Loading summary
Transcript157 lines
- [00:00]
A
Foreign.
- [00:08]
B
Analytics topics covered conversationally and sometimes with explicit language.
- [00:14]
A
Hi, everyone.
- [00:15]
B
Welcome.
- [00:15]
A
It's the Analytics Power Hour and this is episode 289. There's a comfortable trap that a lot of us in this industry could fall into. We spend hours debating the merits of different data tools, what the best visualization is for a data set, or whether our tracking pixels are firing just right. Well, data is our craft and we love it. But there is a harsh reality, and it usually hits about three to five years into a career. You can build the most rigorous model or beautiful dashboard, but if you can't explain how it actually helps the company drive revenue or reduce churn, it kind of doesn't matter. We like to complain about how our business stakeholders lack data fluency, but maybe we need to flip the mirror and ask ourselves the hard question. Do we lack business literacy? So that's what we're going to talk about. Business acumen. Is it the missing link that turns a data analyst into an actual strategic partner? We'll talk about it. And to do that, let me introduce my co host, Val Kroll.
- [01:17]
C
Hey, Michael.
- [01:19]
A
Hey. And of course, Tim Wilson.
- [01:23]
B
Hello.
- [01:24]
A
Hello and welcome. And Mo. Kiss.
- [01:27]
D
Howdy, team.
- [01:28]
A
Hey. And I'm Michael Hellbling. All right, I think maybe to kick things off, we should start with like a brief intro to maybe a definition about what we mean when we say business acumen in the first place. Anybody want to take a first stab at it?
- [01:46]
B
I think there's. When I, as we were thinking about this, the little light bulb that went on for me is there's sort of two types of business acumen, and one of them being like knowledge of business like itself. Like, this is how business, this is how finance works. This is balance sheet, income statement, cash flows, marketing, 4Ps more. Whatever aspect of the business, which is kind of, this is something that you build over time and take throughout and are sort of truisms or practices in business. And then there's another type of acumen which is like knowledge of your business, the company you're working for or with and understanding where they are unique. They're all you know are doing, they overlap and that your business is operating in the context of the broader business. But there is what are the specific external and internal challenges that your business is facing. So to me, that's. I don't know if that's a definition, but that's kind of two flavors of it.
- [02:54]
D
But if you had to pick, if you had to stack rank, do you think one is more important than the other?
- [03:00]
B
I feel like they kind of leapfrog as they go along because you can't be super, incredibly deep in one without being deep in the other. If you were trying to say, I'm just going to know everything about the organization I work for, at some point you're going to run into the finance team who's going to be talking about revenue recognition.
- [03:26]
A
Yeah, because I think on the first level, the first one is like, concepts, right? So, like, how does a P and L work? And then the second one is context. How does it work in our company? And, like, maybe that would frame it like that. And I don't know if I could pick mo to your question, like, which of those is more important? I feel like you need both of those wherever you're going to be if you're going to kind of really, truly own business acumen and the place where you are, where you work.
- [03:54]
D
It's funny, though, like, one of the things that my mind goes to is, is there also, like, a third category which is, like, knowing your type of business, like your industry.
- [04:03]
C
Right.
- [04:04]
D
So, like, you can have really deep experience in, like, E Commerce or FMCG or, I don't know, insurance or government or something, and that's like a different type of knowledge and experience that you can apply as well.
- [04:17]
A
Oh, yeah, I think that's true. I kind of still boil that into the context, you know, awareness of how your, Your business or your vertical works.
- [04:26]
B
But I think that how you're. I mean, it's a good. The FMCG or CPG is a good example. When you. The number of FMCG brands I've worked with who've said, we want to be like, in the insert B2C retailer. And it's like, well, yeah, it's like, so. And they're like, yeah, all we need to do is get, you know, direct information about our individual buyers. I'm like, you're selling soap. Like, that's, that's. Where on earth would you have that permission? Which, I mean, maybe that goes back to the concepts of saying, well, you'd understand what the limitations are in the context of this vertical and what you have to do instead. But as you're talking, I just realized, just this week had a case where two completely different verticals, but both of them had a franchise model was kind of the way they were working, completely different spaces. And I was kind of like, pleasantly surprised to realize that there were some similarities into how that sort of corporate franchisee relationship worked and was managed that was shockingly parallel. And I haven't Worked with that many franchisees. But I was like, oh, wait a minute, does every brand that operates on a franchise model or do most of them have this sort of setup? And that went from learning it for one and then kind of stumbling across it for another. But applying those patterns, which I think, Michael, to your having those concepts and saying, okay, how does that concept apply in this specific context? Is a. I love that phrasing.
- [06:08]
D
It's funny though, the one thought I do have about like knowledge of your business. Well, I have multiple thoughts. One is, I suppose the first being it can really unlock a lot in terms of, let's say hypothetically you're talking about like maturity or data usage and how great it is in the company. And you ask folks to give you a score of 0 to 10, right? 0. Totally shithouse. And 10 being amazing. Industry leading, most data driven. I've got air quotes for those listening along company in the world. And it's like you might have knowledge that actually your company can only get to an 8. Like a 10 is just not possible at your company for various factors. But it's interesting because at the same time I say that I also think it can danger, danger the work that you do and how you provide it sometimes by absolutely biasing your approach. Like you think of Richard Har's book the Psychology of Intelligence Analysis. And for that reason they like, tell analysts to move around because if you know something too well, you can also make mistakes.
- [07:13]
A
No, I think Mo, that's an incredible point because it's very natural for people to kind of get locked in on whatever function they're in as analysts. It's an amazing experience to kind of see different parts of the business and build context around those and see how they work together and build out knowledge across. So like, you know, finance and the way they look and analyze data is different than how marketing does versus how merchandising does versus how sales does. All these different functions within the business, look at it different ways. And yeah, you can become a much more fully featured business analyst by taking time in each of those. And you can sometimes get a little bit too, I don't want to say stuck, but like, there's a bias that can influence kind of like how you even approach or think about what's possible in terms of insight or action or recommendation that then, you know, leaves your analysis not as fresh or capable or as aggressive enough. I don't know the right way to say that. Good. Well, but that's kind of what I, I think you're trying to say too.
- [08:23]
C
And I think that's a good connection point between some of the like concepts we're talking about and what that means to the analyst connecting it to their work. I think what you were just talking about Michael, with like the recommendations and the actions. When I think of business acumen and and analysts and like building the skill, I think one of the first things is just understanding how the business makes decisions. Therefore you can come up with the best ways to think about framing your recommendations or proposing actions for the business. Because it's like what is your relationship? You know, but everyone you know well tread area between sales and marketing. But like what is your relationship with other decision making arms of the business and how is it supporting that whether you're in house or consulting?
