Black in Data User Group 19 Aug 2026

0:01 Alice McKnight: Little Tableau and, uh, get a little bit, a little bit of knowledge.

0:08 Okay. As usual, we start with our leaders here. Um, it's myself, Alice McKnight, um, Chris Williams, and Sekou Tyler. We have been, uh, co-leading this TUG for, um, roughly, I think maybe almost, uh, two years now.

0:29 I always like to start with some quick, uh, quick bites across, uh, the Tableau community.

0:38 And two things that I want to point out here, uh, you can not only hear our voices, uh, during this TUG, but we also speak across the Data Fam, and a couple of, uh, past and upcoming talks are here. Sekou Tyler has done a, a talk, From Analyst to Entrepreneur: Building a Data Consulting Company. If you are una- unable to see that live through the analytics TUG, you can go to the, to the page for, uh, for analytics TUG and view that, uh, presentation.

1:16 And the second presentation is from Angela Henderson. She was our feature speaker at our last TUG and gave an excellent talk during, uh, Black History Month. But she will also be speaking at the analytics TUG, um, at the end of this month, if you want to watch live or see the recording, um, after, on Streaming-Style UX: Building Clean, 3-Step Business Apps in Tableau.

1:43 Um, Angela is a great speaker, and I'm sure she's gonna be bringing a lot of knowledge and her personal energy to that talk.

1:53 And as always, I like to, uh, provide a couple of resources that I have found around the community, um, that I've liked and I'm always, um, wanting to share.

2:08 My focus for this quarter has been Tableau plus AI, and I came across these, uh, couple of presentations that are also available on YouTube.

2:20 If, uh, you have ever wanted to quickly jump into using Tableau and, uh, and Claude, this 15-minute presentation done by Will Fulton, he is a Tableau visionary. Um, 15 minutes you can get, uh, Tableau connected to Claude Desktop, and you can start, uh, experimenting on your own, getting hooked up to your, your own data, and experimenting in Claude in 15 minutes.

2:53 If you have a little bit more experience or wanna spend a little bit more time hands-on building with a great way, a step-by-step process, see- or watching someone build something, um, in Claude code connected to Tableau, this is a great presentation by Eric Summers.

3:11 Um, he has a lot of good information on his website. He's, uh, connecting Tableau and Claude. He's a great teacher, does it…

3:21 You can build as he builds, um, and I think it's a really great resource.

3:28 All right. We're gonna begin with our feature presentation. Um, just, uh, my little experience with Chantilly and her work. Um, I first came into the Tableau community maybe about, I don't know, five years ago. She made a presentation, um, I can't remember the name of it. It's, it's about, uh, designing a wireframe through Figma. I can't remember the name of the presentation on YouTube.

3:52 But it's one of the first resources that I found, uh, that really broke down how to make good dashboards that are telling a good data story. It's more about more than just the technical parts of Tableau, but more about incorporating that data storytelling, and it really lifted up, um, my skills as a analyst, and I'm so happy that she ha- uh, is presenting to us today.

4:22 Chantilly, if you're ready.

4:24 Chantilly Jaggernauth: All righty. I actually know which, uh, presentation you're talking about. Design Secrets for a Non-Designer. Um-

4:30 Alice McKnight: Yes

4:30 Chantilly Jaggernauth: … it's one that I, I gave many, many times, um, at the Tableau Conference before COVID, and then during COVID. We did the virtual conference and a lot afterwards. So I think a lot of the presentations that I do now have bits and pieces of Design Secrets from a Non-Designer.

4:47 Um, but today we're gonna focus on what makes an analyst stand out. So we're gonna talk about building dashboards that users actually want. We're gonna go from data to insight to decisions. Um, with that, I am Chantilly Jaggernauth. I'm the founder and CEO of Millennials and Data. Um, I like to have this picture here of all of the students I've helped, um, over the many years.

