Impactful Insights: Why Sales Data Matters with Scott Parisi

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: Hey everybody. Welcome to another episode of the Making Better Decisions podcast. Today’s guest is somebody that I am extremely excited to talk to. Um, we’ve gotten to work together in the past and he has extensive experience in business intelligence, both as a consultant and an industry.

Um, you know, in addition to his time in consulting, he has a proven track record in both horticulture and retail. Um, excels at sales, leadership, team management. He’s become an industry leader in data modeling [00:01:00] and in DAX calculation. Please welcome senior business intelligence developer at NewBalance.

Scott Parisi. Hey, Scott.

Scott: Thanks, Ryan. What a great introduction. All of it’s true. And I, you forgot humble.

Ryan: We like to gas you up here, man. It gets, it gets the juices flowing when you hear a bunch of nice stuff. It makes, it makes for good conversations. So I want to start you off. We have the same starting question for everybody. What is one thing you wish more people knew about using data to make better decisions?

Scott: I wish that more people knew how much work it takes to get the right base data for whatever you’re trying to answer. Because you start a project, you start a new report, you start something new. And the first question [00:02:00] is, okay, how do we find out X? And then a whole nother team that you don’t even know exists has to go find that data, find it, clean it, create it in a new transformation, come up with new metrics, talk to finance, talk to, you know, sales, talk to delivery people, there’s all these metrics that seem like they’re not that far away.

But when you actually have to go find them, they, And coming from my experience in, in working in small business, large businesses, some of it’s just readily available and you might already have a team that’s ready to provide that for you. But a lot of times it’s not, even if. Even if it’s there in some warehouse, uh, maybe, maybe one team says, yeah, it’s ready to go.

And then you go check that with another, another region or another part of the company. And they say, wait a minute, [00:03:00] we already have a report and it doesn’t match this. So I think, I think that’s probably the most important piece. of data, from my opinion, as someone who’s, you know, a modeler, someone who wants to bring you the data and present it in a way that’s useful.

That’s the most important part. It’s not like, what do we want to know? Or, or, or, or how do we get there? It’s like, okay, How does someone else help get all these pieces together so that we can finally report on it in a, in a meaningful, valuable way? I

Ryan: Yeah, I, I love that. I, I think. Of all of the answers, that’s one that lines up with my own personal experience. You know, I think that that has a lot to do with kind of like what my, you know, history was and kind of what the roles that I had were like, I have also spent a lot of time focused on, you know, building models for other people to use as opposed to [00:04:00] just like, you know, here’s the window with graphs and stuff on it.

I’ve done that stuff too, for sure. But, you know, one of the things that I found was when I. My first job out of college was working for a phone company, doing a lot of this kind of stuff. And I would spend pretty much 90 percent of my time just trying to get all of the data together. And make sure that it was clean and make sure that the numbers were right.

And then like on Friday afternoon, that extra 10 percent of time was when I got to do the actual, like fun stuff of like digging in and seeing like, okay, well, what’s the answer to the question? Can I answer any other cool questions? Like, can I, you know, find us some cool money someplace? Like whatever the case was, like, it was shocking to me how much of that time got spent.

Now, obviously some of the modern tools that we use can make that a little bit better. But I think. All of those depend on the [00:05:00] concept of reusability. Like, the only place that I have seen where there’s actual, like, material gain to be had On the data cleaning and setup side is when one person cleans it and then another person can either use or improve or whatever that information so that you kind of have this like self weeding garden, which is essentially a model right now.

Sure. You know, we have ETL logic and all of that stuff, but talk to me a little bit, like what is a data model for someone who’s not a consultant and why is it valuable to the business as opposed to just us tech nerds?

Scott: love that analogy, the self weeding garden. You snuck that right in. I like it though. It’s, it’s a good one. Uh, I would start with that with our definition of a model. A model sounds complex. It sounds like some [00:06:00] algorithm in the cloud that’s determining, you know, what Facebook ad you get or something. But really what it is, is.

Combination of data using relationships. So, you know, you might have an export from your ERP or your sales platform or your marketing platform that gives you a bunch of what we call transactional data. So it’s something happened. Someone did it, it happened here, um, and we can measure it. So it’s some sort of value associated with it, whether that’s a customer count or, you know, sales amount or cost amount or inventory amount.

It’s just something you can measure. But we define it with uh, what are called either attributes or dimensions in the model sense. So those two words just are descriptions of the data. So we’ll have something happened and we want to figure out where, why, how, and what so that we can either predict what’s going to happen next or you [00:07:00] know, make really good observations about what, what did happen.

Maybe use some predictive modeling or AI or machine learning to help us predict what’s going to happen next. But at its base level, like It’s just a record of what happened. And then the, the essence of a model is that we use those dimensional fields is what we’re called, especially when we’re working with.

You know, Power BI and this type of, uh, interactive reporting. Um, we can say, you know, we have a list of all the stores we have, and that really doesn’t change that much, right? Like maybe we open a new one or close one or remodel, but we have a list of those. And maybe you even have that on an Excel sheet on your desktop.

It’s like, Oh, here’s all the new, here’s all the new stores we were opening. Um, but if you wanted to bring that into, you know, your transactional data, what we call, you know, wide and Frankenstein ish. Those was really wide tables. You, maybe you’re used to working with them, um, from, from different uses, [00:08:00] but you know, if I want to say, okay, at 10 52 AM, I had 17 units of, uh, inventory and it happened at this store with this register and it was this till number.

