The Data Stack Show - 214: Will Salesforce Be the Most Successful AI Company? And Do You Need That in Real-Time? Featuring the Cynical Data Guy

Episode Date: November 6, 2024

Highlights from this week’s conversation include:Cynical Data Guy Explanation (00:00:44)Marc Benioff's LinkedIn Post (1:28)Agent Force Overview (2:23)Speculating on the Backstory (4:08)Top Comment Reaction (6:22)Salesforce's Success in AI (7:30)Distribution as a Key Factor (9:10)Salesforce's Dashboarding Solution (10:20)Data as a Byproduct Discussion (14:22)Historical Data Value Debate (17:15)Real-Time Data Processing Challenges (19:54)Real-Time Data Use Cases (22:10)Legacy Systems and Data Management (28:06)Psychology of Data Usage (29:15)Final Takeaways on Sales and Data (32:21)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Hi, I'm Eric Dotz. And I'm John Wessel. Welcome to the Data Stack Show. The Data Stack Show is a podcast where we talk about the technical, business, and human challenges involved in data work. Join our casual conversations with innovators and data professionals to learn about new data technologies and how data teams are run at top companies. Welcome back to the Data Stack Show. It is a new month, and that means a new episode of The Cynical Data Guy. Matt, as always, it's great to have

you back in the studio. It's good to be here. Who canceled? What are you saying? Why is Brooks crying? Okay, if you are new to the Cynical Data Guy episodes, they're really fun. We do a lightning round where I go through and hand curate some tasty LinkedIn posts related to data. And Matt Kelleher Gibson, who is a cynical data guy, who has been jaded by years in the bowels of corporate data America, gives us some commentary.

We try to balance it out with John, the agreeable data guy, and sometimes I even weigh in with my opinion. We try to let him do it. And I have to call out for the listeners that Brooks just had to go on mute and turn his video off because he was already laughing so hard. So this is going to be a good show. It's going to be a good one.

Okay, free lightning rounds. We'll see if we get to a bonus round, but there's some good stuff today, so I don't know. And we are going to start off with a real banger here, okay? Ding, ding, Mark Benioff, the CEO at Salesforce, first LinkedIn post ever is about AI and is probably written by AI, which I feel like someone... We cannot confirm nor deny it. We cannot confirm or deny it. But I feel like someone would have made this up. I mean, you really, it's that good, but let's just dig in. For my first ever post on LinkedIn, I'm excited to announce that as of today,

AgentForce, our complete AI system for enterprises built on the Salesforce platform, is available for all customers. Easy to set up with a few clicks and a simple description of the job you want done, AgentForce is ushering in a new era of AI abundance and limitless workforces that will augment every employee, build deeper customer relationships, and drive extraordinary growth and profitability. Should I keep going? But wait, there's more. But wait, there's more. Okay, yes. Many enterprises are caught in a pointless cycle of AI experimentation with LLMs and co-pilots that lead to costly failures or proof-of-concept projects with no path to scale.

With AgentForce, you don't have to DIY your AI! As part of Salesforce's trusted and fully customizable platform, AgentForce seamlessly integrates enterprise data, metadata, AI models, workflows, security, and applications. No costly model training, data management, or hyperscalers or AI engineers needed. Going beyond copilot and chatbots, AgentForce agents don't just answer questions or surface insights. They autonomously execute actions like resolving customer cases, qualifying sales leads, and optimizing marketing campaigns. Companies like OpenTable, Saks, and Wiley are already using AgentForce today to extend their employees, expand their workforce, and improve customer experiences. This is what AI was meant to be.

Ugh. Deep, deep sigh. deep deep suck it's all right yeah okay fine let's just word salad our way through all of this i don't know it it's yeah of course it's gonna do everything it'll clean your windows it'll remove stains from your walls and it's a salad dressing i I don't know. I'm just so tired of these over-the-top things. Okay, but for his first, how do you think that it came about that we decided we're actually going to do his first post about this? Let's just make up a backstory.