5:08 So Millennials and Data is a non-profit organization that I started in 2018. Um, I was working at Comcast, uh, within their HR analytics department, and I kept having recruiters come to me and say, "Hey, do, do you know anybody that has your specific skill set that knows not just Tableau, but also data analysis?" And at the time, it was really, it was really hard.

5:30 Um, I wasn't a part of the Tableau community just yet. I actually joined the Tableau community when I joined, uh, Comcast. So I didn't really know a lot of people. Um, and when I was looking to some of my peers and some of the students that I knew, they didn't, they were missing bits and pieces of exactly what the recruiters were looking for. So I started my own organization to help students, um, learn this skill set of how to incorporate data analysis into whatever major they had, so whether it was accounting, finance, marketing, because I'm a firm believer that data can be used in pretty much any department, any division.

6:02 You don't have to have a particular skill set or know a particular thing to be data literate. Um, so I started that in 2018, a 16-week boot camp, um, getting students certified in Tableau, teaching them SQL, teaching them How to be good analysts, um, as well. So some of the things that I'm going, going to talk about today are definitely things that I've taught my students.

6:23 And I've had over 100 students graduate over the last, uh, couple of years.

6:28 Um, I would like to say my sister, my little sister, uh, Sentra Jaggernauth, she's a part of the Tableau community. She was actually one of my last students to graduate, and now she does Tableau contracts. She has a, a job doing Tableau, and believe it or not, she graduated from her university in biology. Like, that was, that was her major. Had no clue about Tableau until she started living with me and saw me doing this really cool thing that, that I enjoyed.

6:53 And, and now she's been doing it for the past, uh, two, three years and actually really good.

6:57 Um, I am a Hall of Fame Tableau Visionary, so the work that I've done within the Tableau community has gotten me to be a Tableau Visionary for about five times, I think, and now I'm officially inducted into the Hall of Fame, and I always like to have my alma mater up here, Howard University. Uh, I am a graduate of the, um, Information Systems Department, and that's actually where I started Millennials and Data at.

7:22 All righty, so let's jump right into it. Um, how many of you all have built dashboards that you just thought were great but nobody really used it, right? It looked so good. It had the best calculations in it, but there was just zero adoption. And believe it or not, you actually produced exactly what the, the business was looking for, but nobody within the business was actually using it.

7:45 The question isn't, you know, can you build a dashboard? The question that we really need to be asking going forward as analysts is, will this dashboard help the business? Will it help them make better decisions, right?

7:58 The best analysts understand that there is a business problem that needs to solve. There's an audience that we need to create a dashboard for. We have data. Now, how can we bring all of that together to create a dashboard that is actually going to be adopted and used by our end users?

8:15 So today, I'm gonna talk about five rules that I feel as though every analyst should know. One, understand the problem before touching the data.

8:22 Wanna know exactly who we're building the dashboard for.

8:26 We don't wanna confuse more information with more value. More KPIs does not, you know, give more insight necessarily.

8:33 We wanna turn data into insight and not just visualizations, and then we wanna connect the insight to a decision or action.

8:42 So let's start with, um, our question. At the, at the beginning, whenever we, we get a dashboard, right, a dashboard project, a user usually comes to us and says, "Hey, can you build a dashboard that shows X, Y, and Z?" Right? How many of you have received that? I'm pretty sure, like, in our careers, that's, that's all we get, a request from our end users.

9:00 I was a consultant for, for many years, and I still am a consultant, and that's usually how the projects start. "I wanna see X, Y, and Z. Can you just build me a dashboard that shows it? Here's the data," right?

9:11 Instead of going out and just building exactly what the end user says they're looking for, have a conversation with the end user and first determine what problem are they actually trying to solve. Sometimes a lot of end users, they wanna take an Excel spreadsheet and put it into Tableau, and to me, that's not really helping them understand their data.

