 Hello and welcome and happy New Year everybody. My name is Shannon Kemp and I'm the Chief Digital Officer of Data Diversity. We would like you to thank you for joining the most recent webinar and the first webinar of the 2024 season in the Data Diversity Monthly series elevating enterprise data literacy with Dr. Wendy Lynch. The series is held the first Thursday of every month and today, Wendy will be joined by Laura Sebastian Coleman, Mark Horseman and Nicole Luke to discuss a fresh look at data literacy. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. If you'd like to check out with us or with each other, we certainly encourage you to do so and just to note Zoom defaults the chat to send to just the panelists, but you may absolutely switch that to network with everyone. For questions, we'll be collecting them by the Q&A section and to find the chat and the Q&A panels, you can click on those icons found in the bottom middle of your screen to activate those panels. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of this session and any additional information requested throughout the webinar. Now, let me introduce to you our guest speakers. Laura Sebastian Coleman is the Vice President of Data Management and Governance at Prudential Financial and has worked in data management since 2003. She has implemented data quality metrics and reporting established data consumer training programs and led working groups to establish data standards in support of strategic data governance goals. Author of Navigating the Labyrinth and Executive Guide to Data Management in 2018 and Measuring Data Quality for Ongoing Improvement in 2013 and meeting the challenges of data quality management in 2022. She is currently writing a data quality management textbook for the Insurance Data Management Association, very cool. Nicole is the founder and president of Signific at B2B, a B2B company that provides analytics and project management solutions. With a solid foundation of statistics, Nicole has over 25 years of experience in data and analytics. Nicole has worked with companies of all sizes and has the ability to distill complex concepts into accessible insights. This has not only enhanced the data literacy of those she has directly worked with but has also contributed to a broader cultural shift towards data informed decision making. Mark is a data management professional and CDMP practitioner with over 20 years of experience and is a data evangelist at Data Diversity. Mark moved into data quality, master data management, and data governance early in his career and has been working extensively in data management since the early 2000s. Previous to his work at Data Diversity, Mark led information management initiatives in both private and public sector organizations. And let me introduce to you our speaker for the series, Dr. Wendy Lynch. For over 35 years, Wendy has converted complex analytics into business value as a sense maker and analytic translator. A talented researcher and consultant to numerous Fortune 100 companies, startups, and healthcare giants, her own work has focused on the application of big data solutions in health and human capital management. Author of books on effective communication and analytics, Dr. Wendy has pioneered the only structured system to empower a new generation of professionals who will revolutionize the successful application of data to solve business challenges. These trained analytic translators allow companies to convert analytics, advance analytics into actionable solutions, building a sustainable alliance between analytic and business professionals. I need to warm up my tongue this year, y'all. And with that, I will give the floor to Wendy to start her presentation. Hello, and welcome. Hey there, Wendy. Thank you, Shannon, and I thought you did just technical. This group is, I am here. I am here. I am here. I can hear you now. Can you hear me now. You're good. We sure can. Okay. I know. Well, I know you need to warm up your tongue. I need to warm up my microphone, I guess. So, I want to thank everyone for joining us here this first week of 2024, especially my esteemed panel members. And for those of you who have joined us before welcome back those of you who are coming the first time. Welcome. And I appreciate you spending some of this first week of the year with us. So I'm going to start with just a little reminder about what we're focused on when we think about data literacy. And what I have to always remind myself when I get buried in a particular topic, whether that be analytic translation or whether that be data literacy. I want to remind myself that in the business world, not the academic world, not where we're focused on the theoretical. In the business world, business leaders do not want machine learning experts. They do not want comprehensive data governance. They do not want optimal data architecture. They do not want employees with high data literacy. Now, what do I mean by that? They don't want these things in and of themselves. They don't really care. I mean, not in a mean way. But on any given day, they don't really care about these things. What they want is to achieve measurable value from timely informed use of data. If they want to apply data in their organization. That is what they want. They don't want to have to think about it. They don't want to have to worry about it. They want it to be happening. And so business leaders, business executives will support the abilities and tools that will accelerate their ability here to achieve measurable value from timely informed use of data. And they will essentially tolerate these other things. They will accept that we need to develop better AI and machine learning capabilities that we need to have consistent governance that we need to have optimal architecture that makes things seamless and useful and safe. And they are willing to accept having data literacy as a priority in training only, and I repeat only if that enhances our ability to