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Now, onto the show. From Data Rails, this is Welcome to FP&A Today.
I'm your host, Glenn Hopper. In this two-part blueprint-style episode, I'm talking to the directors of BI and FP&A at Wasabi Technology.
Wasabi is a hyperscale cloud storage company competing with the biggest players in the market.
With a rapidly expanding global footprint and a complex go-to-market model, Wasabi's finance and analytics teams are under constant pressure to deliver fast, accurate, and actionable insights.
And that's what we're going to talk about today.
Marco Spinto and David Suter share how their teams work together not just across functions, but as a single insight engine to turn raw data into real-time financial intelligence.
Marcos is Director of Business Intelligence at Wasabi, and Dave is Director of FP&A.
From trust and tooling to forecasting and first drafts, they break down what it really takes to build a high functioning partnership between BI, and FP&A.
Gentlemen, welcome to the show. Thank you very much, Corey.
Yeah, I think this is the first time that I've had two guests with a planned two part episode that wasn't part of a big webinar.
So this is kind of an experiment, but I thought know talking to you guys before the show i realized we're gonna have so much great stuff to talk about that i really think there's two episodes worth here and uh I love this conversation, regular listeners, though we talk about it all the time. all the time.
And so having you guys to talk about that partnership, I think is going to be a lot of fun.
I guess let's start with the relationship between FP&A and BI at Wasabi.
And I guess, Dave, if you can kick us off here, from your perspective, what makes this collaboration work so well in your guys?
Yeah, well, in FP&A, we're big consumers of data.
So we rely on the IT and the to make clean a lot of big, messy data.
I mean, that drives our forecast across multiple scenarios, really value that relationship And what makes it work?
Trust, communication. I think both our teams have a real pragmatic approach to problem solving and a real dispassionate approach. get the right answer rather than some pride of ownership.
I think I've found Marcos' team, Marcos and his team's be really receptive to feedback and new ways of looking at things.
There's a lot of back and forth, a lot of dialogue, a lot of communication.
Yeah. And that that ownership of the reports, it's funny.
I mean, you know, on the operations side, people want to take ownership or provisioning time is down or whatever they've done.
And it's interesting as both in FP&A and in BI, reporting and analyzing and forecasting and that ownership, I could see that being a tension point of, trying to own the data and the reporting.
And I'm wondering, Marcos, from your side, as you work with FP&A.
They're a customer, but you guys, I know, I'm sure, are also tracking you know your own metrics and you have other customers too so how do you um How do you build that trust and alignment between the two teams?
And I guess, is that something you have to sit down and be intentional about?
Or does it, in the case of you guys, did it evolve organically?
What was the setup there? Great question, Glenn.
I think it's part intentional and part organic.
The intentional part is, hey, we need to communicate if this is working or this is not working. to Dave's point.
We always love when people like Dave and his team and other folks come to us and say, hey, this data is not looking what we were expected to.
We were looking, we would expect to see something different or Hey, what are the insights that you can give us out of that data?
That's also very important feedback that we can learn.
It's also the other part of being organic and We are as a remote-first company.
There is also another layer in that How can you manage and balance Being on Zoom calls all day long, meeting people, how do you manage these relationships and not having the coffee or water cooler for you too. build the trust that they prefer to.
Yeah, is that... That can be tough on Zoom calls because it's especially, you know, we all get Zoom fatigue and you're you feel like you're on it all day and you get to a point where you're just tired of another meeting, but you still have to have that collaboration.
Is there anything Any insights or anything you've found that works well to keep that up when you are really just back to back Zoom and working together on complex issues.
I think at the most foundational part, it goes back to our culture.
I think that a lot of people here are really good, nice people to work with and to be with.
I generally like to work with Dave and everyone on his team.
It's always a pleasure to work with them and hear and learn and collaborate with them.
It's not like you have your point of the ownership and the responsibilities and the deliverables. how we are doing this.
The journey is always a pleasure and that makes working on these kinds of problems and issues really special.
Yeah, Dave, I mean, you know, you're under pressure to get numbers and you have to have that collaboration and you know, You're not BI's only customer.
From your perspective, and you know when you're managing your team and you know you've got your own reporting requirements and needs how do you handle that that interaction with the BI team.
