 Hello and welcome. My name is Shannon Kipp on the Chief Digital Officer of Data Diversity. We would like to thank you for joining the most recent webinar in the Data Diversity Monthly Series, Elevating Enterprise Data Literacy with Dr. Wendy Lynch. This series held the first Thursday of every month, and today Wendy will discuss overcoming challenges to achieving data literacy. Just a couple of points to get us started. 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 the Zoom chat defaults to send to just the panelists, but you may absolutely switch that to network with everyone. For questions, we'll be clicking them by the Q&A section. 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 and the recording of the session and any additional information requested throughout. Now, let me introduce to the speaker for our series, Dr. Wendy Lynch. Wendy is the founder of analytic-translator.com and Lynch Consulting. For over 35 years, she has converted complex analytics into business value. At heart, she is a sense maker and a 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. And with that, I will give the floor to Wendy to start her presentation. Hello, and welcome. Thank you, Shannon. I am so happy to be here. Thank you everyone for joining. Welcome to those of you who are just joining us the first time. Welcome back to those of us who have been here before. We are talking about data literacy. I am putting it up in the corner there as the bright and shiny goal in the distance. And we're going to be talking about how we get from low literacy to high literacy. And I want to remind us to begin with what it is that we're talking about. In general, what I hear from leaders is that there is a concern that their people are unaware of the data that are possibly available to them. They're not using data or if they are using data, they may be misusing, misinterpreting, and that overall there is a unfamiliarity with the tools that are available to them. There are many definitions and I seem to have one, a new one every time that we talk. But in general, when we are talking about literacy, this is from the Gartner group. It is the ability to identify and understand data sources to analyze data to derive insights and use these insights to make value added decisions. In addition, they add that it's an ability to describe the use case of the data that they have been using and then apply it and achieve a certain amount of value from it. So when we see that kind of a definition, we know that we are asking people to stretch quite high along the literacy spectrum. And there's a goal to be very, very capable when it comes to identifying and using data. So the reason why I believe this has become such a big topic is because of conclusions like this. Mackenzie says that high performing businesses have data and analytic programs that are contributing at least 20% to their earnings. So one out of every $5 that they earn is a result of getting value from the data assets that they have. In addition, reports say things like that businesses that have the highest level of mastery, which means their people, their technology, their policies have all been adjusted to take advantage of their data assets. In these cases, there is a 70% higher revenue per person. So it is not a surprise that we have leaders who are saying that literacy is one of their main goals. And in one of the Harvard Business Review articles. They said that 90% of business leaders believe that data literacy will be critical to their success in the future. And in large what I see is that there are calls, and I will make a mention for the Academy Awards recently, that the way that leaders think of it is they want all of their people to now be literate. So I'm going to go from this case of not using data or misusing data, or being unaware of data, all the way up to being highly literate. So, when we started this, it was a result of the series, I mean, it was a result of focus groups that diversity did over the winter. I would say that the themes that came out of those focus groups we've talked about it a little bit before. But the barriers that have been mentioned on why companies have a hard time going from low literacy to high literacy, the things that are keeping them from achieving higher literacy. And that is like buy in from leadership about why this is important ownership and who within the organization is going to actually drive this effort to become more literate. The measures that they need to use to either assess current levels of literacy or improvement in literacy that there is a question about how do we do a training that is company wide or who do we select. And how often do they have to be trained is this something that goes on forever and ever or is this something that's one and done. And then real questions about do I have the people that can help do this. Do I have the budget because training everyone costs a lot, and probably the most scarce resource we have is how do we have the time to a lot to putting this together and actually having people learn. These are not small barriers. And I would say we can categorize them in three groups here. So there is the barrier that we don't have the scope and vision of what this needs to be and the buy in and ownership. We don't have the structure of how to make it come to fruition we don't have what it takes to put a training together and know how it's going to happen. We may or may not have the resources in order to make it happen. So, we will talk about some of these barriers today. And we'll start by just talking about what the stepping stones seem to be in most of the articles that I read and the discussions that I hear about literacy. To get from low literacy to high literacy, we need people to be aware of the data that are available in their organization that they can identify these different sources that they know how to select the right sources at the right time. And they understand the value and limitations of those data sets. But data literacy efforts seem to go further. They want individuals to be able to manipulate data, at least in the basic way, if not actually begin to be analysts and analyze data. Then once they analyze data to be able to interpret that information and those results, and then actually apply them to the needs of the organization. These are some pretty significant steps that are what gets identified and we saw that in the definition from Gartner in order to get to the high levels of literacy. We have some significant steps that we need to take. And