Marketing Analytics Has A People Problem

Speaker A: Welcome back to what Gets Measured, a Nija Cat podcast about marketing performance, management, metrics and effectiveness. Because what gets measured gets managed. I'm your host, Jake Sanders. Today we get to speak with Shiv Gupta, managing partner at the Volute Group and founder of Quantum Site, a data and analytics consultancy. Over his career, Shiv has helped Fortune 100 brands generate more than 30 billion in revenue impact. And he's become a leading voice on data driven marketing through his work and writing for adweek martech. After 25 years in analytics, Shiv believes most business problems aren't data problems, they're people problems. Today we dive into why dashboards should spark questions instead of answers. Why the best metrics connect to business outcomes and how AI is evolving the role of data analytics teams. If you're looking to turn data analysis into better decisions, you don't want to miss this one. So let's go. Shiv Gupta. Welcome to what Gets Measured. How are you, sir?

Speaker B: I'm doing well. How are you?

Speaker A: How's dc?

Speaker B: DC is not as muggy as it should be this time of year, so I'm grateful.

Speaker A: These small, tiny blessings, um, the data, the weather data don't lie. So we're talking about data analysis before we hit record, um, sort of about decisions, um, just in this modern moment. But before we get into all that, I wanted to just give you a chance to give us a retrospective. You've been in the game marketing data analysis for over 25 years. Um, what do you think has changed since the beginning and what do you think has remained true? I know it's kind of broad, but I just want to see what you, your experience has to say.

Speaker B: Yeah, a lot has changed. I think, uh, at the beginning, analytics was treated as this sort of,

Speaker A: um,

Speaker B: laboratory on the side that came up with ideas. Potentially. Whether those ideas made sense or not was up to the judgment of the managers who were running the various projects and campaigns. So from that very nascent stage now we're at a point where data analytics is pretty much central to every operation that is of any significant volume within, uh, a company. So, um, it's been a big change. I think we started off with things such as just a database, customer database, or an enterprise database. You were sometimes borrowing to do marketing from an enterprise database. You didn't even have a customer database. Uh, now you had the evolution of customer databases. Then you have the evolution, uh, of automation that came in. A lot of marketing technology, um, came with its own separate database that allowed you to really concentrate and focus in on what you needed to do from a marketing perspective. Um, and so the evolution's been pretty good. And obviously today we're at the evolution where AI is now tacked on top of this to really accelerate things. So it's been an interesting journey. Um, I tell a lot of folks, um, I started when the term big data was just data, and now the big term big data doesn't even matter anymore.

Speaker A: I know, it's so pointless. It's so sad because I enjoyed that. I wrote a musical called Big Day to Day, um, a couple years ago

Speaker B: and now I'd be interested in seeing it.

Speaker A: Oh, well, you'll love it. It's audio drama. So anyway, I'll put a link in the show notes. Everybody wants to know about this musical. Um, no, but it's out of touch. You're like, okay, yeah, Big data. One thing you said was interesting is that it starts as a lab, then it's table stakes then. Because I remember this. I remember. So, I mean, I've been in the game for 12 years, so. But you know, I was sort of in that post big data thing. Like, this is what we need. We already know we need it. Just get it, um, a database, a third party database. And then automation shows up with all those platforms and then there's another database layer and then AI is tacked on top of this. So I think it's. Do you see it now as a stack of database layers or are you starting to see some sort of flow in between the databases that wasn't possible? Like, is the. Is. Is it still sort of apt to say if it isn't big data, that it is a stack of databases? I don't know if that seems. I don't know why that's interesting to me.

Speaker B: Uh, no, I think it is an interesting question because what it's really leading to is the larger question, which is, uh, are we just stacking databases on top of databases and not making progress? Right. So yes, there are more databases. Yes. Every platform either will work with your database or your data lake or will come in with its own native data solution.

Speaker A: Right, Integrations.

Speaker B: Yeah, exactly. But at the end of the day, I think the challenge comes out to, um, great, you have more data or more segmented data, if you will. Each database is segmented for a different purpose. But at the end of the day, are you making significant, significantly better decisions as a result of it? That's.