Yeah, I mean, I think, well, to follow up a little bit on what Marcus was saying about, you know, the collaboration between our teams I think it is organic in a sense, but there's also this function of type of people we hire, you know, to Marcus's point, you know, you gotta be, if you're, if you've hired people that are well intentioned and easy to work with and a pleasure to work with, it makes that collaboration so much better.
And that just rolls right into prioritization.
I know they're under a lot of pressure. I try to be respectful of that.
But, you know, the data can never come fast enough in our mind. but it's a balance, you know?
I mean, sometimes there are needs where I just need a fast answer that is ballpark close enough And you communicate the priority and the urgency and okay, let me see what we can do.
Other times it's a little more in-depth.
It takes some projects four weeks, but we've got to get it right.
So I think it's just that collaborative effort to try to understand where they're coming from, understand that we're under demands from or a board meeting.
Um, I think it just goes back to that collaboration and with a good team that, uh, understanding of both sets of problems.
So what does the day-to-day interaction look like between your teams?
Is it project-based or recurring sinks or more on-demand?
How does that work? And Dave, I guess we'll stick with you on this and I'll go to Marcos.
Yeah. It's a little on demand. You know, I think there are times when, you know, we're working on a specific project for a specific period of time and then other times, you know, it kind of my demands kind of fade away and I'm sure, you know, other teams are keeping them busy.
But it kind of ebbs and flows a little bit.
There's definitely times when we're off on other projects and give these guys a break from us.
Do either of you do daily stand-ups with your internal team?
I don't do one, I don't do it daily. I do, you know, weekly, I have calls with our team and then, you know, weekly one-on-ones, but Not unless there's a project going on.
But when there is a project, yeah, daily.
Yeah, the BI team does twice a week. stand-up calls.
We have two-week sprints for deliverables and larger projects, and you have the add-on request that comes and goes. every other time.
Dave, we talked before the show and you're obviously one of BI's biggest internal customers.
How do you think of Maybe you already answered this when talking about the collaboration earlier.
But what do you think about the role of BI in supporting FP&A?
If you're rolling something up and you're reporting metrics, but you're waiting on them, you're never going to never gonna throw bi under the bus and be like well marcos can't give me the numbers so i mean that back and forth and you have to understand, I mean, you're not going to just take numbers that you don't understand.
So how does that sort of collaborative part work when you're maybe it's defining new metrics or understanding the data that you're pulling.
You're not the one going and pulling it all the time, but you've got to understand and work with the BI team for that.
Yeah, prior to having a BI team, I would I would always joke, you know, I don't trust anybody.
So I want all the data is as granular as I can take it and then we'll make sense of it.
To not have to do that, to have a partner that we can rely on is fantastic.
So, yeah, we totally now rely on Marcos' team.
There's been a lot of trust built up. Anytime we're looking at data, there's always this issue of all the nuanced and edge cases that these guys have spent so much time digging into and understanding and mitigated.
Um, it's really fantastic to be able to trust that data and then You know, we're big consumers.
Like I said, we're constantly pulling more data, better data, different slice and dice because more granular we can get it, the better we can forecast.
So the trust that we built up over the past few years has been invaluable.
I want to ask both of you about this because at FP&A, if you're forecasting you can be directionally correct.
And there's, I think about the difference between finance and accounting sometimes.
There's no directionally correct in accounting. is that trial balance has to balance and debits and credits have to, match up and all that.
But if you're forecasting, as long as you're close and can explain what you're doing.
I think BI, I guess it depends if you're doing descriptive or predictive analytics or whatever.
It seems like there can be more of a push.
Obviously, you have to be able to repeatable and explainable all of your answers but There's also that tension, and you guys are dealing with so much data with the industry you're in, and we'll talk... obviously a lot more about that.
But With that tension between speed and accuracy, and Marcos, let's start with you on this.
How do you balance that need for We have to get something fast and this goes to what Dave was just saying, but we also need to be as precise as possible here.
Because you can get really bogged down trying to close that last 2% or 10% gap of making it exact.
Right. I think at a Glenn, I think that you cannot cheat experience.
So if we are a storage company and we are reporting on how much storage our customers have, We need to understand the BI team needs to understand what does that mean?
What does that not mean as well? That goes to all the use cases and edge cases that whatever that can be.
Is that how do you... Think about storage from different customers in different data centers.
