Insightly_Ep27
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Jordan Walker: [00:00:00] Hey, Alyssa, welcome back.

Alyssa McGinn: Hey,

Jordan Walker: what's been happening lately?

Alyssa McGinn: A lot of things. \ I mean, I feel like there's a lot going on with this, which this podcast is not about on a macro level, but I'm feeling good like on a profesh personal level in this queue.

Yeah.

Jordan Walker: Yeah.

Alyssa McGinn: Q1 is always like kind of interesting for us, like business wise it's always like hurry up and wait kind of [00:01:00] vibes. Q2 is when things actually start happening.

Jordan Walker: Yeah. I feel like for me it's closing out end of year.

Q1 is really when a lot of like strategic, planning really starts taking. place with a lot of my clients, and so to me it's kind of like easing into the new year because it's like, more conversation for us.

Alyssa McGinn: do you do strategic plans like in Q1 for 2025?

Jordan Walker: Yeah. Well, and I mean, that's not my choice, but over the last like several years, that has been the trend where the intention is that you do more of that planning in like Q3 and Q4, so that you've got

Alyssa McGinn: because you lose a whole quarter of

Jordan Walker: Yeah. But a lot of Times what ends up happening is Q4. like you might already have your budgets set and whatnot and just kind of taken off of like historical, and some like, you know, higher level ideas of what, you'd maybe wanna explore.

But what I've often found is that the actual, [00:02:00] planning of how we are going to strategically go after some of these initiatives is not hap, that conversation is not happening until Q1. 'cause

A lot of companies are trying to close out. their to-dos and their goals in Q4

And literally once you hit Thanksgiving it's over. like, bye. You know? We're like, we're in cleanup mode. We're all trying to get out at the same time. So my personal preference is that we're starting strategic planning and August, you know?

Like, and starting to evaluate. Okay. Like we've already gotten over the first six months of the year. Like What are we seeing?

Where's the organization heading?

All that kind of stuff so then we can start making some plans on what do we need to kind of workshop and explore. So that by the time we hit Q4, we've got some good insights of what we need to do strategic planning. And I mean, to use my great Grandma Walker's terminology, bless their little hearts, bless their pee hearts. [00:03:00] Such good intentions To try to do it, but like we might even have things on the calendar saying that we're going to explore this, but then it just keeps getting pushed back until like mid-January as a start date.

We roll with it. We're good with it. We kind of like help wherever we can like during those timeframes, but

Alyssa McGinn: I'm sure there's tons going on.

Yeah. Things on fire. Goals to be met.

Jordan Walker: Oh Yeah. Yeah. I mean I'm not,

the most important thing on their to-do list.

Alyssa McGinn: To be fair, I'm sure that what you do plays a huge part in their overall strategic plan. It's just the bigger the company, the more moving parts there are.

Jordan Walker: Yeah. And I mean, especially when

you're going through budget conversations, like depending on what if I'm working even with let's say the marketing director. Sales director, they might have a particular, direction that we think that we're going in. But if the,

CMO or CFO,

In Q4 ends up just like things can change a lot in that timeframe.

It's actually not a bad thing. It's not ever a scenario where like things just aren't happening in Q1. You're basically just [00:04:00] continuing forward

And then using Q1 as that level set of, okay, by March and April now we're really going to town, unless. The only times that I see the typical strategic planning timeframe happening in Q4 is with companies that are very cyclical along those lines, But I also work with a lot of entities where their fiscal year is like June to July. So it's a little bit different than, the January to December.

Alyssa McGinn: super interesting to me because our work is like, not at all like strategy in nature. Like we're not at, we're not having cyclical strategic conversations. We're having strategic conversations that really don't apply to like, oh, what's our marketing plan for this next year based on what we've learned. like you talking about that, I'm like, oh yeah, that's like, not really how we work, but I should analyze some things because.

We had a client that, well, we have the client now that we first started talking to them last June. Oh yeah. [00:05:00] And we just signed to them.

Jordan Walker: Yeah.

