Maximize the value of your data: become a citizen data scientist

Narrator: We are grateful that you are joining us for another episode of the Ag View Pitch, as we know that your time is very valuable.

Chris

Barron: Our team at Ag View Solutions is always here for you for any questions or comments that you may have.

Narrator: Please feel free to reach out to us at cbarron@agviewsolutions.com. And now, here is your host, Chris Barron. Welcome everybody to another episode of the Ag View Pitch. We are here today to talk about a topic called citizen data scientist. And with that said, we have with us a couple of special guests that are experts in this area. We have Scott Plazowski and we have Matthew Bertram. How are you guys today?

Scott

Klososky: Hello, doing well.

Chris

Barron: It's fantastic. I would say which day of the week it is, but I don't want to date this, but it's a day of the week that is my, just about my absolute favorite. Chris, you can— the listeners probably can guess what day of the week this is.

Narrator: Well, yeah, because you have a Hawaiian shirt on.

Chris

Barron: Right, exactly. Hawaiian shirt means it is Friday. Yeah, that's what that means.

Narrator: That's right. So that's the important thing. You know, we want to make sure we get the important stuff out first. So one thing I do want to mention. So, Scott, you were at the executive or the Ag View, I should say, Ag View Executive Business Conference in Florida this year. And you were one of the presenters that talked about just kind of the future of technology, a lot of the things that are coming our way, a lot of things that are here now that a lot of us aren't even aware of, and did a great job. Like to have you start first and then we'll go to Matthew, but have you just give the listeners a little bit of your background if they don't know who you were and weren't at the conference in Florida.

Chris

Barron: Sure. I'm the founding partner of a digital strategy firm called Future Point of View. I also spent a number of years building and selling software companies. And I live in Oklahoma, have a lot of friends in the ranching and ag space. And over the years have done a lot of speaking for everybody from the John Deeres to a lot of the ag lenders around the country, uh, you know, ag software companies. And so, uh, co-ops too, as well. We've done some cool board training for ag co-ops. So we have a lot of experience in the ag space. Uh, we love helping people with digital strategy. And, uh, I'll, I'll leave it at that. And Matthew, I'll toss it to you.

Scott

Klososky: Okay, so I've, I spent 25 years with data and automation and making them weapons. And I have to tell you that, you know, although I've spent my time in technology, I was not born a nerd. I had to work really hard to become one. And, and here's the reason why is when, you know, when I first started my career, I'm easily bored with repetition and I was given a lot of manual processes that out of desperation, I said, there's gotta be a better way. And so that's how I got my start into technology and I realized, hey, I like this. And so what I've been doing over the years, both, you know, inside companies as well as now in consulting, is helping companies figure out where's the data, how do you use it, how do you make it easier to get to, how do you make it easier to make decisions, and how do you do that so that we can become more competitive?

I've done that everything from starting, you know, business intelligence initiatives inside companies to, you know, helping make very specific strategic decisions. And so that's a lot of what I've been doing. I'm, you know, newer to the ag space, but I'm catching up quickly.

Narrator: That's awesome. So one thing I want to, I want to point out and, and just lay out here on the front end of the conversation is that as, as the listeners here can attest, and, and me myself as a, as a farmer, we have a lot of data. We have a lot of fragmented data. We have a lot of stuff that's in this spreadsheet comp computation. We have some over here. We have our accounting here. We take some of this stuff from here and put it over here manually, and we spend a bunch of time doing that. And then we also sometimes forget we even had some stuff, you know. And then we, we want to take information, let's say, from, from a mapping system, and we're doing a pretty good job there as far as taking data from you know, the mapping systems, like, you know, Scott, you mentioned John Deere.

In our operation, we had— we use all of the John Deere software and do a pretty good job of writing prescriptions and those things. But one of the things that happens, though, is you want to take that information and bring it into your financials and analyze that stuff. And there's still some fragmentation in the industry that I think is hard to expect the industry to come in and say, we have a one-size-fits-all. This is going to do everything. So we need to have an easier or a better approach to bringing this information together, this data, quote unquote data, and figuring out, okay, it's one thing to have it, it's another thing to make decisions from that. That's where the money comes in. That's where the benefit is, is from being able to, to make these decisions with, not only information, but trustworthy information too, that, that's meaningful.