so we think about these in terms of skills that each employee needs to acquire. And as we think about how we get people up these stairs, probably the most common way is that we want to implement training that has the characteristics that are most successful in getting people from low to high. And from what I see, there are some elements that make training more and more successful. Some of the key issues that I see over and over again are that they designate an owner and the owner of an effort to improve literacy should not come necessarily from the data governance or data area. It needs to be a content expert who knows how important this is. That there is a clear business case, what it is that they want to achieve in order to achieve higher literacy. That they structure the education in a way that it fits within the business so that it's a part of what normally happens as part of operations. That it's not just individual education, but it's focused on teams as well. That they apply it with relevant examples that are meaningful, and that they are clearly tied to the role of the employee who's going through that. So these are some of the attributes of training that seem to percolate to the top when we are looking at success stories. And we're also starting to see discussions about what doesn't seem to work. The self directed online less expensive programs are not being touted as being as successful. And so we hear discussions from those who are trying to implement education that they want to make it matter. So there's a series of questions that can be asked. How does your job connect to business success. So this is actually a business issue. If a person cannot answer this question it's probably a flaw in the way that we are managing performance, but whether or not they are paid by performance, there should be a direct line of sight. If not, then it's difficult to talk about data. Then how is that measured. So that the individual knows what it is that other people are looking at to decide whether or not that job function is performing well. What does performance look like in terms of data. And what's a good level. What's not a good level how does it change. So that they can start to explore and understand how a particular metric or a group of metrics relate to what they do and spend time learning about those differences making it matter to the individual. We also see some other interesting ways that people are thinking about data literacy and this one I felt like I wanted to mention because it really resonated with me. Mark Palmer from toward data science was has written the several blogs and what his suggestion is that we focus on the question being asked and not the data. So that education is more about decisions that need to be made, rather than about the data that are available. So that when people are learning, they are learning based on a real problem that explores something that needs to be understood, rather than going through a laundry list of things that are already there. His description that I thought was quite insightful was that when you have education that is led by decision makers who are trying to advance the organization. It's like looking through the windshield toward what's coming, rather than looking through a rear view mirror. So I thought that was quite insightful. There are some other very interesting approaches that are coming from teaching younger workers using different kinds of metrics that get them involved. This one I thought was quite interesting using words from Beyonce's songs and having them explore the top word frequencies that go together or words that go together in threes or a discussion about which of the songwriters were most narcissistic by using words like I and me and my rather than you and so they were comparing who was most narcissistic. So whatever you use, it can bring data to life in a way that is important to whoever the audience is whether it be music or climate or COVID or other things we can see examples. So there are starting to be examples that are used in the training realm. And that's probably the most straightforward way that people are overcoming a lack of literacy or as some people call it illiteracy. But I want to bring up a couple of things. Number one. When we have steps identified in training that are in this list and are in most of these definitions, we are calling it more of a data science approach. This is an orientation toward getting people to become more analytic. And some of that is because we believe that if more people could do analysis then there would be more people to answer some of the simple questions, leaving the really talented data in this list to do more advanced kinds of things. But I have to wonder about this for a couple of reasons. And where I see this is, I'm doing some work with medical institutions, where we are looking at the implication of AI in medicine. And I'm thinking that there are instances where physicians are distrustful. They're distrustful of the AI that's being used to diagnose to choose treatments, and to intervene. And so, when I read articles about this. And this one was particularly interesting recently. And I'll highlight this part, this subtitle in yellow. He believes that the next generation of clinicians all have to be data scientists. His solution is that a person who is very highly trained, we know they are highly trained, we know they are smart, they got through medical school, they are practicing medicine, that that person also has to be a data scientist. So I start to wonder, are we really wanting every doctor, every CEO, every psychologist, every engineer, every HR director, every single employee to become a data scientist or become good at at least basic analytics. Is, is that really our goal? Because that seems like a big stretch to me. I also have done work in health literacy, but I don't know that we ask people who have low health literacy to learn how to suture their own cuts or perform surgery. And where is, where do we really need to go? And let's remind ourselves, leaders often overestimate who is highly literate currently when it comes to data. One recent study said that 21% of employees are confident in their data skills. Another study actually said 8%. We're thinking about very few people up here in the reaches of high data literacy. And yet we're thinking that we should require every single person to get to that height. Even highly trained people who have gone through 12 years of medical school, they should go back to training to become highly literate. Let's remind ourselves from this poll last October of 2000 people, adults in the US. One third of Americans do not know that a quarter of a pie is the same as the number 25%. 