 top of the hour. Hello and welcome. My name is Mark Horseman, Data Evangelist with Dataversity. We would like to thank you for joining the most recent webinar in the Dataversity Monthly Series, Elevating Enterprise Data Literacy with Dr. Wendy Lynch. This series is held at the first Thursday of every month. Today, Wendy will discuss the language of literacy. When we label people, does it help or hurt? Just a couple 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 would like to chat with us or with each other, we certainly encourage you to do so. And to note, Zoom defaults the chat to send to just the panelists, but you may absolutely switch that to chat with each other. For questions, we will be collecting them via the Q&A section. 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. As always, we will send a follow-up email within two business days containing the links to the slides, the recording of this session, and additional information requested throughout the webinar. Now let me introduce to you our speaker, Dr. Wendy Lynch. Wendy is the founder of analytictranslator.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 work 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 market success. Through this experience, she has created her new book Become an Analytic Translator and on an online course. And with that, I will give the floor to Wendy to start the presentation. Hello and welcome. Thank you, Mark. It is lovely to have you here and to everyone that's joining us for the first time, welcome. For those of you who have been with us before, welcome back. We have a lot to cover today, so keep your hands inside the vehicle at all times so that you don't run into anything. Today, we are going to talk about language and talk about labels. And I want to start by just having us imagine, because somebody who comes into the data area for the first time, it's like they are learning a new language. Perhaps that's Portuguese. And so I'm going to have you listen to basic terminology in data science in Portuguese. All right, I'm sure you've all got that. And for those of you who don't know Portuguese, I'm going to also have somebody ask you for a simple request having to do with data. So I hope you've got that and that you can deliver that on time. When we think about somebody learning a new field, it is like learning a new language. And if you have been looking at the area of data literacy like I have, you have noticed that we are calling it a lot of different things. We are calling it literacy. Some people call it data confidence. There's the term data abilities, data engagement, data competent, data brilliant, straightforward data skills. They're labeling people data natives, having data savviness, intelligence, fluency, maturity, personas, empowered. And so we'll ask today, does it matter? Do all these labels matter? And let's start just by thinking about labels in general. And when we have the good side of a label, it necessarily in many cases has an opposite. So if you're not data literate, are you data illiterate? Are you data stupid? If you're not data intelligent? If you're not savvy, are you data ignorant? If you don't have data maturity, are you immature? If you're not data competent, are you data incompetent? When we talk about how we measure literacy, the way we categorize people probably matters. If you're like me and you heard terms like ignorant and stupid and incompetent, it matters. So I'm going to talk about some of the ways that different folks in different organizations are talking about literacy. This particular organization, atland.com has an assessment that comes out with categories that seem very straightforward to me. So you are anywhere from a novice to proficient to an expert. And the word novice, I think at least for me, has a pretty benign connotation. A person who is new or inexperienced in a field or situation. If somebody said that I was a novice unicycle rider, I would not be insulted. I don't know how to. And if I was learning, I would categorize myself as a novice. Another organization called EW Solutions, they have their own way of measuring. And they start at the bottom with remedial, then basic, then literate, all the way again up to expert. But remedial has a very different connotation to me than a novice. And when you look up the definition, it's applied to students who have fallen short and they have not necessarily achieved what they need to achieve. And so if we start calling people remedial, and that's the level where they are categorized, that connotation is going to be different. And this organization also categorizes attitudes. And that goes from a data skeptic to a data driven person. Now, it seems like they've tried to make those a little bit less pejorative. But then you just have to look at the faces. And you know that if you're a data skeptic, you have a frowny face. And if you're data driven, you have a smiley sunshine face. And I don't even know what the data indifferent is, whether that's somebody following off a cliff or what. But we get a very clear picture that you want to be an expert. And you want to be a happy data driven person. The data abilities people from data to the people, they have 15 data abilities. And they measure people on each one. And when you are measured on each one, you have a score of a level one to a level six. That's an interesting way to do it. And then you have different levels of ability under different circumstances, whether that's just understanding data or actually doing analysis. And they use descriptions of three categories. So if you are a one or a two in a particular ability, then you are considered curious, as opposed to confident. Or the people at the top who are the coaches, because they can teach other people. And curious isn't so bad. It implies that I want to know. It implies that I'm a learner. And it implies that I am just starting out and wondering about things. So it's not so bad of a label, I don't