Data Team Struggles: Telling Stakeholders the Truth vs. What They Want to Hear (How to Tell The Truth, Tactfully)

Eric Dodds 00:06
Welcome to The Data Stack Show.

John Wessel 00:07
The Data Stack Show is a podcast where we talk about the technical business and human challenges involved in data work.

Eric Dodds 00:13
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 show. This is our monthly show with the cynical data guy. If you didn’t hear the introductory show last month, definitely go back and check it out. For those of you tuning in for the first time, or hearing about a cynical data guy for the first time. Here’s a little overview. So every month, I pick out some really nice LinkedIn posts. And we get Matt Keller, her Gibson, who’s on the RudderStack team, who came to RudderStack from the bowels of jaded corporate data, America straight to RudderStack. And he gives us his hot takes on these LinkedIn posts. So cynical data guy, welcome back to the show.

Matthew Kelliher-Gibson 01:15
Thanks. So I’m back. Yeah.

Eric Dodds 01:22
And of course, John’s doing great. John’s agency is agreeable data. So we do try to pit them against each other. All right, this is Lightning Rounds. So we’ll get cynical data guys take John, you get a chance to respond. Some of these LinkedIn posts, the author’s will remain anonymous, for obvious reasons others will share. And I will honor their safety and security for their safety and security. And their emotional well being. Yeah, right. Suppose everyone’s ready to go. Okay, are we ready? Let’s go. Yep. All right. First post, actually, Clint Dunn, who we had on the show recently, this is a great post. I’m gonna read some excerpts here. It starts out with a zinger. Your data team is lying to you? If people are supposed to be truth seekers, why would they lie? The reason is actually very simple: data teams tell you what you want to hear. That’s why your head of data rarely gives you unfiltered, bad news. Instead, they’ll hint at an issue and try to redirect you towards a better goal. It’s a lie.

Matthew Kelliher-Gibson 02:29
Yeah, that’s for sure. No. But so yeah, they’re lying to you. And they’re mostly lying to you, because you want them to lie to you like, this becomes a thing where it says, Well, I want you to be a partner. It’s like, Well, you say that, but you treat them as a junior service. So that’s what they’re going to act like.

Eric Dodds 02:50
So. Okay. Give us a scenario. Don’t name any names. But give us a scenario where, you know, I mean, lying, I guess maybe I don’t want to put you on the spot and ask you when you’ve lied. But

John Wessel 03:05
but because you had them on the stand asking

Eric Dodds 03:09
corporate America as a data leader, and you’re in a meeting, and you face this situation.

Matthew Kelliher-Gibson 03:15
Okay, so there’s different types of versions of this I can think of right, the most, the one that immediately comes to mind is the one where you’ve been reporting something, you’ve now gone through and realized, oh, man, there was like duplication or a wrong source or like, something has caused it, and the number is about to get very off, when it’s off,

Eric Dodds 03:38
as in, like, from what it was before, but more accurate, right?

Matthew Kelliher-Gibson 03:43
You’re gonna be delivering accurate results, but they’re not consistent with what you’ve been saying, right, or what the company has been saying for a while now. So there’s one form of lying, which is, we just report it the old way, and just don’t even and we come up with like version 2.0 of whatever the metric is, and massage around exactly why? Well, we, you know, this is what we want to transition to and things like that. The other version of it is where you talk about how the numbers are going to be a little different, but it’s okay. And it’s going to be more accurate, and it’s still directionally the same, directionally. We’re actually internationally out here and we’ve done it in the past. We just, you know, there was a little hiccup and now we fix that and you know, and it wasn’t our fault, it was clearly someone else’s fault for not taking lots of hand waving. Lots of hand waving.