accelerate achieving measurable value from timely informed use of data. So when we look at these kinds of issues, and specifically in this series we look at data literacy. We can't be assuming that data literacy is the end all be all, even though many of us focus there and spend a lot of our careers focused there, or spend a lot of our days focused there. It's only a value to the organization. If it is helping us achieve measurable value from timely informed use of data. Are we able to extract the insights that actually make the business better. So business leaders don't really want data literacy. They only want elements of what we can achieve, if it produces value to the business. And so how do we answer these questions today I want to have in the front of our mind. I'm hoping that I can just quickly get through this then I am hoping that we can keep in mind the questions have we shown whether or not. Number one, can we increase data literacy. Number two, can we increase data literacy by enough that it makes a difference. Can we increase data literacy for enough of our population that it makes a difference in such a way that it measurably increases value. So this is where we will focus today. We want to understand how we are doing, and what will have to happen in the new year, so that we can advance measurable value from timely informed use of data. And what has to happen in order for data literacy to be a part of that. Shannon, can you confirm that I'm still live right now. You are. Yeah. Okay. Okay. All right, so I have a series of questions for our panelists. And the first one just to get us all warmed up was, I wanted to know if data literacy has a theme song for 2024. What should that theme song be and why, and I'm going to go in order from left to right here. So if you want to answer up. Mark, tell us. Tell us. What should the theme song be for 2024 and why. My theme song for 2024 would be yesterday to the Beatles but I, I wrote a couple of different lyrics for it so we'll, we'll, we'll, we'll treat, I suppose is a word that I could use the audience to my singing voice. Yesterday. My data was not far away. Now it looks as though it's cloud to stay. Oh, I believed in yesterday. Suddenly. My data is where it's supposed to be. But I have to pay a compute fee. Oh, yesterday came suddenly. Why we had to go well I know but cannot say. We breached something bad now I long for yesterday. So there you have it. That is a good rendition and I like that choice of the Beatles. Very good, very good and I don't know why didn't ask that everybody needed to sing so I will pick the other two respondents off the hook so Nicole. What would your choice of the theme song be for 2024. Well I don't think it's fair that I have to follow Mark to be honest. I don't want to get on getting chat GPT to write me something but then I realized I don't have a singing voice. So that wasn't going to happen. So anyways this is a song that I picked I'm going to just play you a snippet from it and then I'll explain a little bit behind my choice. So Bob Dylan wrote that song in 1963. Pardon. So he wrote that as an anthem to change. So go right ahead. I found it kind of poetically appropriate to be honest in this age of data and just how things are changing. I know it hasn't happened overnight you know how we collect, use and analyze and discuss data. It has changed how we do business in the last decade so you know only in the last few years though the world has started to take notice I think back to the Cambridge Analytica scandal a decade ago or the huge uptake in generative AI really just this last year, and data is not really just from math nerds anymore. So it's moved from a niche skill or industry into the mainstream. So honestly, how we explore understand and communicate with data will become as necessary and ability as reading literacy and numeracy. So just toward the end of the song, your old road is rapidly aging please get out of the new one if you can't land your hand for the times they are a changing. Very good. I like that one. Very good choice very good choice. All right, so we had a sing and then a play and Laura, do you want to share what your theme song would be so yesterday and times they are a changing we have some time orientation here what about you. Well, it's funny that you notice that pattern, Wendy, let me just see if I can play it here. I hope you got that. Let's get it started. Let's get it started. Yeah, so I, I agree with the observations that Nicole and Mark made with their songs that we, we have to change, and we have to change because otherwise we're going to be kind of stuck in the past. And I've now been thinking a lot about data literacy for the past almost five years, and I, I haven't seen a lot of movement within the organizations that I've been part of during that time I think it's great that Data Diversity has this series because I think it data literacy is something that needs to be understood reinforced and and evolved right at both at the organizational and the, and the individual level. So I'm kind of impatient to like, move it forward. And that's why I chose this song. I hadn't previously listened to all of the lyrics only had the chorus in my head. So I didn't play the first verse, which if you guys know the song probably wouldn't have been appropriate for the webinar but I do like that. I do like the, the impatience in the and the excitement actually in the chorus. Yes, I like that too. And if any of you out there listening in all of the attendees have a perfect song to recommend you go ahead and put it in the chat and we'll take a look later and comment on any of those so we look forward to hearing from all of you as well. So let's move on to the first serious question, although I don't know that that that is not serious to hear about where we think we're going and how much we think we need to change and how much we need to actually move forward, because you haven't seen Laura, a whole lot of movement, even in the