Thank you again everyone for for tuning in. We're super excited for you to to join us for the first edition of the data forum webinar. Just a reminder that we are being recorded right now. So, what will happen since you did register for the webinar, all of you will get, a link to the recording once it's done so you can tune in and replay the greatest highlights and hits from this conversation if you so desire. It'll also be posted up on, I think, InterWorks YouTube and Annabelle's YouTube as well. So you'll be able to see it, afterwards pretty pretty quickly. So just check your inbox for those emails, check LinkedIn for that type of stuff. But yeah. With with that, we'll we'll get to introductions. I'll get started. My name is Garrett Sauls. I'm Content Manager at InterWorks. I work on the InterWorks marketing team. I don't work with data directly but for the past twelve years I've worked with data people and so I've had the great privilege of working with tons of people inside and outside of InterWorks, getting their ideas, sharing their knowledge, doing stuff like this. And it's just it's just a lot of fun. So I'll pass it over to Annabelle for her to introduce herself. So a warm welcome also from my side. My name is Annabelle Rincon. I'm living in in the, not in the US. I'm living in Switzerland, and I have been, having, like, fifteen years experience in data analytics. And, for instance, my last role was a Tableau Center of enablement list. And that's why I'm really convinced that enablement is a key of the success of any data analytic platform or any digital inform transformation that you want to do in your company. So that's why we come up with guide, with this new webinar series to try to help you enable people inside your organization. So and today, it's a great honor that I have to, receive Adam as our first guest. So welcome, Adam. Thank you very much for coming. Thanks so much for having me. So, should I introduce myself? Please. Okay. This is the first one. How do I do? Yeah. So, I'm a principal, data and analytics strategist at a a a large firm, company, a biotech company. I'm a Tableau visionary, a Tableau ambassador, part of the advisory board of data leadership collaborative, and, do a lot of stuff with AI and GenAI, primarily, in the most recent, times, and also co lead the Tableau and AI user group or now rebranded AI and Tableau user group. K. Very cool. Thanks for having me, everyone. Yeah. Absolutely. Thanks for being here, Adam. I'm curious. I mean, just to to briefly encapsulate your career in data. I mean, obviously, we're on on a new frontier with AI and everything like that. But I'm curious where from from where you started to where you are now, what's that data journey been like? How long have you been in data doing Tableau things, doing analytics? Where what are some twists and turns that that you've taken along the way? What's that like? That snapshot? So off and on, I've been part of data and analytics since two thousand three in the public sector. So it was actually two thousand five. So that was, I had a whole prior career as an adjudication, invest investigator for unemployment benefits and so forth, but then I had an opportunity to be a business analyst on a project. And I happened to learn, Excel pretty well there and also a number of other business processes. So after that project ended, I came back to the office with these new data skills. So I was known as the data guy around the office for, particular things. So it was around two thousand eleven and two thousand twelve where we're evaluating, data visualization tools. One was business objects, and the other one was Tableau. And that was kind of a no brainer. I selected Tableau, and it's been pretty much lights out since then. So initially, I wasn't changing my careers. At that point, I was an adjudication trainer. So I was training, unemployment insurance and fraud, investigations to adjudicators in the state of Wisconsin. And it was a a fun job, but also split with some of them. It's fun tech work too. But then it evolved to doing Tapo, primary a portion of the time in helping a number of business units, in enable with, new data tools, not just Tableau, but Excel as well. But then I, became a data analyst after forty. So that was pretty exciting, to have a career transition, especially after forty. And then in two thousand nineteen, I joined the DataFam actually in August third of two thousand nineteen. So one month, one day, and and five years ago, I joined the DataFam. And then it really opened up my world. At that point, I reclassed the highest level of a data analyst and a business automation specialist as I can get to. And I was actually pretty bored. So I wanted to do something and thought I can give back. But when I saw my design stacked up to other people's design, I'm like, my design is really bad. So there's a lot of things I need to work on here. And then, working with a a number of great people like Annabelle and so forth over the years, really helped me engage with the greater community and the data fam and also helped that I I'm autistic. So, that was the first time I really came out as autistic to a large community. I did it publicly. So there's, like, a handful of people that knew I was autistic before or I told that I was autistic too before. But I'm like, you know, I'm gonna be my authentic self and give everything I can to see, you know, to see to get some sort of personal fulfillment because I was already fulfilled as a career. So, finally, a couple years later, I was an ambassador, and then I was, hired by Curis to be their temple evangelist. So it's kind of a mix between a cons principal consulting for go to market as well as a lot of marketing and updating their public dashboards and use cases for Tableau. In addition to that, then, I lasted there about a year, and then I was recruited, for my current employer to be a a principal. This is my third principal position since I've, been at my employer. So, it's really exciting to be here because things are changing and, we're riding high in that frontier of AI and data visualization. That's awesome. What a journey. I mean, a, I I love that journey over I mean, obviously, things have changed widely since two thousand three. The emergence of Tableau, you you having been with you know, using Tableau kinda since its inception, since the early days type of thing and then then growing into that. And and kudos to you too for being transparent and really owning, you know, that that, that autism being transparent. And I know that the data community particularly the Tableau community is so supportive and receptive. And so, I can really tell how, how warmly you've been received there. But I'm curious too to hear from Anabel. Anabel, do you remember the first time you met Adam? Do you remember when, when you two kind of came into contact or connection? I don't remember. If you have asked me this question before, I will not remember. I thought maybe you have a better memory. I I