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From DataRails, this is FP &A Today.
Welcome to FP &A Today.
I'm your host, Glenn Hopper.
We have a great episode lined up for you today.
Our guest is Damon Fletcher, a finance leader with a remarkable career that spans roles at some of the most dynamic companies in the tech world.
Damon has served as CFO at Tableau and data robot, two pioneers in data and AI.
And he's now the founder and CEO of Caliper, a company transforming how businesses manage cloud costs through advanced analytics.
In today's conversation, we'll dive into Damon's career journey, exploring how his experiences have shaped his approach to finance and leadership.
We'll also discuss the innovative work he's doing at Caliper and how finance teams can harness the power of AI and data to drive their organizations forward.
Damon, welcome to the show.
Well, thanks for having me, Glenn.
I got to say, when I think we were introduced by Mutual Connection, Nathan Bell, I think, has maybe introduced us.
And as soon as I looked at your profile on LinkedIn, I saw two of my absolute favorite companies in DataRobot and in Tableau.
And I thought, man, this guy's done, he's had the coolest job.
But I guess going back to that, and even before that, you were at PwC and now as a founder of your own company.
I just, I love that career path.
And I was wondering if for our listeners, if you wouldn't kind of walk us through coming out of school, going to PWC and the path that you went on.
So yeah, early in my career, after college, got a master's in accounting like many of the CPA people that started in CPA firms and started at PricewaterhouseCoopers.
I spent about five years in the Jacksonville, Florida office and then relocated my family up to Seattle to work on an IPO out in Seattle, Washington.
You know, the financial markets collapsed in 2007, 2008 with the kind of Great Recession and kind of pivoted my career and started focusing on primarily tech customers and clients of Pricewaterhouse and were with companies like F5 Networks and ExtraHop and Aptio, and then later became the CFO of Tableau Software after joining them shortly after their IPO and kind of progressed through the ranks there and spent about seven, eight years at Tableau.
and then later at DataRobot.
You know, working at companies like that that are so focused on data and analytics and then with what your focus was at the end of your time at PwC, have you always seen the relation between analytics and CFO and maybe different than someone who just had the finance and accounting background?
How have those two skill sets kind of worked together in your approach to finance?
Yeah, I think probably because I grew up in the finance rank and file at Tableau, So it was a very data -centered company.
I mean, obviously, the product is very data -oriented, but I think that the culture of the company was really to make decisions based off data to enable users across the organization to have access to financial data, customer data, product data, to be able to make kind of data -driven decisions.
And I think that experience really taught me the value of being able to kind of dig in and ask questions and constantly never accepting kind of the, you know, the high level results, but really diving deep.
And that really helped us kind of, you know, formulate what we wanted to build here at Caliper.
I mean, were there being at those companies and then your approach to finance as well, I mean, it just, you kind of came up in the data world and as Tableau came out and became this very powerful tool that was out there and then the acquisition by Salesforce and all that, were there key turning points and maybe it came from just the environment and the understanding and role of data at Tableau, but were there certain key moments that you can look at and say, this is what really prepared me for that CFO slot at Tableau and then again at DataRobot?
Yeah, I mean, there was a number of things.
When I first joined, I was handling primarily financial reporting and Sarbanes -Oxley preparation over time, kind of gradually got more and more of the finance and other back office functions kind of reported to me.
I would say the things that were very strategic that I worked on prior to being a CFO was helping the company migrate from being an on -premises software delivery model to a subscription model.
That was one of the key business transformations that we were going through and had a key role in the launch of that product as the deputy CFO. And then I think doing things like as we grew and scaled our business, we had at some point 10, 15 properties here in Seattle and we built two beautiful new buildings and kind of migrating out of the old into the new and subleasing and things like that was one of the kind of strategic initiatives that the CEO at the time, Christian, had me kind of spearhead of how do you transition employees from kind of a group of eight or nine buildings into two and then
sublease the others for a period of time until you need them.
And it was a very complex kind of data -driven process of forecasting, you know, what your headcount was going to be by year and which buildings you needed and things like that.