last five years. So let's go to the second question. And I will start with Nicole. The question that I wanted to pose first is what trends do you see in 2024 that would make it the year that companies will actually be able to make these kinds of changes, the kinds of changes that Laura would like to see so Nicole why don't you start us off here. Sure. Well, to be honest, I don't think that there will be any of these like big advancements or major changes in data literacy but I do think that this snowball will continue to grow and gain momentum. You know companies just they just seem to always be drawn to these like these quick wins these big shakeups they want everything to change so fast. But I think that investing in something that is a slower burn like improving data literacy, which really is a long game. It's harder for companies because it takes years to see the results right so you know I think to like reading literacy. That starts with learning the alphabet not learning not reading Charles Dickens is great expectations you know. But I do think there are three areas where I feel like there will be advancements in the next year or so. First, I think data storytelling. More people are interested in learning how to properly communicate data insights right so communication has unfortunately just been a challenge in the data and analytics world for a very long time. But I think we're finally starting to get to this point where we can communicate some of these challenges more effectively without resorting to like tech speak and alienating a bunch of people. I also think that our educational institutions right from grade schools read a post secondary. They're starting to recognize this gap and data literacy training. You know companies have been saying they need more data literate employees and, and I think finally these institutions of ours are starting to listen over the last several years. So I think that 2024 will continue this snowball effect with graduands entering the workforce and they'll keep pushing companies to improve the data culture and how data is used so I think it will start we'll start seeing more results because of the snowball effect. And then I mean I don't think any conversation would be complete without talking about the fact that you know the generative AI advanced since we've seen in the last year alone will force companies to fast track what they wanted to do for data literacy in the last four years before right from the top down in companies, you know we, we can't discuss these concepts like how to use AI in the office whether AI will replace jobs, or the ethical of you, the ethical use of AI and data without first understanding how to explore, understand and communicate with data. So, like I said not anything like major but I think the snowball has started and we're going to start seeing improvements. Great. So storytelling and advances in education so that we accelerate that. And then also how AI is going to have an influence on that. Yeah, I think that's great. So, let me turn now to Laura. Laura, can you give us your thought on trends, and especially think about whether you agree that these are going to be incremental steps forward not a big leap, the way that it sounds like Nicole thought about it. So, first of all, I, I appreciate the response that Nicole gave, especially about data storytelling and educational institutions. I know when I've, when I've read up on data literacy there is so much really good work that's been done in, in elementary level and high school level, like trying to raise awareness of the need for this. I, when I was thinking through this, the thing that popped to my mind was the last thing that Nicole mentioned, which is with the advancement of AI, we are really, we actually have the potential I think for a giant leap forward, not just an incremental leap. But the reason that I think about it this way is that we all know what's happened in the last year with AI suddenly it seems real and it had not seemed quite real before. I recognize that it has the potential to be a real game changer, but there is a ton of uncertainty around it, and people are aware of the risks, perhaps more aware of the risks than they are of the opportunities. One of the ways to reduce the uncertainty around the use of AI is to actually study and understand the data itself and what data is going in into these, you know, algorithms and such. So, that is only going to come about if people really hone their data literacy skills and build their knowledge. So, they're both positive and negative drivers to that right the positive driver is the opportunity and the negative driver is the risk that things can go wrong. The other thing that I think is going to happen when people explore AI more is that they're going to see their it's going to make them curious more curious about data, right they're going to want to know more about the data because they're going to see it in action in a different way from how they have seen it before. So, I'll just reference here the executive order that the Biden administration issued at the end of 2023 near the end of 2023. With that, I was very interested that it acknowledges both the risk of AI creating greater social inequality, the risk of AI putting national security in, you know, at risk in the US and other places, and also the opportunity that AI could change how we interact and how we advance on other aspects of social opportunity. So both the, the kind of things we're afraid of and the things that we're hoping for. And I think that for me that encompassed the range, the spectrum of things we can do with AI and it also made me realize that if people want to be engaged. They, you know, they're going to have to learn more about the data and how it comes together in order to meet both the opportunities and the risks. All right, so Laura believes that there may be some bigger leaps because of how people are responding to the AI advancements, both because people might be afraid