Speaker A: That's the analysis. Yeah, I mean, that's the thing. So do speak specifically to analysis, not so much the infrastructure that was Happening, but the analysis at those stages in the journey from the beginning, you know, it was a laboratory. That analysis maybe was like, oh, you crazy data people with your crazy analysis. And now, um, analysis seems to, it has to happen across those databases or does it just happen in one place? I mean, I'm just like, how has data analysis sort of evolved since the beginning of your tenure?

Speaker B: So I'd say from an analysis perspective, let's really start to lay out two buckets of analysis. Right. You have your repeatable analysis where you've as an organization decided this is the type of monthly analysis I need to see. Right. Sometimes that's just as simple as KPIs or a family of KPIs that um, lead to a larger business decision. Yeah. The um, other side of it is your deep dives or your ad hoc analysis. Uh, and the ad hoc analysis I think is really the place where you go across databases if you have a great analytics team. Because what they're doing there is they're asking more and more probing questions. And sometimes the answer doesn't sit inside the platform that generates your dashboard, it sits in another platform. It may sit inside the enterprise database, it may sit inside the POS system and that team then is able to dig into that. So um, I don't think any of the advancements done in uh, data management have ah, truly ah, uh, taken the place of what a crack analyst team can actually accomplish, uh, when set free to go and just play in the data man.

Speaker A: You know, it's interesting because I've been, you, uh, know you got your marketing data analysis, you put it in your report and then you share it with people. And it's interesting because maybe we're looking at a recurring data set, but we're asking deep dive ad hoc questions which requires another set of analysis. How often do you see people, uh, kind m of miss attributing the level of analysis that's required based on those two buckets that you're saying? Because I think everybody wants to take a deep dive on a dashboard and people maybe look at ad hoc deep dive stuff, which is really interesting as just sort of a dismissible recurring thing. Maybe can you speak to sort of that sense of data analysis? Did you get my question?

Speaker B: I think so. You could rephrase it if I missed this, but um, um, so I think if it's a repeatable deep dive, then it really needs to be in the dashboard, right?

Speaker A: Oh, repeatable deep dive. Well that's what I'm saying. Do you know what I'M saying, when you're in the analysis phase and you're talking about it with people, do you see how maybe some people are asking deep dive questions of a dashboard that doesn't need to be there? But your point is, if it is, then that should be in there, right?

Speaker B: Then you're going to have to add it in there, right? Uh, absolutely. And yes, uh, a dashboard by its very nature is designed to be unsatisfactory 60 to 70% of the time. And what I mean by that is it's going to pose more questions than answers. If it's a good database, if it's a good dashboard, sure, it's the trend lines are moving up, you have a huge blip. All of a sudden a key metric dropped. Like what happened there. So sometimes it's bad data, but sometimes it's actually a business event of some sort. Sometimes it's an event that's completely exogenous to the company and they say, well, competitor dropped their price. Could this be the reason we're not seeing certain, uh, um, um, uh, close rates or click, uh, through rates because a new product came out. So those things are always going to be part of what uh, a good dashboard delivers is it tells you what you need to know on a systematic basis, but it also points to opportunities to dive into what's actually happening at that moment or at that point in time.

Speaker A: And I'll get to dashboards later because I have a very specific and pointed question about it. But that's a good point. I wanted to talk a little bit more about kind of the different ways that uh, data analysis happens. Quantitatively, qualitatively. Um, you know, it's not that it's a difference, it's just a kind of a degree. You know, you need both is what I'm trying to say. But you've, in your 25 year career, you've been working with a bunch of different data sets in health care, in insurance, advertising. Like what you were saying that family of KPIs, they're laddering up to different things in these different instances. Um, and I'm wondering if there's a qualitative approach required to different types of data analysis that quantitative people might miss. Something about context and downstream application of the analysis. Um, in what way is it one size fits all? You look at the data and it says a thing and how is it unique and bespoke? I guess this sort of gets to that question of the buckets. Um, but do you know what I mean? Like, yeah, health care data ain't the same as advertising. Data isn't the same as like a, ah, you know, work in time factory thing, which is where or ROI came from the factory floor. It's not related to advertising, but we use it all the time. So, anyway, tell me about the quant, the qual.

Speaker B: Yes. I'm, uh, going to get a little bit esoteric here with.

Speaker A: Go for it. You've already said exogenous. You've blown past the. The fat thing. Keep going.