Eric Dodds 04:35
So the reaction to this change I mean,

Matthew Kelliher-Gibson 04:39
well, the reaction or what everyone’s fear is the reaction is gonna be there everyone’s fears. This is like the we’re all getting fired type moment like, right, that’s just the way it is without naming anything. I remember working on one team where I had a boss that anytime we got a problem, the first thing I said is, what do we do? Roll And we had to spend the next 30 minutes explaining to him how it was not our fault on any of this stuff. And it was business users or downstream upstream all those types of things. Yep. The reaction, I think, from people who are if you’re not in that fearful state is kind of the like, Alright, here we go, especially if it’s something where you figured out that there was like a previous iteration of this was done incorrectly or there was, you know, a lot of the like, oh, there was Gary, and he did all of this. And there was this mistake in his code. And so like Gary’s good mistake. It was a Gary process. That’s what Gary dB. And it’s, you want to fix it. But you also know that you’re going to get into this thing of like, well, those aren’t my numbers. And it’s like, well, I know that. I know, they’re not your numbers. Because you’re not you can’t say, because your numbers have been wrong. And every decision you’d rate is often wrong, right? That doesn’t go over well. So you have to come up with these very well, it’s not that it was wrong, or all these types of things to try to move around that. That’s why I say that’s when you end up with just like, you know, sales 2.0 Because we can’t change sales 1.0 Even though it’s wrong, agreeable

John Wessel 06:11
data guy. My first thought on this was actually in marketing in Eric’s territory. So this is a really popular book in marketing called All Marketers are liars. Like, do you know that book? Yeah. So in his point in the book, and he actually, there’s actually two different covers of the originals, all marketers are liars. And then the new cover is all marketers are liars. And there’s like a cross through with like, all marketers tell stories, which is interesting. Fun fact. Yeah. Gosh, we know, I was winning. Wow. Point for one point,

Eric Dodds 06:44
for some point for cynical data guys. I mean, we may just need to move on. Okay, actually, what?

John Wessel 06:50
Yeah, the point being, that whenever you’re taking complicated data, and simplifying it into a story, that’s really hard, right? And then be like, how do you do that? Such that you keep the right level of fidelity or accuracy to communicate what you’re trying to communicate, and not get lost? Everybody lost in the details? Yep. Slyke, I guess on one level, like, you could call, I mean, I wouldn’t call it lying. But like, on one level, like when you roll up enough, it’s like you miss out on so many details that like yeah, like, if you’re not covering all of the

Matthew Kelliher-Gibson 07:29
things, it’s inherently reductionist, when you’re doing that, right, like, you’re gonna leave things out, right? Really just, ya know, right. Taking it from a book to a movie, I can include every plot and yeah, I got to make decisions of what to put in

John Wessel 07:40
and data people are always better people, generally, right? Because there’s more context and what I so that’s

Matthew Kelliher-Gibson 07:47
How do you end up with emails to a VP when they say what sales last month were like? Well, here’s a chart with all of our sales products for the last bullet, you know, every day of the month and they’re like, am I supposed to add up every one of these numbers to get my answer? Well, yeah, it’s in the chart. No,

John Wessel 08:05
I just wanted one number writer.

Eric Dodds 08:06
Okay, actress cynical data guy, we need to move on from this lightning round. But last word. In general, your business stakeholders want the truth or don’t want the truth.

Matthew Kelliher-Gibson 08:20
If it’s just across with them, they don’t want the truth is

Eric Dodds 08:27
the look of light in the caption, like, are you serious? I shouldn’t take a picture. Yeah, okay. Lightning Round one done moving on this. The author of this post will remain anonymous. Okay. I did a poll last year asking data and AI leaders what the biggest blocker to delivering success with the biggest blocker was to delivering successful initiatives. The overwhelming answer was DOT culture. No surprise, really changing culture in an organization is incredibly hard. But might generative AI be the answer to solving all sorts of cultural adoption issues? Join me live on the 16th of July to find out more.

Matthew Kelliher-Gibson 09:07
No next question.

John Wessel 09:09
Remember, go with the no next question as well. Oh, also

Eric Dodds 09:13
hashtag culture, hashtag generative, a

Matthew Kelliher-Gibson 09:15
hashtag wishing.

Eric Dodds 09:18
Okay. That one was too good not to include. Okay, this post the author of this post, we will disclose it’s actually the cynical data guy himself. Oh, okay. And this post went viral. So I mean, hundreds of 1000s of impressions. I don’t know. 80 comments, however many not that anyone not that you’d be obsessively looking at this. Okay, I’ll read it. One of the worst things to happen to data was the sexiest job of the 21st century article. It caused two major problems, one that attracted people to data and data science that were looking for high status jobs. It gave the impression that businesses were already bought, businesses had already bought into data driven culture and had the tech ready for cool work. The reality is most of the job is hard, unseen work, there is very little splashy work, data. And second, data is a support function in most businesses, and the infrastructure is nowhere near ready to just walk in and do quote unquote, or quote, cool work. I’ll stop there.

