What is Your Numeracy? – Ep. 441

Mike and Tommy explore the concept of numeracy — how well does your team actually understand analytics? They discuss skills matrices for Fabric adoption and whether we can (and should) test for data literacy on our teams.

Beat from the Street: Skills Matrix with Fabric

Mike and Tommy discuss what a skills matrix looks like in the Fabric era — how do you assess where your team stands and what skills they need to develop as the platform evolves?

Main Discussion: Numeracy and Data Literacy

The core question: How well does your team understand analytics, and can we test for it? Mike and Tommy reference a numeracy indicator tool from the Government of Canada as a starting point for thinking about baseline data literacy.

They explore what numeracy means in the context of business intelligence — it’s not just about math skills, but about the ability to interpret data, understand what numbers mean in context, and make decisions based on quantitative information. This has major implications for how we train teams, design reports, and roll out new tools like Fabric.

Episode Transcript

Full verbatim transcript — click any timestamp to jump to that moment:

0:28 Good morning and welcome Welcome back to the Explicit Measures podcast with Tommy and Mike. Good morning everyone and welcome back to the show. Hey man, how you doing? I’m doing well, thank you very much. Oh man. Well, today’s main topic today will be around what is your numerousy literacy or numerous numerousy indicator? This is a I don’t know something coming across my radar feeds something and I stumbled across this. I thought this is interesting. This is a report or a a guide basically for

1:00 Employers to evaluate the literacy or how well people can work with numbers for their companies. And yeah, it’s interesting. I just thought this was an interesting concept. I don’t know if I would actually use this exactly in my world. I wouldn’t be like take this test and then we’ll hire you like thing. But this is also a an evaluation method to figure out where people are in their understanding of numbers, math and like word problems basically for your company. And this is interesting. I would like want to unpack this idea maybe not

1:31 Necessarily this test but more about like how do you measure how well your people are your team members are on analytical things? How do we gauge this? Anyways, that’s the main topic for today. So I think it’ll be a fun topic. We’ll probably go all over the place. I could see lots of rabbit trails and tangents on this one. So, that’ll be what we talk about. Tommy, what do you got? Any news? Beat from the street stuff today. , I do. And this is going to be this is going to be a fun one. dealing a lot lately with talking about skills matrixes and training. we’ve talked about this a lot. , this is

2:05 Something we’re both passionate about. This is what we do. But I want to bring it to the forefront here and say, what’s the skills matrix for fabric? Realistically, there is a difference. I’m finding out between what an ideal skills matrix looks like, between the actual available resources at an organization, especially middle to large organizations. You and I could dive into this idea like, well, yeah, you’re going to have the data engineer and you’re going to have those tracks. You’re going to have the business user in those tracks. But Mike, I I’m finding with a lot of organizations right now, they’re

2:38 Like, yeah, that sounds great, but we only have resources for X, Y, and Z. So I want to pose this to you. What really is a realistic matrix of skill of training around an organization dealing with fabric? And again, I don’t think Yeah, I don’t think I don’t think the question is realistic. Honestly, I think I want I think, , I want to separate interesting. There’s a couple things that I think there’s a couple variables that we’re playing with here when I when we talk about skills matrix, right? I think in in the spectrum of skills I think skills should be

3:13 Independent of how many people you have on your team or what people you have on your team right independent. So I think I think when you talk skills you should say these are the skills that we require to do certain things right and then what you can decide as a company is how many people do you need to fill those skills and what are you willing to pay for those skills that you’re acquiring to the company because you Tom you and I know this for sure like when we were looking when it was just pure PowerBI people were asking for like 10 years of PowerBI experience when there’s PowerBI

3:45 Has been out for like seven years you’re like I don’t think this is true and then they and then people want to hire like the admin for PowerBI and the report developer for PowerBI and the and the data modeler all from we want the expert in all these areas for PowerBI and yes those are all skills that you would like to have someone to have but at the time when PowerBI hadn’t been out that long the number of people that you were pulling from was a very small pool of people who had all those skills so

4:16 Let me go back to like the matrix of skills and things right I I think I think my opinion here is let’s focus on the skills you think you need and that may be different per company depending on what you’re trying to do with fabric right let me give you some examples if I’m thinking about doing real-time analytics or event driven based things you might want someone who has some experience around real-time events or the RTI side of things custoto maybe something like that on that side of things if you are a heavy SQL shop and you’re thinking you’re going to build a bunch of SQL

4:49 Servers in fabric you’re going to want a a substantial set of skills around SQL management, tuning, optimization pieces there that you can use the SQL side of fabric. if you’re going to use lakehouse and notebooks, you’re going to want to have a language of Python. You’re going to want to understand, , what is a lakehouse doing, how to efficiently store data, what does the bronze medallion architecture look like? , these are patterns that seem to be appearing when I work with companies and when I work directly inside of the the fabric ecosystem. And honestly, a lot of disagree with you actually what I’ve

5:21 Been telling organizations because they’re go, “Hey, what people?” I’m like, “We need personas, not people.” Like, and I think you need to start thinking that way. But it doesn’t matter who the skills are. Like, if you can find if you can find that one person who’s done everything trying to train for personas, we’re not trying to train for that many people. But the only thing I I would and you might you might have said this, but I’m if you did, I’m going to disagree. because you you mentioned that depend train. Now you’re putting words in my mouth and then you’re going to disagree with the words that you’re putting in my mouth.

5:51 It’s a little Seth flavor thing. So going to bring a little little of that spice back. But that’s funny. well, you mentioned that you train for depending what you’re building for and my only argument to that is a lot of or Okay. No, I don’t think so. If you’re doing real time, you’re going to get someone who’s real time. If you’re doing someone who is more warehouse, we’re going to train for the warehouse. You need the skills for there. Like again, you’re talking about training versus skills, right?

6:23 Yeah. I I think there’s a distinction there. Like yes, you will train for some of these things, but like you someone has to have enough wherewithal to understand like what part of the fabric pieces they’re trying to go get and go use. And that could be a consultant that tells you which way to go. It could be Microsoft themselves. It’s like maybe you have a sales representative that’s saying hey you have a problem I think we can do a proof of concept around solving this with this real- time analytics thing that they’ve got inside fabric great those are the situ like you want to be in having those conversations so someone’s

6:54 Going to identify we need these things the skill regardless doesn’t matter who’s doing it you’re going to need the skill on that team if you identify you’re going to need that stuff so let me ask you then and I to me there’s more what I’m hearing from organizations is more more general where it’s like well we’re moving to fabric not necessarily moving to a particular part of fabric. So let me ask you this and this is probably a better way to phrase the question for fabric. What does your base template look like for that skills matrix of training? Because to me, I’m