9:30 That's literally just replicating a tabular report into Tableau, and it's not really providing any insight. Instead, ask them with that same spreadsheet that they're looking at, "Exactly what are you looking at in this spreadsheet? What does this spreadsheet help you solve?" Right? "Who uses this spreadsheet? Do you email it out to executives, to managers?

9:49 Are analysts diving into it? Are there additional tabs that they're getting insight out of? What decision are they trying to get out of it?" Right? "Are there targets that they're comparing to, if we're above or below target? Are they diving into the data or trying to find data somewhere else to understand why they are above or below target?

10:07 How often do they use this? Is this something that's done on an ad hoc basis?

10:12 Is this something that's sent out on a monthly, a weekly, a daily basis? How often are individuals using, um, this dashboard or this report?

10:20 What action should happen if the number changes?" Right? So I'm currently mostly in the healthcare field with, with my clients, and we're gonna look at, like, patient volume, right? Say, for instance, patient volume has increased or decreased. That's one of the, the main metrics that we're looking at.

10:38 What action happens when that number changes, right? If patient volume increases, are we increasing the number of doctors that are on staff? Are we increasing the number of rooms that are available, um, on the hospital floor? What are we trying to get out of that? And then what does success look like for this dashboard?

10:58 Let's look at an example. A stakeholder comes to you and says, "Hey, I need a dashboard showing patient volumes." We're gonna go back to this healthcare example.

11:06 We're not immediately gonna go and show them the total volume compared to this year, last year, last week, right? We're not gonna just show them the monthly volume. All of that information is great. The weekly volume. We're not gonna show them the daily volume, build out a bunch of charts that show, okay, um, the shifts, the, the volume shifts. We're not gonna show them, uh, the department, uh, volume.

11:29 We're not gonna do all of that just yet. We're gonna pause for a second and ask our stakeholder, "What are you trying to understand about the patient volume?" Right? You're gonna have that conversation, and sometimes it's not just one conversation. It's a couple of conversations that, that need to happen, and you need to dive a little bit deeper into it.

11:46 I always like to say, when I start with a request, I like to go into therapist mode, right? "Tell me more about that. You know, tell me more about patient volume. What have you seen, you know, with patient volume over the past couple of years? What action have you taken off of that?" With– So, you know, a response from a stakeholder might be, "Which locations are consistently below capacity, and how can we adjust staffing?" Right?

12:09 So we might have some locations- They have way more patients than anticipated, way more than, than we budgeted for. Maybe we need to have more individuals on staff, right? Now, we're looking to solve something a little bit different than just showing total volume or total patient volume across different departments and different divisions. So we always wanna walk away, when a user brings us a request, we always wanna walk away and say, "Don't just build them exactly what they're looking for, understand why they asked for it in the first place."

12:41 Different users need different things, right? Don't try to create one dashboard that is going to solve the problem of all of your end users. It's impossible, right? Then you get into analysis paralysis. You have a bunch of different visualizations on there that aren't really helpful, and that's when user adoption actually goes down on the dashboard.

13:01 Instead, ask who's going to be looking at this dashboard first thing in the morning. When that data gets refreshed Monday morning, who's going to be going to this dashboard? Who's going to be making decisions off of this dashboard? Who's gonna be sitting in front of it and sharing it out?

13:15 Executives, right? Executives, they like to look at trends, they like to look at KPIs. They like to look at things according to, uh, goals or targets. Did we meet, uh, the goal or the target? Are we above or below compared to, like, last week? And really they're just focused on, like, what do I need to pay attention to right now, or who do I need to reach out to, um, about a particular number?

13:38 They're not really looking for the weeds of things. Then you have your managers, right? Most of your managers are looking at performance. They're looking at comparisons. They're, they're looking at whatever they manage and maybe it's their team that they're managing. So who on their team, they're comparing their teams, their teams to each other. Or say, for instance, they manage different facilities.

13:58 They're comparing facilities to each other. So they're looking for drill downs and they're looking for some form of accountability when it comes to their dashboards.