So that, you know, those are kind of two strategic initiatives that I think helped me get into the CFO seat that were, I call both of them very data -driven exercises.
And the data -driven being such an important part of the office of the CFO now, thinking about you just being at the forefront and having access to the tools and people in that mindset, how did you leverage data and analytics to drive your strategy at the companies?
And were there, like, are there some interesting KPIs or different ways maybe that someone at a, you know, old school brick and mortar company wouldn't have looked at it that way?
And were there unique challenges as a CFO in such a data -centric environment?
because you're, if you're saying all the data that I care about is our GL, that's one approach.
But if you're trying to actually incorporate data from all parts of the business and the external data as well, I imagine it can be a wealth of information, but could also have challenges with it as well.
Yeah, I think there's kind of two things that you have to do.
One, you have to really define what are the most important KPIs and metrics that the company is going to rally around.
And those, you can't have too many of those.
If you have so many metrics out there that the company is trying to focus on.
I think you can get kind of lost in the weeds and not have kind of a strategic direction of where you're trying to drive the company.
I think, you know, one of the things that we focused on in my tenure as CFO was we were going through a subscription transition.
So what was the percent of our customers each particular period of time that were, you know, subscription as opposed to our traditional perpetual business model that was, you know, delivered as software versus, you know, something that's recurring.
And so we closely monitored that.
We also started looking at annual recurring revenue as another example of a closely watched metric, net dollar retention.
So how much were existing customers from the year prior?
How much have they grown?
And then overall customer retention metrics.
I think those kind of, in my word, those were the four big ones that we focused on and really tried to educate investors on why those were important, educate our team on why those were kind of the highest level metrics that we were really focusing our business on.
And then you would have hundreds of other KPIs and things that each individual kind of part of the team may be focusing on.
If it's, you know, deal desks, it's how many orders can they process in a particular period.
You know, if it's procurement, it's like what percent of our spend is managed by PO.
There's a lot of different things that individual teams would look at, but we didn't bubble those up to our kind of overall OKRs that the company would report on on a quarterly basis.
Those were reserved for those most important top four or five that we focused on.
Gotcha. And, you know, I think about the other data that you're bringing in and the KPIs and OKRs and stuff you're using internally in your departments and across the company.
And it's a stretch from where we start in our career earlier, where you kind of get that accounting foundation and you're just thinking about the GL and it's more just, you know, putting everything in its place, being sure that controls are in place and all that.
And then the transition, I think, of finance over the last, say, two decades, maybe 25 years from shifting that focus from the foundation, which obviously is important.
You have to have your controls.
And you mentioned Sarbanes -Oxley and all that.
And you've got to have all that in place.
But now, I mean, that's not even table stakes.
You've got it. That's where you start.
And then it's what are you doing with this data and becoming more strategic in that finance function.
And in that time, you're seeing the shift where more CFOs are moving into the office of the CEO, which didn't used to be as clear a path.
And I think for you, going from a couple of successful runs as a CFO at data -driven companies to founding your own, would you say that the CFO, you know, if Warren Buffett called accounting the language of business, then you're very fluent in that and you have the data and analytics.
would you say that the CFO role particularly helped you as you were going to found your own company?
My experience being operating a company at the executive level, whether you'd be a CFO or the CMO, others, you're involved in kind of, at least the way that we ran the company in every kind of major decision, it was kind of a committee process with the direct reports of Adam Selepsky and we would have a business review over kind of different functions and provide feedback to those stakeholders in that particular department of here's some strategic direction that, you know, we think the executive team thinks that you should work.
I think that experience really grounded me to be able to see a lot of cross -functional aspects of a company and then be able to roll in.
I think where being the CFO helps most when you're in a kind of an early stage startup is really understanding capital structures, agreements, being able to raise capital, those are all very important, you know, aspects of being a founder that, you know, having that experience as CFO gives you a leg up compared to maybe someone who grew up on the product side who hasn't had experience running a board meeting or raising capital.
And so there's some aspects that it makes it more simple.