love this guy from the first slide. You know? It's like it was a was a, love at first sight. Exactly. No. We we're just very similar people, and we were, chatting, like, in messages and so forth before. But we met actually for the first time in two thousand twenty two at the Tableau Conference. So we knew of each other for three years, but never met each other. But as soon as we met, we knew we're like kindred spirits. And right away, we're starting to, develop a plan for speaking at the next Tableau Conference right at that conference. So within, within a couple weeks, we had our initial meeting. I just posted that, I think, a couple days ago on on Twitter, or x Twitter as it's called now. I can't call it x because nobody knows what that is. So you call it a conference. But, it was kind of like, Annabelle writing down, the notes as far as what we're gonna do to talk about she says server because, she ran enablement for server, employer, and, he says cloud because I run it for a cloud employer. And then, that was the baby steps until, to our conference talk. It was pretty funny. Right before our conference talk, she had the worst migraine in the world. I couldn't talk. So forty five minutes before the presentation, I'm watching Annabelle eat because she loves to eat before speaking. So so He was not eating at all because he cannot eat before speaking. Exactly. But, we took a we took a selfie at that point. He can't speak. She can't or he can't speak. She can't listen. Right before that table of conference, it was our our conference presentation, but it was a lot of fun and it went up, really well. Obviously And I won't. I won't. I won't. I remember I won this battle. That's all. More people, like, agree with me that the setup was better than golf. Yeah. She, she did it while sick, but she had a Michael Jordan game. Michael Jordan flu game. We all won. Yeah, that's, that's fair. I think at the end of the day, everyone won. Everyone got some knowledge. I love that though. I love that, that I mean that seems so indicative of that data community, right? To be like, I feel like we've known each other for ten years probably talking, you know, on internet or whatever and then we get to Tableau Conference twenty twenty two. We meet and then some stuff happens. And similar experience with Annabelle. I met her for the first time at Tableau Conference twenty twenty four. Now here we are collaborating on things. But we spoke before. We spoke before. We did. We did. Yes. That's correct. Yeah. You were on the podcast. So I love that energy. And it's just a good that's just a good advertisement. Whether it's Tableau conference or any other data conference, go out there, go talk to people. You don't know what you some interesting things can spark up new opportunities. So absolutely do it because you'll have a lot of fun. And it's fantastic because it give you opportunity to speak, for instance, with people that are doing the same job. So So at that time, I was leading the Tapo center environment, and Adam has a similar job. So I could ask him question. Okay. How do you do when people are not very excited or when they are very excited about something? How you control? Because for AI, for instance, I can imagine that a lot of people are very excited by the new capability. And maybe you have to say, okay. Let's slow down. Oh, and this is pretty funny too. The first time she met my wife at the Tableau conference, she's like, Adam's just trying to copy me because we became, social ambassadors the same year. We became visionaries the same year. We both had almost exactly the same jobs at that point. So it's pretty funny. The first meeting is, like, Annabelle goes right up to her. Can you tell him to stop copying me? I had a laugh. That was, like, the perfect, entry point to, hanging out with Annabelle. I love that. Yeah. I I hear, Adam, you're looking at houses in Switzerland. Is that right? Yeah. They probably won't hire me. I love that. Well, you you touched on something, and I'd love to get right into it. And so as a as a general reminder, Annabelle, I know you had posted in the in the chat. But, for our attendees, if you do have questions along the way, we're about to kinda get into the bulk of it, of the conversation. But if you have questions, feel free to use that q and a feature, and we'll try to answer some along the way, but we'll also have a few minutes at the end to to cover some questions towards the end in a little more free form. But, yeah. I I'm interested, Adam Annabelle you had mentioned this too with with AI kind of being the the new well we say AI but, you know, for data people it's LLM, machine learning, all the all these types of things. But as we know it in in marketing sense, AI obviously, it's a big moment. It's a big event, and it means a lot of things for analysts and data people and business users. So, I'm just curious broadly to hear how you and your your current role or even a little before that personal life whatever Yeah. How you're starting to use AI to kind of, speed up or upscale you and other other users and save time and just do some things maybe you couldn't before? Great question, Garrett, and thank you for that. Yeah. So, it was kinda like the moment when I was introduced to Tabo. So I had a Tabo I was introduced to Tabo back in two thousand eleven. It was for a leadership conference for our internal leaders, and they brought up the tool, and I was the only one asking questions. Super excited. Mind blown. So, when we came, to, accessing, GenAI and ChatGPT and so forth, another mind blowing experience. But what really blow, blew my mind was when you had the capability of, creating custom GPTs. So you could use them as product agents or do stuff that you couldn't ever think of doing before. So that's what really got me excited about it because, it gave me a new opportunity to apply things for enablement purposes that's frictionless and gives you really accurate information and smarter than I am. So I can build a tool that's smarter than me, which I'm I'm I'm happy to give credit to that tool. But sometimes it doesn't know it's dumb. So every once in a while when it hallucinates or or something like that or, is kind of offline, humans, we're not offline like that. But it's really fun to work with a a bunch of GPTs. And the, type of, custom GPTs I like to build are product agents. So I've I've built one for Tableau, including pretty much all of Tableau's internal knowledge. And then I have worked in long and hard to craft the ability to create really good instructions, for that knowledge. So it adheres that knowledge and provides a lot of opportunities for people to ask questions of Tableau and not bother anybody. So I have a similar version internally, but with obviously more bells and whistles. But I have created a public version, for free for anybody with access to chat g p t, and it's called t data doctor. K. Let me pull that up. Let me share my screen real quick over here. So I have it pulled up, but of