 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 series elevating enterprise data literacy, Dr. Wendy Lynch. The series is held the first Thursday of every month and today Wendy will discuss the missing element of literacy communication. Just a couple of points to get us started due to the large number of people that attend these sessions you will be muted during the webinar. If you'd like to chat with us or with each other, we certainly encourage you to do so. And just a note zoom defaults the chat to send it just the panelists but you may absolutely switch that to network with everyone. For questions, we will be collecting them by the Q&A section and to find the chat and the Q&A panels, you can 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 the next 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 our speaker for the series, Dr. Wendy Lynch. For over 35 years, Wendy has converted complex analytics into business value as a sense maker and analytic translator. A talented researcher and consultant to numerous Fortune 100 company startups and healthcare giants, her work has focused on the application of big data solutions in health and human capital management. As an author of books on effective communication and analytics, Wendy has pioneered the only structure system to empower a new generation of professionals who will revolutionize the successful application of data to solve business challenges. These trained analytic translators will allow companies to convert advanced analytics into actionable solutions, building a sustainable alliance between analytic and business professionals. And with that, I'll give the floor to Wendy to start the presentation. Hello and welcome. Thank you Shannon always happy to be here so thank you for that intro and good morning to everyone on the West Coast. Good afternoon in the East, and welcome back to those who have been here before and a very hearty welcome to those who joining us for the first time. Today we're talking about communication which is a topic that is near and dear to my heart, which Shannon just talked about that I am the author of a book on analytic translation, and also a co author on a book on better communication at work. These two topics are so critical. And I will cover a whole extra dimension today by connecting it to literacy and how it gets in the way of us training people in literacy. So, the first thing that I'll point out is that we have been told in many cases that communication isn't always important. And I disagree. However, there are articles all the time about how you don't have to be a good communicator. And it explains that there are certain jobs that you might want to actually seek out if you're not as comfortable with communication. Three of the top 10 are in data related fields and in fact, database administrators and software developers are listed number one, where they require even less communication skill as a nurse administering anesthesia and only dealing with unconscious people. So we are kind of encouraged to go into certain fields and almost tell us that we don't have to know how to communicate. I saw another article, similarly, that if you don't have strong communication skills, you should be in technical or IT roles or research and analytics because they require technical proficiency instead of communication skills. Those two are actually listed above solo types of professions like a musician or a illustrator or a photographer. So I'm going to push back on this assumption that we don't have to be good at communication just because of the different types of occupations that we're in. And there are problems in data literacy in that handoff between analytic people or data people and the business side. And this article last year pointed out that when we assume that data illiteracy is the basic reason why companies aren't getting value from data. It creates this divide it creates this tension between analytic folks and business folks. And it's partly because we don't understand each other. Our data professions want to have better governance better quality of data better respect for what it takes to maintain a great data environment. The business people want data folks to understand the pressure they're under to get good information from the data that are available. So, while that tension is about the functions of each side. A lot of it is because we don't communicate well with each other. So I'm going to do a small thought experiment with you. If you would take out a piece of paper and a pencil or think about a place to do a little question and answer for yourself. Any of you that want to do it in the chat. Great, you don't have to. But what I'm going to do is I'm going to ask you some questions, and I'm not going to give you a ton of time so I want you to just answer the first way that it comes to mind. The questions are, if you were to improve data literacy and think about improving it dramatically at your organization. What specifically would people know that they don't know now, what would they know that they don't know now. Secondly, if we were to improve data literacy dramatically, what data would they now understand that they don't understand now. What types of data do they need to understand that they don't understand now. If we were to improve data literacy dramatically, what methods would they know that they don't know now. Is it that they need to know measures of central tendency is it that they need to know deviations is that they need to know correlations that they need to know what outliers are what do they need to know, and methods they need to know. And lastly, what skills do they need to have that they don't have now, what skills do they need to have. Well, thank you for participating in that and we will get to more about this topic in just a few minutes. When we will first of all, let me go back. The reason why I only gave you a few minutes to answer each of these questions is I wanted to replicate what happens every day in business. We