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Kirk Munroe Shows You How to Model Data in Tableau
829 Plays1 year ago
Kirk Munroe is a business analytics and performance management expert. He has held leadership roles in product management, marketing, sales enablement, and customer success in analytics software companies including, Cognos, IBM, Kinaxis, Tableau, and Salesforce. Kirk has a passion for coaching and mentoring people to make better decisions through storytelling with data. He is currently one of the two owners and principal consultants at Paint with Data, a visual analytics consulting firm. Kirk lives in Halifax, Nova Scotia, Canada.
Episode Notes
Kirk Munroe: 4 Common Tableau Data Model Problems…and How to Fix Them
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Introduction and Sponsorship
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Guest Introduction: Kirk Monroe
00:01:09
Speaker
Welcome back to the Policy This podcast. I'm your host, John Schwabisch. I hope you are well. I hope the weather is turning nice into spring. But you are listening to a podcast. You're probably out walking in the sun, walking your dog, you're going to run, taking a jog.
00:01:24
Speaker
I don't know, but I'm glad you're here. I'm glad you have tuned into this week's episode of the show where we are going to learn more about data in Tableau. I'm really excited to have on the show Kirk Monroe. Join me for the conversation. Kirk is the author of the new book, Data Modeling with Tableau. Kirk is also one of the chiefs at Paint with Data. And here's the thing.
00:01:46
Speaker
I was on Twitter sort of complaining about, and I'm gonna admit, I was complaining, complaining about how a lot of Tableau blog posts ignore the part about the format of your data, the structure of your data. And a lot of that is because most of those tutorials use the basic built-in datasets within Tableau. Fine, that's great. But in many cases, my data aren't as clean or they're not in the same format, the same structure.
00:02:12
Speaker
So Kirk reached out. He's got this great blog post on the Kevin and Ken Fleur-Lage twins blog on their website, which I will also share in the show notes.
Data Modeling with Tableau
00:02:22
Speaker
But he's also got this new great book, Data Modeling with Tableau, where I am almost certainly going to offer the first two chapters at least, if not the full book, but at least the first two chapters to my students. Ways to think about how to use data in Tableau. I just think this is super important. You can't get to the visualization part.
00:02:40
Speaker
without knowing anything about the data part and particularly about the data structure part. So I hope you'll enjoy this week's episode of the podcast. Here's my conversation with Kirk. Hey Kirk, good morning for both of us. Even though we're like an hour apart, right?
00:03:00
Speaker
I was saying earlier, now that I understand that Halifax time is an hour ahead, when it's four o'clock Eastern, I can say, well, it's cocktail hour in Halifax. So five o'clock somewhere at four.
00:03:16
Speaker
Right. But now I know exactly where it's five o'clock at four. So I feel better. Yeah. Well, thanks for coming on the show. I'm excited to chat with you because let me give listeners just a little quick background. So I was having not a struggle. I was wrestling a little bit with data formats in Tableau. And the one thing I noticed in a lot of blog posts of Tableau tutorials was that people didn't talk about the structure of the data. They're always using the superstore data.
00:03:41
Speaker
And it's in this particular format and you had responded with this fantastic blog post that you had written for Kevin and Ken Flurlage. And that was really great. And then on top of that, you have this great new book, Data Modeling with Tableau, which the first two or three chapters go into even more detail on that. So I reached out super happy to have you on the show, but I want to start with a little bit of background. You have a firm paint with data, but you also have a impressive background before that. So I was hoping you could just talk a little bit about that and then we can dive into some Tableau stuff.
00:04:11
Speaker
Yeah, sure. And yeah, thanks. Happy to be here. So yeah, my background in BI at least without going to the whole history started in 2001. I went to Cognos as a product manager.
00:04:27
Speaker
And I kind of went up the product management ranks there for about five years. The reason I liked being a product manager at that time was BI analytics was still a little bit nascent. So I felt like being on the product side of a bit of a technical background was the place to be. Then once we got bought by IBM, I went into sales enablement because I thought the natural next step of that was to help
00:04:51
Speaker
you know, sellers, customer facing people actually understand what analytics was. Like it's not just reports that you can burst it to people that have static information, right? Like what actual analytics meant and answering business question. And then I went to a supply chain analytics company called Connexus and ran product and marketing there. We did super deep analytics like
00:05:11
Speaker
but very niche. So we could do cool things like if you had a bill of materials for say like, I don't know, two different laptop models, and you could ask a question of it and say, well, what if we took this part from this one and gave it to this one, and you can see how many customer orders would be late within like seconds, which would normally take overnight to run a MRP system or something.