course, you need to sign up to the chat. So I, I was literally trying to log in to my chat g b t. So continue talking. I'm gonna try and log in to that and see if I can get that access actually real quick. So good. And Yeah. So basically, it includes twelve thousand knowledge doc pages of knowledge documents from Tableau. And it you could basically include a picture of, like, a missing, calculation so there's an error in the calculation, and you it oftentimes can give you the answer without even providing context. And a huge thing for me with custom GPTs, it's, responds and works with pretty much any language. So in the past, AI has pretty much always been in English, and it's been a huge hurdle for a lot of people, especially in our global community. Many in our global community don't primarily speak English. So if they have a tool to work with that gives them very accurate information and they could, correspond with it in its own language, there are so many things you could do with them. And, one of the things you could do if you don't know what it does is ask the tool itself, what do you do? What are use cases that you could do? And it's equipped to answer those questions and so forth. So it covers pretty much just think of it as, like, a really good version of Clippy, without the nuisance and annoyance of Clippy. And then, which one are you going to now? Okay. Okay. So here's yeah. Doctor GPT. Mhmm. So, the funny thing too, this is a side note, but it's interesting. Be careful what you name your custom GPTs. This has been on appeal since, probably three months, and I've been actually working with OpenAI to get this off of appeal. But basically, what what the reason why it's on appeal because I have doctor in the title of it. So I'm like, it's pretty clear this is not offering medical advice. Right. Plus, you unless your data has a broken knee or something, maybe it could help that. But it provides a lot of different ways to work with it. So it's kinda like, going to this brilliant the most intelligent person of Tableau has twelve thousand pages memorized and could, talk to you in your language and work with you. So think of the power and the capabilities of that type of tool. It's called I would call that a super agent. What's, my next one here, let's, jump to another one real quick. So this Critique Pro. So The old it's good. Yeah. It's gonna maybe do it for me. No worries. K. I could I could easily come to chat so people Yes. That's super helpful. So basically, all you need to do is include a screenshot of your data visualization. It will give you, feedback based on, best practices from Tableau. So it utilizes their best practices. I have a different version internally that uses my secret sauce, but I'm not gonna share that secret secret sauce internally. And also it provides scoring, but you can ask for it not to score your visualization. Because a lot of people, it kinda gamifies, feedback and it makes it frictionless. So it gives you actionable feedback in a way that's consistent and also, looks at anything that's not super obviously, it can't look at interactive materials because you can't include GIFs yet. But once it does, then I'll be able to look at the interactivity of the data visualization. But it also but primarily what it does, it look at design and looks at pretty much all phases of design, and you could add it to context. So so for example, let's say you have an infographic, you could bring that up front. So it only scores irrelevant pieces based on your infographic and not everything that it would score on a dashboard. So the great thing about this tool is it works in context without sacrificing it it doesn't change the scoring or anything like that. It's consistent that way, but it scores things that are only relevant to your data visualization. I I when I was in the testing phase of this, I put in random pictures to see if it would score it. The ticket said, we we need a dashboard or something like that. So I haven't played with this a long time, but I tested this quite a bit. And right now, it has well over a thousand chats, and it's rated four point three, I think, out of five. I think some people have rated a little bit lower than what it really is, just because they didn't really care for their score. So at that point, I I changed, I changed it so it doesn't, require a score for your data viz feedback because some people would be like, it's a it's a seven point five out of ten. That's ridiculous. It's a nine point nine out of ten. Mhmm. That's crazy. So, basically, I wanted to give them an opportunity to do that. Let's go with Datamaxstar next. I I have a question on this Yes. Of course. Because I see. A lot of, good opportunity to use it, for instance, for your company because you could, help, like, saying, hey, guys. Please use this, this Critic Pro in order to see if it's good enough to go online. Or maybe I don't know, about this version in much detail, but you could, like, maybe make it learn, like, to say if you apply or not, like, the design principle in your company. Mhmm. So, that's a great point, Annabelle. And part of the reason so I have two reasons for sharing public GPTs. First reason is so I can crowdsource the feedback anonymously. So people could, provide reviews and send the review right to my email address, my personal email address. So I could, look at that, correct it here, and provide the corrections for my internal application as well. In addition to that, I wanted to be able to share something and make sure that people had a quality version of an l m to work with. That's not just out there for somebody putting in a couple words of instructions and not really have having, good information or good feedback to provide. So I really focus on making the ins and the funny thing here, this entire GPT, there's no knowledge documents. It's entirely instructions. I learned quickly that the limit of, characters that you could use in instructions for a a custom GPT is eight thousand, which is about eleven hundred to fifteen hundred words depending on, how wordy you are. So, so basically, it it's been on, like, between seven, nine ninety, and eight thousand for the pretty much since the first week of its inception. And basically, it's because, the scoring breakdown is very complex. So being able to provide score scoring across many different things, requires a lot of work and a lot of feedback and a lot of testing. So that's one, main tip I'll I'll share with people when they're if they wanna build a custom GPT similar to this is that you really need to test it, especially something as experimental as a GPT looking at your dashboard without you putting any words, and it scores it right away and provides, feedback based on that. But again, just like I mentioned before, you could score that in you could have it scored or provide feedback in context. You could have it provide feedback in your language. Let's say, please provide this in Japanese. It will provide the feedback in Japanese. I've