 Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer of Data Diversity. We'd like to thank you for joining the most recent webinar, the Dataversely Monthly Series, Elevating Enterprise Data Literacy with Dr. Wendy Lynch. The series is held the first Thursday of every month, and today Wendy will introduce the series topic and will be joined, excuse me, and today Wendy will discuss exploring levels of data literacy, what's needed by whom. And as always to get us started, due to the large number of people that attend these sessions, he 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 defaults the chat to send to just the panelists, but you may absolutely switch that to network with everyone. For questions, we'd be collecting them by the Q&A section and we encourage you to try to highlight via your favorite social media platform using hashtag data diversity. And to find the chat and the Q&A panels, you may click those icons in the bottom of your screen to activate those features. And as always, we will send a follow-up email within two business days containing links to the slides, the recording in this session, and any additional information requested throughout the webinar. Now it is in my pleasure to introduce to you the speaker for the series, Dr. Wendy Lynch. Wendy is the founder of analytic-translator.com and Lynch Consulting. For 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 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 so happy to be here. Welcome to everyone. Welcome back. If you joined us last month for our very first kickoff. And I want to say thank you to Shannon and to Dataversity, all of the folks involved in putting this together. I am grateful to be a part of this series. So I will be talking today about data literacy, and I thought I would start by just thinking about how we got here in the first place. So where is this interest in data abilities coming from. Well, certainly it's coming from all of you, all of the folks who work in data professions and computer professions, but it's also coming because executives are getting inundated. And yet, these sorts of reports, they read management journals, they read leadership journals. And what they see is message after message saying that they need to be more data powered. The other report was very convincing as part of CAP Gemini, which is a respected research institute. And they put together based on a survey they did of a thousand executives, they looked at how people were achieving data capabilities. They put on the left side data behaviors so how people and processes use data. And then they put at the bottom how the structure and the foundation of data capabilities in terms of technology and tools. And this was in 2020 and they identified that 70% of companies really still lagged behind in terms of implementing both the people part and the technology part. There are some who do one but not the other. And then they looked at what they called the data masters who had implemented people and processes, as well as technology and tools, and came to the conclusion based on analytics that they did that if your company was a data master, you had 70% higher revenue per employee. It caught a lot of attention. And so executives start to think about these sorts of advantages and begin to realize that they need more than ever to become a digital force. They also get messages that all of you see all the time that 98% of their colleagues in the executive ranks are investing in big data and AI. That analytics is going to grow to almost 70 billion in the next four or five years that companies will need more and more analysts and data science professionals. And that the amount of data that they have is growing and growing, yet more than half of it doesn't get used. So they get these messages. And it makes them feel like they are behind. So, as we see these calls to be more data literate. It's partly coming from the top. So we see those kinds of messages. And we also see that there is a promise that is being made about big data that your revenues are going to go up that your profitability is going to go up that you will be able to have clear marketing messages, and that these big data discoveries are going to be successfully integrated into their business. Well, there's also another set of messages that they're getting. And that is when the Gartner group and other organizations actually dig into when big data analytics gets applied. We see results like this that 85% of big data projects fail at least the first time 87% never make it into production, only 20% deliver business value. And that fewer than 50% of business decisions are being made using data. We still rely on our old gut instincts in many, many cases. So on the one hand they know that they have to do this on the other hand they're worried about doing it wrong. And so when I started to see these conflicting headlines. I did a couple of surveys online. And I said so how do analytic folks feel about the way that the business interacts with them. And alarmingly, only 8% said this is a great relationship that we have with the business. And two thirds said it's either completely frustrating because we get these transactional requests and we don't actually get any context and we're not asked for our opinion. Half say it's okay or so so. And so the general conclusion is they give me a request with little or no context. Then I did another survey, and I asked the business people. How do you feel about the interactions you have with the analytic group. It's almost a mirror image. Only 7% say it's awesome. It's always great. And two thirds say it's just so