 Hello and welcome. My name is Shannon camp and I'm the chief digital officer of diversity. We would like to thank you for joining the most recent webinar in the diversity monthly monthly series elevating enterprise data literacy with Dr. Wendy Lynch. The series held the first Thursday of every month and today we'll discuss analytic translators how they fit in the data literacy discussion. Just a couple of points to get us started due to the large number of people that attend these sessions. He will be muted during the webinar. We'd like to chat with us or with each other. We certainly encourage you to do so. And just to know zoom defaults the chat to send it just the panelists, but you may absolutely switch that to network with everyone to find for questions will be collected by the q amp a section and to find the chat and the q amp a you may find those icons in the bottom of your screen for the for those features. And as always, we will send a follow up email within two business days containing links to the slides the recording of the session and any additional information requested throughout the webinar. Now let me introduce to the speaker of the series Dr. Wendy Lynch. Wendy is the founder of analytic translator calm 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 and 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 an online course. And with that, I will give the floor to Wendy to start the presentation hello and welcome. Thank you Shannon, I am happy to be here. A heads up to everybody who is out there listening, I am getting over a cold so if you hear a tiny pause once in a while. It's so that I don't pop in your ears so. I am very happy to be here and looking forward to this topic. So I'll just go ahead and get started. If you have joined us before welcome back. And if this is your first time welcome. So, if we think about data literacy. The data literacy goal that we see from a variety of different sources. This is just one. Is a pretty lofty goal ability to identify and understand data sources analyze data to derive insights and use these insights to make value added decisions that is quite an ask if you actually think about it to have everyone. Achieving these abilities. But the reason why it's so important to businesses. Is that leaders are seeing these kinds of statistics that high performing businesses have analytic programs that are adding 20% to their earnings. And that businesses who will have an advanced level of data mastery, which means that their people policies and technologies have fully adapted to the new digital world. And they have 70% higher revenue. So it is not a surprise that 90% of business leaders in an article from Harvard business review said that they believe literacy will be critical to their success. So, before we started this series about literacy, Shannon and Tony and hit their colleagues as part of the diversity system. Did some focus groups and they said, what is going on with your organization in terms of literacy. And there were a lot of responses that were about how difficult this was going to be. They brought up that they didn't know if they had buy in from leadership. They didn't know who in the organization would own the effort. They didn't know how they were going to measure it. They didn't know what approach they were going to take to training, how long the training might be and how high of a level of literacy they were trying to achieve. Whether they had the personnel that they needed, how much it was going to cost in terms of dollars and in terms of time. So there were a lot of questions about literacy. And I think those questions still are pertinent today. I have seen many more questions than answers in many cases around literacy. And so when we discussed the answers from these focus groups, we categorize them into a few buckets. Number one, organizations are wondering whether a scope and vision is there. So the why behind having data literacy training. The how and when so how are we going to structure this where will it happen with whom. And then also how much so is it going to cost us a lot of money. Is it going to cost us a lot of time. What kind of resources are we going to have to throw at this effort. So, as we think about that. As a backdrop, it then puts into focus what the goals are. If we have people at low literacy. And we achieve that description that we heard at the beginning. What we're saying is, is that we want people to not only be aware of data that are available to be able identify the different sources in an organization to select the right source to understand what the data. Tell us and what those metrics are. And then further, it's actually describing that a person should be able to manipulate data. Perhaps do statistical analysis. Interpret those analyses and then apply them in order to achieve good business outcomes. So, we're, we're actually talking about a pretty tall order of having all employees moving along this continuum of skills. And so, when we asked the question and we've asked this a couple of times in the series, how do we get everyone to the top. The first answer is pretty much always training. We want to get all of those low literacy folks on the ladder. Climbing higher getting to higher data literacy. And in parallel to the focus groups. We hear that there are things that have to get put into place. You have to designate an owner. So who is going to be in charge of this. You need to kind of make the business case in order to get everybody's buy in. You need a structured education