of it but also because there are opportunities and it makes people more curious. So, I think that's a wonderful perspective and I think there's a lot of places where people believe that AI is going to have an effect. And so, in your opinion, it's going to have an impact on data literacy efforts in all of these ways. Indeed, yep. So, so then, Mark, why don't you finish us off on these trends. And again, a comment on whether you think that this is the snowball incrementally growing over time, versus some leaps that might be potentially happening in the near term. So, give us an answer but also comment on that. It's something I've been thinking about for a while and I'm seeing the trend more and more and just talking to folks at events like DGIQ and EDW. I see companies that are engaging in data measure data fabric type activities. Folks who are federating their architecture and doing federated models and Wendy, we talked a bit about this not too long ago as well. Yeah, you've got this federated architecture and federated way of being such that folks around your organization must be more literate to survive. Like literacy trial by fire or literacy by immersion, where people are required to across the organization to manage their silo to manage their data product to be more and more literate than they ever have been before. And there's a lot of research right now on mesh and fabric and I think Gartner said not too long ago that mesh is dead, mesh is dead and maybe to some sense it is but fabric isn't. And they're very similar concepts that just slightly different architectural bends on the same thing. You're trying to federate something and spread the data love throughout the organization and who's managing everything. But it's going to require that so many people be more literate to make that function so I think it has to happen in that direction. I'm actually, if anybody's following along with the cool kids articles, I'm actually writing about this in my cool kids column on which will be published on Monday. So just the nature of the federated model, but folks having to be storytellers and data managers in that in that federation, or else things just don't work. So it creates a requirement where a requirement wasn't as broad before. Right. So yours is almost a structural imperative. So you're saying that as a result of how structure and architecture is evolving, it will put some requirements on a wider variety of people to become experts in the data that belong in their area is kind of what you're saying. Yeah. Yeah. So this is a nice variety of answers from the grassroots need to get better at some of these things to the effect of an external development that is being imposed on people, whether they are ready or not and then the structural different causes so those are a nice variety of answers for our audience to really think about in what's going to happen. Can we move on to the next question and I will start with Laura in this spirit of a fresh start which was the title of this particular webinar. So companies should companies change their thinking. So we just talked about changes in actual events or structures or trainings, or outside influences like AI that is sort of appeared through chat GPT and others. How should they change their thinking to help jumpstart what's happening. So, I like this question a lot because I do think that there is a need to change how, how we think about the purpose of data literacy. So, one of the problems that I see is that organizations tend to think about data literacy and other data management problems in really similar ways. So thinking about how can, how can we make value the question that you raise. Instead, they, they are trying to think Oh, how do we prove that we have somehow implemented a data literacy problem, a data literacy program without necessarily thinking about the goals of that program. And I see similar things in data quality. Oh, we have to profile our data. Well, why do you want to profile your data? What are you trying to learn? I don't know, we just need to profile it. They said the tool would take care of it. Until organizations actually, actually define the problems they in particular need to solve, they may just be adopting ideas around data literacy without really having drivers for those ideas. And I think that needs to change. They need to understand their current state and they need to understand what, how to get to a better future state. And the second thing, which is a kind of subset of that problem is that there has been a lot, a lot more focus on tooling than I would have thought for this subject. Data literacy is really not about tooling, although tooling can support how we share knowledge around data, but it's really about how you change your culture, and you help the people that comprise that culture, learn more about data and develop the skills they need to, to use data better. So, Nicole had said earlier that data literacy is a long game, and that, that I think is really true. You know, you've got to invest time in helping the organization as a whole and the individuals actually learn. So, I think the final comment I want to make is that, while data literacy is needs to be supported by organizational processes, there's also a risk that for individuals, they may perceive themselves as not data people. And if, if they think that way about themselves, if they think, oh, you know, there's people that can learn about data or already know about data, and I'm not one of those people. And then that will be a huge obstacle for both them as individuals and for the organization. So, one, I think there's two things to get around this obstacle, right? One is to really encourage people to adopt a growth mindset, right? Help them understand that they can learn and then, and then actually get them excited about learning. And then the second thing is what I mentioned earlier, which is actually building people's curiosity about data. I know I, I find data completely