- [09:06]
D
Yeah, it's funny, I actually had someone ask that in an interview and I to this day always reflect on it being such a great question. The question was how does the business make decisions? Like who are the key decision makers? And like talk me through the process of like how they get signed. You know folks get sign off for something. And I was like it actually is such a wonderful question because it tells you so much about the culture and the ways of working. Like it really is an unlock for someone who is trying to gain that knowledge of the business quickly.
- [09:39]
B
And I think it's. There are cases where I think that's a love that point. And it also is the sort of thing we're talking about like to be a better analyst to be thinking kind of not necessarily just who your direct partner that you're supporting. And I think they can wind up in a group, in a, in a, not a group think but kind of caught in a way of this is somebody who may be a mid level paid media person and they have been poisoned by their media agency as to what metrics matter. And they're not necessarily thinking through the business, the broader business, how decisions are getting made, what sales is expecting, what is coming back. And so there can sometimes be a challenge or an opportunity for the analyst to say I can't just trust exactly who I'm. I mean you want to have a positive relationship, assume good intent. But there are definitely plenty of people in business who are operating with blinders on and often the analysts are the ones. If we're trying to connect the dots, you need to have more than one dot. So that's kind of a weird as you're talking it's making me think about sometimes the analyst needing or having value in having a broader perspective in order to do the analysis that may be providing something to somebody who has a narrower perspective to help broaden their perspective.
- [11:04]
C
No, I like that. And I think even thinking. I completely agree. Because even if you think about the group that you're supporting, it's always helpful to think about who else might have a completely inverse motivation at that table. Like even if you think about resources and thinking about budget allocation or time allocation of resources or marketing wants as many leads as possible, but sales only wants the qualified lead. So there's like this tension that I think you can tap into between these different groups to understand, you know, there was a group we were working with where we were working with the leaders high up enough that we had one group in the retail group that was focused on the sell in to the marketplace partners and then there was a group sitting across the table from them that was only focused on sell through. And so we had to think about how do these two concepts work together and what is the thread between them that kind of aligns to again, how the business is making decisions. But I think that those the motivations, even if it's not a stated goal, helps you understand the friction and who might be not thinking the same way as the client in the group you're supporting.
- [12:15]
B
Okay, time for a quick break for a word from our sponsor. Ask why. You know we're allergic to AI hype on this show. So when someone says AI analyst, my first instinct is prove it.
- [12:27]
C
It's fair. And that's why Ask why caught our attention. It's not about Vibes or Magic. It's about actually automating analyst work. Things like generating SQL and cleaning data and exploring data sets from plain English questions.
- [12:41]
B
And importantly, because this matters, AskWai does not send your raw data to an LLM. They build a semantic layer on top of your data. The model writes the code and that code runs where your data already lives.
- [12:55]
C
What's new and genuinely interesting is that Ask Y now has skills. And memory skills let you save and reuse analytics steps you've already taken instead of redoing the same work over and over.
- [13:07]
B
And the memory piece means it can retain context, business logic, assumptions, definitions. So you're not re explaining the same setup every single time you ask a question.
- [13:17]
C
So the promise here isn't replacing analysts. It's reducing the repetitive friction that slows analysts down.
- [13:25]
B
Ask why is in beta right now. Go to asky AI. That's ask the letter Y AI and use code APH to jump to the top of their waitlist. This isn't Vibe analytics, it's tooling for the rise of the AI analyst. Now back to the show. Can you guys think of times where you all of a sudden realized that you hadn't fully processed how something worked in the business and then it sort of made you say, oh, I'm seeing things with the entirely new light? Because somebody explained that to me.
- [14:01]
A
Usually mid presentation of some analysis I thought was really sharp.
- [14:09]
B
You know what, this is awesome.
- [14:10]
D
I just, yeah, I honestly think starting at Canva and getting an understanding of subscription revenue, like I had worked in Ecom before someone bought a pair of socks. The second that item gets delivered, like we have money in the bank. It's very different when you start thinking about annualized recurring revenue. And like the fact that people are trying to extrapolate out what does this mean over 12 month period versus like this is what has actually hit the bank. I think that was one. Yeah. It takes some like grappling with and then when you've got other revenue streams like print or something that then do hit the bank, you know, when the order gets fulfilled, so to speak. I think that's one that even to this day sometimes I find stakeholders don't fully understand. And it's, you know, I find it's really important to be very specific about if we mean annualized revenue or annualized recurring revenue, which is a subset. And like there's a lot of nuance in that and it does affect things. Like when you're doing marketing, you know, you can't just like assume, you can't wait for every time that the money lands. Right. Like you have to project forward. And so like it creates a lot of complications that I probably never fully appreciated until I worked here.
- [15:24]
B
I mean this was like kind of blew my mind so bad. And it was actually one that the, the team, the business partners were making kind of a. They were, they were behaving in a way that was illustrating how badly they were looking at the data because they weren't thinking through how the business worked. And this was a company that was kind of of, for and by the engineers, sold to engineers, built products for engineers. Everyone in marketing, everyone was an engineer. And so they had like engineered this solution and the R and D and engineering teams were combined. And the VP of that team had said, we're going to start doing a monthly ROI report. And I barely knew roi. I wasn't even officially in an analytics role, but I was helping produce this monthly engineering ROI report. And this entire, which was a printed report that was pretty thick, that was kind of a nightmare to produce. And they went product line by product line and they took the total dollars invested in that month like what was paid to headcount, what were the fully loaded costs for that department and they took the revenue from that department for that month and they did the math and said here's the roi. And they had been working on it and it turned out that people were saying, the people who own those products lines were saying, well, they always had an explanation saying, well, we're doing the R and D now, then the thing has to go to market and then be produced. And then like we're not going to see revenue from what we're working on now for months or years. So everything either looked fantastic or terrible or it was a long time legacy product line that looked totally stable. And that was before I was in analytics and it was actually this was while I was getting my MBA and we were talking about revenue recognition is partly what triggered it. And I was like, oh yeah, wait a minute. This is like the whole company is kind of whiffing, well intentioned, but aren't thinking through kind of the nuances of how it works. Smart enough, great products, they would kind of do the math after the fact and say, well this, this product line looks terrible, but that's because of X, Y and Z. It's like, but you're looking at data that looks terrible. Shouldn't you be looking at it in a way where it makes sense instead of distributing it?
- [17:51]
D
So how did you solve it? I guess the thing is when I hear that, I'm like, what's been built isn't actually trying to answer the question that was intended. So you got to help me out. How'd you solve it?