go from meeting to meeting to meeting, we go from topic to topic to topic. We don't give people a great amount of time to think through their answers. We have top of mind answers that we provide because people bombard us with questions all the time. So, keep that in mind as we move forward and talk about broader communication skills. So, so that you know that I'm not picking on data professionals only. There's a survey that was done on MBA students asking, what are the most important communication skills and they gave them 40 skills to rank. And it's not surprising listening came in as number one most important, followed by asking questions all the way down through number 10. What was interesting is then they asked the same people, what are you most good at, what are you effective at doing. And what we see that even though listening is ranked number one as important, it barely cracked the top 20 in terms of how good we are at doing it. These MBA students were honest in saying they're much better at telling people things than they are at soliciting information and listening to everyone else. And this theme comes up over and over and over again, we're way better at telling than we are at listening. Yet on the flip side when they surveyed subscribers to the Harvard Business Review. The number one quality they wanted in hiring candidates for executive positions, communication skills. The most important characteristic that makes an executive promotable, the ability to communicate, and that was more than hard work, ambition or education. And yet I saw a recent overview that said fewer than 2% of employees have had formal training in how to listen better. So we know it's a problem. We know that it's important, but we don't spend time learning it or focusing on it. I'm trying to be a good listener, but you keep breaking my concentration by talking. We do not focus on this. So when I give trainings in communication, I do hear people wondering out loud why this is such a big deal. It's not as though we don't listen all day, every day. I read that the average person hears between 20,000 and 30,000 words per day. That's one of the estimates. Another estimate said you hear as many as 100,000 words per day depending on your job. One review said that the Fortune 5 Fortune 100 employees who worked for those companies got 1800 emails text voicemails written memos calls and conversations every day. So it's not that we don't have opportunity. We are overwhelmed in the number of things that we have to pay attention to. And all those words are important, but we also have to notice that when other people get meaning from your words, less than 10% of the meaning comes from the word itself. So we say the word, the difference between yes and yeah, and yeah, is very, very different. And the facial expressions that I make while I'm saying the word are critical. So as we think about our interactions, we have to also notice how life is changing. This was a great study looking at how strongly you develop affiliation with other people. Now, if we're thinking about literacy, are we communicating with people who we feel comfortable with? Are the people who are learning literacy comfortable with you? And we see that in person, we develop a certain amount of affiliation and connection that gets worse in video, that gets worse in audio, and it gets even worse when we're talking about Slack or texting. So we are developing new communication methods over the past generation that reduce how bonded and affiliated we feel with other people. So we're getting less and less connected at the time when we need to be more and more connected in the way that we work together. In addition, we are experiencing some distractions that we didn't have before. There's a new word, if you haven't heard it, fubbing, which is snubbing somebody with your phone. And you said, when you pick up your phone, while the other person is talking to you, you are fubbing them. And they have now measured that that reduces the connection that you have with others and it makes your spouse very unhappy. And it actually makes a difference whether the phone is even right there with you, whether you look at it or not. So are we paying attention to each other? Are we connecting with each other? Because if we're asking somebody else to change and learn, they have to know that we care about them. One of the other things that gets in the way that I try and reassure people is that your success actually is a liability. What do I mean by that? The more successful and experienced you are, the more expertise you have, and you think you've heard it all before. And so when somebody comes to you with an issue, you're already applying it in your brain to experiences that you've had before. So you think you already have the answer and it's likely that you can tune out. You also, if you're successful, have been giving way more responsibility, so you feel like you don't have time. You don't have time to listen to what's happening. Also, we get rewarded by having solutions. So when we think that we're supposed to solve a problem, we won't finish listening, we'll jump in and solve it. And lastly, just the fact that you're smart is a problem when it comes to listening. What do I mean by that? Well, we actually think 10 times faster than other people speak. Even people from Boston and New York who I find speak really, really quickly. They probably only speak 250 words a minute. But you think from 1,000 to 3,000 words per minute, so what do you do with all that extra brain space? You think about the last meeting, you think about your next meeting, you think about what the boss said, you think about what's for dinner or something else. Paying attention is not easy. It takes practice and it takes intention to really be thinking about what the other person is saying rather than having our brains wander somewhere else. In addition, we've gotten to where we interrupt all the time. This was an observational study where they looked at the