got a question for you, Adam. It's from the chat, from Anthony Armstrong. He he asked, do signing up for T Data sign you up for the others, or do they each require their own sign in? They each require their own sign in, but what you could do is, look on the upper left corner right before the sidebar. Mhmm. There's you could include in your sidebar. So oh, no. On, a little bit I'm sorry. I'm pointing at it. So I'll put it, right of that. Over here? Up where it says this critique pro. And there should be a no. I'm sorry. No. We are. That that's our mock star. So I'm sorry. Data mock star. So, click the, click the, drop down on data marks mock star. Oh, right here. Yep. Exactly. And then you could, you're at the sidebar. So, you have, oh, you're right there. Go. Please go back to a gear. Keep in sidebar. Okay. Keep in sidebar. Okay. Keep in. Make sure that you have access to this. Over here. Yep. Yep. Very cool. Okay. And, next one, we'll go with dashboard detective, and that's my most recent one. So one of the things that I've noticed and that I've had, trouble with that is, for one, developers creating really good documentation for their dashboards, and two, consumers understanding what a dashboard what's on a dashboard and getting information about, what's on a dashboard without really, being super data literate. You don't have to be. So pretty similar to Biz Critique Pro, you could add a picture of a dashboard or an image of the full dashboard, and it breaks it down and it breaks it down in a consistent format. So it's, I what I do with my GPTs and this is another tip with that is I create a sample output. So when it makes sense to have a consistent, way of providing feedback or, providing an output, it's really important to have a sample output on your GPTs just so the experience is consistent. And, for the most part, unless chat GPT is having some issues. But when it's not having issues, the output will be consistent and you'll be able to, not have to learn what it means each time. You just read through it once and you have the idea of how that feedback is gonna come in. Mhmm. So all you need to do so, one thing you could do in context and one thing I would suggest is if you're developing dashboards, you may wanna include, an image of your dashboard and ask it to create a documentation for you, based on that dashboard. So that could help you save a lot of time, providing that documentation on your dashboard. And a number of people have used it, for that and shared that with the community. And, let's go back to DataMoxstar because I forgot we were on that. And this is a really fun tool. So DataMockster creates, mock data based on pretty much anything. So you can have an idea. You can create, mock data. It defaults to a thousand rows of a CSV, so perfect for MVPs or POCs. And also, you could just have it come up with pretty much everything, and it uses the Kaggle format. So you know the format's gonna be good and easy to work with. And then, the output will be a CSV that you could just simply download. But you could also ask for the output in Python, SQL, or you could ask it to create a schema. So like a common example of of creating a schema. So as you could see, you're using the output. It provides a sample output. And if you scroll down, it should ask whether you want it to, to, create the mock dataset. I think it's a great tool, this one, because you could create a a mock dataset. And after use one of your Yes. Data doctor or that dashboard detective. So that's where you are not sharing any data, proprietary data Right. From your company. And just respond yes to that, and then it will draft that CSV. So basically, it provides a sample output. Is it broken? Not the GPT but chat GPT. Right. It's just not to be any any any adjustments. No adjustments needed. This is this is my worst fear typing in front of an audience. Of course. Right now, what it's doing is it's analyzing. So it's running code behind the scenes, to create this, CSV, based on your request. And then you'll have something downloadable that you could use and, use right away. And if you have any adjustments to be made, it's, of course, it's a live demo so chat g p t is vomiting right now. It doesn't no. So usually, it doesn't go through and that's part of my instruction is don't show the sample output again if there's no changes. But it just provide that, mock dataset that you can access, download right away, and plug in a Tableau. Very cool. Yeah. That's super useful, especially if you're just wanting to play around, wanting to do some stuff, but you don't wanna get in Excel and manually create it. Yeah. It was really fun. It was an idea that I came up with when I was working with somebody internally that wanted to pull data from a PDF into a table. I'm like, if you could do that, how far could I stretch this? It's feasible based on what I know about LOMs and, Jenny and, Chat GPT Mhmm. That this could be done. So it took me probably about twelve hours of work to come up with the entire instructions for this and, and so so forth. And it was just fun to see it in action. That was just the one thing I noticed right away, and this is pretty funny, the geography was really bad in the beginning. It was coming up with, like, Chicago, Georgia and stuff like that. That doesn't make any sense. So one of the things I did is try to make sure that if you're having anything with geography that it does actually have, realistic geography based on certain things. So hopefully, when you pull it and you are trying to build a map or something like that, it comes up with the geography that's useful to you. And let me know if it doesn't work. But right now, if it's scored four point eight out of five, so I guess it works pretty well for a lot of people. Awesome. Oh, so we've got a few questions in the chat. One is from, Julie Mortzer. She had asked, what was the example that you were going to use about creating a schema? So Workday would be an example. So if you, want a sample, Workday schema, you could just go ahead and, please, please help me create a Workday schema, and you'll see what happens. Cool. And then another one is, what are your thoughts specifically about Tableau's AI features such as Einstein Copilot and Pulse? So I did talk about that recently, at the future of Tableau. So Tableau recently had a webinar with Brian E. T, Elizabeth Maxson, and, and all and a number of people, Matthew Miller, Southern Jones. So, and I talked about it a little bit at the end. I really love the potential of Einstein, copilot for Tableau. I was part of the beta testing, very early on, and I was like, this is no good. This is no good. This is not good. But seeing how far they they reached within a few months, it it's truly inspiring and amazing to me. Pulse is really great because there's so many use cases for people that are, not Tableau developers, but maybe SMEs. So you wanna create something of value but don't really have time or the bandwidth or the desire to learn how to build in