7:27 Beginning to develop that. I’m personally leaning that way and then I just expanding from there where needed. But there’s there is becoming now for me a a base that I’m starting from. Would you agree with that or do you think everything’s more hyperfocused? No, I I don’t think I think there is. When I look at organizations starting to move towards fabric, I think about what behaviors are they currently doing and what what how do you migrate those behaviors into fabric? So what are the behaviors of reporting right now? The behaviors of reporting is

7:59 Typically load data in once every night and then that’s the reporting that you use on top of semantic models and reports all day long. Like that that’s that’s typically the pattern, right? So there’s not a lot of real-time anything. There’s not a lot of event driven stuff. there’s not a lot of like so basically what happens is we just look at there are a wide variety of data sources and we can’t figure out how to get them all in the same place. That’s usually the problem. Yeah. So if you’re if you’re trying to build a skills matrix, build a skills matrix around skills matrix around the most common thing

8:33 First and then expand from there as there will be other opportunities. again you’re building for the most common scenario. There’s going to be some organizations that need a little bit more and other ones that are more event driven than others that are not. Yeah, it’ll branch out. But I think I designed for the architecture of get data to the lake from whatever medium you want from lake to semantic model from semantic model to some reporting thing right it could be report it could be pageionated it could be something else on the other side so I think that

9:05 Is the most common pattern because most organizations are doing something similar they’re landing data into a SQL server the SQL server is then being pulled the semantic model is pulling from the SQL server and they’re getting to reports and then are sending out the reports to the broader part of the organization or doing analyze and Excel or something there. there’s something really really important with what you said and and yes I know we’re going to inflate the ego here but that is part of the wisdom of that what you have and honestly I don’t think a lot of organizations understand

9:36 Yet just what you said and just doing that you’re getting the bang for your buck and I think this is I’m finding a lot of trepidation right now with organizations on do we move to fabric because we can’t do real time we can’t do warehouses we don’t have notebook and Python, , knowledge and wisdom yet because this is going to be a big migration. But even if you were a PowerBI shop and all you did was just push data to a lakehouse in fabric, you’re getting your money’s worth.

10:09 You’re get you you’ve seen all the Jurassic Parks basically thing. You’ve seen all the movies and you got entertained. And I and I think that’s where a lot of people and I’m hearing I’m hearing this Mike. It’s like a drum beat where it’s well we can’t do all the fabric things so we don’t know if we’re moving yet. It’s like we don’t have to. And I think that’s the hardest marketing message right now. It’s like you don’t have to. You can. It’s cool but even if you just stuck with instead of having semantic models and data flows and you just moved to lake houses you are

10:41 Changing your organization drastically. and to Yeah, I I completely agree from a training point of view. That’s what that’s exactly what you said is the same place I’m going is the same place my training’s been as well has been with. Yeah, let’s stick to the basics and the basics are incredible. I think there’s a hesitation for some organizations to try and understand all of fabric and figure out where everything fits before they move forward with it. And I think that’s a misnomer. I think fabric has gotten also so big. I definitely hear a lot of comments around well which

11:13 Way is the best way to go right okay there’s a lot of different ways you can store information you could store it all in a data warehouse you could store it all in a SQL server you could store it all in a one lake and I think the number like while a number of a lot of choices is good but also a lot of choices causes indecision as well like people people get a little bit of paralysis by analysis and and we don’t move forward so I I think that I think that the the advantage of fabric is one is it’s very

11:45 Flexible. It does give you a lot of options. You can move forward with solutions very quickly. I think that’s one of its advantages. but in doing that right in moving quickly, you just have to start. You have to decide with the information you’ve got at hand. Make the best decision you can and move forward from there. Yeah. Cuz you can’t sit around and wait forever. keep deciding, keep deciding because they’re going to there’s so many things to figure out. So, I would say that the most reliable pattern that I’ve seen so far has been bring everything to the lakehouse, go to semantic models, go to

12:18 Your reports from there. That seems to be a really good pattern. So, agree or disagree with the following statement. Let’s say I’m my if if I’m my son or my daughter and they’re they want to have a career in fabric right now. Let’s say they wanted to start doing fabric for the next three years. I would tell them focus on notebooks. focus on the lakehouse, focus on semantic models only that become an expert in that you will become invaluable in fabric. Would you agree or disagree? Yeah, I think those are the core those are the core features that you’re going

12:49 To be needing to use. Yeah, I would agree with that. I think I think you can get a lot done at a reasonable cost with those features. And if I think about what , again, let’s go back to like what’s happening in the PowerBI space as they people come over to fabric. There’s a huge wealth of companies that have a whole bunch of data flows gen one or are starting to build data flows gen 2 and they’re like, “Wow, this is expensive.” Well, you’re going to need to learn notebooks and data transformations. And so, at the end of the day, there’s all these different terms and terminologies.

13:22 I would probably even more focus on telling my kids, learn data engineering, learn that, right? So whether it’s through notebooks, a lakehouse and reporting and semantic models or whether it’s through load data to a SQL server and pull it in through semantic models, doesn’t matter what you’re doing. At the end of the day, you need to learn proper data engineering. And you to be frankly honest, everyone is doing data engineering in their Excel notebooks already. They’re already doing it. They just don’t know it. And so whether I’m doing it manually or replacing or fixing

13:54 Or cleaning data at the notebook level, you’re already doing data engineering. All we’re trying to teach you now is an automated way of doing the same thing. Automate the data engineering as our friend Gilbert would say from Australia. Do it proper proper data engineering. Well, yeah. I’m not even going to do the accent, but the my fun my favorite thing about the people from the other coast is the way they use the word proper. And it’s not like us where we say proper. It’s like, , how you hold a fork. They’re like, no, he properly properly did data engineering. So, but I agree. I I’m glad we’re

14:27 Aligned with that. So awesome. Well, we’ll be happy to see what your skills matrix looks like or what other feedback you have, Tommy, as you continue to build on top of that. All right, let’s get into our main topic, I guess, today. So, main topic for today, we’ll be talking about what is your numer to use to go through what is your numerousy. This is again a a test that’s from the government of Canada and they’re they’re using something, , Canada’s interesting, but they came up

14:59 With a pretty decent test here. So, we’ll we’ll I don’t think this test is what you want to use ultimately for evaluating all your employees, but I do think this idea or concept is something interesting here as well. So, the test, excuse me, is in the chat window. So, if you want to see the test or go look at it yourself, here’s the test. And as Tommy and I were just talking just before we got into this topic, right? So, your numerousy is how well how to work with numbers, right? And as I was taking this test, I thought to

15:30 Myself, dog gone it. This thing, this numeracy test, it’s just like a whole bunch of word problems. Yeah. And I’m thinking to myself, I don’t think I made that ever. I don’t think I’ve ever made that connection that when you went through school, you learned a lot of math and it was nice written out for you. There’s a lot of formulas there. You can figure out how to write the formulas. When you got into the real world, nothing is written like in the math textbooks. Like it’s all like word problems. Everything’s a word problem. And I was joking with Tommy. It’s like