Matthew Kelliher-Gibson 10:30
It’s a really good post.

John Wessel 10:31
Wow, wow, should I respond to his post? So maybe Iceberg as you go first? First off, I have to derail this for a second. I was looking to look up an example on company culture. And the top SEO spot for company culture examples is we work right now. All right,

Eric Dodds 10:51
We should do a whole episode. Yeah, that’s where the websites, that’s where they’re all

Matthew Kelliher-Gibson 10:55
we’re all going to have.

John Wessel 10:59
Yeah, yeah. So the data science, do you know, around when that like article came out, like was talking to 15 years ago,

Matthew Kelliher-Gibson 11:05
like 2011? When are we 11? Yes. 2010? Yeah. I’m probably off by a year or so in some direction.

John Wessel 11:12
I’ve actually been thinking through this a little bit, personally, because that is around when I started in data. And I think, like within a couple of years of when you started in data to and it pretty much corresponds to what I think was an influencer, not the main influencer, but an influencer is Moneyball. Right, so that was around that time, too. So I feel like there’s all these like, some cultural things. And then like some technological innovations, it’s a

Matthew Kelliher-Gibson 11:39
little downwind from Moneyball, because Moneyball was like kind of

John Wessel 11:41
three or the movie. Yeah. The movie and the book. I mean, who reads But yeah, I mean, it’s not like, No, you’re right. Moneyball was like 2000 that like

Matthew Kelliher-Gibson 11:50
30 books a year or anything? Yeah, right.

John Wessel 11:54
Anyways, all this to say, there were like, lots of like, cultural wins and like data and like, like Moneyball, the movie and other things, like, wow, this is gonna and then, you know, I don’t even know who wrote the article, some, you know, Forbes or whoever. Brom. Like, but coming off of that. And then even the reality then was, what percentage of the people were actually doing like, cool data work there? Like that was? I don’t know, dreamy, then like, that’s, that was what people wanted it to your right. And it was, I’ll just make up a number, maybe 10%, maybe less than 10%. We’re doing that. And then, yeah, let’s call it like 1%. So then, like, you’re going from that, like, Oh, that’s so great. Like, that’s going to be the future. And then when somebody declares a future enough years go by? And then like, the obvious reaction to it is like, oh, okay, like we’re about in the same spot that we were Yeah. Yeah. So there’s

Matthew Kelliher-Gibson 12:53
a more tangible thing that came out of that, because I remember when I first started having a higher salary, and the number of people that if you put out a post that said data scientist on it, they would go to it, and had outsized expectations for salary. And like we had one, when I first started, as a manager, we were told, We’re gonna hire a junior data scientist, like someone you know, out of school or something like that. We got about two weeks, and we were like, this does not work. And we downgraded the role to a data analyst, because we were like, This is crazy. And this was also at the beginning of when all of these data science master’s degrees started coming out. Oh, yeah, varying levels of quality, much of it dubious. And we went to people who are in the pipeline, and we’re like, well, we’re changing the role, we want to let you know, that gives you a chance, if you want to go there. And we had a couple people that were like, I just really feel like, I’m just a modeler. And that’s like, really what I wanted to do and I saw the same thing, you know, work in other places, and there was one they were not, they weren’t paying market wages, right? Right. Like we’ll we’ll we’re gonna make all these positions a data scientist role, like, don’t do that. Because I’m like, your, your people who just want the title, and they’re gonna leave in a year, when they have a year’s worth of experience to go somewhere to make a lot more Well,

John Wessel 14:10
I was gonna be fair, they want the title to make the money. That’s why the title, status thing to bow was the money because the HR does like the index is against the market and they see the title and you get more money. Like it’s a fairly simple equation about mid and

Matthew Kelliher-Gibson 14:23
large company, and they had a lot of turnover issues, because people would stay for 12 to 18 months. Yeah, go work at a bank as a VP, because everyone at a bank is the VP and you know, get a 30-40% pay increase deal. By the way, can

Eric Dodds 14:37
I just call out that the agreeable data guy just had such a sample? I’m sorry.

Matthew Kelliher-Gibson 14:43
I’ll take what you did have, like the number to comment on it. I know. Yeah.

John Wessel 14:47
Really? Yeah. I didn’t even look at you know, right. And I post that to go back and correct the record Moneyball. 2003 Okay. The book and the movie were in 2011. Okay, yes, yeah. It’s about right. Yeah. Wow. Okay.