Speaker B: Okay, yeah, that's the economist in me. I started off as an econometrician. Oh, great.

Speaker A: You just said econometrician. Get out of here, Shiv.

Speaker B: Okay, keep going. I'm dating myself. Um, yeah. So here's what I say about the soft part of it and actually goes back to the earlier story you asked about as well, like how these analytics groups got started. They were sort of this, you know, lab R and D lab. Right.

Speaker A: Yeah.

Speaker B: Um, part of the problem was also the people that spoke data didn't understand the business. Oftentimes you hired a bunch of statisticians and there was a huge disconnect. Now, over the years, that's resolved itself. I think you have now business experts with analytical chops, um, that have grown over the years, and that's a great evolution. Um, what I think is still missing and where I find our most interesting challenges come from, is when there's larger political and, uh, organizational context to the data. Because as data has become more accepted, it has actually become a vehicle of political discourse in a company, um, and a power source. Let's face facts. I didn't mean that as a pun, but, uh, facts are helpful when you're trying to make arguments and when you're trying to gather resources. And really, in essence, the power to do something interesting in an organization.

Speaker A: Right.

Speaker B: Um, and I think a lot of that is still missing. People don't quite understand the value of data and metrics in helping guide, um, uh, the interpersonal and the political landscape in an organization. And what I mean by this, just give you an example. Uh, you could create a dashboard, but that many times you'll see a dashboard. And we should get off of dashboards because there's so much more happening in analytics beyond dashboards. No, yeah, yeah. But for now, we'll stick to the dashboard. Um, the metrics you choose. M. Are they metrics that matter to you? Are they metrics that matter to your audience? Many times I find that marketing organizations will create a wonderful dashboard. Fundamentally, 80% of the dashboard has absolutely no meaning to the other Leaders in the organization. The CFO doesn't care about your click through rate. The CFO doesn't care about which campaign had which conversion. It's irrelevant to them. Right, um, lifetime value, oftentimes it's not there. But that's probably the greatest way to communicate to a CFO and say this is the incremental lifetime value we've generated through our campaign. Um, so that part is still very much, I think, misunderstood. Um, when it comes to analytics and marketing is the ability to use metrics to speak to the incentives that the person across from you has. Um, and to give them confidence that their needs and their incentives are actually aligned with what you're doing or what you're doing is aligned with your incentives.

Speaker A: Uh, you, ah, it's so yes, the interpersonal thing is just completely uh, missed because it's business. Um, and when you're at the airport, I made this, you know, observation, they tell you, is it business or is it personal? And so when a lot of people in business make some whack decision, they're like, don't take it personal man. It's just business, you know, and you're like, okay, so you're sort of encouraged to not be personal bowl in a way like, uh, don't take it personal, it's just business. But then we miss this interpersonal angle of what I think is the fifth P of marketing before is price, product, promotion, placement. But this fifth is a small P. Politics, man. I don't know uh, how to. Sometimes it works. It depends on if the audience, the stakeholders are in a good mood, you know what I mean? And they're like, good stuff here. And you're like, great. They're in a bad mood, they don't like anything, you know. And I, I think that click through rate is another harsh dashboard truth that marketers have to sort of deliver is like, brother, you might like traffic, but what does it mean? And then you're like, well, uh, and then you have to do that work. And I think it's, it's difficult because we're at this stage where uh, it's hard to hand things off or to know what I'm supposed to be doing and to feel like I should be doing more when I should be asking better questions and focusing on how to work the interpersonal relationships, not manipulating things, but what do they want, how do they need to see it and then how can I work it? So I, I want to know. You, um, Shiv, you must have had a chance to experience this where you had Some data and you got a, ah, political win. Can you maybe just. You don't have to name names or anything, but I just want people to give a sense of like, how is that possible? Because have you had that moment where you're like, man, the interpersonal stuff is going bad. I'm going to use this data to sort of create some. Is there an example from your life that you have?

Speaker B: Yeah, yeah, there is, uh, several of them. I mean, it's pretty much been the crux of my career.

Speaker A: Cough it up because we want those gems.