One specific example I want to point out, and then I'm going to start throwing some questions at you guys, is like in our system, we track all of the trucking loads. And I know there's, there's systems out there that do this. They track all of the data for your harvest information. They bring it all in, but then you end up still having to move it. From that to your marketing or to your sales. And then also maybe looking at the trend analysis over years, right? So this, that's, you know, we got really good data for this year. Okay, well, but how does that compare to the trend over the last several years? So I'm just mentioning, I think, where we have numerous, and there's a, and I could go on for an hour on just all these areas where we have gaps in our data and that kind of thing.

So with that said, I think the need is there for what you guys are going to talk about next, which is citizen data scientist. In other words, teaching us how to, um, compile some of that data and how to utilize that data more effectively. So I'm going to shut up as the non-expert here for a second, and I'll start with whoever wants to go first to explain, first of all, what is the definition of a citizen data scientist, And then where do we want to go from there? And I got a list of questions for you too.

Chris

Barron: All right, Chris, I'll take, I'll take that one. What is a citizen data scientist? And before I do, I just got to say, you know, I'm going to say farm and ranch, Chris, because I'm in Oklahoma and I have to, and I would be kicked out of the state if I don't say farm and ranch, right? It's just so, so in farm and ranch, You gave some good examples of data, but there is going to be a huge amount of new data coming in. I mean, if we think we have a lot of data today, we got to handle it in operation. It's going to double and triple and keep going, right? Because we're going to have sensor data coming from the field, sensor data coming from the livestock. We're going to have all kinds of, you know, things on top of weather data or crop data or things like that.

And so that's first thing is, you know, I think everybody needs to understand in farm and ag or ag and ranch that, hey, we're going to have way more data than we got today. I think then the next thing we have to understand is if we ever want to get to precision farming or precision ranching— and I love the term, right? We've been talking about this concept for a long time. If we really want to get to precision, the path to precision is going to go through data insight, right? Gathering a lot of data and then crunching that data to be able to have insights that we need to make better decisions. And we're not there yet, right? I mean, Chris, I think you guys do a great job. I don't know if you agree or disagree with this comment, but maybe we're halfway, right? Maybe we're halfway to being able to do the kind of analytics we need to do to really get to, you know, to full precision farming.

Is that— you agree or disagree?

Narrator: I would agree with that. I think You know what, where you start to lose some people is, you know, a lot of the operations that are listening to this, they're multi-generational. And so I think what happens is you have the, you know, I'm 56. So from about my age and older, they start tuning out some of that stuff. Conversely, they have the next generation. 90% of the listeners here are very sophisticated. They're, they're executive-minded. They already have that transition plan going. They've got that next generation coming in. I think the idea is, is to assign that role over to that individual to, to say, okay, here's your responsibility because the future is here already and the data is coming already. But it, the data is not going to slow down, it's going to increase. And so I would agree 100%.

Chris

Barron: All right. Well then that, then that takes us to what is a citizen data scientist? The word citizen means this could be anybody. And so data scientists, that term refers to somebody who is able to gather data as a raw material and then find interesting analytics in it that we haven't had before, right? It's somebody that can exploit the value of the data. And it's important to understand these words, right? Data science is not a data analyst. You know, I mean, we've had the word data analyst for a long time, but the reason that the term data scientist was created is because science has a creative component to it, an innovative component to it. A scientist is taking raw materials and constantly trying to figure out how to get new value out of the raw materials. So we've had data scientists, you know, for 10 years now as a position in a company, let's say.

A citizen data scientist means somebody who— this is not their full-time job, but maybe they have 60% of the skills of a full-time data scientist. And, and our belief is that every, you know, medium to large, uh, farming or ranch operation needs a citizen data scientist on staff because of the reasons you're saying. Tons of data coming in, tons of opportunity to exploit that data to be more profitable. And to get to precision farm, right? But that takes some skills. And either you're going to have to rent those skills or hire contractors or get people from outside to do it, or you can get somebody in the family, right, or somebody in your operation to become a citizen data scientist. And what we've seen over the last 4 or 5 years, that is absolutely the best solution for an operation is to develop a citizen data scientist who is part of the operation.

Narrator: I agree 100% with that because nobody knows your business better than you. And so there's information, there's a potential for information overload. There's the potential for garbage information when you have an outside source coming in and managing the data in a way that fits their system over an entire industry. Whereas if you have somebody internally that can say, okay, we need to analyze this and this in this way, and then we need to forego this area, but we need to go really heavy into this other spot. And I think nobody knows that better than, you know, like you say, the, the farmer rancher that owns their own business and can say, okay, this is what I need to know so they can dive into that data.