frustrating. I don't get what I want or if I get it I don't understand what it says. So here we are we need to be more digital more data focused, we're worried about things failing, and we're not communicating well. So it is not a surprise that we are now hearing so much about data literacy. And some of the most common ways that people define data literacy are that people can read and recognize data that they actually understand it pretty clearly that they can use it to convince others or argue about things or make convincing statements about what is discovered. Plus, maybe we get up to being able to explain complex analytics at the highest levels, or teach about complex analytics. So if we're thinking about data literacy we're thinking about a high level of proficiency with the most people that we possibly can. I don't surprise you that when they interview business leaders with everything they're hearing and seeing 90% of business leaders believe that data literacy will be critical to their success. So, this is not a small endeavor this is not a unique endeavor it is widespread that we believe that this is important. And when we ask ourselves how literate, most of the things that we see again in the business journals management journals leadership journals. There is a kind of a rah rah supportive kind of declaration that we can all be brilliant at data, and that data literacy is going to be the second language of business. It's really a call to action. And one description that I saw in a leadership journal is that at the very least individuals should know how key business terms and metrics are defined, especially if it's related to their function. And that ideally, everyone should have a basic grasp of statistics and an ability to interpret charts and analytics accurately. And that's kind of a big deal. And also, there starts to be some finger pointing. There is a lot of definitions and call outs of those people who are not data literate. And that seems to be human resources and sales so we have gotten to the finger pointing part of needing data literacy. So who is literate now. Well, according to business leaders, 75% of them report that they think most or all of their workers are already data literate. That's pretty crazy. But if you go to the middle managers not the executives, only 50% of them believe that most or all of their workers are data literate. So they're starting to hedge their bets just a little. But when we look at who is actually literate now. The one overarching survey that I saw was 25% of employees are confident in their data skills now obviously it depends on what company we're talking about. We're talking estimates as low as 8% of employees are highly skilled and understand how to use data. We're talking about a lack of data literacy in somewhere between 30 or 25 or 8%, or sorry, 70, 80 or 90% of people who lack data literacy, because we are getting down to the bottom 10%. I also started to investigate how much proficiency people have and how open they are to learning about data. I think that there are probably some limits to that. The best overview that I saw was that 62% of US adults operate at a very base level of math. More alarming one in five have what they describe as severe math anxiety, where if you put them in a functional MRI machine, it's indistinguishable. Their anxiety about math is indistinguishable from pain or fear. So we're not talking about just reluctance. It is a an actual phobia. 29% of us adults can't interpret a simple graph, including a third who don't know that a quarter of a pie is equal to 25%. So what we are seeing here is that we are kind of acting the way I remember when health promotion started in the 80s. They would have skinny people tell you that weight loss is easy. They would have really fit people tell you that exercise is easy. I'm afraid now we're having data science people tell you that data literacy is easy. So how do we improve data literacy right now? What I see most is encouragement to have mandatory education on the basics of data integrity, reliability, manipulation and elementary statistics, which is an admirable goal. I have to ask whether it's realistic. If we are talking about the full population. So I'm going to take a detour. Hopefully you guys will tolerate a detour. And I'm going to talk about what's called strategic alignment. It's again, very popular in the business and leadership journals. It's very popular in management journals. And I'm going to call it business literacy and strategic alignment means that all of the people in the organization and all of the processes are aligned with the goals, priorities and purpose of the organization. Because if everybody's rowing in the same direction everybody's pulling the same way, then the company runs better. And again, the journals just highlight this the same way they highlight data literacy employees must understand strategy if they're going to implement it. Your employees need to understand the why of your organization. And they highlight the companies whose people are aligned on strategy, grow revenue 58% faster and are 72% more profitable. That is amazing. Just by having everybody be aware. So, we start to see the strategic alignment or we can call it again business literacy has levels of its own. I do my job, that's kind of the basic number two I know how to connect my job to specific key performance indicators. Number three, you can communicate what the strategy of the organization is you pretty much know where it is that you're going and what's important. At higher levels you can tie different decisions to that strategy. And maybe at the top