Data Literacy vs Fluency... Say WHAT?

Ladies and gentlemen, thank you for hopping back online with us. This is a continuation of our conversation about data and how important data are to us and how there are a few steps we can take to make sure that we are acquiring the necessary needed data that's actionable, that we can make a decision about what the intent of the data collection effort is. So figuring out the data is not hard. We can find data anywhere. The very first part was making sure that we understood this is why we are collecting the data. This is the outcome desired, this is what we want to learn more about. And at this point, I think it's important for us to understand what it means to be literate around data so that we can lean into what does fluency mean? And we'll do some, I think, simple analogies with that. By the time this is done, you'll be able to see some distinctions about how you explain to somebody what the data are showing, what the data are presenting. So we will go through that a little bit at the end of this, but the beginning part is going to. We'll be talking about literacy first, and then we will shift a bit into fluency so that each of you that are listening will be able to walk away with a. Our intention, a slightly better understanding of the distinction between data literacy and data fluency. So, Vince, what are you hearing me say? What are you thinking?

So I'm hearing you say that I. The focus of our conversation is just to really understand the distinction between literacy and fluency. And it was an important distinction that we're going to spend some time talking about. What that meant to me was I would often at least start off my conversations with any group by explaining to them that I'm from the south side of Chicago, third generation. And what I meant by that, that statement was to set the tone of tenor that you got to slow things down now and help me understand what we're going to talk about today so that I can understand, make mean that, and then look at how I could put that in action. So it was a verbal outline quest for both myself and my audience to make sure that we develop a common understanding up front between literacy and fluency. And so as you were talking, that's what was resonating with me. And you're taking me back a few years in leadership.

That's beautiful. So my wife is a Southern woman. She was raised in the South. She calls most everybody darlin, and she says honey and sweetie and stuff like that. Say that in Colleen's neck of the woods, there is an Immediate offense that unfolds like, I'm not your honey, I'm not your darling. Is this a sexist statement? So this question of literacy is so localized in our world that we want to make sure that we can do, as Vince said, set up the conversation so that nobody doesn't get offended by him saying the wrong thing or me saying the wrong thing, or on and on. So there's much here, I think, for us to understand in this setup of a conversation so that we have a shared understanding of kind of the backyard that we're playing in and what Vince described. This is the backyard that I play in, where I am most comfortable, I am most natural. And in the next section, we're going to talk about naturalness in presentation and how important that is. Colleen, how has this all landed on you?

Yeah, I'm looking forward to it. I think that many times people think of analytics and data, just giving them my dashboard, and I'm ready to rock and roll. They skip the piece that we really talked about in our prior podcast, which was aligning your data and matching it up with the outcomes you're looking to achieve. Then this other piece that you both have just nicely laid on the table is the human element. While you might have a dashboard, you might have analytics. It's that human element of interpretation that can pivot and shift and make it so it's no longer necessarily aligned towards that desired future state. So how do we, even if we have to Vince's point, some incredible statisticians and analytics, and there's some amazing dashboard, and he's getting this incredible data. Is it really actionable? Well, not yet. It's that interpretation. So I'm looking forward to having a deeper dive on what does that mean and what does that look like, and how can we support some of our listeners in addressing that human element side?

The human element is dialectic. In nature. We have different dialects in our world, but certainly even in our country, we have different dialects. If we haven't got the opportunity to make the space safe enough. So I can say, vince, what did you mean by that? All of a sudden, we end up in a strange place when somebody says, well, the central theorem tendency is, okay, what is that? Or what? What do you mean? The mode is 20, but the mean is 8. I don't get that. You know, help me. So sometimes there are words that people will use that are truly data scientists, which I admire. In my own study, that was a space I had to enter and stay for a while in order to complete the programs that I had chosen to join. At the same time, if I came home and started telling Ruby about some of that stuff, it ended up being not such a powerful conversation. So there's much for us to catch here about the difference between I can be literate but possibly not fluent because I cannot translate the literacy into a fluent statement that allows people to have a common or shared understanding about my intent. Excellent. Thank you both. So here's our game. Our game in this next section is for Colleen to teach us a little bit about Spanish. I don't want to learn Spanish today, but I want her to talk about the steps that got her from. I'm literate in Spanish but not fluent. I have deep experience with this in the high school settings that I had the privilege to lead. And the Spanish teachers would tell me regularly the kids were literate in Spanish, but they struggled when they went to what they call congre, I believe, where they were supposed to show up and just speak, you know, on stage with other kids that were studying Spanish, if you gave them the sentence, they could translate the sentence. They struggled turning that sentence into a relaxed kind of fluent conversation. So, Colleen, talk to me a little bit about your learning in that area. And, Vince, I want you to weigh in on what you're catching from her. This, because we're talking about developing literacy in a language called data. So, Colleen, please talk to us about Spanish a little bit and the distinctions between literate and fluent.

