 Hello and welcome. My name is Shannon Kampen. I'm the Chief Digital Officer of Data Diversity. We would like to thank you for joining the most recent webinar of the Data Diversity Monthly Series, Elevating Enterprise Data Literacy with Dr. Wendy Lynch. The series is held the first Thursday of every month and today Wendy will discuss literacy as a two-way street, the case for both business and data literacy. 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 would like to chat with us or with each other, we certainly encourage you to do so and just to note Zoom defaults a chat to send to just the panelists, but you may absolutely change that to network with everyone. For questions, we will be collecting them by the Q&A panel and to find the chat and the Q&A panels, you may click those icons in the bottom middle of your screen to activate those features. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and any additional information requested throughout the webinar. Now let me introduce to you the speaker for our 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 companies, startups, and healthcare giants, her own work has focused on the application of big data solutions in health and human capital management. An author of books on effective communication and analytics, Wendy has pioneered the only structured system to empower a new generation of professionals who will revolutionize the successful application of data to solve business challenges. And with that, let me give the floor to Wendy to get the presentation started. Wendy, hello, and welcome. Thank you. And I am so happy to be here and always nice to hear your voice, Shannon. And happy to have many of you joining us again. So, welcome back. And if you're joining us for the first time, welcome. This series is about data literacy, but in a lot of ways it's about the people who we have to focus on when we are thinking about data literacy. So, today is about the two-way street that we might think of for both business and data literacy. I want to start by just making a comment about how literacy efforts often look to me. When people start on that journey of data literacy, it often feels as though it is an investment in a one-direction pathway. So, what I mean by that is we sort of think that as we get better and we get better at people understanding how to ask for, let's say, a report, then further down the line, they start to really grasp what some of those insights are. And then they actually start to have a real understanding. But that's a ways away. And what I see people doing is thinking that there is a journey that they can be on that will get them to the glory days of the future where everybody understands data. And so maybe there is nirvana somewhere down the line. But when we think about this as a single direction, I think we minimize the real complexity that is data in a business setting. When you actually think about how the whole journey of any particular data source that goes through the various platforms and all of you that are involved in governance and architecture and management know that this is a fluid, ongoing type of endeavor. And it can't really be one direction because everything is getting updated at all times and everything is coming in, getting revised, tomorrow, reality is different than today. So if we are thinking about business outcomes and we're thinking about the data that helped us achieve those business outcomes, we can't be thinking about as one direction, we have to be thinking about it from a multi-directional perspective. So I want to remind us of how the business environment and the analytic environment are often structured when we're talking about an organization. Most places the business environment is considered separate from rather than integrated with the analytic environment. They think of data as part of IT rather than data being part of the fabric of the entire organization. And when the business is asking for information, they often just send a request down to what I would say the separate analytic department, the analytic environment. And that department, that environment, those professionals create a design, they do an analysis of some kind or put things together, package it up and throw it back over the fence so that the business can use it. We do this but many of the studies that have been conducted about how successful we are tell us that more often than not we have to revise what it is that we threw over the fence because it wasn't exactly what they meant to ask for. And so here we are with these two different groups and as we think about it there is a gap in understanding and it is partly language which is what literacy deals with but it's also bigger than that. So as we think about this whole endeavor of helping people that first request that comes in from the business usually sends us off to do some type of fancy analytics. So we are working hard on it, we send it back, end up having to do rework and reanalysis because we weren't exactly sure where that business really wanted to go. So if we think about that journey and being way more complex and both directions or multi-directional then we start to think about this in a different way. So I would say it's much more than words and terminology and definitions. It's the context of those words. So I want to give an example and this is a real life example. Business client says oh I need a dashboard. So those of us on the analytic side we hear dashboard and we think oh we have to design something that has a platform on the back end that gives them a chance to look at things so we all of a sudden start heading off into a direction. And if we say oh a dashboard say more about what you need they may say something like a dashboard that shows comparative performance across locations. Okay so again we start thinking okay this is going to have to have information about the locations, information about what's going on at those locations and so we say so the dashboard would be something on an ongoing basis that you can use to track things and they might say oh I don't know that I really mean a dashboard. What I really mean is a report so that I can get an answer right away. Oh okay so you're not really needing a full dashboard that you have forever you just want a report that summarizes things and they say well maybe I don't really mean a report. I just need some information about something that