level you are in charge of creating strategy and developing those metrics. So when we think about data literacy. It's important to start to think about who is literate. Again, executives of executives 52% have confidence that most employees can explain their company's strategy. Although they hedge their bets a little only 20% of executives have high confidence that most employees can explain their company's strategy. Well, how many employees really understand the strategy. It's a little bit alarming. Out of a survey using multiple choice test. So they were given options they weren't asked to bring it out of thin air, fewer than a third could percent could identify what the business strategy was that was kind of disappointing. When they looked at managers as they got further and further away from the executives, only 13% of frontline managers could name the company's top three priorities. And another study by Price Waterhouse said that 93% of employees could not articulate their company's strategy. So once again, we have an essential capability strategic alignment that seems to be a little bit out of the reach of the everyday employee. And we are looking at nine out of 10 who do not have business literacy when it comes to their organizations. What's especially important about this is that there is also a need for strategic alignment between business and technology. So we see this overlap between not only having strategic alignment and not only having data literacy but having that occur together. And it turns out that when those two areas are completely aligned, it explains 80% of the difference in company performance across organizations. That's pretty nuts. So what's difficult though, is that the executives in charge of the business and the executives in charge of technology differ in their opinions about how aligned they are. If we look at an assessment of a thousand executives, those in blue are the technology executives, the majority of them think, oh yeah, we're absolutely aligned with the strategy. But only a third of the business executives feel like technology is aligned. So we are struggling, not only to have this separate level of alignment, but in having alignment between technology and business. And I start to feel like executives are acting like the skinny weight loss instructors. Strategic alignment is easy, because it's easy for them. So how do companies suggest that you increase business literacy? It's not unlike what we see for data literacy. Mandatory education on strategic priorities, consistent messaging and aligned KPIs. And it sounds a little bit like what we saw for data literacy, we need to have people have basic understanding, but start to actually become much more capable. So apologies in advance, but I'm going to take one more detour. And I'm going to talk about one other concept, emotional and social awareness. And I'll call that people literacy. So, when you look again at these business journals for Sloan, MIT, Harvard Business Review, over and over again, leaders and executives see that emotional intelligence is critical for the success of a business. It's critical for high performing employees, 80% of long term job success depends on their emotional quotient, not on their IQ. Managers who have high emotional intelligence have teams that average 15 to 20% higher revenue. So as we look at this, we start to see, again, a series of messages. We need people who are socially and emotionally aware or people literacy. And 52% of HR leaders say they're going to start hiring managers based mostly on their emotional intelligence. So, here we have another capability that is important that leaders hear a lot about. What it means, again, to have this people literacy and the different levels across all of us is that they are self aware of their own emotions that they can recognize emotions in others that they empathize with that they actually know how to put themselves in other people's shoes. We have the social skills to communicate well to deal with situations that are emotionally difficult, and perhaps teach or guide others on how to have better communication and better involvement when things are difficult. So to give you an example of how we test people literacy. There is a eyes test where you see 36 sets of eyes. And those eyes are showing a particular emotion. And that emotion is one of the four that is listed around each set of eyes. The average person can only identify the emotion in 23 out of 36 sets of eyes. And that is even though they're only limited to four options. What is interesting is that again with the finger pointing. They are happy to identify that people majoring in science and business have significantly lower people literacy than those in social sciences. While the computer science and data folks are pointing fingers at sales and HR. HR is on the other side pointing fingers at science and business people. So, how, how many of us have people literacy. Well, only 36% of people in general are able to recognize and identify and name emotions at a high level. And 95% of people think that they're self aware, but only somewhere between 10 and 15% actually are. So people literacy is also lacking in a lot of ways. Now, why is this important. Well, number one, it's a skill that they are highlighting as related to business success. But actually, if you look at the number of companies who want to accelerate digital transformation accelerate making data driven decisions and incorporating a data foundation to the way that they operate. Converting some of the manual processes into digital data oriented processes. However, 70% of those transformation