 Hello and welcome, my name is Shannon Kamp and I'm the Chief Digital Officer of Data Diversity. We would like to thank you for joining the very first installment of the new monthly Data Diversity webinar series, Elevating Enterprise Data Literacy with Dr. Wendy Lynch. I'm so excited for you all to be here for this first webinar. This series will be held the first Thursday of every month and today, Wendy will introduce the series topic and will be joined by two esteemed panelists, Laura Sebastian Coleman and Melissa Dupwig to discuss is Enterprise Data Literacy possible. 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 chat with us or with each other, we certainly encourage you to do so and just to note Zoom defaults the chat to send you just the panelist, but you may absolutely switch that to network with everyone. For questions, we will be collecting them via the Q&A section and we encourage you to share highlights via your favorite social media platform using hashtag dataversity. And to find the chat and the Q&A panels, you may click those icons in the bottom middle of your screen to activate those features and as always, we will send a follow up email within two business days containing links to the slides, the recording in this session and any additional information requested throughout the webinar. Now it is my pleasure to introduce to you the speaker for our series, Dr. Wendy Lynch and our two panelists, Melissa and Laura. Melissa is the director of analytics and data governance enablement at N2A. Melissa has been in the data and analytics space for almost 20 years and is passionate about building teams and improving ways of working through analytics capabilities and governance. Laura is the VP of data governance and quality at Credential. She has worked in data quality management since 2003 and has implemented data quality metrics and reporting, launched and facilitated data quality of working groups, contributed to data consumer training programs and led efforts to establish data standards and to management of data in support of data governance goals. She is the author of several books, the most recent being Meaning the Challenges of Data Quality Management. Wendy is the founder of analyticstranslator.com and Lynch Consulting. For over 35 years, she has converted complex analytics into business value. At heart, she is a sense maker and translator, a consultant to numerous Fortune 100 companies. Her current focuses on the application of big data solutions in human capital management. In 2022, she was awarded the Bill Whitmer Leadership Award for her sustained contributions to the science of corporate health. As a research scientist working in the business world, Dr. Wendy Lynch has learned to straddle commercial and academic goals, translating analytic results into marketing success. I heard her speak first at the price analytics online conferences and I'm so excited that she has agreed to join us and help us through this challenge of data literacy. So with that, and just so you all know, she has a book and an online course on how to become an analytic translator. So Wendy, hello and welcome. Well, thank you for having me and thank you for all of you who are joining us. A special thanks to Melissa and Laura. We will be hearing from them in just a few minutes. I'm going to start by having an orientation to this whole idea. We're so excited to launch this series. This is the first one. And we're talking about data literacy from the standpoint of a whole organization, the whole enterprise, not the spaceship, the entire organization. And our question today is, is enterprise data literacy even possible? It's not a surprise to any of you that in the past 10 years, especially the past three years, data literacy has become a huge topic. Before 2010, that decade, it was 6 million searches on Google. The following decade, 117 million searches on Google. And the past three years, 143 million searches on Google about what data literacy really is. It's a hot topic for leadership. It's a hot topic for management. And it won't surprise you that 90% of business leaders report data literacy was going to be critical to their company's success. But what are we talking about when we talk about data literacy? Well, there are lots of definitions out there in articles and books. And most of them start with something like this, the ability to read, write, and communicate with data, or read, write, and argue convincingly with data. That is a great start, a great overview. And it was the first sentence in the data versity definition that came out in June. But it starts to get more in depth. So in this definition, it's read, write, and communicate. But in context, including how you understand the data sources, analytical methods, techniques that you use, and the ability to apply the data and to explain the results of an analysis. So it starts to get more comprehensive than simply that read, write, communicate overview. If you look at one of the references that I know Laura thinks of very highly, Michael Larson says that in order to be data literate, you must be able to do these six things. You must be able to know what data is appropriate if you want to answer a particular question, to be able to read charts and graphs and interpret what you see, to understand that whole path of data from sources all the way to visualization of results, to know which kind of data and how to see data once you've done a particular analysis, how to know what's going wrong, whether it's improperly used or biased or misleading, and then finally to be able to communicate about data with others. So we see that it's starting to be way more comprehensive. It's starting to get more and more definition. And probably the most detailed that I've seen is from a group called Data to the People. And this one is used by the nation of Canada to try and advance data literacy nationwide. And this organization has