Eric Dodds 15:02
How many projects? Have you worked on it? How much datas? Let’s get specific. You’ve done tons of types of data work. Tons of types of data work. How many data science projects have you worked on? What would you describe as sexy? Oh, I

John Wessel 15:19
I love this question. Well,

Matthew Kelliher-Gibson 15:21
This is all on a sliding scale, because we’re all data people here. So one or two,

John Wessel 15:26
way out of how many? D hundreds of projects. Yeah. Or at least dozens,

Matthew Kelliher-Gibson 15:31
or at least a dozen? Yeah, it will. Yeah. Because yeah, so if you remember, I worked at several places where it was like, I spent a year going like, Alright, we’re gonna fix this. So we can actually do data.

Eric Dodds 15:44
And then I realized, it’s probably actually just the standard that’s existing. everywhere, everywhere.

Matthew Kelliher-Gibson 15:49
The analogy that I use is, at the time when we first started, like trying to hire people, as I said, it would be like if you went into a law firm, right, and especially if you were like, I don’t want to go to law school, I want to, but I want to work in a law firm. And so you got a master’s and lawyering, and it was all about how to be a trial lawyer, and how to pick a jury and how to do a closing statement and all that, and then you walked into a law firm, and you’re like, I’m ready to go. And they would look at you and go, that’s like 2% of our work. And real Boys that whenever. Like, that was part of my job as a manager was kind of like crushing data scientists dreams when they’re like, We could build a model. Or we could take the average, let’s say

John Wessel 16:36
the dream or actually, I gotta, I gotta decide if agreeable, more agreeable take on this, but I think we’re pretty aligned on this one. The only like, I don’t even know if it’s a caveat is that some of it isn’t, though that, like, some of it’s a cultural problem. Sure. Like, we laugh at that, which

Eric Dodds 16:57
is gonna fix, obviously, so we can consider that assault, right?

John Wessel 17:02
Ship the boats. But I think there’s this, maybe it’s kind of a cultural problem. But I do think there’s a component we haven’t talked about that people really like to understand how things work. Yeah. And models are inherently opaque. Unless you are pretty good at math. And then if it’s AI, then basically no one knows. So

Eric Dodds 17:21
except for Sam Altman. Yeah, just Sam. Well,

Matthew Kelliher-Gibson 17:23
I will say one thing about this that I like, because there were some comments in that post, too. And it was, like, I used in the post, I compared it to being like a professional athlete, where it’s like, you gotta love to practice, right? Like, there’s a lot in this. And there were a decent number of people who have gotten into this, and maybe they got into it because of that. And then they went in and found like, Okay, I’m gonna have to do a lot of SQL work. There’s a lot of this stuff that I have to do, and they, you know, for lack of a better term fell in love with the process. Yeah, so there is that out there. You know, it wasn’t all bad. It was just, it was easy to sift through a lot of other people to find them. Yeah, it makes the job when you I was a manager harder, like, I put a lot of emphasis on like, training culture, and specifically on things like how to think, because I was like, I would much rather take some I mean, one of the best data analysts ever hired 50 year old woman who would come back into the workforce after raising her kids. And the only reason I knew she was 50 years old was because that was the first thing she told me when I asked about myself. She was one of the best analysts I’ve ever worked with. And it was because she really embraced, like, I want to learn how to do this better than I want to learn how to think, so she was really great at that. And she had super strong SQL skills, too. So you know, you just got to work through it a lot of times.

Eric Dodds 18:40
Yeah. That sounded almost positive.

John Wessel 18:43
I know. Yeah. I think we’re, I think we’re reversing today. No, yeah. My only other take on this, though, is I don’t think this is unique to data. Like I remember talking to having this conversation with somebody about being successful in general, like at work. And it was, you’re going to have to spend an inordinate amount of time like around the things you’re actually doing, to be able to execute on what you’re doing. And in any organization, whether it’s politics, whether it’s like I need access to this thing, or I need this, like you’re gonna spend a ton of time on that not just IT or data marketing is the same way. And there’s just a lot of like, reps and discipline and like, yeah, you know, relationship building all these things around that to be successful at the one thing and even like a time percentage breakdown, like Yeah, especially as you progress in the organization, you spend even more time on the like auxiliary things last time on like the core like what needs to be done? Yeah,

Matthew Kelliher-Gibson 19:36
I think the one thing you’ll notice is that a lot of other professions where you ‘re like, let’s call it high prestige, or where like, a lot of people would like to do them or there’s a lot of money. There’s a lot of barriers to entry. Yeah, and especially 10 years ago, there weren’t a ton of Yeah, that was the like, if you a certain amount of like SQL skill or Excel or else Girls, yeah, you could get in there or I mean, you know, there’s people that were just like, I just love data, which I always those people want to write off.