projects fail. And when they look at why they fail. It's because of people not having the literacy to bring the everyday worker along. It's the people issues that make or break the digital transformation. So, here we are looking at data literacy as a separate issue. And strategic alignment as a separate issue. And emotional intelligence as a separate issue. And yet they all come together here. As you try and align your digital strategy with your business strategy and your people strategy, they all come together. So how do we train people. Well, it's again not unlike other trainings, mandatory education to improve awareness of emotion in ourselves and others. And along with that probably coaching and feedback so that people can learn it and apply it. So it is not that different from the way that we are thinking about these other areas. And I'm sure psychologists and HR folks will tell you. Yes, emotions are easy. So I'd like you to just think about the answers to these next three questions just give yourself a rating. This is just for your own recording. And let's start with data literacy. And I'll start at the bottom. My guess is this group is all going to be way up high but in other audiences it isn't always the case. So one is I'm allergic to data and math. Number two, I can read charts and basically understand them. Three, I'm comfortable explaining what basic analytic results mean. Number four, I know what statistical tests should be applied to answer a question. And number five is I choose and perform advanced modeling techniques in my job. And I would think of a five as being probably equivalent to at least a bachelor's degree maybe a master's degree. Now I want you to rate yourself on business literacy. Do you think about the business as I just do my job I do my part. I know how my work ties to company goals. Number three, I can give comprehensive explanations of the business strategy. If someone asks me. Number four, I know how our company is performing on all of its key metrics right now. And number five, I actually develop corporate strategy and create the metrics that measure it, and also communicate it. Again, we might think of number five as being an MBA level or the equivalent in real life. So give yourself a number on that one. And then lastly, how would you rate your people literacy. One would be, you know, emotions get in the way I just want to do my work. Number two would be, I can't always tell what others are feeling. Number three, I can accurately identify my own and others feelings. Number four, I empathize with others and uncomfortable handling and acknowledging when negative emotions are involved. Number five would be I teach others how to recognize and handle emotional issues and situations again that probably would be a training like a bachelor's in social work or in communication or psychology. So, think about what your numbers are. I say that when I do this, sometimes with an audience. I see a lot of people who are three or higher in multiple areas, but very few, if any, score a five in multiple areas, which shouldn't surprise us. Because we tend to be experts in one area more often than in multiple areas. So that number five, as at least a bachelor's if not a master's. How many people have a bachelor's. Well, a little more than a third, about 10% have a master's, and only about 3% have a PhD, if we're thinking that real skill is somebody who has invested all that time. If people who have a bachelor's degree with a double major is only 9%. Then our people to master's degrees is less than 1% and the number with two different doctoral degrees is itty bitty tiny tiny. So, we have experts in our organizations and those experts have strengths in certain places. And so if we're thinking about literacy what companies want is they want literacy in data, they want people who are people literate, they want business literacy. That's what leadership wants is everybody to be literate in these things to be capable. So what we have looks more like this less than a third with data literacy less than a third with people literacy less than a third with business literacy. So there's, there's where we are. So let's think about what this actually means when we're trying to tackle lower levels of abilities. What happens that I notice is that we have different efforts being run by different groups. So we have the data it groups who are interested in promoting day literacy. We have the management groups and project managers who want you to be focused on business literacy. And we have HR and some other groups trying to focus on people literacy. So, these end up being disparate separate kinds of events. And when I think about this, I think about people as having a variety of different types of capabilities. Each person has a certain amount of data literacy, for example, a certain amount of people literacy, and a certain amount of business literacy. So we have these combinations of capabilities. And the next person may have more people literacy, or more data literacy but a little bit more people, or any combination of these three. Each of us then has a capability of bringing certain strengths or weaknesses. And the way that we see the calls to improve data literacy the calls to improve strategic alignment the calls to improve emotional intelligence has been weakness focused. And what we do is we grab some people who have low data literacy and we enroll them into a data focused intervention. We find the people who have low emotional intelligence and we enroll them in something that improves. We find people with low business intelligence business literacy, and we enroll them