- [18:06]
B
So I was a little into that story who was like pulling this stuff. I honestly don't, I think it kind of petered out and they're like, this report actually doesn't make much sense and we need to be thinking through. I think they shifted to more of a planning, like an annual plan. This is what we're going to invest in this product line and this is what our expectations are and this is kind of when. So they kind of shifted, I think to more of a forecasting model so they could forecast the costs and they could forecast the revenue and track against those instead of trying to put them together. But I was a kind of pretty low level and I was like, this is. And I went to my manager who was more senior, but he was kind of one of these people who'd been kind of marginalized like, pushed to the side. He'd been around the company forever. And I was like, does this make no sense whatsoever? And he was like, well, yeah, I think it, I think it doesn't, but we kind of know it doesn't. Like, it wasn't like, oh, I brought some grand insight. It was kind of like, yeah, we've slowly been figuring out that this actually isn't all that helpful. But there were also plenty of people who were flipping through it and saying, our product line is doing great. It's like, well, I mean, incidentally, it's.
- [19:21]
C
A good story when it's a good story until they're on the other side of that.
- [19:26]
A
Well, and it, it happens all the time. Like you, you mentioned a little earlier too, Tim. Like, some people grab onto metrics they want to optimize just because it fits within their constraints, but don't really think further into the business of like, okay, if I optimize for this metric, what are the downstream impacts of that thing? So, like, what if I go optimize for customer acquisition in a channel that isn't really a good fit for a product and returns go way up? Well, now we're costing the business tons of money over here that, you know, maybe me sitting in my customer acquisition spot never sees or thinks about. You know, those kinds of things happen all the time. You can, you know, optimize for the wrong metric or don't think through the flow of the money, through the system to, to encapsulate what you're looking for. So that's kind of why it's important.
- [20:16]
D
So my mind always goes to the, like, annoying practical shit. But how do you develop this business acumen? Like, I know I probably have ways in my mind, but I think sometimes the frustrating bit is people like it. In some cases it does take time. Right. Like, part of knowing a business really well is the experience of working there. But are there other things that have worked for you all to develop this quickly, especially when you're not sort of in house and you need to develop that business acumen fast.
- [20:43]
B
You've brought people in many. You've hired many people, presumably many not coming from a subscription services background. How do you ramp them up outside of the mechanics of the data? Like, what do you have them do?
- [20:58]
D
Why did just turn it back on me?
- [21:03]
A
Well, you had a really good example earlier.
- [21:06]
B
Yeah.
- [21:08]
D
What I do tend to find is part of our onboarding is normally getting to know one of the data spaces really well. So it will be something where you basically have to really Dig into the data warehouse to for example, understand how subscriptions are calculated, how is that revenue calculated? And so often or if you're going into the people analytics team, it might be something to do with a particular metric that needs to be calculated for that space and by really getting your hands dirty on the subject matter that like we intentionally do that as part of onboarding. But I mean I, I have a very different working style than I would say to lots of data people. And the thing for me is talking to people. My advice to every new starter is spend your first month having coffees. I don't care if you have five coffees a day. I mean you might want to swap to herbal tea at some point but like have as many catch ups with people as you need to and have a list of questions. Because for me, I mean that's how I absorb information and learn. Which is really irritating for other people because it involves talking things through. That's been my approach. But I know that's not the case for everyone. Right.
- [22:13]
B
I mean I feel like that's a pretty common onboarding is giving these are the people you should meet. And I think sometimes that that's not given because they can't just go set up coffees because they don't know who they're supposed to have coffees with. So it's on the manager or the team or whoever's onboarding to say these are the people. And here's why I think it falls to the analyst. I'm trying to think when I've started at places in house and I'm like, I guess I'm just going to try to meet them and I'll come up. I, I didn't frame it is I need to deeply understand how they think about the business and it's a great opportunity to say I want to know how Joe thinks about the business. Who's in this role. And now I want to go talk to Ann and I want to see how Ann thinks about the business. She's in a different role. Does it match, does it jibe? You know, and you get, by the time you get to the third or fourth one you're like, okay, everybody knows that so and so is the big dog competitor and we're never going to beat them on price. Like that's consistent. I seem to be hearing inconsistent things here. Which means in the business there's not agreement as to the value of email marketing or you know, whatever it is. But I think having that framing of starting a new position to say I'm Just trying to figure out what does everyone agree is the case? Because it probably is. I mean, sure, maybe there's some. Every misguided assumption that everybody's bought into, but framing it that way of like, I want to be able to go and understand how each one of them think about the business means I'm going to learn about our business.
- [23:54]
A
It's like a flow discovery type of thing because you have to figure out how the money flows to the org, but you also have to just figure out how people's decisions or their work flows through the org as well. So like, to the earlier point about like, how do decisions get made? Okay, yeah. What part of the P and L do you care about? What part are you motivated the most by? Because if you can understand motivations, I honestly find that, like digging into this topic actually helps me increase my empathy for business users quite a bit because I start to understand their motivations for, okay, what are they trying to do? And then I feel like I can find ways to help them that they aren't even able to really enunciate back to me in the first place sometimes because they don't really know what I do. But then I can go back and say, okay, here's three ways we can get you data that actually helps the thing I hear that you're trying to solve for. And, and you know, kind of helps like build some really nice bridges because that's. I always think about it like, okay, well, what decisions are you trying to make? Or kind of like, how are you motivated? Right. Like what? You know, in a crass sense, like, what are you bonused on? You know, if you hit these targets, is that going to make you happy or make the business happy? And then you do find, Tim, to your point, all these disconnects when you start doing that process and you're like, what is going on in this organization? Like, this person is motivated bonus this way, this person is bonus this way. Like, they're at odds with each other.
- [25:32]
C
Who.
- [25:32]
A
Who built this alignment?
- [25:34]
C
Yeah, and the, to the other part of your question, mo, like, and how do you do that, especially if you're not in house. And I would say that there's, you know, pros and cons, advantages, disadvantages. But because you usually start a consulting engagement like that with discovery, like Michael was just kind of talking about, like you have permission to take audience with all those people and ask those questions directly. And I do find that it's like this, like a game of guess who, like understanding, like asking questions like, oh, and where does this person sit inside the organization? And after they answer that, it's like, okay, I don't need to ask those four. I'm gonna put those down. So they're more in an operations function within marketing. Understood. Got it. Okay.
- [26:15]
D
So.