Eric Dodds 20:09
Not hiring you. I don’t care

Matthew Kelliher-Gibson 20:10
about how passionate you say you are about this, because this is not an emotional gig.

20:14
I love

John Wessel 20:15
digging ditches, right? It’s

Matthew Kelliher-Gibson 20:17
good. But like, there just wasn’t a lot of that there. I think we’ve seen a lot of differences now, just in the fact that like, you know, if you want to really work in algorithms, let’s say like, you better go get a PhD now. There’s no jumping into

Eric Dodds 20:30
Yeah, right. Yeah, there’s a Is it okay for me to insert a comment here? Yeah. I guess I actually make the rules. Yeah, you do. Wow. Yeah.

Matthew Kelliher-Gibson 20:41
You think we’re just got where weirdness guests

John Wessel 20:43
on that show? You’re

Eric Dodds 20:46
driving the car and you would exit? I should get off that. Okay. One brief comment from the moderator. There’s a really good paper for those listeners. For listeners who are interested. It’s a, I think, college commencement speech called the inner ring. And it talks about how like people try to Oh, that’s CS Lewis. Great one. That is great. Well, yeah, it’s really good. And basically the summary is, there are people who like trying to get in the inner ring, or chase a title with prestige, or you know, whatever, and any context of life, you know, whether it’s, you know, I wanted to design a saddle or whatever. And the whole point of it is, if you just focus on getting extremely good at a craft, you’ll realize that you have entered an inner ring of crafts, people who, to your point, love the process, right? I just want to, I want to hone my skills in this craft. Okay, we have time for one more since the culture AI one was, you know, brutally and swiftly disposed of, we’re going to try to squeeze one more in. Excellent. Okay. The author of this post will remain anonymous. What makes a great data leader. This may be controversial, but most of the time, I get a gut feeling when I determine if a data professional is right for the job. Or if you’re the cynical data guy, if they say I just love.

Matthew Kelliher-Gibson 22:06
I’m just passionate about data. Obviously,

Eric Dodds 22:08
This gut feeling is produced by what the candidate and their references Tell me. But I’ve been doing this job for so long. It’s a well rehearsed process and knowing who’s right and who’s wrong for a role. When I am asked to speak to data professionals, I know that a good leader in data will always hold a mixture of characteristics like being a storyteller, strategic thinker, skills and cross functional collaboration, people management and be a risk taker. I

Matthew Kelliher-Gibson 22:35
I think those are all true. And I look forward to the day when companies start hiring for the use of data leaders. That’s probably when I’ll be a data leader again.

John Wessel 22:44
What did they do? What did you say? Wow, that’s not what I expected. Guy know,

Eric Dodds 22:52
what do Okay, so they’re not hiring for those, whether you’re hiring for

Matthew Kelliher-Gibson 22:55
technical skills. When was the last time you coded at a neural net? That was literally a question I had in the directors interview, like, three, four years ago. And I was explaining to the person like, Well, I haven’t done that, because I’ve been managing, but I can talk to you about how I got this, you know, in a regulated industry, we got this model into production. And over here, it’s like, yeah, we’re looking for someone who’s coded more recently than you have.

Eric Dodds 23:23
Even though that’s not part of the job description.

Matthew Kelliher-Gibson 23:25
Well, it shouldn’t be. But I think for a lot of people, they think of directors as super individual contributors. Yeah.

Eric Dodds 23:36
Agreeable data guy.