There are all these kinds of nuances. But you can only learn about this if you have the experience, if you're talking with the folks that know about the business and it's either your customers, but how the input of the data comes from.
So that comes from engineering and from sales, and that gives us context to make the right assumptions when we're building the correct database.
I think that over the last two, three years, we built the foundation of what's the essential, what's required for us to run as an organization, as a fast-growing software as a service company.
Now, the next step is what else can we do with this data?
That's where this gets interesting and it goes to your speed and accuracy. at maybe the first forecast that we did three years ago.
We were off by 10%. then we make improvements and it goes from 10 to 8 to 5.
I think that Dave now has a good enough forecast that we are within a couple percentage points from what our plan or our forecast.
So that gives us That's probably how you also build trust is that the data that you're showing is consistently reliable and you can playing what's underneath that.
And Dave, I think about that speed and accuracy and the potential for drift.
If Marcos' data that is delivered to you is not as precise as it needs to be, and you're building a churn forecast or whatever you're building off of it, it can exaggerate the error and difference. you're deliverable if you don't have it as close as possible to what you need. could drift from that.
So from your standpoint, and you alluded to it earlier where you You used to have to get all the data and figure it out yourself, but now you have someone upstream who's providing it for you.
How do you balance that knowing, does it start with understanding what you got from BI and where there could be error there or how do you approach it?
Yeah, I think it's just the classic cost-quality schedule trade-off.
I'll call it a schedule. You can only have two, uh, in our case costs are relatively fixed.
So now it's yeah. Speed versus accuracy.
Yeah, like I said, I think there's times when I just need a ballpark answer quickly.
Other times I need a solid answer and understand it's going to take a little while. of time.
To your drift point, though, I'm always looking for It's a third data point to validate is what I'm getting correct.
We sell cloud storage, we sell it in dollars per terabyte.
So I'll get data from Marcos' team around our storage, our terabyte usage, and our ARR usage.
But when you divide ARR by storage, you get a dollar per terabyte.
And when I'm looking at this and it's 10x what our list price is, I know something's wrong.
So now which one is wrong? Well, if I have ARR, I can forecast revenue.
I can understand if that's directionally correct.
So I know whether the error is in revenue, error.
So it's things like that where I'm always looking for outside data sources to validate is what I'm seeing right.
And if I can go back over six, 12, 18 months and get something that is directionally correct to these outside sources, I know we're on the right path.
So that kind of helps mitigate that drift.
And if it does start to drift, okay, now we have a new project.
Another layer of the onion we got to peel apart to figure out why are we drifting.
And then again, I think where Marcos and his team add a ton of value is they've peeled this thing apart and they know these edge cases and it's like, oh, you're not factoring in X, Y, or Z, And those are outside of the bounds of this regular view of what you're looking at.
It's really helpful to be able to peel that onion apart.
Yeah, and you guys are both... I'm trying to suss out if there's a difference in mindset because you're both, when we talk to FP&A guests, all the time we hear him say to be really good at FP&A, you have to sort of be an investigative journalist.
You're, constantly asking why you're trying to get to the root cause of something and you're using what you find there to create a narrative and to the facts as you've seen them through your experience investigation and bi is very similar to that so that's really it's almost like two crime-solving agencies both trying to solve the same problem with different approaches.
But I guess what got me thinking about this, Dave, is when you said talking about you have to know your business and and I think that that domain expertise, we keep hearing about this promise and Marcos, I think you're going to laugh at this, but The idea that self-serve data is going to be so easy, it's going to be easy to get all this because generative AI,
We're just going to be able to access everything and pull all this.
But where humans will add value, even if it does get you know, if Cortex or whatever and Snowflake really starts to work great and you can chat with your data, there's still that domain expertise.
And I think about maybe because FP&A has been evolving as a field longer than data science. that it used to be you could sort of sit in this ivory tower of finance and and not have to know the rest of the business.
Because it didn't matter what the cogs were, you were just presenting the numbers.
But now the way that FP&A works, You have to have business partnering.
You have to truly understand the business because all the work that used to go into putting the data together and all that, that comes a lot quicker or you have a team that's coming from Marcos that is helping with that as well.
But I'm wondering... if you refine down your approach to the metrics in this, if I'm trying to picture a meeting where you guys are both together trying to solve for a new KPI or whatever.
