A Look Back With Some of our Favorite Guest

This is Making Better Decisions. I’m your host, Ryan Sullivan. Decisions are where rubber meets the road for organizations. Each week, we’ll be learning from people who are on the front lines of turning raw data into better outcomes for their organizations. This show is sponsored by Canopy Analytic, helping companies make better decisions using data.

Ryan: So what is one thing you wish more people knew about using data to make better

decisions?

Jeremy: Yeah. I would say the, the one thing is. And this is probably something that most don’t think about, but it just always, it never fails to, uh, you know, kind of excite me is, and it sounds, it sounds maybe kind of silly, but I just get excited about, you know, taking the data, [00:01:00] taking the information and, and then figuring out what it, what it’s telling me to do.

Right. So, so in our business, there’s just, there’s a lot of lease comp information. You know, this deal was done at five dollars a square foot. This deal was done at four dollars a square foot. This was this type of building. This was that type of building. I just love to take and compile all the information, which is kind of like a, a bit of a, uh, Well, that’s a challenge in and of itself.

Then coming up with the metrics to see what all of this, all these data points are saying. You know, what are the trends? What are the rents in the southwest side versus the northwest side? So it’s, I think people Maybe it’s the difficulty in compiling that information. So they never get to the joy of saying, Oh, wow, that that’s what it says.

But that’s what I wish people would know is that, yeah, there’s some, there’s [00:02:00] some really cool stuff on the other end of gathering the data.

Ryan: Yeah, I absolutely love that. I think one of the things that was most interesting to me about your experience with data is that it contains such a large component of primary research. There are a lot of folks, um, within big and small companies and their, their business kind of generates that data for them.

That could be, you know, financial or supply chain or something else. And so they’re really trying to dig in and look at the trends, but you actually get to be a part of the creation

portion of that. Tell me a little bit about that.

Jeremy: Yeah, it’s true. Um, And yeah, it’s a, it’s an interesting point you make because, and I hadn’t really thought about that, but yes, when you’re in a big company, and actually I do think about it because even CBRE, hopefully, you know, the, the top brass isn’t listening to this. I mean, I’m an independent contractor and a broker.

They kind of call us the gunslingers out there in the, in the field. [00:03:00] But, um, you know, they rely on some big data sets, some big systems that they never really had any, any. Direct act of participating in. So what’s, what I love about what we do is we’re the ones out actually doing the, the transactions.

We’re the ones negotiating the lease rates, for example, which everyone wants to know after the fact, you know, what space leasing for in this market. So we’re the ones creating that. And then most. Of the folks in my position, just kind of, that’s what really lights their fire. That’s their passion. Of course, that’s what, when the rubber hits the road, that’s how you make your living.

I like to take it a step further and actually compile the information, create a platform to repository, you know, like a repository of information, which is what you helped [00:04:00] me create in Power BI. So that I can, first off, so I can just. Set it in there, forget about it. No, I can call on the information later in the form of metrics.

What was 2023 like? What was 2022 like compared to 2024? So I like to do the deals, create the original research as you refer to it, and then actually compile it. And then. Use my relationships with my peers. The other Jeremy Woods is at my competitive firms that I can’t do all the deals. I wish I could or did.

Um, but you know, you have to rely on your peers, um, to say, Hey, I did deal with Acme Sprockets, you want to trade that comp for XYZ Corp that you did. And then boom, now we both have both deals that happened.

Ryan: That’s, that’s really interesting. So one of the, [00:05:00] the other things that I think is really unique about some of the work that you’ve done is that it is also kind of, uh, reporting as marketing tool. Um, I think, again, there are a lot of folks that most of the reports they build are either to inform an existing customer of the status of something or to help them make a decision or something internal.

Uh, your work has been really unique with data. I think you found a really cool new space to use. Can you tell me a little bit about how the stuff that you’ve built allows you to interact with prospects and new

customers?