14:06 And then analysts. Analysts, they dive a little bit deeper, right? They want the detail. They want to go as deep as they can go into your data set. And when you think about it, one dashboard isn't going to solve each of those things, right? It shouldn't at least, because if it does, then there's too much information on your dashboard. An executive isn't going to look at the dashboard the same way an analyst would.

14:29 And an analyst is going to need more information from an executive dashboard than what you'll be able to present.

14:36 When you're creating your dashboard, less is, is definitely more. Um, I run into a lot of dashboards today that have way too many KPIs on them, way too many charts. Don't get me started on the number of colors and filters that are there. Too much text. Just too many hierarchies, too much drill downs, and the user doesn't need to, doesn't even know where to go.

14:59 Um, and I always tell a analyst, um, do a so what test on all of your visualizations before you, you put it on there. Your user is saying, "Okay, I need to see patient volume, uh, per division." So what, right? What decision is the end user going to make from seeing patient volume per division or per facility, right? What are, what are they actually looking to get out of that?

15:23 Maybe they need to have it compared to another facility or compared to last year or compared to some type of target or compared to how staffing is, you know, in those same, uh, facilities. Always ask the so what of visualization before you just decide to place it on the screen. And if you can't answer that question of so what, right, what are they going to get out of this, then consider just removing the visualization altogether.

15:47 So instead of showing a bunch of different charts that show revenue, expenses, profit margin, volume, growth, the information versus prior year, the forecast, right? Instead, ask your end users, "What are the top three things that you need to know from this?" Right? "What are your top three to four KPIs?" Maybe even five KPIs, and then drill a little bit further into each of those.

16:11 Do the so what test on them, right? If this KPI is up or down, what does that mean for your business?

16:17 And that's what you should put on your visualization. I can go on and on about how to b- build a dashboard, but I'll always say, um, design to a grid for one.

16:28 Lay your KPIs up top. I would recommend not having more than, let's say, five KPIs at the top. And understand that your end users are going to read your visualization the same way they would read a book, right? They're gonna read it top to bottom, left to right. So have your most important information in that top left corner and your least important information in the bottom right-hand corner.

16:50 Only display information that's relevant to your end users. I did a, a dashboard overhaul for a client a couple of months ago, and I got a lot of pushback on the fact that I cleaned house on the dashboard. I took away all of their charts, and I left them with about four or five different visualizations. And the analyst that created it, they were stunned because they said, "Hey, what if our end user is asking for this?

17:12 What if our end user is asking for that?" And instead, I, I went back to them and I said, "Hey, you know, if your end users are asking for those things, then those are the charts that you need to display here instead of displaying 20 different charts at one time," right? So you always just wanna hone in on the top four or five things that your end users are looking for, and that's the visualization that you're gonna place on your screen.

17:35 Tell the user exactly, uh, what matters. So a good dashboard isn't gonna make your end user hunt for that insight, right?

17:42 It isn't gonna just say, "Here's the data, here's your volume, and here's your target," right?

17:48 Yes, you know, it's accurate. It does give them some type of insight. But you wanna take it a step further and allow your end user to get some additional insight out of that instead of just telling them, "Okay, target, your volume is up or down compared to your target." You wanna have actionable insights, so have some drill downs. Have…

18:10 Tableau is really great with doing, um, actions, dashboard actions. You wanna have some dashboard actions or a way for your end user to figure out- What the detail is behind the scenes. And your executives may not know how to use this, you may have to teach them this, but your managers and your analysts are definitely gonna n- wanna know what's driving that number.

18:30 So instead of just presenting with them with that number, teach your end users how to get to the underlying information producing that number. So volume is down compared to target, but only two locations are driving 80% of that decline. So now let's dive into more information about those two locations. Are they managed by, uh, certain people? Are those people, are those locations understaffed?