Tableau. You can create something very quickly and very, with a lot of impact within ten to fifteen minutes, utilizing Pulse, assuming that your data is in the right shape to utilize it. So main thing with, with Gen AI or any AI tools, you have to make sure that you're using a clean governed and, data that's, without redundancies. So that's a a huge thing, just because you need to have clean data to work with the AI. Just like anything else, the what you get out of data is only as good as what you put into that data. So you really have to work on that data and make sure it's in the right shape so you have something that's useful. But I Pulse has the most immediate impact, but Einstein Copilot also has an impact, especially for those people that are new to the tool. And, my favorite feature of Einstein Copilot for Tableau is the suggestions. So it looks at your data and it comes up with questions a stakeholder would ask about the data. And as a stakeholder building this because you don't really know Tableau and don't really have the time to utilize it too much, you could utilize and cycle through questions based on its, observance of your data. So it provides something that's pretty basic but pretty quick and impactful. And those were the use cases that weren't there in the beginning that have come up and come up really well. So the suggested questions is probably my favorite feature right now of Einstein Copilot. But based on the road map I've seen, it's there's a lot of cool things that are gonna gonna happen, especially, mentioned the future of Tableau as a calculation help. People hate calculations, especially starting out with Tableau. Why doesn't it work like Excel? Why doesn't it work like SQL? So it it's confusing to people. So it providing calculation assistance is really helpful, and it knows regular expressions pretty much more than anybody would ever wanna learn, especially, in the near future. And that's something that they brought up, the calculations. But what I noticed, especially in the the beginning of, its working calculations and, in prep was its capabilities with, regular expressions, which is quite unusual in a day to day work of a developer. The only problem with both, for instance, is that it doesn't exist in a Tableau server. Right. You have to have cloud. Cloud. Ultimately, I will Here we go. Yeah. I know. I know. Right. I knew we'd get here eventually. Thanks, Brett. That's really cool though. I I I love hearing that though from from someone who's who's been intimately familiar with with Tableau tools, for for a long time and and kind of how it's evolved and and to and to see your perspective on, where stuff like, Copilot and Pulse have began and where they're going in the future and what each represents individually. You know, the the the people that they are most likely going to serve and who's gonna get the most mileage out of them. That's that's super interesting to hear because either you hear it and again, we I love Tableau. You know, they're a partner. But, you know, reading something reading marketing is different than hearing, you know, an actual human perspective on it. So And I gave some really strong constructive feedback in the first iteration. Pretty much anybody on the product team could tell you. I I didn't hold back, but I always try to make sure that my feedback is constructive, rather than but it does I do have a lot of passion about the utilization of AI and GenAI in products, but I wanna make sure that it's done well. So that's where I'm passionate about and wanna make sure that if I have any input anywhere, that it it's being utilized in that way. So I I'm just so thankful and happy that they're able to do this, and I really think it's gonna prolong the shelf life of the tool itself. If they didn't and I wrote about this in a blog recently. If they didn't include GenAI or AI in their in their tooling, and didn't focus on the cloud, it would just really shorten it it would be a legacy tool very quickly in this new, environment of GenAI AI enabled in, tooling. Pretty much every, every database, every data tool, any product is gonna incorporate AI. And if they're not doing it, it's gonna be a legacy tool because stakeholders are gonna be like, where's the AI? It's like, we we just wanna build these products, and it'll be a legacy really quick. So I'm just happy that they're, really going forward with it, and they're really improving quite a bit. That's awesome. So I think we have one more GPT to go through, and that is Blueprint Navigator. Yes. So let's talk about that. So you get logged in. You have to sign in every time. I know. Like, come on. You know who I am. Apparently, the EI is not that good on that. I'm kidding you. But, so this is, for customer success. So, often what happens is that customer success is kind of overlooked with a lot of people. When I was a Tableau evangelist, I did, go through, the customer success, training, which I found quite valuable because for enablement, you really have to be proficient in customer success because it's not only your external customers you have to worry about if you're a consultant, it's primarily your internal, customers as a person that works as an enablement leader for your company. So having this out there, although it's not gonna have the thousands of discussions and conversations that others will have, it's an important tool for those that are trying to leverage, customer success in their environment. It also has, data visualization best practices too, which is super helpful for users and, enablement leaders to have something there to highlight, data visualization breast best practices, because unfortunately, it can go all over the map, and you really need to have a handy dandy guide to really help you along to help others, align with those best practices. For sure. That's super helpful. I love all these. I really, I particularly love the best practice things because yeah, I, so many times you hear design is subjective and sometimes, yes, some, sometimes there are subjective preferences. But when we're talking about what people respond to visually as humans, like mark sizes and colors and all these different things. There are, there are absolutely best practices that people can check against. So I, earlier when you had mentioned like some people being a little upset about their ratings, I think that's a healthy thing. You know, for them to be like, okay, there are some things you need to fix. So it's really cool that that's incorporated into a few of matter at scale, especially. And it's a lot easier than going through a large document and trying to find what you're looking for in a large document, just asking a question of a GPT. Simple, as long as the instructions are really well, put together, it's gonna provide, very good and accurate information only based on the knowledge that's presented to it. So one of the things I do is I enable, so, what you could do is, remove web browsing so it doesn't include potential noise