fascinating. So when I think when we talk about data, if we can do so in a way that gets people curious about it instead of intimidated by it, then we can, we can make advancements. So I think a, you know, a new start involves thinking about the problems you're trying to solve and then really trying to engage people in a different way so that they are excited about solving those problems and they're, and they're interested in the answers that they can get from data. Those are some really good points. So, number one, the purpose behind it rather than just doing it. So, why, why might an organization be interested in improving literacy and how does that shape what we do. Also, shifting from tools and metrics to culture. I think that's a wonderful one. And then I like this shifting our thinking on how we label ourselves. And I do think that that is one of the big challenges for, for how we help somebody who doesn't believe they are a quote unquote data person a math person. How do we help them get curious as you put it, and help them really get interested in a way that they want to achieve that kind of growth. Wow. And they look forward to that. So, wonderful thoughts about that anything else Laura. That's all I have on that one but I'd be curious to, I'm curious to hear the other responses. Yes. Okay. So, Mark, what about you? How should we be changing our thinking. Well, Laura said a lot of things that I agree with, but one thing that I've had a few discussions with folks on this now. And I see it coming up in the chat to so I love that our chat is so vibrant. When we call things data literacy, then we call people illiterate. You still have the same issue with fluency and not fluid affluent. That's not the right word. But I think I think the true nature of these labels and how we approach data literacy is a little bit I think we're to blame a little bit for this. Which is maybe a bit of a hot take. The way we as data management professionals and data leaders treat folks who don't understand might be part of the problem here. I think it requires a much gentler hand and and why I really like Laura's answer is we want to encourage that curiosity and folks we want to encourage people to have fun with and learn about data, but not couch it in terms of, well, you don't know anything. So let me help you along the way. I think I think we share and shoulder some of this this blame as to why folks at the C-suite aren't as engaged as they should be. And you see that the symptom of this is you see a lack of understanding of requirements when it comes to fixing the quarterly report. In the end, like really, what is data literacy is fixing the quarterly report? That's a bit of a joke, but I mean, it's true, right? Like, what are we fixing? Why are we fixing it? What do you think it is? And Wendy, it goes to a lot of what you and I talk about and what what you've talked about over the last year in this series is the analytic translator, somebody who can bridge the gap between data and business. I think that's where the secret sauce lies and communication has to be more respectful both ways. And I think I think we as data management folks can take a lot of that on ourselves and speak in more business terms. So that that's the kind of direction and change in thinking that I've been thinking about for data literacy for the last couple months. Yes. And thanks for that plug for anybody interested in analytic dash translator.com. We are trying to train a thousand people, hopefully to change all of this. So McKinsey says we need two million more translators by the end of the decade. So I agree, obviously with that. I think that your perspective really does synergize with what Laura was talking about on how we self label. And I agree with you that how we approach that and that's probably part of Laura's culture really can make a difference. So Nicole, I know they've covered a lot of topics. I'm curious what you think we ought to change with the way that we think about literacy. Yeah, I can't say I disagree with anything that was said to be honest, especially what Mark just said now about, you know, the fact that we can have this judgmental attitude towards people who don't know data. To be honest, I think in the past anyways, like when I've been doing this for, you know, most of my adult life, not going to tell you how old I am, but that's a long time. And, you know, in the early days, I think that bit of an ego was a bit more to be taken seriously, you know, that my skills are useful that you need my skills that my job is useful. It was a long time ago to being more of a teacher and a mentor to people who are not the most data literate so trying to take them on a journey in terms of, you know, you call it a growth mindset Laura I call it a building a data mindset. And to me a data mindset is just, you know, you look at a problem and ideas from multiple perspectives, and then you just integrate data into all the elements of your thought decision process so you you're just open minded and you ask questions like, what's really going on? Why is it like that, you know, where's the proof? It's not about the math. Like, I know I was reading an article recently about marketing people and how they have math anxiety, right? And they think that it's all about numbers and you have to be a numbers nerd to like data. But, you know, when you ask your waiter and you say, what's good the chicken or the beef and they give you some feedback that's technically data helping you make your decision right. So you just have to you have to be more open minded in terms of how you approach people who aren't in the data professional field, because everybody uses data to a certain extent. So I definitely liked that idea of being more open and being less judgmental. Other thing I just really wanted to touch on to that Laura had said was, I really don't like this idea that companies think that data literacy starts with this new tech or this new tool. You know, no one will take a shovel