Speaker B: Yeah, yeah. I mean, so there's one early on in my career, actually, we were, um, you know, working on, um, a project where we were trying to estimate lifetime value for a, um, insurance product. Um, and I think the sales organization never fully grasped the value of lifetime value because they said it's some crazy metric being generated in an analytics department. The corporate side of it. I need to have more policies sold. I need to generate numbers. Yeah, policy revenue. Um, and so we had to sit there and say, okay, we could talk about lifetime value with them and it'll just be completely over their head. Or we could talk about it from the context of retention and the actual value of stability that it's providing for polygons under force. So the sales team did care about having enough of a stable book of business. Right. If you have people leaving every six months or every year, that's problematic. So you translate that into the language that they will understand. So I define to them and say, look, if I can get this type of a segment with this lifetime value, you're going to be able to not have to write as much business to replace this. You'll be able to have a more stable book of business. Um, that resonates when you start to identify the segments. You can identify the segments and they kind of get it. Or you can go right into their book of business, take out 20 customers and say, according to lifetime value, this is going to be your best customer. This is your second, your third. And their sales intuition kicked in and they said, you know, I never looked at it analytically, but you're absolutely right. That is, that is my best customer. I already know that will be my best customer. I got them because I already had their dad's policy and therefore I know that I have that family. Right. Um, so what that analytics was showing only made sense and actually was able to deliver momentum when it was translated into the language of what mattered to that team.

Speaker A: I'm, um, writing something down because I was like, man, I wish I could rewind this conversation. Um, incentives blind people's actions and analysis of things from the interpersonal relationship. Again, which you're just like, who is your best customer? And salespeople would be like, anyone with a pulse. And you're like, well, of course, uh, you know, and any, any product person is like, who do you want? Who's this product for? Anyone with an Internet signal. And you're like, well, of course, you know, they'd be like, who's this drink for? Anybody with a mouth. Okay. But if that's the incentives, you don't figure out those long term, again, interpersonal relationships. It seems like it comes back to a lot of analysis is getting to the relationships between data points. This next question was, and I think you've answered it a couple different times. Um, but regardless of that, you just said, you know, the data management, you know, stack and all of that, the talent. There's always the issue of getting people to actually look at the analysis and not their interpretation of it. And we talked about those different buckets and you know, how you can sort of analyze rejigger LTV to be something, you know, so we talked about this. But is there uh, do you have a different or something more strict or data specific piece of advice for making data points sticky in a presentation? Like sometimes you want people to hear this great line and then they blow through it and you're like, how do you get people to set their kind of presentations up to be like bam? Oh, uh,

Speaker B: I think it begins with an understanding of who your audience is going to be that day.

Speaker A: Naturally.

Speaker B: Um, then you have to understand what are ah, they incentivized to deliver. If you have a good senior manager on staff, that senior manager understands not only the explicit but also the implicit metrics that this person would care about. M. Um, when that happens, um, you put that forward. So your presentation, if it's the same presentation to every person in the organization, you're probably missing the boat. You're not personalizing your presentation enough. Which is funny because marketers should be the first people in an organization. Cobbler.

Speaker A: The cobbler has no shoes.

Speaker B: Exactly.

Speaker A: No, they don't. Okay.

Speaker B: Anyway, that is exactly the issue. You're uh, absolutely right. The cobbler has no shoes. Um, and so, you know, they think about that. Right. Who's your audience?

Speaker A: Yeah.

Speaker B: Oftentimes you also have immensely, especially with analytics, you have these deep, you know, data filled presentations. Exactly. And the person actually just cares about two or three metrics that are relevant to them. And if you can excite them and say, this is how it's M moving or this is how we plan to impact it. You have their attention because tomorrow, the next day, and the next day they're worried about moving that metric as well. And if they see an ally helping them move that metric, they're in M. If they see an ally talking about 100 other things, then you've already told them you take up about 5% of my mind space and therefore how much cooperation and how much in sync are you going to be with that individual? Um, so it's super critical to think about your audience and then don't have the same presentation for everybody because they're

Speaker A: not going to understand it. But that's why it requires a ton of work and all we're being sold is automation. And so you're like, man, do you remember? Because you mentioned at the beginning, you know, I remember, you know, you were working with third party databases and then automation came in. We've been through automation. Digital transformation is what I think it was called. Um, we've been here before. Like, how do you feel that people are learning lessons faster with AI or are we forgetting long term truths quicker?

Speaker B: I don't know if I'd phrase it that way, but you're right. You're definitely right. Um, but I'd say I don't think we're forgetting long, long term tudes.