system so that you know what you're going to teach and at what rate. You're going to decide whether this is an individual activity or whether this is a team activity. And then customize it with relevant examples so that it pertains to your industry. And then actually tie it to a person's role within the organization. So it's a pretty tall order when we think about everything that we have to do. And so it doesn't hurt for us to ask. Are we really wanting to get everyone to a level and I would say everything in yellow here is a pretty high level. And it's a data science orientation. Are we trying to get everybody to this level of analytics skill. And so that's what we're going to talk about a little bit today. What is it that we really want to achieve and how do we best do that. So we talked about these three categories of potential barriers. There's actually one more. And that is the human factors. We've talked about this a little bit before but essentially I'm saying. How do we decide who has the interest and not only interest but aptitude to learn these skills. Especially those ones that are manipulating data and analyzing and interpreting data. And then also do we have the empathy and teamwork that we need in order to generalize literacy to an entire workforce. So I will I will be asking today. Is it realistic and if it's not why is it not realistic for every organization and every employee to climb those ladders all the way from low literacy to high literacy. So let's start with some assumptions. What we've seen in many, many articles is that leaders overestimate the level of literacy in their workforce. In one Harvard Business Review study when they interviewed several hundred business leaders. 75% of them said they thought that most or all of their workers were already data litter. But then you look at the studies and actually it's more like 10 to 20% that are literate and confident in their data skills. So rather than most of them already being there and you're just trying to get a few of them up to the top. What we're talking about is the majority of them have to get to the top. And then we hear from organizations like Forbes who say there's a problem because assuming that data literacy is the reason why things are failing now. Actually puts us in this category of blaming certain parts of your workforce. And last month we talked about this labeling of people who are illiterate versus literate. Are we really wanting to label folks in such a way that we're causing a divide and a conflict between these two groups. The reality is if we look at the data US adults are not that math oriented. We've seen these stats before a third of Americans don't know enough about fractions to know how much a quarter of a pie really is. We've heard that half or more don't understand statistics but they're not going to admit that to you. And then as much as a quarter can't follow basic numeric information that they get every day. So we are not seeing that it's typical that people understand data and yet we're asking them to adopt not only just an awareness of data. But also. Manipulate and analyze data. Now let's add to this that according to the click survey group. Only a third of our senior executives are data literate. So they are assuming their workforce is literate. But actually when you look at what kind of capability they have. It's not very high. So if we are thinking about what it is that people will choose to do naturally. My guess would be that the majority of the workforce. If given a choice would walk on the walkway to the left. They would rather. Not be put in a position where they have to learn. About data. So let's talk about what's actually happening. And I'm going to do this from an analytic point of view because that's where my experience has been as a data analyst in the business world. So I actually have three graphs that are the result of polls that I conducted on LinkedIn. In the first poll. I asked my business professional contacts. To describe their relationship with the analytic team that they collaborate with the people to whom they make requests. I said how often do you get and understand the answers that you really need. So I want you to think about if you're one of the business folks. How often do you get and understand exactly what you need. Well, I was actually kind of surprised. At what I heard. Only one in 10 say they always get what they need and their relationship with the analytic team is awesome. One in five said they really don't get what they need and their relationship is frustrating. Half of them describe it as adequate. And a quarter say that it's good. So what we're seeing is that two thirds. Don't get what they need most of the time. And they barely think that that relationship is functional. So then the next poll that I administered was to my data professional colleagues analytic professionals. And I said why don't you describe your relationship with the business folks who ask for things from you. I said what is that relationship like. Do they give you some context or is it kind of one way where they just say please do this. And they don't ask you for your expertise or your input or your knowledge. So I want you to think about it if you are an analytic professional. What is your relationship like. What kind of request do you get is it just give me this or is it you know where we're trying to accomplish X and we love your thoughts and insights about it. Well, here is the result again almost a mirror image of the other direction fewer than one in 10 say they