- [26:15]
C
And then you're, like, kind of building this, like, understanding as you get to be like, direct fire versus, like, trying to keep it casual over a coffee. Sometimes it's. It's almost beneficial to be able to. This is the intent and purpose of this meeting. I'm gonna pepper you with 20 questions. And then. And then, similar to how you would at the end of a coffee date, if you're starting to say, who else should I speak with? Is also, you have permission for that at the end of, like, stakeholder interviews or that kind of thing. Like, who else should we be chatting with to fully understand, you know, what we're trying to solve here or what the opportunity is to. To give it, you know, its fullest shape.
- [26:51]
A
Yeah. Do you. Do you guys read, like, 10Ks and in, you know, like, company decks and stuff like that?
- [27:00]
C
Oh, yeah.
- [27:01]
A
Yeah.
- [27:01]
B
As a consultant, definitely. And when I was in house, I would at least. At least would hop on the quarterly conference call when it was public. And always.
- [27:11]
C
I never did that until I was a consultant. I should have, but I never thought to do it.
- [27:15]
D
One thing that's on my mind, though, is as data folks, it's always like, ask for more context, keep asking questions, be curious. I do feel like there's, like, another end to the spectrum, though. And we had this recently with a partner, and it almost got to the point where, like, they kind of kept asking for context and kept asking and kept asking. And it gets to a point where you're like, you have a lot of information about our business. You know us really well. We've worked together. You need to stop asking for context and start coming to the table with some ideas. And I. I felt that was a pretty fair place to be, and we had a really great conversation. They were, like, very different culture at their company, so they were quite cautious. But I was like, it does. It gets to a point where you're like, I understand that you're trying to collect this information so you can present something good, but by waiting and waiting and waiting, there are ways you could check in on your thinking earlier that might get us to the outcome. Whereas if you're always in that collection mode, sometimes I think it can also be problematic. Curious to hear perspectives.
- [28:30]
B
I mean, if an analyst, consultant in house, whatever, shows up and hasn't even made an attempt to sort of figure it out. I think it's always much more useful to say with whatever knowledge I have of whatever this space and drop me in somewhere completely foreign to me. I can still say, well, I would assume that it works like X. And I think it's. I've found it more useful to say, let me put forth where, how I assume it works. But let me also illustrate that I don't know for sure this is a logical way to think it. And that gives them the okay. You're trying to think you came prepared and that's not sitting in a vacuum. Like, go and try to figure it out. Who are your competitors? I think that is actually where ChatGPT can be a huge. Just going to say that, like, yeah, go and spend some time with that, whether whatever you're doing and then say, okay, is this how this works? And then they're reacting and saying, oh, that's like 70% correct. But what you're missing is this one other piece. But yeah, I would be pretty annoyed when people are like, what's our list of stakeholder interview questions? It's like, well, here's your stakeholder interview question, but here's the research and prep you damn well better do before you show up in that room because you are taking their time. You want to find out what they can tell you.
- [29:53]
A
And to be honest, Mo, I think I probably struggle with the opposite problem of what you're describing, which is I tend to have ideas of what I think the solution is before I've gone in depth enough to really understand the context sometimes. And so I find I have to hold myself back from being like, oh.
- [30:11]
B
I think I see it.
- [30:11]
A
Here's the idea, here's the solution. Instead be like, nope, gather more information. Gather more information so that you don't miss important nuance. But you're absolutely right. There's diminishing returns to trying to, like, measure four times and cut once versus iterate through, like, kind of what you talked about, Tim, like, created iteration, expose your assumptions and allow there to be a flow of information back and forth on top of something. And the old saying, it's always easier to edit than it is to create. Right? So if you get something set up, then people can react to it and give you so much more valuable information as opposed to, like, them sort of wondering, like, what the heck is this person doing in here not talking about anything, you know? So, yeah, yeah.
- [30:58]
C
The one thing that I'll say, though, if, like, you were trying to Think through like, what is this, like striking this right balance is like, you know, if you're in an analysis, you know, validating some assumptions, some hypotheses, like, do you feel like the recommendations you can make to the audience you plan on delivering this to feels like something that is a lever they could pull, something that they potentially actually have control over. Because there's been so many times where we'll see examples of work where it's like making recommendations that are so off from like what the person could actually do that it makes it feel. It almost invalidates the first 10 slides because it's like, do you even know what that team does or how long ago they had to make that decision? Like, that is so unhelpful. And so like, that's like a.
- [31:46]
B
But we had a, we had a business context case where, I mean, we wouldn't have known. We were talking to the CEO and we said, would there be an appetite for geo lift tests? Like you're not doing it that would address this issue? And she said, absolutely not and here's why. And it was a damn good read. I was like, cool. And she wasn't upset. She was like, yeah, I would love to do that. And here's why we can't based on the nature of our business and we're like, boom, ding, ding, ding. Like better understanding of kind of what parameters we were operating in. So I don't know. Like, Michael, I feel like even if you bring and say I'm bringing a potential solution, my assumption is this is probably not workable. But if it gives you something to react to, to tell me why it wouldn't be workable, that's going to help both of us. And they very well may be saying, well that's 30% workable and I love that. And I never thought of that. This other 70% won't work. So. But I think mo you're hitting on like the, the balance of bringing stuff and getting stuff back. Like it does need to be a. Not just going and expecting to be. It all being kind of a pull. It needs to be kind of a back and forth.
- [33:04]
D
How do you bring it back though? Like I've had so many times people have walked in and presented stuff and been like, here, this is the insight and that thing you should do next. And I'm like, cool, we knew that that's not helpful. Or like the example you just gave Tim, like that's not feasible here for reason X when you're in that situation and you realize that you've Done this. How do you, how do you course correct. How do you like repair that relationship And I suppose your credibility to some.
- [33:33]
A
Degree you're technically correct, but business wrong.
- [33:39]
C
Well the example that Tim gave that was in a discovery session that that happened. So that was like to Michael's point, like exploring solutions but making sure that going to get to the end of, of some piece of work, making a recommendation for a geolift test and they're like, like get the hell out. Like press the buzzer and your seat ejects you. But I think if you, if like if I'm thinking like in a consulting context, like if you make a recommendation that is like so off base and you, you've, you hit that, you hit that button live, I think part of it would be kind of unwinding some of your thinking as quickly as you can to show why you got to that conclusion because there might be just a slightly different path forward that still relies on some of those same assumptions or some of the dots we were able to connect because like again you went too far. But I also think that sharing some of that context and then maybe coming back with a couple questions to understand what might be a right fit, but I think it's turning it into a discussion as quickly as possible is like where my head first goes to it because that's the only way that you're going to dig yourself out and be able to make a sharper recommendation next time. I don't know.