That's what I love about this citizen data scientist concept, because it allows the business owner to manage from within, no different than if you, you have a ranch that's large enough, a ranch that's large enough that you can maybe even have your own, you know, veterinarian on staff, or, or if you're a crop operation, you have your own agronomist on staff. Well, if you're an operation that's executive-minded going into the future, you need to have a, basically a data scientist on staff. Like you said, I like your 60%. You don't have to be an expert in all data all the time, but at least understand what is it that you need to know and know how to navigate that stuff. Because, because we have the data. I mean, we have tons of it. You saw that at the conference.

You you know, you made a comment to me after the conference even that, man, you guys got a ton of data, you know, let's do something with it, you know, and, and it really hit home with me that, yeah, we need to, we really need to go down this path. Matthew, did you have any comments on that too?

Scott

Klososky: I do. And actually, I have a question for you, Chris. How many decisions do you think you make every day?

Narrator: A lot. So if I'm at the farm operation, let's say it's in the fall. I do, I run the grain system, I run the grain dryer. I'm doing the marketing. I'm managing basis, I'm managing a lot of different stuff. And we have trucking tickets coming in. At the same time, we're starting to do fertilizer applications, we have fertilizer coming in, we've got 5 trucks going, 2 combines, some tillage equipment, and a lot of different things happening all at once that we do keep track of. And I'm as guilty as anybody listening to this and probably maybe even more so in some cases. I have a, a ton of data, but I'm not sure we're making any decisions from that in a way that, that makes it a difference. I don't know if that answers your question.

Scott

Klososky: Well, I'm actually, I'm gonna press you for it. Give me a number. Like, what do you, if you had to give a number, what do you think it would be?

Narrator: In what way? Like how many decisions I'm making?

Scott

Klososky: How many decisions do you think you make a day?

Narrator: Oh, it's more than 100. Oh yeah, especially in the fall because there's just so many things going on. I get more than 100 phone calls.

Scott

Klososky: Okay, well let me set this scale because this is one of the things we talk about in the Citizen Data Scientist program. So an Ivy League school, I believe it was Harvard, did a study about 20 years ago and they looked at how many decisions the, uh, the American adult makes every single day. 30 5,000 decisions every single day. Now, some—

Narrator: more than 100 then.

Scott

Klososky: Yeah, more than 100. Now, some of those decisions are what shirt did you decide to put on, right? There are those. But so let's just be, let's be a little conservative. Let's just say that a third of those decisions are decisions that you make that are farm related. Well, Chris, that's 10,000 decisions every single day. Let's say you have 10 people who are working with you. Well, now you're talking about 100,000 decisions that are made every single day. And so there's this idea that we talk about as a citizen data scientist, one of the skills is being able to impress your will onto a set of data. Now, here's what that means. You were talking about the multi-generational. I want to come back to that. So you said you're 56.

So imagine you're trying to, you're trying to take the, the wisdom, the experience that you've had from a lifetime of farming, and you want to make sure that continues on, you know, when you retire, when you move on, how do you do that? Well, there's really only two ways to do that. One way is to teach a person how to have that kind of experience, right? And, and that's what we've had from time immemorial, how we transfer knowledge. It takes a long time and it doesn't scale well because you might be able to do that with just a couple of people. What we have the ability to do now is impress your— is to take that wisdom and experience and turn, put that over a set of data. And so the way you would do that is let's say you build an analytic. For, you know, let's say what time to plant. So you're looking at weather, you're looking at temperature, you're looking at these things.

If you put that into an analytic, well, now that lives on, that lives on whether you're still there, other people can see what you've done. And so now you can, you can transfer that knowledge to a machine. Also take this back to, okay, but you're still here. Well, now what you've done by offloading those decisions to a machine, instead of spending, let's say, this much effort on that, you're now spending this much effort because it's helping you offload the bulk of that heavy lift on those decisions. So that's one of the things that's very important as a CDS is this idea of imprinting your will by moving decisions over to machines.

Narrator: Interesting. Yeah. And that, it just makes me think, I mean, as we record this, we're heading into planting season and And you're right, there's a lot of, of data. There was a lot of questions, I think, this week. The weather was warm and people were wanting to go ahead and plant, but the calendar isn't— is a little on the early side. But what's, what's the data say, right? What's the data— not only the data say for what the forecast is, but over the last 10 years, um, based on planting date, what's the data say, you know? And, and You know, I think companies like Corteva and stuff have that data from a grand perspective, but think of the value of that on your own operation geographically specific to your spot and your operation and your land and your environment and your management practices and all that.

So I think that's where I want to see things go with the citizen data scientist is being able to get more granular with the data internally is what I'm looking forward to and what I'm excited about.