have an awesome relationship. And it's a great collaboration. One in five say it's frustrating they don't ask for my input at all and they don't give me any background at all. Have say it's okay. I get a little bit of context but it's not a real relationship a collaboration and a quarter say it's it's good. I get I get asked for my input. But here we are again two thirds say it's frustrating or whereas a third say it's either good, but very few say it's awesome. Now let's talk about our final poll which was last month. And again I asked my analytic friends. When you get a new analytic request. How often do you give the requester exactly what they need on the first try. So I want you to think about it how often are you able to give people exactly what they need because you understand exactly what they are asking for. You know exactly why they're asking for it you know what format it should be in. Well, it's even worse than the first two. One in 20 say they get it right all the time. Almost 40% say never or almost never. 30% less than half the time. A little over a quarter, most of the time. So more than two thirds report that they are not giving. Their clients essentially what they need. So we are in a really bad situation. And if you're wondering well this is people that know Wendy so it's really not that big of an end so maybe we don't need to listen to that. Well, if you go out to the experts who analyze this level of information about how well analytics is working in business. They reported even worse than what my colleagues said. Reports are 85% of big data projects fail 87% of data science projects never make it into production. In 2022 Gartner said only 20% of analytic insights are going to deliver business value. And only 50% of business decisions are made using data, which means we are not doing well. So, given our challenges, given the miserable state of providing value to the business through data. Is literacy training really enough? Well, I've been thinking about this a lot for many years. And so what I thought I would do is I would walk through some of the root causes for why so many analytic projects fail. Let's start with something obvious personality. If we think about data scientists on the right and business leaders on the left. How different are these people? Well, if you look at the Myers Briggs personality inventory. They are actually opposite on three out of four dimensions. Business leaders extroverted data scientists most often introverted. Detail oriented on the left big picture oriented on the right. They're both thinkers, which is a blessing. But the one on the left wants to make decisions and be definitive and the one on the right wants to explore possibilities. Now, I will admit that these are stereotypes, but there's a reason that stereotypes exist. And that's because we are different. And very often these stereotypes reflect a good aspect of reality. Second thing, they speak different languages. They have been trained and immersed in their own worlds. And the business leaders are going to be talking about net revenue, KPIs, EBITDA and price to earnings ratio. And the data scientists are going to be interested in variance and P values and R squared. These are where they live. And they speak differently. Now, there's a reason that they speak differently because as you're trained as an expert, you get very, very clear and you have your own terminology so that you can speak to each other. But that makes it harder to speak with someone in a different environment. So there's a reason for the style that each of the groups has if you look at Sheldon from Big Bang Theory. If he was talking to other scientists and he was asked, does your company's product improve employee performance? You can already imagine what he's going to say. Something like this. Analysis controlled for demographics, 10-year previous performance, location and job type. We did a time series analysis. Removing seasonality, transforming the outcome into a binomial and showing that yes, participants had significantly higher likelihood of improvement at a P value of .02. That is a reasonable way that data scientists would talk to each other. However, if you were talking to Don Draper from Mad Men and you said, does your product improve employee performance? He would probably say yes. Next question. The personalities already make it hard. The terminology makes it harder for these two groups to talk to each other. Then add to that that they're trained in really, really different ways. When you get an MBA, they want you to be clear and action oriented. They want you to look for differentiators for your product or service. They want you to be in search of opportunities that are coming up. They want you to make decisions quickly. They want you to be able to pivot if the marketplace is changing or customer needs change. They want you to take action for success. Now, the data scientists are trained to formally doubt the results that they get. What do I mean by that? You are asked to understand all of the reasons that you may be wrong. Threats to internal validity, threats to external validity. You are trained literally to quantify the likelihood that you are wrong. A confidence interval is literally how confident or not confident you are at what you discovered. You are trained to list your limitations up front. You are trained to give your level of uncertainty and to admit to potential bias. So you are supposed to hedge your bets. That's the way that you're taught. And oh, by the way, on the left, they