number of people in a meeting and how often others got interrupted. And as you can imagine, the bigger the meeting, the more interruptions are likely. But what was more telling about this particular study was he measured who was doing the interrupting. And in this study, level E was the CEO or anyone in the C suite. C is reported to ease, C is reported to ease and so forth. And so when he looked at it, the highest level executives interrupted six times more often than the entry level people. So as we get more advanced, more successful as we move up in the business world, we don't get better at listening, we get worse at listening. So it also applies to very high level professionals. There was a landmark study done several decades ago and recently it was redone. How long before the typical doctor interrupts what their patient is telling them. In your mind, take a guess how long this is and see whether you guessed 18 seconds. So again, all of us, every professional, every professional spends way too little energy and emphasis on listening. So why am I spending so much time on this because I've really looked into the science of what listening does. And I'm guessing that you will be somewhat surprised. So in studies where they measure how well a boss listens, and that's usually a combination of how people felt when that boss was listening to them, but also an external observer, seeing how well they listen. And there is a concept called psychological safety, which is very, very impactful and it's a big deal right now in HR circles. Psychological safety means that you feel comfortable expressing yourself that people will listen to you that they won't dismiss your thoughts that they won't discount the validity of what you say that they won't make fun of you or hold it against you. You feel free and safe to really contribute. Well, when a boss listens well, psychological safety goes way up. Now measured that workers who have a good listener boss, they're more creative. They have a more accurate memory, they actually could remember more because they weren't using a brain space wondering if they should talk. They have better job satisfaction, better trust. Their teams have lower burnout. And their workers have lower turnover. So an investment in good listening by a boss makes them better leaders and impacts worker performance. Now let's think about this in the context of literacy. Literacy is often an effort to help people who aren't as aware of data issues, learn more. So if we're asking them to explore areas they're not good at they need psychological safety, because if you remember and we've covered this in other sessions. Almost a third of adults can't interpret a graph. Almost one in five has such high anxiety that on on an MRI, you can see it register in their brain as pain. 62% operate at a very base math level. So if I don't feel safe, how am I going to overcome these reservations and really engage in learning. If you are a listener, who's effective and create psychological safety your investment will have an impact on how people buy in when you want them to become literate. What else does listening do listening actually has an impact on how people disagree. So if you totally disagree with somebody else. But you listen attentively, you don't roll your eyes you don't argue you don't interrupt them, you listen to try and understand their perspective, even if you don't agree with it. That person's thinking will get clearer. That person will start to actually align with you, even without you trying to convince them. They become more aware of how mixed their feelings might be about a topic. And they become less extreme in their thinking. Listening is an investment in teamwork. And also a solution to conflict. Lastly, if you're not convinced by performance and teamwork. So listening actually has an impact on corporate performance and sales teams, good listeners have higher revenue. In client management, their clients are more loyal. In manufacturing plants where the boss is a good listener, they have fewer accidents, and their plants are more efficient and earn more startups who have bosses that listen. They succeed. And in places where you have poor listening, especially something like medicine, malpractice rates increase. And that's not the mistakes increase. It's that the patients sue them more often. I say all of this because when we're thinking about literacy when we're thinking about engaging between business and data professionals. If we are not open to each other if we are still blaming each other for what's wrong will not make progress. What does listening mean? This isn't a training session but just to give examples so that people have an idea of what we're talking about. Listening means that you're totally paying attention that you are focusing as much as possible on quieting your brain and the rest of the thoughts and not trying to create your response or figure out what you're going to say next you are just paying attention. If you have video or you're in person, you are making eye contact and using body language. You make empathic words or sounds you say oh that's too bad, or yeah, oh I get it. You slow down, you don't try and rush somebody. You get rid of your distractions, you don't multitask, you put down your phone, you don't fub them, you don't interrupt, and you make sure that you confirm what you heard so that they know you have heard them. When you do these things, it has a powerful and powerful impact on other people. So listening is also critical for data driven decision making in organizations. What I find is that data literacy efforts often are about trying to change the non data people. There isn't a flip side to that, where the data folks are trying really hard to understand what the business needs. It requires us communicating to see both sides of this and improve both groups in terms of not only their literacy in data, but their understanding of each other. And what I notice when I work with companies as an analytic