John Wessel 23:37
I think it’s funny how it starts out, where basically, it’s like, I use my intuition to make decisions rather than data. So that’s fascinating, right? For me,

Matthew Kelliher-Gibson 23:50
I have other thoughts about that, but they’re not relevant. Right. We’re gonna

John Wessel 23:54
Yeah, but yeah, okay. Beyond that, I got a little hung up on that, honestly. Yeah. Beyond that. Yeah. Like, I understand why we picked the post. Yeah. I mean, I don’t think people like hiring data leaders, period, I think if you already have it ends up being the first data person in the door. Like since nobody likes it, people don’t really know how to manage data, like the business doesn’t either. So they just keep going down the train or up the ladder. And then they’re going to self promote to be whatever leader there is, and when they hire somebody, even if they’re hiring or for Director, they’re looking for, like some sort of technical skills and to offload work, because, I mean, if you are hiring, like I just, you know, like, that’s pretty much what happens, like, Oh, we’re gonna like, we want to keep you like, you know about the legacy stuff like, we’ll promote you and hire in a new guy. I mean, that’s typically what happens. I

Matthew Kelliher-Gibson 24:50
i also think it’s a bit of a I mean, you know, John, if you think about what are the profiles of most people who move up to being like VPs and stuff? Yeah, we actually talked about this one. Oh, yeah, they come from a specific background, they’ve typically come more from the data engineering or architecture side of things, which is a little more of like, it’s farther away from the end product, right? Yep. But it’s also one where it’s a very, like delivery type thing. The people who get promoted are the ones that were there. They’re like, we need this yesterday. And they say, okay, and they get it done. Right. Yep. Rather than as you go up the ladder, like then they’re like, well, we want you to be a strategic partner. Well, it’s like, well, you have not been rewarding strategic partners this year.

John Wessel 25:32
You’ve been rewarding technical skills and project management, and just

Matthew Kelliher-Gibson 25:36
get me to get it done. Right. Yeah. Right. Hottest put out the hottest fight. Right, but not the hottest fire and get it done quickly. And so like, it’s not like it’s the person who’s in that position’s fault, necessarily. That’s what you’ve been hiring for? Because it’s also because, you know, you viewed it a little bit as a junior service. Team member, right. Yeah, that’s what you want them to be. And so that’s what you’ve been hiring for those skills, which I think is also a problem, because those people are farthest away from kind of like the end of the data pipeline, you know? Yeah. And so when you didn’t say like, Okay, well, how are we going to use this in a strategic way? I mean, they’re dealing with infrastructure, they haven’t dealt with the actual, like, what the number is this? So why would they know what to do with that? Yeah,

John Wessel 26:21
I will say that I do think it depends on who is hiring, is the CEO hiring is another executive VP. Is the CFO hiring is the CIO hiring? I think it depends if you have an analytical CEO, which is actually not a lot of those, yeah, that is a great hire for this to actually, like, value data later, because it’s their job. But if it’s the CFO, like, then, like probably pretty mixed bag, like, and then if it’s a technical like CTO, like pick on my old role, like odds are and again, it’s a CTO that came from a developer or ops background, they’re probably least likely to value like, yeah, do leader type skills.

Matthew Kelliher-Gibson 27:01
I think it’s like his he is John said, you know, a lot of the first ones either were like, a lot of the first data leaders, when it first started becoming popular, were either like academic PhDs, which were hired because of how smart they were on the topic, which we saw had issues early on with many of them not knowing how to run a budget and the team looks

Eric Dodds 27:20
as an academic. Is this a tenured position?

27:28
For anything,

Matthew Kelliher-Gibson 27:30
or the other one just being like, this was the person who had worked here the longest, and they had been the person working in Excel in that creed. archetype. Yeah. about it. They think, what’s the data leader? Oh, well, let’s Gary over there. That’s been there for 20 years. He

John Wessel 27:44
knows so much about the business, which is valuable. Yeah. And

Matthew Kelliher-Gibson 27:48
then when they go, and they look for other people, they’re not looking for it. And it’ll probably evolve over time. But right now it is that we’re not looking for those skills. We’re looking for your technical ability. That’s super nice.

John Wessel 28:01
And for all our listeners named Gary. Yeah, sorry.

Eric Dodds 28:04
We love your Gary. Sorry, Gary. All right. Well, that is a wrap on this month’s show. If you’re out there and you find a LinkedIn post that you want the cynical data guy to comment on, send it to us, you can go to Data sack show.com Reach out to us there or tag us on LinkedIn. I think we’re on x two if anyone uses X anymore, and the cynical data guy has a blog now so it just looks good. You’ll find them on substack if you haven’t great shows coming up for you and we’ll catch you next time. The Data Stack Show is brought to you by RudderStack. The warehouse native customer data platform RudderStack has purpose built to help data teams turn customer data into competitive advantage. Learn more at rudderstack.com.