Turn Data into Your Company’s Superpower

Cole Abbott (00:00:00 -> 00:00:12)

So today we're talking about data <laugh>. I love data. It's a, uh, probably something that people think is more simple and straightforward than it really is,

Mark Abbott (00:00:13 -> 00:00:13)

Probably.

Cole Abbott (00:00:13 -> 00:00:16)

Right? As we say, data is a superpower. Yeah.

Mark Abbott (00:00:16 -> 00:00:20)

At least we want to help people turn data into a superpower for sure.

Cole Abbott (00:00:20 -> 00:00:22)

And we're working towards that. Yeah.

Mark Abbott (00:00:22 -> 00:00:24)

Yeah. But progress, we'll get

Cole Abbott (00:00:24 -> 00:00:32)

There. Yeah. Um, but with any superpower, right. Obviously with power comes responsibility. Yes. But then also

Mark Abbott (00:00:32 -> 00:00:32)

Great point, by the way,

Cole Abbott (00:00:34 -> 00:00:45)

For us, in our situation. For sure. Yeah. Um, but with great power comes great responsibility. Yeah. But it also means, and anything with great responsibility, that means it's not easy.

Mark Abbott (00:00:45 -> 00:00:46)

Right.

Cole Abbott (00:00:46 -> 00:01:09)

In many ways. Yeah. One of the easy, and, you know, you go into the philosophical end of that, but I think the real thing that is not easy, and it's more complex than people realize is sort of the behind the scenes of, you know, aggregating data, making sure we know what's important, having those KPIs, and then using those to our advantage.

Mark Abbott (00:01:09 -> 00:01:20)

And even understanding really how to, what the best practices are, obviously associated with setting up KPIs. So, lot, lot we can get into where you wanna start.

Cole Abbott (00:01:20 -> 00:01:25)

I think just broad overview wise data, one of the nine core competencies.

Mark Abbott (00:01:26 -> 00:03:37)

Well, because there's so many different reasons. So I'll, I'll just try to go into, you know, what, what comes to top of mind? Well, number one, when we have decent data, we can see what's working and what's not working. Um, and when we have a sense for what's working and what's not working, we obviously can allocate our time a heck of a lot more, um, efficiently and effectively. Um, and, uh, and, and, and, and if you don't have good data, especially for like founders, you've got a sixth sense whether things are going well or they're not going well. And if you don't have data, then your, you are probably going to be sitting here all the time with this sixth sense that either things are good or not, or not good. And sometimes your sixth sense will be good, and sometimes your sixth sense will be not so good. But without data. For me as an example, I feel like it's like flying an airplane in the fog with no instrument panel. I mean, it's like, it's really, really hard. Um, and it's, it's scary, right? If you have no idea how things are performing. Um, and so, uh, you know, the same, you could use a different metaphor, which is imagined coaching a team and, you know, they, they kick you out. They kick you off the sidelines and they throw you into the, you know, into the locker. And you're, and you're still supposed to be coaching the team, but you have no idea what's going on. You don't know what down it is. You don't know anything, right? So it's almost impossible. So everyone has data. The question now is, you know, is, is, is how useful is it? And um, and, and, and, and how good of a job are you doing with regard to helping everybody in the, um, in the company understand why data is good for everybody? 'cause some people, as we were talking to a little bit, you know, you were getting into, you know,

Cole Abbott (00:03:38 -> 00:03:38)

Uh,

Mark Abbott (00:03:39 -> 00:04:22)

With power comes great responsibility. So a lot of times what we'll see in the feedback we get from our clients, in particular from sort of people who aren't on the senior leadership team and most often who are sort of in entry level positions, they're like, well, you know, why the, you know, I, they feel like they're being micromanaged because people are watching their data. And it's like, no, it's actually, if it's done properly, we all understand when things are going well. And as they're going well, and we've talked about this before on the podcast, so they're going, well, you know, a great team leader or a great coach is gonna let you just run, run, run. Right? I don't need to worry about everything's green. Awesome. Well, you know, just let me know if I can help.

Cole Abbott (00:04:23 -> 00:04:31)

Well, if you don't have the markers to figure out where things are going wrong, when they're going wrong, right, right. It's like not giving your doctor access to your blood work.

Mark Abbott (00:04:31 -> 00:04:32)

Right, <laugh>. Yeah.