Is there a difference between how you guys approach problems or the universe of data that you deal with.
And I know that I just said a whole lot there.
So David, I'm gonna start with you and I guess it's, If you're trying to figure out something, you're trying to squeeze some extra margin out or you're trying to find some new area, Are you initially going to do that just through the data that you have,
Or when do you bring BI into that? I mean, is that maybe, did that distill it down a little bit more?
Boy, that's a tough one. I like your analogy about investigative reporter.
Yeah, I mean, I definitely view us as problem solvers.
That's a tough one. I mean, obviously we rely on data from BI to give us historicals.
If that then looks off trend to a forecast, that might give us a little hint. dig into.
But you're right. You're right. We are investigative reporters here.
And Glenn, to be fair, I think that a lot of times we work with the same set of objectives.
And that's what the overlap happens quite frequently. maybe with some different lenses we are looking at hey, give me the raw data and what's the output of that.
And for David's, Okay, give me the output of that.
Now, what can I explain with this set of numbers?
So that makes the work our collaboration also work because if we're looking at ARR or storage or churn or neck retention or whatever the metric is, we have the same set of objectives.
If we are trying to make or create one of these reports, we need to understand what's the context, what's the audience that this is going, And we try our best to be on the same page.
Are you both doing predictive analytics or So I know, I'm sure Marcos, when you are, you're using machine learning algorithms and everything and I'm wondering, Dave, if you're doing something predictive, is your team, using machine learning or are you using Excel or Python?
What are you guys doing? I guess, Marcos, we'll start with you. if you can tell me about what kind of predictive analytics you're doing and your approach and who the customer If you are doing that, who the customer of that analytics package is,
Great question. So one of our predictive or machine learning models is to forecast the amount of storage of a customer in the next six and 12 months.
But the primary user of that, it's not Dave, it's sales.
So we use that to engage sales into better discussions about the future. hey, we're looking at similar customers for the same amount of storage for the same, amount of years with wasabi, they tend to grow at X, Y, or Z rates.
Now, if you're going into this rate, We can probably offer you better discounts.
We can probably offer you some discounts or some terms. and that makes some actionable items for the sales teams to act on.
We are actually, as we speak, we are working to try to integrate this model into the FP me.
So Dave does a forecasting on the entirety of the P&L.
At some point, we want to use some of these machine learning models to feed whatever Dave is using.
And how about on your side, Dave? Yeah, we're a little bit still in the stone ages.
We're not doing any sort of predictive stuff other than just manual forecasts.
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I know everybody has their annual plan and we know especially it's got to be in the hyperscale mode.
That's got to be it. It's crazy to try to look 12 months out, but whether you're looking at 12 months or 36 months or you're re-forecasting at the a quarterly basis, you have a lot of data that can feed your forecast, how complex are your forecasting models, and are you using variables that are using internal and external?
Could you tell us a little bit about your approach to forecasting on the FP&A side?
Sure. I guess quickly on the expense side, it's fairly straightforward.
If you take away all of the capex and data center stuff, we look an awful lot like a software company.
78% of those costs are people related. So that's just workforce planning.
On the revenue side, it's a little more complex.
We have a very detailed forecast down at the monthly cohort level across different geos, different payment types across different channels, across different products.
So we can get very granular very quickly.
Or we can roll it up at a very high level and just do... a classic ARR roll forward with churn and net retention bookings functions in there.
So it's a little bit of both, you know, try to triangulate on an answer that you all feel good about.
But it's a lot. It's complicated. And Glenn, to be fair, I think that Dave is underselling his capabilities and of his teams because the amount of work that went to the revenue forecast. at the cohort level is just absolutely incredible.
The amount of details that were put into this and It's, Obviously, he and his team is very good at doing Excel modeling using all the advanced formulas.
But the most important piece is what's the output of the results.
And the accuracy is very good. Even at our growth rates, the ability to do accurate forecasting is really hard to do.
And as I said before, when they just started, we were off by 10%.
So it was very hard to understand. Now that he can go deeper and see per cohort, per month, or whatever type he wants to slice and dice, this gets into a better cadence of forecasting.
Thank you. Yeah. Thank you, Morris. We're getting better.
And is that, is machine learning for revenue forecasting, is that some I think you guys have sort of spitballed or kicked around the idea of because there just i just think with your industry you have to have so much data that um that I could see that potentially working but they also,
And I think I've mentioned it in the past couple episodes.