I need to work on. Oh so you need information okay. So what kind of information might you need? Well I need to make a decision. Oh so what kind of decisions are you needing to make? Well I actually have to compare those locations because I need to make a decision about whether one of them isn't cutting it. Oh so what criteria might you use to decide which of those departments, which of those locations isn't doing well? Oh I haven't really thought about the criteria but really I need to have information about the profitability of each location because I need to make some budget cuts. So when we have those kinds of conversations, even though we're talking about helping people with literacy, literacy only takes us so far if we don't understand what is going on in the business. So they came to begin with as oh I need a dashboard but really what they need is to figure out how to make a decision about their budget and whether to cut funds for one of the locations. So we see these kinds of interactions all the time and the analytic people need to understand more about what they mean when they first come with a particular request and the business people need to understand a little more about what's possible and what kinds of things can happen as the result of the data that they need in a format that they need. So they have to get some clarity and so whether we're talking about dashboards or other things, think about the magnitude of this problem. Every day in corporations around the world there are literally millions of requests where the two teams do not understand each other and it has more to do with communication than it does necessarily with terminology. You all I'm sure have examples of this whether it's somebody saying dashboard when they really just meant information or criteria, whether somebody one person asked me for an ROI and when you ask them what they meant by ROI and which things they wanted to compare and what was their investment so that we could get the return on that investment and they say oh I don't mean an ROI I just want to know whether we got better because that's what their terminology meant to them. It can be ROI just meant improvement. It could be I've had leaders say well we need to correlate these two things. Well if I took them at their first request I would run a whole bunch of Pearson correlation coefficients but no what they really meant was compare. It isn't necessarily that they don't know what they want they just not may not say it in exactly the right way and on the other end they may say we need to compare earnings and if the analytic folks don't really know whether they mean net earnings net revenue what they exactly mean they may provide something different and I have seen that happen before because they don't understand the context of the request from the business perspective. So as we think about these two directions we have to remember that it is terminology it is awareness fluency literacy but it is also how we interact with each other. So when we think about it from the big picture what leaders want is timely innovative insights from the data and they want to know that those insights that those insights are delivering measurable value that's what they want. Now the data teams they really want meaningful challenging work in an environment where they can actually achieve things that make sense that the data are reliable that they can do it with minimal hassle and they want that work to be appreciated and value. In order for both teams to have achieve these outcomes we need a little more data awareness which is why we talk about literacy from the business folks but we also need more business awareness from the data folks. It isn't simply that we need these guys to articulate exactly what they want in a terminology that these guys understand we also need data folks to understand what it is that is the priority for the business. So I often tell people that the problem is not that we have insufficient resources or that we don't have talented people or that we don't have good tools or systems. The problem is that we often see things from only our own perspective and so today is about seeing things in both directions. Now I spend most of my work on being an analytic translator and training analytic translators and so what I thought I would do is share with you how an analytic translator looks at this challenge at this lack of communication this lack of comprehension on both sides and the very first thing that an analytic translator does is to have some empathy. So that empathy comes from appreciating each other. We have to appreciate first of all that we have different languages that it isn't on purpose usually that somebody is using jargon. I have been working with somebody on a completely separate project that has to do with finances and she keeps on saying do you have the EUI? Do you have the AM? Do you have the blah blah blah and I do not know what in the world she's talking about. So we have to know that everybody operates with different languages and it's not that we want every business leader to be able to define the equation for variability but we do need to start to understand what's possible and what challenges each other has. We also have empathy because these are two different groups of people with two different types of personalities. If we look for instance at the Myers-Briggs and what is the most common now I realize that there are exceptions but the most common personality in each of these groups business leaders tend to be extroverted and make quick decisions based on details. Data scientists are introverted and like to consider possibilities and what the other options might be. So we already see that we have different language we have different types of people so those personalities don't always jive and there's a good reason for these. So as an analytic translator we understand that each of these groups has very very specific needs in the work that they do. So if we think about Sheldon from Big Bang Theory being a typical data scientist and we ask him does your product improve employee performance well he might say something like that our analysis controlled for demographics 10-year previous performance location and job type we did a time series analysis removing seasonality transforming the outcome to a binomial showing that participants had significantly higher likelihood of improvement at a p-value