because I don't want, data visualization best practices, from some random blogger to come up Right. Based on a scientific essay of that. Also, preattentive attributes are so important. So understanding the science of data visualization. So this is a good tool to help you not only understand what's best practices for, customer success internally, but also, the science behind data visualization and working with that to enhance that. And then you can go back to, the Biz Critique Pro to even, further evaluate and test the design based on what you have. So you're looking at this. You're like, okay. I understand best practices. So let me put this in practice. So I can create a dashboard. I'm like, okay. I want it evaluated. So you get it evaluated with, Biz Critique Pro. And that with that particular dashboard, you'll get the feedback from that, which is custom to what you're building. So this is more general feedback but, utilizing scientific approaches to, that feedback with, Tableau's blueprint, for customer success. Very cool. Here here's a question from the q and a. It's a good one. They're asking Evan Zato is asking, is it possible for the data mocks start to produce data from an institution whose data is confidential? An example of such data would be hospital patient data. No. I automate so basically, what you do in the instructions I'm not I can't give away everything, the entire sequence size. But you make sure that it's that it's not using anything from an established dataset. So what it does is it codes, and, basically, within the confines of, that code looks to generate something that's realistic, real world, but, not based on anything that's out there. So, basically, I have it to look at the format of Kaggle, but not copy anything from Kaggle, and it's, in, DataMockster. So I've never encountered a situation where DataMockster with the public version or even an internal version that somebody said, you know, this is real data. It's real confidential, because it's not, it's not programmed, if you will, to do that. It's programmed to try to give you the best representation of fake data, so you could utilize it for your POCs and MVPs. And that's really important, because there's a lot of us that work in, very high regulatory, business lines that you can't utilize or share data similar to that, but a lot of people have, used DataMockStar. In fact, it has a number of visit the days from people utilizing it and then putting it in dashboards and creating a really cool visualizations out of it. So it's really exciting to see people use it and see the feedback of, this because it was, like, kind of a dream that kinda came came at me, hit me at once. I'm like, this is like it's like when somebody writes a really good song. It's not like my really good song. And then Data Moxtra, and I'm really happy what it does. It's not perfect all the time, and sometimes you do have to, provide feedback to make it better, but work with it like a data engineer. So you're working on a team, you're a business stakeholder, or you're a data visual visualizer, and you're not working as a data engineer, but you're like, this is not quite right. So something needs to be done, and then you could work with it just like you would be talking with the person, that you're working with on your team as a data engineer. Very cool. I really like this, to come back to this, team blueprint navigator because it's like kind of speaking with a friend who is, like, maybe a little more expert than you in enablement and asking him question, hey. I have this question. How I can do that? And, of course, you don't have always the capacity to call Adam on the direct line or some of of our guest or the future guests like to say, hey, Nisa. I I I have this question. How do I do that? So it's, second best option. I think it's very good. And you look really smart. You come back with all the answers, and it's like, I how how did that happen? Oh, I just came up with that myself. Nobody will be the wiser. I'm curious, Adam. Have these saved you a lot of time as someone who kinda leads enablement for a lot of things? Just kinda answer those, like, surface level, entry level questions so you're not, again, having to get on a on a call every five minutes with someone to go through this. Great. And that's a great segue because with me with enablement, it's not doing everything for the person. It's, it's working with the person so they could learn themselves. So, the tools I like to create for these types of purposes aren't just giving you information. They're helping train you as well and doing it in a way where you not feel like you're bugging somebody. So one of the things you may ask as enablement leader, and if you have something like this internally is, you know, did you consult with this? And it's like, oh, I didn't know I was out here even though you may have shared it forty times internally. And then they go through it, and it's like, okay. You don't hear from them for two months. And then, two months later, you just check-in to see how everything's going. Oh, it's great. This tool works really good, and it's helped me save a lot of time. And I haven't had to schedule meetings with anybody to talk about it. I could just jump right in on it and get my questions answered. Oftentimes, it does cut down the number of questions that people will have because they're getting the right answers and not having to worry about dealing with, the asynchronous approach where you send an email or you send a ping to somebody and it's like a day later or a few hours later or whatever and then kind of forget what you asked about in the first place so it's hard to provide the context. So when you have it right there and you're working with the tool in real time, it's a little bit harder to forget what you're trying to ask it and have it come up with. And if you do, you could just go back to it and ask it later when it comes to you, and it's always there just hanging out waiting for you to, relate, with them. For sure. We've got one in the chat real quick, and we can kinda segue to more of those classic tools, kinda what we're talking about here, you know, tried and true enablement things. Keith, Bressel has asked, since it has a one k limit and we need more data, he would be asking for sample data multiple times. So will the results for data mock star be different every time that you query it? So, wink, wink. So I have one thousand, so it doesn't run out of memory. So there can be a memory issue when you're working and it's analyzing, coming up with code, and then it generates something. So it can go up to ten thousand pretty easily in most cases without a problem. So you could ask it to do it and see if it comes up with the number that you have in mind. But generally, for MVPs or POCs, you're not gonna need anything more than ten thousand. That's a good that's like the kind of the perfect, point there when you're working with that because you don't want anything that's too much and too overbearing, but