Jeremy: So, so I guess I would go back to, and I’ll try not to use too much like jargon or lingo because the jargon and lingo is as unique to each market as like, [00:06:00] languages to, you know, to countries oftentimes. So, um,

what I think what I did early on was I, I looked at the market and I said, look, you know, Indianapolis, if you counted, well, let’s start with the commercial real estate umbrella.

Right. So commercial real estate has, has different, uh, uh, disciplines within it. So it includes office space, which you’re hearing about a lot today. It includes retail. It includes, uh, multifamily like apartments. It includes self storage even, and then it includes industrial warehouses, which we heard a ton about during COVID.

Um, And so the first thing you need to do is determine you can’t in a market like Indianapolis, you can’t be a retail guy, an office guy, an industrial guy. Well, you could, but you wouldn’t be very good at it in my opinion. I wouldn’t recommend you use that person, but, [00:07:00] um, so you really have to kind of specialize.

So you have to pick one. So I picked the food group of industrial. And then what I did was just take it a step further because you could be within the industrial umbrella. There’s all kinds of different subsets within the industrial. It’s, you know, you could be a manufacturing specialist. You could be a data center specialist, which is also in the news these days.

You could be an office warehouse guy, which are kind of the smaller units. I chose the biggest, why not, you know, just choose the biggest buildings that are the most impressive looking. And we call that type four modern bulk. Um, and I live it. I literally have a car and my license plate says type four. No one knows what it means.

Even my friends who are in this business and specialize, but they’re like, what is type four? What’s that? Is that the type? I’m like, type four. It’s the space we lease. Oh, got it. So anyway, so, so I really think you need to immerse [00:08:00] yourself into it. So, so when you take it down from commercial to

industrial to a specific product type within industrial, now you can really build a platform to, to, Show clients and prospects, and whether it’s a tenant looking for a space or an owner looking to find a tenant for their space or a developer looking to build the right building to fit the market.

I use this CIR, as I call it, the Competitive Intelligence Report. To market, that’s all I use to market myself. That’s it. I mean, and when you think about it, why would you want to hire me? You don’t want to hire me for a bunch of flashy collateral. You want to know what the information is. If I’m going to build a building, how much am I going to lease it for?

You know, what should I build?

[00:09:00]

Seth: So that’s a pretty broad question. And I think the way to the best way to answer that is to. Begin with the outcome in mind. So understanding what the business is trying to do, what it’s, what its objectives are, and, uh, deconstructing that to finer and finer degrees of granularity, uh, because if you just say, well, we want to increase sales, we want to reduce costs, that’s kind of ambiguous.

That’s kind of meaningless. So we have to say exactly how. Do we want to do that? What are all the supporting, uh, business outcomes that will support that? That’s a big picture, you know, outcome, but there’s other things such as increasing the, uh, uh, the customer base, the amount of customers, the customer base, uh, increasing the share of wallet, right?

There could be a lot of other things. It could be, you know, expanding the sales force, whatever it might be building new marketing campaigns. So there’s lots of elements that go into that big picture objective. And then we, when we look at [00:10:00] those elements, we have to say, well, what processes are now going to be needed to support those bigger objectives.

And then within those processes, we need to deconstruct those processes to understand who are the users. What information do they need at each step of the process? So I think a lot of times, you know, people are attacking, uh, uh, problems, big picture problems with tools, right? Rather than getting to, or starting with what is the most important thing to the business, uh, uh, itself.

And then who are the players involved? And. What do they specifically need? I talk about, uh, information leverage points, meaning where can you get the biggest bang for the buck when you have a new piece of information or a more accurate piece of information, or, uh, you have information faster, right?

What’s going to help you with your processes and your downstream, uh, systems. And so having that, that place where you say, Oh, here’s a really, here’s a pinch point. Here’s a critical point where, [00:11:00] um, we have some kind of a blocker or we’re not able to make the decision quickly, or we have to go through too many manual processes.