to a pile of dirt and assume that it will plant the seeds and grow the garden right. So, yep, we think that we're going to get this new data warehouse this new data visualization tool, and data insights will just magically appear. We don't think about that when we think of a shovel. So why do we think of that when we buy, you know, a new piece of tech or a new tool, right. So, I mean, obviously we still need data professionals we need people to analyze the data, find the insights, but then also communicate to the audience in a way that's respectful, and that gets you that measurable value, right, because that's really what we're going for. The other thing that nobody really touched on that. I really personally think it's a huge misconception is, when you look at job descriptions for anything in the data world, it's always about these hard skills. Can you do Python, can you query with SQL, can you build a statistical model. And I mean these can be a skin essential skills depending on the organization, but that's not the starting point for developing real true data literacy and organizations. Because I mean you can hire the most technical data scientist or analyst, who has the most modern tools, but that doesn't make your team data literate, because they might be great at exploring the data. But who knows if they have good communication skills to speak to their insights that will help with data informed decision making, or maybe the audience still believes in gut feeling intuition. And the results they present will just fall on deaf ears anyways, right so, and then my final thought also is that this idea that data literacy starts with individual contributors. I personally don't think it does. I think there's tons of people out there just looked at look at on LinkedIn everybody's getting a certificate and data something or other these days. I personally think that we need to start with the top. We need to start with the leadership. And we have to start, as Laura mentioned, with the problems we're trying to solve, not the fact that I have this, these terabytes of information and data at my disposal But what do I want to solve with this information what is my problem what what do I need this how do I need this company to move forward. And I just, I don't think that the way we approach data literacy right now from the bottom up is necessarily the best way. So, if I was to summarize my thinking, I just wish companies would stop thinking of data literacy as tools or hard skills, because they can be bought or trained to be honest. If we approach problems with the right data mindset, that's the foundation. And if employees right from the C suite down to the individual contributors, don't have the right data mindset, then companies will struggle to build a data litter organization, no matter how many tools they buy or training courses they send their staff on. Yeah, I do have to say, I love all three of your perspectives and Nicole I think you are spot on and I'm seeing a ton of comments about how what you're saying resonates with people. And so you wonder whether we even if we're talking about language, do we really want to even use the word data, because it freaks so many people out. So, why are we even thinking about it as a data issue when it's more about understanding or producing value, or some of these other things. And might we think about it differently. If somebody doesn't put a data hat on it or a number hat on it because that's where everybody with math phobia instantly goes. Yeah, so wonderful comments to all of you. And there is one more question that I would like each of you to comment on. And I will start with you, Mark. If there is something and we've heard a whole variety of comments already about where people ought to go and what we ought to stop doing what we ought to start doing. I'd ask you to prioritize if there's somebody today who's saying okay, it's the beginning of 2024, and I need to start somewhere, because this is in my KPIs this is what I need to be producing I need to help with literacy whether it's the literacy mindset or the literacy abilities or a literacy culture. What is the one thing that you would recommend that somebody do right now. Yeah, this is, this is something I've had success with at the last several places that I've worked at and I recommend everybody do something in this vein and and really it's about communication but what I've enjoyed doing is newsletters. Hey, this is what's new in data, this is what's new in data and organization this is what the data teams working on this is why it's cool, and really just bite sized, entertaining chunks of newsletter type content. The last place that I did this at you had a, I used to put like two short paragraph size articles and then like a food recipe at the end because I'd like to pretend I can cook. And, and so I get the occasional person yeah like I look at the news I really don't really just there for the recipe it was always page two, but eventually what I started hearing was folks talking about the topic. The data topic of the day whether that be AI or, or a master data management initiative or something but it was always written in the style that would be hey this is why this matters at our organization this is why it's, it's important for everybody. It's sort of a vibe, but maintaining a good communication strategy, fun communication strategy that has a regular cadence to it. So that people can expect something whether it's like a short 32nd or two minute video that you do on Tuesdays and you like two minute Tuesdays that I've heard people do before. That is going to help move the needle even though it's a small thing. It's a small tiny thing that you can just do. And, and, you know, as long as people start thinking about data, you'll have success with something like that. So it's a fun, regular communication tidbit that highlights things that are either intriguing or useful so that people get used to seeing examples that maybe they haven't