54% of adults admit they simply smile and not rather than reveal that they don't understand data or statistics. Results that 22% don't understand everyday numeric information like bank statements and rely on friends or family to tell them whether there is an issue. And last time we talked about how 20% of people have severe math anxiety that actually freezes their brain. So we want everyone to become data scientists, but we are starting at a place that is difficult. I sometimes wonder if those who are trying to teach data literacy think that this is what reception they will get. Yee-haw, we get to learn all about data and analytics. When actually, it probably looks a little bit more like this. There is a certain amount of hesitancy here. There is a certain amount of aptitude. There is a certain amount of interest. And if this is the way it feels when we are being asked to stretch, we have to wonder what our goals really need to be. So if we are trying to get to this high level. Is it realistic for every organization is it realistic for every employee. There are certainly ways that can help train a good portion of people. Is that the way we want to structure this and I think it's worthwhile to ask those questions. Because we don't know if people are going to get there. Plus we're seeing and now this is six weeks ago out of Forbes, that there is starting to be some questions about data literacy about whether or not it can be effective and one of their conclusions was that assuming that data illiteracy is the reason why companies are failing creates a toxic divide between the people who are considered illiterate and the people who are trying to get them to high levels of literacy. So we are actually making assumptions that the reason why companies don't capitalize on the value of their data is because of these people who aren't willing to or able to become analysts. So it makes me think about this problem a little bit differently and I'm going to expand one of these barriers is human factors. And those human factors involve not only the individuals we want to have increased their literacy where we have to wonder about their interest in aptitude. But also, whether or not those who are teaching have the empathy and I toward teamwork that we need in order to bring an entire group of people to a different level. So I'm going to have a little flashback here for those of you who joined last time in March. It just is a little bit of a review otherwise I'm going to catch everybody else up so it's a five minute flashback. And when we talked last time. I talked about different types of literacy. So for example, what we talk about here is data literacy and what a company wants when they are hoping to achieve literacy is that they achieve high levels of literacy here on the left. They achieve it with all the people that they possibly can. So if every single person was highly literate, it would be all these boxes. But we also pointed out that there are other types of literacy that are really important companies. And there is a huge effort right now to increase what I would call people literacy are people in emotionally intelligent. Do they have good communication skills, do they have empathy. And we looked at all of the evidence of how that improves corporate performance when you have leaders that actually have people literacy. We also looked at business literacy, which is, do all the employees understand what the business priorities are what the street strategic imperatives are and how their job connects to that. And what we found was that all of these things create tremendous value for an organization. However, fewer than a third of employees are data literate fewer than a third of employees are people literate and fewer than a third have high business literacy, and these are being quite generous. So we don't have experts in people we don't have experts in data we don't have experts in business across the board. So, when we are faced with this. It does make me think about data literacy differently, because in context, we think about that data literacy is over in one department, perhaps it's under data governance or some other group. We have business literacy that's under operations possibly trying to get everybody aligned at what's important for the organization and making decisions that align with that. And then we have an HR people literacy trying to get folks who are more emotionally intelligent and can communicate better. When we think about data literacy in combination rather than isolation, then we can start to look at people, rather than single abilities. So we may have a person who has really high data literacy, not as high people literacy, and not as high business literacy. We have somebody else who has great communication skills, really great emotional intelligence, but less in data business, or we have somebody who's high in data and quite good in communication, all of the above. We're just looking at these three types of literacy these three abilities these strengths, we see that everybody is different. What we talked about last time was that we can capitalize on those particular differences, rather than forcing, for example, this gentleman to go to remedial communication and remedial business training, or having this person who's great at business go through remedial data literacy training and communication training, and instead think about whether or not we take that expertise, like I'm doing with the medical folks where they have AI experts and clinical experts, and we implement a way of having translators that help each side, because the translator is not enough about each side, even though they're not running AI, or they're not treating patients, they actually can do a really great job of translating from one side to the other. Or we can take somebody's interests. For example, let's take someone who's really great at analytics and has high data literacy, and maybe her interest is in teaching. Let's take the person who has great communication skills and business skills, and let's say he has a great interest in managing processes and collaboration. This individual who wants to become more capable in terms of data and allow her to grow her data skills. That way, she can be a member of a team where she is the person who provides a lot more data queries data manipulation, and can collaborate with a