Cole Abbott (00:04:33 -> 00:04:53)

I mean, those markers is pretty helpful. Yeah. In trying to figure it out. We can sit here and, and try to just, you know, find the needle in the haystack, but if we can have something that gives us a direction to go in and find out where things are read and where we should start looking, and if it's organized appropriately, then we can dig into those and, and find everything. Yeah.

Mark Abbott (00:04:54 -> 00:09:19)

And we, we talked about, um, in a prior podcast on process, right? We talked about how, you know, the big idea of when you think about a process, you should think about what it is that you want at the end of the process, and then you should work your way backwards in terms of the steps to get what you want. And so, yeah, the example we used in the last, uh, podcast on process was if the, if one of your processes is processes is to end up with a client, and let's just say the client is signs a contract, and the contract signing is, that's the end of the process, right? Um, for going out and finding and bringing a client on board. And then what's the step before that? Well, the step before that is they get a contract, and the step before that is maybe there's a letter of understanding, um, you know, some, some form of whatever. And the step before that is, you know, maybe it's dealing with, um, um, people having objections. And the step before that is, is, you know, walking them through the offering. And the step before that is getting, you know, is presenting and the step before that is getting the meeting and the step before that, et cetera, et cetera. Right? I know they're gonna stop that. Like four steps. Wow. You're really pushing is gonna continue. Well, I'm trying, right? And, but every one of those steps is measurable, right? And now the question is, is what does good look like? Is it, is it that, you know, once we get a lever letter of understanding, 98% of the time we have a contract, et cetera, et cetera. I could go all the way back if you wanted me to. Um, but that's what we do with the KPIs, is we think about all these different things that we're doing, um, in particular things that are repeatable. Because just like on the processes, the KPIs should be measuring things that are happening all the time. The best KPIs are leading indicators, which means that they're at the beginning of that chain I was going through, right? As opposed to the end, the, you know, 'cause the end, the long chain, I could gone on, uh, I saw your look. It's like, are you, are you <laugh>? But, but you know, in the end there's these, you know, there's revenues and there's margins and uh, there's a number of customers and you know, there are these, yeah, this is what we want, but how do we make sure that everything we're doing right? Like the guy like to say is, number one, we don't make money because we want to make money. We make money because people value what it is we do. And so what we do is an input for them, right? But we have all these inputs to become an input for them. So let's measure those inputs and make sure that, um, you know, we're, we're, we're, we, we're getting to the plate, we're doing the things that we think we need to do consistently enough. We're having the results that we want to have. And you know, one of the things that people, um, you know, tend to do is they, they'll, they'll create KPIs and targets that are like these goals. Like, wouldn't it be amazing? But we don't wanna measure the, wouldn't it be amazing? We want to measure the, hey, if it's not performing particularly well, if we're not hitting sort of the, the basic targets associated with decent performance, is there an issue? And so you want to have that target equal to that thing that says, Hey, there's an issue. And obviously for this to be healthy factor with power comes great responsibility is we need to agree on what the KPIs are, then we need to agree on, hey, if this number is at this level, there's an issue. And then, by the way, if there's an issue, then it's cool for us to have a conversation, right? So ultimately you want to be able to have KPIs that are leading indicators. The best of 'em could be daily. Almost everybody mostly is using weekly KPIs, weekly leading indicators. And we have a sense whether or not, you know, everything's working really, really well and the things that we need to do are progressing for us to be able to, um, generate the type of outputs that are consistent with the cost of, that we need to generate in order to justify the cost of all the inputs, which are the humans and the machines and everything else's. You know, it's part of our business.

Cole Abbott (00:09:21 -> 00:09:56)

Yeah. I think I could go down a lot on the various things there. Yeah. I think one of the, going into like the responsibility and how people can misuse data, I think that that's an, an interesting place to go down. 'cause I think everyone understands, yeah, it's important, right? But Right. There's times where we can be too reliant on it. There's times where we can underestimate it and then there's times where we can just misuse it, right? And so I, I think one of those things regarding KPIs, it's people have, some people have want to just make everything a KPI, right? And you have a list of 300

Mark Abbott (00:09:56 -> 00:09:56)

KPIs

Cole Abbott (00:09:56 -> 00:10:13)

On one thing. Yeah. And right. When you make try to make everything important, then nothing really is. Yeah. And so do you ever, do you see that, or what, I guess what issues do you see surrounding data and scorecard or anything vegetables when it comes to coaching?