of 0.02 that would be a reasonable response if a data scientist is talking to another data scientist. If you ask Don Draper from Mad Men does your product improve employee performance he will likely say yes so as we begin to understand both sides it's not just the words we use it's the way we like to present information and we are trained so differently this one always cracks me up as I was thinking about it originally thinking about training when you go through a MBA program you are trained to see things in a way that you can easily take action you look for differentiators you look for opportunities you look for how to be decisive pivot quickly and take actions to be successful on the other side data scientists are literally trained to formally formally I can't always say that formally doubt ourselves what I mean by that is you go through semesters and semesters of understanding all the reasons that you might be wrong you actually quantify using things like p-values and confidence intervals the likelihood that you are wrong there are types of errors types one type two there are subsets of errors and you are trained to document the limitations uncertainties and potential bias in the way that you look at things so that caution is the way we are trained to look at the world now think about how that sounds when you on the other side the MBA training is telling you to move ahead use what you have and move forward quickly on top of this business leaders aren't trained in advanced statistics and usually not in communication skills and data scientists are not trained in business management or communication skills so we have this chasm we have this big gap between the two so it isn't just the language it's how we use information and lastly we have different preferences about how we express ourselves and I grew up with a theoretical nuclear physicist for a dad now when you listen to what's happening on the right you think of my dad business leaders they want to deliver results that are simple clear understandable indisputable and convincing data scientists or scientists in general want you to understand how complex the possibilities are because they love learning something new and presenting something different they think everything is interesting and want to explore it tell you how unique it is and share just how advanced the techniques were if you're on a data team like the teams that I collaborate with they can't wait to use the newest and greatest methodologies so what's interesting is that the person on the left our business leader wants it to be so clear that it needs no explanation the data scientist wants it to be so interesting everyone wants an explanation so when they have a fancy exciting result they want the business to be really curious about how they got it done the business leader wants them to just get to the point and quit throwing it on and on again we can't simply say literacy is understanding the exact way that people describe a particular concept it also has to be that we understand where each other is coming from we are so different in all of these ways our language our personalities our training our expression that it's almost comical that we are trying to work together and if we don't appreciate what each other has to contribute and we don't have empathy and appreciation we are going to struggle so it's not just what we know about each other it's also how we learn to talk to each other and I always emphasize for people learning to be an analytic translator that yes it's about language but it's also about context so let's describe some of the things that get in the way I did a series of polls on LinkedIn this one was for data analysts and analytic teams and I asked them how often are you able to provide the exact answer that the business wants the first time with no rework well you can see it's pretty discouraging only one person in 20 this was actually 40 people so two people out of 40 said I can provide that every single time more than two-thirds say less than half the time or almost never what was interesting was that one of the respondents actually sent a note and said I answered most of the time because technically I did give them exactly what they asked for even though it wasn't exactly what they want so he or she proved by point exactly we do not figure out what the other person wants so let's talk about what gets in the way when we don't have two-way understanding and that first one there is expectations of mind reading we often get requests like dashboard when we really need budget comparisons we expect the person to know what we're talking about another thing that we have to recognize is that expertise requires us to have a unique terminology if you're a cfo if you're a pilot if you're air traffic controller if you are a marine biologist an artist a social media marketer you have specific terminology that allows you to communicate about a particular issue we need that we want that and when we find someone else in our profession who understands that insider language we feel instantly connected if you go to a party and you are an avid bird watcher and you meet someone else who's a bird watcher you love to talk about what you just discovered however between two different professions insider language divides us rather than uniting us it actually alienates other people because they feel like an outsider so when we think about the way that we translate when we think about literacy it has to be in both directions where we need to both understand the priorities of the other person and avoid assuming that other people know what we're talking about so another problem is that insider jargon and that operates in both directions so the next poll that i'll talk about was again to the analytic team and i said describe how your relationship is and how your requests come in i mean are they are they asking you for your input do they give you context for why they need it and what's they're going to use it for and what you see is that fewer than one in ten think that it's a really great relationship and that requests are collaborative and over two-thirds say it's either completely frustrating with no context or sort of okay so what we see is that it's a frustration the requests that come in have very little context they don't ask for the input which means there isn't a respect for what that data person probably knows and the intelligence and