Complex aggregations allow you to ask intricate questions of your data. You can even nest one aggregation into another for richer, more valuable insights – things like trends, ratios and running totals. But folks avoid these types of calculations or don’t know how to use them, instead doing a hack job using simple calcs.
In this on-demand webinar, we demystify complex aggregations and show you the available options for performing them in Tableau Desktop. Learn how complex data aggregation requirements can be easily addressed in Tableau by leveraging table calculations and level of detail (LOD) calculations.
You’ll also learn
- How to filter data displayed in a visualization without filtering the underlying data necessary for aggregated result sets
- Techniques for creating dynamically recursive calculations such as weighted moving averages
- The purpose of LOD calculations
- How to leverage LODs to show data at various grains within the same visualization and address other common aggregation challenges
If you’re a Tableau rookie or on your way to becoming a Tableau guru, you’ll want to add aggregate calculations to your toolset. Understanding the options in Tableau Desktop will open unlimited possibilities for comparing metrics across dimensions and highlighting important insights. You’ll improve data clarity, reduce the number of visualizations needed to communicate insights and increase dashboard performance.
Consultant and Trainer
September is one of our most sought after instructors. Students love her, giving her 4.9 out of 5 stars. Fluent in both Tableau and Cognos, she has over 15 years of hands-on, expert level experience with a variety of BI platforms. September loves mentoring clients and helping them solve their most complicated problems.Read more
Greetings and welcome to the latest installment of this Senturus Knowledge Series today. I’m very excited to be presenting with my colleague on the topic of uncomplicated complex data aggregations in Tableau we’ll be speaking about leveraging table calculations and level of detail calculations.
First of all some housekeeping items. I’m taking my time through some of these upfront things as we give you all a chance to get on. I know you’re all sitting at home with your dogs and your kids and all your entire extended family. Hopefully sitting six feet or more apart from each other and washing your hands. By way of housekeeping, we have the GoToWebinar control panel, which you’re probably looking at right now, you can maximize and minimize that using the orange arrow.
In the upper left hand corner. And while we do mute all the microphones because of said children and dogs. We encourage you to enter questions in the question pane in the control panel and while we’re usually able to respond to those questions while the webinar is in progress.
If for some reason we run out of time or otherwise unable to reply we will cover all the relevant ones in the Q&A section or via a written response document that we post on our website at Senturus.com. We understand that most of the teleconferencing providers GoToWebinar’s no exception maybe having bandwidth or you may run into some issues getting on or if you have audio issues go ahead and post those in the chat and we’ll do our best to get those resolved for you. But we ask for your patience while we are all going through the same issues and adjusting to life on the remote side of the fence here.
So the first question we generally get and peppered throughout the presentation is can I get the deck and the answer is an unequivocal yes. It’s available on our website at senturus.com where you can select the resources Tab and go into the resources Library which by the way, you should bookmark because it has a ton of great resources including all of our past webinars. Similarly. You can also click on the link that should be posted in the GoToWebinar control panel.
Our agenda today after a brief introduction of our speaker will get right into our topic du jour will discuss what is aggregation then get into default aggregation behaviors dive into level of detail calculations, table calculations, do some demonstrations. We will do a brief Senturus overview and provide you with some additional great free resources.
We encourage you all to stick around through that very brief section for the always valuable live question-and-answer section. So today, I’m very happy to be joined by my esteemed colleague September Clementine. She is a consultant and trainer here at Senturus in addition to being much more photogenic than I am. September is one of our most sought-after instructors with students loving her and giving her very high ratings. She is unique in that she’s both fluent in both
Tableau and Cognos and as over 15 years of hands-on expert level experience with a variety of BI platforms. And she’s one of our most expert report developers on both of those platforms September in particular enjoys mentoring clients and helping them solve their most complicated problems. So she is uniquely qualified to handle this particular topic. So before we jump into the main presentation.