Mark Abbott (00:10:13 -> 00:10:57)

Yeah. Well, I'm going to be honest, like I'm always honest about, we have too many KPIs across most of our departments and most of our teams, um, we're guilty of that because we're, we're big data, you know, freaks, hogs, whatever. It would be awesome. You know, one of the things I wanted to build into the software is you have a toggle and it says basically if something's green or yellow, don't even show it on the scorecard. I just wanna see the reds. Right? Um, now you, now, you know, maybe you do that or maybe you just say per every single KPII have that switch, right? If this

Cole Abbott (00:10:57 -> 00:10:58)

Cool in a meeting, huh?

Mark Abbott (00:10:58 -> 00:10:59)

That

Cole Abbott (00:10:59 -> 00:10:59)

Feature would be really cool

Mark Abbott (00:10:59 -> 00:15:30)

In a meeting. Exactly. Yeah. If this is green, I don't want to see it. I, um, and imagine that for the company, the departments, the teams, the individuals, right? And you know, and I've said, I know I've said this before, you know, you know, if you have a really good set of KPIs, forget about, I'm not gonna answer your specific question. I will get back to it, right Around the 50 KPIs as an example, per, per, per team. But if you have, if you have let's say hundreds of KPIs and 98% of those KPIs are green, you know, as a CEO, I can tell you I'm a pretty happy bloody camper. Right. Especially if they're all, if they're all built around targets, that, that, that, that help us understand that, that whether or not there's an issue someplace, um, you know, that it, it, that is one of my, my dreams. It's been way too long, right? But it was one of my dreams is to give every one of our clients that ability, right? To basically be able to see the screen. And if they want to, they can see 'em all, or they just see the greens, they can see the reds, they can see the yellows, right? And they can see that the company level, department level, the team level, et cetera, right? It's just sort of like, imagine the accountability chart, um, or the org chart, right? Blown up and giving you all those colors. So now the question is back to, you know, how many's too much, right? And, and I think that if you don't have sort of a red, green, yellow approach to it all, and you just have 50 right? And you're going through every single one of them, that's overwhelming. And, and you know what a lot of the operating system coaches teach is every single team should have five to 15. Well, once again, like I said, we don't have 15 now we have on our senior leadership team, we have 16 departments now. So even if you just get it down to three, 'cause that's a, that's a conversation I want to have. If you get it down to three, obviously three times 16 is almost 40, right? No, no, it's 48. 48. Right. Okay. So eight. I wanted, I wanted to let you try do that one <laugh> 48. I thought you was gonna be slow, but get the right answer. I was too slow and got the wrong answer. I don't why I was thinking 39. Um, it's data point 13 by 13 by 13 times three. Right? Um, what's 13 times 3 39? Thank you. It's different. I know, I know that. So I, whatever reason I was there. So <laugh>, I know you're enjoying this high five on my behalf. So, so, you know, when, when, when, when, if, if, if you've got no red, green, and yellow and you got 50 things up there, right? It's obviously a hell of a lot. Um, and you're probably gonna lose the force of the trees. But this is the other tension I have, which is if I believe if you have one KPI on a team or for an individual or for a department, right? You're over emphasizing something. It's always over emphasizing something. So my bias, and it's not consistent with what some people teach is I like to have like three to five three's awesome. You know, but sometimes five. But if, if you just have one KPI, let's just go to the simple seat. The simplest of seats, stratum one entry level position, they've got a quantity to produce, they've got a quality to produce, you got following the processes, right? And you got showing up and giving us 40 good hours. That's at least our company, right? And so now if I just emphasize showing up and, and the only one I watched was 40 good hours, they could show up, they could twiddle their thumbs, right? Those are good hours. That's a discussion, right? Right. But, but fair enough. Yeah. Right. Um, or if I just had quality, yeah, one amazing thing, but they're supposed to produce 50 if I just had quantity, right? They produce 50 things, but they're all for crap and they're all going back to, to, to, to, to redo or whatever it's called. Right? So my, my bias is I, I I deeply understand and appreciate the notion of five to 15, but I think that you almost always find three to five KPIs per seat, per team, per department, right? That make sure that we're not over emphasizing one thing versus another thing. Does that make sense?