16:02 You’re at a you’re at a company, they’re like, “Okay, you have 60 lbs of sugar now. which product should we develop so we can build the me revenue out of our customers who live in Australia? You’re like, I don’t know. This doesn’t make any sense to me. Yeah. Where’s our Yeah, it it is insane. And I I I love you brought this topic up, Mike, but it’s insane when we think about gosh, I there’s a lot of thoughts here, but when we think about us as analyst, when you think about people in our space, even people who

16:34 Deal with Excel, you’re dealing with numbers, but it’s worrying a lot of times because I I’m seeing this with my daughter Mike. It’s the same thing. She does great with her her normal math problems, but as soon as it’s like Susie had a gram of sugar, it falls apart thing and putting the numbers together. And it’s part of it, I think, because the other things you have to accomplish. But let’s break down before we get into our our thoughts. Tommy, it’s funny that you blamed your daughter for having problems with word problems when we know the really the

17:05 Issue was you. We know you’re the one who doesn’t doesn’t know how to do word problems. Michael, no problem. I have problems. My daughter too, Tommy has I just have problem with words. It’s not the order of words is my problem. So, how many times on this podcast have I asked you a question? You’re like, okay, I don’t think I understood. Let me let me rephrase. Let me rephrase. Make sure I understood. But the audience can understand. Well, , I think a lot I think a lot of this is just commun good like communication challenges in general.

17:38 Like, okay, I’ll I’ll be I’ll be real here. can’t tell you the number of family communication issues I’ve had. I’ve said one thing and someone else has interpreted it as an entirely different lens on things and so communicating to people is hard. And this is another great point. Yeah, I I’ve read the Rob Collie had something around like Power Query or something for Rob Collie’s book or something around Power Pivot with Rob Collie. I don’t remember the book name of the book but I know I know it was Rob Collie and he was talking about if you had someone sit down and write out all the lines of code for a program

18:09 Just type them out just literally copy paste them in it would have it would take you like a couple weeks done but whenever we do data projects it takes like months to get done because of all the extra communication and talking that needs to happen between departments and what do you understand what do you understand how do we compromise we can’t do that which is the data we actually have like how do we go find new data where does that exist like there’s a whole bunch of communication that has to happen and getting to the final result usually is if you had the final result

18:41 Up front it’s not hard to do it it’s just all the communication that is in charge and every time you add another person you’re adding a whole bunch of required communication to get that part done what you just said is why I have a problem putting business intelligence in the IT department because it it seems so easy to do so because that assumption it’s technology and it’s nerdy so it should be under it but to that literally to Rob’s point we have to do such a good job of not just the nerdy stuff and the data engineering and all the things we

19:14 Talked about but we have to communicate and allow the organization to trust communicate and be able to speak to their data to their information there is a hu and this is where the gap is this is where people don’t understand where your skill and you need to measure your skill So, we It’s funny enough, Mike, that we just talked about the skills matrix right before this, but let’s not forget, Mike, if I I could be the best data engineer in the world. I could have literally 18 trophies saying number one engineer thing. But if I have a

19:48 Hard time speaking to the business and to my boss or to the other teams on what their numbers are, it’s for not. It’s to me you have lost all that credibility on regardless of how good you are with everything else and this this numerousy test or this idea of measuring your numerousy plays a part in this. It plays a huge part in this because I have to be able to speak not just to what the numbers are but I have to understand what those numbers mean to other people.

20:22 Mhm. Mhm. Exactly right. So, , thank you very much, , chat for coming up and filling in my gaps. I know. I knew we’re getting chat. I knew you’re looking at the chat. Yeah. Yeah. Well, all right. Give it to me. Give it to me. Well, well, chat’s filling in my blanks, right? So, this is I was blanking on the name of the book. And so, I want to I do want to give Rob Collie the the the due service that he deserves for the book, right? So, the book is called Power Pivot and PowerBI. That’s the book. And I think the book was it was it was written in 2016 and Donald was talking about the original book which

20:56 Was like Rob Collie in 2013 DAX formulas and for power pivot but the one I remember reading which was power pivot and powerbi and that was the one where I think I saw the analogy of like this time and how much communication is required to be there. So anyways I just really felt like that was a really relevant book. The book if you want to go read it which I do think it’s still very relevant today still great. Yeah it’s still great. It was written by Rob Collie and Avi Avi Shing. , which I think he also runs a pretty substantial YouTube channel and training program as well. , he’s also still in the PowerBI space additionally. So anyway, so the

21:28 Book’s in the chat window in case you want to go check it out. , pretty cool book. Really neat. , good thoughts in there. And that was probably one of my first data books that I physically last I laughed out loud when reading the book cuz he just puts a lot of funny data things in there. I thought it was just a good read. So, I really enjoyed that book. Do how I knew you were looking at the chat and it is a little distracted because you just said blanket I agree with you. Oh, really?

21:58 Yeah. You just went, “Yeah, I agree with you.” I’m like, “Yeah, that ain’t the case.” So, so no. And I and I agree like it’s amazing how some of those books a lot of them can be already like old legacy but Rob some of those DAX fundamentals are huge but Mike let me ask you a question here when we’re talking about the numerousy you for a for a business intelligence team let’s just break it down here how important is to you their level of numerousy so let’s say they were to take this test I’ll give my I’ll give my

22:30 Answer I’m fine to give what I got on this because it’s right in line with what I did in college, eight out of 10. So, two I disagreed with, but it’s fine. Obviously, the numbers are the numbers, but I I was I’m right in line where I was in school. That was a frustrating thing. I’m like, I guess I’ve not gotten any smarter. , still sitting around 80%. Still still around that 80 in school thing. But how important is it to you that they do well? Like, , above what they say. They say that you should get at least six on this. That’s I believe what they said.

23:02 Anything below three there in each level they said is is something to level up. But how important is to you for the people that you work with that they would score well on here or does it mean anything to you their the value of this test or a test like this? Well, so so I guess maybe let’s I don’t want to focus so much on the test, right? the test is interesting a measurement of numerousy. Yeah. Yeah. Let let me go back. See, let me take the I’m going to take the conversation slightly differently,

23:33 Right? Maybe a slightly different direction. I think this there’s probably many ways we could me measure numerousy, right? So, I think there’s there’s probably more to this one. This just might be a reference point mentally to say like this is a good score. So, like to your point, Tommy, I think I got like a nine on this thing. I think I missed one of them, right? So, okay. I felt like it but I felt it was also extremely simple and like when I too short extremely simple and it maybe not really applicable to what we’d be doing as a data anal I would expect your data analysis to be knocking this out of

24:04 The park every single time so I’d almost want to say there’s a lot of people their brains work different ways right so a lot of people have different mentalities of how their brains work right u and the reason I bring this up is some people are very analytical- minded love numbers and that just is the way their brain ticks. Totally get it. Other individuals are not that way and they’re much less, , creative and numbers aren’t their thing and they’ve steered away from shying away from numbers. Now, think think about this. There are people

24:38 In your analytics team that are thinking this way and they be getting eight or nines, but when you’re giving out reports, when you’re handing things off to the rest of the organization, you’re handling this off to like a wide range of skills. Some people may have been math majors. Other people may be in the finance majors. Other people might have been like a music major or some theater major or history majors that decided to move into the business world. Like there’s nothing to say they can’t do the work. Like they they have earned the skills and they’re there for a reason, but how they like to think about numbers, the way that you’re

25:10 Portraying them or again you as the analyst as you’re trying to convey the information to other people, you have to get it to their level. And if you don’t understand the different teams and members of people across your organization about what levels of skills they’re hitting, you could put this, and we’ve talked about this before in the past, you could put this very elaborate, beautiful, fancy chart in front of someone and they may they just may not understand it. It may even give them the exact answer that they want, but two people can look at the same bit of chart and data and not understand it, right?