are not taught in advanced statistics or communication skills. And on the right, they are not taught business management or communication skills. So their orientation, in addition to their personality and their language, on how to be good at what they do are in complete conflict with each other. So one embraces doubt, the other insists on certainty. So then on top of that, let's look at the values that different groups have. I've presented to and provided analytic support to many sales teams. And you learn very quickly how different you think and act and what you value. Sales teams want to beat the competition. They want to win and get commissions. They want to leverage every advantage they have. They want to highlight the most positive results that they can. They want to stretch it as far as they can. I had a salesperson that said, Wendy, I don't lie. I just provide the truth in its most favorable light. So they will do what they can to leave out what is negative, to claim the best outcome possible, and to do it as quickly as possible. They're not going to wait until you've double, triple checked. They want it now. Data scientists really want to be correct because the worst thing you could do is claim something that wasn't true. If you're going to publish, you don't want the academic community to ridicule you or decide that you don't know what you're doing. So you want to get it right. So you're cautious, you're factual, you don't embellish, you highlight what might be wrong, you admit what you don't know, and you take the necessary time. So you end up with one group pushing us one way and another group pushing us the other way within the same business setting. So one wants significance and certainty. The other one wants to move ahead and win and sell. So another way that we're different is the preferences for how people will hear us. And this one cracks me up the most because I'm guilty of it at all times. But if you start to look for it, you will see. So a business leader wants to guide people and keep them heading toward a shared goal. They want to provide simple guidance. Clearly, they want it to be understandable to everyone so that nobody's wondering what they ought to be doing. They want as much as possible for the direction that you're headed to be indisputable so you're decisive. And they want it to be convinced. They have investors that they have to talk to. They have a workforce that they need to lead. And you can't do that if things feel uncertain or if they're questionable. Now, what's funny is that data scientists want to teach people. They want somebody to understand that there are so many complex possibilities that are really interesting. They discovered something new and possibly and hopefully different than somebody else discovered. They want you to know how fascinating this discovery is. They want you to know that it's brand new and unique a discovery. And they also want to brag about the advanced techniques that they used. We would all rather do a gradient boosted model than to do something simple. We want to show how excellent our analytic skills are. So what we see is that the business leader wants it to be so clear that it needs no explanation. Whereas the analyst or data scientist wants it to be so interesting that everybody wants an explanation. Think about that difference. How much it leads to people not being able to work well together. And I have an example to share, which is again why it cracks me up. I was working with some colleagues and we were evaluating a program that they had implemented to improve employee health. And what typically happens for those of you out there who are evaluators is when you do something where you try and intervene in health. Let's say it's cholesterol. You're trying to get the people with the worst cholesterol to improve. And what normally happens is exactly that you target those who are doing the worst. And it shifts the distribution a little bit, but it's because the worst people got a little bit better. That's just kind of how it works. Well, we were evaluating a different kind of a program where actually the worst people got better, the average people got better. And the people who already had the best levels also got better. So we shifted the whole distribution and we were psyched. So if you're a data analyst, you're like, wow, this is so cool. So we ran to the CEO to explain how monumental it was that his product, his service actually shifted the whole distribution. And he said, oh my God, I don't know what in the world you are talking about. You are making this so complicated. Give me one graph with one line that goes up into the right because when a line goes up into the right, that means that the result is positive. So we start to realize that we are in a position where it's not just about terminology. It's about how we like to have a result perceived. I have to always be reminded that those of us who love statistics and analytics are unique and not part of the mainstream. And so one other area that's important for us to realize is that we forget sometimes when we have grown up or spent decades in the same industry or in the same role. We forget that other people don't know. And adults like to be comfortable. Adults like to look smart. And if you're an expert in one area, you tend to not want to look dumb in another area. So when you end up with any kind of complex stats or math, very often people who are extremely talented in other areas of the business do not want to be involved. I would say there's been dozens of