Speaker A: I got a question about, I got a question about that later. But yeah, keep going.

Speaker B: Okay, I think what we're doing is we've never acknowledged some long term truths or challenges that have always been there and we've constantly tried to push them to the side. Um, the advent of AI does not make that problem go away. In fact, it now makes it more acute because AI has the ability to take every one of your shortcomings and escalate them at a higher velocity.

Speaker A: Damn it.

Speaker B: Uh, and so you do have to start to acknowledge it. Just even having good data governance in a company, data governance has been the hardest. If anybody who leads data governance, and I've never led that for a reason because it is a thankless, difficult job in most organizations. It's painful.

Speaker A: Yes, it is, it is.

Speaker B: Um, and it's herding cats. And then everybody wants in on whatever this project is so they can have priority. Um, it becomes unwieldy and we still as organizations haven't figured out how to make that a more disciplined, um, approach. Um, and so, yeah, that's one of those truths, if you will, that we just never acknowledged or we Acknowledged, but never wanted to really tackle. Yeah.

Speaker A: And yeah, I have an image, uh, I draw cartoons every once in a while and I had an image of somebody sweeping dirt under the rug and pick your, uh, tech debt favorite, uh, meme, you know, the one with the Jenga and all of it. And they're like, this is like the code the, that we're trying to work around, you know, like, yeah, we all have that, but you sweep enough rug, you know, dirt under the rug, now the floor is dirt. Uh, you're not hiding anything. You've just actually changed the floor, you know, so it's like, that's why those, those questions and those kind of concerns and thoughts and the evolution of data analysis from 20 years to today. There hasn't been a change to the requirement of having a governance respect role. Maybe it was knowledge management, you know, maybe it's qa. I hear QA is coming back. But anyway, um, really quick. Dashboards, uh, they're dead or are they dying? Just kind of just give me a hot take. Dashboards dead.

Speaker B: I think that that's more, uh, clickbait title. I would say that they're. They're not dead. They are, though, being put in a proper place. Um, in that. And not put in their place, but in a proper place. Let's put it that way.

Speaker A: Be nice to dashboard ship.

Speaker B: I am. I'm being very nice to dashboards. They are actually the spine of how a company.

Speaker A: I know, God bless them, I know. I don't want them to die because, man, what are we looking into? But you've already met, made some really good. Like, it's kind of going to be a letdown unless you figure out that intent, package it Right. I mean, am I right?

Speaker B: Yeah. But here, let's now praise dashboards for a minute. What they do is they offer continuity in a business environment where you are constantly being distracted, distracted by a new opportunity, a new technology, a new customer. I, uh, mean a new product that goes into the market. There's always these things. If you want to be neurotic as a business, you can be right. And some organizations are more neurotic than others. Dashboards are the sense of continuity to say, back in fall of last year, we decided this is our strategy. How is that playing out? How are the top metrics working? And can we see that we're making progress against our original thinking? They are your last savior from distraction, which is very, very easy to do, um, in business. So from that perspective, I, I praise them, uh, and then they can be better Designed, without a doubt.

Speaker A: And, but it, it, it kind of like, when does the tweaking end? You know, and that's, that's where I think it never ends. And then you're like, well, I think that's where a lot of people get off the bus. You know, I think a lot of people are like, I, I wrote something about analysis amnesia, um, or maybe like workflow amnesia. They're like, why did we. You made a good point about managers being this sort of necessary filtration mechanism between the desires of the high and the performance and capabilities of the team. Um, I just kind of like, is, is. There are. What ways can we prevent the amnesia that happens when you automate things, um, and sort of, you sort of disassociate from those. The, the function, like, why did we even really do this in the first place? Um, how do you. Have you experienced people like that? It seems like if the governance is strong, they're carrying that historical knowledge with you. But unless somebody's doing that, it does feel like there's a lot of amnesia. You know, that that kind of goes throughout organizations. And that dashboard is a great touchstone. Like, come around children. Let's look at the thing. It's going up, you know, what's your thoughts about preventing amnesia and the importance of workflows? Is it about again, uh, improving your inner relationships, uh, bringing up the politics, strengthening up that game? What's your thought here?