wisdom that they bring is not being recognized and so when we think about requests so we're talking about this part of a project that request that comes in at the beginning before the analytic people create a design we have to think about how that happens how is that request made because too often the request is cryptic rushed unidirectional as we said we're not asking them for their ideas it's transactional rather than part of a bigger collaborative effort that recognizes everybody's contribution and they come in and they're ultra urgent they needed it yesterday even though it's possible that it could take a considerable amount of time just to get the data together so we have to understand how we are starting projects because these tend to be what I call drive buys as they used to in if we were in person they used to walk by by your office and throw a request in the doorway before they move on what we are talking about here is often it just comes in in a two-sentence request we also need to make sure that we're understanding how much goes into the work that the analytic teams do and that also applies to the way that we update those requests another thing that happens a lot that I see is that changes come in without a whole lot of understanding of what it's taking for the other team so business environments are constantly changing priorities change things come up emergencies happen things are going to change that's not the issue it's not that you can never change but if they do change how much do we update the other team if the business is going to change what they need do we acknowledge what it takes do we let them know that we appreciate how much they had to do even though they don't need it anymore do they appreciate what it took to get everything ready or does it come across as oh yeah yeah we forgot to tell you that you don't need that so this again isn't the straightforward literacy about oh we need to have them understand the terminology we're talking about how we communicate and how we work together so that there is ongoing trust between the teams so I call those fire drills how often does a team ask for results and then not necessarily meet them so the teams end up spending a lot of time and energy rather than it being a case where they get it wrong they just don't need it and if we look at it from the other direction I also asked business professionals who work with analytic teams and asked how often do you get and understand the exact answers that you need and it's almost a mirror image of what we hear from the analytic side fewer than one in ten say they always get what they need and they totally understand it and again two thirds are saying wow I don't know what I'm getting I sort of get what I need I don't really necessarily understand it and so they are frustrated and feel like nobody is giving them what they need in order to make the decisions that they need to make both sides are frustrated both sides are contributing to this lack of understanding so when we think about this whole area and we start to think about this other side so giving the business what they need in a way that they can understand there are also a few pitfalls that I can describe on this side because there are times that the folks in the analytic environment take for granted that folks up here are going to understand them appreciate the way that they see the world appreciate the effort that went into it and sometimes they can be so absorbed by what they believe is the right way to do things that they don't think about the language of what they are delivering so I'll give you a short story when I first got into the working world out of academia I was working for a startup and it was in the first year of the company's existence and we had just done a six month study to look at the new product so the question was does this product really do what it's supposed to do and the future of the company kind of dependent on it so I was working for the head of the analytic group and the head of the analytic group gathered everybody together and included the CEO, the head of sales, the head of marketing so everybody was anxious to hear what their results were and I don't know why but the head of analytics decided that the best way to start the presentation was overall the program didn't have a significant impact now technically that was a true statement and as you can imagine everybody in the room was starting to think about dusting off their resume because oh my god we're not going to make it if we can't even have good results for this first trial but the real answer was that the people who enrolled in this new product slash program really did improve but enrollment was lower than they hoped it would be which is a very different answer now technically if you included people who didn't enroll with the people who did enroll which in academic situations and research design is technically the way that some people like to evaluate it but in business we need to know all of this and so there was a lot of damage done with that relationship because of how that meeting started because this answer is we need to really improve our marketing and recruitment but the program itself seems to be solved so what we have to avoid when we are delivering information is avoid being disruptive avoid sharing a lot of details that don't matter avoid delivering messages that are confusing or more complicated than they need to be we can spend the time digesting things in a way that meets the needs of the business and so we need to avoid throwing what I call grenades you don't throw a grenade into a meeting and then hope that everybody recovers when you get to the real detail similarly you don't want to vary the treasure if the good result is what matters in the meeting don't vary it after the 18th slide about methodologies and populations and limitations get to the point so that the business leaders understand and then describe it also similar to insider language we don't need to use terminology that doesn't matter to the business overhaul they need to trust that we know what we're doing they need to trust that we use the methodologies that are necessary they need to trust that we can provide them with information that is credible they don't need to know what the words headers gastricity or gradient boosting really mean if they want to sure we can explain it but they don't need that and too often I find