Yeah, I need a backstory. I need a cynical backstory. Well, let's see. What would this cynical backstory be here? I don't know. Perhaps it has something to do with the need to do something to draw attention to this in a different way. And so someone had the idea of having it be posted on his LinkedIn,

possibly after some comms or marketing people had been asking for months if he would actually post anything on LinkedIn, and he was not exactly sure what they were talking about. Well, see, I like to think of a very like years long political battle over who, you know, there's an internal competition with this guard who has survived many years at Salesforce, you know, and is at sort of like the VP executive level. And there's like, maybe actually there's even God, am I like usurping the cynical nature, like a large sum of money on the table for who will win in like getting Mark's first post out the door? I mean, that's a real possibility. I would imagine this is part of some kind of campaign,

right? Because everything has to be part of a campaign of course so it's a like a pr blitz and like oh let's do a linkedin post and then somebody was like what you've never posted on linkedin this will be perfect well so my big question that is is the story going to be that he's never done it before has never wanted to and like they finally broke through or is it going to be one of those where he's been talking about this is going to be a great idea he's going to he's going to do this and everyone was like no you're not doing that and they finally came okay that's even better and they finally came to an agreement that you can post if but only if we can take what you wrote and rewrite it and be the ones in control of it. It's actually like a masterful redirect of like the...

Yeah. He's like, I want to be like Elon just on LinkedIn. And someone's like, okay, we actually, we're going to get you there. Somebody's like, baby steps. Let's trust this.

No. Well, and we have to read, we have to read the top comment. We have to read the top comment we have to read the top comment okay we're more cynical than even i think the cynical yeah i mean this is savage okay ready your first post is shameless self-promotion let's hope your agent does better on the next post but if you read it it really is like i mean i think we were talking before the show. You're on LinkedIn. The post is totally on brand for LinkedIn.

And of course, it's all shameless self-promotion. It doesn't feel like it's more shameless than any other LinkedIn post. Yeah. I mean, at least he has more than six months work experience before he's strictly on LinkedIn. Yeah, I mean, okay, that's the other thing. We can armchair quarterback Mark Benioff so hard, but he gets a last laugh.

I mean, I'm going to extend... Should we talk about the content of the post a little bit? Or should we just... I'm extending round one a little bit here, which is probably why we won't get to a bonus round. But I knew this was going to be a meaty one. I want the cynical and the

agreeable response to this. Salesforce will be the most successful AI company. How are we measuring that? That's such a data leader answer. Well, I mean, it's one of those things. Are we saying they'll be really successful

because they'll have this AI thing with a lot of revenue? Or are we saying specifically people using the AI? Outside of the... Great question. That's why we love you, CynicalDataGuy. Okay. I will put parameters around it, but I had to

ask it in a short and catchy way, of course. Outside of GPT and the consumer facing use case, we're talking about AI integrated into a SaaS platform, which so many companies are trying to do. Salesforce, I think, will be the most successful at that. True or false?

I just said I introduced my opinion. Salesforce will be the most successful at that. True or false? I mean, I want to say no, but then I think about it, and it's like whenever you kind of count out these legacy platforms, they seem to find a way to be like, everyone uses it and everyone hates it type of a thing. So maybe?

Agreeable data guy? I think there are very few companies still, and this is funny to say out loud, that can out-sale salesforce i think they're so good at sales so good at it and it's silly right because obviously they sell a tool that helps you like sell better but that's not a given there are a ton of tools to help you do something better that the company internally is not that good at but salesforce i will give them credit they're excellent at sales yep so well if the key to success in AI is who

can sell it the best, yes, they'll win. And that's probably the key. Okay. I think there's another aspect though, and I'm going to take the liberty to introduce my own opinion here, which I try to minimize, but I feel strongly about this. There's another aspect of this, which is distribution. And so the example I'll use, and I'll take Tableau out of the picture here because Salesforce bought Tableau, which is, you know, one of the, one of the largest, you know, sort of BI, you know, sort of traditional BI solutions. But if we take Tableau out of it and we just think about Salesforce's own dashboarding solution with, you know, so they're reporting products within the Salesforce suite, right? I would guess that it's probably one of the most widely used

dashboard solutions in the world, right? In terms of end users accessing data in some sort of dashboard with charts or whatever, right? The distribution is just so immense. And I think like their dashboarding solution, even if it's not the most incredible AI

solution, like a dedicated AI company is going to come up with a much more elegant, whatever, you know, solution to the problem. It just won't matter because if it's moderately useful and Salesforce, you know, A, can sell it. And if it's moderately useful or useful enough to people to use it, like they just will within the Salesforce platform, right? Well, they can get it to update people's opportunities and sales forecasts. Then it will come.