Sure, sure. Well, I think that's. Anytime we are beginning to learn a new language, you begin by learning words. You've got to know the words. That's the first piece. What is the vocabulary that's being used? Well, guess what? There are so many words in every language. Same with Spanish. So what did many people do? They learned these words in isolation. Get out your flashcards, Vince, because we're going to have a test next week in Spanish. So many times that's what they would do. They would have these new words that they were learning out of context, not related to what it is they were trying to achieve, which is the fluency piece. When I worked with small kids and when I worked even in middle school, I always got things in. What are we trying to do or achieve? If my outcome is we want to be able to go to a restaurant and order food, then I focused on the words necessary in Spanish to produce that particular outcome, to be able to have a greeting and. And order some food and say thank you. So I always had. Everything was focused on the application of the words that we were focused on in Spanish. And I think that that's huge because you could learn a huge book of vocabulary. But if you can't walk in and apply the vocabulary in a specific setting to achieve a desired purpose, then you're missing the boat. Why are you even studying if you can't apply it? And that's that fluency piece. When I can take what's here, have it come out of my mouth, and get what I want achieved.

That's really good. Thank you, Vince. What are you hearing? What are you thinking?

Yeah, so I thought that was a very good story. And so what I heard Colleen say was, you know, the first thing that she did in Spanish, the first thing most people do is they start talking about vocabulary and you're breaking out flashcards, you learning things. Right. But they're not connected. And I thought it was powerful the way she connected outcomes as something that supports fluency in what you're doing. And so for me, what that really meant was if you have a team and you're trying to get a team to do something or to go someplace, literacy can mean defining what's on the dashboard, looking at some bars and graphs, but that doesn't connect to what you're trying to achieve. You have to be able to get people to understand at the most basic level, we want to get here. And to do this, it is going to be supported with these three components that I need everyone to know. And so I was thinking that how important it is to develop a very simple baseline of common understanding within the team of literacy so that we're all speaking the same language, but how important it is that it's connected continually to your outcome, which supports fluency. So great story. And as she listened as Colleen, you were talking, that's what was really going around inside my simple mind.

No, I like it a lot. So in this world of data, then there are some words that matter. So when you were figuring out the mirrors you were going to get or the house you were going to buy, what were some of the data driven words that mattered to you? What were some of those pieces? Do you recall.

Words? Well, I mean, size for me. I wanted to know what was the size, what was the material, what were the, you know, the dimensions, price? Was it in stock? When is it? When am I going to receive it?

Excellent. Those are all words that you can actually find a data point that'll tell you something about that. How about you, Vince? When you think about the house here. Yeah.

You know, for me, it's interesting. So our economy is going through a lot of changes. So I was thinking about interest rates, you know, in terms of cost. What can I get it for? I was also looking for location. I have a kid in high school and I wanted this kid to stay in the same area. So location was very important to me. And then amenities, you know, because I was thinking about long term investments. You know, when I leave, my life changes again. Will I be able to sell this house based off the school system and the environment? So there were a lot of factors that were weighing in on my consciousness in addition to quality. But price was a big driver, location was a big driver, and the quality of the build. I wanted maintenance, free experience.

I think all of these. And there are other sub variables involved here. Variable is simply a point, a fact, something we can tell. I'll give you a great example of a movie that I think everybody has seen in our country now because it had a lot to do with a change in the way Major League Baseball acquired players. And it's called Moneyball. And if you haven't seen Moneyball, it's worth it. And you'll see that there were some people that really did not have literacy about the data. And I'll give you an example. Should we keep this player or get rid of this player? Guy says fans love him. What a great presence he has. He's great on a microphone. He's just fantastic. And then somebody else said, what's his on base percentage? Oh, I don't know that, but I can tell you what he looks like and I can tell you he hits home runs and they really like him a lot. That was probably for me, one of the most revealing stories about two gentlemen in the organization who were developing fluency in the data that is produced by individual players and how that data had an impact on the team's ability to ultimately win. Colleen, do you remember that show?