thought of as a quote unquote data example or a literacy example. Exactly. Yeah, that's really great. Well, if you had to select one thing that should be on their minds to advance literacy in 2024. What would it be? Well, for me, I wouldn't start obviously with buying a new tool or sending off an employee. I personally would start with training the leaders to be data literate. In particular, I would focus on getting the right champions in the organization who can truly affect change, especially I've worked with some people who are like yeah yeah no we totally need to do this. But then after the meeting it doesn't really go anywhere right or that champion goes away and then nobody takes their place and then everything just falls apart in the organization. It really needs to start from the top and kind of then trickle into the rest of the culture, because I think so many organizations think that it's the individual contributors who need to be data literate first. But in my experience, I've just found that if I didn't have good solid leadership and a champion, then everything that I was trying to bring that was a value just never worked out properly, because the data culture unfortunately starts the top. Grassroots movements tend to be favored and there's, I don't know why but this kind of disdain for a hierarchical structure sometimes, but I think when organizations are structured hierarchically without support from the top there's just a good chance that any new programs or ideas will fail. And so if organizational leaders are brought into the program first then then they've got more of a fighting chance to succeed. And just kind of what what what to echo what Mark was saying in terms of making things fun. Like, one of the things I've tried to do when I've been an employee in organizations is to be relatable to people to make sure that I'm around that I talk to people that they realize that we're not off to the side and we're separate group that you collaborate with people that you bring them into your work they bring you into their work, and then involve the leaders and all of that stuff give them you know a lunch and learn type thing. So I wanted to make sure that leaders are involved in all of the processes when it comes to implementing data changes and organizations. Got it. So, you are an advocate for starting at the top in some ways. It doesn't mean exclusively at the top. Yeah, definitely not exclusively. I just need to understand what they're trying to achieve because if they think it's going to come from bottom up and then the individual contributors aren't meeting their, their needs. They have to think well what are my needs in the first place like they need to define that. So I do think that in a large case. Yes, we need to train everybody, but I think that the change needs to start at the top. So it's partly modeling, because they would be modeling behavior that they hope the rest of the company would adopt. Yes, partly awareness so that they understand what other people are going through. Yeah, especially if they are having to learn. That's a great way to put it yeah. Yeah, so that's a great. That's a great step. So we've got fun regular communication that helps build grounds well of awareness and has it data issues and data learnings becoming a part of the. I'll use the word fabric even though that isn't how the you use fabric exactly mark, and then also an adoption by the, the leadership, so that they are aware and modeling these issues. And so let's ask you Laura. If there was one thing you think the companies could do right now what would it be. So, what I had thought through before this is very similar to what Nicole was talking about. I'm going to tie it back to a comment I made earlier about curiosity and getting people engaged in thinking about data. So I think if there's one thing that companies can do it is to start to use data themselves in a way that in that employees get engaged and and to bring that into the culture of the company and obviously that needs to happen among leadership and it also can happen among other people as well. So when I talk about getting people excited or getting them curious about data, I'm, I'm not actually, I think there's lots of ways of being curious and it can be that you find it fun or interesting I know, I have a lot of data really interesting, but I think the way that you can change the organization is if you make that direct connection to the business goals so the point that you made to introduce the webinar Wendy is, you know, business leaders want value. And show how the data itself allows you to understand the value that the business is generating and the data then contributes to that. That can be very powerful. And I'll give an example. I've been fortunate to, to work in several companies where leadership has been very good with data, and has used data themselves to demonstrate how we've been improving or changing the business. And the example I'll give is during the pandemic I work for a health care company. And as you guys can imagine, the, the events of the pandemic had a lot of different effects on the business of the company right large managers were, were, you know, furloughing or laying people off so the way that that affected monthly reporting and the way that it affected the types of claims that came in and everything it was just, you know, potentially chaotic if people did not have a handle on data. And so our data science team put together some analyses and, and particularly good visuals that showed how the pandemic was changing our business, and how we needed to get ahead of that in order to both serve our customers and understand what might be happening from a financial and in other points of view. And so to me that was a major illustration of why we needed to have good reliable data, because otherwise we would not have been able to navigate the, the the maelstrom of the pandemic. So, you can't always have examples that are that vivid. But if you, if