18:53 Are they in particular zip codes that we haven't been, um, serving? That's what's going to help you get some action out of your dashboard. So you wanna make sure you're teaching your end users and your audience exactly what to look for, right? Don't just present them with the information. Train them on how to get more information out of the dashboard.

19:12 Help them understand what's happening. Why is it happening? Is it good or bad? Um, is it changing? Where exactly is the problem at?

19:20 Don't just give them information, just give them more context, and I think Tableau's a really great tool at being able to provide your end users with additional context, especially with that view data and with those, um, drill downs that are available. I love to create dashboards that have the drill up and drill down feature, where you don't have to display it, you know, on the dashboard until the end user clicks on it.

19:45 So you click on a action, and then, uh, you could have a collapsible container that shows them the details associated with whatever they just clicked on. I think that there's so much power, um, into how we can really use Tableau and enable our end users.

20:01 All right, so making dashboards that are, uh, more actionable. So what happens after somebody sees this dashboard, right?

20:09 What's the so what out of it? We wanna make sure that our end users are going from data to insight to decision to action.

20:18 A lot of times, a lot of the dashboards we create just stop at that insight. It's just providing them with those raw numbers, right? Let's go back to our volume, um, example, our total volume.

20:32 Um, if volume increases and wait time increases, then we need to adjust staffing, right? So we're saying the insight is that volume has increased that particular location. The decision is that, uh, we need to…

20:49 The decision is that wait times have increased because volume has increased, right? The action is that we now need to adjust staffing based on the volume increasing or decreasing at certain locations. Sales has declined. We need to investigate the different products and the locations. The action is going to be maybe we need to remove a product or a lo- uh, from a certain location, or we need to, um, adjust the product line, or we need to adjust what we're doing, uh, with the sales at our various locations.

21:19 Expenses have exceeded budget, right? Here's another one. We need to identify exactly what's causing the expenses to exceed the budget, right? And then make a decision off of that, and based off the decision, what is the action that's gonna precede that?

21:34 Your dashboard, as I said, should always go from data to insight to decision to action.

21:40 If nothing changes after somebody looks at your dashboard, then you're not really gonna get any type of user adoption out of it. So the question's gonna be why does this dashboard exist, right? Just to give us some numbers? We need to make sure we're creating dashboards that provide action once our end users look at them.

22:00 So what makes the analyst actually stand out, right?

22:04 Technical skills, yes, and this is what I used to tell my students all the time. It goes beyond, um, being technical. I always say that a good analyst wears two different hats. They have a technical skill set, and then they also have a business skill set. They're able to understand the problem, talk to the end users. They're able to communicate effectively.

22:25 Not everybody is technical, so you have to understand how to communicate in business terms. They're able to always ask the question of why, right? They're curious. Why does this number look like this? Why is it increasing or is de- decreasing?

22:39 They're able to think beyond the numbers and dive a little bit deeper. A lot of critical thinking. They're able to, uh, have empathy with their end users and actually understand what are they looking for, right? A end user isn't just looking to replicate an Excel file into Tableau for no reason. They're looking at something very particular in that Excel file.

22:59 They're doing some type of comparison, and they're… A good analyst is able to spend that time with their end user and figure out exactly what they're doing and the insight that they're trying to derive from that spreadsheet.

23:11 And a good analyst also cares whether or not their dashboard gets adopted. It doesn't just stop at creating something that looks good.

23:19 It goes back to are my users actually using it and getting exactly what they need out of it? I like to go through a process of not just handing a dashboard over, but going through a test process, right? A UAT testing is very important, um, when it comes to developing dashboards and having a small group of users actually test it out.

23:38 And I take that feedback very seriously. It's always, um, an iterative loop, you know, when it comes to, to feedback with our end users. And when they use it and they say, "Hey, you know, I'm, I'm not understanding this number. I wish I could dive a little bit deeper into that," don't always push that off to phase two. Try to see, you know, ways that you could possibly incorporate that into the dashboard without maybe adding in a new chart.

