How to get more budget for your data team

Mark

0:04 Topic that I'm gonna cover, folks, is something near and dear to my heart, and I think it'll be near and dear to a lot of your hearts, which is how do you get more head count or how do you get more budget on your data team? And the, the context for why we're talking about that today is if your annual planning process if, if you're on a fiscal year end of December, January, a lot of companies right now are in the middle of annual planning. And so there are CFOs and there are CROs and CMOs right now battling over how in the heck are we gonna grow next year? How many people do we hire?

0:40 And what I've often struggled with is data, data teams end up at the back of that line. And I'd like to, you know, help change that. So what I'm gonna do is talk about kind of how the process works for adding headcount to teams, and then I'm gonna talk about why I think it is that data teams end up often in the back of the line. I'll give some examples of how you can change that, and I'll end with one really specific example that I've seen work really well to help you. But first, just a quick introduction. So as Austin said, I'm Mark. I lead the business team here at Atlan.

1:10 I have, before joining Atlan, experience at, you know, companies like Gap Inc. And Adobe. I've led sales, I've led marketing, I've led customer success, I've led customer business operations. So breadth of experience, but there's really, like, really one thing that's consistent about everything that I've done, and, and that's data. And, you know, like I, I'm dyslexic. I didn't read a book until tenth grade, but I've always been good at math, and I'm really passionate about customers and understanding what they do and why they do and how data works.

1:40 But I, I never managed a data team until about four years ago, and that's where I built this empathy for, you know, some of the challenges that, that data teams go through and, and that's what led me to Atlan. But a different challenge is, you know, that they face is what I'm gonna talk about today. So I wanted to just explain kind of how the process works. And so typically, you know, I, you know, there's CROs banging their fists on the table right now saying, "We need more headcount." The math is pretty simple, and, and depending on the size of the company team, you know, there's quota, right?

2:10 A sales rep might have a million-dollar quota, and so a CFO is gonna say, "Oh, we need to add revenue, we need to add quota." There's some problems with that, though, in that, you know, ramp time is four to six months, so you knock off a few hundred K here and then, you know, not every rep hits their target. And so now you're down to, like, 500 something K. And there's more considerations here, like s-sales reps, they don't, you know, they don't sell the product on their own, right? Sometimes the product's really the more valuable part of the equation. Some customers come and they're just ready to buy, right? So it's not all incremental.

2:48 Oh, and then, and the sales reps get paid, so this is revenue, it's not profit. And so the question that I always have is, you know, like, why not hire analysts instead? And why doesn't anyone do this math for analysts? And I'll reflect on a story. So I spent a bunch of months going through this math while my team had just hired a person called Shuey, an analyst, and we like people to get, you know, started right away and have an impact right away. This was at my last company. And within three months, she had analy-analyzed the data and found nine hundred K of revenue.

3:29 And not only she find it, but she found a way to work with the teams to actually realize the revenue. So in three months, she had made nine hundred K while the CRO is pounding his fists on the table saying, "I need to hire all these sales reps." And so this is a conundrum that we, you know, at data teams face. And I wanna, I try to, you know, like, there's the five whys. I, I try to go deep on understanding why. And so I recently saw a chief data officer's LinkedIn profile that got me really excited, and I'll show that to you in a second.

4:02 But this is what most CDO LinkedIn profiles look like. And whether you're a CDO or a director or a manager, like, this is pretty common. But, you know, there's great stuff on here. It's like improving data quality and security. You know, th-the data teams are big, managing lots of people, partnering with business leaders. This is true. This happens and, and I see the, you know, like, data leaders are really great at partnering, building and maintaining relationships with vendors like us. You all do this really well. Staying up to date on latest trends in tech. This is all great stuff, but the truth is, like, CEOs don't care about any of this.

4:43 Th-this is not what's gonna get you into the front of the line. The the LinkedIn profile that I saw recently looked like this. It's totally different. Led the development and implementation of a new data-driven marketing strategy that resulted in ten percent increase in customer acquisition and a thirty million dollar increase in revenue. You know, increasing customer churn prediction by twenty percent, fifteen percent increase in win rates, twenty million in revenue, time to market reduction of twenty-five percent. This is what data teams do, and this is the impact that data teams have.

5:18 And I think the question that I have and I wanna talk about is, like, how do you change from this left side to this right side? I have a few ideas, and then like I said, I'm, I'm gonna share a specific example. But the way you kind of can ev-evolve. First, and I, I'm, I have a strong point of view on this. I know not everyone agrees, but I think there's strength in numbers. I think data teams work best when they're one team, and I've worked with both kind of situations where the data team's in one, one team and they dotted line to the business, and I've seen it the other way.

5:52 I, I think this is the best way to go. You, you get cross-business insights shared more easily across the team. It fosters career growth, team growth. You can hire better. You get best practice sharing. And whether you agree on this approach or not, I think one thing everyone will agree with is however you're organized, whether you're under one roof or not, find ways to get at least a tent where you all are together as one team and doing the things that data teams do best together. The second thing is building champions, and I think Michael talked a little bit about this, but you've gotta find data-minded business leaders who get it.

6:33 And oftentimes, that's in the marketing organization, sometimes it's in the sales organization. But you have to find, you know, people who have budget and care about, you know, the growth of the business to get that— well, how data can have a huge impact there. And one way to do that is Shantanu, the CEO of Adobe, used to always say this: "Never waste a crisis." He would say this to all of the leaders. When there's a crisis, use that to go after and make a change. And I'm not talking about the crises like the dashboard's broken or arguing about the data. I'm talking about the crisis like you missed revenue targets.

7:08 "CRO, let me show you how I can help you with forecasting." "Okay, poor customer experiences. Let me show you how we can leverage product data to improve that." Business risks and crises, budget misses, they're opportunities to go in and build partnerships with the people who care about these things the most. And so now I'm just gonna give you a very specific example that I saw work amazingly well, and hopefully it can be useful for you. But everyone is doing QBRs these days. Like, quarterly business reviews is how businesses kind of run. Sometimes they're run by geographies, sometimes they're run by business units. But have you done a data QBR?

7:52 And if you haven't, I strongly recommend thinking about it. A couple things to think about. Start with the impact, and depending on how big your company is, this might be five point seven million or fifty-seven million or five hundred and seventy K, whatever it is, but start with the impact that your team created. And then the next thing is organize all the data projects you've done by, you know, how the business is organized. And, and this one's organized sort of by functional area, but it could be, you know, by geography, it could be by business unit. But you want to organize it by how, you know, the, the teams that care are organized.

8:30 Share wins and losses, right? Like, it's not just about the wins. There's learning and, and failure, and it's really important to share these as well. And then lastly, it's insights, right? It's about understanding the why behind the data, the why behind— You know, how can you help the teams understand the customer and, and the experience from the wins and the losses? And then lastly, probably most importantly, is end with a page that shows, "Here's all this other stuff that we can do, right? If we had more resources." And when I've seen this done well, I've seen the different business u-unit leaders, myself included, clamoring for, like, "Can we get you more budget next year?

9:12 Can we fund a data scientist? Can we fund these projects? This five point seven million is gonna grow to fifteen million or et cetera if we can just do X, Y, and Z." And so I'll leave you all with that. My, my hope is that you all get to hire huge data teams and get all the projects funded that you want for twenty twenty-four. Thank you.