you wanna have enough where it's meaty. So, you could ask for ten thousand, ten thousand, rows, and it should produce for you pretty well unless you have, like, fifty columns or whatever. But normally, it's gonna be, like, six or seven columns, You know, you just ask for it up to ten thousand rows. And if it can't do that, just ask for, two versions. So version one and version two using the same logic as the previous one, and it should do it exactly, what you would expect. Cool. I have a question. Important that you ask for it to do, to that it's in the same logic. Even though it's cached, usually it does, but sometimes it doesn't know. I have a question, Adam. You know, I feel like yeah. That's fine. Saying before, like, for instance, with, some internal, they said, like, things to this tool, they, save some time. And, it's always a recurrent question with people working in enablement is, how can I prove my value, or how can I prove that enablement works? So I had, in the past, like, work with some use case, like how much or how many time did you save? Is it easier for you? And you can also evaluate maybe how many if your time in support decrease so you can do more meaningful work. But maybe you have a secret sauce and other metrics that you evaluate or things. So I'm opening my ears. I can't give this internal secret sauce. Of course. Primarily, what you wanna do is see the growth of your community. So if you see the growth of viewers and you see the growth of developers and then you see the growth of assets. And when that's going and it's consistently growing and it's almost exponential, you know, you're doing a pretty good job. Obviously, you also have to provide, avenues where people could provide feedback. So it's important to, make sure that you send, forms or whatnot to your community to make sure that they have the opportunity to provide feedback on what works for them and what doesn't work so you can know what you're focusing on to improve. The other thing too that's really important is to make sure that you have a community. So, in order for people to be engaged, with Tableau, because oftentimes what happens is that people are stuck with Tableau. They're working in a business and it's like, we need somebody to visualize data. You're the odd person out, so you you get stuck with the job. So oftentimes people aren't like us where we see the tool. It's like, wow. This is a whole life changing experience. They're like, this is dread. So you have to make it as fun as possible. So you have to gamify it as much as possible. You have to have, internal user groups, and you also wanna participate in things like a Tableau Day or m d or or I'm sorry, or, or vis games to make sure that, they're really engaged and give them an opportunity to really have fun and meet others in the community and others that are working with them. The other thing that's really important is, it's interesting because, in different places, I've kind of gauged, and I've always assumed that people would love external speakers. That would be, like, the number one thing. But the number one thing is always internal use cases. They wanna see how other people are using it at work so they can get inspired to use it in a certain way. So highlight those and highlight those champions that you, helped, you work with. So that's another piece of information too is, having a Tableau champions community is super helpful because your voice gets really tired after a while. If you're the only one on messages, it's just like white noise at a certain point. So if you have other people talking to business lines and evangelizing for you, it's super important to get that out there because it engages an entire community and business lines that you don't have an opportunity to reach. I'm curious, Adam, on that topic of just talking about enablement in general. I know when you work for a larger organization, sometimes even medium to smaller organizations, you're likely working with a lot of different types of users. And so I'm curious in your day to day and you don't have to be overly specific. But what what are generally the types of users that that you're working with and how do you reach each of those diverse users? Like what are some ways that you can kind of serve everyone, you know, to get them on the same page? So it's an one thing, because Tableau has viewers, explorers, and creators. So developers, explorers, I would consider more the SME class, and viewers are the consumers and interactors with existing data. But you have to have something for everybody. You can't just set them in a wild and just say, have at it. So you have to have something for the viewer group. You have to have something for the explorer group, which is often missed, with with those that are running enablement or center of excellence, teams, just because it's the primary focus is on developers. But, obviously, you really have to highlight your work with developers, meaning that, they need to be feel like they're part of it. They need to feel like they're part of something important. Oftentimes developers get overlooked in business lines, so you really need to give them an opportunity to feel like they're seen and have an opportunity to be part of the community and engage with their audience. So a lot of it depends on your work with the developers and how they engage their community. You can, especially with the larger organization, you can't engage with everyone. But you could primarily engage with your developers and make sure that they have the resources, to and also the the voice, to be able to work with their business lines to really encourage those, to utilize the dashboard. So you wanna give them usage statistics and all that stuff so they know who's using it, what's working, what isn't, and what is not. But you also wanna give office hours as well, so they have an avenue to talk and, receive advice and so forth on on certain things, not just data visualization, but, you know, how do I, how do I, reach out and, get my, stakeholders involved because nobody's really looking at my dash important. So those are things you could talk about and really work with the developers. But you still can't ignore your explorers or viewers because they're as essential as anybody else. Because pretty much, I would say, a good rule of thumb would be you have fifteen percent developers, five percent explorers or SMEs, and the rest being viewers. So your primary community, although you're working with the developers more, is gonna be the viewer community. So you wanna make sure that they are utilizing best practices, have high standards with the dashboards that align with your, with your design, requirements and so forth. So having a a data visualization style guide is really helpful, for the developers to make sure that you have, pretty consistent looking and feeling dashboards throughout different business lines because one project could be a specific team, but a person on another team may be part of a