And then it was, Cloudy, and it was, you know, raining, like you don’t need to say that every time at 1053. You don’t need to also say it was cloudy. You don’t need to also say it was this till number. So we’re able to keep data separate in an efficient way. Like the store really doesn’t change that much, and the weather could change, but like I don’t need to duplicate it every second that we want to check inventory.

So a model lets us, like, separate out the ideas of what’s more static, but describes the data, and then lets us keep a transaction that’s really efficient. And we use relationships to say, okay, well, you know, this store number was, didn’t change, but if we want to know who was working and, you [00:09:00] know, uh, what the location of that store is and when it opened and what time it closed, like we keep that separate and we can bring it in as we need it in a model.

So model really lets you say, I want to know everything, but I don’t want This much data to repeat itself every time we want to answer that question. So let’s, as, as modelers be more efficient in answering those questions, but allowing you as an analyst or, um, an executive or someone that wants to get some insight from that data lets you still access all that information.

Ryan: Yeah. I love that. I mean, I think

in my mind when I think about it, I think a little bit more about kind of like the flow into the model and the flow out of the model. And, you know, coming in it’s like, all right, well we have all this data from all these different places and. Am I supposed to include this or do I need to exclude that? Or do I need to filter this out?

Or maybe I got to [00:10:00] multiply this time, some number to take care of like taxes or fees or say, you know what I mean? Like there’s the go get it, put it into the shape that you want and add in any of the business logic. And that’s. Time consuming enough and then we have this other layer that you talked about in depth as far as how can I?

Structure this information right because like if we think about it, you know, you mentioned that giant Frankenstein table, right? That’s, that’s what I did in the beginning, right? Just like, all right, pull everything together, make one giant rectangle in Excel and make a pivot table. That’s what reporting was for me.

Um, you know, as we start to have these tools that are capable of kind of doing a little bit more. Well, that certainly wasn’t fun or easy for me to make. So maybe we can make it a little easier and more efficient, but additionally, it also makes it much, I think, easier for people to interact with, like you mentioned, right?

If it’s like, if I just have a table called stores. [00:11:00] And I got a column in there called like store ID, store name. All right. I get that. That makes sense. You know, I got another table that says sales and it has a column called sales amount. I’m like, I know where the money’s at. Like, you know, so it just, it kind of like that outflow to the consumer, it makes it easier for them.

So one of the things that I think is most interesting that I want to get your take on is, okay, so I, we, you know, kind of started our journey. Just like grabbing stuff from everywhere, making a giant rectangle in Excel. We make some, you know, pivot table or hard coded, right. You know, who knows, whatever.

Right. And then you kind of get the next step, which is like, Oh, okay, well, maybe I’ll use a modern tool, like something like Power BI, where I can kind of pull things in, you can make the model right. Inside Power BI. And then maybe it moves to the next level where you’re like, Hey, I need [00:12:00] actually a database.

There’s just so much data. I don’t need all the data for all the reports. I don’t want to build one Power BI model that the entire company is running off of. Uh, so we’re going to make a database and then. Each Power BI report can just pull the chunks that they need. And then even beyond that, right? Like there’s scales of databases and different technologies and all that stuff that keeps going.

I kind of view it as a natural grow up story where you just kind of like take, take each step when you’re required to, but I’m curious to get your take on how can an organization know which Of those kind of modeling, infrastructural investments are the right ones. Cause I see a lot of folks that either want to just jump all in and it ends up kind of being like an overspend and the business isn’t ready for it.

And then there are other situations where it’s just like, you know, you have, you know, big complicated company kind of running on Excel spreadsheets and it’s a bit of a house of cards. So like, what’s your advice for folks there? [00:13:00] Hmm.

Scott: um, my number one advice here would be listen to your people because your self weeding garden analogy was real. It really was really good because when you have an analyst. Who is making these large tables and they’re coming up with great answers for you, but they look tired and they’re saying, you really want to look at it that way?

Okay. And you say, I need to. Yeah. That’s going to be that point where they say, That analyst wants repeatability. They want a garden that self weeds itself. They love to pick you some zucchini. They love tomatoes, but like, you know, they’re also picking weeds most of the time, so let’s focus on the fruit and let’s get some value from that for you because you aren’t going to be able to say.

I, I see that it needs to happen. That’s not something that’s just obvious to anybody. But when you hear your people saying, okay, [00:14:00] yeah, I’ll redo it that way. I just built that two weeks ago, but I’ll do it a different way. What they’re really saying is I need a new tool and I need, uh, I need to create a data model because this data isn’t changing like, okay, I need to look at this for Europe now.

Well, we just did China and it was. A huge project. Well, maybe we create a global model and that’ll help us say, what are our attributes? What are our KPIs? And then we can translate for region. You know, you could, you could say, okay, well now we can look at Europe with just a couple of clicks. Those type of things that seem like you’re stuck, stuck in the weeds of like the data.

It’s like, okay, it’s telling you I’m not being efficient with my people. I’m not being efficient with my data. I’m not being efficient with anything. And like don’t discount efficiency because it adds up not just for the time you’re spending with your people, but for burnout and for people who just are saying, you know, I want to create value.

I [00:15:00] don’t want to just recreate, uh, Excel sheets all day. Like that’s great. Sometimes a quick answer comes from Excel so quick and you say this was so valuable. What a great, what a great spend of three hours. But when you’re spending that three hours multiple times a day, Week in, week out. You know, that’s more than likely the time when a new tool and a new data model and a new way of thinking is going to be really productive for you.