Writing Process and Challenges
00:05:35
Speaker
And then anyway i did a start up again that i went to tableau actually for four years to do customer success i was drawn to that because i thought you know this stage we've gotten to is that people didn't understand the industry analytics at a high level but they didn't know how to make it happen.
00:05:54
Speaker
I did that for four years. And then on the paint with data thing, my wife actually, who's a Tableau ambassador, a user group ambassador, started a company called Paint With Data to be a consultancy. And of course we used, you know, take data and actually paint with it. So we're a consultancy that work with companies of all kinds of different sizes. I joined 18 months ago.
00:06:16
Speaker
It was an intersection of two things. One was we had always said we wanted to work together, and then we kind of went, I'll just run out of time if we're going to do this. And part of it was just the customer success role was good at Tableau. It started to get a little bit frustrating that I couldn't get hands-on. The things that really made customers successful, the job became a little bit too much.
00:06:37
Speaker
relationship as opposed to the things that actually make people like is their data structure, is an example. And the role got really far removed from that kind of stuff. They didn't care that new hires were trained in Tableau really, or certainly at that kind of level. I would go get every Tableau certification you could get. I thought it was really important to, if you're going to be a trusted advisor, you've got to know more than two people you're advising conceptually.
00:07:07
Speaker
Okay. So you joined 18 months ago. So were you working on the book? Now it's the book. It was kind of a funny thing because, um, I joined and then I was in for maybe six months. And then the publisher called me and went, we want someone to write a book on data modeling when you write this book, uh, and tableau. And the first thing I thought was, well, who am I to write a book? And of course you get into, there's so many smarter people than me that could do this, which is just a natural thing to do, I think. And I thought.
00:07:37
Speaker
And I think in my head I went, there's so many biz books in there all the time. There must be a lot of data modeling books. So I asked for 24 hours and I did a search and there are none. So to be fair, Carl Alton is awesome. Like Carl's got a book on Tableau prep, but like not on data modeling, you know, that's kind of the stack for Tableau. So I went, well, then you quickly, this has happened to me a lot of my life. I go from why me to why not me? And then started writing the book in about April, I guess.
00:08:06
Speaker
So it took about six months from April to October to write the book. And then I thought it would be, you know, a 200 page book and trying to be as concise as I could possibly be, I got it down to like, it turned out to be 325 pages in the end, like there's kind of more there than I thought.
Overcoming Tableau Challenges
00:08:25
Speaker
Right. Yeah.
00:08:27
Speaker
Um, yeah, I've been there. So, okay. So let's talk about data. I prefaced this whole conversation with this difference kind of between wide data and tall data, but maybe I'll ask the question sort of a more general way, like from your perspective, what is the biggest challenge? In particular, I guess new Tableau users face when it comes to the data modeling and the data structure in Tableau.
00:08:50
Speaker
Yeah, to do one step back from that, which I get to it in a little bit in the book, and I think we'll probably have more blog posts on this, is what makes Tableau so special that no one sees actually is VisQL, which Tableau used to talk about. So until Tableau came along, you would have to query your data, and then you'd have to format your data a little bit, and then you would visualize your data. And most other BI tools, I think,
00:09:17
Speaker
thought spotter, doing a pretty good job at the same kind of approach as Tableau now. But traditionally what it is, is you open up a product and the first thing it'll ask you is, how do you want to chart this data? And my frustration until I saw Tableau, I was working at Cognos, so we were one of them.
00:09:32
Speaker
frustration at the time. It's like, I don't know how I want to visualize it yet. I'm just trying to interact with my data. So Tableau solved that problem. And what it is is, so basically, when they write their sequel underneath the scenes, they have this clause appended on the end that basically goes display as, which is why people fight Tableau sometimes and find it unintuitive at the very start.