Scott

Klososky: Absolutely. I, there's one farming operation I was just, you know, familiar with, I was working with. They operate over about a 60-mile footprint. Well, I would imagine 60 miles is just enough that the rain might have hit the south side, but not the north side. So that would affect how you would adjust your planning.

Narrator: Mm-hmm.

Scott

Klososky: Yep.

Narrator: Scott, any other comments? We hitting everything?

Chris

Barron: Yeah. Well, I mean, what we haven't talked about is, all right, so how do you, uh, train?

Narrator: Right?

Chris

Barron: How do you get a citizen data scientist in your operation? And so, you know, that might be interesting to the listeners is, you know, this— some people think, well, do I have to send them to Harvard? I mean, do they have to go to Stanford for a couple of years to learn to be a citizen data scientist? And, you know, it might be interesting for listeners to know that there are programs that are 6-month-long programs that you can do, that people can do part-time, and they can completely get up to speed to be a citizen data scientist. And that's, that's one thing I think we could talk about as much as you feel like talking about, Chris. The other thing we can talk about is just, hey, if you have somebody at your operation that becomes a citizen data scientist, the win-win with that is they're adding a very powerful skill to their capabilities from now on.

So let's just say, you know, it's one of your kids, right? I mean, you decide, Chris, one of your sons is going to become the citizen data scientist. It's not just that your son's going to be able to play a really valuable role for you. It's also that he's adding a skill that is going to be extremely beneficial to him the rest of his life and wherever he goes and whatever he does. And so I think that's You know, those two things, if you want to talk about them anymore, we can, which is, hey, you can learn to be a citizen data scientist. Anybody can. You don't have to be a technology person. You don't have to be— have an IQ over 150 like Chris does, right? You, you can just be a regular person and learn to be a citizen data scientist. And when you get that skill, it's just something that will help you in whatever you do from now on.

Narrator: So let's talk about that for a minute. So, so the answer to the, to your question is yes. You know, I, I think, you know, with Ag View and, and with the partnership, you know, with you guys, with Future Point of View together with us, you know, what we'd like to do is, you know, and we'll be announcing, you know, a launch date here in an, in another episode. We definitely want to go forward with that. We do have some operations that we know are interested in participating in, in learning to be a citizen data scientist and have someone in the operation that's going to want to go through the process. So I guess I would say yes. And we want to throw that out there.

So the listeners understand that we are going to, we are going to proceed with this because I think from, from Ag View, from, from our perspective, it's, it's one of the next steps for a lot of our operations, as far as what they can do to improve their business and become more executive-minded in their decision-making and that kind of stuff. So with that said, I guess what I would ask you guys then is, you know, is to talk a little bit about the training. You know, we're probably looking at, you know, just guessing right now, probably looking at that July 1st timeframe when the majority of the row crop operators are in a position where they have a little bit of time available to have somebody in the operation participate. So with that said, go ahead and give us a little bit of kind of what, what does the training process look like?

Chris

Barron: Hey, Matthew. Yeah.

Narrator: Why don't, why don't you take that?

Scott

Klososky: Okay. So first of all, the training is broken into two parts. We have a foundations part, and that's where we teach you how to be a, a citizen data scientist. And that's where you learn this idea called data activation. There's five steps to data activation, and it's important to understand what those are. And just to give you a, we can talk more about that, but to give you a sneak peek, just one of those is the idea of creating charts, analytics, visualizations. That's just one of the layers. And so that's a, that's a 6-month program. That's what we'd be starting in July. And so what we do is we walk you through the foundations of how to think like a citizen data scientist. And we've got the, we've got a step-by-step, it builds on itself. So after that, you've got the idea, you've got the foundations, you know how to think.

The second part, this is where it really begins to apply. We do a lab. We have about an 8-week lab that we take you through. Now what we're going to do is mentor you through projects. We will do projects that apply to your farm where you choose the projects, you choose what's important to you. And then what it— now it's hands-on. It's not so much now, you know, you've been taught the fundamentals. Now you're going to learn how to do it and you're going to be doing it. And so we're going to walk you through that., we'll mentor you. So you go all the way from being able to know how to scope out, find a good project, how to describe the project, how to identify what a valuable project is, how to build it, and then how to put it in a place where other people in the farm will be able to get access to it. So those are the two parts.