Ryan: Yeah. I love that. I mean, I, I think it keeps coming back to this idea of like, are you redoing stuff? Cool. Well, you know, it’s 2024. AI’s cool. I’m not entirely sure that it’s just, you know, Skynet or anything yet. It’s not going to like take over everything, but, um, you know, really, I think there’s a ton of.

You know, juice for the squeeze, if you will, in just trying to use a modern tool to automate repetitive behavior, right? It’s [00:16:00] 2024. I don’t need to do the same thing every single time. And if I am, and there’s, like you said, like pain and frustration there, maybe it’s time to just take a look at saying, Hey, the, you know, 60 percent of the process for these 10 reports, that’s all identical.

Let’s make that one thing and let’s have a computer remember how to do it, you know? So I want to talk a little bit about something That for some listeners might seem like a totally different topic and I want to kind of show that it’s a bit connected Tell me a little bit about What is data engineering and what’s your experience with that?

Scott: Sure. Uh, the way I see data engineering is, and I’ll try to look at it from my current perspective at New Balance, is it’s a group of folks that really are focused on getting things from, you know, source to [00:17:00] as close to insights as someone like me, a developer can, can grab them. So we have data generated in different languages.

We have data generated in different cultures where they consider things differently. We have data generated by. paper sometimes. We have factories in Maine and New Hampshire use paper still, um, and then translate that into some sort of, you know, digital language at some point. But there’s all these inputs that are not identical at all, but they all do translate eventually to some sort of, you know, similar value higher up.

So an engineer is really speaking the language of the data itself, being able to translate that into code to make sure that it’s being transformed and brought together, um, so that we can gain insights from it. You know, a developer analyst, someone like that, is going to really need an engineer to [00:18:00] translate all that business, um, data, add some logic, maybe.

Upstream logic where it’s consistent across the board. Uh, another thing that I really see them doing is, um, timing. Um, when you’re talking with a global company, data isn’t always generated at, at 1am, you know, well, they’re awake while we’re sleeping. They’re selling millions of shoes in Asia and Europe while we’re kind of cooking dinner or sleeping or something.

So it’s like, I can’t just run it at 1 a. m. And so it’s their job to take the data, transform it, but also make sure that we have a consistent way of looking at it. What is yesterday? Well, that’s a very easy question for us to answer. But from someone from Australia. It’s, it’s a different answer, uh, so being able to have a, a report or, or analyst, um, you know, analysis that’s looking globally is, [00:19:00] is, is timing is very important.

So, some of those things you don’t necessarily think of right away when you’re thinking of, you know, How do I want to measure something? Um, but an engineer has a lot of experience with getting something ready to be analyzed and thinking through the process of what does it mean when I say, when I say yesterday, because it, it’s just something, you know, I, I never really thought of before I got into it.

Um,

Ryan: hmm. Mm hmm.

Scott: and. And these type of folks are, um, really well versed at getting something ready to be analyzed, um, by someone like me, who’s a developer, an analyst, and who helps create insights.

Ryan: Yeah, that’s really cool I mean, I think a lot of this also has to do with kind of the scale and the complexity of, of the organization and the data and everything like [00:20:00] that. You know, I, I kind of liken it to like something around the house, right? Like if I’m helping my kid build a Popsicle stick house, right.

I don’t need to like hire an architect and an engineer to build the Popsicle stick house. Right. You just like break out the hot glue gun and call it a day. On the other hand. If I were building a real house, right, well that requires one type of help. And then if I’m building a skyscraper, that kind of requires a very different type of help.

And so like the, you know, how much do I invest in that? What is the role in that again? Like, I think it comes back to what you said with the last question, right? Like you got to listen to your people, listen to the pain and focus on return on investment. Now, you know, we’ve talked a little bit about the backend right around like data engineering and modeling and all that stuff.

I’m kind of interested to pivot a little bit to more of the front end, like the actual visualizations and working with, with folks to actually kind of [00:21:00] generate the insights and, you know, realize impact where, where decisions and, you know, changes actually happen. One of the areas that you have a lot of experience in is, uh, sales, right?

Like do like looking at sales reporting. And While data is data, there’s kind of, uh, different nuances and ways of thinking about each kind of type of reporting. And I’m, I’m curious to know, like, what are your thoughts about sales reporting? Like, what is unique about that? Like, what is something that a listener could learn about how to change and impact a sales organization through reporting?

Scott: I think one of the most Unique things about sales reporting is how personal it is. Uh, this is people’s livelihood that’s at stake, whether that’s through their commissions or, you know, performance improvements or year over year. So really having so [00:22:00] much attention paid to the fine details. You know, not saying detail isn’t important in finance.

Of course, it’s very important there, but if you put out a bad piece of information and someone sees something that they aren’t expecting or their numbers are not matching yours, because You did it wrong. You’re affecting someone’s livelihood and it could affect their, you know, compensation. It could affect, you know, the way they stay employed.

Like this is very, very important information that’s coming through. Um, it’s important for leaders to see the high level, but like a lot of sales reporting is meant for salespeople and salespeople, helpers, and you know, inside sales and all the people that contribute to it. So I would say one of the most important things, um, You know, important factors is really being extremely thorough with everyone involved.

And that includes managers, but also salespeople, because salespeople can have, you know, some quirks about them. Like there’s some tricks [00:23:00] people use in their sales bag, and um, it’s not necessarily something you want broadcast across the company.

Um, there might be relationships that you’ve had for years that, you know, really, you’ve spent a lot of time on, and You know, you can’t really show that in data, but you can say, I’m going to show you accurate data so that these people say, you know what? There’s things I like to keep private, but I’m going to trust your report because you’ve worked on me with it.