Alyssa McGinn: Yeah. And I think there's like some cyclical stuff in the sales cycle in terms of budget and conversation. But it's really hard when you work across verticals to really understand like who works in what way.

Yeah. But we're not at like, the strategic planning level in the core departments. We don't really have that. It's just, yeah. But Sometimes the work that we do precedes some of the strategic conversations. And then sometimes they get into it and they're like, oh crap, we don't have the data.

Jordan Walker: They don't have the data or they realize that other partners within the organization need to get involved. And Yeah. That's, where I think a lot of those best intentions to get jump started really come in because depending on like time of year, they still have things, that they have to close out.

They still have goals that they have to achieve, and then they also have to add whatever we are serving like on top of their workload. So like, I get it, but I think it would be interesting for like to kind of, in that vein of things like maybe even choosing like five client [00:06:00] types to like start with, and maybe you're going out and getting just some like, qualitative

feedback. on, well, What does your, company structure look like? Like how do you guys operate throughout the year? When do you do strategic planning? Just asking

Some of those questions to see if you're seeing differences across verticals Or like what aha moments. If they say that like, the best time to get in front of us is actually February, then change your process.

Alyssa McGinn: Right. Take that note

Jordan Walker: Yeah. Thank you for letting me know.

Alyssa McGinn: For all the other people that I wanna work with. Like you.

Jordan Walker: Yeah, yeah, Well, today, I wanna talk a little bit about a topic that has subtly come up on our podcast every now and then.

But this is a topic that I kid you not has come up like at least five or six times just in the last couple of weeks where my clients are asking me about data dictionaries or they're bringing up, we should probably do a data dictionary. And in some [00:07:00] cases, here's actually my real question for you before we really get started. Have you ever been in a meeting where everybody is using the same word but they're using it differently?

Alyssa McGinn: And they're just talking but not stopping to define it. Uhhuh.

Jordan Walker: And so like everybody in the room thinks that we're all on the same page. But then you leave and you find out that you're actually not, because the five different people in that room were all thinking about that word or that category or whatever differently.

Yeah.

Alyssa McGinn: A hundred percent. That's been on like a topic, but that's also been in like a role. So like we need to bring on an advisor, a business advisor, and like people think, there's like coaches, there's like, you know, EOS

Jordan Walker: Yes.

Alyssa McGinn: there's this and that and the other. Yeah. So I've had that happen where it's like, yeah, we should totally get in.

Like we need help, you know, with this, this, and this. And like the, like who they go out and talk to is completely different. Yes. So yes.

Jordan Walker: That is why I find this topic of a data dictionary [00:08:00] interesting. Because even as I've been asked about data dictionaries, the way that people are using that term is even different.

Alyssa McGinn: Oh.

Jordan Walker: And, or like, I guess to the same point as what you just made, maybe not as in depth as what, others would consider. So I wanna have a discussion about data dictionaries And what they are, how you can use 'em, and just some like good advice that comes along with. So, okay. What is a data dictionary? It is ultimately like if you think about what an actual dictionary is it's a glossary of terms essentially, right? Like what are the definitions of these different words that we use commonly in our language? Same thing, but think about it from the data that you have within your business,

Whether it's data fields or labels that are in a CRM system. Or it's metrics and dimensions that you're looking at on [00:09:00] a website analytics platform, or it's even like all of the convoluted terminologies that come through with what is customer engagement?

What does engagement. Actually qualify as to us for our business? Because if I asked you what does customer engagement look? like, you would probably have a very different definition of it than I.

Because we come at customer engagement from different departments and different goals. So the data dictionary is like a standard operating procedure or a glossary that helps teams get on the same page that if we're going into a meeting and we're using these terms,

We all have the common definition of what that is and what it includes. but It also helps with data governance.

Because if You're starting to allow more accessibility within the tools and the platforms that you have where you're reporting data or you're capturing data, [00:10:00] it helps people be able to jump in and start Making some analysis or start doing some analysis or start pulling some, reports based on maybe whatever goal that they're trying to achieve.

Alyssa McGinn: could there even be multiple layers of this? Like so,

Like custom engagement, you use that for example. So like there's the definition of like, what do, what does this, what does what, how are we using this term?