The first part is the foundations and the second part is the lab. So if you look, now let's go back and let's talk about the foundations, what we would be starting in July. The, the training is a, it's centered around 6 different sessions and you can look at as a total spending about 50 hours, investing 50 hours in this. And it's a little bit of, there's flipped classroom videos where you'd be able to, for example, for each session, have about 2 to 3 hours of videos that you might be able to watch while you're on equipment. You could watch on your own time. The idea is that you're just learning the knowledge. That's when we get a chance to speak. The live sessions, each one we have about a 2-hour live session. And this one's really important because this is when you get to talk.

And by you get to talk, what I mean is, Chris, imagine you go through the videos and we're talking about, for example, analytics. And analytics is a way of taking data and applying calculations to it so that you get some insight that you didn't have before. We call that enriching data. So you listen to the videos and you say, okay, this sounds interesting, but I'm not exactly sure what I get to do with it. The live session is where you get to ask questions. And you get to say, hey Matthew, what would I do with this? And so it's a chance for you to talk. But on top of that, what we do with the live sessions is we have exercises where we will put you into situations where now you have to start thinking about, for your situation, for your farm, how would I start doing this? Uh, let me give you a situ— an example. One of the, one of the exercises we have, it's one of my favorites.

It seems to be well received. Is on different types of cognitive bias, right? Ways that we think we're thinking correctly, but we're actually not. Well, what we'll do is I will give an example of a cognitive bias and then I'll break you up into groups. I'll say, okay, Chris, you're gonna be with these 3, 4 other people. I want you to now think about how does this bias affect your farm and how can you use data to be able to overcome this bias? And then you spend about 10 minutes talking amongst yourselves and you, and now you're applying it. And then you'll come back and then as a group we report out and then you get to share what it is you came up with. And so it's a good way of being able to not just learn the in— or be exposed to the information, but really begin to start to wrestle with it in your mind as far as how this works.

Narrator: Interesting. So all of the, the education and the labs and things are all pretty much on Zoom, is that right?

Scott

Klososky: So the live sessions are on Zoom. We, yeah, we do those remote. The flipped classroom videos, they're available of course on demand, so you'll be able to watch them. They're on YouTube right now, but you know, you can just watch them remotely. But yes, they're all, everything is remote. There's no need to be on site.

Narrator: Okay. So what other things are important for people to know during the process?

Scott

Klososky: Is there anything else, Scott, or anything else that we didn't hit on Or, or Matthew, I, one question that I would imagine your listeners would be asking is, do I have to become a programmer? Do I have to become an, you know, data expert? And the answer is no. No, you do not. Matter of fact, you do not have to write a single line of code. And Chris, this is why it is a really, really good time to become a citizen data scientist, because 25 years ago when I started, the tools that were available then compared to the tools that are available now. They are so much better now. There's one, Power BI, for example, that we were talking about. Microsoft puts this out. It's available for free to download. So you can download it, you can start using it. And Power BI, if you don't know what it is, it is a tool for being able to import your own data.

You can build visualizations and analytics on your own data, and then they make it to where you can publish it up to the, you know, to the cloud, to a place where you can share it with everybody. Microsoft has invested billions, literally billions, in building out a network that makes it easy to use. And so, and this is available now. And not only does Microsoft have a lot of content available because they want to teach you how to use it, if you go out to YouTube, there is thousands of hours of high-quality content of, you know, how-tos and tutorials on this. And so the point is, you're able to use these things, they've made it easy, and you know you don't have to become a programmer. Now that said, here would be something else I would, I would want to know if I was, if I was a listener. What if I get into this and I find out that I'm a data savant?

I mean, what if I find out that I'm really good at this? Okay. The nice part about that is your farm is very lucky to have you because there are so many places you can go now, especially where if you want to go deeper and you want to look more like a data scientist than a citizen data scientist. There's everything from, you know, we're talking about ChatGPT, these different AI tools, the ability to actually go deeper and start writing some more of the technical script and be assisted is huge. It's there. It's a very good time to be getting to become a citizen data scientist. Scott, what else?

Chris

Barron: I think that covers it pretty well. I, again, I would just say that this program is such a win-win for an operation. The people who go through it, they love getting the value of this kind of knowledge, and then the operation gets the value of what that person's learned. And so we've always— everyone who goes through this program, we've always gotten really great feedback about how much they enjoyed it and how much it's influenced the arc of their career.

Narrator: So one, one comment I want to make, and I appreciate the, the explanations and the comments there, is that, you know, as I listen to this, I, I know for a fact I've got 2 people in our operation I want to go through it. But I would also say that I'm not sure I want to on the first round. I'm going to probably assign a couple of the younger guys to go through it, and then I'm going to sit down and learn from them and then maybe go through it on the second round. So what I'm saying, the reason I'm saying that is I've guarantee you there's a bunch of people listening to this that are thinking, I don't know if I want to do that. But okay, then who in your operation do you assign to go through the process? Because then you have an asset, you know, no different than, you know, we invest in, you know, I always tell people we invest in machinery, and we spend a lot of time on that.