identified 15 data abilities all the way from collection to manipulation to analysis to visualization. And each one of these data abilities gets rated on six different levels. So we're starting to see a real drill down into what do we need to have people able to do, comfortable doing, if we are going to reach data literacy at the enterprise level. But let's take a moment here and think about this. If I look at this overall definition, can you read, write and communicate with data? There is now starting to be a real examination of who is comfortable with this. According to Accenture, only 21% of employees nationwide say they're confident that they have those skills to read, write and communicate with data, which means four out of five are not confident. Then if we add all of these other things about understanding sources of data, analytical methods, techniques, how to apply it, I think it would be generous to say, my guess is fewer than half of those 21% start to feel comfortable here. So we're talking about enterprise data literacy, but we're thinking maybe nine out of 10 people in general across all organizations are comfortable with this idea of digesting and being able to make use of data. So what I want you to do is I want you to imagine what it's like for a person who doesn't understand data to be presented complicated data-oriented information. So I invite you to just sit back and listen and imagine what it's like. Latid literacy, a corporative imperate. Dumpani seed data, dues data, drive with data. Criticical that we each occur, build skated dels, drake made of chiven doices, under del mostants to pre-make good diktions, and engorm accurate, devry a. Dowing data makes you more malleable, band is-nesses, poor, moffitable. Spet garter, dute, belate it's two four. Moingy, learn rutun, majestic lottles and dill's bashports. Let's try that one more time. Latid literacy, a corporative imperate. Dumpani seed data, dues data, drive with data. Criticical that we each occur, build skated dels, drake made of chiven doices, under del mostants to pre-make good diktions, edding gore, accurate, devry a. Dowing data makes you more malleable, band is-nesses, poor, moffitable. Spet garter, dute, belate it's two four. Moingy, learn rutun, majestic lottles and dill's bashports. For people who don't understand data, for people who didn't go to school for economics or data science or computer science or math, data are scary. And so part of this series will be about communication and about the empathy that we all need to have when we are asking people to do something brand new that may not be natural to them and how to make it non-threatening. Imagine that my next announcement is real. At the end of this session, every attendee on this webinar is gonna be asked to turn on their camera and either sing a show tune or do a handstand. If you happen to be able to do handstands and sing show tunes, then you're gonna be asked to sing a show tune while having a handstand. And we're gonna rate your performance and we're gonna send that video to your boss. That's how it feels when you aren't naturally good at something. I know if I had to sing a show tune or do a handstand, I would be very, very, very nervous because I know I couldn't do it well. So we're gonna keep talking in this series, not only about what we need to accomplish, but the format and the culture that goes around accomplishing these things. So to remind ourselves, because most of us in this field, we went into it because we love data and analytics, we love it, we know it, we live it, we breathe it. But a recent survey of 2,000 representative members of the American adult population show that one third of Americans do not know that a quarter of a pie is 25%. 54% of Americans say they just smile and nod rather than admit that they don't understand something. And almost a quarter actually say they can't understand numbers well enough to even read their own bank statements and they need their family to help them. Now, these may not be all the people in your organization, but there's gonna be some organizations where there are a lot of people who have a ways to go. The bad news is about 60% of Americans avoid dealing with numbers, but the good news is that over half of them realize that if they could get better, if we could do this in a way that encourages people and helps them grow, they know it would help them in their lives and in their work. So I'll finish up setting the stage by reviewing the results of a series of focus groups that Dataversity conducted in December. And it included people like yourselves, people who work in the data field. And out of six hours, all of the transcripts were put together into this world, this word cloud. And so I will use this word cloud to give you a synopsis of six hours of focus groups in about six minutes. So the themes that we heard were number one, yes, data literacy is important. It is something that people need to know and they need it now more than ever. Also that it's getting absolutely critical that everyone become more data literate. But there was a lot of questions in the focus group. Where does this effort to build literacy belong? Is it part of data governance? Is it system-wide across the organization? Is it part of training and onboarding? Or maybe it's a separate program that's not part of other training? Should it come from management from the top down? Or should it come from the bottom up within teams? Another question was, who's supposed to be literate? Is it everyone in the whole company? Or is it by level? Should we be making it required for just the right people? Or do we have the need for everyone to actually be able to do it within a team or within a specific job or across different stakeholders? And once we know who, how literate do we need them to be? And this was a very significant topic because on the one hand, we think, well, depending on your role, you might