25:43 And it’s totally foreign to them. And they don’t and I don’t the answer here is and this is why I’m getting so bent about this and I’m maybe get a little bit heated on the call here. I’m with you, man. But I I think I think the idea here is when we look at reports, we want to drive people to ask questions about the charts and and think through what’s going on. And when you leave that report, you need to have an action you’re going to go do. That’s the whole goal of this. there’s something there’s an activity and now you’re seeing like translitical which is Microsoft’s way of like putting

26:16 Transactional information inside an analytical report they’re now even trying to blend this like even while I’m in the report maybe there’s an action that I’m taking or something that I’m sending the report can now send data out to go do something so I really think this is a really useful or important measure across your organization. So again, I just I want to talk conceptually about this, not necessarily the report. So I I know. So honestly, I’m glad you’re taking this direction because I was

26:50 Going to say something that I thought was going to be a very hot take and it may be for the audience, but what you just said, I think you’re going to agree with me to to a degree. So let me phrase this and hear me out here. Mhm. To me, this type of test is irrelevant and this idea of numerousy because to me it’s actually missing the point of what I’m looking for in an analyst. So, you can do math problems, great, but Mike, well, hold on. Let me pause it right there. I like I like your thought. That’s a great thought.

27:21 Okay. As an analyst, I would agree. You’re right. Yeah. An analyst must be far above this. However, what the analyst is building for are potentially people that fit in the middle of this range potentially. But this is the thing. I have to identify where people are numeracy level. If I don’t know where they are, I don’t know what to teach them and we don’t know what training to put people through. I’m not going to be naive to say everyone should just know how to use

27:53 A report that I build out regardless. Right? So this is this is why the center of excellence exists. So to your point Tommy the analyst yes needs to be far above this on the analytical level but the analyst also needs to have a very good empathy relational part to the people they’re giving the report to and I really do think right I think the organization should spend some substantial time across many people in the organization and say look everyone gets analytics training it’s everywhere everyone’s using data in every part of their job across the organization so

28:26 Just landing person someone in a giving them Excel and saying go forward that I don’t think that’s helpful. I think what’s helpful is saying look everyone needs to go through some form of like IBCS training. There needs to be like a some online class something that you’re paying for for everyone to get on the same page. So again one of the a proxy of this right it’s just a proxy concept here is I went to a company one of the major requirements for them hiring people was that they all knew SQL. There was a there was a SQL

28:59 Everyone who came into the company had a gated SQL test they had to get through and you had to be able to do some semblance of SQL things to be hired by the company. Right? So that meant everyone in the corporation no matter who you were who came into the company had a level of SQL knowledge that set a a bar and what came out of that was what the company desired which was people can do some analytical things but it also came with a double-edged sword. It came with 12,000 access databases that

29:33 Everyone was split up across the company because they all knew how to write SQL. So like it it I guess my point here is the the corporation made a decision, right? And from that an outcome was was produced, right? So if we want our whole organization to think analytically, we better give space for people to think analytically. We better give training to allow people to think analytically. I’ll just pause there. I said a lot of things. No, no. And I think that’s great, but I this is going to be this is going to be a hot take, but most people when we’re dealing with our reports, the

30:07 Numbers are subjective. And hear me out what I actually mean by this. So obviously the numbers are what they are, right? Year-over-year, what did we sell? How much of it did we sell? But they’re subjective on how we have to measure it. Because why people care about how much they sell, but what they really want to know is how much should we sell? And those numbers are either made up by that team or are there a goal and they become very subjective. Yes, they are hard numbers. Yes, they need to be calculated a certain way and yeah, they

30:40 Better be right and accurate, but Mike, how many times do we I feel like we miss the point of a report or what the numbers are because we’re not hitting a threshold or pain points of what we’re measuring and what actually matters. There’s a difference between what we talked about with the numer test or this idea of numerousy and how well can we do math compared to asking the right questions to what people are actually trying to measure. To me, there are honestly two diverging skills. And this is this is my

31:13 This is something I the hill I will die on when it comes to what our skill is, what we need when we say training for analytics, right? What let’s just that idea or that phrase, , analytics training. What does that actually mean? And and I I think a lot of people miss the point. It doesn’t mean I can calculate oh 7% and if we can do another another 2% because it’s in the eye of the beholder Mike it’s in the eye of that department and if I can’t ask the right questions to get there I’m never going to build a report or

31:45 Calculation or number that’s going to be adequate for that team. So, let me back up from here and and I the the point that I I I want to focus on here is this idea that a lot of numbers that we deal with that from an analytical side are subjective. Would you agree with that? No, I totally disagree with what you’re saying there. So, I think I think what you’re So, if a team doesn’t know how to ask the right questions, so let me let me say this way. This is going to come off I think a little bit harsh.

32:17 Okay. I think if you have a team of people so let’s I’m going to talk huge organization like OKRs right objective key results I think is that’s what that stands for but OKRs are are basically driven at the top level of the company right so at the top of the company we have an objective these are our objectives we’re going to be producing becoming this company like there’s some main objectives of the organization we want to grow our market share by 5%. Whatever that thing is,

32:49 Right? So top down, the top part of the organization will be setting some goals. As you build departments across the organization, you look at those departments and they’re all supposed to support some level of those goals, right? And so, , I think companies that focus too on too many goals get distracted. I think companies that focus on too many different things can’t necessarily focus on the main point of what they’re trying to do. And I to give you an analogy or an example here, I think Microsoft was talking about their internal dashboards.