But, you know, do we spend enough time on the most important piece of equipment in our operation is us, is, are the people resources. And so it's, you know, and I always talk a lot about health. Well, it's not just physical health, it's also mental health. It's also mental growth, right? And so that's kind of where I'm going with it, is I see it as a value to the business intellectually, to expand and grow the business intellectually. The other thing, other comment I want to say too, after listening to you guys, it kind of explain it, that the, the gives me what the benefits are, which is I think the important part is, you know, you mentioned ChatGPT and I've been using that ChatGPT-4, which is awesome because it's very accurate on a lot of stuff. You can ask it tons of questions and it's almost scary, but it's really a cool tool.

But one of the things that Shay and I always tell our clients is, and is asked this question, is how much time do you spend working in your business versus working on your business? Business. And to me, becoming or having someone become a citizen data scientist is an absolute avenue toward the business investing in working on the business instead of just in the business. Because I mean, we can all go out and, you know, work in the shop and we can do our bookwork and we can do all those things. But what are we doing to invest in the business for the future? And so that's the other reason. So I'm just like the cheerleader here, I guess, but I'm just telling you what's in my mind as you guys talk through that stuff.

What's So I'm just spewing that out, but I think it's important, you know, and I guarantee you the people who are listening to this are thinking, well, it'd be pretty valuable in this area and that area too. So with that said, I guess, um, another question I would have is, is for those people that, um, are, are like me that maybe don't want to, you know, and I do a ton of data analysis. I just am very, um, fragmented with it. So. Assigning somebody else to doing that. They're perfect. It's very attractive to me to assign my kid to do that.

Scott

Klososky: It's awesome. But Chris, you're perfect. Jump out there. So let me, let me tell you that what does a good candidate for a citizen data scientist program look like? What is that? Because we get this question all the time. Here's what that looks like. First of all, age, age is not a condition. I mean, we've had people who are well into their careers. We've had people who are very early. Both have done well. So you are not too old, Chris. Come on in. Here's what you do need. This is, these are, there's, here's what makes a good candidate. First of all, they're curious. You have got someone who has a curious mind who's willing to ask questions and go find answers. That's one of the biggest conditions of being a good citizen data scientist. The second, let's talk about technical skill.

All you need in terms of technical skill, if you think of someone who's moderately good with Excel, moderate, I mean, you don't have to be a ninja, but you just know how to put some data in. You might know how to create a pivot table or a chart. That is an acceptable level of skill. We have lots of people who come in. So the fact that you're doing data analysis already and you're, you're already doing something to look at data, that tells me you're more than qualified.

Narrator: Yeah. Well, I actually built Profit Manager over the years, so I'm, I feel like I'm very qualified. But I also though feel like it's important to not only maybe have one person in the operation, but to have multiple people. And then like I said, this multi-generational component that a lot of these listeners are, I think having one or two people go through it so that it's not just one person saying, hey, this is how we're going to do it, or this is how, you know, you get, I think, better feedback from having you know, a couple of people in the business, maybe, maybe at the same time go through the process.

Scott

Klososky: Absolutely. Absolutely. Chris, there's one other thing I want to bring up. I want to touch on. I can see this is the— this question irritates me more than any other question because I get it all the time with our clients and having to— and this is when I see clients who don't move forward. This is the question that they, that they get wrong. They say our data is a mess. We're working on it. We've got this, you know, initiative in place. And in 18 months, in 2 years, these are actual numbers. I mean, they actually tell us this in 18 months or 2 years, we'll be ready for a citizen program. And, and I get when people say, yeah, my data is a mess, but here's what I want you to think about. How many decisions are you going to make in the next 2 years? Right. All right. Are you, are you willing to keep doing it the same or are you going to stop making decisions? Right?

Because obviously you're not going to stop making decisions. All right. So there's got to be a way to be able to improve the process, you know, as you're going along. And there are. And here's what I would tell someone who's listening. If you're thinking, man, my data's a mess, I've got the blizzard of Excel spreadsheets all over the place. Yeah, I get it. But people are making decisions. With that same data. So let's start putting a process in place. This is where becoming a citizen data scientist becomes like the tip of the spear. And this is where having that person can help start to herd the cats in the right direction. The other thing that it does is, let's say for example, let's say that you have 20 spreadsheets with data, Chris, and you think it's a mess. Okay, well, it's a mess, but it's what you have. If you took that, you could import that into, let's say, Power BI.