have different levels. But it's important that people understand certain aspects of data, especially quality and definitions of the data that they use. We actually want them to be able to ask questions that are intelligent about the data and to be able to talk about the data that they use on a daily basis. But there were some people who said, we need everybody to actually become an analyst so that everybody can be self-serve and be able to find information that they need without so much help. What is the goal of data literacy? Well, first and foremost, it's creating business value. We want enough change to happen that there will be leveraging all the data assets to make information-driven decisions. And on top of that, we actually want a level of transformation so that we have better and better use of data to advance the needs of the organization but also advance the skills of our population. And lastly, the groups also talked about the challenges. What gets in the way? And first and foremost, it's the burden of what it's going to take to spend the time and the money to actually train everybody across an organization. And then when we think about implementation, whew, so is this something that we do certain number of sessions each year and then we're done? Or is this an ongoing thing where everybody needs to have continuous education over time? So this is the setup. This is the landscape that we wanted to provide to you as we go into our discussion. And I am so excited to have Melissa Depweg and Laura Sebastian Coleman here. Both of them are going to give you examples of companies who are doing this well and who are further along because their companies value data and have already begun to address this issue. I will ask them a set of questions, but we will also take your questions toward the end of the session. So I want to start with you, Melissa. How did your company decide that literacy was an important goal? Thanks, Wendy, and hello, everybody. I'm really looking forward to our discussion today. So into it, we have a main strategy to be an AI-driven expert platform. It seamlessly blends digital and human financial experts. And that requires us to build work-class data, models, and AI-driven experiences. This, of course, means that our product managers and our leadership must make data-driven decisions with speed, and that requires data literacy. And for us, our data literacy definition really matches Wendy's second definition. Understanding the context, communicating data in that context, having a basic understanding of sources, constructs, data flows, analytical methods, and the techniques applied, and the ability to really describe the use case application and the resulting value. And so for us, it's the cornerstone and foundation for us to do our strategy and achieve our goals. Right. And so is that, it sounds like it's happening at all levels of the organization. Is that correct, or where would you say that sort of started? I would say that the need for data literacy really crosses all roles, specifically, and I'm seeing it even in the chat, trying to focus on specific personas. The personas we're really focusing on are the product managers and leadership. Of course, we have a huge population of data workers, which includes data engineers, data scientists, analysts, and they're all trained and do this every day. And so in order to make them the most effective, all of their partners or stakeholders really need to be data literate. Yeah. And that really is like what's driving our focus there. Got it, got it. So Laura, same question to you. How did Prudential decide that this is an area that is critical to your business? And Laura, I think you're on mute if you want to unmute there. There you go. I am computer literacy too. So Prudential has not directly launched a data literacy initiative, but I can speak to the importance of data literacy, nevertheless, because of the type of company that it is, and also commonance on other companies where I've worked and why this is important. So Prudential sells insurance and financial products. Those products are dependent on understanding the current, understanding how to underwrite an account, understanding the risks associated with any kind of condition that you would want to ensure. And then from a financial products point of view, understanding market conditions and likely outcomes from investments and the like. All of that really is about data, right? Those products are completely data dependent. So it's critically important that the folks that are creating those products understand the data that they're working with and can interpret that data and use it to build the products and to sell those products and to make them valuable to our customers and also to make money from that process. So everything in the industry is dependent on data. And that means that the people that are using the data definitely need to understand what it means, how to use it, and the like. If you don't have a level of knowledge about the data itself, you can't make good decisions about it. Right, right. So, so your years, the way it sounds is that you're believing that this level of data knowledge and data literacy extends way beyond the folks who are building the product and manipulating the data all the way you're talking about sales and interpreting for clients. Yes, and, and I think one of the things that's interesting when we talk about data literacy, right, is sometimes it gets presented as if it is a new thing. But in many businesses, understanding your data is really understanding your business. So think about, I'll give a simple example of, if a banking, right, I know banking is complex, but I used to work in a bank on the teller line. And for us to be successful as tellers, we had to balance our cash accounts. And that meant that we were creating data throughout the day that indicated how much cash we had. And at the end of the day, we