24:03 Maybe actually just teaching them and showing them how to get down to some of those details that they're looking for. Sometimes it just takes training users to get that insight out of the dashboard that they need.

24:13 So technical skills obviously make a, a really good analyst, but the ability to be able to connect the data to the business, um, and drive that impact, um, takes you a whole lot further.

24:26 … than just a technical analyst and a business analyst. So it takes you really to the next step of what a good analyst is and, and who a good analyst is when it comes to creating dashboards.

24:38 So I'll wrap up with those five rules again. So one, make sure that whenever you're starting out building a dashboard, you wanna understand the problem before even touching the data. You wanna make sure you're having those conversations with your end users.

24:50 Understand exactly what they're, what they need out of it instead of just, "Hey, can you build me a dashboard that shows me X, Y, and Z?" What are you looking to get out of this?

24:59 Know exactly who you're building it for. One dashboard isn't going to fit the needs of both, uh, an executive and a manager or a manager, uh, and an analyst. Know exactly who you're building it for, and if it takes multiple versions of the same data to achieve that, then that's okay as well.

25:15 Don't confuse more information with more value. Just because you have more KPIs on the screen doesn't necessarily mean that you're, you're getting, your end user is getting more value out of this. All you're doing is telling them another number, right? You're not telling them, uh, any decisions they need to make off of that number, right? They're not getting any insight, additional insight out of that.

25:34 So just because you have a bunch of different charts on a report or a dashboard does not necessarily mean that you're supporting the business or you're helping your end user drive business decisions.

25:44 You wanna turn data into insight and not just visualizations.

25:48 Tableau is really great at creating visualizations quickly, but you wanna get to that underlining, underlying meaning of your data, right? What is this visualization telling us, right?

25:58 How can we dive a little bit deeper into this visualization and allow it to give our users actionable insight rather than just an observation?

26:07 And then last but not least, you wanna connect the insight to a decision or an action, right?

26:12 What can the user take away from this? As I keep going back to this healthcare example and this staffing example, okay, our executive asks for, uh, patient volume, or our end user asks for patient volume, right?

26:24 Now we understand that they wanna see how does staffing relate to patient volume. We dive a little bit deeper.

26:31 We see that some facilities are understaffed, some facilities are overstaffed compared to the patient volume.

26:37 Now, the action that we're gonna take off of that is that we're going to test maybe adjusting staffing for those locations where we see some peaks. Are there seasonal peaks? Are there, are there time peaks, right? Are we, do we have staffing issues between, let's say, lunchtime and the afternoon time, right, and then things die back down?

26:55 Once we analyze that and we find that information out, then we're going to take action off of it.

27:00 So you wanna make sure that you're building dashboards that are actually being used and being adopted by your end users and not ones that just look good.

27:09 And that's all I have for today. I'll open the floor up for any questions.

27:15 Uh, Alice, you're on mute.

27:18 There you go.

27:18 Alice McKnight: All right. Um, I have a quick question. I'm in consulting too, so-

27:25 Chantilly Jaggernauth: Mm-hmm

27:25 Alice McKnight: … always being brought into different consult- uh, different clients looking for different things. And I think one of the most common things that I see is that they, they love an Excel spreadsheet-

27:38 Chantilly Jaggernauth: Oh, yes.

27:39 Alice McKnight: … and will, and will hold onto an Excel spreadsheet, uh-

27:45 Chantilly Jaggernauth: Yeah

27:45 Alice McKnight: … to the, to their dying day. Um-

27:48 Chantilly Jaggernauth: Yeah

27:48 Alice McKnight: … what's some ways that you have found that, uh, can quickly get, um, some stakeholder buy-in to move-

27:55 Chantilly Jaggernauth: Um-

27:56 Alice McKnight: … to move from just an Excel spreadsheet or exports?