We like to get a sense for our audience by asking you a poll question. So we we’d like to know what Tableau complex aggregations do you find yourself regularly using, you can select all that apply. So do you use table calculations?
So quick table calculations are the actual calculation editor to use level of detail calculations do use regex expressions, or do you do it old school where you using seen if-then-else our case statements in your calculations, or are you really old school and you go back and you do In SQL, so go ahead and get your answers in there. I’ll give you a few about a minute to do that.
So we have a good representative cross-section.
About two-thirds of y’all got your votes in there. I’ll give it a few more seconds.
People are stingy. Hey don’t want to give their votes. All right, so we go about 3/4 view there today and it looks like well over three quarters of you use table calculations less than half using LODs, interesting but not really surprising and a very small percentage using regex. Either those are complicated and some people don’t even know they’re there and then fully half of you go old school with the insane nested if/then/else statements and a good 40.
sent to it back in the SQL. Okay, that’s very insightful. Hopefully find these insightful and entertaining. So with that I’m going to give the microphone and the floor to September. Go ahead. Thanks Mike. So welcome all too uncomplicated in our complex data aggregations. So as Mike mentioned, in this webinar we’re going to cover some common challenging data aggregation. Use cases will show how you can.
You can often use level of detail calculations or table calculations to easily address some requirements that you may have and this is going to be a really small sample of our new full-day training where we’ll have over 25 demonstrations on complex data aggregation scenarios and Tableau. It’s now on our public trading calendar as well as going to be available soon with our subscription offering and for those who are already
considering Tableau desktop certification the full day class will be really helpful as part of that preparation as well. So first of all, what is aggregation? It is the process of combining data into groups. If you wanted to count the number of sales, orders within a city you would be aggregating or grouping sales orders by City.
The type of aggregation can vary you might want to count the number of cities, some city revenue or analyze average city spend whatever the type of aggregation you are combining data into groups. Those groups are defined by the dimensions that you dragged into a view.
The default way that Tableau aggregates data is at the most granular level for the dimensions that are brought into the visualization. So if you are counting for instance records for each state, the default aggregation would be a count of line item transactions per state as opposed to a count of orders which could include multiple transactions.
Any dimension that is included on the rows columns pages or Marks card, excluding the tooltip Mark will be used to aggregate data. There may be times
however, when you want to aggregate your data at a different level of detail for a particular group level of detail refers to the granularity of data level of detail calculations, allow you to override that default aggregation behavior if you are analyzing average sales instead of looking at average sales per transaction the default. You may want to look at average sales per order or per customer.
There are three level of detail calculations frequently referred to as LOD calculations that allow you to modify the default way that Tableau handles the aggregation and those are include exclude and fixed. So again, let’s say we’re interested in average sales.
We know that the default would be average transaction sales, but what if we want to know the average sales per city include computes aggregates for engines that are not directly used in the visualization. So those aggregated values can be further aggregated based on any other fields in the view so we could use the include function to some sales at the city level even though city is not in the view and then average those based on whatever dimensions are in the view perhaps state or some other combination of dimensions.
X glued will ignore the specified field when computing the aggregate if it is present and fixed only uses the specified fields when calculating the aggregate. Let’s look at a few use cases and we’re going to build out a few of these during the demonstration portion of our webinar today include Expressions. Typically use aggregated data. That’s a higher level than the transaction.
So if I wanted to compare the average amount per transaction to the average amount per order in the same visualization and LOD calculation is a really easy way to do that complex or mixed aggregation scenarios, of course are not unique to Tableau. The equivalent in Cognos might be to use the keyword for in an aggregate expression to specify the grain with relational data or keywords within set if you’re using Cube data DMR models or TM1 and Power BI you.
It use summarize all selected or all except functions.
Exclude expressions are often used to omit dimensions in the View when you’re aggregating data, there are literally hundreds of use cases for using and exclude expression, but a really simple one might be wanting to create a stacked bar visualization. And in this case our bars are showing sales by ship mode. And while we do want to see sales values for each ship mode, perhaps we’d also like to see the total sales value.