But then with some CFOs, they haven't had that experience being an operator like we did at Tableau and be part of that executive team of making key decisions.
And I think both of those experiences helped me prepare for this role.
Gotcha. So tell me about Caliper.
You started the company, what, about two years ago now?
And I guess, you know, making that leap from CFO to, you know, Blue Sky, brand new startup, I mean, it had to be, you know, something you thought about for a while.
So tell me kind of your mindset and what inspired you to start the company and walk me through Caliper's progression.
And first off, maybe tell us what Caliper does as well.
Absolutely. Yeah. So Caliper, so our website is calipersoftware .ai.
We help companies get better visibility into their costs and usage of their public cloud and other consumption services.
So in my experience at Tableau and then later DataRobot, the cloud spend was the variable cost that is most likely outside of bookings, top line bookings, to impact your ability to hit your numbers in a particular quarter because of the way that different people throughout the organization in a very decentralized way can turn on servers or save more data, change their storage, all types of things that can kind of impact the amount of funds that you spend on the public cloud.
in a particular period.
And so there was kind of three moments in Tableau's history that we saw those kind of costs escalate faster than we anticipated.
One was when we originally moved from data centers to the public cloud.
The second was when we went from perpetual to subscription, as I talked about earlier.
And the third was when we launched data management, which had a different spend profile than our traditional Tableau products.
And so all three of those moments really meant like deep introspection about how do we get this cloud spend under control?
And so what we try to do at Caliper is take all those lessons learned from that experience and then bundle them into a product that's ready to go in 15 minutes of setup time for a customer.
So we're trying to kind of disrupt the existing providers in this area who take weeks and weeks to implement and have tens of thousands of dollars cost.
We brought the cost significantly down of managing this spend.
And then on top of that, we get you set up in 15 minutes or less.
So we're highly disruptive to some of the legacy providers in this area.
What I love about the origin story of Caliper is that you firsthand saw the need for this.
So it wasn't like you just got the whiteboard out one day and were like, I need to come up with a business idea and just started writing on it.
It was, here's a need that I have.
How do we solve this?
And it sounds like that Caliper came about very naturally through that.
Is that accurate? Yeah, absolutely.
I mean, this was kind of the number one problem that we faced when I joined InRobot.
They had rapidly escalating cloud spend and it was a big cross -functional initiative to get that under control.
And actually, the individual who we hired to help with that has actually been an advisor for our company and kind of the voice of the customer as we informed our roadmap.
He left data robot and joined another company, but he's been helping us kind of steer the product direction and doing a great job for us.
And so you have .ai in your domain name and you have, obviously, if clients can get onboarded and set up in as little as 15 minutes, Obviously, you have a lot of automation.
Are you guys leaning heavily into AI right now?
Can you talk about how that's integrated into your platform?
Yeah, so we're trying to, we're doing a couple different things that are, I think, relying on machine learning technology.
One is we're trying to bring in kind of advanced predictive capabilities.
So predictive ML and statistical data science have really been able to kind of look at seasonal patterns within the data and predict what is going to happen in the next period of time and then use that to find anomalies.
So that's an advanced statistical kind of model.
And then the other thing that we're doing is taking the results of our visualizations or that statistical model that I just mentioned and then feeding them into a generative AI model that then provides the customer with action points.
And so those are kind of some of the exciting features that we're building that we're calling kind of intelligent insights is the overall kind of feature that we're working with customers of kind of a prototyping phase to see if we can add more value by not only providing what's happened in the past, but really saying, hey, this is what was anticipated to happen.
These are the anomalies we found.
Here are some specific action points that you should take to kind of bring down those anticipated costs.
Yeah, and I love your approach to it because right now, I mean, there are so many companies out there that are just AI washing their company.
They're just sprinkling, they want to say AI.
And because obviously with your background at DataRobot and even Tableau, you come from a data and analytics background.
And so machine learning and predictive analytics and everything that you've done, I mean, that's all deterministic, set, reliable, tested technology that can help your clients.
So you implement that, you know, whereas most people don't understand what that means or whatever.