33:21 Their their internal dashboards were like they had I think they talked about like they had like something like 150 different KPIs they were using to measure the company. And I know yeah to have 150 KPIs that are all marching the company in the same direction. Some some KPIs could be conflicting across other KPIs or two KPIs could be too similar in nature. And so what are you what are you really measuring? I think Microsoft even talked about this when they talk about their internal dashboards for reporting for PowerBI and other things. They themselves said look

33:54 We need to pair this down and I don’t I don’t remember what they paired down to 35 something smaller. So they took a big number white paper. Yeah. Yeah. A big a big number of KPIs and they had to pair down to just 35. These are the 35 things that really means what we want to do. So all I’m trying to say is okay let’s let’s look that out. So 35 KPIs, you roll those out to the different departments of the company. Each people have different products, different teams, they have different P&Ls, all this stuff. If a team can’t recognize what their OKR is, if a team can’t ask the right questions to start

34:30 Driving to that AK OKR, there’s two problems. either you have the wrong person in leadership of that team and they need to be removed and another person needs to be replaced or that department needs to be eliminated. Something has to change. You’re not going to spend money in a in a part of the business that can’t ask the right questions. So, I think there’s absolutely no excuse for teams to not be able to ask the right questions and move themselves towards the right direction. And if you have something in a team that is not able to do that, you probably

35:03 Have the wrong members on that team and you need to shake things up a bit because why would I be spending money on a department, a team, a group of people that can’t move the needle in the direction of the OKR period. So I think there’s I think what you’re describing is a fundamental fault of the organization and that is a different problem entirely than to numeracy and literacy stuff. So I’m I want to separate that issue away. Let’s separate. Yeah. Because that’s more of I think a fundamental failure of the business and

35:35 Like why are we even spending money on these teams and members harsh at all when you said if they can’t deliver what they’re supposed to be delivering. So again there’s a lot of weaknesses here. Sometimes it’s not clear from the top down like what expected, right? Goals change all the time. Like it gets very muddy. It’s not it’s not very as clear-cut as I’m saying it is, but it it plays a part. Now, what I would say though is I really do think that if you if the if the company wants to hold on to a we’re an analytics driven culture,

36:09 That company better put the money where their mouth is. they better spend time on bringing in people, training individuals, doing something that is going to help those team members at all levels of the company start to understand things analytically. Now, I’m going to have a higher standard for my analysts 100%. They’re going to need to be way better on this numeracy scale than the broader part of the organization. But if I find this numeracy scale that we’re talking about here is probably too simplified. But when you think about your company, what is the right test? How do you

36:42 Measure this? Right? And I I was actually having a conversation with a client recently talking about, hey, we’ve got a lot of Excel files laying around all over the place. Wouldn’t it be nice if we could say, hey everyone, take your Excel file and upload it here. And in the Excel file, it would be, , it’s a company, it’s a company tool, basically, right? I would argue your literacy, your data literacy could potentially be projected from how complex your Excel files are. Right? So I don’t agree with that. So take take an

37:18 Excel file anyone someone has built and then based on so upload that Excel file and then from inside that Excel file you have a whole bunch of knowledge that that person has poured into that Excel file whether they understand it or not they have they’re using it they’re working with it. So, couple things that I would like echo on, right? , , is is that Excel file using advanced formulas, index match, are we using XLOOKUP, VLOOKUP? What is that doing? Does this Excel file have VBA macros recorded in it? What is the structure of those VBA macros? Does it

37:51 Look like it was just automatically recorded from the program or was it actually made by the user? Right? So, you can you can So, there there could be a series of tests you run against your Excel file. And so I think to some degree it’d be interesting if there was a tool that said a complexity score or an analytical score on top of these Excel documents. And that way it’s it’s a passive way of measuring everyone who’s working on things in your company and you could just take thousands of these Excel files from hundreds of users or thousands of

38:23 Users. you could quickly run them through this tool and then you’d have scores on people based on like how well they’re handling analytical things because Tommy we said it earlier your data engineering if you’re doing Excel you’re already doing data engineering you’re already using it are there tables in there are certain scored metrics that I would put against users who were building it are there lots of pages in the report are there lots of interlin data points between the pages like what is is there conditional formatting like I think all these things are speaking to an analytical mind

38:55 That’s trying to solve problems. So, so and at first I vehemently disagreed what you said with the Excel, but I think you’re getting to something that’s I honestly I think even diverging from this idea of numerousy because this numeracy test or this the numer test that was proposed in front of us was math. It was math. But that’s what you said I like better than this idea of the numerousy test because it’s really more what I’m looking for is I want someone comfortable working with numbers and honestly if you’re dealing

39:28 With Excel or DAX I don’t want to say it because it’s going to sound really bad but you don’t have to know math. You don’t have to know math. The bad the computer’s doing it for you. Yeah. And the the numbers get so large at some point you’re not going to do it in your head anyways. You should know some basic math. Fine, two plus two. But for the most part with Excel and de and PowerBI, I don’t have to do the math. I should understand how numbers work. But more importantly, you’re the the thing I’m more focused on is how comfortable that person’s willing to explore

40:01 Thus the complex Excel file because they start diving into the different formulas and they have to learn them because not all of that’s on the top of their head. And with DAX is also how comfortable I am trying new things out, which to me is completely separate than the concept of the math problems. So, and again, I know this sounds terrible, but you’re not doing math more. How many times, Mike, let me ask you this. This I think is going to I think hopefully prove my point or maybe it’s just me. How many times have you

40:33 Been to the one of your complex PowerBI reports and you had a calculator out at the same time for most of the things? Yeah, dude. I don’t never if if I ever if I need a calculator, I’m going to Excel. Like I if I need a like seriously Yeah. I will So there’s two there’s two places I calculate things from. I go to a browser, I type it in the browser. So So one it’s like you just type it I type the math right in the search window. So, one, that happens and then Google just knows, oh, you’re doing math formulas and then

41:04 Spits out a formula calculator for it. So, one, I just do that. And then two, if I if I’m doing more math and I need to like keep track of my steps, I immediately go to Excel and I’m immediately building all of the I type in the numbers I want and then I do the sums or the averages and make tables and things like, so I’m fast enough in there that I can just go do it. And I’m to be to be clear, I’ve w Have you ever watched the Excel Championships, Tommy? Like in Las Vegas? Have you seen these things? There’s Of course there is. Of course there’s this dumb Have you not seen this?

