct that AI agent to fix that for you. So, you know, correcting, you know, within certain parameters, you know, things that they can do, by itself without humans interacting with it. I think it's gonna be important to obviously keep a record or a log of all those changes that they are making. And. then also within that prompt of that agent, really understanding, hey. This is something a little more complex. I need a human to review it before I action it. Yeah. I think it's I'm excited about it too, and this is something that that, you know, I get to talk to customers all the time about is really thinking about, you know, the AI strategy of of automation augmentation and agentic. Right? And and to think about a system of record, you know, I'm excited that we're you know, from Monday's perspective, what we're doing is you think about, like, we're limiting the subjectivity. Right? We live in a world where cars drive themselves, planes fly themselves, right, and a refrigerator can order service on itself. Why are we using a system of record that still relies on the subjectivity of a seller? Right? I think that's a little bit of a kind of obsolete there. And so, you know, we're looking at bringing that AI world to it that allows objectivity. I think about it from the sales process. Right? Can't tell you how many times I look at a sales stage and isn't qualified, yet the rep has already sent out a proposal. Right? And you're like, the system should do this. Right? And and think about those things that we start to get really, really reliable and an exciting world in terms of that. So what we wanna do here is as we've kinda wrap up, we wanna have that last poll question. Two poll questions here as we wrap up today. The first one is, how much are you currently using in terms of AI in your revenue workflow? Love to see this one because I know that it's something every day that we're utilizing day in, day out. And I think to Brandon's point, it's only become faster. Right? You mentioned Claude. Amazingly used Claude. I'm enjoying that. And and we think about, like, what's gonna be in a year. I think we're moving so fast. Like, what's gonna be in a week, honestly? Right. You know? I agree. It's it's hard to keep up. Everybody in this conversation is something different in terms of their Right. you know? So I'd love to see that. But for you, how much are you using every day? I so me, personally, I'm using AI every day, primarily as a chatbot, really, you know, doing research, you know, on prospective clients, you know, understanding certain clients. Like, what are they interested in? What are their initiatives? So I think it you know, we can as a BDM, that's sort of more in my wheelhouse. But as it relates to rev ops, what I'm using AI today is to automate certain workflows that, you know, used to be manual and really manipulating data agentically. Yeah. Yeah. I I I'm using it almost all the time in my world now, and I think that sometimes that that's pretty much the norm with everybody of where we're going in terms of this piece. And so you're not you're not alone in terms of theirs. We're thinking about that piece. So, team, thank you so much. Brandon, thank you so much for your insight today. Before we let you go, we just wanna thank you all for being here and attending. Been really exciting. We enjoyed the conversation, enjoyed the questions. We got one more poll here. Love to know. Are you interested in learning more about monday.com? We're really just appreciative to sponsor today. Love to hear from you and love to continue more. If you have more questions for Brandon and I, please look us up on LinkedIn. Give us a a ping, a follow, you know, and happy to accept some time up to answer any questions that you may have. So, again, thank you all for today. We really appreciate it. And, Martina, anything else from you or the or the the organization? Guess we're good. So thank you, guys. I appreciate it. Thank you for the time and attention, Brandon. Thank you for all your insight, and have a great rest of the day. Good luck on the rest of the quarter, and we'll talk soon.