Certainly, everybody's talking about generative AI.
And then the way that you're able to layer in that generative AI piece to use it where it's valuable to add to.
So it's not just, you know, you have a chat bot that is using some rag setup and doing minor features in the product.
It's really, you can see how that machine learning algorithms and things that way are adding value, but then maybe generative AI is just a better way to communicate with that data.
Yeah, so we thought about different ways of approaching it and what we fear about you just purely relying on generative AI, if you're not using kind of an advanced statistical model to find the anomalies, is that if you follow some of the news stories about generative AI, it's not really good at math sometimes.
And it can hallucinate.
So we're really using that only to communicate, you know, try to take data and communicate the findings as opposed to doing calculations.
And so we're using, we're doing all the calculations based off, you know, more advanced statistical machine learning models.
And then, you know, really using the general AI for what it's best at, which is summarizing, you know, a prompt or a query.
If we generate a crosstab of your spend by a particular period of time, it can then summarize those peaks and those valleys and what action points the customer can take.
And we think that's a better approach than just purely pumping cost of usage data and then allowing General AI to try to do the math.
I think if you did that approach, which some companies are, I think you'll find that customers won't rely on the findings and then they'll get little value, little utility out of the product.
It's that trust step.
The first time your AI hallucinates, that trust is out the window.
So, you know, with your background, I've pushed for this for a long time because I've started my career in telecom where we had just a well, this is a million years ago.
This is, you know, right around 2000.
And we had so much data back then and we didn't have all the reporting tools that you do now.
But in my first finance role, you know, predicting churn in a telecom company, I was trying to get data from whatever sources I could to predict these churn events because we would, you know, we were churning 2 % a month.
And that's a pretty significant, you know, it's 25 % a year.
And so as much data as I could to bring into the finance side to help predict that.
It's to me, analytics and finance have been tied together forever.
And I imagine your career is probably very similar just because of the places that you've been, even sounds like back to PwC.
But I'm wondering, out of your clients and people you talk to in the industry, how do you feel that the application of analytics in big data in finance, do you see more companies adopting that now?
And how do you kind of see the role of finance evolving in these data -driven companies with, you know, you can speak to generative AI if you want, but I'm just thinking like classical AI machine learning and statistical analysis and all that.
Yeah, I think there's really a divergence in two types of companies, from what I can see.
There's companies who very deeply want to control the data.
They tend to be more focused in highly regulated industries, industries that, you know, have a fear of, you know, the employee base kind of leveraging that data for kind of inappropriate reasons.
And those 10 companies tend to move very slow and are not able to react to changes in their business as well.
And then the companies that I feel are generally higher performing are the companies that really unlock as much data as possible and give that and put that in the hands of the frontline employees that are trying to kind of make a difference in their business.
So in your example, kind of churn, you know, that's a very common, you know, area of focus for any kind of technology company is, you know, deeply trying to understand churn because it impacts your valuation so much.
You know, just one or two points of improvement in churn can dramatically change your long -term valuation.
And so, you know, you know, providing data, telemetry data around, you know, how the customers are using features or how many times they're logging in on, you know, putting that data in the hands of customer success reps is one kind of very easy way that you can make them be able to kind of spot, identify potential customers who may be more likely to churn than others by whether or not they're using the product and how frequently they're using the product.
And so there's some people who say, well, we can't give that information to those particular customer success reps and they try to hold that back or for whatever reason.
And so I just, you know, I would encourage many companies to really think closely about, you know, is this financially sensitive information that can move, you know, capital markets?
And, you know, sometimes you have to control things for, you know, Sarban's Oxley reasons and things like that.
Or, you know, maybe it's privacy if it's, you know, individual information about employees or health records or things like that.
But for most companies, I think there's an opportunity to unlock more data for people to make better decisions.
And that's what we're all about here at Caliber.
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We've been talking about democratization of data for probably 20 years now as well.
And we've been talking about digital transformation for 30 years at this point.
You know, I think, and still, it's in my day job.
I was actually just right before this podcast recording, I left a meeting where we were talking about a digital transformation project.