had to reconcile that. If anybody was doing that relatively simple job, and didn't understand the relationship between the data they collected, and the outcomes at the end of the day, then they wouldn't really be able to do that job very well. And that, again, that's a simple job. But when we think about the multitude of processes that people have to engage in, that if you understand what you're responsible for, and the data you create or use, then, you know, the organization itself is going to run better than if people are not aware of that. And so when I think about this problem, I think about it really as educating people first in the data that they actually interact with and knowing their own jobs and their own processes and inputs and outputs. And then you do need people with higher levels of understanding of the operations of the entire business. But the first step is kind of realizing, wow, our enterprise, whatever that enterprise is, is bound together by data. And so each person who has responsibility towards data, at the very least, needs to understand how their data works. And the more they understand about the wider organization, I think the better off that organization will be. So definitely there are different levels and such. But it starts with each person's work. Right, right. It starts with where they are. I heard somebody call data the actual bloodstream of a living organization, and I thought that was a wonderful way to talk about it because if it doesn't flow and you don't use it correctly or you stop the data or don't do the things you need to, then you actually harm the whole organization. Yeah, yeah. Or if it's of poor quality, that brings harm as well. And especially if people can't recognize it, which brings the literacy question in like, how do you, not only are you able to use the data, but how do you know when something might go wrong or the like? Right. So Melissa, either from this particular role that you're in now or from the past, we just heard from Laura, an example of how literacy or low literacy might show itself in just balancing what's in the cash drawer. But what other ways would our listeners here notice lower data literacy in an organization? Well, I think there's two ways to think about data literacy. It's either currently blocking you from, you know, performing and achieving your current goals, or it's required in order for your company to grow and achieve future goals, you know, three, five years, 10 years out. Right. And so into it, it wasn't so much us noticing the lack of literacy, but the continuous need to increase the amount of people who can self serve data and insights so that our analysts and other data workers can focus on more advanced analytics, such as building complex statistical and AI models. You know, we're at the leading edge in the industry for personalized recommendations and predictions, serving our millions of customers. Yeah. But that means that most of our descriptive intelligence activities, like what happened last week, why did that happen? What were the key factors that drove that KPI down? Need to shift to, you know, product managers, leaders and other business type analysts who aren't always in the data. And also it becomes important, especially for product managers to have a good understanding of what's even feasible so that we can continue to grow and improve and push the boundaries of our experiences. And so, you know, I think, I think what was really interesting that you showed in your presentation was that continuum and maturity of data literacy in the key areas. Yeah. And I think that continuum is very much linked to the company's maturity for their data and analytics areas, right? Right. In the early days, if maybe only a handful of analysts exist in the company, even if their skills are really high, they're supporting everyone else in the company to explain their work, what it means, how they got there, you know, why it should be believed over someone's intuition. Right. And that limits their impact, right? Right. But as companies evolve and they start requiring data to make those types of decisions, you need to add more people even on that continuum and then continue to grow their maturity to open up analysts and data scientists to do much more complex things to really increase what the company can do. Right. And so you really are of the mind that we don't just have people learn a little bit for the most part and then leave the analysts to do the big work. You would like to see everyone maturing as you use that word toward better competencies all across the company. Because if you depend on the few analysts to do all of, answer all the questions, even the simple ones, then they can't be doing the complex work. Yep, exactly. You know, I'm also kind of seeing some of the things in chat. I think it's critical, not just for our ability to move at speed, to have people data literate and able to self-serve things like what their key KPIs are and even get into the curiosity of why the KPIs are doing what they are. In my mind, it also pushes the ownership to people like product managers and really get them to own their own KPIs. Instead of, oh, the KPI dropped, you know, analysts, tell me why that is. Right. They get even more invested because they can be curious on their own and drive their own answers. Right. And you know, something that Laura had mentioned as well, the trustworthiness of your data is so ingrained with this. Because if you don't have trustworthy data, I think a lot of people who are maybe not as data literate or don't understand the complexities of the data, they see a KPI go down and their media question is, oh, well, what data issue is it? Like what's wrong with the data, right? What broke? Yes. And it kind of takes away that the ownership of like, hey, well, let's actually find out why maybe there's, you know, business factor variable that's driving it. Right. Right. Well, it