At the top of the bar so we would want to essentially exclude ship mode when we’re aggregating sales for that particular label fixed expressions explicitly define the dimensions and filters to be used when you’re computing the aggregate.
So a really common use case for using fixed Expressions is when you want to create a calculation that is unaffected by filters, perhaps you want to give users the ability to filter the data for particular year but for certain calculations may be labels and tooltips those sorts of things. You might want those filters to be ignored. The fixed calculation would allow for that in addition to LOD calculations table calculations are also incredibly helpful in working with aggregated data, whereas level of detail calculations are computed at the database level and allow you to change default aggregation Behavior.
Table calculations are computed locally after the data has been aggregated and this allows for second pass aggregation. So for instance, if you some Regional profit over time, that might be the first past you could then rink each region based on that set of profit that might be the second pass. It allows for ranking percent difference and percent of total comparisons moving averages even weighted moving averages and many other second pass out.
Patience there are also several table calculations that return values based on row position within the table or visualization index first last look up previous value. These are really useful for simplifying a variety of challenges. But one Theory really common example, let’s say we wanted to show a running total of sales over time our data set begins in January of 2016.
So by January of 2017 we have a little over 500,000 in sales.
Well what happens if I create a year filter to view a smaller time period than lifetime sales if I filter for the year 2017 then I don’t have the running total sales data from 2016. I’m only seeing the 18,000 and sales from January of 2017 instead of that cumulative $500,000 value table calculations used as filters can easily resolve this.
As table calculation filters are evaluated last after all other filters and after any table calculation expressions like a running total Okay. So let’s go ahead and get into the demonstration portion of our webinar and we’ll see a few of these techniques in action.
I’m going to switch over here. There we go.
to Tableau desktop and we’re going to be using our standard sample Superstore data source that comes with any install of Tableau desktop. We’re going to start off first using our include level of detail calculation and I’ll create a view much like the one I showed just a moment ago where we’re comparing aggregated regional sales at both the transaction level the default and the order level in the same view.
It’s only this first one average sales.
Per order versus transaction and I’ll bring over region to the column shelf.
Sales to the rose shelf going to change the fit here for two entire view.
And let’s change our aggregation for sales to average.
So now if I hover over the bars, I can see my average sales in the central region. We have $216 east region $238 and so on. What are these values as we’ve said Tableau by default Aggregates at the lowest grain of data. So it is going to be the sum of sales divided by the count of transactions for each of our Region’s.
Let’s create a calculation that gives us our order sales. We want to some our sales at the order ID level. I’ll create a new calculated field and name this order seals and we’ll use our include.
Order ID for the sum of sales I’m going to also set the default format to currency.
I’ll bring this new order sales to the Rose Shelf.
And change our aggregation to average I’m going to make sure that all of our marks stay bars.
All right. So we’ve got our average sales here on the top row and our average order sales below it going to combine these and create a dual axis.
And then also make sure that we are in sync here for both of our measures will synchronize the axis.
On for our average sales I move these Marks here to the front.
And make them a little bit smaller so that we can more easily compare.
And I’ll pick a different color here.
All right, and then let’s also add some labels so that we can see what the values are. So I’ll add labels for the average sales. These are our transaction the default and then labels for our order sales.
Eight. So with that calculation we’re easily able to show average sales at two different grains of detail in the same visualization just with that single calculation. So you might think well I could have just created a calculation some sales divided by account distinctive order ID. Let’s actually test that I’m going to create an ad hoc here at the bottom of the marks card some sales.
Count distinct order ID Right, and if I hover over one of my bars, I can see that I’m getting the exact same thing as I did with that that include calculation, right? So why choose the include calculation instead? Well for one thing they include calculation is a lot more flexible.
Let’s see what else we can do with just that one calculation going to remove this ad hoc calculation here and drag order sales and again, To tooltip and I’ll change the aggregation type for this instance to minimum.