This would be targeted for clients in the like $2 to $20 million revenue.
So, you know, small SMBs.
But the thing is, a lot of these companies, you know, they don't have an analytics team they have their tax accountant closing their books for them or whatever you know they're just very immature companies and we're trying to figure out a way to help bring these companies into be doing data -driven decision making and and part of that would be the fin ops side the cloud management and everything but i think about it can be a hard sell because a lot of times if you're talking to and you know maybe i'll move away from the very small end like pre -cfo level companies and maybe get to you know more
than maybe 25 million range where they have a CFO and all that.
But a lot of CFOs are in that category of you were talking about, those old companies where I know GL data and I don't think we need to be sharing this other data with these people.
So it's hard to shift that mindset.
And I'm wondering, just because with your background, you've been in companies that were so forward thinking, can you think of any practical steps that finance teams, if people are in that first category where they still, and it really feels like as AI becomes more prevalent and with generative AI, as it becomes more accessible.
If you haven't done your, I'm doing air quotes here, digital transformation at this point, then you're really about to be super behind the curve.
So are there, what would your advice to companies who are maybe laggards in this area of data and analytics, what could they do to sort of start moving in that direction?
And I'm not even talking about getting to AI yet, but just getting to base analytics.
Yeah, I think the most important thing is get started.
I think the most common reason companies fail They say they want to clean their data before they're ready to analyze it.
And that is something that is continuous and never, you know, and by analyzing the data, you sometimes find kind of why it's not clean and then can make changes.
So I would say, don't wait to get your data in a central repository or clean it completely up before you allow your team to analyze it.
I think that's, you know, so get started, get started immediately.
immediately start finding the things that are most important to your business and look at those every day.
I mean, for me at Tableau, it was, I got up every morning, looked at bookings.
I looked at employee ads for the week and how many employees we had to hire and how many had left.
I looked at CloudSpend, things like that that were big impacts of drivers of RP &L, T &E spend.
Those are kind of things that are really easy to unlock based off your apps that you use.
If you're using Salesforce, you can create a bookings report and then pull that into an AI -like software.
And I think it's pretty simple to do.
And so I would say for most companies, it's just really get started.
Don't try to get everything organized.
The other thing is just define what's important for your business.
Even at a small company, even if you're at $25 million in revenue, if you focus on too many things, you're going to fail.
And so for a company that size, there's probably some really key product milestones that you're trying to deliver to kind of increase the kind of size of your TAM.
On the business side, I think things like bookings or trial conversions and things like that are probably super important at that early stage.
You can tell I got really hung up on data and analytics just because, like I said, you worked at two of my favorite companies and all that.
But I do want to talk a little bit more about cloud spend for our listeners who are, you know, at companies that have a big cloud spend and sort of how they're managing.
Could you maybe walk me through the kind of client that would come to you that would have, where you guys would have the biggest impact?
Like where, you know, where are they size -wise, kind of their spend and what are they doing now that Caliper can come in and really make a difference?
Yeah, so we help a number of companies at different levels of maturity.
I mean, we have, you know, startups who are spending, you know, $120 ,000 a year on AWS and they're using our product.
Maybe one person in their FBA team or finance team or their CTO has access and they're kind of looking at things like which of their machines are turned on, you know, nights and weekends or you know what storage have their engineers selected should they upgrade to the latest kind of source they get to take advantage of the best price are they you know looking at their spend by day by hour to see if there's kind of unusual fluctuations those are kind of things that the early stage companies could do as they mature i think let's say they're spending five or ten million dollars on um the public cloud
you know at that point they probably have multiple public clouds so we're helping them connect both their aws maybe their gcp data, pulling it into one kind of single pane of glass, allowing them to maybe create virtual tags.
So, you know, create departments or cost centers within our platform that they can analyze their business based on their, you know, that level of maturity.
They're probably starting to explore doing things like reserved instances or savings plans.
And so we can help them, you know, see, you know, how much of your spend this, any particular period of time, day, week, month has been covered by a savings plan.