That's the conclusion. Is updating opportunities that? I mean, the sales pitch for it is there. Because if you ask any salesperson in the world what they hate about Salesforce, it would be that I have to use it. I mean, like, and then I have to use it. I mean, and then I have to go in and manually update things. Any salesperson is like, oh, I have to update this manually. If this works

as advertised and they have to do less of that, then I think everybody will want to buy it. I have a feeling that if it does get to be very big, it's not going to be because of all the things they talk about. It's going to be because it does one or two really boring things

that are high frequency over. Which is the case with Slack AI. Yeah. Right? Sure. Did it completely change

the Slack experience? No. Do I want Slack without it? No. Because it's really useful for things like searching for something specific across like a wide set

of channels or summarizing or whatever. It's so useful for that. Any other hot takes on Benioff? I mean, he's a master. He can post whatever he wants. One other take on this post before we move on is, it is interesting that they choose to jump in

at this intersection. Because they could have done it a lot earlier. And they've jumped in right after Anthropic announced the new abilities, at least for developers, I think they're rolling it out more broadly soon, to actually perform actions on your computer. Think macros or whatever you want to call them, like actions. So it's interesting the timing of it. that comes out in a week or two later

this comes out with allegedly similar features where it can actually perform more actions and it's not just summarizing information or whatever as soon as the curve going into the trough of disillusionment and the budgets and the lackluster results start to become acute, he comes in and he's like, hey.

The only thing I'll say is agent force, every time you said it, all I could think of was space force. Yeah. I thought of the matrix for some reason. Yeah. Okay. All right. Round two.

This poster will go unnamed. When I led my webinar on how to get value out of your data, I asked attendees why they were interested in the topic. Here are some of the most common responses. We want to figure out how to monetize our data. We want to better leverage our data. I want to be more data-driven. At first glance, these responses might seem vague,

but upon further reflection, it's pretty clear to me where most of these people need help. I will elaborate, but first let's talk about composting. Composting. I knew you were going to love this one. Imagine someone learns about composting for the first time. They're curious, so they buy a compost bin to try it out. They start cutting some vegetables, and before they know it, their bin is full. Wow. Okay, now I have all this trash. I heard that I can use it to feed our garden.

How do I do that exactly? Maybe I just throw these carrot peels into my azaleas. You see where I'm going here? Now imagine the early stage founder. They have a digital product. They're integrating digital systems into this operations. Maybe they're scraping open data from public APIs, or maybe they have a bunch of random documents from their customers in an S3 bucket. When founders bring their ideas to reality, it starts to become very clear to them what this whole data thing is. They recognize they have a lot of it,

and they've heard how valuable it's supposed to be. They know how important it is to be data-driven. They just have no idea how. Meanwhile, many people who can help them get lost on learning new tools that vendors shove down their throat. But I'll save that rant for another time. So, first of all, are we saying data is trash like the carrot fields? Or this sort of, you find this really useful thing, but it's like a, it's a byproduct of,

you know. Oh, so we've gone from data is the new oil to data is the, A carrot? Your leftover carrot fields. I mean, this opportunity to not just name the post,

like what I learned about data from composting. Like we should have just led with that. We should have led with that. We should have led with that. Five things I learned about data from composting. Like we should have just led with that. We should have led with that. We should have led with that. Five things I learned about B2B sales from composting. Yeah, exactly. So is data a carrot peel in your azaleas?

I mean, there's like some decent points in there that like when people first start, but I think it's very specific. Like if you have a digital product and you're tracking certain things and you kind of realize, wait a minute, I can use this for more, that makes some sense. And there is some mismatch to it. But I mean, there's also kind of, you know, there's a lot of people who have that idea that like, I remember I was working like kind of internal consulting at one company and we, a guy wanted my group to come in and it was, he was like, like well we want you to

evaluate our database i'm like what what do you mean by that well we want you we feel like we can get more of us we want you to like evaluate how it could be used and i'm like i don't know what you're talking about so you know like there's still kind of that cold start problem yeah out of that when you say like well i want to be more data driven well what does that mean to you that you want to be more data driven yeah john i think i think i understand what he's saying as far as like he started out with we want to be data driven we want to better leverage data monetize our data and then he goes straight for like the data implying that the data is bad and we have to transform it and do things with it to make it better because that's what composting is. I don't think starting there

typically helps people. I mean, starting in the inverse of like, what are you trying to accomplish? Like, not like, oh, I want to leverage this data to be data-driven. It's like, no,

what are you in the business of? What do you do? Like, define that and then define like, what would provide value to customers and then you build backwards from that with a data product. Which may data may not be or it may not yeah yeah