Then I feel like there's a secondary part of it is like, and what does that mean to us? Yes. As an organization, so like we define it as, let's just say, clicks on a website, click through rate, let's say that's actually what we mean by customer engagement. That's probably not

Jordan Walker: It's fine

Alyssa McGinn: if it, but how do, how are we thinking about click through rate?

Jordan Walker: Yeah, I think so. One example that I would maybe think of is, okay, from an aspect of customer engagement, marketing individuals, think of the interactions that occur on platforms. [00:11:00] So like you just said of visits to our website, clicks on our ads. Likes on our social media posts.

That's customer engagement. Sales or business development, though, probably considers customer engagement different in that,

okay.

Well they attend our webinars, they're coming to our happy hours, they're visiting us at trade shows. Like that's a different level of engagement. Where operations might literally just think, customer engagement is the frequency of how often are they purchasing from us? And what kind of growth are we seeing from, them. you know, like maybe they're correlating customer engagement really just to the customer lifetime value type

metric. So, to your point, there are layers to this. So it's what does, like, what is customer engagement in this case? Where does this play out in our organization? And ultimately, what are we, like, what does, [00:12:00] why does this matter? To us? And what are we hoping to learn from it? But even beyond just that like basic definition, what a data dictionary should also include is the definitions of the labels, and the metrics and the dimensions that are used on the platforms to help you calculate that particular category.

In a non-technical sense, the data dictionary, is a glossary, of terms, but in where it veers off of just being a glossary is by helping to connect. These are the platforms and the outreach models. or the departments that we Utilize.

Here's how that works. Here are the metrics and the dimensions that we use to calculate this, and here's typically why we want to know this.

So in like if we're talking about customer engagement, well maybe customer lifetime value does actually take into consideration the [00:13:00] activities that our customers are doing with us. Like They, we are taking into consideration, maybe we're giving like a point to whether they follow us on social. They're signed up for our email and they attend at least one of our, webinars.

Alyssa McGinn: a good customer engagement scale.

Jordan Walker: I love a customer engagement scale,

But then beyond that, maybe you're then looking at other things that show you customer engagement from. Okay. What is their product mix like? Customer engagement to us means that they've made it beyond product one. And now we've got them on number one and number two.

Alyssa McGinn: And on some of the metrics, this could even include parameters.

Yes. Like not all, it wouldn't be applicable to everything, but let's say. It's more SaaS and you're looking at lifetime value and you're looking at churn and you're looking at reoccurring revenue. Like, okay, we, our standards are, we want our churn to be below X percent. And if it's, [00:14:00] we want this to continue to get lower and lower at lifetime value, we wanna get to higher and higher.

Our standard is this. Yes. So then everyone also knows what's good and what's bad. Yes.

Jordan Walker: I love that you just made that point because really a lot of conversations, and I'm sure you hear this too, like.

When you're sharing information or insights that you're gleaning from the data, like a lot of times I get, well, what's industry? benchmark? Well, okay, well industry if we've got, it looks like this, but for you it actually looks like this. You know, and so being able to show like good, better, best, From a benchmarking alongside the different data definitions that you have, I think is a really good point to make.

So at minimum, like if you're thinking about what a data dictionary kind of entails, think about it similarly to like a standard operating procedure. One, you're going to want to have just like the basic definition of the different data labels that you are looking at. So we talked about customer definite [00:15:00] customer engagement, but it could also just literally be something as simple as revenue transactions. You know, like.

What is an actual transaction? In some cases, like a transaction is always gonna be a sale. You know, but what if we're talking about transactions from like an IT ticketing system? That doesn't actually come with a dollar value, sometimes, but that is still a transaction. of sorts, You know, so being able to like really define, like when we say this, this is what we mean. Here are the areas that we use it in. And then here are the ways that if you have to input the Data, here's how we need you to input it.

So like we've talked a lot about CRM Systems on here and one of the biggest challenges with CRM systems is the fact that it requires a human to enter in the data. A lot of times.