- [34:50]
B
That's just some of that goes to the stakeholder management like you don't want to have. If that's happening at a super high stakes, there were probably some relationship and there were some planning stuff that you whiffed on. Like if you're going to go and present it, who did you vet it with beforehand in a lower stakes to make sure that the, the assistant or the team member say hey, I'm going to present this. This is kind of a big deal. And I mean we're excited to say, wow, you know, we're going to present this amazing thing. But it may, it may completely backfire. Better to figure out, okay, who are the people looking around, whether it's in your group or another group that you have the relationship that they seem to have their finger on the pulse of the business, that they would be a good sanity check. I think just knowing who those people are, that that same company years ago when I managed the BI team, had a lady who had been a analyst supporting sales forever and she knew everything about how they worked. She knew the data inside and out. She knew what they cared about, she knew all the personalities and she was kind of gold for the team. If anything was going to get presented to sales, it was like, you better run that past Shelly because she's going to make it better and she's going to make sure like it needs to be Shelly approved before you go put it to the business. And that was because she was just a super seasoned and approachable analyst in the team. So if it went through her, it was going to be good. But it can also be somebody on, you know, who you're presenting to. If I'm going to present to the head of sales, you know, maybe I should, whoever, my buddy who's in sales management, I should kind of run it by them first to make sure that I'm not about to have the eject button hit on me when I take it to the higher stakes.
- [36:41]
A
We've lost so much from in person meetings because one of my big signals is like when the most senior person picks up their phone, you course correct instantly like you're like, okay, lost you, now I need to get you back. But even sometimes people will be like, you present all your ideas and then you don't hear anything. And that's almost like even worse. Like they don't, they just write you off as okay, that guy's an idiot. And you don't get any feedback. That's brutal. And then basically you're just trying to scrape, claw your way back in to those conversations after that. And it's really, it's just a rough thing because trust is so hard to build and being influential in a business is so hard to build. And so that's why like, like kind of all your points, Tim, are super important. Like do the prep actually run through it with somebody who can give you great feedback on it. Collaborate with somebody else on like, okay, this is the analysis I'm thinking about. This is the direction I think it's going. Does this ring true? What do you think this will look like in the room? I remember back in the day when I was a business analyst when we had an important analysis to present, we would do pre reads with all of the like most of the people who would be in the meeting just to make sure there was no big alignment issue with what we were going to present to the bigger team. Now you can't do that with every single analysis. There's not time to do that. But if it's like an important one, that's really going to drive a big decision. Like yeah, grab that director. Grab that director. Grab that director. Make sure they don't come in with, oh, that you're missing in a very important piece of context that's going to derail this whole thing before you even sit down and present this to the broader or leadership team or whoever. And that can really help you. And you just have to think through who might have impact or who might have something to say about it.
- [38:31]
B
If having that sort of forum and this again, I'm going back a ways, but having, managing a bi team, which was where the business analysts lived and we would do in our, I think weekly, every other week, staff meeting and we would do the whole, like have somebody present the analysis they were working on or had done. And it would have the senior people because there was a good way for the, for kind of cross training and the junior analyst. So as you're describing that, Michael, there's like the. I'm Michael, presenting this analysis. These are the three experts who I want to make sure I run it by. Who are the three up and comers or who are other lines of business who should be there, who probably, probably aren't going to weigh in, but they should learn by listening, Which I know it sounds like Pollyanna, like, where are we going to find the time for people to witness this? But if you're looking through the lens of saying we need to understand the business, it was a manager, that same company is the first one who said, you know, as analysts, sometimes we need to know the business better than our business partners. We need to have a broader understanding of where the moving parts are and we're sitting in a central function where we can have that. But that means there needs to be time expended to actually learn that broader context.
- [39:54]
D
And it's funny, I think one of the things we haven't touched on though is understanding business timing, which I feel like is almost its own whole area. And this is something that's incredibly top of mind for me right now is about speed to decision. And so what I do observe is the data folks going away wanting to put their best foot forward, wanting to work on this really incredible, complicated piece of analysis. And it's like, well, the business needed a decision made last week and so now you've presented it and the decision's already been made and they're like, but they, you know, and like sometimes I find coaching people through that where it's like needing to understand the like level of rigor you need and the like speed, the level of confidence for the business decision Being made, I find is one of the most, I don't know, like, underrated is probably the wrong word, but I feel like it's something that people kind of forget a bit about and it's one of the ones that trips up data folks very often.
- [40:55]
C
That goes back to Michael's point about empathy. Right? That's building the empathy or building your business acumen helps build. Okay.
- [41:06]
A
That'S right.
- [41:07]
C
Building your business acumen helps build your empire. Empathy for those stakeholders because you're really in tune with the decisions that need to be made, the pressure that they're under, the risks that they're considering taking.
- [41:18]
A
Or being forced to take and, and should guide. Basically write kind of what kind of analysis you end up doing. Like, is this a massive project where we're going to go six months on this or is it sort of like quickest, dirtiest sum information to help you make a decision tomorrow? Because that's. You're so right, Mo. Like that's one of those ones where I've seen this a lot where somebody rolls in with an analysis that would have been great three weeks ago and now is completely out of priority. And then especially in a growing, like a, like a fast growth company that happens almost overnight. It's like, oh, we moved on, we're already on to something else. Not, not even talking about that anymore. And you just look so out of place at that point.
- [42:01]
B
Point.
- [42:02]
A
It's terrible.
- [42:02]
B
You do. But to be, to be fair, and this is a separate challenge and I feel like we've brought this up, we have as the industry writ large, this belief in the truth and precision and magic of the data, that the challenge of saying I'm going to give you directional, crude stuff but it sort of shows that X is greater than. Yes. What will be heard by the business is that is the X is greater than Y in an absolute, you know, truth perspective. So that, that's like a separate challenge. Like we, we can have marketers railing about speed to decision. I need it. What they're thinking is I want the super precise, in depth, all that detail, which probably wasn't that hard. You just need to give me the right model or put the right AI agent on it. I need it now. And if you say, well, I can give you something now, but it's going to be, it's going to be pretty blunt and it may be wrong and it's going to be a little risky, they'll be like, fine. So you'll get what you'll give me right now will be perfect. Just because I asked for it harder. So that may be a whole other episode of, you know, decision, decision science, decision skills.
- [43:17]
D
You know, if we move really fast, are you comfortable we're going to be wrong 30% of the time, 60% of the time, like really getting folks used to having those conversations and realizing, sure, if you need a decision tomorrow, I will give you something. But there will be assumptions and I can list them out very explicitly. But like, it is back of the napkin. That's, that's what we can do with the time we've got it. It does require a whole different level of, I don't know, maybe maturity is the word or like depth of understanding.