How much is on demand?
Are you leveraging the opportunity to do some kind of tasks that make sense on spot instances and what's that coverage?
And so that's when you get into more complex use cases and then allowing them to use our product to kind of drill in and ask questions by different departments or by different teams of kind of those kind of more complex processes.
And I think that's where we seem to do best.
I think we're helping companies at the early stage all the way up through earlier public companies that are just getting started with analytics.
And then one thing we're really trying to do is expand the number of things that we help them with.
And so we launched a Snowflake cost and usage connector so we can help them with not only can help them with cloud use cases, we can tell them which queries are costing you the most within your Snowflake instance and what teams are driving that increase you saw this past month.
And those are insights that people have generally never had just from looking at their billing console on Snowflake.
And so we've been able to really help companies get that cost under control, which may be not as big as CloudSpend, but might be growing more rapidly.
At FP &A Today, we think a lot about KPIs.
And I think about if you were on the FP &A team that was watching CloudSpend and all that.
I know there's the dashboards that are built in and all that, but there is domain expertise that you have as an FP &A person.
And I know you could be embedded and learn it, but then there's sort of the IT expertise and understanding this is why we spun up these clusters and, you know, this is the burstable part of it, but understanding the variable cost.
I mean, regardless of size, what's your impression?
I mean, I guess this is kind of speaking to the TAM of Caliper, but how much of a sense that most companies have on their cloud spend or how to control it when you come in, what do you think?
Yeah, I would say the vast majority of companies that we meet with, unfortunately, don't focus on it.
It's a lot of money that's wasted in our broader economy on public cloud.
So you have kind of a challenging situation.
You have the office of the CFO who cares deeply about, you know, budgets and kind of getting costs under control.
And they, like you said, they just feel uncomfortable asking questions and kind of digging into, you know, a cost that's being managed by another department.
And so I think it's very important for kind of the, you know, whoever owns the dev budget within your FP &A team, maybe if you only have one person, that's the VP of finance or the CFO. If you have an FP &A team, you might have someone dedicated to the dev team.
They should be using a tool like Caliper, whether it's our product or some other company, to better understand what's going on in their cloud spend and holding the engineers and asking questions, holding them accountable, asking questions about spikes, asking questions about, you know, did they use the right size machines?
Could they go down a size, you know, like just really kind of digging in and looking at the spend on a regular basis.
Because I think the engineers, they have a different, while they, generally, someone in the CTO's office owns that budget, they have a competing priority of delivering high -performance software, delivering on schedule.
They're not necessarily thinking, how do we do it as most efficiently?
And so I think there's got to be a partnership.
And that's one of the things that I think we do best compared to some of the other tools is because we're really an analytics platform as opposed to kind of an out -of -the -box kind of dashboarding tool.
I think it really allows kind of that inspection and really diving deep and really understanding what's going on.
And I think the best teams that we have, you know, have a partnership between FB &A and IT or the dev organization.
And they're looking at it collaboratively on a kind of maybe five or ten times a week from what I can tell from our data on telemetry data.
And that's, I think, the best performing organizations.
I think, you know, if you want to if you want to say how big a market is that I think most companies based off where we're at, we generally help them save 30 percent on their cloud spend.
So if they're spending a million bucks, that can be 300 grand for our investment in Caliper for 10 seats is going to be $9 ,000.
So you can you can save your company, you know, $290 ,000 by spending $9 ,000 to provide them with some tooling to better manage that spend.
Wow. So years ago, I worked at a couple of e -discovery companies, and we had a lot of data, you know, as you're bringing in, you know, all the discovery information in a lawsuit, could, you know, terabytes and terabytes of data from, you know, from single clients.
And one of them was using AWS and Cloud.
The other one, we had our own data center.
And when we were using AWS, it was, as a CFO, I didn't know what I was, and I consider myself kind of a tech forward CFO. But, you know, I didn't understand the intricacies of the cost on AWS.
And we hired a firm to come in.
And the ROI on that was still, I mean, you know, it was a lot more people than systems coming in.