And you could create visualizations on it. You could publish those visualizations in one place. So at least now everyone's looking at one place at a dashboard together instead of going to 20 different spreadsheets. That's an improvement, right? That's— you're going down the right path. The other thing that it allows you to do is it instantly starts to prioritize what you're missing, what you need to work on, and you realize, oh, if I just had this next set of data in Power BI and I was mashing that up, that would really help us with this type of decision that we're trying to make. And so it allows you to begin with the end in mind and see where you are. So if you're thinking my data is too much of a mess, I would encourage you, don't think that way. Use the CDS program as a way of beginning to, you know, get the process, get the journey started.

Narrator: Yeah, I would also just, when you were saying that, made me think of, you know, there's a lot of already preexisting programs out there. There's 3 or 4 of them I can think of off the top of my head, including Profit Manager, but that they could take that data that they have in the systems they already have and they could put it into like your Power BI or whatever after going through this process, and you'd have a pretty good idea of how to take the information you've got and analyze that because there's, there's a lot of exportable, you know, already existing programs, but you can set them up in an Excel format, export them pretty easy, move that stuff around. And create whatever it is you need to create to be able to communicate, to see, and to make decisions from. Um, yes, I like that a lot. That's awesome. Um, I also, I want to backtrack just for a second.

When people are going through this program, you know, I asked the question on, um, you know, it's going to be, it's going to be online, it's going to be, you know, on Zoom. Um, you know, it's probably, you talked about 8 weeks you know, for that, maybe that first one. So maybe what we would do is do a session this summer and then follow that back up. And so what we'll be doing is sending out details once we get with you guys more on absolutely the launch date when we're going to offer this. Would ask people to reach out to us as well to send us an email if you're interested in this so that we have you on the list because we're going to target some people that we already know that would really benefit from this. But if we're not getting to you and you're interested in this, make sure you reach out to us, send us an email or send me or Shay a text.

So with that said though, when you go through the program, go into the nuts and bolts real quick, just, you know, from start to, to finishing the first phase, that 8 weeks you said?

Scott

Klososky: Well, the first phase is 12 weeks for the foundations. Okay. So yeah. Okay. So nuts, nuts to bolts. Here's, here's how it works. The first thing that we do is we have a kickoff session, which is just for making sure everybody understands the tools, the platforms that we're using. We make sure you're comfortable with it. We call that a session zero. It's a kickoff. Then we have, what we do is from there, we have, you have an assignment where you have about 2 to 3 hours of videos to watch and you've got about 2 weeks to watch 'em. We space them about 2 weeks apart. And then, and sometimes you'll have some assignments and we, we make these, they're not onerous, they're not burdensome. It would be, for example, here's a document with some data science terms. Just, you don't have to memorize it. Just make sure that you're familiar with this list.

So if you hear the term, for example, API, you know what an API is. You've at least heard that, that term before. So there's some things like that. Okay. So that's what happens in between sessions. And then we'll come up to a session that we'll have a live. Then we'll have the thing that you've been listening, talking, or watching the flipped videos on for the last, you know, watching 2 hours of content. That's where we do the 2-hour live session, and that's where we talk about it. We have exercises on that, and that's where we discuss it to make sure that you've really got those concepts. Session 2 is actually worth highlighting because Session 2 is about your data environment. And this is, this is actually some of, one of the most effective sessions, some of the feedback we get.

Because what we do is we have a, we have a, an on, a digital tool where we help you identify what your applications are, what your data is, and you start to build your own map. That's one of the things you get, and you get to keep that as part of the program. It's very helpful just to be able to get all of that on one page. And then what happens is we start building on that for the rest of the course. Because for example, then in session 3 with analytics, we come back and we'll put you into a breakout and we'll talk about, okay, these analytics we've been talking about, Now go look at your data, and I want you to identify data that you have not created an analytic for, but that would be valuable for your farm. Go identify that, put that on there. So now you're going to put new information on there and then come back and discuss it with us.

We start doing things like that so that by the time you get all the way through session 6, you have this built out, a lot of data and a lot of notes about your own data environment. Session 6, the, the last session where we graduate is great because that's one of the places where we talk a little bit about automation and how to, because with some of these things with data, how do you do it to where you can create an analytic and then step away from it? And so that's how we, that's the, the nuts and bolts of how we do it is a couple hours of video, a little bit of study on your own time. Then we have a live session with about that last about 2 hours and then rinse and repeat.