in a way that they can improve each of them in an isolated way. There are some other options I think that we can consider. One of them is where I spend a lot of my time and that is on how we can connect experts in two different areas by training individuals to be what you might call the tweener the in between. So, one person is an expert in analytics. One person is an expert in business. And there are individuals who have sufficient expertise in each maybe there are three. They know how to interpret what analysts say, interpret what business folks say, and do it in a way that helps individuals collaborate more successfully. So that might be a role of a translator. Another possibility would be that we highlight what people are interested in learning about. It's not just what their strengths are, but which direction they might want to go. And an example of that might be that we select these folks. And one of them maybe wants to not just do analytics but teach it. Another person may love the idea of collaboration and figuring out how best to develop processes so that people communicate even better. And then we identify folks who want to learn new skills. And in this case, perhaps she wants to learn better data skills and become tech savvy and users sometimes called citizen developers or other kinds of roles. Business technician, I think I've heard used to. So this person wants to learn wants to get to the point where they can collaborate with their business colleagues in a way that they become that front end, rather than requiring that all analysts be answering every single question for every single business person. So there is a hybrid role that is possible, if we do it that way. So, if we think about this collectively that we want to maximize strengths and interests, then we also then can become strength and interest focused, rather than deficit focus which is what we seem to be doing. And if we do that, then we highlight the value that each person is bringing, rather than taking the person who has severe math anxiety and forcing them to enroll in data literacy or taking the person who couldn't understand what the other person is feeling if they, their life depended on it, and forcing them to figure out which eyes show disappointment and which eyes show excitement. So, if we think about it this way, we might actually say that we are going to educate about making a line decisions using evidence and empathy. We could combine many ways that we want to emphasize strengths and interests, and actually build on that combination in a way that advances how everybody collaborates, but respects what people bring. What I invite you to think about as we wrap up the content that I will present for today and then we can have a discussion is, should we be thinking about data literacy separately, or should we be thinking about it within this context of other needs and priorities. If we think about data literacy as a solitary solution, or whether maybe it belongs in a broader integrated context. By itself data literacy will actually make decisions, data driven, or will there be strategic and social requirements that we have to consider before we can have that outcome. I mean data literacy by itself will it achieve that or do we need these other capabilities. That must become highly literate is seems to be the call. But realistically, do we want every person to be highly literate in every area, or can we leverage the strengths that they have. Ask that a lot of non experts are supposed to become expert. When I saw that the definition of data literacy included everybody becoming, you know, basic statisticians. It just really worried me. What about if we thought about combination roles, whether that's translators who can go between two experts or tech savvy folks who learn a whole new set of skills because they have that interest, or similarly citizen developers. And lastly, can we can we actually focus on this stuff separately without creating this superior inferior dynamic. Because if we point fingers at every science or business major and tell them they're not emotionally intelligent enough, or we look at every HR and sales people and tell them that they are data illiterate. I'm sure that that heads us in the right direction. So maybe we give people opportunity to learn more without it having to be a good or bad situation. So I will ask you to consider. Can your data literacy efforts be put into context in some way in your organization. What about which departments and professions within your organization that might collaborate to consider how a holistic perspective might shift the way that you think about data literacy. And think about which of these combination roles might be useful, taking advantage of those who can operate in between and who value and enjoy doing that. And I did want to give a shout out that if anybody has been incorporating or integrating data literacy into other areas and have examples of that. I would love to hear from you and speak with you. So feel free to email me at that email. And so Shannon I'll expect you to help me here because I have not been looking at the comments while I've been talking. But I thought you could guide me in terms of questions. Absolutely, Wendy. Thank you so much for another fantastic presentation. I'll be sure to send over the chat as well. After a seat can see that it's been quite entertaining throughout y'all and just answer the most commonly asked questions just a reminder I will send a follow up email by any day Monday to all registrants with links to the slides links to the recording and questions that have been posted. So there is a