think. Erin does personas. And when you fill out their assessment, you get a persona. And they start at the bottom with the data skeptics, who not only don't know much, but they don't believe in the value of data. Then the next level is a data enthusiast. So they don't know a lot, but they're excited about it and willing to learn. And so they're sort of more like the curious. And when we put these kind of labels, again, if you are calling somebody a skeptic, what does that imply about them? What does that do when we label them that way? And then the click folks have four personas that are quite interesting. At the bottom level is the data doubter, which is a lot like the data skeptic. And they are people who are fed up. They don't really want to deal with data. They'd rather ignore it and leave all the analytics up to somebody else. Then the next level is a dreamer. So that's an interesting name. I guess a dreamer could be curious or could be enthusiastic. So maybe they're like the enthusiasts and they at least recognize the importance of data, but they don't know how to use any kind of data skills. And so they are categorized as needing to improve their skills pretty dramatically. So you can be a dreamer even though you are low and probably would get a frowny face if you were in the other categories. Their third level is a data knight, which is kind of interesting. They are skilled at what they call battling with data. So these are the folks in the trenches. They have good analytics skills, but they aren't quite at the top because they tend to get overwhelmed by everything that they have to do. So these knights don't have shining armor. They are just still fighting their way to the top, which they designate as the aristocrats. So the aristocrats are like our experts. They are the folks that are the leaders. They have a very high aptitude, and they can be like the coaches that we heard about in data abilities who help upskill and uplift others. So we have a whole variety of ways that we talk about people. And I want to think about that because I have a question about who has to get to that high level of literacy? How much literacy is enough? Or to use the other languages? How much data engagement, data competence, or data savviness is enough? Now it's very clear with the labels that being at the top makes you a superhero and being at the bottom makes you something not great, remedial, or doubters, or skeptics. And so we never have anybody put some weird labels at the top. We don't have a persona that is called a know-it-all or a smarty-pants. So there is nothing bad about being at the top, and there's a lot bad about being at the bottom. So we have to think about the implications of how we talk about these categories. So if we think about data literacy and we think about a population of people, the proficiency would be up on the left axis and how many people would be at the bottom. And companies are saying that they want everybody, the whole population, to not just be able to read and understand data, but to convince people with data to explain analytics and then learn how to teach and or perform analysis. So yes, we know this is important and we see all the time that leaders believe that literacy is going to be critical to their success. We absolutely hear that all the time. And we hear that literacy is not something that we should do a little bit of because when you read about it, literacy is going to be the future. There are calls to have other professions all be data scientists. When an explanation of literacy comes out on Forbes or the MIT Press, they will say that everybody needs to grasp statistics, interpret charts, and be able to understand analytics. Oh, and we also start name calling because the summaries that I see, I was going to say something meaner, the summaries that I see point out who is good and who isn't good. And least literate teams are often identified as those in the more social sciences or the interactive people kinds of roles. So human resources and sales are called out as being the least literate. So we would put them in the remedial, the curious, the skeptic category. Now, leaders say they want everybody to be literate, but they overestimate how many people already are literate. 75% of business leaders in one Harvard Business Review article said that they believe that most or all of their workers are data literate. Now, the middle managers are a little more realistic, but still half of them believe that most or all of their workers are data literate. But what is the truth? The truth is Accenture found that less than a quarter of employees were confident in their data skills, which would be put them in that proficient or confident category. Another study found that only 8% of people could be put in those higher categories, the aristocrats, the even the data knights. And so leaders think that most are all of their people are literate. But if only one out of 10, not even are aristocrats, then what are we actually doing? And what can we expect? 62% of US adults operate at a very base math level. One in five can't read their bank statements or understand it. One in five have such severe math anxiety that if you look at their brains in an MRI, the activity is similar to pain and fear. And if we, oh, come on, and 30% cannot improve, can't interpret a simple graph like the fact that 25% is the same as a quarter of a pot. So leaders think that most are all of their people are literate. The evidence says it's somewhere between 10%, let's say, and 20% who are highly literate. A huge portion of people are afraid of numbers and will have trouble. And so if the majority of employees are not highly literate, then should we change how we think about this issue? Because 80% of them at least are falling into the remedial, curious, bottom, skeptic, doubter, novice level. Does it help us to have the majority of people labeled in that way? Because that corner where the illiterates are is going to be very