I do. And I'll never forget because they were trying to select their team and they'd say, well, what about this guy? Does he get on base? What's our number one thing we're focused on? Does he get on base? And that was it over and over and over. And I think it is a shift in your mindset and in the way you think and you view the data and interpret it. Because if you don't. Once again, they were developing their fluency. There were laser focused on the win. But for them, the win meant get on base. The rest of them didn't have that same perspective of how do they win. So I think that that's another great example of it's so critically important to continue the conversation because maybe, you know, Ray and I have a vision of what the wind looks like, but Vince doesn't. So he's over here pulling up all these other data points, going, no, Colleen, what about this? What about this? What about this? If we haven't communicated that effectively and got everyone on board about what that looks like, your data isn't going to be aligned to the outcomes and you're going to continue to pull additional data points in that might be a distraction and might be a confusion.

Yeah. So there's been a few wonderful examples of this. There was a movie with Clint Eastwood, who was a scout, and he was losing some of his facilities, and they wanted to. The organization was in love with this potential player. Right. And that's right. And this guy said, the guy can't hit a curveball. Everybody. Everybody else that saw this kid play said, yes, he can. He hammers the curveball on and on and on and on. Oh, as it turned out, he had a little hitch in his swing and that meant he couldn't hit it. And when they finally went to practice and the guy's job was on the line, he says, I'm telling you, can't. And they went out and he hit a few out of the park that were just fastballs up and in. And then all of a sudden he said, throw a few hooks at this guy and his life changed. So we had a couple of variables that we were focused on, but not the entire package, and something seemed to change. Back to you, Vince. It's good we're talking about sports. I love sports. So what are you thinking and learning?

Man, those are two incredible movies that you laid on the table. I couldn't think of any two better examples. I watched both of those movies multiple times. But, you know, what really stood out to me was the fact that not everyone bought into the data sets that were initially being presented, and it was absolute resistance and non acknowledgement. And so if you have a data set that you believe drive towards outcome, your ability as a leader to really synthesize that and to get it down into the simplest terms so that everyone can understand is probably the most important variable. And sometimes people are not going to get it the first time you lay it on the table. Right. Because we all come from different places and we all have different points of reference. So your ability to communicate and develop a common understanding of your data is critically important if it's driven towards outcome. And I think those two movies man were prime examples where people didn't believe it, people didn't support it. The data turned out to be absolutely accurate and it really changed outcomes.

Yeah.

Great stories.

Beautiful. There was such a shift in that too, where I had a mental model that caused me to have a blind spot so I couldn't see anything. Then my particular worldview. My worldview was driving my conversation and driving my decision making. And unless I can actually open that to your word. Use this word a lot and I love events. If I can open the aperture a bit, I can actually let more data in and hear different interpretations, limitations. Back to curiosity. Did anybody ever say, why do you think that you know? And in some level, as they explained why they thought that that other person's worldview mental model was so powerful they could not hear him. Literacy. What are the key performance indicators that matter to us when we're looking at a data set? We did wait time before in a hospital waiting room or an emergency treatment facility, or just an MTF in general. It could also be how much time does a teacher spend in a 45 minute lesson actually teaching? How much time is actually spent greeting everybody, saying hello, sitting them down, handling discipline problems, blah, blah, blah. We need to be clear about the key performance indicators. And believe it or not, one of the ones that was so important, that was not really talked about a lot in the world of education is it really matters that teachers start on time. It really matters that teachers orient the class to the new work. It really matters that teachers do reviews, make sure the schema that's necessary is in working memory as they are teaching the next lesson. Those are very important variables. Very important. And many times we get taken with the outward appearance of the person. Wow, that looks like a great teacher. Boy, that person looks like an elementary, middle or high school teacher. And we back to Moneyball. Some of those scouts really thought this was the right next person for this professional team because of their appearance and their demeanor and the way they behave because it was consistent in their worldview. That's what's necessary in order to be successful at this particular effort, this particular career, this particular job. So today what we're doing here is saying that what matters is there's lots of data. What are the key variables? What are the ones that really matter to you that are going to tell you something moving forward? And I have examples, but I'm curious about you guys. Do you have an example of what were some key variables you were looking for when you were both in leadership positions that mattered to you? You Got one, Colleen?