Cole Abbott (00:15:31 -> 00:15:57)

Yeah. 'cause if you just have the, it's hard to distill everything into like, into one thing, right? Uh, I mean, unless for for a Strat in one seat Yeah. You could kind of do that as how it relates to the rest of the team. I'm just thinking, you know, if have a social media manager and their thing is just total followers or subscribers across the thing, right? And it's like, that could be something, but also it's just, it,

Mark Abbott (00:15:57 -> 00:15:59)

It, it, it, it could be, I mean, it's possible, right?

Cole Abbott (00:16:00 -> 00:16:24)

But if you go up, you're not gonna have a a, a stratum three. Right? In this case we're talking about SLT, right? Stratum three team member has one KPI, right? That doesn't Yeah. That there's nothing you can do with it, right? But three, I mean three's, we like three. I know you like odd numbers, right? So three makes sense. And then five, but then you go over five if we use six times, right? 16. Right? I don't wanna do that.

Mark Abbott (00:16:26 -> 00:16:30)

96 get right. No, I didn't get it right. Again, <laugh>

Cole Abbott (00:16:31 -> 00:16:41)

Bad damn number actually. Huh? I'm not doing that right now. Yeah. But yeah. Yep. It's nine six. I know.

Mark Abbott (00:16:44 -> 00:16:46)

Okay. What else?

Cole Abbott (00:16:47 -> 00:17:14)

So I think, right, so, okay, I have two areas we could go down first because I think we've experienced both to some extent. One being an over reliance on data, right? Sort of just being see right as a, I guess the quote that I that comes to mind is like, data is everything data's our creator, right? And where is the issue with that mentality?

Mark Abbott (00:17:14 -> 00:17:19)

Yeah. Well, are we getting back to, are we data driven? Are we vision driven to some extent? Well,

Cole Abbott (00:17:19 -> 00:17:35)

A little, I think vision driven versus market driven would be the correct, uh, spectrum, right? But right. Data driven is kind of, is is very similar to market driven, right? I I just think market is more, uh, all encompassing for that type of things. Yeah.

Mark Abbott (00:17:35 -> 00:17:45)

I, I think, I think the, the, the, the big one of the things to talk about here is that if you look at

Cole Abbott (00:17:48 -> 00:17:48)

Data,

Mark Abbott (00:17:49 -> 00:21:07)

Interestingly, if you look at it through the lens of one of the assessments that we use all the time, which is called Kolby, right? And kolby helps us make sure that we're not putting people into seats, that they're just not going to naturally be good, um, at, in terms of problem solving. So Colby helps us identify people's problem solving preferences. And there are some seats where people are, um, they need to be very much data driven and process driven. So that's, uh, that, that means that they're, you know, they're mid to high in terms of fact finding. They're mid to high in terms of being able to follow through on things. And then they're typically lower in terms of quick start, which is comfort with risk and ambiguity. And the last 1:00 AM is, is implementer, which has to do with, you know, in some regards it's more complicated than this, but in some regards it has to do with whether or not, uh, the nature of the work you work with is more to sort of conceptual and or, um, versus, um, versus physical and three dimensional, right? And you can, you can have conceptual three dimensional work. But what we've found is that engineers as an example, even though in theory they're working within a computer, a lot of them are high on implementer because they, they're dealing with three-dimensional, um, models in their head when they're working on, on, on things. And then they also love to, as we know, they're great at we on our channel of makers, they're, they're always making stuff, right? Um, so you have a lot of, but a lot of it engineers who are high in implementer. But let's go back to the fact finding. If you, if you think about dominance, if you had four people who were equal, Colby's research shows that the person who has the facts tends to dominate the conversation. So people who are more efficiency and fact oriented tend to dominate people who are more creative and effectiveness oriented in terms of just like how they think and how they process and how they engage in conversations. Um, and so that's part of the issue with, at least within the business world Yeah. That's outside the different thing. Yeah. But yeah, but in the business world, um, it's the no got fired for buying IBM thing. Well, that's another thing, right? Yeah. But, but, but, but in the business world, in terms of, you know, having a conversation around, around options, right? Um, that's a good example of where if you over index on data, you may be sort of under appreciating the longer game, the qualitative side of the decision making, the, the more, you know, um, possible serendipity that comes with exploring the unknown and the, and the, and the emotional side and, and, and other things that are frankly, you know, very important as well. So, you know, so the, the, the, the, the data is, you know, it, it's like everything, it's a, it's a double-edged sword. But I do, I do think that when it comes to certain areas within the organization, those areas where efficiency is important, data's, you know, a very, very powerful tool that should be understood and leveraged.