- [43:49]
B
But I think it's the other piece is that the getting that understanding of at the core, what are the big boulders that we as a business are trying to push can help. Say, these are the things I need to always be thinking about and trying to figure out ways to kind of bring those to bear, as opposed to every request that comes in. I need to kind of slot that into the context, the decision speed, all these other pieces. Like there's, there's another part of. The deeper you get that business context, the more you can just make smarter decisions about where you're spending your time. Which things are total throwaway. Like, I have to spend 15 minutes on this, but it doesn't matter, it's never going to come up again because this was a one time thing with one pointless question being asked, oh, this other thing. I need to respond just as quickly in 15 minutes. But I'm also going to keep working on it to give a more thorough answer because this, if we can crack this nut, then like, we really will have moved the needle. I feel like I'm talking all in abstractions and I have various examples floating through my head that I can't figure out how to generically articulate them.
- [45:06]
C
Well, it's good we're following, Tim, but the thing that this conversation, this part is making me think about is when speaking of empathy, is when you're delivering an inconvenient truth. Like when you're saying like, oops, sorry, that painted door test, like in the experimentation world, says it's not worth building out that feature or it happened a lot in my market research days when they were doing concept tests and it's like, oh, sorry, everyone kind of attributed that to your competitor. It's like not cutting through. It's like not giving you the credibility. And it's like, shit, what do we do? How do we go? And so I think this is your other opportunity, and I don't have perfect answers here, but to be a good partner of what this could mean and how you can help tell this story and delivering, helping your business partners deliver the message to their higher ups. I think that's one of the areas where you get a lot of points in the partnership realm for, again, having empathy for what this means to that team's roadmap or budget or they're planning. That's never, never a fun moment to have to be in.
- [46:13]
B
Sometimes also a good opportunity to pull in somebody from another group. I think the number of times that the analyst get hits with something and you're like, what? I understand what you're trying to do and you deeply want to answer this question and answer it well. And we're not the most equipped to do that. We should go to the research team, we should go to the experimentation team. They may already have something. We understand what you're trying to get at. I don't know that they're going to have a simple quick fix either, but let me loop them in, provide them the context, see what their thoughts are, see if something can happen on that front. I feel like that happens a lot of times with behavioral data where the question that comes in and if you really understand what they're thinking, you're like, you're, you're just trying to cram the big behavioral data answer something that is really an attitudinal data question. But you have to understand what they're really trying to get at.
- [47:14]
A
This is a question for the team. So we've been talking a lot about data analysts. Do we think everybody in the data org should be building business acumen or there are some roles that. It doesn't matter as much.
- [47:30]
B
I think everyone, everyone, data engineers should be.
- [47:35]
D
I mean, I literally, I had this conversation yesterday where a team of engineers have built out probably one of the most useful data sets that I could imagine. And they left out a particular property because, like, they didn't understand that that's how. That's like the connecting fabric for everything we need to make that data useful. And you're like, how. And that's the business acumen. That's the, like. Here is how it's going to get used. Here it's, here's how it's going to, like, work with our systems. Here is how a product manager is going to answer a business question. And it's like, yeah, anyway, I'm on the very strong fence of everyone.
- [48:15]
B
I think that's the Battle to fight against that. I watch. This is like the path that the analyst will get pulled down, the data person will get pulled down as they get into the complexity of the data, which is interesting in and of itself. It is this interesting engineering challenge. There is plenty of understanding and exploration to be done and clever solutions to be come up with. And that doesn't require going too, too far out of your comfort zone. You can just dig in and figure it out. And then they just sort of spiral down into, hey, I'm getting smarter. I'm getting a deeper understanding of the business, when really I'm getting a deeper understanding of the way the data is landing in various tables, which is important and needs to be known. But it's easy to get. I mean, I think that happens. I mean, Adobe analytics, cja, Google Tag Manager, like those all, there's an infinite level of spiraling deeper and deeper into the data that can be done. And it's got to take a conscious effort to say, you know what, for the next hour, I'm just going to go understand what the business gives a shit about instead of figuring out how to better normalize this one metric. And that takes conscious effort.
- [49:32]
C
Yes.
- [49:33]
A
It's kind of why I asked the question, because I've run into people who kind of will be like, well, in my role, I don't have to. And what's always surprised me is like, I've never really. Yeah. Like that. Or not. Maybe explicitly, but like no interest. And. And it's so weird because, like, I, I see the connection immediately from like any data role you can think of. But I've seen data scientists do this. I've seen data engineers do this, even to a certain extent, even analytics engineers or analysts who kind of live within their function. But I think it's because you get down into the layers of complexity or arcane knowledge of the specific tools that you're interested in, and then they sort of feel like that's enough. And I just, you know, the challenge would be like, I don't think you're gonna, you're gonna have as fulfilling of a career if you don't spend some time trying to go up into the business itself and understand it going back into that. Like, we've talked a little bit about how to do that. Do you think there's benefit in like going and getting an MBA if you're like a data analytics person. Or other education? Like, it doesn't have to be an mba. Specifically me, I'd love to get one.
- [50:49]
D
But that's more just like interest. I, I don't think it's a requirement.
- [50:54]
B
But I like I'm interested having gotten one before I was officially in analytics and kind of stumbling backwards into it and finding it very interesting. And it was largely just because I wanted to go do something else. And every passing year I find more like wow, there was some good, like how did I just take one microeconomics class? And 23 years later I'm still pointing back to some of the game theory that happened in that and seeing those patterns in the world. I don't think it is a, you must like it's an investment of time. But there were things that I was doing then when I was taking it that I had no idea was like getting is embedded in my brain and I think did give me deeper understandings of different aspects of the business. So I'm a fan, but I'm also not a reliable assessor. You guys now take the FedEx commercial and say so easy. An MBA can do it, you know.
- [51:59]
C
Well as someone who doesn't have their mba, similar to Mo like it's something that's always interest me but my, my very small scale proxy for like ways that you can get deeper in some of that outside of some of these conversations and two different roles that I've had in the past, you know, similar to how you go through these exercises to get closer to like what the experience is in your customer shoes. I've had like sitting in rotation with some people. Like some of my business partners are stakeholders. So when I was at UBS within the investment bank, I spent a couple days like with Rydal alongs with some of the research analysts themselves from like the top of the morning to the end of the day. And like I'll tell you that the, my mind was blown within like the first five minutes because the analyst that I was following said oh you know, I can't, I can't take the subway into work and blah, blah. And I was like, what do you mean you can't take the subway? We lived in New York City. I'm like what do you mean you can't take the subway? And he's like, I literally can't afford to not be reachable during these hours. And so I have to be able to hop on a call because like that's how I service my, my customers. And I was like that is crazy. But I just felt like I speaking back again theme of empathy here understood so much deeper like what was at stake for him or you know, others in that role and how Data like, you know, I'm thinking like, well, what do you mean? He didn't read the thing that I sent you and he can't even get on the subway. Right. So not really going back to answer your MBA hate question, Michael, but just talking about like, how do you find like really deep ways to extract some of that? And that's. That was the thing that, that cropped up in my, my head, like rotations.