Speaker B: Yeah, uh, uh, I think there aren't always cookie cutter answers to some things like these. And so there's no systematic way to say this except to say, look, if it was worth doing and there was a manager that led this, it showed up in the dashboards as these are the activities we want to do. That manager is responsible for maintaining consistency. M. That manager is there to deliver on what that vision was. And if that vision needs to change, fine. You change that vision. Now, if you're changing it every three months, every two months, then maybe something's wrong, you're not thinking about the problem properly. Um, so at that stage, I'd say it is on the human being. I think in a distracted world, we're becoming more and more distracted by things. And that amnesia is not a function of the fact that the data isn't there or the documentation isn't there. Sometimes it is, sometimes it is, but it's not always. Sometimes it's a function of the people who are in charge are constantly chasing new shiny objects. And that, uh, in itself doesn't allow the institution to carry that legacy. Of understanding and that knowledge. And that's where it's important to have some managers who say, look, every year we'll review this, we'll think about our plan, certainly maybe even every six months if it's appropriate. And the dynamics are pretty rapid. But at the end of the day, um, if we don't measure what we're doing and if we don't keep an eye on those dashboards, we're not going to be able to achieve our goals. It's really basic and not AI, not anything is going to save you from that background reality.

Speaker A: You're bringing up a good point, man. Uh, Ninja Cat were deep into data governance, deep into AI agents like threading across unstructured data. I've seen some amazing things happen. I have, I have seen awesome things. But I also understand that at the end of it, it doesn't have anything to do with the software, the data, the tech. It has everything to do with the people in the processes and that it, it sucks because you want to sell a technical solution, but it only works when the people have it together. They have their processes together. And that's why I think there's uh, it's so great that it starts with marketing data analysis, but it ends with a respect for interpersonal like relationships that, that small p of politics and, and realizing that maybe if you're a cobbler, make yourself a pair of shoes and, and go out there and sell some of this analysis to people and get some buy in. Uh, man, you said some amazing things but we got to wrap it up. I want to know if you had to give one piece of advice about marketing data analysis in the age of AI, what would it be?

Speaker B: I'd say, um, a job of data analytics has been sort of combined historically. It's going to separate. You're going to either decide to take the path of data governance or you're going to take the side of being what I call commando analytics. This is analytics designed where you take the data infrastructure and you use it to answer very sophisticated, complex questions with large organizational implications. Um, I don't think the skill set is going to be the same. So you have to pick a path going forward. Where do you want to be? And I think there's value in both, tremendous value in both. But I do not think it's ah, a skill set that one can have. Some may have both, but mostly I think people have to split.

Speaker A: Yeah. And it feels like that, um, there's a manager track to the governance, like if you like sort of organizing teams and sort of seeing the tournament angle and the trees and the respecting the different players. That's a great skill set. But most people are horrible managers. Like, you know what I mean? Like that. God, I've, I've, uh, you know who's. I've had a couple great managers that I've came across and most of them just are haplessly in there, hitting on those incentives, hitting on the most present number. Um, man, I think maybe is that another piece of advice is just sort of maybe think uh, right now about what your skill is. Is it more towards analysis or is it more towards management? Do you think? Is that a fair assumption to.

Speaker B: I think that's a fair assumption as well. Absolutely. You're uh, going to need more sophisticated managers from a manager perspective. So let's just say one skill, let's say for the managers think about is, um. Because AI is going to democratize the access to data and information. What you need to now be able to do is to be able to manage and convey that information. The implications of that information at a higher plane, your moat around your relevance is no longer access to the data and having a data team that can answer those questions. It is now being able to understand how this impacts the organization because you understand the data and where it came from. You understand the lineage of that data, what the implications are, how much you can actually do with that data and where you might be overstepping because the data isn't really set up to measure that outcome or that um, bit, ah, of information or insight. That's where you're going to have to spend more of your time and then the politics, then understanding. How do you, um, make this, um, relevant to the organization. That that's really where management needs to step up and not about optimizing a campaign or optimizing um, you know, your click through rate or something like that.

Speaker A: Yeah, stay out of there. Yeah, stay out of there. Quit. Quit backseat driving.

Speaker B: It's AI's job now.

Speaker A: Let the Waymo handle it.

Speaker B: Exactly.

Speaker A: I love it.

Speaker B: Man.