00:09:54
Speaker
But if you get in Tableau talks about, used to talk about at least the analytics flow, you just start clicking around and asking answer questions and dragging things to marks cards and the visit keeps changing without you explicitly going drop this on columns or on one color on. It just, it's smart enough to mostly know. Like I never drag anything to...
00:10:12
Speaker
I rarely drag anything to a column or row shelf as an example. I'm a big double clicker. And I let Tableau figure that out. I've done it my swap rows and columns, but I rarely drag.
00:10:25
Speaker
That background is important, I think, because Tableau always assumes, and this is where the data modeling comes in, is that your data structure underneath has a series of columns that it's going to convert to fields. And every one of those columns is going to be distinct. So not discrete, but distinct in that.
00:10:46
Speaker
If it says customer, the only thing in that field is going to be customer. And if it says revenue, the only thing that's going to be in there is revenue, sales, et cetera. And that's why a lot of people pick up Superstore. It's already formatted that way, and they have a lot of fun, and they bring in their own data, and they choke, and they don't know what's going on. And the reason Tableau does that is it makes this FishQL thing easy to do. It makes it easy to know how to visualize it, because they know how the data structure is.
00:11:13
Speaker
I guess they, they assume how the data. So that's why, right. So that's why you can run into problems without needing to be super technical. The way CPUs work, like I've always worked is that is they work well when you pass them an array of data and then you can filter slice, whatever that, and they're really good at aggregating it.
00:11:33
Speaker
So analysis at the end of the day is about some level of aggregation of data, visual analytics, which Tableau does is that visually. So basically it's really easy if you have a row called revenue and then you pull on region, it's easy for Tableau to go, okay, some, and then break it down by that region, right? And then color it by sub category.
00:11:54
Speaker
I'd be ugly but it does this stuff very fast if the data's structured that way. If, for instance, your field was conditional and it was called name and the next column was vendor or customer and then there was a name and the next column had vendor, customer and you had to try to dynamically write a calculation in Tableau to go, if this column equals vendor then this one,
00:12:18
Speaker
Tableau is going to be terrible. It's just awful, right? Because it assumes that's truck, which I knew as soon as I went to Tableau, we had to do a demo at a time to get hired. And I went, I refuse to use Superstore because no data looks like this. I've been around long enough that I knew that. So I brought in a bunch of Airbnb data or inside Airbnb data, which is great data.
00:12:41
Speaker
to show, and then I made a whole scenario about, um, you know, if you were working for the city, how happy would you be with this or your potential hosts? And I did a whole demo around that. And then I realized, oh, this like formatting data thing is tricky. Like you've got to get this right. So, um, so yeah, that's it. So that's fundamentally it's 325 pages of how do you get your data like that? And because, you know, there's a lot of nuance beneath that, but
00:13:10
Speaker
So I know you're not at Tableau anymore. So this is, you know, just dreaming, but like if you had your druthers, would you have Tableau focus there? Presumably they have an AI and an ML team working on a variety of things. Would you have them do something similar to the show me tab, but not for graphs, but for data where it says it looks like your data in this structure. Would you want in this structure and you click a couple of buttons and you're good to go.
00:13:36
Speaker
Yeah, for sure. Yeah. Yeah. And then it may be, you know, on the, um, uh, what's the new feature called that workbook optimizer? It should be more than just, it should be able to go, then it's getting there a little bit, but it definitely has to, I think, get better at going. The reason that you have all these calculations and weird parameters and whatever is because your data is not shaped, right? Right. I think it would be not terribly hard for them to pick it up. I know like Ken Fleerlich has a great line, which I love, which is,
00:14:05
Speaker
If you're doing something in Tableau and it seems like it's more difficult than it should be, it's probably because your data is shaped right. This is more complicated than it should be, like nine times out of ten, that's because your data is not right. Your data is not shaped right. So for those who are, let's say like me, sort of
00:14:26
Speaker
relatively tableau newbies. What do you recommend for folks to do when they're in that position where they're struggling and maybe they even realize, oh, my data's wide and it needs to be long or tall. Like what tools do you use to do that reshaping when you're working on it?