Well, humans are really great at being [00:16:00] lazy.

That means that we're, I, if we're not doing it, then that's a problem. But if we are doing it, something as simple as like, okay, maybe you've got industry type or company type as one of your data labels in there.

Okay. If I am like is company type, the company, like somebody might interpret that as, well, it's the company name,

but that's not what we mean. Do we mean industry? Do we mean a sector, within, maybe we have a vertical and we're wanting it to be labeled by sectors within industries. So defining like what we mean when we say client type in that data label, like this is what we mean by that. But then also giving, how, what is that appropriate input? Let's say that you use that data label to indicate the different, industry verticals that you have.

Easy thing is to have a dropdown in there already, but even in that maybe your dropdown has like industrial manufacturing, healthcare, blah, blah, [00:17:00] blah.

But what happens if healthcare and manufacturing can also be industrial as an example, right?

So then being able to say, okay, these are our categories and these are, these are the definitions of, those categories. And here are companies,

company examples of what that looks like and why industrial is industrial, and not manufacturing kind of a thing.

Alyssa McGinn: Also, I have like five thoughts, so I need to like order them. Also, which maybe you're getting to this, but I feel like it also could be important to say, okay, what do we mean by revenue? And then for example, and then how is that actually identified as a true data label in the data? Yeah. And so a lot of different systems show that differently, and that's part of the hard work of combining data sources

Jordan Walker: Yes.

Alyssa McGinn: QuickBooks may call it customer id and your ordering system calls it client id, right? And it's like, are those both our, you [00:18:00] know, that's a simple example, but are those both our customers and like, is that actually an order? Is that a customer? And I think that's true. You know, as you get down into like even more nuanced data points,

Jordan Walker: that's a real like, so that is a great example because like outside of the data labels, you do need to couple that with the data sources. So here's the definitions of our data labels. Here are the data sources, and maybe you actually start with, source and to make it more organized.

Here are the sources, here are the data labels within those sources, but here's how they correlate to other things? Like something maybe at I love the example. that you just gave of customer ID and client id. They're sa and

maybe in this example they're the exact same

Alyssa McGinn: Mm-hmm.

Jordan Walker: but different platforms that are labeled different things. So then when you get to the, point where maybe you have an analyst coming in or somebody that needs, to pull a report, if they wanna correlate. you Know the [00:19:00] customer interactions or whatever from each of these platforms, they know I need to look at client iD over here. Match that to customer ID over here. That then starts sending us into like customer data platforms and data, warehouses and all of that kind of stuff. But if even when you get there, a data dictionary is still incredibly important because like if you're building out a cdp,

you still need to know what are the data sources that we're pulling from, and how are these labeled so that we can correlate it to the true customer that we're talking about.

Alyssa McGinn: There's another layer of reinforcement that could be done on the reporting and analytics layer, which is, we build it in a lot of hovers typically. So when you have a dash, and that's again, not to replace a data dictionary, but to reinforce it is like when you're looking at, a metric shown in a chart and you hover over customer engagement, it would say, customer engagement means X, Y, and Z.

Here's the industry standard and here's where we're at now. And it gives you maybe a red, yellow, [00:20:00] green. This sets the standard. Yes. And then you reinforce it in multiple places.

Jordan Walker: That's a really good example of that helps people that like, okay, if they're not going to go mine through anything, or if they're not in that like exploration mode, but they're seeing a dashboard now you're kind of alleviating that challenge of if we're talking about this particular data set or if I'm not in the room to describe it to You you understand what I'm ta, like, what we're talking about. here, and whether it's good or bad.

Right?

Alyssa McGinn: And that's not just made up by the analyst. Yeah, that was like back to the dictionary that everyone talked about and hopefully across departments. Exactly.

Jordan Walker: That's exactly where I was just getting ready to go.