Because I came from a similar to where you guys are with private equity-backed companies and you know how private equity folks are, they're they love their models and they want you to build really great models of stuff so i I have a tendency sometimes because it's so fun to build these things to sort of confuse the map for the terrain, you know what I mean?
I'm so focused on the model that I forget there's a real world out there.
So I don't know, if you're doing this, unless it was quicker or you could get more precise or whatever.
I don't know. Obviously, Data Rails loves Excel and all of our listeners and even with data science or Or finance, Excel a lot of times is the first place you start.
I don't know, is that a collaborating on using machine learning for predictive revenue forecasting?
Is that something you guys have talked about?
Between the three of us, this is something that we are actually working on.
We have a great senior data scientist working on this model.
He's fine-tuning the model for a couple of different variables to understand not just at the cohort level, but in the tenure, the billing methods, but also looking at the behaviors of each individual of one of our more than 100,000 customers. what Excel is pretty good at, said, okay, give me a million rows and let's do a forecasting.
Now, when you're looking at hey, you have a billion rows, how do you manage those databases, right?
So that's where some of these data lakes joining and the idea is to speed something for Dave so that that he and everyone can use this to create better actions out of that. but it's still a working process and we're going to refine And Dave is probably going to see some problems in version one, version two, up until a point where he saying, okay, I can get away with this.
I can explain. Let's now roll this forward and make this plan even better.
Yeah, and I think it's early days for that kind of thing.
I'm not trying to be like a Luddite, but I'm...
You know, I'm a little skeptical of AI's ability to just like forecast everything into the future.
I think it's going to be great for anomaly detection, variance analysis, looking at where to highlight anomalies that need to be dug into.
I'm a little less... bullish on its ability to, you know, hey, just like put in all the data and like out pops a look proof Revenue forecast.
Yeah, especially if you have to have that explainability and you can't just say I threw it into the black box and this is what it figured out.
Yeah, exactly. But that said, I guess to Marcos's point, With a machine learning model and you have a boatload of data, you can...
Excel has its limitations and whether it's crashing or slowing down or what are just the cumbersome nature of trying to use Excel for everything.
I think there's a lot of, you know, if you're doing, uh, Serena forecast or something, it's a lot easier to do that in, in Python.
Um, than it is in Excel or if you have other forecasting models.
But the problem is how many people can build forecasts in Excel versus the few people who are not on the BI side who could build the forecast in Python and then make it explainable to the board, to management and all that.
So there is that sort of chasm between the output the explainability and how much better is it going to be?
Or how much time is it going to save? Maybe it ultimately comes to that is, once you set it, that it becomes quicker to re-forecast and do that.
You brought it up, I didn't have this as a planned question, Obviously, this is an area I'm very interested in and then it seems like I have to bring it up on every episode.
But when you mentioned flux analysis and what AI is good at, We do talk a lot on the show about generative AI.
And I know a lot of SaaS tools out there are building in their generative AI wizards and genies that you can talk to your data, And there's some stuff you can do with off-the-shelf chat GPT or Gemini or whatever. and I'll ask this of both of you, I guess Dave, we'll start with you.
Are you using generative AI successfully in anything in your department now or is your team using it for anything?
Not in any sort of official capacity right now.
Like I said, we're a little bit in the Stone Ages, in Excel, still love it.
You know, you'll pry down my cold, dead hands.
But we're definitely bumping up against the limitations, you know.
Like we said, you can only fit so many rows into a spreadsheet.
And we're definitely pushing the bounds of that today.
So we're in the process now of moving to a planning tool that has some of those, you whiz-bang AI features that we hope to take advantage of in the future.
But like I said, we're still early days. we'll see what pans out how about you marcos is your team doing anything with it We are.
There are some interesting use cases here and there. uh one of our data analysts he writes a letter to or a summary e-mail to the executive team every Friday, Initially it was by hand.
We were writing the summary. Hey, this is how much storage we added.
This is where... the pipeline came and closed and opened month over month.
Six months in and drafting these letters, that took a couple hours every day. he realized, okay, we now have enough data and text that we can train a model to do this for us.
So every Friday morning, he clicks now a button, generates the text, he now just edits a couple paragraphs here and there.