Yeah. I mean, I think key variables when I was working for another organization was specifically we would look at our events. We ran several different large events. One was a three day event, one was a two day event. And we scheduled those out and we did like every other month we scheduled these out. And there were a lot of different variables that we looked at. How many butts in the seat did we get? Right. How much did that conference room in that conference because we had 500 people coming, how much did it cost for that particular hotel? Right. How much did we have to pay for all of the food, all of the staffing that it took to get there? I mean, there were so many things that we looked at. We wanted to look at our expenses and then we wanted to look at our profit so we could make informed decisions on do we want to go back to that same city? Do we really like that hotel? Does that hotel make sense for us to use again if we return back to this city? So all of those really helped inform whether or are planning our planning and growth for the company.

They were key variables. And in the world of sales and medicine leading in general, there are key variables that are sometimes called performance variables. One of the places that I thought was a fascinating deal was, you know, how the different cities bid to get the super bowl, the National Football League Super Bowl. Jacksonville bid to get it and they got it. And guess what the problem was? No hotels. No hotels had no place for them to sleep. So somebody was now curious. Well, we got the thing, we're happy to get it. And let's be curious about this. What are some possibilities? What they found out is that there were plenty of cruise lines that could dock their boats at the, at the docks in Jacksonville and turn them into floating hotel just during the Super Bowl. That was brilliant. So here we go. Okay, I'm curious and now I need to be creative. I'm curious about how this event is going. So I'll do an after action review, then I'll be creative about how to respond to those variables. You got one for us, Vince?

So, yeah, just quite simply, you know, I had a situation where I think in the last podcast I told you that I had metrics and things that were important to my boss. Right? My boss laid out some things and if it was interesting to him, it absolutely fascinated me. But I had to find ways to get my team to buy in to how are we going to answer the mail with this defined criteria and have it make personal meaning to where we were each and every day? And to the people that we served. And so what I did was I just tried to get our team to go out and connect with the community and say, hey, here's some things I'm looking at. What does this mean to you? And once I was able to make that connection of what was important to the customers with my team, it created personal, shared meaning within my team on how to align what we needed to do with what we were being asked to do. And I was able just to make an easy connection. So I was able to connect my team with the driver of data that I was being asked for form and what was important with the voice of our customer.

I love it.

And I just tried to really keep it simple and to do that. And that's an example of how I tried to take. Create literacy within my team and then to help that drive fluency in the way in which we operate each and every day.