crowded. And the number of them who are going to be the superhewers is going to be small. So how is it that we talk about this? And how can we put this into perspective? Well, I'd like to remind us of a few other areas that are important. Oh, and by the way, one third of leaders, meaning CFOs, CEOs, COOs are considered highly data literate. So if we are in that mode of labeling people, then the headline would probably look like this. Two thirds of business leaders are illiterate would be the headline if we are going to be labeling, or incompetent, or ignorant, or remedial. So we have to think about whether or not these labels are helping when the majority of people actually fall into those lower categories. So there's another type of literacy that I will call business literacy that is very important also to the success of organizations. It's called strategic alignment, I'll call it business literacy, but it's the phenomenon of when everybody in the organization is aligned in what they believe are the priorities of the organization. And you see as much in the management journals, if not more, about this issue than you do data literacy. So employees have to know what the strategy is to implement it. Employees have to understand the context and why they do what they do. And it's so important that just like with data literacy being important, companies whose people are strategically aligned have more revenue and are more profitable. So if that's the case, then we want business literacy with everybody, not just doing their job, but connecting their job to critical performance indicators, being able to know what strategy is, knowing how to make decisions according to that strategy and the priorities of the organization and actually become good at understanding and creating strategy. And leaders just like with data literacy believe that their employees, most employees can explain company strategy. Now only 20% of executives have really high confidence that most employees can explain strategy, but still more than half believe that most of their employees can explain a business strategy. How many actually understand business strategy? Less than 30% can pull out what the strategic priorities are from a multiple choice list within organizations. Only 13% of frontline managers can name their company's top three priorities. And in one Pricewaterhouse Cooper study, 93% of employees could not articulate their company's strategy. The ones that can, do we call them business aristocrats? Do we call them business experts? And all these 93% are they remedial in their business expertise? Are they business curious or business doubters? What do we say about the people who don't understand this? Do we categorize them? And another interesting connection between business alignment and technology. So we might think of that as our digital and technical and data literacy. When you have alignment between business strategy and technology strategy, it is one of the most prominent indicators of whether the company is performing well. But there is a discrepancy between business executives and technology executives about whether there is alignment between the technological strategy and the business strategy. And so in fact, when you ask technology executives, they believe that their strategy is completely aligned with the business strategy. Whereas fewer business executives believe that they are totally aligned. And so is this discrepancy? Is this discrepancy because the technology executives aren't business literate? Is it because the business executives aren't technologically literate? Is it because they disagree on what the definitions are? We don't know. But we know that there are things missing between these two efforts. And so I ask, just like with data literacy, if a majority of employees are not highly literate, in this case in business strategic alignment, then should that change how we think about it? How do we move people along the continuum when it's the majority, if not 93% of people? We want everybody at the top, but we don't know if that's realistic. So one of the ways that I've started to hear people talk about data literacy is using data fluency. And I actually think that fluency sounds a lot more realistic of an analogy because we are learning a new language and new concepts. And the Gartner Group, which is a big management advisory and consulting group, put together these levels, which are really interesting. But what struck me to begin with was that their lowest level was conversational. And if you're like me and you go to a country where you do not speak the language, you hope, you wish you were conversational. You really can't have a basic conversation if you don't know the language. And so I added another level at the bottom that I'll call introductory, where you're just getting familiar with the terminology so that you can at least maybe ask for directions or point to the right food or do something right as you enter this new country. And so a friend of mine from Women in Data, Karen Jean-François, helped me with this to look at these levels. And I want you to imagine that this is your entry into the data world. You've come into the data country and you are just learning terminology. So you are learning basic words, just words to describe something. The next level at conversational is where we have to put them together so we know how to describe them or compare them with new kinds of words. So again, if we just got to this country, we have to start to understand these things. Once we understand this much, then to be literate, we need to be able to understand an example result, for example. So how do we describe, and if we are now asking people to understand how to interpret, then it's almost like now we have to be able to have a conversation using these terms that we don't necessarily understand yet. So let's go to competent. Competent means that you can actually start to do the analysis and do some of the analytic work so you