41:35 No, I don’t know this. But of course someone tell me about it. Tommy Tommy, you’re you’re your your fellow MVP. Oz. Do Oz? The MVP? Oz. Oz solo. Oz is the host on the Excel World Championships. Go Google this. Tom. Tommy’s going to be I’m looking up right now. All right. For everyone on online the knockout rounds. I’ve just literally lost Tommy for the rest of the day. He’s going to be watching Excel Championships all day now and just getting in. So, these Excel Championships, I don’t know

42:07 How anyone would ever solve these problems, but these things are incredible. And watching people shortcut key their way around on a keyboard inside Excel is phenomenal. Listen to this. Listen to this. This is not Excel. This is geniuses. An example is saying this is on ESPN. This is an ESPN event. 7,000 gold coins. He starts mining copper on J. Wait, wait, what copper? He will mine 999 units. Nine units of ore. 49 and a half stacks of ore. Player D. Where? What? Why? Like,

42:41 Okay. Okay, I’m done. World. Oh, it’s World of Warcraft. Of course. Of course. Excel in World of Warcraft are tied in. So, let let me also give you some other things. So, back back in middle school, my dad was a math teacher at middle school. And so my dad was running like we had a at the time, this is back in I don’t know the ‘9s or so like that. We were doing a lot of space shuttle things. And so the one of the middle school activities is the school would build a project. It was a it was it’s a teaching educational thing around building a space shuttle. So what they would do is they would build an entire

43:14 Space shuttle. The space shuttle was built inside one of the hallways of the school. It was like wood platform outside. They had a little entryway and they would bring a bunch of students into the space shuttle and what they had was they had the space shuttle built into the school and they had an Excel document. They had a mission they had to go on and the Excel document was what they used as the interface to run the rocket ship. And so they had like little missions. They had little science experiments and they would all it was all done and there was like a camera crew. And so like every day there was

43:46 Like a report in from the camera crew and you could like you you would you would it it was like a couple day activity where the kids actually spent like a full mission through this but the whole project was run on top of Excel. all missing the point why why this is great Tommy. No, this is great. But Mike, we have the like this is the problem I have with all like the Excel thing’s really cool, but for us with lake houses and what we’ve been talking about like my this is why these

44:20 Numbers are all subjective. This and I’m going to prove it right here. Look at my bank account and I want the same bank account with someone Wall Street. Who’s freaking out if we had the same number? Right? If the if the guy on Wall Street had the numbers that I had in my bank account, he’s having a very different reaction or she’s having a very different reaction than I have. Same numbers. And we can do the basic math. The numbers are subjective. I don’t understand where you’re going. I don’t understand where you’re going with this. I’m lost.

44:50 Let me Let me rephrase it. Let me rephrase this. Let’s say whatever is in my bank account right now in my little old house in Chicago was the same numbers all of a sudden someone living in New York City and Wall Street had. They’re just adding zeros to it. They’re just adding zeros. It’s the same stuff. It’s just adding zeros, right? But no, but I’m saying let’s say they had the same number I did, right? Which is not a lot thing. Highly unprobable, but okay. Highly unprobable. But they’re are going to have a very big different reaction than what I have. I’m happy with my

45:21 Numbers. They’re okay. They’re not bad. That person’s freaking out. And the what I’m trying to say is same numbers, same math, but two different people are going to have two very different reactions. Also, what changed the game in Wall Street 2 was Lotus, was Excel. And we have this technology. All this math is well and fine and great, but what I’m trying to say here, Mike, is you don’t pull up a calculator and are doing basic math in your dayto-day. You’re not. Neither am I. My kids, my kids don’t even do that. They They throw out their

45:53 Apple watches and they’re like, “Hey, hey, AI agent, what’s this time this?” I’m like, “Stop doing the math.” I’m like, “Stop that. Just you’re telling you’re asking You’re adding 42 and 17. Just figure it out.” Like, don’t don’t go to the computer all the time. Even the people doing math who who should be doing math aren’t doing math. But, but this is what I’m trying to say. like I this concept and yes I know basic math is important but at some point like there’s at some point there’s there’s a there’s a threshold we need fine great but at some there’s a certain level

46:27 Where it doesn’t matter in the space we’re in and I I I I’m failing to see someone who let’s say is light years better at me than complex algebra and geometry in what we do it has an advantage , numbers aside, smarter. Yeah. Like likely smarter than me, but yeah. The thing the thing I’m falling apart on your analogy here is I I feel like you’re you’re focusing on like the one person who’s the analyst. There’s a lot

47:01 More people than just the analyst in the company. And I think that’s my my my my nugget here is like look, let’s dive into that. We have to look at the whole company as a whole. Like there are going to be certain individuals or pockets of analytics in the company that are super sharp and really get it and like know how to do these things. But like what I’m looking at here again going back to this numerousy test is we need a we need a health read on our entire organization around you think the whole company and so it’s not just the analysts. So I think you let me say it this way.

47:33 I don’t know if I agree with that either. Well, I’m going to try and build I’m going to try to build an analogy here that might make sense a bit more. Right? If you’re if you’re building let’s see here. how do I how to describe this one? Let’s let’s say you’re let’s say you’re building something simple. I don’t know what it is, right? You’re you’re let’s say you’re printing t-shirts. Okay, you’re printing t-shirts. Pretty straightforward. There’s there’s definitely, , you’re you’re acquiring shirts and then you have a printer and you’re printing stuff on top of things. Okay, in let’s imagine now that you go out and hire like the

48:07 Aerospace engineer to come work at your company who has studied very complex things, right? You bring them into your t-shirt making company and say, “Go to town.” That aerospace engineer may come in and have like all these analogies of like, “Okay, we’re inefficient here. We’re wasting time there. We’re doing these things. I’m going to build all these reports. I’m going to like really and so that p that one person might have skills way above the rest of the team in building the the the new t-shirt process we’re going to develop, right? And so

48:40 What might happen is there’s two things that one person is got to either like simplify things so that the rest of the team can understand what’s going on or we’ve got to work on the rest of the team and bring their skill levels up to match our expert. And so why do we need to build all this new hardware? like are we over complicating things? Is this really going to make us faster? Like what what’s really going on here? So I think I think when there’s a disparity gap in knowledge in your team around, , analytics people or analytics developers or analytics engineers and they’re so far removed from the broader

49:14 Part of the organization, it it you’re the gap is widening. So you can focus on yes, I want my analytics people to be very sharp and very analytical and be able to read those word problems and high have a high numer. But if they’re giving out content to a broader audience that doesn’t quite get it, it’s almost like you’re you’re you’re capping them. You’re holding their you’re tying their hands behind their back and they’re not actually able to effectively lead and develop reports. And to to some other point here, we don’t want a BI team building every single report moving forward. We want the BIT building like semantic models. We want the BI team we

49:48 Want some level of self-service across the broader part of the organization. So how does that look? So to me, I look at this going like I really do want some level of self-service. I think a hybrid approach between central IT and the business is very important to making the company function well and I I hear that. I I I hear that and but to me you’re saying something different. You’re saying something in terms of what I’m looking for from the company. It’s it’s the ability rather

50:21 Than to calculate a number to value a number that I need my organization to have. And at least this is what I’m hearing and maybe there’s a difference there. I don’t I don’t I don’t understand the difference of what you’re describing there. Like calculate versus valuing a number. it goes it goes it goes back to my analogy of the OKRs the top level company. Yeah. But if you can’t but who’s what who those numbers where they’re actually coming from, right? So, if I’m in the marketing department or the operations department and I know that let’s go to the t-shirt thing. Let’s I I because I