I don’t need to know every detail about how you do your job, but let’s collaborate on like what we’re defining here. As the KPIs, what we’re saying is important because the, the other component is like having everyone part of the process. That’s another thing I’ve seen is really important when you focus on one person’s point of view, whether that’s a manager or a, you know, division leader or CEO.

You know, they see one number that’s important to them, or a couple numbers, and that’s great, [00:24:00] but you’re really missing the big picture by not having input from all the folks that would be viewing this report. That comes down to the design of the report, the design of the model, the design of the visualizations, the accuracy of the KPIs, the testing regimen.

You know, there’s a whole component to creating something, which is testing, you know, like making sure these numbers And making sure you have the right group of testers and making sure that they represent the whole, because I’ve seen it many times where you create something and you display it to a group and they say, yeah, it looks great.

And then it rolls out and they. A new group is having access to it and they say, nope, this is all wrong. And you kind of burnt that bridge. You’ve lost that trust. Um, so really say like, and this comes back to the, that sales component of saying, like, let’s include the salespeople as part of the UAT testing.

Let’s include them as part of the KPI design. And that’s where sales [00:25:00] really is unique because it does touch on very personal things, but also, um, it flows all the way up to the top line. So every little piece adds up and people are very, very aware of their portion of the sales because a lot of that does come down to commission and performance.

So, um, yeah, long winded answer for saying sales is unique. It needs a lot of fine detail. And it needs to be inclusive of the whole process, not just, um, you know, the high level KPIs. Yeah.

Ryan: love how you talked about, I definitely, I’ve done a lot of sales reporting as well. I do agree that it’s very unique. It is exceptionally rare to generate reports where you have large numbers of people going through every detail with a fine. Tooth comb. And like you said, right, it makes sense, right? It has to do with their performance evaluations, their compensation.

They’re going to, they’re going to check every single number. [00:26:00] You know, I also think in, you know, obviously I don’t want to overgeneralize, but I find that sales people respond really strongly to gamification, right? Which is why everybody has these comp plans. You want to really connect the input that they put Out, you know, in the market trying to close deals to their compensation.

And like, you know, there’s like putting up a scoreboard. Um, that works really, really well, but you know, you gotta make sure that it’s super accurate.

Scott: Yeah. The game, but also the, um, the idea behind the game. Like that’s, that’s, that’s a great, a great idea. Let’s, let’s, let’s have this happen. Okay. Well, how do we turn that into a game and then how do we track it? And like all these questions just snowball into, we need a report for that. Oh,

Ryan: yeah, yeah. So there’s a, there’s a funny story like back in the, uh, the early, early days of [00:27:00] Power BI, um, there was a, uh, a small amount of kind of custom visualization. So you get like your out of the box visualizations, like a bar chart and a pie chart and blah, blah, blah, all this different stuff. Um, and Microsoft in order to kind of get the custom visualization store, To have a few things in it, so it wasn’t just blank.

They made a couple, and one of them was kind of this silly aquarium where, like, you could take a visualization that would be like a bar chart, and it turned it into like an actual aquarium. And instead of having the biggest bar, it would be the biggest fish. And so when I was training people on how to use Power BI, I would always, like, leave, you know, 10 or 15 minutes at the end just to show them, like, some fun stuff.

And, you know, it’s kind of like just like a hey, you guys sat through a giant. Technical training all day. Like, let’s have a little bit of fun and goof off a bit. And so I would show them this and they would always laugh. And it was funny, I talked to one of the people that I trained some months after and he told me, he goes, Hey, so you remember the [00:28:00] aquarium?

And I’m like, yeah, yeah, of course I remember the aquarium. And he goes, I made our sales report, right? Like kind of like the monthly sales amount. I made that the aquarium and we bought a TV and we put the TV In the sales bullpen. So all day, every day on the TV, there’s just an aquarium and everybody got assigned every single person in the bullpen, like had their color.

They knew what color fish they were. And the sales team went feral. Like everybody just wanted to be the biggest fish in the aquarium. He was like, our sales are skyrocketing. It’s incredible. I know you showed it to us as a joke, but it’s been a huge help. And it was so funny. But, um, I want to circle back.

There were two things that you talked about when you were talking about sales reporting that I actually think are really interesting to dig a little bit more into. One of them was inclusiveness, like making sure that the [00:29:00] people that know your logic, the people who are going to sign off on the project, the people who are going to realize impact, the people that know how to calculate the number, make sure that all of them are involved in the process.

And the second one is testing. And the two of those, there’s some crossover there, but I want to dig in a little bit more to testing. So I think being, uh, you know, a consultant, one of the big things that I focus on is just like maximum value, maximum impact. I want to like come in, crush it. Like I want to put out as many, you know, reports and influences, many decisions.

I want the company to receive As big of a boost to their decision making profit company goals, whatever it is that we’re going after, I want them to get as much of that as is possible. And so I think there’s sometimes this mentality of like, you know, testing and documentation. It’s like, well, just do it right the first time.

And then we don’t have to test it. It’s like, well, you [00:30:00] know, so talk to me a little bit about like, what is the right balance? Of testing where you want to make sure that it’s right, but you also want to make sure that you’re getting a lot of impact out to the team.

Scott: Yeah. I mean, I’ve seen both sides now. Um, I’ve worked with you in consulting before and now working at New Balance, a very large org. Um, I do have two different, you know, experiences that have combined to my answer. So like you said, documentation and testing is always the afterthought sort of, um, in the consulting world.

Um, it’s something that, you know, the way you said it is. That’s what you want to provide and we we look at a budget when we’re creating anything, right? Like we have a budget for let’s create something. Well, no one ever wants to say let’s boost the budget for testing and documentation That’s never gonna happen.