Understanding those points are what’s going to give you the biggest bang for the buck. I think the other thing is making sure that you’re measuring your outcomes. So starting with baselines and making sure that you’re, Tracking against those baselines. So, so I think that it’s, it’s kind of a, what I guess I would also say, so that’s kind of motherhood and apple pie, right?

I think the other thing is starting with a good semantic architecture and, uh, data architecture. That’s a critical piece because a lot of folks think that the technology will take care of that. And as you know, having the right foundation is really critical to any tool technology or business

process. Wow. Yeah. Uh, couldn’t agree more. I mean, I think like, first off, I love that term information leverage points. That’s a good one. Um, but you know, what I, what I really heard, there were two big categories of things. Number one, [00:12:00] data is just a tool. Right. So if you don’t know why you’re breaking out a socket wrench in a given situation, it’s just kind of like, you know, you’re not going to be driving nails with it.

Ryan: So I think there’s this idea that a lot of people have like, Oh, we’re going to do data. Cool. Love that. Why?

Seth: We’re going to be data driven. What does that mean?

Ryan: What are, what are we doing with that? Yeah. So it’s really about what is the business strategy and then how can we use this set of tools to get there?

Seth: Very precise, very

precise points. Sorry, Yeah, yeah, no, great. Um, I mean, I think the other thing that I would love to dig in a little bit more, I mean, in an, in a non technical way is this kind of marketing promise from, you know, every vendor out there of like, just use our stuff. You won’t have to think like there’s no code, there’s no effort.

Ryan: You’re just going to like, like plug it into all your [00:13:00] places and it’ll be magic. And it’s like, you know, um, In like the least technical way possible, help our listeners know like, what is a semantic model and why should they care?

Seth: So I think, you know, the other misconception that I didn’t mention is that people will think that the tool is all they need, right? And start off with, okay, we have this problem. Even if I understand my, my, uh, process and my business objectives, now we’re going to apply a tool to it. And the problem, of course, is that.

You know, it’s, it’s the data that is critical to leveraging the tool. If you don’t have the right data, if it’s not in the right structure, what happens sometimes when we say semantic architecture, that means that there could be a term that’s used in different systems that has a different meaning. Like sales is always a great example.

What, how are you defining sales? at what level of granularity, uh, et cetera. And is it net [00:14:00] sales? Is it gross sales? What is it? And then the other is that you can have different terms that mean the same thing in different systems. So you can have the same thing, same term that means different things. You can have different terms that mean the same thing.

Mapping those together so that you can Disambiguate, right? The two terms that have different meanings where it’s sales, uh, net sales versus gross sales, right? They’re not sales. Sales is too ambiguous and then being able to map together those, you know, disambiguate those but also when you have Uh, uh, retail sales and, you know, gross sales.

And those are really the same thing, mapping those together. So in one case, we’re splitting them apart. The other case we’re mapping them together, but that overall is the semantic architecture when you have all of these, Entities representing in different environments and different tools and technologies.

And that’s always the case. You know, I read something the other day that said, um, that it had related to [00:15:00] AI, uh, and it said that, uh, 46 percent of executives Admit that they have a data problem. And my response to that is the other 54 percent are in denial.

Rebecca: It’s such a great question. I think that data is at its most valuable when it is accompanied by two additional questions. Why and so what? So,

Ryan: that.

Rebecca: When we think about a data point, and this really speaks to the name of your podcast, understanding what caused it and then understanding, great, who cares? What is the value of that?

How can we make a better decision based off of this data point? That [00:16:00] really separates one data point from a really robust way of thinking about data. When we think about the why, it can, that is a tricky, it’s a tricky question to answer. Identifying differences, not so hard, but identifying why that difference occurred, requires, collaboration with other teams.

potentially to understand what occurred, um, understanding greater context, um, it can include exploratory data analysis to understand, uh, what other factors may have involved, uh, this change. And so also keeping in mind, um, the so what, what am I going to do with this information, um, after I have it, how does this.