50:53 Like where you’re going with this. I really like this for for those people though like if I can’t whatever department I’m in that whatever the OKR number is as it goes down to me, right? I need a number that’s going to support that. And if I don’t understand how my numbers work and what’s important to me and my team and what we’re shooting for, I’m not going to be able to generate a number that we’re gonna aim for, right? So, yeah, let’s say we want to build we need the company needs to make a thousand t-shirts, right? Well, if I’m in marketing, I need to be able to

51:25 Figure out the number that’s going to support that, not necessarily calculate what that eventual number is. So, there’s a value to a number that we have to think. thousand t-shirts doesn’t mean the same to every department because they do different things. They have different functions in the organization to support that. I disagree. I disagree with what you’re saying. I disagree. Leadership sets this. Leadership determines what’s important and what numbers they should be looking at. If you are debating what a valuable thousand t-shirts looks like, it’s a fundamental failure at the leadership level of your company. Your your leadership is not emphasizing

51:58 What’s important to you. So I I don’t but at the same time every department doesn’t have the same value to for whatever the final whatever the metric that you’re looking at for marketing. I have to have the ability to say what we’re going to invest in getting people to go to the stores because that’s what’s going to support that number. You’re over complicating it. I think I think you’re thinking a lot of extra things in there that’s just it’s too complicated. I think it’s got to be simpler. I think it’s got to be everyone works towards generating more revenue. , if the company doesn’t make money,

52:29 No one’s got jobs. I think I think it’s much more simple to that. And everyone should be marching towards that thing. And the leader the the top level leadership, right, they’re going to be like, “We need to make the same amount of money we did last year, if not more next year.” So, they’re trying to grow. And this is I like this the book Simon Simon SK, he talks about the the neverending game. We’re in business. the the game. We’re not playing a game with defined rules. We’re not playing a game with a defined timeline. There is no winner in

53:01 Business. It’s just a game you play. And so, as as soon as you start understanding like this is a neverending game that we’re playing and all we’re trying to do is continually stay in the game and continue playing, there’s going to be new competitors. Our existing competitors are going to change how they do things. We’re going to change how we do things. Everyone’s vying for positions and jockeying for the next place, right? I agree, Tommy. Like each team will have different objectives and they may be contributing in different ways, but if leadership doesn’t say this is the

53:33 Number we die on. Yeah. Or the leaders of their department. This is the number that we’re working toward. It goes back to OKRs and KPIs. If those things aren’t clearly defined, then you’re right. All of their ambiguity comes down here. And I’ve been in I was working with a a gentleman who came from I think it was Budweiser or one of there’s like a brewery or something downtown Milwaukee. I don’t know what it was. but a pretty big company. but anyways their their metric was the analytics team would produce the data

54:05 Even if the data was wrong. Anything that came out of the analytics team was what we what we decision off of. They so there was a clear delineation between the analytics team like and again beer companies spend an inordinate amount of money on marketing and product discovery and like what products are selling well and where are they selling well and what should we be building new and what are our competitor so there is a ton of money in the marketing side on the the alcoholics beverage industry right there’s and there’s the ton of money there so derby yesterday yeah there’s

54:38 I really right I really liked the ability for that leadership to say, “Look, anything that comes out of the analytics team, you trust it and if there’s questions about it, you have a team to go back and ask questions to. They can research it and figure out those problems, but they will correct it, correct it, and they will give you the new numbers. But anything that that team produces, right or wrong, that’s what we make decisions off of.” And leadership made the decision to say that team has the authority around that data. And I really like that ability because it gave clear it gave clear direction to the analytics team what their

55:12 Responsibilities were and then if people had questions they could fire questions back at the team and that it was their duty to respond and answer to those. So to me there was a very clean break point between where analytics team ended and where self-service and BI and reporting started on the other side of the wall. And I think that’s a that’s a leadership level decision that has to be made at the top level of the companies. And once they get that, that establishes the culture and then people can march forward. And if there’s weak culture, what you’re describing, I think more is

55:44 Weak culture and not actually like solving the problem. Well, I and I I’m going to tie in with the you mentioned OKRs and I I I don’t think I did a great job explaining it because we’re really tying in I think three essential skills. We’re talking about numeracy, we’re talking about literacy, and we’re talking about culture. Those are really the things we’re looking for. So, let me just at least, and I’m going to put this in the chat as well because I think it really deserves to be said. You mentioned OKRs. This book by John Door, Measure What Matters is the 100% Tommy. Love that one. Yeah. And so, the idea in the book about

56:17 OKRs is the the example that John gives is an owner of a football team has two objectives. He wants to incre He wants to win the Super Bowl and he wants to increase sales. He wants to have the best fans that they had. So correct, that’s the objective. Neither of those are measurable, right? In a sense. So the the numbers that come down he brings to the his next two guys, the GM and the the manag the the director of the stadium, those are the ones who actually come up with the measurements. Like

56:49 Okay, the GM says we want to win the Super Bowl. Okay. Well, we should have the number one passing team, , because we know the best defense and the number one passing team. But that that that person’s developing that the director of sales is like, well, we need to do the most promotions and fill up the bottom seats. That keeps getting passed down. Okay, so now I know objective is we’re going to have the best defense. Okay, defensive coordinator, what do you need? Oh, well, what? We need to have the best defensive line. And it keeps going down the line on terms of the objectives.

57:21 What those K or the key results are. Everyone has a misconception that KPIs are your goals. They’re not. But so my original point and I and I think this aligns what you’re saying at the end here. I need a department where when they’re tasked with their objective to come up with the numbers that are going to support that objective. And this idea of numeracy, this idea of literacy, right? It ties in because you gota Yeah. I the more I think about this Mike I I’m I have to agree with you again and I hate it’s like the worst

57:55 Words have to come out of my mouth sometimes but I I agree with you completely where like honestly you have to have we need to develop something for an organization where everyone not just the analyst can handle numbers and be comfortable with them. Now, how that looks, this is where that’s where we may disagree, but I I need them to be able to be comfortable with numbers, whether they’re dealing with an Excel file, but people have to own their own numbers, and it’s not going to just be the win the Super Bowl. Their numbers may be,

58:28 , having the best def we’re going to stop the run or whatever. We’re going to stop, , stop the pass and then develop that team there because that’s the facet that they run. But no, I I think when we’re dealing with this numerousy test, it’s it may not be Canada. They may not have it completely right, but a lot of organizations, they don’t focus on this. And I think you and I can both agree on this where they expect reports to tell them everything, but we don’t expect the business or the business doesn’t expect people to ask the right questions either because if

59:01 You’re car to would you agree with this? If you have a team who has a high level of numeracy and a high level of data literacy, they’re going to ask the right questions about their numbers. Yes. I I think I think that goes hand in hand. I think, , I think if you if you clearly defi to your point, Tommy, if you clearly define the topper level top level objectives, here’s our main vision, right? We’re going to be Super Bowl champions. We’re going to out compete. We’re going to And sometimes these goals are very lofty. Some of them are they’re like look we were 13th in this in the club last year we want to be