have to understand a request. And then lastly, in order to be fluent, you have to be able to put it all together. And what the Gartner folks say is that you aren't just fluent in analytics, you are also fluent in business. So you have literacy in data and literacy in business. I know that I'm beating this analogy to death, but I can't help but think of how we label people as being incapable when all of us know how uncomfortable it is to go to someplace new. If these words didn't make any sense to you, then are we putting a lot of people in that situation of making sense. So they have another level called multilingual, where the people are not only fluent in data and analytics, but also in multiple business domains. So to them, this is the top level of literacy. What I started to wonder is whether or not we have one more aspect that we need to attend to. And I will call this people literacy. So are you socially and emotionally aware enough to understand when somebody is uncomfortable? Are you able to figure out whether someone is following or not? Are they struggling with this new language or do they seem like they're doing okay? And this is a critical leadership and business skill. In Forbes, we hear about it all the time. Harvard Business Review, we hear about it all the time. 80% of long-term job success depends on your emotional quotient rather than your intelligence. Managers with a high emotional quotient have teams that perform better. And HR leaders are saying they're going to hire people based on this. So let's start to think about this one more skill and what it involves. So people literacy is that you are self-aware. You recognize emotions and social situations that other people are in. You empathize authentically with them. You have the skills to interact regardless of what that situation looks like. And you can guide others in how to do this. So if we think about people literacy, once again, how do we label them? What do we call somebody who is not emotionally literate, who is not people literate? And how many people actually have high emotional people literacy? Well, once again, here we are. 36% of people are able to recognize emotions, which means that 64% are not. And 95% of people think they're self-aware, but only 10% to 15% actually are self-aware. And now, just to take this all the way back around, people majoring in science and business and technology have significantly lower levels of people literacy, the same way that people in HR and sales have low data literacy. So this is especially important in data efforts because 90% of companies in the next few years plan to accelerate large data transformation projects, whether that's how they collect or store or implement AI or whatever they intend to do. But it appears the majority of them do not accomplish what they want. And when you look at those failures, 80% of it happens because of people issues, not technology issues, not talent issues. So we can't ignore the fact that it's about people as much as it is about tools and process. So once again, we're going to ask the same question. If a majority of people are not highly literate, so they are emotionally curious, emotionally remedial, they are people skeptics or people doubters, or they are emotional aristocrats and they are doing the best that they can. If the majority are not highly literate, should that change how we think about the issue? Because in all three of these cases, the vast majority are not at the top. Yet we pretend that that is the goal that everybody be at the top. So I'm going to ask a question that I have asked before. And that is whether or not data literacy is our goal. And I would suggest that it's not. Data literacy is a means to achieving something else. And that is company-wide information-driven decisions. What companies want is that they make use of timely information. They notice problems and opportunities quickly. They ask better questions, they make better decisions, and they extract insights at all levels of the organization. So the goal is to be highly insight driven. Now the reason why we talk about data literacy is because it's high literacy that gives us access to those insights. Because some insights are accessible to everyone because they are the very basic pieces of information. But then as we get to where we need to be able to manipulate data, the need to be able to interpret and analyze data, we have to be able to use advanced, sophisticated machine learning or other types of AI. In order to get all those insights, we ask people to be highly data literate. But in order to gain access to insights, the question is do we need to get all of these people, all of these curious remedial doubters, skeptics with the brownie faces? Do we need to get them all up there to the high level? I would say no. Can we take some of the lower level folks who are interested and have an aptitude and train them to help other team members get a little higher access to more of these insights? Can we train people who know how to do basic manipulation and understand some of that data integrity and data analytic capabilities? Those folks who are proficient, the confident, the data knights, so that we don't have to rely only on the aristocrats to give us what we need. And then can we designate analytic translators to help pull all of the highest insights out in a way that takes into account business knowledge and people knowledge? So are we focusing too much on these low levels rather than trying to find the intermediaries that can help pull more people up? Because it does make you wonder whether you could train people with the aptitude and interest to get us part way there and embed translators to get us most of the way there. Can that happen quicker than educating every employee who is currently a skeptic remedial doubter novice? I think it's worth asking. So I would put one more at the top just like I put one more at the bottom. I think we have to acknowledge that there are people