request here. Can we get a study for the math phobia, it'll help me convince some people. Sure, I will, I will get that to you. Yes, I will get that to you. Shannon to put in the in the email that goes out. Thank you. The 70% digital transformation project really fail or fall short of achieving the goals and how is fall short defined. Again, send you the connection to that. And it was a, I believe it was a Forbes article but I can't tell you because I had to read so much for putting all of this together. But what they had said was is that it did not achieve what they anticipated and I think in some cases absolutely failed to reach adoption, but I can send that article on what that means. Thank you. And is there an online test to measure people literacy you can recommend. You can look up the ice test that and emotional quotient tests are out there. So, I can, if you'll remind me, also, I can look up that but the ice test is available you can walk through those 36 sets of eyes and test yourself. I don't like it. And what is our why is data literacy so tightly coupled to data science and not in broader perspectives like data management data governance, etc. Yeah, I actually think that's a really good question. I think that what happens is companies take an area just like I'm showing here. They decide that the best people to teach it are the ones who are expert, which may or may not be true. Because when we think about those of us who have been trained in data science and get really, really, really excited about machine learning and wonderful new interaction terms that we found or, you know, exponentials. And those of us who really get excited about that have long sense forgotten basic things. I don't mean forgotten like you don't know them but you take for granted, basic things about how data need to look, and what is the basic part of assessing them. It is a question for me whether data literacy does belong in data science, or whether it would be better suited to be part of this overarching advancement, where we all need to understand some basic things. And maybe we have questions that we ask about what's important for their job that isn't even talked about as data, but perhaps talked about as evidence, or talked about as a way to make decisions, rather than saying you have to become data we talk about how we decide something. So it is a very good question. And I don't know that it should belong in any of the data areas necessarily. I think it needs to be collaborative with that, but I don't know that it belongs there only any more than emotional intelligence belongs in HR. Thank you. So, are these tools instruments to measure literacy skills data people and business and are there training programs to address literacy skills. There are many many trainings for data literacy for sure there are many trainings out there everything from some basic ones. And I've had a hard time knowing and this will be something coming up in the months to come. How do you select one. Do you, what situation is your company in. Who is it that you're trying to train what level do you want them to achieve. There are some that are, you know, hours and hours and weeks and weeks and weeks. There are some that are just very rudimentary and basic. There are some that are thousands of dollars and others that are available for free. So it is really difficult and we will be focusing in one of our sessions coming up on the assessment part. I, again, there are many assessments in many ways. There's one assessment out there that just is more of a qualitative interest based assessment like what you would like to do with data. And then there are others that are very very specific I think I highlighted last month, one that had some amazing number 15 data abilities with seven levels of capability on each of the ability so it just was massively detailed. So, it is all over and all over the, the map which is I think why Shannon, you and Tony said we need a regular session on this, because we need to try and tease apart all of this and figure out what will work for what kinds of companies. Yes, indeed. So, and I see some requests coming in for the upcoming links I'll get those two all as well. And thank you Wendy any advices on how to put in place a guide to help the team, one self assess their data literacy and to take on their own learning curve though, for example, training material and other resources based on where they land on their self assessment the challenge comes from your point of different people have different levels of the same skill set. Right, right. Yeah, and again I'll point to this is where we hope to go with all of this. And when we talk about different assessments depending on what it is that you're trying to achieve. I'm sure there are. Well, we all know that there are specific roles where it would be malpractice to not have the highest level of data skills, if that's what you're in charge of. Yet there are jobs where it would be great if people understood it a little bit. So, I think that's going to be a matter of finding fit. And there are so many. It's hard to digest which direction it's going to go and most of them are for profit groups. So it's groups of people who are dedicated to this and that is their livelihood. So I don't want to point to one or another being great or horrible. So we're just going to have to try and figure out a way together that we all can find what is the best for their situation and their people. Thank you so if you take something like reference data, which should be highly informed and own from business expertise. Yeah, often there's not enough of an understanding of