translator is that the literal and figurative distance between those who set business goals and those who manage and use and analyze data is quite vast. There is a big gap between the two. And so there's an entire set of people and structures that operate the business and try and manifest these business goals. And on the other end, there's this whole team of people involved in data management data governance analytics and data science. They are focused on the right side of making sure that we are doing things right to have the best quality usability of data. And what happens is that when we have an information need that is at this juncture between business operations and the management and curation of data. That we are far apart from the origins of the goals and the origins of the data. So, for example, if we decide that there's an important decision, let's say the business, the leadership team decides there's an important decision that they need to make. And as they think about it, they realize that they need some data and they need something to help them understand the issue and make a decision. And because they are not close to the data, they attempt the best they can to define what they need to know. So here we are with the business folks realizing they need something that may take some data, understanding what they think that might be, and then trying to articulate that into detailed information. And from a data literacy point of view, our goal is to have them be better at these steps, be better at defining it so that on the data side we know what to do. Now, on the data side, they are aware of all the possible data sources, all the analytics that might be available to answer questions. They know that this is a huge universe that's getting bigger all the time, the ways that we may be able to make use of data. And then when it comes to any given question, we know that we have to whittle it down to some data sets and specific methods that we might need in any given situation. And so what happens is that we try and create clear specifications about the fields and the outputs and how those data are going to be managed. And that puts us into a framework where we identify what I'll call for this case the question. The question is where we have this information need from the business where they have a decision or an action they want to take. And where the data folks can finally whittle it down to what it is they actually need to do. The problem is that that question is often formulated without a whole lot of context. And we end up sort of in this isolation far away from the data and far away from the business goals. I see over and over and over again that a query comes in from a business leader. And dozens and dozens of times I see the use of forms or questions that limit what it is that they can ask. So what data do you actually need? What specifically do you want us to do with it? How many cuts do you want? What format will it be in? Because if we are looking at the question, the data folks want to be able to do it right. And so their approach is to have it be smaller, smaller here. Because otherwise there's too many things they have to decide over here. So we take this and we turn it into an individual question. So then where does that lead us when we restrict like that? Well, a survey that I did asking business leaders, how often do you get the answers that you need in a way that you can understand them? And two thirds of the time they say not often or sometimes. Only one in 10 says I always get what I need. When we look at the analytic team, when you get a request, do you understand what this is about? Do you get asked for your opinion? Do you get the context? And again, fewer than one in 10 say yes, it's a collaboration and I understand everything. Two thirds of the time say no, I just get these requests out of the blue and I don't know what they are or I get some context, but they don't want my opinion. So this is not a talent problem. This is not a data problem. This is a communication problem. So the issue here for me is that we have asked people who are not necessarily data literate. But also, we're asking them to take on a huge task by defining a business need in somebody else's terms. And so the first thing that they say is what we run with. They say, I need to know ABC and we say, well, how do you define A, how do you define B, how do you define C, how soon do you need it, what are you going to do? And that is how projects start. That's how requests start. And data literacy tries to say, well, if we could just get them to say this better, our life would be easier. And what I'll say today is probably not. I think today at all, I want you to hear this. The first thing someone says is not the full story and probably not what really matters to them. I'll repeat it. The first thing that somebody says, especially an answer to a question is not the full story and probably not what really matters. And so communication is about having a conversation that gets from the first thing they say to what really matters. And if you go into the conversation knowing that the first thing that they say is incomplete or not even right, then that opens our eyes and opens our perceptions about what else we might need to know. Now, people will say to me, oh, my God, she's calling it a journey. That must mean we have to talk to other people for hours and hours and hours. No, I'm talking about a few minutes, just a few minutes. And the reason for this is that, like I said earlier, we go from meeting to meeting to meeting to project to project to project to topic to topic to topic. And what always happens before that meeting is that there is some background. So the first thing that they say can't possibly encompass all of what's behind it, because everybody has baggage and I don't mean that in the bad way. There's so much more of a backstory that people bring with them that shapes what they say first. So when somebody has a backstory, it reflects how they're going