that scientific types believe that if they use a lot of syllables it will make them look smart instead it actually makes them look kind of disconnected and unaware so as we think about these aspects people may not think that this is literacy and technically maybe it's not literacy but if we don't have these kinds of ways of communicating that avoid those problems then we're going to be in trouble so some of the things that we learned for two-way literacy is identifying and prioritizing what matters to the other team which means we have to understand it we have to take the time at the beginning to clarify and understand rather than guessing and looping around again to rework and reanalyze we need to build the skills to have the right questions to begin to uncover the context that matters to both parties some of the tools that we use as analytic translators are simple open-ended invitations to say more about what they're thinking to tell us how this came up or who might use it and also understanding that there are specific questions for guiding a conversation in different directions to explore what we call levels of meaning whether it's in specifics or whether it's in motivation we can uncover things that the requester hasn't even thought about yet that will guide how we are most helpful to them and helps them get clarity so I want to do a brief little exercise with all of you what I want you to do right now is think of an important question that your organization needs to answer using data it can be anything just a question that you know right now your organization wants to answer preferably one that hasn't been completely answered already but I want you to just jot down what that important question is so you have the important question in front of you and now what I want you to do is listen to a series of questions as it pertains to that data need for your organization and just think about how you might answer these I'm not going to have anybody raise their hand and do it but think about how you would consider the answers to these questions as you think about your issue so how did that question that need for data originally come up how will the answer specifically be useful to the organization who else might be interested in that answer and for what might they use that how will you know that we've answered that question satisfactorily or sufficiently what's the important time frame for that specific data need is there anything about it that needs to be included or excluded that you haven't thought of or that I might not think of is there anything else that I might need to know first before I might do something to get the data so now I want you to answer this that any of these new questions make you want to adjust how you worded the first question would you qualify it would you shift it a little would you think about it in a slightly different way or actually ask it completely different because when I ask a similar series of questions not exactly this but when I ask a similar series of questions when a data I mean an analytic translator asks a series of questions like that nine times out of ten the original request has evolved so when we think about these drive-by requests the chances that we got it exactly right the first time are very small and analytic translators learn in the first week that the first thing that somebody says is not the full answer and probably not what really matters it's not because they don't know it's because they haven't had a chance to think it through your job is to help them think it through so as we think about it this way literacy can't just be the words and the appropriate terminology it has to be how we help somebody get clear because terminology is not enough it's not just the words that we choose what those words mean to them to us what matters about the request how we deliver what we discover and what the context is and how we might use that context to inform the way we answer the question so when we think about what the data environment is behind a business it really has to be thought of like this it's not a straight line it's never a straight line and it's not the same straight line tomorrow as it was today or yesterday so we have to be thinking about all of the ways that it's evolving and how to provide information that is appropriate that is useful that is timely and also understandable to everyone so it's not just what business leaders want to separate from what data teams want it's connecting these two things for what we all want which is to do our work in a way that it actually benefits the company as a whole and that we all feel like our knowledge and our abilities are being used in their best most complete way so if you're interested in analytic translation and you like to think about literacy in this bigger way we do have a book how to be an analytic translator and I also offer trainings as well as coaching using recorded videos that show just how difficult it is for these these opinionated business leaders like Lee on the left and these very conscientious but a little bit misguided data scientists like Anna on the right and we use those examples to help people understand how things go wrong and how to improve so I will stop there and I will rely on you Shannon to help me go through some questions if there are some Wendy thank you so much for another great presentation really appreciate this and 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 links to how to continue the education with Wendy so diving in here Wendy my organization is starting on its data governance journey and data literacy is a key component any advice on how to start any practical tools methods suggestions um well I would say that when you start out in this area of analytic translation when you when you start out becoming an analytic translator a lot of this is some basic communication skills that we as professionals and I don't mean just analysts or just business people pretty much everybody has had insufficient exposure to the ways that we can talk to each other in a more constructive way so asking questions taking time having the intention to really support the other person and hear what they need rather than only focusing on our area so I guess I would start there thank you and Wendy the dashboard to budget discussion is very important of course isn't that isn't what you're describing here just fundamental analysis it seems