Cole Abbott (00:21:08 -> 00:21:37)

I, I think Right? The best person who has the facts is gonna win. Yeah. But I think the better thing is you need to have the facts, but then if you could tell a story with that and you can internalize that and express it Mm-Hmm. <affirmative>, then that is, that's the best. Because I think persuasion through the emotions is the best way, right? As long as you can back it up with facts otherwise, right. One's gonna win in some things, but you're always, you're gonna win every single thing if you have both of those tools. Yeah. Because, because the emotions

Mark Abbott (00:21:37 -> 00:21:47)

Sink to an, to to, to a deeper level and, and of understanding and, and, and just being, um, seared into the brain,

Cole Abbott (00:21:47 -> 00:21:53)

For lack of better term. I'm trying to think what the actual number is. There's a like a data point,

Mark Abbott (00:21:53 -> 00:21:53)

No

Cole Abbott (00:21:53 -> 00:22:18)

Pun. Yeah. About, uh, basically they told people just a bunch of facts about something. They told people a story about something. Yeah. And then right after people were more persuaded by the facts, whatever. But then the next day going back and figuring out what happened, it was 60, basically two thirds to one third favoring of the story versus the data. Yeah. So I, I just think that that's an interesting, well,

Mark Abbott (00:22:18 -> 00:22:39)

Even, you know, I went, I, I did a, um, several hours on improving your memory, right? It was a thing through Vistage and all of the stuff that we were challenged to memorize. He gave us tricks that basically converted it all into stories and emotions.

Cole Abbott (00:22:40 -> 00:22:53)

Well that's, that's how we evolved. Yeah. That's what matters, right? Stories, emotions, patterns. Yeah. The things that Right. And the numbers is Yeah. And, and those things, like they're important, right? But it's a tool and it's, you're kind of fighting evolution and Yeah.

Mark Abbott (00:22:54 -> 00:22:54)

But, but things

Cole Abbott (00:22:54 -> 00:22:55)

A little bit. But,

Mark Abbott (00:22:55 -> 00:23:04)

But you know, it's interesting, right? Yeah. Obviously motions have been around for tens of thousands of years, if not hundreds of thousands of years, right? Hundreds of thousands

Cole Abbott (00:23:04 -> 00:23:07)

Of years through a whole Right. There. A logical breakdown here, but Right.

Mark Abbott (00:23:07 -> 00:23:15)

And exactly. So you go, go to maybe millions of years, right? That that's not the point. The point is that not

Cole Abbott (00:23:15 -> 00:23:16)

Prefrontal cortex numbers

Mark Abbott (00:23:16 -> 00:23:22)

Have been, numbers have not been around for, you know, numbers have been around for, I don't know, 20,000

Cole Abbott (00:23:22 -> 00:23:34)

Years. And I guess take some of this might not be a great analogy, but I'm gonna try to take the data thing right. And make it a little archetypal. Okay? So I'm sure most people have seen the Lion King,

Mark Abbott (00:23:35 -> 00:23:36)

Hopefully Lion,

Cole Abbott (00:23:37 -> 00:24:46)

Check it out. It's a good movie. Watch it. I think it's out of theaters. It's back on DVD now. Um, but in Lion King we have Mufa to the king, right? And then you have Zou the, the bird that flies around and is always keeping an eye on everything. Right? And Zou is not the ruler, he is not the leader, but he informs the leader. He informs mu Foss about everything that's going on in the kingdom, right? And, and he's a, he's a big part of that. And so when Simba goes out into the elephant graveyard, <inaudible> is whatever, he knows everything, right? He is the eye, he is the keeper of all things. All the information, whether that's, you know, what's happening or that's numbers of things, right? It's pretty much the same sort of deal, right? And when Mufasa dies and scar becomes king, scar locks zsu in a cage because Scar wants to be willfully blind to the things that are going on around him. He doesn't want that information. Right. And I think Right, it's very important that the leader has access to all that and does not hide unwanted things in the fog. Yeah.