person who is not so skilled in data. And she learns from the person who wants to teach. So we can start to think of different roles, perhaps, that makes sense. And that way we take advantage of people's skills and strengths and interests, rather than this divide where we're saying that the reason why the company is failing because all these silly illiterate data people. Where it gets us is we can make aligned business decisions based on data and evidence and using empathy, which was a nice combination that we thought of. And I brought this up this flashback for a couple of reasons because I'm going to continue on overcoming barriers. But I wanted to especially pay attention to the idea that when we think about data literacy in isolation as a solitary problem that we need to solve by grabbing every person with low literacy and forcing them into that. I was going to say hellscape, but into those classes that they really don't want to take. And we want to avoid the superior inferior dynamic. And we want to think about these combination roles, where you have what they call citizen developers the ones who are learning how to analyze data, or we want to think about translators. So in that context, what I want to think about is this first goal, we said the first way to overcome barriers is to have excellent training programs that help every single person become litter. So that was the premise. But now I'm going to actually change how we look at this. So bear with me. What if literacy isn't really the goal? What if data literacy isn't the goal? I would say that the goal isn't that everybody become highly data literate. I would say that the real goal, like why you want people to be literate is that organizations want everyone in the organization. Every employee to be able to make intelligent information driven decisions and take actions based on that information. What they want is people who can use information in a timely way to notice problems and opportunities to ask better questions to make better decisions and extract insights at all levels. That's what we want. Now the reason why we say, and actually I'm just going to call it insight driven. So we're going to talk about insights, that's what people want they want their people to be insight driven. And they want them to use that information but the reason why we say we want literacy is because the only way that we can access those insights is by being highly literate. So there are certain things that are available to everyone with minimal expertise. And I was thinking an example is everyone can get up in the morning and see what's happening in the stock market. I don't have to analyze trade data and be an expert analyzing data. I get a report that tells me which stocks have gone in which direction. So I can look at that. I don't have to be an expert. There are things that are available through reports that I don't have to be an expert. But then there are things that require some manipulation and knowledge. There are things that require pretty good skills and being able to know what the data sources are and idiosyncratic aspects of those data. And then there are these really highly sophisticated predictive models that need to be done according to a lot of trained individuals who know what they're doing. And so if you're going to do prediction from some aspect of machine learning we need to have highly skilled people. But I started to think that the reason why we need more data literacy is not because we want everybody to love Matt. It's because it would give us better insights if we had more people. And the higher up you go, the more you can get at these insights and what employers want what leaders want is access to those insights and access to more of them and having more people. Responding based on their access to timely insights. So the way we can think about it is that the people in low data literacy have to be told the answers and they can be given information with minimal. They don't have to to work on it at all they just have to receive it and see it at the moderate level. We might be able to do some manipulation. We understand the basics. And then in order to get to these higher levels we need high data literacy. What we want is the insights. What we need is people who can get us those insights. What I wanted to think was maybe there's another way to do this. Maybe we have trained individuals who help get access to these insights without every single person having to be trained in all of these skills. What if we have team members who are the ones who are familiar with the different data sources we have team members who have basic skills in sequel, and that they are part of the team that helps everybody have access to more of these insights. What if we have translators who are highly trained, and those highly trained translators understand what's happening in these instances, and know how to have the communication skills that give the information over to everybody else. So what if we can stretch these other roles to get access to the insights that have not been accessible, rather than asking everybody to reach the next level, or the highest level. And so I think our question here in terms of overcoming obstacles is for your organization or for an organization that you're working with, which can happen sooner and more efficiently. Training interested and people with the right aptitude to become embedded translators or embedded junior analysts and have them be a part of the team. Or is it educating everyone to be littered. Those are two of the potential options. So instead of having everybody trains. We start to look at what these roles could be. So that's number two. Now for another thought about this. I want to take us back to when computers were not really available to everybody. I'm old enough to have started my computing experience on punch cards. And you would put all of your code on to cards and then you would hand them over the desk to the guy who was operating the, the computer that is in the back room. So, when we had to operate that way, only programmers were able to use the computers. And so only people with really specialized skills had access to computing. It wasn't until we started to have PCs and graphical user interfaces. And now all of the touch screens and everything else. Now, everybody can have access to that computing power. My question for all of us is, will there also be a democratization of analytics and how will that happen. We are already seeing that we can ask machine learning or AI algorithms to