is, it's much easier to blame somebody else than it is to start to take responsibility. And what you're saying is if they own their KPIs and they have to sign off that the way it's being collected and managed is accurate, then 100%, then they would have to now answer the question themselves of why it's going down. Yeah, exactly. Exactly. So, so Laura, you also, besides the example you already gave, how else would, does low data literacy manifest itself in ways that, that make the business, put either put the business at risk or make things not as effective as they could be? Yeah. So, so many organizations today claim to be or trying to be what, what they call data driven, right? So they, you know, they claim they want to take advantage of their data and to get value from their data. Yeah. And the, we get value from data only when we are actually using data. Everything else costs money, right? To store the data, to make it accessible and such. So if you don't have people who can use the data, then there's a direct cost on the business because you're, you're preparing data for use, but not being able to take care, to actually take advantage of that. Yeah. Sometimes it, it shows up in, in, in complicated ways, but more often I think the, the, the problem of a lack of literacy, lack of data literacy or low data literacy often shows up when people simply don't, they don't have the skills to understand what they're looking at when they're, when they're looking at data. I loved your, I loved your introduction with the, with the translation of our assertions about data into this pseudo language, right? Because I, I think you really did effectively remind people of what it's like to be looking at something that you don't understand. Yeah. And, and there, and yet most of us, I think in watching that, at least I was, you know, puzzling out, oh, how did she put this together? You know, like did you, how did you generate the scrambling of those words so that they still, they still sounded like language, right? So my curiosity was piqued with, with the example that you gave. I think a lot of people who don't have data skills look at that and they really are stymied. And that's not good for the business because as I said, you know, data binds most organizations together. And so you have to be able to talk to each other across, across business units within teams and stuff. You, you need to represent the work that you've done and you need to understand it. Yeah. Even, even things that I think are relatively straightforward because I learned them when I first started working with data, like, you know, how to construct a meaningful time graph, right? Or how to organize data, even in a, in an instrument like an Excel spreadsheet. A lot of people don't have that knowledge in their heads. And so you present them with, with information that is, that you hope the structure itself will help them understand and they don't necessarily see that. So I feel like at those really low levels of literacy, it's a, it's a short, it should be a short learning curve to, to get up, up to the next level. It should be, but the things that you identified, people are intimidated by data. It is scary. It is. They do feel like it's a different language or something that they can't understand. And, and what that means is if they're not, if they're not able to understand or not willing to try to learn, it means they don't really understand aspects of the business that they need to understand, right? Yeah. Like how people in, you know, how parts of the organization depend on each other and interact and the like. And that is very, you know, very bad for an organization. Yeah. You know, when, and I'll step back for a moment when, when I think about data literacy, I almost always compare it to general literacy, right? So we all have a stake in having an educated populace who can at least read and communicate with each other through written language and understand written language, because we need so much of our lives depend on that ability and that ability, right? And if we don't have that in relation to data in most organizations, we're really, really doing ourselves a disservice. And at the same time, we can build that because it is a question of helping people understand better and giving them opportunities to learn and also, you know, delivering with that message the importance of those kinds of opportunities so they can get better at their jobs and contributing to the organizations that they are part of. Right. Yeah, I totally agree with you that, at how important it is for organizations to have as many people as possible understand. I do know that when I get hired as an analytic translator, often it's a leader who is not data oriented, who is not getting what he or she needs from the analytic team, not because the analytic team isn't talented or doesn't have the resources that they need, but because they do not know how to talk to each other. The data people don't know how to explain it in English and the non-data people don't know how to ask good questions of data, which is where I come in. And one of my main goals is to demystify each side and have them appreciate each other and build a mutual respect. And that way you have an environment where people are willing to learn because if you're feeling like you're being belittled every time that someone lectures you about their, you know, logistic model, then you tune out and you think they're just trying to be jerks about it. So there are communication issues on both sides, I think, that have to go along with it to get us there. Yeah, and I love the word that you used, demystify, you know, because I do think that sometimes we talk about data as if it's some kind of magical thing and people who are very knowledgeable about data, you know, can get off on tangents or, you know, use vocabulary that gets in the way of that communication, and yet when it