what reference data actually is from a business perspective. How do we bridge that gap between tech business to drive better collaboration. This is just an example. Gosh, can you interpret for me a little bit what what they, they mean specifically by reference data. Do you mean a benchmark somewhere reference master data. So, if the questioner wants to expand on that a little bit. I don't know what reference master is actually deep. Can you say more about what what what that means reference master data that I lose you. Sorry, I accidentally muted myself against reference data is is typically data that's used to classify or categorize other data data. Okay, I see what you're saying. Okay, so I think that that comes down to it's in the areas that I that I work in we we classify things and do benchmarking using other sorts of data and making sure that we can refer to things accurately and make sure that things are behaving the way they're supposed to. And again, I think if we can find ways for any of those kinds of roles, where if you have a boundaries around what makes sense, or doesn't make sense, or boundaries around how something gets classified as a or b, boundaries that helps somebody know whether it's a valid score or not. It's the language that we use just like me not knowing what you mean by that. It's a language issue so many times, because I can understand what you're talking about now that I know what Shannon said to me. We often get hung up on our language, and we insist that other people use our language, whereas I think we can get to a point where what we want from them is can be described in ways that aren't as technical, which is what our hope is is we can start to talk about literacy without being really hung up on what language they have to use to understand it. Hopefully that makes sense. Definitely and if experts aren't necessarily the best trainers. Would that mean that the classic train the trainer model is flawed. I don't know. It doesn't mean that that experts that there's always going to be experts who love to break things down and teach. And so I'm not saying that experts can't be great teachers. I am saying that we can't assume that every data scientist would be really great at teaching remedial information about data, because they may get hung up on such incredible intricacies about what's interesting to them that they may not have thought about how to best talk about the basics. So there will always be like my example of interests. If somebody really wants to be a teacher, then that teaching is part of their capabilities. But you can't assume that every really advanced data scientist wants to be a teacher or will be good at deciding how to teach some of these skills. And Wendy, can you point us to any research or case studies explaining how to design upskilling programs with businesses and human needs in mind. I will do my best. I will, I will do my best if it's not in this week, it will be part of what we focus on when we keep on going with in this series. Yeah, and then although not necessarily a direct tie and I would I would actually recommend your book as well the translator. Oh, that's a good, good. Let me Yeah, in your class. Yeah. Yeah, so I have a course called become an analytic translator but also the book. And then I also co authored a communication book that gets into how you see I'm such a lousy self promoter I didn't even think that. So these are available. That are also helpful. Very good. All right. I love and I will have links to that as well in the follow up email to both of those. So, when you say we regarding futurist future steps do you mean. Oh, sorry, I'm going to go back to that. So when, when you say to we future steps do you mean data receive consulting or another group so we being the future steps are going to be in part of this webinar series and I think that's what I was referring to is yeah, we meaning me in collaboration with Shannon and folks at data diversity that we will try and cover each of these topics that keep coming up and do our best to synthesize what we see out there. Absolutely. And let me pull up the I'll pull up the link here to the upcoming webinars in this series. And to that point, just, you know, all this feedback so there's a request to publish the chat so I can certainly do that that's really helpful. You know, Wendy and I are kind of learning along with y'all and so if you have, you know, additional topics and stuff for the series, please let us know and how you'd like and help us to shape the series. Yes. Yeah, please. All comments and suggestions welcome. All agreements and disagreements welcome. We are moving along seeing that there's a lot of things that don't work. And we are trying to identify the things that do. And I would not say that we have all of the answers but we're hoping to find a few things that will be helpful for sure. Absolutely. All right. Well that is all the questions that we have for today again there's so many great comments I'll send the chat over to you as well Wendy make sure you have that as well as I'll get a copy out to everyone. Thank you so much for your participation and helping with these webinars. Wendy thanks for another great webinar. Again just a reminder to everybody I will send a follow up email by end of day Monday with links to the slides links to the recording and links to the all the different requests that we had throughout for different topics as well as how you can get Wendy's book and sign up for her class. Thanks everyone. Hope you all have a great day. Thanks Wendy. Thank you. Thanks Shannon thanks everybody thanks for spending an hour with us.