to me that any report writer who does not conduct that type of inquiry analysis before building a report is just not doing their job your thoughts on that well I would agree that it seems like it's very straightforward and we would hope that anybody who's going to do an analysis actually dives a little bit deeper into it but I will continue to emphasize that often when somebody asks us for help on an issue they haven't really thought it through yet either and so if we don't as a as a partnership really explore it or if we don't also ask like who the other audiences are or if we don't figure out exactly how it could be used or what the context is surrounding all of these issues we often miss the miss the point so there are groups out there where they do this really really well and it may be that the person asking this question does it really really well already and it seems very straightforward so if that's the case then great for you I just see over and over and over again that analytic teams are given very cryptic urgent out of the blue requests that are difficult to answer because they don't have the information they need perfect so Wendy have we lost the essential skill of business requirements analysis that would presume that we had it we had it to begin with I would agree that there isn't enough of that and those people who do it well are very useful to the organization so yes I guess I would say we have either lost it or haven't emphasized it enough in general I was I was giggling with you through that so you know we can data analysts do to improve their business literacy so one of the um simple things that you can do is you can make a request of one of the business leaders or somebody who you know on the business side who is who you are acquainted with and interview them about their priorities right now so it's a great time of year to do that say as we're looking forward frame the frame the conversation as I'm looking forward to 2024 I want to make sure that I really understand what you guys are facing as the priorities for the business in the coming year or coming quarter I wonder if you can describe to me the the main things that are a priority to the business and as they described that if there are things that you don't understand or that the terminology is a little different you can say wow can you say more about that what else do we need to know how do you um decide what those things are so really explore with them with the intention of making sure that what you provide is going to support their needs and I find that most people are willing to have those kinds of conversations when it comes from a standpoint of you trying to learn so then continue on that so in the world of self-service analytics and citizen data scientists translators should also be skilled in knowing how to keep their business counterparts from running with scissors it's a statement but I love that and wanted to get your take on that um yes I there's only a certain amount of protection that we can provide I mean obviously there is we don't want folks who don't understand what it is that we have provided to misuse it in a way that's harmful to the business so so there is that um and I think that trust is where is is the best defense against this and what I mean by that is if a business person and an analytic person that you work with both understand that you have their um success in mind you have their priorities in mind and you don't want to see them um fail but you also don't want to see them embarrassed you don't want them to feel like they don't know what's happening and so as you build trust they are more likely to hear you when you say you know I am not comfortable using the results in that way and would worry about that they're much more likely to listen to you than if you're the person who always says nope you can't do that nope you can't do that nope you can't do that um so there there's a fine line between protecting themselves protecting them from themselves and uh simply just telling them they don't get it and I think it comes out wrong sometimes indeed so what is the difference between business analysis and data analysis translation um when I talk about analytic translation what I am talking about is a combination of the structure of how work gets done and the communication aspects of how to best um define the work as well as um deliver the work so it isn't just the from my understanding it isn't just getting the terminology right and defining the variables right it's about building a collaborative alliance between the two things two teams that is bigger than simply getting the question right perfect well I'm going to give the community just a minute here to ask to submit any additional questions I don't see any questions coming through I love these I just love these your take on this Wendy I mean we talk about this a lot trying to educate a few on how to communicate versus educating an entire company on on many different things yeah it may or may not be excited about yeah and it's what's interesting is is that this problem is pervasive um I just released a report where I interviewed 20 um executives in analytics and overwhelmingly they say that this lack of collaboration between teams is interfering with the ability to be successful um and the most common estimate was 50% less effective than they could be if in fact you had good collaboration and communication and understanding between the two teams so it is huge it's millions and millions and millions and millions of dollars and uh excess angst and disappointment and feelings of un being unappreciated and turnover and analytic talent I mean it is it is a gigantic problem that is being ignored for the most part because the analysts feel like the business people don't get it and the business people think that the analytic teams don't get it and so we need to solve it we do indeed it's so important and so I agree with that and Wendy again thank you so much that is all the questions we have and and really all the time that we have for today's webinar thank you so much lots of positive feedback in the in the chat there and thanks to all of our attendees for being so engaged in everything we do again just a reminder I will send a follow-up email by end of day monday with links to the slides and links to the recording so thanks everybody I hope you all have a great day thanks Wendy thank you all right talk soon