00:14:44
Speaker
Yeah, well, I mean, first they should buy the book. Obviously. I think it's just that the first exercise is whether you use a piece of paper or you mentally do it or whatever. Just like if you were going to, I know Chantilly talks about this a lot is not the only one who talks about if you're going to create a visualization, you should kind of map it on a piece of paper somewhere first to see
00:15:09
Speaker
You should think about what would it take before I even get to a tool, what would it take to get these fields into these very distinct columns. And then the thought would be sometime, and most of the time, what it's all it's going to take, if it's not, is pivoting rows to columns or pivoting columns to rows. And it's that simple, usually. And then you can pivot columns to rows in either desktop or prep.
00:15:39
Speaker
And you can pivot rows to columns in prep. I feel like that's a feature that's been in there now for at least a couple of years that almost no one knows about. You know, other than the prep community knows is there. It's the slickest little feature. And it just makes the world a difference. Because when you open up a workbook and you see if people have those type calculations I was talking about, like, if
00:16:00
Speaker
this field equals profit, then profit. And then they've got a string field to put dollar signs in front of it. I'm like, just restate that. You know what I mean? Yeah. Just restate that. Yeah. I've done all that too. Yeah. Yeah. And in prep it's literally.
00:16:17
Speaker
Pivot rows to columns and it's like which one do you want to and then you drag two things over and it's done Yeah, I mean like prep like prep is terribly underrated. So let's let's talk about prep because you have I think it's like part two of the book I think there's like four or five chapters that are dedicated to using prep. So is that section in there?
00:16:40
Speaker
because you feel it is the right tool for Tableau users, or because there really isn't like in my reading, there's just not enough, I would say enough materials out there to really help you dive into it. Yeah, sometimes I wonder a lot if prep like a lot of features come out. So it's probably a few things like Alterix had this great partnership with Tableau, right? So a lot of people probably knew that, right? And if you're already licensed for that, then you could substitute everything in prep, right?
00:17:08
Speaker
That's fine, do you know what I mean? But I think for people that don't have it, especially, I think a couple of things happened. First off, when Prep came out, it didn't do a lot. It was okay, but it didn't do a lot. It was still kind of a cool product back in 2018 now. And I think a lot of people might have evaluated it then and written it off a little bit.
00:17:27
Speaker
and not kept up with all the innovation that's in it. But certainly, it's a really valuable tool for people who already know Tableau well. First off,
00:17:39
Speaker
It's, it comes with the creator license. I mean, there's the annoying thing, the schedule that you need data management, but, um, but it comes with it. It's, it's the same calculation language. It's the same UI ends as far as you could have the same UI and UX, like they're different UXs because they're different processes. So, I mean, from a cost to ownership, it's just so long to use prep. Like I wish more people use, I'll tell you, we talked about this before we started a little bit, like, so I've been in data for.
00:18:04
Speaker
22 years and I probably haven't other than a very simple line of sequel probably haven't written the line in 18 Even though people insist on asking for these sequel skills. I'll give you an idea of how much I avoid it lately I've been using snowflake. All right, so technically I'm writing a sequel statement to create my snowflake table But it's it's not really a sequel statement. So I create my table and then what I do is Relationships are also a very powerful thing in tableau, but they only work against live connections. So you can't use prep per se but
00:18:34
Speaker
But prep, I know so well now, and it's so familiar that I usually create my snowflake tables, and I prep the data and load it into snowflake with prep. Like I don't even always use a published data source. Yeah, like I'll go into prep, and then do what I need to do to it. And I'll move it to a snowflake table. Because let's say I've got two tables at different levels of aggregation, right? And I don't want to explode those, I'll put them into two snowflake tables, and then I'll get tab load to create a relationship. Oh, wow.
00:19:03
Speaker
So I don't even always use it, a published data source. So sometimes I'm using something like Snowflake actually as the output. So you don't even have to think about it always. But the last thing on it is I don't know why Tableau has been so hesitant to call it an ETL tool because it's what it's always been. Like you extract data, you transform it, and you load it somewhere else. I think they were afraid to do it because they're like, well, we don't want to compete against all these like ETL specific
00:19:32
Speaker
You don't have to say that's what you are. Do you know what I mean? Like I would, if I was going to some data engineering team that never did visual analytics, I would not recommend Tableau prep just because there's probably more powerful tools. But for someone who's used to Tableau anyway, like why not? Like the CASA ownership or something.