Yeah, sometimes the words themselves. There's two big things you just gave us, and you and Colleen are great in this. First of all, you made your boss your customer. What do you want? Boss, you know? And that person created a shared meaning and common understanding with you about expectations so that he could have actionable data to make decisions. And you did that, and you did that by building that same conversation with your team. So they realized what we were producing was actionable data. Notice this keeps coming up. So what variables does the boss need? So the boss can make a decision about deploying resources, Make a decision about reducing FTEs at a particular place, make a decision about adding FTEs at a particular place. All of this is beginning to create a language of literacy around the use of data. And of course, we've all seen this. People have dashboards in their office that I couldn't make heads or tails out of. Just, they love the data. So it was everywhere, like, okay, here's a lot of data. Look how good I'm doing with my data. Good. Help me understand which of those ones that are there. And this is almost like when you prepare a presentation. We have lots of things we can say. Which ones are the most impactful? And in this work of literacy and data, the ones that are most impactful are the ones I need to become very comfortable. Comfortable with and very able to talk about. This is just terrific. So notice where we've been back to that same space of what's my audience? What do they want? How do I produce that for them? I can produce as much as you need and want. What are the significant variables or the key performance Variables or indicators, depending upon your business, that matter to. To us. How do I get those in space? And then we're going to talk a little bit in the next section about once we can see the data in a visual way, how do we tell the story about what the data is telling us? That's really a big deal. The construction of a story that matches the visual display is the same in any presentation. Once you create a story, then we need to come use some interesting media to help that story move along. And I don't mean 100 bullet points. Okay. In case you haven't watched some of our other stuff, we. We have explained that the, the PowerPoint keynote constructions sometimes take away the story. They don't allow the story to unfold. But we'll talk about. We have talked about in other episodes. In other episodes. So today we really reviewed a couple of big pieces. One, be very centric around what do we want the data for? What's the big goal or end state? Second, making sure the audience clearly understands that we have the same interest. And if they don't know what that is, we have to build that common understanding with the audience. We began to talk about which metrics are meaningful. And the metrics that are most meaningful are the ones that are actionable. I need this data. I need data that will help me make a decision. And it could be about a lot of different things. Doesn't really matter what the big thing is in your organization. It's just make sure you're clear about what is the most important decision you wish to make, you wish to make so that you get the data that help you make that decision. I think what we've done here is set the stage for the last piece of this because we're going to talk about fluency, data fluency and being able to tell a story in a fluent way. As a setup for that notice Colleen laid on the table. I can teach you a lot of words in Spanish. I can show you the verbs. I mean, I had six years of Latin. I can do all of kinds, kinds of things with Latin that no one cares about because no one speaks it anymore. I never really got to understand fluency, though, until they said, here's a situation. Your job is to direct these particular people to do X, Y and Z. If I didn't have that, my fluency was very lacking. I could do declinations forever, but didn't mean anything. I could do a grickological arm still and tell you many ways to say farmer in Latin doesn't mean anything. Unless I can turn it into a conversation that reaches an end state. And I thought, Colleen did that beautifully. I can be literate, but I'm not fluent unless I can use the words that I know that lead me to an outcome. Where's the bathroom? Was one I wanted to learn in any language when I was going anywhere so I could, you know, because I'm not a young man any longer. So Banyo. So I would figure out how to ask the right question so I could take care of personal human needs. Well, in this use of data literacy, we want to be able to have the words. We need to know what a significant variable is. We need to know what a key performance indicator is. We need to know what matters in the context of is this a bullet point or is this a series of data that are telling us about a central tendency? So these are words and phrases that don't have a lot of impact until we can become fluent in those. We're going to do that in the next episode as we talk about telling a story with data. So to this part here, and I'm going to get each of my friends to tell you a little about what they want you to. Here we did intent, very focused on the intent, very focused on the outcome. We can then figure out what data are most significant and important to us in order to meet that particular intent. Then we talked about how to start to become somewhat fluent in using those words that represent the most important data points and are the data actionable, or do I just now have a hundred metrics sitting up on my Wall? Maybe, maybe 5% of those metrics may be useful in making a decision. So this is so cool that you guys are interested in this notion of I can be literate, but I may not be fluent. And the next section, we're going to become really more fluent as I get Vince and Colleen to tell me a story once I give them a little bit bit of data. And that should be a lot of fun. So, Vince, what would you tell our audience right now from this particular section on literacy and fluency?

I would say to the audience, just begin to appreciate that there is a difference between literacy and fluency. I think the Spanish example that my colleague Colleen gave was an outstanding example. And I say, stay tuned for part three. I'm excited as we move into the storytelling mode.

Colleen?

Yeah, and I think I'll just say that fluency comes with intentional, deliberate practice. You know, it's one of those things where, you know, I intentionally went to Guatemala, so I was in situations where no one around me spoke English. I had to ensure I was working very diligently every day to become fluent. So my invitation is perhaps you're listening, thinking, well, I'm not fluent yet, or maybe a couple of these things that Vince and Ray and myself have brought up. You're going, oh, maybe I need to really delineate what are those data points or key performance indicators I want to focus on because things shift and change. So my invitation is just to continue to recognize you might not be there yet. However, with intentional, deliberate practice, I think you can really enhance these capabilities, develop them within your team, and see a huge impact on the results you achieve.

So I believe this literacy game is a muscle you can develop. Just like strength, courage, resiliency. These are things that you can develop. Colleen made the most fascinating point, and that is for me, in going through all these reading I've been doing and listening, sometimes I don't think in data, and I think I should think in it more. And Colleen is the second linguist that I've ever met that taught me that once I was enmeshed in the culture, just think about enmeshed in the data culture. I started to think data. She started to think in Spanish, not going, what does that word mean? She started to think in Spanish and became fluent because she actually thought about Spanish in her subconscious. And I had one friend tell me I started dreaming in Spanish. That's when I knew I had it. So I imagine I've had some nightmares about data. At the same time, I do understand the distinction between understanding being literate and being fluent, which means I really can use the language of data to communicate the meaning and message I wish to communicate. That'll be the next part when we talk a little bit about. Tell me a story about the following, and I just love their examples today, and I hope you enjoyed this particular piece. I sure did. Thank you for joining us and being with us. I'm Ray. This is JLC Media. Take care.

Sincerest thanks for listening to this episode of the Everyday Leadership Conversations podcast. The Jorgensen Learning center offers a variety of programs for individuals and organizations to enhance their communication and leadership skills. To find out more about programs and upcoming webinars, check out our events page@gojlc.com.