who are below conversational and we should think about whether there is somebody who is not only fluent in business and analytics but also understands people in a way that they can get the most out of the different types of literacy. So I'll take us back to the beginning. We can choose ways that we talk about this, whether it's confidence or engagement, whether it's competence or brilliance, whether we just talk about skills, whether we talk about people's native language, whether they're savvy or intelligent, we can think about personas and try and figure out how to refer to people. We can think about whether we are assigning the superheroes and revering them a little bit too much with aristocratic kinds of labels while we put bad labels down here. We have to wonder which of these things are the most useful and also leverage the abilities and interests that people have rather than thinking only about the folks at the bottom needing to get all the way to the top. So yes, you've heard before. I believe analytics translators are likely to be part of the solution and I will stop us here and open it up to any questions and Mark, I invite you to make any comments or tell me how much you agree or disagree with this position. I love how you've presented it here today and I really enjoy, as we were discussing in chat on the side here, the image of folks being pulled up to a new level of literacy and rising to the challenge with the ladder there. I thought that was very well illustrated and the point made. So yes, if people have questions, I would love to see them in the Q&A. I've got a couple here. So one question in here. How do you identify people who may have disengaged by being labeled? How do you see those folks in an organization and how do you understand if that's happened at your organization? Yeah, that's a really good question. My guess would be that they will not raise their hand if that has happened to them, which is going to make it much more difficult and especially if they have data phobias or math phobias, it's going to make it much harder. And so my bias would be that as you start to find the folks who want to be a go-between, whether that is the citizen analyst or whether that be a person that wants to connect with others, they may be identified more likely on a one-to-one basis when we start to have these mid-range people. So if somebody down here is talking with a team member that has some data familiarity or a team member that understands how to interpret it and deliver it in a way that's not scary, then you may be able to re-engage them. But my guess is that if you have already ticked them off because they feel like they've been labeled, they are not going to raise their hand right away. Well, what strategies have you experienced or developed to help bridge the gap between data and technology? My work has been as an analytic translator, my whole career, and then lately as a instructor teaching people how to be an analytic translator. And what it involves primarily is communication skills. So it sounds kind of backwards because you have to understand the analytics first. But if I don't know how to ask a person questions in a way that help them get clear about what they need, if I can't explain something in language that is more digestible and be able to recognize when that hasn't worked so that I can look for another way to translate it, then it doesn't matter how good my analytics are because I'm going to either answer the wrong question or deliver something in a way that nobody understands. So that has been my bias as a solution is to train more people to get past the differences in language because sometimes it is almost like one group is speaking Portuguese and another one is speaking French. That's a great segue to another question that's in Q&A here. Is there a minimum level that everyone should get to to successfully operate in the kinds of businesses that we have today? Yeah, I think that is the million dollar question. And I would love to have a debate with somebody where we take extreme positions and figure out where we end up landing in the middle. I think that with the right language and it sort of is dependent on the job is the other thing. There are some jobs where if you don't understand what the data are telling you then you could really mess up. So I think it's somewhat job dependent. I think we can aspire to everyone understanding the data that are involved in the job that they are doing, which means that as we teach, we would teach about what their performance indicators are and why the data are numbers you can trust or why they are numbers you can't trust. And if we tie their own performance and understand what they think about the numbers that reveal their performance, I think that's a place that we can start. But I really don't. That's such a great question. And I think it's the question is what do we all need to know? I mean, you almost would believe that every citizen needs to understand basic numbers so that they understand what's going on in the world, but I'm not sure that that is likely either. So we've got a very popular question in Q&A here. How does one consider negative reinforcement versus positive reinforcement within the discussion of labeling? Some people may be motivated by perceived unfavorable labeling. Well, I guess it depends on what your goals are. I tend to think that kindness and encouragement and empathy get you further than insults do. But I also know that there are some people who respond well to being challenged and they may see this as a challenge. So not everybody is wired the same way. But my bias is always more toward kindness and especially since we're talking about the majority. So we're talking about 80% of people not being even moderately data literate, 80% of people not understanding the strategy of their own organizations and 80% of people not being able to read other people's emotions. So if we're talking about the majority, labeling them all