00:19:47
Speaker
It is an interesting thought about who they target, like what is their avatar of their core customer base. And I always find that interesting because the folks that I work with tend to be, you know, it's a nonprofit of, you know, six, eight, 12 people. And there's like one person or two people have demonstrated this interest in creating visualizations, but they're not necessarily maybe, you know, data people. They haven't coding experience. Maybe they never use a tool like this before.
00:20:14
Speaker
And I think they often get, as you've mentioned, they get frustrated by these little but crucial things. And maybe being able to help those folks would unleash it. I don't really know. Yeah, you know what? That's a great point for consulting with a small company. I would say it's worth learning data at the level of Tableau Prep.
00:20:36
Speaker
first, especially for the non-traditional technical. People don't come in from a programming background because not only is your vis going to be easier and faster, they're actually not going to have to write nearly as many calculations and struggle with that kind of stuff in Tableau, right? Because otherwise they're going to get frustrated because they're like, I'm not a coder and I have to write all this. And I'm like, you wouldn't if the data were structured the right way.
00:20:59
Speaker
And this idea of every column being distinct is not that hard of a
Data Relationships in Tableau
00:21:05
Speaker
concept to get. I don't know why people don't go back to make it that way. I just neatly need it in a column. You need pretty good SQL skills before this pivot rose to column. I think that's the secret feature.
00:21:18
Speaker
Because it's so fast, and it's a little bit hard to do in SQL. Like, it would be daunting to try to split that up. And it's counterintuitive because it makes your data longer. And people think, oh, long data's slow. I'm like, long data's not slow at all. Right? Yeah.
00:21:36
Speaker
Yeah, it is interesting. I grew up in the SAS Stata world. And there is a burn into my brain. There's a little image in the Stata help file about the reshape command, which goes from, they use long and wide is the language they use. But there's this image. It's like, here's a wide, and then it's like a little arrow. And then here's the long. And if you want to go left to right, this is the syntax. If you want to right to left, this is the syntax.
00:22:04
Speaker
I agree. It's not a complicated concept to get, but it's so crucial to everything that you're going to do down the road. Okay. So almost without intentionally doing it, we've talked about the first two parts of the book. So I think it makes sense. We should, we should get to the last part. So the first part is really about the types of data models, setting up your data. The second part is about prep. And then the third part is about connecting and building relationships, which. Right.
00:22:31
Speaker
I have found also to be a frustrating, especially the relationship part, so frustrating piece. So I'll just make it a super general question, which is, as we walk through now, through the book and sort of the process by which someone would work in Tableau, what is this third part about when it comes to connecting and building relationships in the data?
00:22:52
Speaker
Yeah. So the next thing becomes almost, I wish there was a term for this, like it's treating tables instead of tables as distinct analytic units that sometimes need to be combined to perform a different level of analysis. So, um, I also, like you referenced the blog posts that I had on the, on Ken and Kevin's site on the Florida twins.com. We have two more coming one on when to use prep and when to use desktop.
00:23:19
Speaker
And then another one on when to use relationships versus joins and just a little bit on blends, because the blends answers almost never. So there's just one use case, just one very specific use case. So let me imagine this, this will probably be in the blog post, but imagine this Airbnb data, I think we can do it, right? So you've got, you've got, let's say we have five tables.
00:23:41
Speaker
And the five tables are, one table contains a list of all the properties, say in a city or whatever. It could be all of them, but with a city column is split it up. Let's say even for a city, and then we've got reviews. And so reviews are at a different level of granularity than the properties because one property can have many reviews, but a review can only be for one property.
00:24:09
Speaker
Right. So, so you don't, until relationships came out, that's a perfect example of tables you don't want to join. Um, and the reason you don't want to join them is you're going to explode it. And then you're going to have to watch your level of aggregation because you're going to have many rows now for individual properties because it had many reviews. Right.
00:24:30
Speaker
So what's just magical about relationships is you create a relationship on those on property ID, listing ID, and now what happens, and I mean by individual units of analysis, is I can ask questions about reviews without asking about properties, and I can ask about properties without asking reviews. So if I ask a question on the other side of that tableau, it's only going to generate the SQL behind the scenes against that one table, like no join. I won't even look to join it.