That's the thing that I actually love about this data dictionary conversation is because we know that data exists, in every ounce of our organization, but as we've talked before, there are silos between different departments. And that's the challenge with companies having great accessibility with data because every department kind of owns their Own thing. And the minute you start [00:21:00] breaking down those data silos, if there's not

a consistency across the board of like, we all understand what each of these things mean and how they correlate to the work that we're all doing together. then you just get the extra challenges of how do you actually report on success later on. But this gives you that foundation of this is what we mean, this is where it's coming from. here is how it's correlated to other areas within our business or a sales model or whatever the case may be.

And that actually can help you foster that data driven culture because Now you're teaching people and it could be served as a good onboarding tool for people. as well.

Alyssa McGinn: Oh, a hundred percent. I hadn't even thought about that because that's how you use SOPs. Uhhuh is like, here's how we do this, and it's like a an SOP. I think this is like unlock for companies to like, that's such a basic thing, but if you can actually achieve the feat of getting all of these [00:22:00] stakeholders in one room, plus other, so that's probably a lot of people across an organization to actually hash this out and talk about it and get it written down and documented in an SOP that you can then clearly give to a new employee and say, here is how we do things.

When you look at a dashboard. That tells you about your sales pipeline and you hover over stage of sale. Yeah. Here's the stages and here's what this means to us. Yeah. And here's where we wanna be in terms of time duration on each

Jordan Walker: Yes.

Alyssa McGinn: Yeah. Like, I hadn't even thought about that. I was just kinda like, dig data dictionaries.

I was like, cool. Yeah. But I'm like.

Jordan Walker: there's so many uses for it. We live closer to the analyst side of things where we understand what analysts have to go through to clean data and blah, Blah, blah, blah. Well, and you know this as you're like starting to like onboard clients and you're having to like audit their data and then map metrics and all of that kind of jazz well. If as A company you're going through the exercise of developing a data [00:23:00] dictionary, everyone is now starting to kind of think like an analyst.

You're identifying dirty data opportunities that need to get cleaned up. Like if you wanna visualize something down the road, but it's not matching up and it's not done consistently on the front end that's going to give, it's gonna take longer to get to where you're at.

So this is a good exercise that brings cross-functional teams together. And then it fosters collaboration. So like there's a

Alyssa McGinn: and build culture.

Jordan Walker: 100%. Like, so I have one client right now that is actively building a CDP and their customer data platform is connected to, will be connected to a custom CRM system that they've developed for their organization. They also have a custom marketing automation tool that connects to the email and their text app notifications. So they've done a lot of infrastructure to get these platforms to fit within. There [00:24:00] wasn't anything directly off the shelf that fit what they were trying to achieve. It was a better investment for, them to actually go through and do their own.

So they've got all of these platforms, but as they were going through that, they knew, and they started working on all this before, CDPs really became a big buzz term in the industry.

But In order for them to do this? Well, they were working with software engineers. They were working with their internal like data team, which existed in it.

This team is essentially like they, they're a growth team and so they do a lot of like the advertising, and community engagement and things like that.

but They're the ones that are in charge of this. And so they're having to like make sure that they're speaking the same language with Terminology as the software engineers that also speaks the same language with the data team, which is housed within it. So like as you can imagine, they're probably all thinking about these terms very differently.

So they did the really [00:25:00] smart thing early on by starting to like establish, okay, when you say that, what does that mean to you in A CRM or marketing automation software? Okay. When we say this, what does that mean to you? So that they were able to start correlating it. Now they're at the point where they have, because of all of the different things, there's buku IDs for one customer and one customer, like technically if you think about it.

You've got companies and then you've got clients within a company or customers within a company.

You might have five or six different decision makers within one company. So we've Got a company ID and then each one of these has like a sub ID on 'em, same thing, but blow that up 10 in the number of IDs to keep track. They're able to fully map all of this now and connect the data and they can kind of slice and dice things however they want.

Alyssa McGinn: That's truly amazing.

Jordan Walker: But they would not be here. Like, I mean this has been maybe like a year and a half journey so far [00:26:00] for them that is significantly faster for them, like to have adopted all of this.

And it's because of that data dictionary now when they go into meetings and they're talking to all of these different stakeholders, everybody knows what's going on and there's not this like back and forth of, well, that's not what I consider.