recognize catalog and curate data. We can ask it to put data into different types of categories. So there's business rules and working definitions that can be applied so that you have a data set that is known and that you know it's advantages and disadvantages. And every day AI is starting to learn more and more data types to be able to do some of that process. On top of it. Now that we are seeing natural language and chat GPT chat GPT for can actually convert a spoken natural query into sequel and produce graphical results. So if we start to think about analytics becoming more and more accessible, the same way that the computing has become accessible. Then what we might see is that instead of only the most basic things that have already been put into a dashboard being accessible. We might be able to access some other things using natural language queries that are converted into analytics. And so we see that more and more of what used to be only available to those of us who have high data literacy and analytics skills. Now are going to have access through other methods of doing analytics. Now, there are some things that of course scare people about this. But it's going to get better and it's going to get more accurate all the time. And so, once again, we can ask ourselves, which can happen sooner and more efficiently. Is it educating every employee to be data literate, or is it going to be the capability of AI and chat to get smart enough to drastically improve access so that it isn't just people up here and here who can get insights, but anybody can get insights. So, we have to think about whether there are other solutions to this problem other than training. And I think it's worth asking these questions. Is it that we're going to train everyone. Or we're going to select certain people to get trained. Is it that we are going to train different types of team members to understand the data. Understand basic types of analytics or translate the complex analytics between those experts and the content other experts. Or are we going to see new ways of giving access to insights that don't require as much training and literacy. I'm not sure which of these or which combination of these was is going to be appropriate for any given organization. But I think it's worth having all of us think about which of these options. Make sense for which of these organizations and which subgroups of people before we ask every single one. To go through that climb of eight steps in order to get to high literacy. So, I will stop there Shannon, and I will take questions. And I would love to hear comments as well. So, let's open it up. Wendy thank you so much for another great presentation just a reminder I will send a follow up email by entity Monday for this webinar with links to the slides and links to the recordings if you have questions for Wendy feel free to submit them in the Q&A section of your screen there. So diving in. Why do you, what do you do if the lack of literacy is at the senior level, misinterpreting not understanding the importance. Yes. This happens a lot. Because what they don't know they don't know, which is, which is difficult. And in as an analytic translator I work on that all the time. So the most important thing is to have use case and business justification. So what is it that you are unable to do now that your organization could do. If, in fact, there was higher literacy across the board, or if in fact, you could have a translator who translates to that senior level. So, the, the issue here is that on top of not understanding its importance. There is a certain amount of fear that nobody wants to look dumb. And a person who is a leader has a clear identity around being competent. And so asking them to be to go through education feels like it's a threat to them. So understanding what opportunities are missed. Putting that into a clear business case. And perhaps giving them an opportunity to work with a translator rather than having them decide that they need to become more analytically focused. That would take the pressure and the threat away from, from that situation. So, and I would be happy to follow up with Sherilyn, if, if she would like to. Perfect. And Wendy, can you provide the name of the bloggy referenced early. Sure, sure, I will give it to you so that you can put it with the follow up. Yeah, that. Yeah, Mr Palmer, I think it's his name. Yeah. Okay, perfect. Thank you. Sure. And what are the current efforts in data literacy in higher education? Is there the same energy behind this as there was around information literacy 20 years ago. You know, I, I can't say that I know what's going on in all higher ed. I know that there are many of the examples that I see from higher education are unfortunately a little bit behind. For it's I think it's on the same level. In many cases, as I see in analytics, where the instructors, I mean, if you think about everything that happens in analytics these days and how quickly new approaches appear in our. It takes a lot of effort to stay up to date. And I don't know that all of them are staying as up to date. But I do see some really great examples out of on literacy out of different specific institutions. MIT has a big effort. There are a couple more that I have seen. But I apologize. I'm not totally up to date on what's going on in the higher higher ed. Only understandable. But yeah, so how do you identify if a person has low or high business data people skills. Yeah, so there are a few things that we can do. And it's, there are very, very intricate measures on all of them if you if you want to do that. In terms of business, business literacy, the way we were talking about it. All we were talking about is a very simple set of questions of. So what are the top three or five whatever you want to use. What are the top three strategic priorities for the organization. And how does your job connect to those priorities. That's all they were talking about in this and what they found was something like 90% of employees couldn't name them even from multiple choice list. In terms of people literacy, there are quite a few different ways to measure. Whether or not you have high emotional intelligence, whether or not you have communication skills. And there's a thing called the eyes test. If you watch the recorded session from March, you can see what that is, but there's an eyes test that sees whether you can tell what a person is feeling based on what their facial expressions are so that's one kind of official way to do it. And other ways are