How does this matter? Uh, why does this matter? Um, is it important to [00:17:00] also balance the work? Um, because also as data analysts, you can get really focused in on a question and dive really deep and then get it and be like, Oh, okay, this has no value. Like this, like, um, for instance, There was, we’re looking at trends over time and we saw a big peak in terms of spike in impressions.

Um, and we’re trying to understand what happened and then we found, oh, it was due to, um, you know, um, one of our markets spent a lot more money, um, on this, you know, they spent more money on, uh, on something and so that’s peaked it like okay great like that’s so fine sometimes finding the why something occurred isn’t always like always has finding some why something happened always has to be balanced by what are we going to do with this information um and i think identifying the most important metrics as well is part of that as well In terms [00:18:00] of thinking through, um, the data point, why and implications.

So if we say, okay, great. Um, Columbia had more sales than Peru. Um, what does that actually mean? Like what, not only the why is pretty obvious here. We want more sales. Sales are very important for a business. Um, but understanding the why, uh, is really tied up with, uh, what, what is the implications of sales?

Does sales tell us how many people bought it? Did it, does it tell us how many people, um, uh, how many transactions occurred? Uh, does it tell us about our distribution? So understanding the metrics that we use, um, and the implications of those metrics. is also incredibly important to answering questions of why did this happen?

Um, and as well as also answering those, so what? [00:19:00] And that speak, speaks to the one other point about, um, working with, with people that ask for data points. So even in a data driven culture, I’ve noticed that, um, there are times when People will ask for like, I, I just want to understand this one thing. And I think in a, in the best case scenario, um, again, if we want to use the, if we want to make the best use case out of data and analysts, It needs to be collaborative in terms of understanding what are we going to be doing with this information?

Um, why is this data important to you? Um, and what is the greater context around this question is really necessary, um, to help, um, analysts understand how we can best answer this question, [00:20:00] um, and answer it in a way that’s really meaningful to what the ultimate goal is. There have been a couple of times where people on different teams will say, I would love, I would love to know, um, the number of users that used this specific, um, tool on our website versus the number of users that, um, Use another tool on our website and as analysts, we can do that.

Sure. Great. Boom. Here, here are two data points, but it’s much more valuable if we understand what, what question are we trying to answer? You know, why is this important? Um, and, uh, also understanding like, what are you going to do about it after we, you know, what After talking with a stakeholder, um, understanding that, Oh, what we truly want to understand is how do our, was the, was the cost of putting this specific tool on our website worth, [00:21:00] um, the value that we got out of it.

That, that helped me partner with them to create, um, the, a different measurement framework, um, to help answer those, that so what, and answer those why questions a little bit, um, more meaningfully for them. And then similarly, I think that even in great data driven cultures, I’ve also noticed that sometimes leaders can ask for, have a hypothesis and then say, Um, I, yes, I think that this platform or, you know, this approach is the right one.

Can you find us some data that supports that?

Ryan: Hmm.

Rebecca: you know, again, it is, it is using data, but I think the, again, the most valuable way we can use data is through a more exploratory, um, more exploratory way of, um, coming in with a more scientific and discovery based [00:22:00] mindset where we’re saying, Hey, we found this thing.

We saw this difference and, you know, here’s why, and here’s what the value is, instead of kind of working back from saying, like, we, we think this is right. Let, let’s kind of. back into, let’s back into supporting it, which may seem like a, uh, pretty obvious one, but it’s worth stating because it’s, I’ve seen it, uh, happen.

It really, in great

Judah: You know, when I think about it, there are a number of things. The first thing is that you have to ensure that the question you’re trying to answer can be answered by the data that you have. Right. That’s like a sort of 101 thing, but I think it’s really important to understand, you know, my experience, and this even happened this morning.

Actually, um, I got a, uh, email from a CEO and, you know, he had some questions [00:23:00] about forecasting and, you know, thought it would be really cool if we could, you know, do something with AI. And indeed, we can certainly do something, but the data that he wants to use to predict doesn’t exist, or it doesn’t exist in the right format, or exist in a system that, you know, the data collection, the ETL hasn’t been set up for, right?