So we do have to build that in And [00:31:00] we kind of shortchange ourselves sometimes there too, because it’s just an after, like, oh, we have, we have a little bit of runway left. Well, let’s put that feature in. What about documentation? Let’s worry about it later. And it’s, it’s just reality. Now that’s my experience in consulting.

And when I’ve moved to the larger org here, I can’t stress enough how much time we spend on testing and documentation because Everything you build is a framework for what’s next. And if you are not documenting and testing what you’ve created, and you’re putting your name on it, and your department’s name on it, and your division’s name on it, and it’s going outside of your scope of what you control, And other people are going to be using that as a source.

It just doesn’t happen in the corporate world. Because no one is going to say, I, I give you this and I take no responsibility for it. That, that doesn’t happen. [00:32:00] Um, and it’s not to say, That’s how we view things in consulting. It’s that a consultant is usually coming in when they need help, when something is a behind schedule, when someone is trying to plan something that they’ve never done before.

So I think there’s not necessarily a butting heads here. It’s that, I think, um, if we could work as, As someone who says, let’s, let’s come up with a testing plan early and let’s come up with who those people are and let’s come up with how we can be inclusive in that. And let’s have a conversation that can be an hour, an hour call and say, here’s how we’re going to plan that out.

And then it’s on everyone’s mind. And then as you’re having these development conversations, it’s just a part of five minutes of the meeting. Okay. Let’s check through our list. Did we say we’re going to be doing this and this, how are we testing that? Who’s responsible? Oh, I know, uh, you know, XYZ over in this department.

And maybe that’s not even a good enough answer, because you only know what you know. [00:33:00] You only know that one guy you know in shipping, and he’s But like, you’re hoping he has a pulse of what the whole organization of that shipping area is saying. So like, as much time as you can spend, uh, doing documentation, testing, testing plans, um, You know, really having a record of it.

I like to use, um, like really detailed Excel sheets to keep track of everything that I’m doing because I need the answers for myself and I need to be able to provide answers for other people. They say, who did you test this with? Who validated this number? Like, that’s going to be probably the first question you have when you create something new.

Who validated this? Anyone who has any merit, especially at a higher level, they’re going to, guaranteed, going to ask that question. So if you don’t have a detailed testing plan with documentation of how you got to where you got, um, I can’t see any corporate environment saying, yeah, we’re going to move [00:34:00] forward.

This just has to happen.

Ryan: yeah, yeah. I agree with that. I think I, I agree very much with the necessity for like some sort of solution for the, like, how do we get answers about this and also how are we making sure this is right and I agree with you. I do think that the, the best way to make those two things happen is different for consulting versus like working.

Um, because the roles of an employee and a consultant are different as you highlighted, you know, one of the things I can say, at least from the consulting side is I’ve had the most success getting those things done when they’re kind of done in parallel.

Scott: Exactly.

Ryan: You know, so like, as we’re having discussions and doing discovery and I’m figuring everything out, like, we’ll just write it down then, instead of just like keeping it in your head, building something, and then having to write a doc at the end when you’ve already forgotten pieces of [00:35:00] it, you know, just like it, so long as, as you mentioned, right?

Like if we’re keeping track of things as we go and we’re involving the right people at the right times, and we’re, you know, recording, you know, you know, writing down and documenting everything that they say, we’re going to be in a much better position at the end. Ted. Hey, let’s make this a part of the work as opposed to this separate stage that nobody wants to pay for.

Scott: Yeah, and keeping roles. Like, who is responsible for that? And when does it happen? I also think it’s almost in consulting, you know, thinking back, it’s been a little while, but thinking back, maybe that, that approaches phase one, you know, phase one is let’s get a prototype. That’s fine. That’s more than fine.

Phase two should be how do we, You know, make this real. How do we make this something we’re proud to put our numbers on and proud to say we created? And then there’s phase three, which is, um, let’s add some stuff and let’s validate [00:36:00] it again. I was joking this morning with a coworker. About a project that’s on phase one.

And we’re already talking about phase two and then phase three. And I said, and phase, uh, LVIII, where they’re selling a commercial space and NBC is picking up a sponsorship.

Ryan: It’s pretty funny. So I want to, I want to, Pivot a little bit. So just from, you know, knowing you and knowing a little bit of your experience, we’ve obviously had a lot of discussions where, you know, each of us brings personal experience in when we were working together. And one of the things that I personally, uh, found most interesting was your experience in the horticulture industry.

over my consulting career, I’ve gotten to work with a lot of different industries and, um, I’ve gotten to work with a lot of farmers, I haven’t gotten to work with the horticulture industry very much, but [00:37:00] there are some similarities, right? You know, the idea of growing things. Um, and one of the things that I was, I thought was so cool about the farming industry was, you know, Eating is one of the most simple things.

Like it’s just like, you know, table stakes. It’s like a fundamental part of life. It’s not as like abstract or complicated as like, you know, financial investments or some of these things. But when I actually dug in with these people. It was incredible how dialed into data, not only they want it to be, but they needed to be like so many of them.

Like I picked the financial industry as a setup for the joke, right? Like so many of these people are like hedging a particular harvest with like futures for like offsetting crops or like, Hey, we’re going to go corn this year, but in case that doesn’t work out, we’re hedging with soy or, you know, like, you know, it was absolutely incredible.

How [00:38:00] focused everybody was on yield reporting, predicting demand, hedging, looking at futures prices. It was actually unbelievably sophisticated. And so having conversations with you, like even something as simple as like, Hey, I’m going to go down to the plant store and like buy a couple of flowers to throw in the front yard.