cross functional one, and they're building really nice dashboards and they're stuck in this project that's not, so they're not really engaging with that dashboard. So it's really important to work with those users to make sure that they're applying best practices throughout just so that people have a consistent experience and can really get value out of the data visualization work that's being done because it is quite valuable and oftentimes overlooked, just because best practices aren't followed. Yeah. Something that I found also very useful is something like a training for you viewers, dedicated training, how to request access, how to click, how to interact with the bees. Mhmm. And I make sure to give them face to face, but also virtually and also have it recorded. So if they have a question and don't dare to ask, they have the answer here. Yeah. Always offer to record because you could just ask them. Did you look at the recording? Especially if they're asking you seven or eight, versions of the same question. Oftentimes, they could be, answered if they looked, reviewed that recording or even created a transcript of that recording, using ChatChiPT or something along those lines. Yeah. Yeah. You can do that. I mean, anymore, even even on Zoom. Obviously, Zoom has the the AI assistant type of thing that people can use. That's been super useful in terms of documenting, but I love that. So we we've got about three minutes left, and I love this because obviously we could talk forever. I could talk for three hours doing this but I'm sure people need to get back to their days as well. But I'm curious, we'll open it up to questions real quick and there's one that was sitting in the chat. I think this is a good one. So Emmanuel was asking you know how do you get started in this? He's essentially asking how do you get started with something like an internship? How do you get into this world? How do you get your your foot in the door if you're a person who's just kind of looking to get started as an analyst, as a data professional? Any professional. Any any tips from Yeah. Either of you who who have obviously been in been in the the field for quite some time? So a big part of it would be, to reach out to the data fam. There's a lot of us that's been around forever and people been along, around longer than Annabelle and I. One thing we all have in common is that we're trying to help people upskill and trying to give them the resources in the, in the backing, that they need to excel. So, you know, maybe it's not like one on one internships and so forth, but you have opportunities to work with people to really develop your profile. Another thing I would suggest is utilizing Tableau Public and ensuring network, publicly. Even if it's not very good in the beginning, it will improve. And that's a great thing to see as the improvement of your data visualizations from day one until, you know, month three, year three, and so forth. The evolution of what you're doing as as long as you're practicing sharing that information and also supporting others in the process. So there is a give and take where people often may be content creators, but the data family, relies on you working with other people and not just thinking about what you're producing, but also supporting others and being there for other people as they're, leveling up or have questions regarding, this and that. Absolutely. Annabelle? You always learn better when you help each, another person. So, yes. Exactly. Mhmm. Unless it's Annabel. No more. I'm kidding. I love it. Those are those are some fantastic tips. Truly, it is just to get involved and to help and to put yourself out there and and to always find those opportunities that you're just going to. Yeah. And like you said, that data community really will come around you because again, I know we we have all been given opportunities right from that community and that family and so kind of paying it forward is something something that matters. But I'm not seeing any any other questions right now, so I'll just kind of do do a little recap. I'll remind everyone that this will be available. This will be recorded. We'll send it out via an email since attendees here registered. They'll get the they'll get the highlights of it. It'll be posted to YouTube. But, Annabelle, Adam, is there any, best best way that people can reach out to you if they wanna know more? Obviously, we posted the GPTs here, but if they have further questions, wanna connect, what's your preferred mode of connection? LinkedIn is perfect. My DMs are open. I can't get respond to everybody at the same time. But, yeah, feel free to, link up on, LinkedIn, and I'll be happy to connect with you and chat with you as long as you're not trying to sell me something. Uh-huh. And now we have a last question. I need between Adam and Annabelle. Oh, let's let's go. You have to speak about that. That was always a pleasure to collaborate with Adam. Same with you, Annabelle and Garrett too. That's my new favorite referee. Love it. Love it. I'm always here to referee for the viz battle. We'll put it on pay per view. We'll sell tickets. We'll go to Vegas. It'll be great. Nothing physical, she'll kick my butt. I know, well I want to say thank you, to everyone who tuned in for this first episode. Really, we really appreciate you tuning in. And Annabelle, obviously, thanks for being my co host on this journey. And Adam, thank you so much for being our first guinea pig and for sharing your knowledge. It was a lot of fun. It was a complete blast. And, obviously, when Annabelle, calls, I pick up. So, perfect, perfect way to collaborate. And it was an honor for me to be part of the first, incarnation of Dataform or EnableMe, which is a secondary title. That's right. Love it. You gotta have you gotta have more than one name. Yep. And we have Can people on their feet? Exactly. And we have a we will have a new piece of next month. Yes. Yes. It's it's very good. We will next month, I think it's October second. It's gonna be the first Wednesday usually of of each month. And we do have, guest, Michael Sandberg from USAA, and there will be details for that webinar coming shortly. We'll share that out. Yeah. We're super excited to get to get Michael on there as well. So And he did talk about, t blueprint navigator during the conference. Oh, okay. Well, that's we tie in. And, fun fact really quick is, he was my first interviewer, from the data fam. As I started with the data fam, he was the first person that I was on their blog. So it's really exciting to be it's like, I'm being seen. It's awesome. So that's what the data fam, does to you. When you least expect it, people actually recognize and and reach out to you. So, you'll have friends all over the globe, even, without realizing or really trying to. For sure. I love that. Love that connection. Well, with that, I'll say thank you to everyone again. Thanks for being here, and have a wonderful day. Bye. Thank you. Bye bye everyone. Thank you for coming.