times a leader has asked me to come and co present so that we could describe some of the findings that we had. And he or she would not be put in a position of having to answer technical questions about how the analysis was done. That's because as adults, we like to be competent. We don't want to be put in that situation. So even if we give people basic literacy, it may or may not be enough to make them comfortable in describing what they need to know. So the last area is their orientation. And this comes a little bit from the wanting things to be so interesting. Everybody wants to know about it. Data scientists don't want to do the same thing over and over again. They want new solutions and new ideas. Once again, that flies in the face of how businesses become successful. Businesses become successful by making things systematized. What you want to do is discover something and then put it into something automated so that it's done over and over again. You want to take a model and implement it, not try and figure out whether there's an even better model. Scalability and profitability requires that you repeat something rather than invent something. I've had several times where a leader will say, Wendy, just stop making things up. We're here. We have the products we want. So don't improve it just yet. We still have to implement it. So this is what we are up against. And when people tell me that literacy will get us to a position where we can actually overcome this. I have questions about it because what we're saying is that these two groups are so different. They are different in all of these different dimensions. And what that does is I find the same way that we saw in my polls on LinkedIn, the relationships are not good. The relationships have tension if not direct conflict. And so what I hear and what I witness is leaders say that data scientists are wishy-washy and uncertain. Data scientists say business leaders are overconfident. On the left, they believe that data scientists move too slow. On the right, they think leaders jump too quickly to a decision. Business leaders think analysts make things too complicated. Data scientists think leaders oversimplify and ignore some of the intricacies of what they found. Leaders think that data scientists don't care enough about the business objective. Whereas analysts and data scientists think business leaders overlook how challenging something might be methodologically. Business leaders may think that the analysts get distracted because they're too busy wandering around in the data and they don't give them what they need. Whereas data scientists think that business leaders are one track mind. So we have put ourselves in a situation where we are trying to collaborate between teams who don't necessarily get each other. And not only do they not get each other, but they presume that the other one is intentionally making things more difficult because of all of those differences in training, values, goals, personality. So when I think about this, it makes me wonder, given these differences, is the solution really to just educate one of the professional groups, meaning business people, to be more informed about the data used by the other? Which is essentially what data literacy is about. So is that going to get us to a point where we are collaborating? So it makes me think about a story that I heard and it really had an impact on me. A story from World War II. In World War II, there was a group of meteorologists. So what we're talking about is in the late 30s, early 40s, a team of weather forecasters offered their resignation because it was proven that their predictions were no more accurate than random chance. So these are our data scientists. These are the folks who are saying, gosh, we really wanted to do a good job, but you're making decisions based on data that we can't prove is actually providing you that. And they got an answer. The commanding general is well aware that the forecast are no good. However, he needs them for planning purposes. Now that really struck me for a couple of reasons. Number one, businesses or the military have to move forward and do the best that they can based on what they have. But also that we tend to rely on a an answer that may or may not be out made it. And so I wonder are we relying on literacy as a solution to a much bigger problem because it's popular. It's written up. There's ways to do it. And in many ways, it blames the people that we would rather behave differently. Because in many ways, data literacy blames those folks who aren't enough are talented enough or not trained well enough in the things that we as data people think are important. And so we can ask ourselves. Do we train everyone? Or I've used this graphic before. Can we use these people with the T analytic translators? Can we take people who have data familiarity and have them boost some of the folks who don't. Can we take people who have some basic analytic skills to help their teammates get move higher along the insight spectrum? And can we use designated analytic translators to make sure that we are able to communicate between business. Team members and analytic team members. So we'll talk about this more as our series moves on, but what does a translator do? It's not simply to take the answers that may or may not be the ones that they asked for as we see. But a translator speaks both languages fluently. They know what somebody means when they say certain things. They know what's going to get an analyst excited and what's going to get a business