- [53:39]
B
I feel like I should also throw in that the. There are, there are all the various, like executive ed stuff and some of those are like just ways for schools to just print money. Like, they. It's really hard to know what's. What's garbage and what's not. But like my, my wife went through like, like one of those mini MBA type thing, Harvard's core program, capital C, O, R, lowercase E. And it was courses in semesters, but it was six months or something and it was totally doable. And she was like this. And she's in a PMO type roles and she's like, this has been really useful because the people that I'm working with are sometimes talking about, you know, useful to get just a surface level accounting background, surface level finance background surface level marketing. And that was a much, much lower lift. And even as she was doing it, she's like, oh, this is like really useful. And then there was other stuff that she was like, I could give two shits about this. I don't think I'm ever going to need it. But so I think there are those. And that also goes to learning style. If somebody is a. If you are, I need the structure of some sort of a program. There are scads of them out there.
- [54:58]
A
Yeah, I think I was more negative towards MBAs earlier and now I'm more ambivalent. I'm personally not gonna probably pursue one at any point in time, but I could see where they'd be useful. So I'm. I'm more open to them now, probably because I know you, Tim. That's helped me learn bas. Yeah, exactly. All right, we've got to start to wrap up. So hopefully this has been a good discussion about business acumen and you feel like you've got a couple directions. I think now that AI is so prevalent, I feel like any business question you could think of, you could get a really decent answer at least at a fun. At a base level from an AI. So those kinds of tools or things like, you don't have to guide me. Go buy a whole book on, you know, the innovator's Dilemma. Although you should read that. But you know, you could also just get a rundown on a topic pretty easily with AI these days. All right, before we jump into last calls, I want to take a quick break and have a chat with our friend Michael Kaminsky from Recast. They're the media mix modeling and geolift platform, helping teams forecast accurately and make better decisions. You've heard Michael sharing a lot of bite sized marketing science lessons over the past few months and they hopefully are helping you measure smarter. Well, let's get over to you one more time, Michael.
- [56:18]
E
Before running any analysis, we need to ask, do we have the right data and model to answer the question we care about? Often we don't. The problem is really common in economic analyses where you want to estimate a demand curve from your data, but if you only have data on price and quantity sold, you can't actually determine how much is driven by supply changes versus demand changes. The model can't be identified statistically with just sales data. You would need data on some other external factor that only affects supply or only affects demand in order to be able to identify the effects you care about. Identification issues can stem from data limitations or model structure problems, and in very complex models, these issues can hide in the structure of the model really easily. Even in simpler models, multicollinearity or correlated variables can make models practically unidentifiable. The during due to insufficient data variation to estimate parameters. My favorite way to check for identifiability issues in a model is via simulation and parameter recovery exercise. We can simulate data where we know what the values of the parameters of interest are since we use them to simulate the data and then we can check if our model can accurately estimate those parameters from the data. If it can do that consistently, we don't have identification issues. But if the model fails to recover, then we know we have a problem. So the takeaways are Be thoughtful about your ability to learn the parameters of interest from your model and use simulation exercises to check for identifiability problems in your analysis.
- [57:38]
A
All right, thanks Michael. And for those of you who haven't heard, our friends at Recast just launched their new incrementality testing platform, Geolift by Recast. It's a simple, powerful way for marketing and data teams to measure the true impact of their advertising spend. And even better, you can use it completely free for 6 months. Just visit www.getrecast.com geolift to start your trial today. All right, let's do some last calls really briefly, Mo let's start with you. What's your last call?
- [58:13]
D
Okay, so about a month ago, there was a really horrific incident that happened in Bondi in Australia. And it's. It's been a really shitty time in both our city and our country. But I've been really grateful because it just happened that I started reading this book a few days before, which is called A Hopeful History. I can't say his first name, so the author is Bregman. But his main premise is that human nature is fundamentally good. And I won't give away the whole whole book, but what I will say is one of my favorite examples is he talks about Lord of the Flies. So if you're not familiar, bunch of kids, deserted island, they all end up blowing up at each other. And he found a real life Lord of the Flies situation, which I can't remember if it was five or six kids in the Pacific and how they actually cooperated and came together, never let the fire go out, and ultimately ended up getting saved. And I just, it was. It just happened to randomly be a book that I read at the right time. So if you're feeling a little bit pessimistic at the moment, it's definitely one that I recommend just to remind you of the good in people.
- [59:37]
A
Yeah, that's a good reminder. Thank you, Mo. All right, Tim, what about you? What's your last call?
- [59:43]
B
There was a podcast about the real life guys and I can't find it quickly. I'm dying to tells their story. And they actually went back and talked to a couple of them because the guy, the kids had like stolen a boat or so they'd done something and then they got.
- [59:56]
D
They stole a boat.
- [59:57]
B
And yeah, they stole a boat, but they wouldn't actually talk to one of the two of the people who were still around. So I'll track it down and throw it in the show notes as well. So mine would be just super entertaining. I realized that if Ben Stancil is on stage describing a ham sandwich, I will pay money to go see it. He did a talk at the Small Data San Francisco 2025, 17 minutes long. The title is in the long run, everything is a fad. It is like it zips along. It's grounded in kind of the Jordan Childs Olympics third bronze or not how long was she challenged Gymnastics thing. But it is so good. And his point, actually, Mo gets to kind of that decision making. He kind of makes the case that maybe AI and LLMs being good at sort of picking up the vibes from stuff that that may be a more effective thing for people to make. Decision is just decisions getting quickly getting a sense of the vibes, even if it's not hard and quantitative data. He has a whole thing about kind of different generations and what they think is kind of the core. Core of making decisions. But it is like 17 minutes that just blows along and it's. He is just one of the most engaging presenters ever and I can't recommend it highly enough. Nice.
- [61:28]
A
Thank you. All right, Val, what about you? What's your last call?
- [61:33]
C
It's a twofer, but I'll be quick. So part one is, Michael, you took a break from the APH mic to join another podcast recently. We're not, we're not too jealous. But it was a new podcast, Knowledge Distillation from the Ask why team and Michael appears on episode four, which is about why. About trust and bottlenecks and the difference between data retrieval and actual analysis. And Michael, we loved it and so wanted to give you a shout as one of my two last calls here.