Narrator: Awesome. Any other comments, Scott, on the process?

Chris

Barron: No. Some people ask us how much time, and normally you can look at a rule of thumb. It's going to take 10 hours of your time over 2 weeks. So every 2-week session, there's about 10 hours of either you need to watch videos or you're doing the live session. So just think in terms of 10 hours a week, you know, spread out every 2 weeks. That's the actual time you'll have to invest.

Narrator: That's about 5% of the screen time that people have in general anyway, right? Probably.

Chris

Barron: Yeah.

Narrator: If you're, you know, it comes right back to, you know, investing in your business, right? I mean, I'm a firm believer of this and I think this is the next step for a lot, like I said, for a lot of our our listeners that they— that are executive-minded and wanting to, to work on the business and go to the next level.

Chris

Barron: Yeah, when you said that, that really resonated with me about, uh, if you go back 40, 50 years and you think about the skills you had to have in a farm or a ranch, a lot of the skills that you had to have were mechanical. Like, you needed to know how to repair things, uh, you needed to understand the animals, or you needed to understand the plants, you needed to stand the weather, right? If you think about the skills that you have to have, uh, you look at a John Deere tractor today, or a planter, or a harvester, uh, it— now there's less that you have to be able to do as far as repairing it, and there's more that you have to be able to do to consume the data that's coming off of it, right? Or to be able to, uh, you know, program into it.

And so I just think it's natural that you were right on that, hey, We have to keep improving those intellectual skills in an operation now, and data is one of the basics.

Narrator: Yeah, for sure. So I think we've covered a lot of it. Like I said, you know, I'm, you know, we're committed, Ag View is committed to proceed with, with the process and to generate some interest hopefully with this discussion. Would like to follow up with some detail once we have a specific launch date set. And, and we're going to be reaching out to some people, sending some emails out. But I would also encourage you that if you don't hear from us to please, and if you're interested or have somebody in the operation that you feel should be going through this process, we'll have more on the, on the details of this here as we proceed. But I wanted to first get the information out of what is a citizen data scientist, Why is it important and why is it the next step for working on your business as an executive-minded business owner?

So with that said, any final comments, you know, from Matthew or from Scott?

Scott

Klososky: I just look forward to seeing you in a live session.

Narrator: You'll probably— I guess you can talk me into it, but You know, you're going to see my son. Go ahead, Scott.

Chris

Barron: Go ahead. And we'd love to help a lot of people do this. I mean, we've seen the results from this and it's— it really— it makes us feel so good to be able to see somebody be able to take data and be able to turn that into more profit in their operation, more risk control. And so we always look forward to being able to run a cohort through the program and see the impact it makes.

Narrator: Yeah, it's going to be, it's going to be an awesome opportunity for, I think, a lot of the listeners. And I'm definitely excited to have a couple of the guys in our operation go through it. And I'll probably do it too, Matthew, but I'm, you know, I'm just excited to see the next generation and in a couple people in each operation have the opportunity to go through it, or one, if they're just one, that's great too. But, um, with that said, I think, you know, really want to thank you guys. I think this is a great opportunity for people, um, to really grow, grow their business. And, and one thing I want to, I want to finish up with is, Matthew, you made a comment that something that frustrates you is when people are like, well, in the next 18 months I'm going to have this fixed. My analogy to that is, is there's two different analogies.

One of them is is when I can afford kids, I'm gonna have them. Well, you'll never be able to afford them. You just have to start. The other one is, um, I'm gonna start working out next year. I'm gonna work— I'm gonna start working out at the beginning of the year, you know. Well, give me a break, you know. Don't do that. Just start. Just go. There's never a right time. Never. You know, if you wait for something, all you're doing is delaying the inevitable. Potential improvement. And so it's, it's never too late. I guess that's my, my final comment. Any final comments from you guys? We'll wrap it up.

Chris

Barron: I think I'm good, Chris.

Narrator: All right.

Scott

Klososky: Thanks, Scott. Chris, thanks for having us on.

Narrator: You bet. Thank you guys for being on. And again, we will be back to all of you to kind of give you some update on exactly when we will be launching the Citizen Data Scientist program. We'll have some particulars on there. We'll also have a link on this podcast to Future Point of View, to their website, and to information that, that revolves around citizen data scientists. So if you want to look that stuff up, you can, and then also reach out to us if you got questions and want to be on the list for this opportunity. Appreciate everybody. Again, thank you to Matt, thank you to Scott, and we will catch everybody again next time on As the you pitch.