person excited. They know that. And they are native to both domains. So it's not just they know the terms. They know what a P value is or they know what the KPIs are. But they're familiar with all of these biases so that they can help each other be comfortable. They have expertise in communication because you can't just say, so Mr businessman. Is this exactly what you want? That does not get you there. We can't say to the data scientist. So dumb that down for me. We have to be able to ask high quality questions. Listen for the right things. And help each side clarify. So the other team really understands what's going on. They are absolutely dedicated to converting data into maximum business value. So the data scientists and analysts can do all of their work. And focus on the things that they're good at so that the translator can help them package it. And also focus on the things that are most important to the business. And lastly, because it's really kind of humorous just how different these groups are. And a translator has to have authentic appreciation and empathy for both teams. They have to understand both perspectives and why it's so difficult. And when you become an ally to both teams, that is when you actually can understand what's not going right and make sure that the value is getting produced. I like to think about moving forward in terms of a combination of not only analytic translation, but also communication. And so when I teach analytic translators, we use both of these resources. How to have good conversations that provide meaningful information and then how to guide a project in such a way that you can serve both groups. And as we move forward and thinking about doing things differently, we can keep this in mind. Our biggest challenge is the time zone difference for our analytic team in Oregon. It's 245. But at headquarters, it's 1987. So we do need to be thinking about how to construct this differently and get over all of these barriers that keep us from providing business value from data. So I look forward to your comments and questions. Wendy, thank you so much for another amazing presentation. If you have questions for Wendy, feel free to submit them in the Q&A portion of your screen. And just to answer the most commonly asked questions, just a reminder, I will send a follow up email by end of day Monday for this webinar with links to the slides and links to the recording along with anything else requested. So diving in here, Wendy, do you find that when data science and data analyst teams live within the business that the communication barrier is not as high? And I assume from that question that you mean that analysts and data scientists are embedded within a business team rather than having separate divisions. And I'm assuming that's what you mean. And yes, it does help because they aren't sort of isolated in their own environments. But it still takes a certain amount of awareness either from the business person or from that data scientist to be able to translate how they think about the world into the other person's area. Because I still see a lot of miscommunication and a lot of misunderstanding because of the basic language differences that they use. For example, I watched a project where the business person said, I really need an ROI on this particular investment. And so the data scientist was like, well, how much did it cost? And they said, I don't care how much it cost. I need an ROI. And they went back and forth. And what the business person really meant was, are we seeing good outcomes? That was what they meant by ROI. Whereas the data scientists very appropriately was trying to know what the I was so that they could calculate an ROI. But when we don't understand each other and we use different terminology, it can make things go sideways. And Wendy, you talk a lot about data scientists and analysts, but this also can apply to like data architects and engineers and in all of the data community. Yeah. Absolutely. So I have given these presentations before and somebody who works in it, for example, they say it's exactly the same. They think that somebody is asking for X and it really is why. So any technological difference. The other place where I'm seeing a lot of interest right now is medicine. Because physicians do not necessarily understand AI. They don't understand digital twins. They don't understand all of these new mechanisms that are being used. And so they're not trusting that these new models are telling them what they ought to be doing with their patients. And they've been practicing for 25 years. So there is a communication barrier again between 2 groups who are very, very smart. But have different orientations toward how you use data and how you use modeling. Perfect. I love it. I think I could use some translation skills myself. So Wendy, you know, we have a lot of dashboards that our area creates, but they are not used by the areas dashboards for whom they were built. How do you suggest that this gets mitigated? Yeah, this is a really great question. I've been working with a team where they learned analytic translation skills. And they had run into exactly this where they had built an entire dashboard system. They thought they had outlined exactly what sort of data sources they needed. They rolled it out and spent 3 or 4 months asking for feedback, asking for what kind of information they needed or what kind of training they needed to use it. And by the end of whatever that time period was, maybe a year from very beginning to end. The