yeah i mean i think it's kind of inverse yeah the composting one is a little weird too just because i mean there's composting is kind of like i let it sit around and it breaks down oh yeah it gets better over time that would not be true that is not that's not true it's like i mean because that but that does feel like the rotting aspect is true good point it is true but it doesn't turn it into something useful at that point i mean it feels a little bit like the way some people perceive data and companies where they're like well we've got 10 years worth of this 10 years it's been sitting around it should be valuable and it's like well no there isn't like a whole micro you know going around and breaking it down and turning it into nutrients like that it's just you know dirty data that's been sitting there for 10 years yeah i mean what's your opinion on i mean

if you have history like maybe that helps if you're trying to build models because you have more data yeah to build a model from that good, but it's not like it's gotten better over time or something like that. And then I will agree with him on the part that there's a lot of people who focus way too much on the tools aspect of it. But that more has to do with where people get very lost.

It's kind of like the person who says, I just really want to make models. I just really want to make dashboards or something. Okay, but that's like a component of this that's not like a job you know this isn't a widget factory where we're turning stuff out yeah i love over analyzing and i love these like way too deep unfair analyses of people's analogies right right you don't want me to overanalyze your analysis, you should have thought it's better.

One more question on this. One thing that's interesting, and of course this is an unfair analysis of the analogy, but there's this implicit idea that you can't have any data waste. We have to figure out some use for all of this.

That's wrong. It's illegal. There's a ton of data that's just pointless and not going to do anything. Yeah. I mean, that's because I remember when I started, that was like the way people thought was, well, we're going to find, we're going to refine all of this. It's a real mindset. Yeah. It's a real mindset of like, well, we're going to collect everything and then

it's all going to be useful. And it's like, no, 80% of that is probably going to be a waste. I actually like how the industry's gone with that where collecting more data, storing more data is marginally more expensive, but collecting more data is

a lot more expensive now because everything's usage-based. Because before, if you just scoped out, I have this fixed amount, then I'll just collect whatever I want, and then I'll ask for more resources later. But now that it's so scalable, I think there's a lot more pressure to be collecting useful

data versus just all the data. Well, the other thing is it's getting a lot cheaper to... With things like Iceberg, it's going to drastically decrease the pain. I mean, a data-like idea is one that it's like you can collect a lot more. Yeah, the storage is cheap. You can see what might be useful later. You just run into the problem then of, oh, crap, how do I figure out the useful?

Sure, yeah. Well, and I think people are more concerned about liability with keeping data forever than they used to be. Because that used to be a thing where it's like, we'll just keep it forever. Now people are like, oh, maybe it's better that we don't have that data.

Yeah. All right, round number three. This has been fun. Oh man, I forgot what three was, but this is such a good one. Something we've talked about on the show for literally years. Okay, as a data practitioner, I can't count the number of times I've been asked the same question.

Can I have this data in real time? And for years, my answer has been some version of, no, you can't get data more often than once an hour. It's absurd we've come to accept this as normal. Stakeholders often have perfectly valid reasons for wanting up-to-the-minute data, but we've been stuck between the limitations of batch processing and the complexity of building real-time systems. At Twirl, we're rethinking orchestration to address this. Traditional orchestrators force entire pipelines to run in one go, meaning the whole pipeline needs to finish before starting the next run. Twirl changes that by allowing each part of the pipeline to run independently on its own schedule.

We treat every step in the DAG as a microservice and constantly evaluate if it's time to run. By default, a job will run if all its inputs have processed new data, but users can override this and specify different triggering logic for each node. This means high-priority data can refresh continuously, providing near real-time insights. Less critical processes can run hourly or daily, aligned with when the data gets used. By decoupling the cadence of each step, we blur the boundary between batch and real-time data processing. This means teams can deliver low-latency data where it makes sense

without overhauling their entire infrastructure or rewriting their code. And they teed this up perfectly for me. We think this approach is much better aligned with real world use cases. What do you think? I think that it highly depends on

what business you're in. I mean, I've worked at a lot of places that had brick and mortar type locations. And the idea of like, we need minute to minute updates. It's like, we don't even get downloads until the end of the day. So what are you going to do? I mean, I think this is like probably leaning more

towards a digital type product that you're going to be there. Because I mean, I don't know if I've had a scenario where someone asked for real-time data where it was actually needed in real time. That was going to be my question. John, have you run into a true real-time use case? I don't think, I can't think of any off the top of my head.