You know, and so they, one of the things that I heard from the client is that they've even heard from the IT and software engineers, how much fun the project has been because of how much collaboration has occurred, and they feel like they're being able to spend more time on developing the solutions and getting to the next step instead of

Alyssa McGinn: just misunderstanding each other.

Jordan Walker: and I'm like, wow, like this is such a common sense thing that we don't a lot of times people don't do because it takes time.

To do it, but I'm here to advocate that like data dictionaries, we just need 'em. And it doesn't matter if you're doing anything like super duper complex. Like if you've got, you know, an email system, you've got your [00:27:00] QuickBooks and maybe you're, you know, running e-commerce or

CRM or something like that, like just develop a standard operating procedure around those platforms if you're using them constantly, because over time you're gonna need that organization if you wanna get to the next level, of how to use data to help you innovate, and make, better decisions.

Alyssa McGinn: and just a side caveat, if you ever wanna sell your business, this is, this would be a huge value add and I think a huge leg up in terms of like actual valuation of your company. Like people think that these things actually don't add valuation, but valuing a company is not the same as valuing real estate.

Like, it's not so cut and dry, it's more subjective. If you can hand someone like a lot of times we've built analytics that go into kind of the deal room for a deal, and we've heard, you know, anecdotally, like that's super helpful because the buyer has confidence in, okay, they have the data that they can even put this together.[00:28:00]

They know the story that they're telling, they understand their customers, you know, the list goes on. I think the same thing is true for this. Here's the analytics or here's the data, or here's our reports and here's what we mean by all of this. And we have documented

Jordan Walker: Yes.

Alyssa McGinn: That is seriously, like, and even if you don't think you're gonna sell your company, you just, don't know what's gonna happen.

Yeah. And so to have that, just as same as any other SOP, but with the increasing value of data and what that means for ai. Just like future innovation, I think it could be more critical than just your average SOP on how to do another

Jordan Walker: I think it's a good goal for every company to make because, you know, we talk about it every episode, but data is a huge competitive advantage. It's a huge opportunity for innovation and increasing profitability within your organization.

It's also, the way that you break down these data Silos, between different [00:29:00] departments and foster more of a data-driven culture where everyone can really participate.

And helping drive toward the mission that we're going after. There's really no con to taking the time to do this because I think what I have, the conversations that I've been a part of, even with companies that have not yet made it to this point. What I'm hearing from them is that desire for everybody to get on the same page so that they can be more collaborative, even if they're not going to the full like scale. that of What I just described. Like they see good opportunities for good conversation to also see how can we cross pollinate insights

across the board and get that good understanding of, okay, maybe you don't see customer engagement as important as return on investment or profitability metrics. But this can help people understand how customer engagement actually, applies to that.

So it also helps with just like bridging that gap and

Alyssa McGinn: Oh, [00:30:00] I'm sure. Insights come just from going through the process 100%.

Jordan Walker: Like I actually think that a lot of good ideas and new directions could come out of this kind of activity.

Alyssa McGinn: I'm more convinced of data dictionaries now than I was. In all honesty. I was like, going into this, I'm like, cool. Yeah, good idea.

Jordan Walker: Like, okay.

Alyssa McGinn: Like I, I mean, I'm on board, but now I'm like convict. I'm,

Jordan Walker: I wanna do this just for bonfire now.

Alyssa McGinn: Yeah. Even for our company sizes, like. I'm a believer.

Jordan Walker: Well, I'm glad I could convince you. I,

Alyssa McGinn: I did. I wasn't like totally skeptical, but I was kinda like one of those things where I'm like, yeah, it's a, it's a good to

Jordan Walker: It's a good todo that has longevity. So the time you go, you put into this, you will get a return on it

Alyssa McGinn: and just so many benefits that will far outs seed the work Yeah.

That you put into it. Great,

Jordan Walker: Awesome. So everybody go out, make a data dictionary.

Let us know how that works out for you.

Alyssa McGinn: I love it.

Jordan Walker: Awesome. Well, with that, we'll end this episode and [00:31:00] catch you on the flip side for the next one. Bye.