- [62:12]
B
Why don't you bring that kind of quality to this, this podcast?
- [62:16]
A
I don't know, Tim. Maybe there's someone always talking over everybody.
- [62:22]
B
Damn it, Mo. I'll talk to her about it.
- [62:25]
C
Yeah, we'll take that offline. And then the second one is for an upcoming conference. DataTune is in Nashville. Two day conference, March 6th and 7th. Tickets are on sale, but it's all about analytics, AI, some of the things you'd expect to see. And Tim and I will actually both be there. So we're, we're speakers at that and we're looking forward to it. So if you're looking for something in March, we'll see you in Nashville.
- [62:57]
A
Nashville.
- [62:58]
C
How about you, Michael? What's your last call?
- [63:00]
B
Should we actually say is there any other conference that we're going to be at? I'm trying to think.
- [63:05]
A
Well, I believe we will be at the Marketing Analytics Summit in April. The Analytics Power Hour will be there and I believe that's April 28th and 29th, if memory serves. And we're excited because it is actually the 25th anniversary of that particular conference. It went by a different name in its early days, but now it's called the Marketing Analytics Summit. And so we're pretty excited. Celebrate with the industry, go get tickets, come. It's going to be a blast. There's some amazing speakers already lined up up. And this little crew will be regaling you with our chit chat and something we'll, we'll, we'll come up with a topic. All right, my last call just come back to sort of the business acumen thing, because I got all my business acumen the hard way. I like finding resources or places where I can learn things or I consistently find good information about this kind of stuff. And one of those places is commoncog.com, which is run by Cedric Chin, who has been a guest on the show before. They evaluate cases, they write up big long form articles. So there's a lot of explanation, there's a community, so there's a lot of discussion. You can talk to other business people. So I really enjoy that website, commoncog.com, so if you're in a role where you're trying to ramp up on your business acumen, that might be a great resource to potentially leverage as well. Well, all right. Well, this has been fun. I think this is something we come back around on semi regularly, but I think it just, it's, it still maintains its importance in the life of anyone working in data and analytics. So thank you everybody for putting your time and effort and thoughts into this episode. All of you, thank you.
- [64:56]
B
Thanks for having us, Michael.
- [64:57]
D
Yes, that's fun as always.
- [65:00]
A
You don't have, you don't have to say anything thing. I'll take it from here.
- [65:04]
B
No, I'm just saying too much dead air. Couldn't take it.
- [65:06]
A
Couldn't take it. That's right. Let's talk a little bit about how you can reach out to us. We'd love to hear from you. And the best way to do that is on our LinkedIn page or on the Measure Slack chat group. Or you can also email us at. Contact analyticshour IO. And you know what, we also take a look and see what ratings and reviews people leave for us on the various platforms. So if you leave us a review, we're excited to see that also. I'd love to hear those also. All right, go get better at understanding the business. It's going to help your career. It's going to make you more influential, it's going to make you more impactful. And I think I can speak for all of my co hosts when I say no matter what your level or you years of experience, keep analyzing.
- [65:57]
B
Thanks for listening. Let's keep the conversation going with your comments, suggestions and questions. On Twitter @NalyticsHour, on the web at AnalyticsHour IO, our LinkedIn group and the Measure Chat Slack group.
- [66:12]
A
Music for the podcast by Josh Crowhurst.
- [66:15]
B
Those smart guys wanted to fit in so they made up a term Called analytics. Analytics don't work.
- [66:22]
A
Do. The analytics say go for it, no matter who's going for it. So if you and I were on the field, the analytics say go for it. It's the stupidest, laziest, lamest thing I've ever heard. For reasoning in competition. The most accurate business acumen. It's interesting, Tim. I forgot that you had an mba.
- [66:44]
D
Tim had a whole book on. On it.
- [66:47]
A
Is that what that book is about? His MBA getting an mba? Listen, at some point this afternoon, coffee is going to kick in and I'll be ready to start the show. Mo is literally recording this in a rainforest right now.
- [67:07]
B
Oh, so many trees.
- [67:09]
D
Places I've recorded. Like under the kitchen bench in that place in Italy.
- [67:14]
B
There are so many trees, and some of them are even organic. There's the coat tree. You got the.
- [67:19]
A
Yeah, Decision tree.
- [67:22]
B
All right, that would actually be a cool.
- [67:25]
D
Random forests, you know, Random forest.
- [67:28]
A
Exactly.
- [67:30]
B
Giving everyone a decision tree.
- [67:32]
A
That's right. It's mine.
- [67:35]
B
Fits in the envelope better. You can use it.
- [67:39]
C
I knew you were gonna say that, Michael.
- [67:44]
A
We belong to the same generation.
- [67:46]
B
That's right. It includes what. What did they jump.
- [67:52]
A
Jump to conclusions, Matt.
- [67:54]
C
Office space. We were joking about office space yesterday, Tim.
- [67:57]
A
I was gonna. Yeah, come on, Tim.
- [67:59]
C
When Tim was working on postal code cleanliness, whatever project when he was at Nationwide, I just imagined. I said, I imagine he was Milton in the basement.
- [68:08]
A
Like, just give me. That.
- [68:12]
B
Was one where, like, in my memory I was told I could use.
- [68:14]
A
Excel at a reasonable volume.
- [68:23]
B
So good. What's interesting, because of the lag and you being in the southern hemisphere, we're actually hearing what you say before you think it. It's kind of interesting.
- [68:35]
A
Which one do you think will. Mo, just try whatever you think will work best. I don't. I know that you don't issue.
- [68:41]
D
The reason. The reason I use this room is because it has no aircon, so no whirring sounds. And it has, hypothetically, some sound bound things up. If I go to a meeting room downstairs, the WI fi could be better, but my Internet could be really shoddy. I might.
- [68:59]
C
How many tree. How many trees will be there in that room?
- [69:08]
A
All right, we'll think of cool tree jokes while you get reset up. Oh, oh, you guys are going to the datatune conference.
- [69:16]
B
Okay, good.
- [69:17]
A
I'm glad some of us are going. Cuz I completely whiffed on submitting anything to that guy when he reached out to us about it. And then I remembered it like. Like a month later and I was like, oh, crap. I missed the deadline for that completely.
- [69:35]
B
We're gonna be. I'm glad the top is some decorum.
- [69:51]
C
But it's a day long workshop.
- [69:54]
A
You both are doing a whole day workshop.
- [69:57]
B
Or maybe it's a half day.
- [69:58]
C
First I'm hearing about it.
- [70:00]
A
Oh my gosh.
- [70:10]
B
Rock flag and concepts and context.