Like somebody had to predict that that’s the type of flower that you would have wanted. So that Tell me a little bit about your experience in that industry. It’s one that like, I find very interesting and I think the listeners will think is really cool.

Scott: Yeah, sure. That was my job. I was a product manager for one of the largest greenhouses in America and a really popular plant brand. And what we would do, I worked in mostly annuals, but also some perennials. So annuals are plants that come back, uh, don’t come back every year. Perennials are plants that do come back for one or more years.

And My job as [00:39:00] product manager was to talk to consumers, use data to figure out what that demand was. I would work with salespeople to determine how many we should grow of those plants. I worked closely with an R& D team, an on site R& D team, and then scattered throughout the globe. We had R& D teams to come up with new varieties of plants, so like, super pink, you know, instead of just normal pink or something.

Um, and so the data component of my job was as in depth as you can imagine. Um, we had on premise servers at, you know, everything’s still family business, mostly in horticulture. And just so we’re clear, horticulture is growing plants for, you know, enjoyment, basically flowers. Um, it does include, you know, the, the.

The veggies you’d get at, you know, your garden center, little starts and things. Um, but mostly it’s ornamental plants, uh, trees, shrubs. [00:40:00] Um, flowers. And so a lot of these places are still family owned, uh, at least for now. Um, one thing I loved about the industry is it’s one of the few industries that’s almost a hundred percent made in USA.

You know, when you think of it that way, it’s a lot of hardworking people that. That all across America are making these things work similar to that farming, that farming idea. Um, and then, yeah, like I said, it’s all family business. So I worked for a family business. They were part of a larger group, um, that made up this plant brand and, uh, you know, a day to day would be walking through a greenhouse, checking on some of the things that are growing, working with the R and D team to come up with some data points on what we’re looking for of.

You know, vigor, plant growth and, uh, applications of a chemical. There’s all these interesting chemicals that called plant growth regulators that keep this plant size to a certain [00:41:00] compactness, you know, you don’t want your plant to overgrow your container. So there’s a plant growth regulator that’s applied to them.

Um, those levels are maintained by the growing operation. The most data driven person in the company I worked for was the The head of growing, um, because there’s metrics on how long it takes to grow a crop and then the turnover and then the percentage that we have floor space for. And that’s still just on the production side because this was, you know, production all the way through to wholesale and retail sales.

So it’s the amount of data that’s generated from any process that you’re starting start to finish is just mind boggling. And then you have control over all the pieces. So If I say we should grow this color and this many, well then we still have to make decisions on, do we still do that when the time comes to plant it?

And then do we ship this? Is this ready to sell? Is this colorful enough that someone’s going to want to buy [00:42:00] it? Is this ready to go? And then finally, Okay. We get some sales data back. Can we increase the price? Like, can we have any more margin? Like, where do we go with this? So it’s any, it was a great experience for learning really how to work with data, because of course it’s a little messy with, you know, any family run business, there’s all sorts of systems that are like, you know, from the eighties and from the, from new stuff and it all has to talk to each other, but it really gave me a holistic overview of data that’s generated.

Cause we’re in control of generating it. It has to go somewhere else. It has to be cleaned and transformed and then analyzed. And I had visibility right into the CEO’s office, which was great too. So I learned how to talk to those people, um, you know, what they’re looking for, what’s important to them. And then it’s just nice to get your hands dirty and feel like you aren’t in an office all day.

So I, I really enjoyed [00:43:00] that part of my career. Um, along with the greenhouse, I also worked at just a family run nursery as well, which is like more of the retail side of things where I saw a lot of retail data and we weren’t growing as much there. It’s more like selling and buying and selling. Um, but, you know, similar experience where, you know, we had a lot of just generated data from a small family owned place that really sort of taught me how to deal with messy data, but also kind of messy questions like, we don’t even know what we’re looking for, but let’s, let’s dive in and see what we find.

Um. Both of those experiences led me to Power BI as the tool that I graduated to. So obviously Excel is a place to start. There was a time, just like anybody, I didn’t know what a pivot table was. I didn’t know what a VLOOKUP was. I didn’t know what any of that stuff was, but just having a curious mind and in a field that I enjoyed.

I wanted to know the answer. So [00:44:00] eventually, um, now I’m a senior, um, BI developer at New Balance. It’s just this very strange path. Most people I talk to are like, what, what are you doing? Okay. I thought you worked as a landscaper. Like, no, not anymore.

Ryan: Yeah.

Scott: in technology, you know, and I drive a Tesla.

Ryan: Yeah. Yeah. Yeah. It’s, it’s, it’s very different. I mean, I think one of the things, one of the, the reason why I brought it up is because I just, you know, feel very similarly about, you know, learning from your experience as I did when I was kind of working with this, um, you know, group of farmers that it was just like, unless it’s something that you Until you have the experience to know how much goes into it, like it’s very easy for me, you know, I walk into a grocery store and there’s just like [00:45:00] know, we’re very lucky in America, right?

Like, it’s just like, okay, cool. I go into a grocery store. There’s all this food, all these different varieties, got different colors and organic and that, that, that, like all of these different things. And I just get to walk in and pick out whatever I want, you know, same thing with, you know, ornamental stuff, right?