simply to have them talk about whether they're comfortable communicating in different situations. In terms of data literacy. We are going to talk about that next month actually because there is everything from a really detailed 15 attribute six level intricate measure, all the way to level of comfort with using data. And I think which kind of an assessment you use really depends on what your goals are and where you're trying to go with it. Thank you. And is there any generalized design pattern for achieving functional data literacy in any given organization. Yeah, I'd have to know what a design pattern really means I'm not sure that I, I know what that means. There are many, many organizations out there who sell services to achieve debt or data literacy. They either have instructors that come in, or they have designers that come in, and they do an analysis to see whether or not what levels are in your organization now, and how you might train the trainer in order to implement that. And if the goal is to have data literacy across the organization, then there are many options out there that you can, you can certainly look at. Nice and you mentioned your previous webinars a few times I threw a link into the chat there for everyone to the previous recordings, so y'all can take a listen. Currently I'm building out a data literacy learning path path on degree self learning. If, if that is not effective avenue for training knowledge sessions, what is the best training avenue in the post COVID age with a large number of people working remotely, I have limited access with internal webinars for learning due to employees scheduling conflicts. Yeah. I have seen reviews saying that it is not as effective to use the self learning. If that is your only avenue, because you can't have people gather, or you can't have individuals who can't attend at the same time even remotely, then it may not be that you have other, other options, they may not be as effective, but for those who are interested and really want to learn it, it can be. It's certainly useful I'm not saying that it never helps. I take some of those courses myself, all the time. So it's not that you can't learn from them. I think it more relevant to the individual specific job is probably the most important thing to try. Very good advice. So, what do you say that online data literacy courses, or why do you say that they don't work and what is that based on. I didn't say they don't work. I said there's questions now about whether that is the most effective way for people to learn. So the reason why I would lean against that approach, unless that is your only option. Is that it's hard to make it relevant for the individuals specific job, their specific data sources, and so they end up learning a lot about data sources that aren't relevant to their jobs. So, it can be interesting and it can be helpful and it can, there are certainly ones that are put together by very highly trained people who have done this for a living. It is not necessarily as good as helping somebody understand their own work and how data applies to their own situation. So, a source of that for the data on that. Is there a site source that you can any study that you can share. I will send you what I have and that you can send it out. There's just a comment here that says design pattern is closely associated with framework. Yeah. Great. Thank you. Yeah, I didn't know what design pattern meant. Yes. That's perfect. Thank you. And as data literacy increases and and as companies catalog and make available their data sets to internal employees. Are there any security risks with someone combining disparate data sets in unconventional fashions and how do you combat that situation. Yeah, I think that there will always be concerns about having people. And security is one thing and misuse is a different thing. So, certainly I'm not advocating that we give people access to individual records that they shouldn't have permissions to access so I'm not, I'm not talking about that but I am talking about some abilities to make queries on bigger data sets. And I believe that that number three solution that we were talking about the idea of democratizing access to insights will require that the company culture is a culture of learning and curiosity. Because we can all imagine that if data sets are kept up to date and they change every day or every minute that an answer today may not be the same as an answer tomorrow. Which, if people are used to being handed an answer that they think is the answer. It's going to freak them out a little bit. So, as they learn, and they understand what the limits are or the variability is in answers, based on definitions or based on timing, or based on the way they ask questions, that will have to be a learning opportunity, not an opportunity for freak out. And we all know how those kind of things can go. So that is my question. I think for everybody is as it becomes more accessible. What are the ways we need to put parameters around what we, how we talk about an answer, how we talk about information, how we talk about the various options that we've put together. And as we know as people who manipulate data that things do change and that definitions matter and filters matter. And it will be a requirement that we learn but we'll be learning based on getting access, rather than learning by waiting for someone else to make those decisions about the way it's going to be answered. So, it's, it's going to be a wild wild west I do believe when we get there but I think chat GPT is already opening that. And so the question is how do we start to work with that. Very nice. That is the end of the questions there. I'll give everyone a little bit. One more moment here to add any additional questions. Next month, just to give everyone a heads up, we will be talking about what are we talking about next month. I think it's, it's assessments. Oh, it is love that topic. Yeah, I have my work cut out for me trying to summarize that. That was an amazing job so far so really appreciate it. Wendy, such another great, great webinar. Thank you so much and thanks to all of the community for being so engaged in everything we do really appreciate it. Again, just a reminder to everyone, I will send a follow up email by end of the Monday with links to the slides the recordings and anything else requested throughout. Oh, do we have, we do have one. Oh, just comment. Thanks. So, thanks y'all. Thanks Wendy. Hope you all have a great day. Thank you. Thanks everybody. Thanks for attending.