to make decisions. It reflects what's at stake. I think their job is at stake. It reflects the context, the experiences they've had before, what they value, what they believe, what the project goals are, what they hope to accomplish, so that you can get to what really matters about this comment or request. We can't simply take the first thing that somebody says. So the goal is to acknowledge that when somebody comes to you with a request. They say something to you like I need X. Your job is to have a brief conversation by using the right kinds of listening and questions so that you understand what they really need without making assumptions about what that is. So, even small differences in how you ask a question are going to affect the quality length and content of the answer. Today is too short of a session to get into all the different ways. But I want to go back to our original question. I asked you if we were to improve data literacy what specifically would people know what data do they need to understand what methods should they know. We answered those specific questions. Well now I want you to think about it slightly differently. Again, keep track of what your first answer would be to these questions and put it in the chat if you would like. If we were to dramatically improve data literacy at your organization. How is literacy important do you think how is that important. Next, if we were to improve data literacy. How will that be useful to the organization. What kind of answers do you have to that question. How would you decide that that data literacy. program was successful if it were to improve data literacy how would you know that it was successful and if we were to improve data literacy. What would you both be looking forward to so as you think about these answers how as you think about how it be useful how you would know it's successful. What would you most be looking forward to compare that to the answers to your questions about what would they know what would be their new skills. What would be the methods and think about how these new questions might shape those answers differently. So the levels of what matters to somebody are driven by certain levels of the way that we see the world. So these are just some examples of how good communicators can use different types of questions to move people along. So, for example, in our book, get to what matters. We talk about three levels of meaning motivation specifics at strategy. These things drive how people make choices and what it is they do specifics are how I know things strategy is how I decide things and motivation is what really matters to me my beliefs and values. I asked you a couple of these types of questions just now in thinking about literacy. How will you decide that data literacy efforts are successful. How will data literacy be useful to the organization. If we explore all levels of meaning and all levels of what matters, we get different answers than if we dive into the specifics right away. There's also a series of questions that move conversations in different directions. If somebody is stuck. And they're at the corner of anywhere but here and oh no. We can help them see the possibilities and move toward what they want in the future. We can ask them what are you most looking forward to which was a question I just used about what are you most looking forward to when we have literacy. When we make progress in data literacy. Conversation allows us to navigate conversations. It allows us to broaden people's thinking. It allows us to set a direction in the conversations that we have. So, when you start to think about it this way you realize that when we take the first thing that somebody says. We've probably jumped on a specific answer of what that group says they need. When they haven't had a chance to really think about it yet. Because remember the first thing somebody says is probably not what matters. And certainly not the whole picture. So what we typically do is we jump on that request. Try and make it as specific and narrow and clear as possible. Even though that's probably not what they really need. So, instead, if we back up and understand the context of the business goal. Huh, I see. So you're trying to make this kind of a decision and allow them to elaborate and answer multiple times at different levels. We can find out what really matters about this request. Use that information to actually think bigger. Because most of us don't even really want to be doing these tiny little answers. They want to contribute. Based on their knowledge of all of the possible analytics and data sources that are available. So then they can help define what the real question is. And I'm going to tell you that when we use this kind of communication rather than trying to interpret this small little definition. Every single time that request gets modified. And most of the time, it's not even the right question. It's not surprising when we focus in rather than broadening, we get the answer wrong more than we get it right. And the estimate is 60 to 80% of the time. We give the wrong answer, not because we didn't do a good job. It's because we asked the wrong question to begin with. So when we look at the first thing that people say. When we respond there. We are missing an opportunity to have a broader conversation about what they know. And when it comes to literacy. If we're asking people to get better at defining this. Then we also need to get better at how we talk to them about it. So if we want them to be able to articulate their big goals better. We should also get better at helping them understand all that's possible at the depth of information. The breadth of insights that are possible. So if we ask this question. Why communication. What I would say is we end up arguing over something where we all want the same thing. We want others to understand what we are up against. But in order to do that, all of us have to be willing to engage in better conversation. Because seriously our