Like I can just go, you know, buy a bouquet of flowers for my wife, or I can, you know, get something to throw in the front yard and it’s, to me, these are like fundamental parts of life. But I had no real way of conceiving like exactly how much really went into it and how much thinking goes into it and like having this window into the hidden machinery that actually makes our life function and like makes it so I can walk into a store and like Buy flowers or buy fruit or buy meat or like any of these things that, you know, I took for granted before I learned, uh, it’s just so cool kind of getting to see that hidden side and how much work really goes into it.

Scott: I think that’s how a lot of people feel on the [00:46:00] inside, you know, like when you are part of something where you’re creating. It’s so different, especially in an organization larger than yourself. Like when you start seeing, you know, a hundred, 200, 500, 000 people, and there’s people whose job it is to, you know, just travel and inspect things.

Like. someone’s job. Like they go to different places and are like, just testing things. You’re like, wow, like that’s all you do, but it’s so important. Yeah.

Ryan: Oh, it is. Yeah. Yeah, absolutely. I met a guy who similarly, he was just like, yeah, like I go out and like I put out steaks and like, that’s the measuring spot and like every single farm that I’m measuring yield and like, how big is the corn and does it look healthy and how many, you know, failed and that, I was like, wow, man, you know, like it’s, it’s crazy.

Like there’s all of this machinery and there’s a ton, a ton of hard work that goes into making a lot of the things that, you know, as I was growing up, I [00:47:00] just very much took for granted. It’s very cool. One of the, the kind of key threads across your career has been around the, the concept of data culture.

And, you know, I think when you were coming up in, you know, the horticultural industry, there, you know, Obviously is that kind of like inherent data culture, like we were just talking about, like it’s, it’s a necessity. There’s just so much that needs to be known. And then as a consultant and, you know, eventually a manager in that consultancy, right, you did a lot of not just being a part of the data culture, but also crafting it.

And now as a part of a much larger organization being in a senior role, right? Like, again, you have had A lot of experience in different places, and you’ve had a voice at many of those places in crafting data culture. And it’s one of the topics that I’m really [00:48:00] interested in, because I think almost every listener can get something out of trying to make a more data driven culture and making sure that the people who interact with data at the organization have the things they need and are happy.

And what does that look like? So because you’ve had that experience, I wanted. to ask you, what is your take on data culture? You know, what’s important about it? How do you build or improve it? You know, give us some of your experience.

Scott: When I think of the data culture throughout my career, I mean, one of the things I’m focusing on, I don’t know if it’s just cause we’re talking, but I think of a time. Of us together where we had, um, a recurring weekly meeting. And when we first started them, it was new to the company, but it was focused partly around metrics and, you know, tracking and measuring things.

And, um, it was new to me. I had never been metricized before, but [00:49:00] it, it wasn’t that bad because, uh, my point is it wasn’t bad because it was open. Uh, I told you. Hey, these are, these are wonko. These are not right. Um, and you said, okay, well, how can we fix them? And we talked about it and then we fixed them.

And that seems so simple and basic, but that’s data culture. That’s openness. That’s, it’s not me telling you what you are. It’s us talking about what we want to be and using metrics to help get there. So I can’t picture how data culture can work when you don’t have that connection between someone who’s trying to help you, right?

They’re trying to help you. The metrics are just a tool. So if I don’t agree with like, how that’s being dragged across, like, then let’s talk about it. And we did. And that really set a good tone for how I approach it everywhere. Um, [00:50:00] if I see something that doesn’t make sense, I say it. And whether that someone says, Oh, you know what?

Um, I was wrong and I just need to adjust that. Or, Oh, you’re, you’re looking at that the wrong way. You, you’re using this context that was true, you know, last year, but now it’s not true. It’s like, Oh, okay, cool. Now I know what was wrong. So it’s, it’s less about like, here’s how I’m going to tell you how it is and more about let’s talk about it.

And I really feel like that stuck with me. Because, you know, earlier the data culture in some of the horticulture places, it’s out of necessity. It’s out of like, we have to do this. We have to make a decision and let’s harness the data we already have. Where I felt like where we kind of, you know, grew out of that in the consulting space when we worked together was You know, how can we create something new?

Like, how can we, how can we get better? Let’s just like dive in and like come up with [00:51:00] something new and say, this is how we’re going to say we’re going to be better. And that’s what I feel like data culture really should be. It’s not about like, Oh, let’s, let’s figure it out. Okay. We already have this data.

Let’s. Let’s figure it out. It’s like, okay, let’s, let’s come up with a plan and use data to help us get there.

Ryan: I love that answer. Um, you know, one of the, The big things that I’ve found over my career, I’m, I’m by no stretch, you know, like an old salt of business leadership, but I’ve had enough experience to have, you know, like a, I guess, half baked take on what makes, you know, decent culture. And one of the biggest things that I have found is number one, the organization has to have clarity around where they’re headed.

Right? Like, what is our goal? How are we going to try to get there? Right? Like, if there’s no strategy at all, it becomes very difficult because everyone’s just kind of like making up what they should do, right? Like, how do I decide [00:52:00] between two things if I don’t know which of them helps me better achieve a goal?

So like, obviously that’s, that’s kind of table stakes. If an organization doesn’t have that, a lot of stuff breaks down. But then once you have that, if every person within the organization can’t wake up in the morning and know what is my part of the company getting there And how good of a job am I doing at that?

It’s incredibly demotivating, like being able to understand, like, this is my responsibility. This is how I complete it. And then, as you mentioned, right, like working together as a team to figure out like, okay, well like, how can we figure out a good picture so that you know you can measure your contribution every day?

You know, if you don’t have that, it can sometimes feel like really, you know, impactless.