comes down to it, you know, data is representing, it's a representation of aspects of the real world. Yes. So it's so, and that's so fundamental to it. If we can get people back to that, like common sense, you know, this is, to go back to my Teller example, this is the number of transactions you did in your eight hour stint. And, you know, this is the amount of cash you should now have in your drawer, that's what those all represent. Yeah. That simple concept, you know, not everything breaks down as simply, but many things do when you break them down are much simpler than they get presented as. And we just need to kind of, as you said, demystify it. Let's, let's learn from it, not treat it like it's magic. Right. I like to say that my favorite word is sesquipedalian, which means the desire to use very big words that you don't need to use. I love it. It's its own definition. Yeah. So we have to avoid as data people being sesquipedalian. So, so Melissa, as you listen to what Laura had to say about, especially how do we find this middle ground? Is there a particular way that you've seen introducing the idea of data literacy in a way that's non-threatening? I think for us, we're actually more focusing on the skills that would make someone data literate versus calling it data literacy. Because I think, you know, to earlier points, both, you know, spoken by the two of you, as well as in chat, telling someone like they need to increase their data literacy can be a little bit jarring. Right. So we, you know, we've done a few surveys, right, sending out to our employees, just especially for non-data workers. They're like, hey, what's your interest in all the different aspects of data, whether it's data storytelling, visualization, pulling data, leveraging SQL, building models, et cetera. Where's your interest in learning? And surprisingly unsurprisingly, 80% of our respondents wanted to learn data storytelling. They see the value in it. They want their messages to their stakeholders to the leaders of the community. And so, you know, we're a data-driven company, right? And so that is the expectation. So there definitely is a healthy poll, if you will, from our non-data workers to continue to increase their skills. And so maybe I'm, like, a little bit lucky in that aspect that there is just so much interest already. Right. Yeah, I would say our approach really is to focus on the skills and not necessarily, you know, saying that this is our data literacy program. Yeah. And can you say a little more about, when you say focusing on the skills, is it just that you've started to get people's interest in those surveys? Or is there a mechanism that you actually already have to start to implement that level of learning? Yeah, we've had a variety of grassroots campaigns to increase data literacy and train different parts of the company. And they've been very successful in terms of the interest. Now we're looking to formalize it much more broadly so that we can drive consistency in what that education is, while also being a little bit more efficient in how we're creating and pushing out the training. So we're going to focus on a combination of computer-based data literacy courses and then also some live hands-on workshops. I'm a big proponent of like hub and spoke models or even like train the trainer type models. I think there's a lot of companies probably out there where they have a very small team focused on data governance or training and being able to provide that service. And so as that company grows, expecting that one small team to be able to handle everybody becomes just unscalable. And so finding like those subject matter experts within each of the different areas that can then be responsible for increasing the literacy for that area becomes a more scalable option, which is the approach that we're taking. Yeah, and so you're saying though that you're not necessarily calling it data literacy. Is it just that you're calling it skills? Like what are the skills you would like and do you want to learn new skills? Is that... Yeah, and for us it's also... We have an approach called Follow Me Homes where we really sit and observe how people are doing their work and almost organically seeing any sort of challenges they may be coming across with data. And so that's more around how the survey is represented. Like, hey, what are some of the challenges you're facing when dealing with data or trying to move at speed? And some of the answers can be, well, my analysts don't have enough time to give me the answers I need or which then is followed up with, I'd love to be able to do this myself. And so when you're getting those types of verbatims over and over, it really just creates like that grassroots campaigns that we've seen. Got it. And so I did see a question in the chat. Like how do you... And I'll start with Laura and then come back to you, Melissa. So how do you convince people that it is important to them individually? How do you help people get on board of, you know, if it is the lifeblood of the organization? What steps have you taken or have you seen in other places that help people go, gosh, yeah, I really do need to learn this. And it would be really great to do that. Yeah, so I've led data quality management teams in several different organizations. And within those teams, people often come to the work with different levels of knowledge about data. And so I have used my own experience to try to help team members understand the kinds of things that they need to know about data. So when I started in data quality management, I had not worked directly with data at all. I had, or at least I wasn't, you know, a data analyst in using information technology to solve problems or anything like that. I had the kind of day-to-day experiences with data that I described earlier. And what I found works best when you're trying to get people to understand data and especially how it relates to their