59:35 Above 10 right so maybe the goals are a little bit more reasonable and but whatever is set at that top level you’re right the main objective is established and from the main objective the teams evaluate what that what it looks like for them and they have responsibility. My other part my other point earlier in the conversation was well what happens Tommy when you say we want to be the best defensive team in in the league. What happens when your defensive coordinator is coming up with strategies that doesn’t cut it and you’re

1:00:06 Continually losing and your defense is falling down that to me I look at that going that’s a failure on the leadership under that department for that team. the main objective was not being met. And what do you do in those teams? They replace them. They put someone else in new that has a different strategy, a different way of thinking, a different way of hand. Again, it’s all people related. , it’s at the end of it’s how you communicate to your team. How do you convey messaging? , what are all your team members doing? And it’s it’s again, this is yes, this is football or

1:00:39 Or sports or whatever. there is a defined set of rules. But like when you’re talking about being the best at one of those departments inside the league, it’s an infinite game. It’s there is no rule. There is like how do your people train? What do they train on? Do you have a lot of injuries? Do you not have a lot of injuries? How do you handle? Like there’s so many different pieces and you’re competing against every other defense who may be taking a different approach to what you’re doing for defensive stuff. So maybe you’re learning from

1:01:11 Them and you’re taking some of their knowledge and using it. But let me let me like I do want to we’re at time here. I do want to bring it back here back to the main topic here. Right? So we talked about this numeracy test, right? The conversation here is not really about the numerousy test. The conversation is really about can your organization align to your corporate objectives, the OKRs, the top of the organization. Are you able to clearly articulate those down? And my thinking here is how much of your company is actually focusing on analytical mindsets and can handle analytical things and you

1:01:47 Know don’t I think a lot on the podcast we focus purely on just the analyst as what they need to be doing to produce reports for produce models produce data engineering produce fabric things for the broader part of the organization. I think the conversation is much bigger than that. Honestly, if we think about who should be touching reports or things that we produce from the BI team, it’s a huge team. There’s a lot of people that need to be touching this and they’re going to have a huge array of knowledge around how to read and interpret data from charts. So I really do think the company it’s in their best interest to

1:02:21 Push for more people in the company to have knowledge around analytics or analytical things. So that way the the company can push on push out better looking better reports, more data, different visuals that might answer the questions even faster and provide better insights. But in order to do that, you’ve got to make sure that everyone on the team is playing at the the professional level. Everyone’s got to be skilled up. We’re not going to go out and hire a bunch of, , what what do they call that? , when you’re

1:02:55 In baseball, Tommy. Oh, double. In baseball, you have like the major leagues and you have like the league below. You have the farm system. Yeah. So, you have double A, AAA, minor. Minors. So, the minors. So, I’m I’m not going to expect to win world championships by replacing half my team and pulling half of them up for the minors, right? That doesn’t happen. You pull one or two of the best people from the minor leagues to pull into the team. So, what we want to do is we want to focus on how can we get our entire team playing in the major leagues. That’s the point. Where can we where can we address that? And how can the company invest in their employees

1:03:28 Because I think it’s an investment. How can they invest their employees to get them to be on that next level to be playing in the major leagues? No. And my last point here, Mike, is I love the topic that you brought here. Just real real quick, I there are I don’t bring many, but this is a good one. I don’t have Listen, I may not be smart, but I am dumb. So, , even a blind squirrel can find a nut, right? But I just really I this these

1:04:01 Are the things that I I I personally love talking about on this podcast because these are the things that people just don’t are not brought up enough and are so fundamental to what we do. Like we can do all the lakeouses in the world, but if you don’t have an organization or or the people involved who understand the value and are comfortable working with numbers, it’s going to hard it’s going to be very hard to succeed. I was working I literally yesterday I had this meeting and someone asked hey is this common this issue that happens in our industry and I said let

1:04:33 Me put it this way all industries have the same problem your industry actually bigger than that I said yeah I said not only is this your industry it’s any industry with data and that happen all of them right which is all of them all of them all of them all of them exactly and I I don’t think we realize that the hardest thing that we deal with is the thing that’s we don’t see and this is this idea of how comfortable people are working with numbers and this is if we can keep continue to preach that

1:05:04 Gospel and I think you and I do a great job of that is hopefully a lot of people here got a little a little value from just understanding this and understanding the value of this I I would argue our audience is probably heavily analyst centric level people right so these are the we are the people that are doing the anal we are the analysts We are we are the ones batting 400 right in the team right so how how do if we’re come up with a good number there by the way with baseball

1:05:37 Out of a thousand right I think I think three 300 would be decent but 400 would be great 400 is only happened once in major league history there we go so we’re all betting 400s or 390s or two 375s whatever something like that I’m proud of you but what happens when we have a chat GPT is greatly helping me out here. , no, just kidding. , so what happens? So, how do how do we get the rest of the team who’s batting lower up to our our speed? What can we do trainingwise? It’s going to have to be intentional. We’re going to have to invest in it. And so, that way the

1:06:09 Literacy, the the numerousy of the entire team gets raised. And I think honestly, when you raise the literacy of the entire team, it it adds it benefit across the entire team. And again, if I’m communicating to you, Tommy, in visuals, language, and charts, and I’m not talking to you in words, then when I’m handing reports to people, if we don’t read and talk the same language, I might as well be speaking a different language. I might as well speaking Portuguese to you. And you would be like, “Yeah, sure, whatever.” Like, we’re literally talking a different

1:06:40 Language. So, the the literacy part here is just, I think, so important here that we’re all talking and batting at the same level. Anyways, that being said, super fun conversation. We had a hard time cutting this one off because it was so fun and kept adding more thoughts and points about this one. Thank you all very much for participating today. We really appreciate you listening to the podcast. this is super fun for us. We really appreciate you listeners joining and hearing about our podcast as well. If you like this conversation, if you felt this was value, we would really appreciate it if you would share it with somebody else. let them know on social media that you found this

1:07:12 Conversation to be useful and maybe even put a little summary around it what you thought was one or two bullet points around why you thought this was good and why people should listen to it would be helpful. That being said, Tommy, where else can you find the podcast? You want to keep batting a thousand, you keep listening to us and you can find us on Apple, Spotify, wherever you get your podcast. Make sure to subscribe and leave a rating. It helps us out a ton. Share with your friends since we do this for free. And do you have a question, an idea, or a topic that you want us to talk about in a future episode? Well, head over to powerbi.tipsodcast.

1:07:44 Leave your name and a great question. And finally, join us live every Tuesday and Thursday, 7:30 a.m. Central, and join the conversation on all PowerBI tips social media channels. Oh, that’s funny. , I I I think our chat is hilarious. , they said goodbye in a different language, which I thought was actually very funny. So, You’re switching languages on me. Not good. Anyways, thank you all so much. Appreciate you and we’ll see you next time.

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