Okay, I need to change the numbers are already there.
It's played because it pulls the data from the correct place in the data.
Now it's a matter of adjusting and improving how the letter is written or maybe we want to highlight something different than the usual cadence that it sent every week.
That's an interesting use case that we are working on.
There is some more, they're still not in production that we are trying to figure it out with software. sales operations, how to improve and automate some of the processes on the order to cash.
And I know you guys use Snowflake. Are you doing this with Cortex?
That's right. That's right. So how are you finding Cortex?
I mean, I guess you have to pick your use cases.
Oh, absolutely. In general, it's been great for us. they have developed and created so many new functions in the last six, 12 months.
Their roadmap seems very exciting and interesting for the new use cases that we have. in our roadmap, we were just using one of their functions to predict from all the activities that our sales reps do with partners and with end users during the pipeline creation.
What are the most important activities that our sales reps need to do?
That can predict a closure of a deal in the next 3, 6, 12 months.
We were using three different models from Cortex.
We ranked them. We now have one of our favorite models. we're now going to double-click on each one of these activities.
That's super cool. And I think the use cases you mentioned really are That's the area where generative AI is going to be the most valuable and I picture the The problem is if your data is not in Snowflake or if you're just trying to work Excel, obviously you blow out the context window of the AIs pretty quickly, but When you have AI integrated into your data, if you can look directly at your GL or if you can look directly at your production data or whatever's in Snowflake.
And Dave, you mentioned variance analysis and explanation.
Obviously, very quickly, Janae, I could go look at your monthly financials and see where there's a variance and even look for things.
It could find correlations Computers are much better at finding correlations and variances than people are.
But if it can't tie in to your GL data, well, you're still going to have to go dig it in.
I do think though that my whole first round, I'm, I think back to my days as a CFO and whenever I got the financial statements, the first thing I did was, manually scroll through.
Obviously, the FP&A team would have given me notes too, but I wanted to see what those variances are.
And I think that whole first level of financial statement review right after it's closed.
If you could have it pull and say, hey, revenue was up 2% and cost of goods were down 6%.
So that sounds great, but it's going to ask you the question of, why were our cost of goods not keeping up with revenue?
So then, but if it's not tied in the jail, you still have to go do it.
But I do. I'm an optimist around this and I do see a future and especially with Marcos and I were talking about where it's integrated into systems or into your software, because a lot of the SaaS tools now are integrating it out there, but that's where it's going to be valuable.
I think Right now, having a probabilistic engine doing journal entries or reconciliations, that's not the practical use case of it, but yeah, I think those summaries and analysis like that, there's a, but for most people,
And I don't think, Dave, I don't think at all you guys are in the Stone Age with this, but it is.
What's the sort of... manifesto or approach around GenAI at Wasabi?
Because you hear it everywhere. We have to AI this.
We have to AI this. And I guess, Dave, start with you.
Is there... Do you get any questions from leadership or from the company of, can you AI that? or anything along those lines?
Not yet, but I feel like it's coming and I'm like you, I think, um, I think the ability to do that first pass of variance analysis is going to be huge.
Why was this off? Oh, well, there was one anomalous transaction.
And then, okay, great, we can go look at that and figure out what that was. it'll just really speed up that whole process.
I'm sure there's still going to be some manual interpretation of what it is, but, you know, to point big red flashing arrows, a couple of different things to go dig into, I think is going to be Pressure is coming.
Our CFO is definitely a fan and sends us articles once a month of like, hey, I read this cool new thing that it can do.
So it's coming for sure. And I think we are intentional.
It's not applying AI just for the sake of applying AI. it has to have an outcome.
If we can prove that the outcomes are going to be better, if we can use any GenAI platform out there that can speed up and automate any of our processes. then I think our executive team will be all in if and once we understand a use, a killer use. case.
I don't think they are owing just for the buzz and they're not against just because.
I think we are all open to explore and understand, and we have the green light to move these tools.
Obviously, not with Wasabi data unless we've added with legal and all the processes. but we can definitely use to understand where we need to go next.
All right, so I can step down off my AI soapbox now and we get back to our regularly scheduled programming.
Thanks, guys, for the insight on that, though.
So we haven't talked about dashboarding and... sort of the standard routine reports that go on, thinking about trying to have your data dictionary and have it consistent and write. do uh before i dive too deep do you guys both create dashboards of certain types that Are there some that FP&A is responsible and some that BI is responsible for?