mutual success depends on it. So I am going to stop there. I am Wendy Lynch. I have a course training people to become analytic translators. I also teach communication. And my goal is to create clarity, confidence and powerful partnerships between business business and analytic teams. So I will stop there and we can take some questions. Thank you so much for another amazing presentation. There's been so much love given to you and shout outs in the chat. I'll make sure you get a copy of that. And just to answer the most commonly asked question, 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. If you have questions for Wendy, feel free to put them in the Q&A panel. And somebody, I got a nice snarky question here, you know, have you been to my agency secretly Wendy and hanging out? Well, you know, it's amazing. Yes, I have. But it isn't yours. Yes. It just is pervasive. This is what happens is we try really, really hard to narrow, narrow, narrow, narrow. And we should be expanding, expanding, expanding. And people say to me, well, I don't have time to have those kind of conversations. And my answer is always, wouldn't you rather spend six to eight minutes than waste three weeks of work? I mean, that's kind of the deal. Yeah. Perfect. And where can we find information about your course? Well, I would, well, you can reach out to me directly. And let me put in the chat. My email. But we are just revamping where that will be available. And it is a several week course over time, both live and recorded. And so that was the other question. How long are your courses? You say several weeks. It's, it's a few weeks, like one hour a week, two hours a week. Yeah, it's the ideal amount of time and it depends on what the goals are because I tailor it to whoever it is, but it is between 60 and 90 minutes if they can spare it five to six weeks. I love it. Great questions. And if we don't have time to do it right the first time, how we have time to redo later, I love that question too. Just a general question out to the community there. Yes. I'm sorry to say it again. Yeah, it's if we don't have time to do it right the first time, how will we have time to redo it later. Exactly. I mean, that's, that's my, my point. And there needs to be buy in from leadership that we have a culture of following up for a few minutes. So it is not just asking that person who fields the questions. But it is a commitment culturally to allowing somebody to come back and clarify, rather than making them jump through hoops, knowing that it isn't right. Because there are so many demoralized discouraged individuals out there in the data professions who feel as though not only is nobody using their talent, but they are being set up to fail because they don't get enough information about what they're asking. And yet, the people who are asking are getting the feeling like if they don't understand enough in the data, and they're illiterate, that they shouldn't have the right to ask. So we've put each other in a really, really bad situation. And it's, yeah, it has to get fixed, or we are all going to really have more and more trouble the more advanced things get like AI and machine learning, because we will not be able to upskill people to the extent that they know how to ask for the right machine learning model. That's not possible. So we all have to get better at talking about what we really need. Indeed. And the comment, there's a comment that says thanks for saying this, Wendy, you know, it's prevalent. This is all too prevalent in IT as well. Yeah. And I, the reason why I went through so many examples at the beginning is that people feel like I'm picking on them. But doctors don't listen. And business people don't listen. And computer programmers don't listen and IT people don't listen. So it's not one group or another. Yeah. Very true. So, do you offer courses for individuals? Yes, absolutely. Yeah. Individuals are great pairs of people are great teams are great. And when communication with leadership and other higher ups, whose time is limited already, what's the best approach to figure out what they want? We have a series of questions that we use as analytic translators. And we also have some techniques that we learn. One of them is framing. And framing helps open the other person up to the idea of you asking some follow up questions. So rather than pepper them with follow up questions, we say something like a frame of, you know, I want to make sure I get this right the first time. And I want to make sure that I get exactly what you need. And so it's very hard for them to say, no, I can't answer your questions when you frame it that way. And then you start big. You start big with so how did this come up? And how would that be most useful? Because then they usually have to back up and articulate what it is they're really trying to accomplish and what decision they'd want to make or what action they want to take. And so we continue those questions. But it is only about five minutes at the most when I've gone from beginning to end with the project changing like three times during the conversation, I think it was 20. So it's not a lot. Perfect. Just another question about the recording here. Of course, yeah, we will ask you to follow up email and the with the recording out to everybody on innovate Monday. Lots of great comments going on. I don't see any additional questions. Give everyone a quick minute, but Wendy this again has been so amazing. Thank you so much for another great great webinar. You are welcome. I'm always happy to chat about this stuff because I'm an evangelist to make us all happier and more productive and better connected. So, Absolutely. Well, again, everyone, just a reminder, I will send out a follow up email to all registrants within the end of day Monday with links to the slides and links to the recording so you can all share with your colleagues. So, thank you everybody. I hope you all have a great day. Wendy. Thank you again. Sure. Thank you, Shannon. Bye bye.