users came back and said, can we just go back to our printed reports because we don't have time for this. So what happens is that the data folks think about this from all of the ways that they would use it, not how the other team might use it. So this team, we've been working on this together. They have gone back to start incrementally because that's something that often we don't do. We figure, well, we have to design it with the back end covering as much as possible so that we don't have to redo it. But that's not how people learn to use something. They are interested in being able to cut it 45 ways and see it trended over time and have it be able to be printable this way and that way. They need a specific piece of information at a specific time. So what we're doing is rolling out incrementally a piece of information followed by another piece of information that is very simple. Because we overestimated what it is that they could absorb to begin with. Anyway, that's one of the ways that we've tackled it. Very nice. So, Wendy, have you surveyed people in the public nonprofit or academic sectors? Anecdotally, your framework rings true in the public sphere in similar ways as you described for the business here. I'm curious if you've seen differences among different segments of your survey respondents. I haven't surveyed specifically. I do think that this applies. And I do think one of the groups that I work with is from a nonprofit organization and they have similar challenges where they're trying to provide information to decision makers. And the decision makers aren't able to articulate exactly what it is that they need. So they are now using this framework. I call it a maximum business value framework to understand what they're truly asking and using their communication skills in order to pull that out of the decision makers. Because it's hard because we think that what they're saying is what they mean. So, oh, and one really great example, that third poll that I did, where I said, how often do you give them exactly what they need the first time? The person wrote in in LinkedIn, a comment that said, well, technically, I gave them exactly what they asked for. Now, it wasn't what they needed. But I consider that a success and I, I'm not sure that I agree. And there's Dion. So, that's all the questions we have for now. I'll give everyone kind of, oh, we got another one. What role does data governance play in data literacy? Well, I think what I've heard from my data governance colleagues is that we have to have a foundation. And a place where there is a keeper of the structure and the definitions and the usages so that you know where to go back and understand it from the standpoint of literacy. So, it's so easy to get three years into it and be using metric a and then questions come up. Well, was that a monthly score? Was that a, did it include the part timers? Did it have this in it? Did it have that in it? And so, if you don't have a designated keeper of what the variables are, what, how they're collected, what they mean, how we might use it. If there's no keeper, then it's hard to have literacy sort of stand on its own two feet. So that would be how I describe that. Very nice and another comment on that specific question. These 2 are often grouped together, which I find limits the depth needed for both. Oh, absolutely. And I think. I think we're spending a little bit too much time on literacy versus figuring out all of these other barriers. Because I don't think that data literacy will fix the communication gaps by itself. And do and people say, well, that does that mean you don't believe in literacy? Of course not. I would love for more people to get excited about data and understand how to use it. And I, I would love for that to be the way things happen. But shaming people who aren't data oriented into trying to be data oriented is not going to fix these big communication differences between both groups. You and I have talked about that so much over the last year. Yes, absolutely. And yes, Lawrence. Yes. Yes. Yes. I really do think data translators, analytic translators will be the in demand role on every analytic team because you can fix so many problems that have nothing to do with. And that's the thing is the problems that where we're describing the 85% of projects failing have nothing to do with talent, nothing to do with technology, nothing to do with systems. And it's not that people aren't trying. So it's not about bad intentions. It's that we don't get each other. And if you have people in there who can help build that collaboration, it makes all the difference. Sure does. Yeah, well, I am glad in, and I know I've never been hired as an analytic translator, but that's what I did my whole career. So I agree. So, awesome. You've heard a hair post and it's a new role, new title. Yeah, we're going to start a new trend. Yes. Let's hope so. I love it. Hey, you know, it wasn't that long ago that CDOs were born. So, right. Exactly. Exactly. Yeah, and look at us now. So, oh, Wendy, thank you so much for another really great presentation and thanks to our attendees for, for the great questions. I really appreciate it. And again, just a reminder, I will send a follow-up email by end of day Monday for this webinar with links to the slides and links to the recording. And it looks like, and, and so we'll get that information out to you all. So, Wendy, thank you so much. I hope you feel better. Me too. Thank you for, and thanks everyone for joining us. Thanks y'all. Have a great day. Bye bye.