you focus on having examples that show that connection between how we understand the business through data, and how we make decisions about what to do in the business. I think that that is the most powerful thing that can change people's perceptions of of their own relationship to data. So, I say examples, examples, examples. Yes. Yeah, that's a really, really great point. And I'll add another example along that line in a previous life in my work in human capital. We did a lot of work on pay for performance and adjusting compensation for all workers to reflect what they are accomplishing. And I will tell you that once you give somebody a metric and say, this is how your job links to your department and this is how your department links to the performance of the organization. And so these are your goals because this is how it links with a line of sight to how the organization is doing. So we are going to partly pay you based on your achievement of these metrics. And I will tell you that people get very curious and very intent on understanding whether those metrics are valid, whether they are distributed reasonably, whether there are outliers that don't make sense, whether they should be used to make comparisons. So there are great ways that you can start to think about measures that get people a little more attached, essentially, to understanding. So, but that was a great example Laura. So, I think we are down to the last couple of minutes, and we do have a question. And Shannon, I'll just go ahead and ask this question. And it's the anonymous attendee asked, how do you see data literacy advocacy in 2024. In a way that avert the pitfalls of being data liter or fluent and versus not. And how do we advocate for data literacy without forgetting the less data oriented people. So we've touched on that a little bit. But, Nicole, why don't I just go ahead and throw that one over to you. And some of the comments you made. How do we advocate for it in a way that does not highlight people who are non experts and call them out. You know what to be honest. I don't think too many people use the phrase data literacy or data fluency outside of the data sphere unless you're talking to data people I know when I've worked in, especially larger organizations but definitely smaller startups as I've never been, I've never thrown around the terms, you know, are you data literate are you data fluent. I just start talking to them and I just are asking questions. And then you kind of get an idea of where they're at, and what they need. So I think that even even using the terminology like I mean we use the terms literacy and numeracy, although, to be honest, I don't even know how many people use the term numeracy anymore, because it's I think a lot of people call it number sense and stuff like that so if the terms make people uncomfortable, we don't necessarily need to use the terms to train the skill in all honesty. That's, that's a great, that's a great point. What we don't, it's sort of like going back to the beginning where I introduced this topic. Just like companies don't care whether their people are data literate, except to the point that it helps them perform better as an organization, and helps people be successful. And I think you're right, we don't, the individual doesn't care and doesn't really identify themselves as literate or not, but they have a certain level of comfort. We might be able to help them differently if we're not identifying it in that way. I mean, to Mark's point, it's just a way to, we can engage their curiosity while being respectful and not using terminology that might, might make them be more self conscious about what they know or what they don't know. Yes. Any additional comment on that topic Laura. I, over the last year I've been humbled by people who have talked to me about this because I, it never occurred to me personally that using the term literacy would that people would take it the opposite way and be concerned about illiteracy but I've had several conversations with people that I really respect on this and they have made that observation that you know the term can get in the way. And Nicole said about, you know, we don't want to use and Mark, we don't want to use terminology that's going to get in the way. At the same time, I think it is helpful to have models of literacy, because you, you, you don't have to have everybody be an expert in data to contribute to a data driven organization but you need people to be comfortable enough, and to have the right knowledge for the, the work that they need to do so that they feel confident in making connections. And so I think the concept of literacy can be applied to data to create models of how we, of the things that people need to, to learn to know, and the ways that they can gain knowledge and experience about data. We don't necessarily need to put those upfront and say this is this is a test you have to pass it. Instead, we need to use them to help people get to the place that they need to be and to help organizations get more value from their data. Great. Great answer. So I know we're running out of time at the top of the hour and Fred. Sorry. Sorry about that. And we will be getting for those of you asked about the Biden declaration about AI, then Laura, you, if you can provide a link to that, then we can send that out when we send out information. I would be glad to. Yep. Okay. Great. Thank you. Well, thank you, Wendy, as always, and thank you to our guest panelists today for such a great conversation. But I'm afraid that is all the time that we have for today. Just a reminder, I will send a follow up email by end of day Monday for this webinar to all registrants with links to the slides and links to the recording with and with that additional link and information as well. Well, thank you all. Happy New Year everybody. And we will see you in more webinars. Have a great day. Thanks, Shannon. Thanks, Wendy. Thanks, Mark. Thanks, everybody. Thanks, Nicole. Laura. Mark.