as remedial and insufficient, I think is counterproductive. Yeah, I really agree with you there. One thing that I learned a number of years ago from somebody who was my mentor back in ye olden days, as I like to say, is to catch more flies with honey. While you may catch flies by other means, I think you're trying to cast as wide a possible net as you can and a positive message is always going to do that more effectively in a corporate environment. So I love exactly how you put that, Wendy. I think that's perfect. We've got a couple of related questions that I'm really interested in your take on. And one's kind of leading the questioner a bit. So I'll ask it second here. Who do you encourage to take the lead on deploying this change to the organization that seems like a senior level directive? And the other question is, how can a data governance team support this initiative? Right? You know, it's so interesting because my career has wandered around in a whole lot of different fields. And whether it's corporate health, or it's other types of training, it always goes better when a top leader shows that they are interested and they value this, and they tie it to some real essential strategic aspect of the organization. But they allow themselves to be vulnerable and to express the ways that they also need to learn and that they also need to grow. Because top down is important. But when they say things like, well, it's easy. I do it all the time. That's not motivating. And even just saying this is critical. You need to go do this is not motivating. But explaining that this is going to help all of us succeed. And I am working hard as an individual to also learn some of these things that are important for me to learn. I think that is critical to have the top be interested in it. Especially since we now saw that only a third of the C-suite. So a third of CEOs, CFOs, COOs, CHROs are data literate. And if they can't admit that they have gaps in the way that they look at things, it's going to be very hard to convince other people to do that. Now the data governance people, I have such an incredibly difficult job unless they have support like that. Because folks who don't understand data are going to be less aware of how important data structures and data governance and data literacy could be. So it is an education that has to happen from the bottom up too. And hopefully you have an ear that will listen to you somewhere at the top. I love how you put that to you and in chat somebody was saying 32% seems a little bit high to be honest. And I know from my experience whatever I've rolled those dice and talked to someone at the C-suite results definitely varied. I would agree that I think that that's a little high. Maybe they were surveying companies that included a lot of tech companies. But yeah, I have lots of stories of conversations with top executives that where you just go, oh my gosh, I didn't realize this is where we're starting. So yeah. Yes. It sounds like we have a shared lived experience there. Yes. But I will also say in all fairness, all of those leaders that I'm talking about that were not particularly adept at analytic results were excellent leaders in other ways. Oh for sure. I think the whole point and for the most part the people who are in leadership or doing a good job are there because they are excellent at what they do. So calling them remedial, skeptic, doubter, novice, whatever is not helpful, which is I think the whole point of today. So yes, I can laugh as a person who thinks analytics is like the best thing ever that I and I can laugh when I find out that someone doesn't understand a bar graph, but they do a lot of other things well that I don't do well. So. Yeah. I think we share that experience as well because a lot of leaders that I've run into in the past, they've always shown me their excellence in other ways. And I'm always in awe at folks in the C-suite. So yeah, definitely, definitely. We have a couple minutes left here and there's another very popular question and I'm very excited to hear you speak to this. How do you recommend measuring data fluency? You know, we had one session in the spring on assessments and those assessments varied so much like how people put those together. And what was interesting is probably the one that I was the most impressed with was one that was homegrown by a committee inside of Evergreen Health Organization. They created their own. So Heather Wilson shared some of what they did because they had very specific things they wanted to measure in their health environment. And it almost felt like that was more straightforward and appropriate than the ones like data abilities, which is widely used. It has benchmarks around the world. Those are all important skills, but it may or may not help with your organization, with your tools, with your mechanisms for sharing information and how much people understand about that. So it makes it more difficult because there isn't one off the shelf necessarily. But I think that more people being able to interpret the results of things that are pertinent to your organization is when you get an indication of fluency in your environment. So I think we all have different languages and different terminologies. So that's unfortunately the way that I'm starting to think about it is that in some ways it's native to your culture, which is your organization and your priorities and your metrics. That makes so much sense. And again, I just love the way that you put that and couldn't agree more. Well, that brings us to the end of our session today. So thank you so much, everybody, for sharing some time with us today and talking about data literacy. And thank you very much, Wendy, for the excellent, excellent webinar. As a reminder, the slides and recording will be made available within a couple of business days. Thank you, everyone, again, and have a wonderful rest of the day. Thank you. Thank you so much, and thanks, Mark, for all your help.