Mark Abbott (00:24:47 -> 00:24:51)

Well it is, it is an expression we use all the time. Average players want to be

Cole Abbott (00:24:52 -> 00:24:53)

Left alone.

Mark Abbott (00:24:53 -> 00:24:54)

Good players

Cole Abbott (00:24:55 -> 00:24:55)

We told

Mark Abbott (00:24:56 -> 00:25:00)

Want to coach. Yeah. And great players want to know the truth.

Cole Abbott (00:25:02 -> 00:25:04)

And so it's all about knowing the truth. Yeah. And that's what

Mark Abbott (00:25:04 -> 00:25:05)

Data

Cole Abbott (00:25:05 -> 00:25:06)

Helps you do.

Mark Abbott (00:25:06 -> 00:25:17)

Yes, a hundred percent. But like you said earlier, you gotta be, it's like everything, you can't over rely on data, but to not have It's foolish.

Cole Abbott (00:25:17 -> 00:25:28)

Yes. It's, it's right. It is the i is the thing that sees into that. But you have to be able to take that and interpret it Yeah. For what it is and make decisions utilizing that. Now

Mark Abbott (00:25:28 -> 00:25:31)

Do we have, 'cause I have one more comment. I'd like, do we have time? I think yeah,

Cole Abbott (00:25:31 -> 00:25:33)

You, yeah, go for it. Alright,

Mark Abbott (00:25:33 -> 00:27:01)

Cool. So one of the things, um, that I think is really, really helpful about data is not just it using it to understand when there's an issue or when there's not an issue. And Yeah. But ultimately, you know, when you look at every single seat, if you really think about what's the data that's really important and informative of how, how this seat's working, just that exercise alone is extraordinarily useful, right? And, uh, you know, part of my interviewing process years ago, and I don't use it as much anymore predominantly 'cause I'm sort of last interview and it's probably would be weird, but is, you know, when I'm talking to someone, I'm like, okay, so what are the roles, accountabilities and responsibilities? And, and how would you measure success in terms of each of those, right? What would, what would be the KPIs you utilize? And they'll say, you know, look, I'll give you five minutes and I'll, and I'll walk out and I'll come back and let me know what the KPIs are. And if they don't understand the math of the thing that they're doing and they're applying for, that's a pretty interesting signal to me. So I, I think, you know, my brain is, you know, that understanding the math of the business is really important. You know, we want every department leader to understand the math of their business. Um, you'd like the team leaders to understand the math associated with what they're doing. And so just, you know, part of KPIs is just getting clear on the math. Yeah, yeah.

Cole Abbott (00:27:02 -> 00:27:04)

Because that's right. Math is the

Mark Abbott (00:27:05 -> 00:27:05)

Objective

Cole Abbott (00:27:05 -> 00:27:08)

Universal language. Yeah. That we all can,

Mark Abbott (00:27:08 -> 00:28:04)

Which gets back to the one final comment on data, which is, look, if we have, you know, I think it's James Parkdale Parksdale quote from, you know, the Netscape founder or co-founder, um, from, right, what was that, the early nineties or something? I can't remember. Late eighties, early nineties. But he said, look, you know, if we're gonna make decisions, let's make 'em based upon facts. If we can't make it, if we can't make a decision based upon objective facts, then we're gonna base it, uh, make it on based upon opinions. And if we're gonna make decisions based upon opinions, guess whose opinion's gonna matter more than everybody else here. And so you want the data to support the perspective that you have in particular when it comes to sort of sitting there with someone who's got a more authority Right. Within the organization. So it's helpful for all of us. Good data. Yeah.

Cole Abbott (00:28:04 -> 00:28:21)

It's, it's the easiest way. 'cause you can go out and create an amazing argument in your favor and try to get all the rhetoric right. And do all that. But if you just come with great data Yeah. Great information, all that research, that's a much wiser data

Mark Abbott (00:28:21 -> 00:28:27)

Plan. And simpler. Yeah. Lot simpler. Yeah. Cool. Data, data. <laugh>.

Cole Abbott (00:28:28 -> 00:28:30)

Alright. I was like a minute ago. Okay,

Mark Abbott (00:28:30 -> 00:28:31)

Cool.