00:24:58
Speaker
But then what's really smart about relationships, let's say I want reviews on a given property, Tableau's smart enough to do that, it would probably be a right join the way I'm describing it, doesn't matter, but it would create that join dynamically to answer your question and handle the level of aggregation so you don't get it. Because I think, especially for non-technical users, understanding levels of aggregation is really hard to wrap your head around, and then Tableau takes your mind away from that.
00:25:27
Speaker
So imagine those two tables. Next thing you want to do is I want to bring in neighborhood information, right? So I want to bring in a shape file of neighborhood so I can map it. And then I want to bring in the walk score and bike score or whatever. I can go get that off the internet. And maybe I want to go get apartment information.
00:25:42
Speaker
on how expensive apartments are by neighborhoods. So I could answer the question, does it look like Airbnb are driving up the price of apartments? Or it's a tricky question, right? The best part of analysis. Or am I helping people afford it because they can use Airbnb to help them offset? So, but those three, those could be three separate tables.
00:26:03
Speaker
three. Yeah, one with the shapefile, one with the walk scores, and one with the cost of. So the temptation would be, oh, I should, and you could do this, but it would be a little bit complicated. You could bring all those in those relationships, because that would be the default. But if you think about it, those three tables are all about neighborhood information.
00:26:22
Speaker
And they're all at the same level of granularity, which is one row per neighborhood. So what I would do is, and this is why relationships and joins go together really well, I would join those three tables together, and then I would create a relationship on those three tables joined together, because Tableau would then effectively treat those three join tables as one table, because
Tableau Server and Optimization
00:26:43
Speaker
it really should be one. But don't we happen to do a whole data engineering job to put those in one?
00:26:48
Speaker
in the background so and then i can ask questions just about neighborhoods or again i can ask neighborhood how many reviews per neighborhood how many postings per neighborhood or whatever but it's just an example of if you think about it in terms of is it a unit of analysis on its own once neighborhoods ones reviews ones listing as opposed to tables and what their level of granularity is that also takes the complications out of that a little bit
00:27:11
Speaker
It's also interesting from the perspective back to this single person in some small organization, back to they need to pull all these data together. Maybe not even for a visualization purpose. They just need to have these data together for whatever it is. And they could use Tableau to do that because it's so efficient at doing these different things.
00:27:30
Speaker
Yeah. Yeah. A hundred percent. So that, and I still think in even a big order, so in little orgs you have no data engineering support. Right. Right. My experience at least, maybe people have seen other things. The one place where you're, where I still see waterfall process, heavy, slow things or data engineering teams, like, uh, like cloud data warehouses didn't magically solve that problem. So sometimes you'd be like, I need my data shape this way.
00:27:57
Speaker
And they'll be like well it's gonna go through this you need to get this approval it's gonna cost this much of a charge back we'll have it in three months it's like well you know what i'll take the data you've already given me without going all the way back to source and later in the data pipeline all clean it up right so i talk a lot about in a completely idealistic world you and one analyst doing this stuff right but.
00:28:17
Speaker
But you have to, but you're never going to get answers out to the organization. If you wait for the data engineering or to do it or whatever, that would throw us back into the eight. The last part of the book, which admittedly I haven't read because it isn't as.
00:28:33
Speaker
useful for my use case, you know, this is, you know, everybody's got limitations, but the last part is on Tableau server and Tableau online. So what are the differences in that section versus a working in desktop?
00:28:48
Speaker
Yeah. So that the last sections cover, um, and I wish that there was a word for Tableau server online together cause they're synonymous in this. So I keep going Tableau server or cloud and it's like the exact same for all intents and purposes. Um, it there's basically that you could think of in three different ways. The book's not exactly organized this way. One is all about data security and data security at two levels. So one is who can see the data model? Mm-hmm.
00:29:13
Speaker
Right. And the next level is who can see data within that data model because so imagine the first one, you just want only the finance team to see it. How do you make sure that only the finance team can see it? The next example of who can see it is like, imagine you, you were giving this out to your customers and, uh, you had thousands of them. You wouldn't want to build thousands of workbooks. You would want to say only the customer blogging can only see their own data and produce one workbook. So that row level security. So part of it's about that. So part of it's about how do you secure.