28:00 Chantilly Jaggernauth: So I found that actually giving them both has gotten a lot of buy-in. So what I would do is, the collapsible containers I think in Tableau are, are wonders, right? So I'll have a visualization that gives them the insight out of that spreadsheet, because it does take time to get users from looking at tabular data over to more actual visualizations that, where they can get some-

28:24 Alice McKnight: Right

28:24 Chantilly Jaggernauth: … some insight out of. So what I'll do is I'll show them, "Okay, this is the information that you can get out of your Excel spreadsheet that you're saying that you're even looking at in this Excel spreadsheet." So I present them with that information, and then I present them with a way to still download their Excel spreadsheet within Tableau. So we'll have two tabs.

28:45 We'll have the visualization, and then we'll have the details tab. And you'll see that, you know, they start with the visualization, and they still go to the, to the details tab. But we're helping drive user adoption, you know, from Excel to Tableau that way. I've never seen users just toss their Excel spreadsheet and just go right into T- into Tableau.

29:04 In the 16 years that I've been doing this, I haven't seen that. So, yeah.

29:10 Sekou Tyler: So I got a question for you, Chantilly.

29:11 Chantilly Jaggernauth: Yep.

29:12 Sekou Tyler: So in the 16 years you've been doing this, we have the rise of AI, right? Everything's automated, Agentic AI, so on and so forth.

29:19 Have you seen an increase in the fundamentals being needed now, or have you seen more people trying to automate different things?

29:29 Chantilly Jaggernauth: I see both, actually. Um, I see a lot of end users coming to us with, um, AI-created dashboards or wire frames of what they're looking for. And those same users also lack the data literacy to understand exactly what is even being displayed by… What, what did AI even create, right? They just see a pretty picture, and they're like, "Hey, I need you to recreate this," not knowing they d- they don't even have a clue, you know, sometimes on what's even being presented.

30:03 So I see a r- I see a rise in both. I see, I see a rise in end users being more comfortable using AI, and analysts obviously using AI to support the work that they, they're doing. But at the same time, I do see an increase in the need for data literacy, right? Like, if we didn't need it before, we definitely need it now, like way more. Over my 16 years, we, we need it in year 16, for sure.

30:33 Alice McKnight: I would say the same thing about data governance. I think-

30:36 Chantilly Jaggernauth: Okay

30:36 Alice McKnight: … a lot of companies, uh, wanna jump all the way into AI, and wanna ask questions of my, of, of my data.

30:45 Chantilly Jaggernauth: Mm-hmm.

30:45 Alice McKnight: And the foundation that they're built on is j- is, is not where it need, where it even needs to be-

30:52 Chantilly Jaggernauth: Exactly

30:52 Alice McKnight: … to be able to trust the answers, uh, that they're receiving from-

30:56 Chantilly Jaggernauth: Yeah, the model-

30:57 Alice McKnight: So, yeah

30:57 Chantilly Jaggernauth: … isn't built correctly.

31:05 Alice McKnight: Are there ways to, uh, practice these, uh, skillsets that if you're not in a cli- at a client, working on a client, uh, or are you, like, new, new to the field and you're trying to practice these skills?

31:21 Chantilly Jaggernauth: I think in the Tableau community there's still… I haven't been as active, um, as I used to be, but there are def- definitely, uh, there are definitely projects out there, um, that allow you to practice. And before I was in consulting, so I started consulting in 2019 or 2018 when I joined Lovelytics. Before that I was just working for Johnson & Johnson and Comcast, and I had very specific use cases.

31:49 So I would say if you wanna practice those skillsets, there's projects and opportunities within the Tableau community that allow you to do that, that allow you to wear a consulting, um, hat. And then also use some of your peers, right? Um, I would say I used to role play with my data, right? Like, okay, if I were an executive, what do I want to, to understand about this data, right?

32:15 And then I'll play the role of the analyst in creating that. So sometimes it takes you, you know, being your own imaginary friend when it comes to understanding, you know, some data and, and, and learning.