It depends on what you're doing, though. Because if you're in reporting and analytics, I'd say almost never. But there's other things that are not really reporting and analytics that can cross over. Like I work with a client right now that's basically written a whole analytics type add-on. They're using a really old legacy system,

can't update it, there's all these restrictions around it. So they basically wrote a reporting add-on to it. And you can also edit data through that interface so now it becomes like does it need to be like pretty real-time like yeah it does because they're editing data and saving it because it's a full-featured app now so to me that's like the differentiation between like is it read-only which reporting analytics that's that's like 99.5% of all that. If it's read-only, does it need to be real-time? I mean, unless you're trying to use it to physically monitor something.

If you're actually monitoring something with data through a reporting app that you just need to know exactly what's happening, then no, and that's really rare. I haven't seen it much at all. Right. It's also, it's dependent on a lot of other services in that chain usually.

So a lot of times you're not gonna be fully self-contained at every step. And that's gonna put a damper on your ability to be real time anyways in a lot of things. I mean, I've worked at places where, you know, we sent data out to get it, you know, appended and matched and things like this. So it's like, that would be, you really couldn't do that in real time.

Sure. You can do certain things where you're like, okay, if it's an obvious match with this, then we can move it forward or something. Yep. But you're not going to be doing none of that. Right. I think the use cases, they just don't always end up in reporting and analytics. I'm thinking of one right now for sales where it's like,

hey, I want to notify salespeople as soon as this prospect returns to the website. Timeliness in that, near real time, is really helpful and important in that. Which is more alerting and monitoring to your point though. Yeah, it's more alerting and monitoring. But in a sense, you're capturing data from a website and then streaming it into Slack. Right, it's part of an analytics pipe. Yeah.

It's part of, you would, yeah, the data would run through a pipeline that also serves analytics use cases. Right, which is, I mean, I think part to the point here, where in this post, it's talking about you can decouple these things, and i can have different components of the thing make this like near real time make this not and like that's cool like i understand that yeah i mean i think other

people have done some of that stuff where you kind of have like there's the normal pipeline it goes through and then there's the hey when this person leaves the store i want to be able to ping them with whatever and that just has its own thing that bypasses everything to get it more real time there. So, I mean, if it can help with that, it might be useful in that sense. But I mean, the majority of the cases that you're going to run into are one of those where, you know, it's like the meme. It's like, can I have this real time? Well, what's it for?

A monthly review. QBRs. It's for QBRs. Well, I mean, the most, the real-time reporting, when I think about real-time reporting, I think about refreshing a Salesforce dashboard, which there are limitations. It's funny that we talked about Salesforce dashboards earlier in the show, but it's getting towards

the end of the month, right? It's the last day. The sales team hasn't purchased AgentForce, so they're having to go in and update opportunities manually themselves. And so as a marketing leader, I'm refreshing the dashboard as fast as sales work. Let me refresh it to see how close am I going to be to the number. So it's like that 15 minutes or whatever it is. Yeah, and you can imagine a scenario where you're not using Salesforce

or for some reason there's some other outside data you want with the Salesforce data where you'd want that, like near real-time in that scenario. Yeah, I mean, I think that, you know, I haven't, that was the first time I've heard of Twirl and sort of the orchestration. It is actually interesting to think about the orchestration layer as a way to set up pipelines that run at different cadences for different uses. I'm sure there's a lot of utility there.

But we've had multiple real-time vendors on the show. We've had companies, you know, data practitioners come and talk about real-time. And I think to the point that both of you are making,

the actual use cases are more rare. I can't remember the name of the company, but there's a company that does financial information. It's like stock ticker stuff. Like, you know what?

That's real-time. And they like have a super, a deep haven is the company. Super cool product, right? But they come from the world of finance and they have a lot of customers in the financial world.

And it's like, yeah, I mean, they literally are doing, serving all sorts of real-time use cases with giant feeds of stock ticker information that are literally happening. There's changes happening all the time. So it's legitimate, but it has this slight feel of throwing a really cool solution to a problem that isn't practical for a lot of companies. Yeah, exactly.

John, you do bring up something beside this point that is one of my favorites, which is when you have a legacy system and everyone gets caught up in, we got to replace it, We got to throw it out. And it's like, nah, let's just hollow it out. You just hollow that sucker out and you place something on it. If somebody does a good job of that, like I think it's one of the best solutions often you can do because especially like in a large company where it's going to be millions and millions of dollars to rip out a system and replace it, to hollow it out, like it really can be good. Well, and depending on what it is, I remember one place I worked,

we actually ran into this where it was like, we got to completely replace this, blah, blah, blah. And my team, we started using it. And one of the things we realized was the data model inside this system was actually really strong

and straightforward, made sense. Like it wasn't overly complicated, but it was well-made. And we were like well this works the way it is. So let's just sit something on top of it.