own job is to give examples, try to show them what they can see in the data. Now, when you're dealing with data quality analysts, they're looking at two kinds of data, both there, the data that they are trying to understand problems in and the data that they generate through data quality management processes that they have to interpret. And they need to know both of those. And I've had, I personally had the moments that I've found most useful are when people have shown me how they analyze data and what they're able to see because they have the skills to do that kind of analysis. And so those are the kinds of things that I try to share with my teams. You've seen this at a larger level, you know, at an organizational level. For example, during the pandemic, right? I was working for a healthcare company during the pandemic. And so we had, first of all, a lot of effects on the organization because of the pandemic. And our data and analytics team did a lot of work for the medical officer so that he could communicate to the organization as a whole, how he understood the situation as it was evolving. And his examples and the work that the analytics team did to me were just really very powerful because they were able, they as a team communicated to the rest of the organization how they saw possible progression of the pandemic and what it meant to our business both in terms of, you know, how healthcare would be provided and how we would get basic functions done like claim adjudication and the like. So that was like a huge example where I think everyone in the organization simply by paying attention could understand the power of reliable data and also could understand the limits of data, you know, there are so many things we did not know about at that point. So I feel like every opportunity that we have to show how you can use data to understand the world and to show that it really is, it's not about the technology, it's not about something showy and glitzy, but it really is like what questions do we want to answer and how do we use the data to answer those questions. When people see examples, I think that's where they both learn about data and get excited about what can be done with data. And if you have that message that, you know, the connected message that, hey, this is something you can learn to do, then that makes it, that makes it seem accessible in a way that sometimes it doesn't when people are just throwing statistics at you. Right, right. So making these examples real and tangible so that people are recognizing that this is an outcome related to data. Yeah. And I, as I was listening to Melissa, I wrote down a note, just the benefits of being part of a data literate culture come when you have the conversations between people and when your leadership can understand the business better and communicate that out to employees and other stakeholders. So it's like the more we are able to help each other understand data, the better able we are to be an organization that's data literate, not just individuals who know how to use data. So I feel like some of the points you made at the beginning, Wendy, about, you know, what's the cultural context, how empathetic are we, right? If we find, if we're talking to someone who clearly doesn't understand what we're talking about, is our reaction to be, you know, angry that they don't get it or is our reaction to step back and figure out how we can help them? I think that, you know, that's part of the elevating part of the interview. Yeah, I agree. Yeah, because it's really about helping people. It is. So that leads to a question that I see. Because you're giving examples of how you're taking tangible results that are specific to a person's job or a company's experience like the pandemic and applying that in a way that you get, you get people's attention, you get people's buy in and you have them maybe get inspired to learn. So, Melissa, there's a question here that's, is there a universal set of skills and definitions that everybody should be learning that can be sort of off the shelf purchased or is it better to sort of do an in-house homegrown approach to this? Because the data sets are so unique. Do you have a sense of that or an opinion about that? Yeah, I think, in my opinion, there's so much awesome training out there for basic, you know, how to understand data, how to read a chart, how to almost like develop that curiosity when you see numbers to dive like that extra layer lower. And I know all of us are feeling, you know, the cost constraints, resource constraints, we always have that. And so being able to leverage outside training as much as possible is definitely a way that we're going to go. We, within our company, we have access to those types of trainings, and so for us, it's really creating pathways for people so that they're not having to hunt around and figure out which training they should take. We're providing strong recommendations of, hey, if you want to learn the skill, take this particular training. And we can almost put it in like the series so that we're slowly building their skills up versus, you know, somebody kind of just throwing a dart and hoping they hit the board. I think there will be a need to develop more in-house trainings for maybe more specific tools. So, you know, we have some in-house tools around data discovery, what tables are available. So, of course, we'll have to create trainings just so that people can understand the tools and leverage them effectively. But I do think it's a balance of both. And so, are you saying that you, because you were talking about some training, the trainer, is there an internal training platform, and this is part of a much bigger, broader set of trainings that everybody's aware of, or is this separate? No, no, it's part of it. You know, I think we all have mandatory training that we have to take from a compliance perspective. And of course, those are available