And, you know, we paid them whatever a month, but the ROI was simple because they got the savings in there.
And it sounds like where you are now, and maybe this is where the industry has gone, it sounds like what you're doing is not consultant intensive, right?
It's a lot just the software and then maybe a little bit of handholding from you guys.
How does that, you guys are doing compared to some of these old companies that would come in and help you manage your AWS spend?
So we rely on partners to do the consulting.
As a business model, we're just trying to stay away from offering professional services.
But we have seven partners that we've onboarded since we launched last year, and they provide based off different what the customer needs from an expertise.
We have some partners who manage cloud on behalf of customers.
So I think small businesses who need a website or need some AWS infrastructure and you don't have an expertise on your team, we have partners who can do that.
We have partners who can help you, hey, I have a $10 million AWS budget and I want to find some strategies to bring that down by 20%, 30 % and they're consultants and they come in and they use our software but then do hourly consulting or outcome -based consulting where they say, hey, we can migrate your on -demand workloads to these particular savings plans, we think we can save you $300 ,000 and then they'll take a fee for that kind of onboarding work.
We provide the tooling and then the partners provide the consultation.
Just being a technology company, we do try to help our customers.
And so many of our customers, they don't necessarily need a full -blown consulting engagement, but they just need a few insights from their data.
And so when we onboard them, we show them how to use the product.
And then we also give them some observations based off kind of benchmarking that we're seeing from other companies.
And I think that's sometimes just helpful for them to say, Hey, you know, Oh, I didn't realize I had my test environments on 24 seven.
If I shut those off on the nights and weekends, I can save 30 % of my cloud.
Yeah. That, that little bit of help is all they really need.
And then we give them some white papers and things on how to do that.
And with the compute growing so much right now with everything around generative AI and you, And, you know, every day you're hearing about how many more billions of dollars are being spent on new data centers.
And, you know, we've been digitizing everything in our lives for the last 30 years.
So I'm wondering if you're already seeing or what do you think the impact on cloud hosting, and this could be everything from Snowflake, AWS, just, you know, all the cloud expenses.
How much are you seeing already or do you think that the cloud storage is going to grow based on this new tech round of generative AI?
Well, I can see from my customers, just from the small amount of customers we have, several of them are AI first companies.
And so they are experiencing sharp increases because they're buying more expensive or they're renting more expensive compute types than they had traditionally.
We had a small startup that was very tiny and ended up going out of business, but they were renting a machine for $35 a day that they had turned on for six months and they didn't realize they had for an AI experiment that they had left on.
So that was like $12 ,000 a year savings on one seat of caliper we helped them find.
So I think it's one where they're renting more expensive machines.
A lot of times I'm helping customers with, they're doing like single tenant SaaS where they're standing up environments on behalf of individual customers.
And I'm helping them with like, well, you're charging a million dollars for this customer, but it's costing you 1 .6 million.
So helping them understand their margins on particular customer relationships.
And so, and from all that experience, all I'm seeing is dramatic increases.
And so this is kind of an exciting time to be in this market, because I think that's why the cost management kind of tooling kind of category is expected to rise.
The TAM is supposed to get so big is because there's so much spend going in this area.
And there's very little tools to help you manage, you know, this kind of consumption based pricing.
very interesting so yeah it does seem like your timing is very good as you're kind of riding the wave of this uh you know just when you thought it couldn't get any the growth couldn't get any faster now here we've got the past couple of years where uh we're actually right you guys kind of caught the wave right at the front of it and uh yeah so hopefully that continues to do well for you for someone just starting out or just a finance professional uh who's starting to realize okay i've got it i've got to get up to speed on the integration of of ai and data analytics into of the roles for at an individual
level, what advice would you give them?
I think it's important to kind of make sure you understand kind of the latest technologies and how that's evolving.
I mean, there's been so much change just in the time when I first started Tableau, we were competing against companies like Click and Microstrategy.
Now they're a Bitcoin company.
And now people were doing data cataloging and things like that.
And then now everything's moved to either Snowflake or Databricks.