32:28 Sekou Tyler: All right. We got a question in the Q&A, uh, from CF. Let me see if I got this person's name. No, we got initials. Can't… I don't know what CF stands for. But from CF we got, "How do you go about figuring out and sketching the layout for a dashboard?

32:43 Uh, for example, which info goes where-… picking the right colors. Like, what's your process for laying out?"

32:50 Chantilly Jaggernauth: So, uh, for me, I s- I start with the wire frame, um, instead of just building out visualizations. So I do a lot of talks on either, uh, high fidelity or low fidelity wire frames. So I'm, um, I'll use either PowerPoint, uh, pen or paper, or Balsamiq to sketch out some black and white wire frames for a client. Or if they're one of those clients that you need them, they need, like, really good buy-in, then I'll use a tool like Figma to actually build out a wire frame.

33:20 And that helps me get my ideas on paper without actually having to have data, and get feedback from the end user and implement that feedback rather quickly. That's if I'm building from scratch. There are times where a end user already has an idea in mind, and if they have a starting point, then I'll go off of the starting point. I always ask them for their brand colors whenever I start.

33:41 If they have a report that they're used to seeing, then I'll take bits and pieces of what they're already used to seeing and maybe enhance it, put that into a wire frame to see exactly how they, how they like it. But I never go right into Tableau building from scratch. Like, I, like never. I always start off of, um, an example that the user has pretty much signed off on before I go and build.

34:05 Alice McKnight: I was a- also gonna say if you are part of Tab- the Tableau community, um, and you're on Tableau Public, and just put in, uh, dashboard layouts, there's, there's several, uh, dash- uh, Tableau workbooks that are just layouts, like common layouts. There's certain layouts, you know, four KPIs at the top, two big sections for charts.

34:32 It'll be a Tableau workbook of just these common layouts that you see all the time across, um, dashboards.

34:42 Um, I have one that I use, uh, all the time as a starter. It has a sidebar, a header, and some blocks, some block containers that I just bring over and start putting things into.

34:58 Chantilly Jaggernauth: Oh, yes. Always good to have-

34:59 Alice McKnight: And just picking the most common-

35:01 Chantilly Jaggernauth: Yep

35:01 Alice McKnight: … templates.

35:02 Chantilly Jaggernauth: Any other questions?

35:03 Alice McKnight: Okay.

35:05 Sekou Tyler: Got one more from…

35:08 Oh, no, that was it. Never mind. That was from CF.

35:11 I think that's the only one.

35:13 You see anything else? I don't see anything else in the chat or anything.

35:16 Alice McKnight: I don't see anything else in the chat. Andre, thank you for that, uh, link. There's some, uh, nice information about dashboard layouts there, um, and the, the common ones that we see all the time, so that's a good link. Thanks.

35:31 All right. This is, this is our, uh, way to, uh, stay in contact with us. Um, we usually have our meetings, uh, at least once a quarter. Um, but if you want to stay close to us, uh, during that time, in contact with us, these are ways that you can, um, reach us and be notified of when, uh, our next, uh, presentation is going to be.

36:02 If you are interested on giving a talk, um, there are TUGs. We're not the only TUG, but TUGs are great opportunities to, uh, share your knowledge, get that practice in presenting and sharing, um, information with like-minded people. Um, if you would like to have a present during one of our TUGs, please let us know. Um, we're always looking for, um, additional voices and different perspectives for our presentat- for our presentations.

36:38 And as always, thank you so much for your time. Um, this presentation was recorded, um, so if you want to share this information with other people who could not be here, uh, this will be available inside of our group in, on Bevy.

36:56 And also there, we, there are links to our other presentations.

37:00 Um, thank you again for your time, and we look forward to seeing you guys at our next session.

37:07 Chris Williams: Thanks, all.

37:08 Chantilly Jaggernauth: Thanks, y'all.

37:10 Bye.

37:11 Alice McKnight: Thanks, everybody.