00:29:41
Speaker
these data models and how to secure the data within them. Part of it's on distribution, so when to use published data sources versus embedded data sources. Another thing, lots of people have been using Tableau, including Tableau Server or MindCloud for like a long time, and don't know the difference between an online versus an embedded data source and when to use one versus the other.
00:30:04
Speaker
And it's important to get that right from a cost to ownership thing. Cause you could be rebuilding the same data model over and over again on one side, or the flip side is you could be publishing a data model that was really only intended for one workbook and people are trying to build workbooks on it. And you know, a lot of them do because it was very specific for the work. So they both have their plays. Um, and Tableau doesn't make it very explicit, although at least now on Tableau cloud, Tableau server, you can say, you know, new published data source. So they at least have taken it at a.
00:30:34
Speaker
desktop enough that it feels like a distinct thing. Um, and then the last part is around all the other things that come with Tableau data management. So how to schedule prep flows, how to have data quality warnings, the Tableau data catalog at lineage to see who else is using this. So there's only one of 15 chapters on data management, but one of them covers all the things. Right. Right.
00:30:59
Speaker
So before we wrap up, I want to, um, come back to one of the things we were talking about before we, before we actually started recording, which is the size of data. Cause you mentioned a couple of times when we were chatting, but, um, you had mentioned a very interesting piece of extracting data in Tableau that I don't think I even really recognize. Cause when I do the extract, I just do extract and good and publish and I'm good. Right.
00:31:21
Speaker
But I wanted to finish up with that data extract tip, because I think this is something that probably most people don't know about the sort of way that you can modify or option in that extract menu. Yeah, in the background for this, I think I heard recently, I hope this is a true stat, but that the average Salesforce deployment as an example has 100 custom fields in it.
00:31:42
Speaker
I'm sure some people go, a thousand, I don't know. But what happens is, anyway, that's the kind of thing that leads to really wide data. Even if all the fields are discrete, sometimes the data still gets incredibly wide with all these ways you could slice and dice the data. And that wide data definitely makes Tableau slow, especially if they're string fields, because they're usually string and descriptive type fields.
00:32:07
Speaker
And so when we're working with clients, you have to get, well, the business might want to analyze by all those different things, right? Like anyone could possibly filter by all those things. Yeah. So let's say whether you're using a published or embedded data source, but say you're just using
00:32:23
Speaker
Tableau desktop to make it easy. And then what I say to people all the time, then if you don't know which of those columns you're going to do, like leave them in your data model. This is if you're using an extract, leave them in your data model, build all your visits. And it's your very last step before you run your extract, just take the little down arrow, you make calculations, everything else on and go hide all unused.
00:32:46
Speaker
And it will hide all the fields you're not using. And then what most people don't know is when you run your extract, it doesn't bring all that data into your extract. So it's going to perform way better and no one's using it any. And then people come back and go, well, what if I want to use those in the future? Well, what slick is Tableau will bring in almost like a ghost field where you can say show hidden fields.
00:33:07
Speaker
You just go to the field you want, you add it. Of course, you can't add it to the visit that point because the data is not there, but you run the extract again, and then you can add it. So this is the short fire way to make sure that you're not bringing in data.
00:33:20
Speaker
from a width perspective that you're not using. So it's a, again, I don't think Tableau talks about it much. It's just a terribly hidden feature. No, it's just like, it's just like this thing that kind of showed up that like, Oh, you have to do this. And yeah, it's super. Yeah. Well, Kirk, thanks so much for coming on the show. Um, the book is really great. The lessons are fantastic and something I think more people need to learn and read about. So, uh, yeah. So thanks so much for coming on the show. I really appreciate it. Oh, thank you. Yeah. And I really enjoyed
Conclusion and Podcast Credits
00:33:49
Speaker
the time. Yeah.
00:34:11
Speaker
obviously in Tableau, but also just a better person to work with data, just a better data visualizer, a better data scientist, a better statistician. Anyone who works with data really needs to understand this content and these lessons even better. So I hope you enjoyed that episode. I hope you will consider sharing today's episode with your friends and your family, put it on your social media networks, share a review or rating on your favorite podcast provider. And until next week, this has been the Policy Viz Podcast. Thanks so much for listening.
00:34:13
Speaker
Thanks for tuning
00:34:42
Speaker
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00:35:06
Speaker
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