Scott: It’s a great way to put it. Um, not at a [00:53:00] contrarian point, but as just like a, a, a thing I’ve seen also is, is knowing when to say that’s enough. Like, this just needs to be a conversation because that’s one thing I think we also did a good job with in that, you know, when we had that role was the data says, this, um, we’re using it in a great way.

But we need to talk about something. Like, it’s not, the data can’t just be the end. It should spur a conversation. You know, it should spur something moving forward. So, making sure you, you use it in the appropriate sense of like, it’s not just somebody behind a keyboard, you know, just only looking at a single number.

It’s, it’s like, how does this play into What needs to happen next and do we have an open line to have that data communicated properly and then have a discussion about it and what it means and why it’s impactful because the context easily gets lost, even with the best report with the [00:54:00] most helpful like tool tip things.

It’s like, you know, You still, it should just be people talking to people and then it’s just a tool. So as long as you can keep that as the basis for any sort of rejuvenation or a project or something you’re trying to improve, like, it’s a really great tool. I don’t ever want to replace that with, you know, Communication, you know, first.

Data, data help, helping, you know.

Ryan: Yeah. That’s, that’s a slam dunk point. I love that. Yeah. I think that sometimes there is that push to make everything a number and like make it more passive and there’s just like no replacing human relationships and having connections with people. It just like comes up over and over and over again in every single forum.

It’s just the most important part of business, right? It’s networks of humans. Yeah.

Scott: Two data nerds can probably get pretty far with a couple metrics, [00:55:00] but not everyone speaks our language. Part of our role is to translate data for other people, so it’s just as important to have, if it’s a conversation based on data, even better. Another helpful way.

Ryan: that, that’s such a slam dunk. I think I’m going to, I’m a call it on the tech right there. I want to talk a little bit more about you. Give everybody the opportunity to get to know you a little bit. So we’ve actually talked a fair bit about your background, but give us a little bit of a picture into, into Scott.

Like, what are some of the things you like doing outside of the work? What makes you tick?

Scott: Well, uh, my location, so being in Gloucester on Massachusetts North shore, just above Boston, uh, that defines a lot of who I am and what I do. Uh, beaching and boating is very popular around tier fishing. Um, it’s summertime right now where it’s July, late July, it’s just [00:56:00] prime time here in Gloucester. And me and my family are just, Making the most of it.

Um, we do dinners at the beach. We do, you know, my dad has a boat. We go out with him on his boat. Um, it’s just. Everything that like a summer retreat town should be is where I live. So I just love it this time of year. Um, and I mentioned my, my family. So whenever we have, um, time to spend together, um, working from home, mostly I do go into the office sometimes, but I do work from home as well.

That’s been a blessing. I’ve been able to do that since both my boys were born. I have a just turned four year old and a two and a half year old boys. And I get to spend a ton of time with them. I got to bring them to daycare this morning. I’ll get to pick them up this afternoon. Um, those types of things working in this type of field, I would have never been able to do that working in, um, horticulture still.

So that was one of the main reasons for my career switch. Um, And then [00:57:00] music is a big part of my life. Um, I don’t have any hanging on my wall here because I’m just in my temporary living space. I’m building a house right next door to my house right now. But usually I have my guitars hanging on the wall.

So this is a pretty plain background. Usually I have my guitars up there. But yeah, I, I played in bands. I sing with, um, a big band out in, in the Gloucester, you know, Cape Ann area. Um, that’s a 20 or 18 piece big band, you know, saxophones and trumpets and all that stuff. Um, I’ve been with them since 2011, I think.

So. It’s a really long running band, and um, after my kids grow up a little bit, I’m hoping to get more back into playing in bands and rock and roll and singing and stuff, but it’s a big part of my life as well, and I really like the creative outlet, for sure.

Ryan: Yeah. [00:58:00] Yeah, absolutely. So if anybody likes what you, you know, have said, or wants to check out any of this stuff that you’re talking about, or wants to check out some of your musical endeavors, what are the best ways to, to look you up or get in contact?

Scott: Yeah, I’m on LinkedIn, just under Scott Parisi, you can look me up. Uh, should say something about Power BI in there. Um, um, I, at the Cape and Big Band for anyone in the Metro Boston area, we’ll do a Christmas show this, this, uh, Christmas time and Shaolin Lee

Ryan: vouch for that one. It’s a good show. Had a blast.

Scott: came a couple of years

Ryan: 10 out of 10.

Scott: Um, and, um, I do have a website, sdperisi. com just for my personal You know, whether it’s data or other things, but, um, yeah, I don’t keep up with it too regularly, but it’s there.

Ryan: Awesome. Well, Scott, this was. Such an enjoyable [00:59:00] conversation. I think we have just like the perfect amount of like overlap and disjoint that like we speak a common language, but get to like, learn so much from talking to one another. I, I super, super, super enjoyed this conversation. I can’t thank you enough for giving your time and your expertise to all the listeners and I, and I hope that they enjoyed it as much as I did.

Thank you so much for coming on, man.

Scott: Oh, you’re welcome. I really enjoyed it too.

Ryan: Yeah. I also want to make sure to thank all of the listeners. If you’ve made it this far in the podcast, thank you so much. Uh, if you liked anything that you learned today or you laughed, please make sure to, you know, write us a review, tell a friend about the podcast, you know, all that, you know, like, and subscribe, yada, yada, you get it, but please do it.

It really helps keep the, the engine running. Um, Scott, I want to thank you one more time, man. This was just so awesome. And this has been another exciting episode of the making better decisions podcast. Thanks for [01:00:00] listening.

Outro: That’s a wrap for today’s episode of making better decisions for show notes and more visit, making better decisions dot live 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.