And so it's kind of deeply understanding kind of the trends in the industry.
Obviously, there's some really powerful companies that are helping organizations.
The two best in that are kind of Databricks and Snowflake around getting data ready for analysis and providing, you know, some basic AI tooling to be able to analyze that.
But from a career progression standpoint, I think it's important that, like I said earlier, that you really define what's important for your organization.
And that's going to set you up to be kind of a thought leader in your organization if you can provide a point of view on what are the insights that you think are going to best drive the business outcomes of the company and investing the time to really think through that and interviewing different parts of the business.
and understanding those drivers.
I think you're gonna set yourself up for being instead of just kind of report the news type accountant, you'll be a trusted business advisor to the rest of the organization.
Absolutely, really, really good advice.
All right, well, we're at the time of this show where we've got two questions that we ask every guest and it's always fun to see what kind of answers we get here.
So the first one is, what's something that not many people know about you?
Maybe something they couldn't find just by Googling you real quick.
I love being on a sports field I help you know I coach multiple teams.
I coach uh high school baseball age kids and I coached my daughter's fast pitch team and so you on the weekends and nights you can find me on a field somewhere uh almost every night of the week so uh that's a lot of fun and and something kind of invest in kind of mentoring you know young young kids and helping them prepare to be uh kind of better athletes.
That's awesome man and travel ball That's a, that's a pretty serious commitment, right?
Yeah, it is. It is a lot of work, but I, you know, I enjoy it.
And, you know, really the most important thing is I enjoy kind of getting the, you know, seeing the kids grow from year to year and improving their skills.
All right. And our last question here, so you've been a bit removed from it and you were, you were a Tableau guy, obviously.
And, but you're also a finance guy at heart.
And we asked all of our guests, what is your favorite Excel function and why?
I was pretty good at the Excel shortcuts.
You know, I've completely transitioned to Google Sheets at this point in my career.
Just after I started at Salesforce, they were a big Google shop.
And so, you know, I think the, you know, when I try to open up Excel, I still have trouble now with being such a transitioning to the cloud.
And so one of the things I guess I really liked about that transition is just having documents available at all times, whether you're on your phone or your computer or maybe another computer.
And so the sharing functionality of Google Sheets is something I love.
I can work pretty quick with the Excel shortcuts in the old days.
People were like, what did you just do with all the shortcuts?
I'm able to move things around without having to kind of go in and use a mouse or anything like that.
Um, but, uh, my days of doing pivot tables are long gone.
I'm, uh, I would struggle right at these days.
Yeah, I know. I'm in, I'm in the same boat where I, you know, I used to be so proud of just how quickly I'm, I was one of the guys that had my mouse, you know, uh, the, the cord tied a little noose on it and hung the mouse, uh, on my thing.
I was like, we're no mouse here.
We're gonna do everything through, uh, through shortcuts.
And now I, I don't, I open it up and I'm like, I'm not even understanding the formulas that are in the cells anymore.
It's just, you get removed from it and you, I tried to, I went back a few years ago and got an FMVA certification just to get back into Excel.
And of course, if you're not using it every day though, at that level, you know, you're getting, using it more as a tool that's being presented to you rather than building the model.
So I do miss that a bit, but I'm right there with you.
Yeah. You know, I think when you, especially when you're a Tableau, you tend to, you know, rely more heavily on kind of your analytics software than your Excel to do most analysis.
So I think I got completely away from it at this point in my career.
But I do have a big budget document for my small startup and kind of update that on a monthly basis.
And I love the ability to share and how quickly information can move around these days compared to the old days of when I first started my career.
Yeah, yeah. So, well, Damon, I really appreciate you coming on the show.
Just one last question, and I know you mentioned it earlier, and we'll put it in the show notes too.
But if our listeners want to get in touch with you, learn more about you and what you're what you're doing at Caliper, how can they do that?
Yeah, so it's calipersoftware .ai.
You can do a free trial of the product.
You can contact us through the website.
So again, calipersoftware .ai.
All right. I appreciate it.
Thanks for coming on.
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