So there’s going to be some work to enable that. Um, and obviously analytics people, AI people, they’re very busy and they work on scheduled roadmaps and prioritized projects, so. You know, interpolating that in, um, we have to be really judicious about it, right? So that’s my first piece of guidance, which is, you know, know if the question you’re asking can truly be answered by data.

Um, you know, another thing related to that is to, you know, understand the type of question that Your [00:24:00] type of answer that would be required for the question. So this actually happened to me in a different scenario this morning, uh, just before we got on the call where, uh, a team wanted to understand the impact of, um, current events on sales essentially, right?

And. You know, that makes sense, right? If you ask that at a high level, it’s, you know, seems a reasonable request, but what really are they looking to answer, right? And it turns out when I started to dig deeper, they’re looking to answer, you know, what will be the duration and days of the sales impact post the event. Um, and then, you know, related to, you know, knowing that you can answer it and understand that with the data and understanding, uh, you know, what is going to be the acceptable answer, what your stakeholder is looking for. There’s also understanding whether the. And I guess this relates to my first part of the answer is whether the data you [00:25:00] have already collected that you believe to be sufficient truly is sufficient.

Oftentimes there are gaps. In the second use case this morning that came up, it’s clear that in order to answer it, There’s going to be some feature engineering required and some new data collected and some new ways to think about, uh, you know, the existing data that would help make that analysis relevant.

Um, I also think that you have to look at, like, resource availability and, you know, You know, infrastructure availability, um, in order to, you know, actually deliver. So I would probably concentrate on those, you know, things, right? Knowing that it can be answerable with the data. Knowing that you have the data.

Understanding exactly what you want to answer. Making sure you fill in any gaps based on that and then ensuring you have like the correct allocation of resources and like potentially, you know, systems or server capacity infrastructure to [00:26:00] do that. Oh, and then one more thing that comes to mind, revenue impact.

Like this is a super important thing for me because, um, well, I’ve been, you know, an analyst and, you know, had a lot of people asking me to, you know, if, if, or me asking them if they want fries with that, uh, I’ve, I’ve sort of, you know, over the years. That’s sort of certainly gained in seniority. And you know, one of the things that companies live and die by is revenue, right?

And everything costs money to do. And so when you’re looking at a company that let’s say doesn’t have maybe the free cashflow of a Google, right? Or, you know, has a CFO or has, you know, economics within PNLs and business units, you want to make sure that the projects that you take on and the work you do, is actually actionable and has a direct impact on the business.

And you know, there’s two really ways to impact a [00:27:00] business, right? It’s either increasing revenue or reducing costs. So if given those constraints I put on, you can then also wrap that request around, you know, an ROI or a financial impact pro forma, you know, that you think will result from getting the answer and being able to take action.

Well, you know, you’ll get further, right? If it’s a, if there’s a big impact then, uh, from a revenue or cost savings perspective than if not, right? And I feel like that, that is often one thing that is devoid of, um, in, in many requests that I’ve gotten over the years. Um, even, even today, like the first one, like I understand, You know, the time savings for the executive, who’s been asked by his boss to do it, right, but I don’t really know what action they’re going to take.

I, there’s a few I could name. Um, whereas in that second request this morning, I know from the gentleman who requested it, that the, um, [00:28:00] the direct revenue impact is there, or at least it’s, you know, an influencer of a direct revenue impact, so focus on those things and, you know, always in the business, tie it back to the To the dollars in the simplest way, right?

Increased revenue, reduced costs could argue boosted efficiency, but that’s really reduced costs, right? So that will get the attention of the people in the, you know, in the leadership roles or in the C suite, right? It’s not just analytics for analytics sake, or, you know, Oh, great. We have more data, um, to help us or confuse us.

Uh, it’s that it’s tied to, you know, Wow,

Ryan: I love that. I love. One of the things that I’m interested to learn from you on is

kind of the intersection of, of data and AI. You know, I think there’s, there’s kind of a, [00:29:00] a couple of different populations out there. And so you have one population that’s just kind of like. You guys do computers, right? Then you have another population where it’s kind of like, all right, well, you know, like data and AI are kind of one thing, kind of, you know, different.