Speaker A: This has been so wonderful. Tons of insightful commentary. I love that. It's, it's, it's landing at the personal level, um, but keeping us sort of visible of what's possible with technology. You're so great, Shiv. People want to connect with you, learn more about you online. How can they do that?

Speaker B: Sure, absolutely. I'm on LinkedIn. Uh, it's data brand leader. It's an interesting, uh.

Speaker A: Go for it.

Speaker B: Yeah. Or, uh, Shiv, uh, Gupta, uh, uh, at Volute Group volute. Grp. Sorry.

Speaker A: Get it right.

Speaker B: Uh,

Speaker A: send an email. All right, cool. All right, well, I'm not going to let you go until we play a game really quick called Cheese or Chocolate, where I ask you two questions, give you two options, and you got to choose one. Are you ready?

Speaker B: All right.

Speaker A: Cheese or chocolate?

Speaker B: Uh, uh, chocolate.

Speaker A: I know I stress you out. I don't want to stress you out. Maybe the next one's easier. And this dates me, Dan Marino or Joe Montana?

Speaker B: Montana. I thought Montana was definitely, you know,

Speaker A: something cheeseball about Dan. Right.

Speaker B: I don't know about the personalities. I let the personalities sit.

Speaker A: Otherwise, in a true analytic fashion, I don't, uh, shiv. You're better than us. Okay. But you're right, it's Joe Montana. Okay, moving on. Um, facts or figures?

Speaker B: Figures.

Speaker A: M. You had to think.

Speaker B: Yeah, well, the answer is again, figures are used to paint a story, and at the end of the day, nobody actually has the fact they're always incomplete. Um, figures, though, help you move. Organizations, they help you. And, you know, one sort of the sarcastic way to view it is to say, oh, liars lie and liars used to, you know, what is it? Uh, you're right.

Speaker A: Words.

Speaker B: Yeah, I forgot the old saying, like statistics, use statistics. But, uh, nevertheless, um, it's not that when you have figures, you can start to paint the full story, and some of that story has to be filled in with your intuition, your business understanding things where you won't be able to really get data or facts around. So I think facts are limited. They just are. And figures are more powerful. I love it.

Speaker A: I love the philosophical angle. It's required, kids. It's required. Um, okay, here we go. Window. Uh, seat or aisle seat?

Speaker B: Oh, aisle seat. For sure.

Speaker A: For sure. Because what, you gonna put your legs out or you want to be claustrophobia?

Speaker B: I can't. I don't want to be at least on one side of that, of that trip. I'm not being smushed against another person.

Speaker A: I know it's whack. I love it. But you get. You get to Cincinnati, you know? Okay. Anyway, donuts or beignet,

Speaker B: I can't tell the difference between a good donut, not a, uh, not a mass product, not a crazy donut.

Speaker A: These crazy donuts that have bacon on them now.

Speaker B: Yeah. Or even, like, even these, you know, mass produced donuts or one thing. But if you go to a proper bakery where they're, you know, do that. I. I know, I know people from Louisiana are going to get. But I can't tell. Right. Well made. Uh, donut and a beignet.

Speaker A: Oh, man. Well, one of them is going to be covered in powdered sugar. You know, um, both of them can be. Let's not fight. We don't. We've done so well, Shiv. Oh, we got into a horrible fight about donuts at the end of this one.

Speaker B: It went so well. Uh, this was. This was our red line. Who knew when we started this, this was going to be the red line?

Speaker A: I should have sent the. You know, the. The disclaimer ahead of time. Why did I ask him the donut que?

Speaker B: It'll teach you to ask provocative questions like that. I mean, come on.

Speaker A: I did. There's a third rail, bro. I'm sorry. Everyone's like, jesus, have some decency. Okay, anyway, moving on. Last question. Um, robes. Are they necessary or are they useless?

Speaker B: It depends on where you are. If you're in public, wear robe.

Speaker A: Well, wait, there's a lot of assumptions happening in this story.

Speaker B: Well, I mean, if you need a robe, get a robe.

Speaker A: Yeah, and if you need a robe and you're in public, what are you doing? Who invited you to this donut convention? And that's not a beignet. And did you fly on a window seat or an aisle seat? Oh, I hope you. It's a sign. My Joe Montana poster. Stop. It's over. It's over.