Yep. In that dashboarding, is it, you're defining the metrics, you're building it out, you're trying to tweak it.
People always want to drill down to one level deeper and a lot of times what they see in the dashboard drives more ad hoc reports.
If there's a variance or something that they can't explain, I guess each of you, and maybe Marcos, we'll start with you on this.
When you are I'm saying dashboards, but I'm thinking of slide decks and the monthly presentation and everything.
Sometimes we can get People want so much information that the board deck becomes 80 something pages of charts and graphs.
And they'd be to me at that meeting, They become like billboards that you drive by on the interstate and you just, You're not seeing it or every manager or every board member or whoever, has the one graph that they go see they know it's on page 30 or whatever so um i guess you guys are both delivering to others that want these reports, but Marcos, what,
When people are asking about data and metrics they want to track, and you start hearing the scope creep and all the stuff they want to see on the dashboard, Do you have some sort of simple advice or guidance or a way that you structure that dashboard from the beginning?
It's a great question. And we can probably use three episodes just on this, Glenn.
I would say that in general, it goes back to context, what we initially started.
We can only create a dashboard that is actionable and useful if we understand why people are asking for those questions.
Sometimes we will go back and push back and say, hey, from all our, I don't know, 300 reports available reports that we have in our BI tool.
Can't you really use whatever we already have today?
We have built tons of every single potential view and cuts of every single one of the data sources that we have.
In general, it's a matter of educating the users and saying, hey, do you know that you have this report available?
In most cases, people are saying, oh, this is exactly what I needed.
I never knew that we had. and this is on the BI team to constantly educate and inform people. about what data is available.
But to your point, Glenn, every Monday we have to develop a 200 slide deck and When we meet with the executive team, we will just summarize that.
We're like, okay, for historical purposes, we have all these 200 slides for you.
But for this week, the important ones are slide 2, 14, 50 set and let's walk you through all of them.
And then we spend a full hour just talking about the three most important ones because the others are in plan or whatever we were expecting.
What the executive team needs is what's new, noise versus what signal around all this data.
That's great, and that's where you add value, I hope, I hope.
That's where, I mean, there's all the foundational work.
Obviously, you've got to pull it all together and it's massive, but That's turning... information into knowledge.
You're taking it and refining all that so that they don't have to read everything. every billboard that they drive by on the interstate or every street sign, you're telling them, these are the actionable items and they're trusting you to pull that out.
So that's Totally get that. And I get that these are metrics that are important. maybe not every month but if they're indicative of something if there's causation tied to them or whatever, if they're a leading indicator or even if they're a lagging indicator, whatever it is, having that information historically is good, but cutting through all that noise and saying, here's shining the light on and here's what your focus is.
And I guess, Dave, what about you? sometimes maybe not dealing with as massive amounts of data, but there are as many financial KPIs as there are probably across the rest of the company.
So what's your approach with these? Yeah, I've had a similar experience.
I worked at a company where every week we'd get a spreadsheet emailed to the entire company of, I think it was like 400 metrics.
And, uh, I would look at it, I'd read it, I'd try to digest it, and every time I would reference it to anybody who'd been there for more than six months, They would just dismiss it.
Like, oh, I can't even look at that thing anymore.
And so to your point, though, it just becomes these billboards that like just nobody looks at.
So all this work that gets done, nobody looks at.
So my approach to dashboards has always been you got to resist that metric bloat.
And really, you should be able to, any department should be able to distill it down to three to five metrics tops.
A couple of historical looking views, maybe a couple of forward looking view metrics, but like three to five max.
You don't need anything more. I mean, of course there'll be like ad hoc or we got to dig in because something went haywire.
But in terms of regular reporting and dashboarding, you got to keep it to three to five.
That's it. With that, we're going to wrap up part one with Marco Spinto and David Souter from Wasabi Technologies.
We've covered the foundations in this episode, how FP&A and BI collaborate, build trust, Navigate the tension between speed and accuracy and in part two I want to dig a little bit more into the metrics, systems and strategies that bring the partnership to life.
We talked before the show about net retention, data governance, Wasabi's approach to scaling insights, and all that so let's save those for episode two uh and and thank you guys for uh for being on