within the platform. But there's actually a lot of more external trainings available that we can easily link to. And so, we're fairly lucky that we have access to, you know, thousands of trainings, to be honest, across not just data, but other software and stuff as well. Got it, got it. And so, Laura, your opinion or experience about that same question, is it better to be homegrown, better to link into resources that are out there that are available? What's the... I definitely agree with Melissa that it's a combination. It is never an either or. It's never an either or. The way that I like to talk about this is there are certain things that people should learn about what I call data as data. Any data that you might need to look at will have certain characteristics just because it is data. And you should understand things like how data is created, how it moves through your organization or any organization and how it's stored and accessed and used. That everybody should have a conceptual idea of what those steps are and the like. But then, of course, you're going to have to work with real data. And there will be characteristics very specific to the data in your organization and in your industry and then in your organization. So I don't think we should think of it as either or. We should think of it as how do we learn foundational concepts and then how do we apply those within our organization or how do we learn about our organization through the lens of those foundational concepts. So to me, learning about data is similar to learning about any other subject. You have to bring multiple questions to the table and you have to understand the big structures and how to put details in context and the like. So definitely a both and. Okay. So it's, it's, it's homegrown versions. After you've made accessible some other good resources that are, that are out there. Yeah. Either, either direction to, you know, I mean, I started learning about data because I was right there in a pond of it. And then I, in order for me to understand it better, I had to step back and ask myself about the bigger structures, but it started from the, you know, from being. Suddenly. Dealing with big sets of data every day. And so it can go in either direction. Yes, it can. So I think with a couple of minutes left, I'm just going to have each of you comment. As you think about people who are just starting down this journey in their organizations, perhaps they don't have buy-in yet. Perhaps they don't have resources allocated yet. But they know that this would be really beneficial. And so I'm going to ask you to think about if there were a couple of things that might be a great place for them to start, whether that's a conversation with certain people in the organization, or it's an assessment of where they are, or any other idea that you might have. So I'll start with you, Laura, just briefly, if they could take one or two steps, what might they be? Yeah, so I'm a great believer in assessing current state in order to get to the future state that you want to get to. So I think really understanding something about both the culture of your organization, how it works with data, and the kinds of challenges that you're currently having, and then envisioning how improving people's knowledge of data will help that. I would take those steps. And I love the reference that Melissa made to the survey that she asked, talk to people, ask them what they need, or what can help them, and kind of pull that together. Got it. And so, Melissa, you can have the last word. If you could suggest a step or two. Yeah, I mean, I think the first thing I would focus on is, what's in it for them, right? How is this going to really change the trajectory of what we're doing? And fighting advocates at all levels within the business. I think it really needs to be kind of tackled tops down and bottoms up. But last, and certainly not least, start small. You can't boil the ocean. You're not going to, you know, make your entire company data literate overnight. So really start small, find those key areas where you can show significant impact with even just the smallest, you know, smallest of work. And that will build the momentum that you need. Right. So start small. Is Melissa's first step to see how you could actually make a change and Laura appropriately saying, let's assess where people are. So I have put here the resources that actually Laura provided that she recommends. If you need more information about that, and I'll make sure Shannon has that so that we can distribute it to anybody who would like it. And I just want to thank both of you very much. I just want to make sure it's been helpful for all our listeners to hear those two people who are in the trenches working hard at these things. So thank you again, Melissa. Thank you again, Laura. Really appreciate it. Thank you, Wendy. Yeah. Thank you to everybody on the call and all the comments and chat. I've learned a lot myself. So I always appreciate this opportunity. Yes. Thank you. Thank you. Thank you. Thank you. Thank you. Who chimed in. It's a, I wish we could get to all of the different questions. Thank you so much for kicking us off and Laura and Melissa, thanks for joining us to help kick off this webinar series. Again, it's going to be the first Thursday of every month. So excited. And just as a reminder, I will send a follow up email by end of day Monday with links to the slides and links to the recording. I know we didn't have time to get to so many questions that you have. And we'll, um, collect those and analyze those and really kind of start building out the rest of the series based on the questions that you all have. So really look forward to that. Keep that feedback coming. So thanks everybody. Hope you all have a great day again. Wendy, Laura and Melissa, thanks so much for kicking us off with such a great discussion. Thank you. Thank you. Thank you. Bye bye. Bye bye.