There are just different tools in the same bag. And then you kind of have people that actually like really do that work and know that. Pulling in a rectangle of data that’s already been cleaned and making some visualizations on it is very different from saying we have siloed, unclean data in many different locations, and we’re going to pick a very specific machine learning algorithm or a neural net or like something very specific and generate a very specific result with it.

So, I think one of the things that would be very interesting to listeners who don’t have a deep background in [00:30:00] artificial intelligence is maybe if you can share a little bit about what are some of the The most tangible limitations of AI. I think the possibilities of AI right now have really captured the public’s imagination.

We’ve seen, you know, artificial intelligence tools come into, you know, public conversation. Everybody is talking about chat GPT and they don’t have to understand anything about back propagation or neural nets or transformers or any of these things that actually make it work. So I think that there’s, You know, sometimes this idea of going to shoot the moon or this’ll be the first initiative that we do, or we’re going to like make a lot of what you talked about with like, maybe we don’t have the data for this.

Tell me a little bit in very simple terms, what are these artificial intelligence tools and what are some of the things that [00:31:00] maybe shouldn’t be our first step with them?

Judah: that’s a really big question. So I’m at a loss on back there. So let me sort of reflect on the question and kind of give some context and I’ll, I’ll start like broad level in a broad space, narrow it down. Um, first of all, what is AI, right? Like

Ryan: Hmm. Mm

Judah: I have been working with like. Statistics and machine learning, quote unquote AI, for, you know, could argue almost two decades now, um, starting when I was, um, at Monster Worldwide, and even before that, I mean, in statistics for, for three decades, but I really, in, in Monster, and, shh.

Big job site, you know, 16 years ago, we did churn models and, you know, predicted who was likely to, you know, no longer be a customer or we would do personalization and predict like what based on behaviors or demographics or firmographics, you know, what experience to [00:32:00] render. And those were all machine learning based, you know, predictions.

Um, so we go back to the question, like what is AI? It means a lot of different things to different people. Um, and, you know, You know, even vending like predictive AI, um, with Squark in the early days, there were, there was still a lot of confusion, you know, people, uh, you know, didn’t have the data, they didn’t have the time, they didn’t understand, you know, the outcome.

So when you start to like group AI, Um, and there’s just a, there’s a lot here, and I’m sure, um, there’s some folks who’ll say I may, in this, answering this quickly, omit a few things, but largely there’s, um, supervised machine learning, you know, semi supervised machine learning, and unsupervised machine learning.

So, you know, starting with the latter, like, unsupervised machine learning would be something like clustering. You know, where you’re passing a parent child relationship, like the IDs on [00:33:00] receipts and the SKUs on those receipts and you want to cluster the data together to see like set co occurrence, to see like, oh, when people buy bread, do they buy milk and cheese more likely?

Do they buy bananas, right? And retailers have used that type of, um, Unsupervised machine learning, these task focused clustering algorithms to predict how to merchandise shelves for a long time. Like what, if you have three shelves, like an end cap and two sides, should you put, uh, based on the affinities between the products so that people will like pick them up, you know, like putting, you know, a sunscreen next to beach towels or, you know, beach toys.

Right. It’s like, those things are not just necessarily serendipitous that are merchandise, so you have these, um. Unsupervised, you know, algorithms. And this would, it could include, you know, affinity analysis, market basket analysis, clustering, things like that, a whole bunch.

Outro: That’s a wrap for today’s episode of making better decisions for show notes and more visit, making better decisions dot live [00:34:00] a special thank you to our sponsor canopy analytic canopy. Analytic is a boutique consultancy focused on business intelligence and data engineering. They help companies make better decisions using data for more information, visit canopy analytic.

com. There’s a better way. Let’s find it together and make better decisions. Thank you so much for listening. We’ll catch you next week.