We'll do all the work it won't do and then we'll just drop it in as basically storage at the last minute when we need it. So it's just an underrated thing of like

hollowing out legacy systems under one of those things that people get very caught up in. No it's got to be this big fancy thing. No, it doesn't. One other thing though, and I think it's easy as like a data practitioner to kind of downplay real time because it's hard and we don't want to do it. So I want to call that out.

But I do think if they were easy, the same amount of easy and the same amount of cost. I do think there's a human psychology component that probably long-term will help adoption of reporting analytics solutions if they were all real-time by default, long-term. The problem is they don't scale that way. The cost

is the thing that makes people go, we can have this once an hour or once a day. I do think there's a psychology thing with some of that. Like you're saying, nobody's going to go to their tableau or whatever dashboard at the end of the month to refresh, to look at the number, because they have to wait until the next day they're going to log into Salesforce and look. Right, right.

Okay, but that brings up a question. So do you, okay, I agree with you, right? If that could increase adoption because of the dopamine hit, right? Because of the dopamine hit, right? Okay. And I actually, I mean, to your point, Matt, like there are cost implications of that,

but those are increasingly going away, right? Like the technology is getting to the point, and at huge scale, there's obviously still issues, right? But there is a path towards that, towards it becoming more reasonable from a cost standpoint. I mean, even some of the stuff we're working on internally is pretty cool.

But is it healthy, right? Do you want to, of course, it's going to increase adoption of analytics, but Matt, cynical data guy, is that, do you want to? No. I mean, I've built stuff for sales teams before. So my big thing with them a lot of times is, like, you're chasing noise. Like, just let it, you know, it's like you will make better decisions if you're looking at it once a week than once an hour.

Because you're just going to get very hung up on some of that stuff. I mean, though, if you really wanted to increase engagement, which you should do, is like Rory Sutherland, who does marketing, talked about this. The problem isn't that people like need it real time. It's they're not sure when it's going to come. So have it go every like 10 minutes with a countdown clock. Yeah, that would get people doing it just because they like a lot of times also like when we think it's like, I need to have it think it's, well, I need to have it now, it's not that I need to have it now,

it's I don't like the uncertainty of when I'm going to get it, basically. It's the freshness thing, right? Like if I knew exactly how fresh this was, when it would update again, when it last updated again. Because BI tools are pretty bad at communicating that. Yeah. Most of them.

So that solves a lot. And a lot of teams try to hide that fact, too. Oh, yeah, of course. They don't want you to know that this is two weeks old. I'm surprised you didn't have more of a cynical, like, we can do a Pavlov's dogs thing, where you have to update your opportunity information, and it's a random number of clicks

that actually triggers the report refresh. No, I think you get enough people addicted just by doing the like, oh, look, it's going to update in three minutes. I could walk away, but now I'm going to sit here and wait for three minutes. Oh, nothing changed.

But it's going to be another 10 minutes now. I got to sit and wait for that. Yeah, the countdown. I get behind that. I mean, to answer your question, is it healthy? Like, I mean, yes and no. Yes, and that you want people to care about the numbers.

And like, that's still hard to do in a lot of companies. Yeah. To really get them to care about the numbers. No, and that like, there's another extreme of like, I want you to care about the numbers, but I mainly just want you to be selling. Or marketing or whatever. Can I point out that we just, both of you,

I mean, I made several jokes about sales, but both of you went straight for sales. We launched this off with Salesforce. That is true. We got crimes. We got tricks into it. I mean, I can go after marketing too. That is very true. We normally go after marketing, I feel like.

That's true. Sales day. You know what? It's end of month. It's actually end of month. Sales day. You know what? It's end of month. It's actually end of month. For a lot of SaaS companies, it's end of quarter. True. And we started off with Boss Benny off at the beginning.

So I think that was appropriate. I stand corrected. Well done, gentlemen. Well done. All right. Well, that's all the time we have for today. Tune in next month. We'll pick on marketing and pick another couple great

LinkedIn posts for you. And we'll catch you on the flip side. Stay cynical. See ya. The Data Stack Show is brought to you by Rudderstack, the warehouse native customer data platform. Rudderstack is purpose built to help data teams turn customer data into competitive advantage. Learn more at ruddersack.com.