I’ll drag it in again.
Change the new instance aggregation to maximum.
And drag it in a third time.
And I’ll leave the aggregation set to some for this instance. Right and I’m just going to update our tool tip here to make it really clear that this is the order detail that we are looking at.
Now move are some of sales to the top of the list.
All right, so we can use our level of detail calculation to not only view the average order amount per region. We can also use it to see what was the maximum sales amount on any single order in the region but was the minimum order amount on any single order in the region?
And what was my total sales amount for the region also because we use the include keyword any other dimension in you is used to further aggregate those values. So always start it off looking at comparisons by region. We could look at them as well by any other dimension. Let’s say we want to see these comparisons by Year. I’ll drag over order date and replace that replace region with order date night. So now we’re comparing transactions.
For each year to the order, right if we expanded our year out to quarters again, we have our exact same comparisons, but now we’re able to compare at the quarter level. So a really simple solution to a potentially complex requirement and it can be used for really many other things not just in this visualization, but potentially other visualizations that might be used on a dashboard.
Next we’re going to create a stacked bar visualization and we’ll use code for this example. So we want to create a stacked bar that shows Regional sales by ship mode. We want value labels for each ship mode. But we’ve also like total sales values at the top of each of our bars. So we want to exclude ship mode from that instance.
I’ll name this new sheet.
Regional sales by ship mode and again, we’ll bring region into the columns.
Sales to the rose and let’s drag ship mode to color.
And let’s also add our labels here. So I’ll bring in sales again to label.
So the sales values were seeing here are aggregated by both region and ship mode by default again. Anything on the rows columns and Marks card, excluding tooltips will be considered when aggregating measures. We would like a sales value that is not aggregated by ship mode. We want to exclude that from the automatic aggregation and create a new calculated field.
And I’ll name this sales.
Excluding ship mode and we’ll use our exclude function. So we want to exclude ship mode from sum of sales.
Now also set the default format for this two currency.
I’ll drag this new instance to the rose shelf, right? We have our second row here. And for this second instance. I will also make sure that our Mark types stay bar for the second instance. I will remove the some sales that will take into account our ship mode as well as the color by and for our labels for this instance. I want the label to be using to use the calculation where we’re excluding.
Mowed it. So these are our total values.
And then we’ll go ahead and create a dual axis.
Synchronize the axis it go ahead and hide this header here and then we will move our initial Marks to the front and then to really call out that we have totals here. I’ll click on label and make the font bold.
So we now have our total sales values that exclude ship mode at the top of our stack bars and since our labels are separate instances, you could for instance change the labels that do take into account ship mode 2 percent of total it. So for our first instance of labels, I can say for these I’d like to actually know what the percent of total is. I’ll use a quick table.
Calculation percent of total and we’ll compute that at the cell level.
So again we’re able to override default aggregation behavior. And in this case excluded dimension that is in the view ship mode from being used during aggregation. Those are just two of the many level of detail use case examples that we are going to cover in the upcoming aggregation class that show really simple ways to solve common challenges will also introduce parameters and sets and other Tableau functionality to give you the tools to create really dynamic really versatile.
So now we’ll switch gears a bit and look at how we can use table calculations to resolve some common challenges. We all create a visualization that looks set profit percent change month-over-month for a user selected year and will leverage the lookup function to filter our view for a selected year while not filtering the underlying racket records that are needed for that that percent difference.
Name this new sheet month-over-month.
Profit trends and I’ll drag order date.
To the column shelf and we’ll change this to continuous months.
And profit to the Rose shelf. I want to make sure that this stays aligned visualization.
And I’ll drag a second copy of profit.
Also to the rose shelf, so for the second instance, I’m going to add a table calculation.
And the calculation will be the percent difference from and I do want the percent difference based on the previous month. So I’m going to leave this relative to set to previous.
I’ll also add labels here for our percent difference.
As well as coloring are Marx based on percent difference in for these let’s choose bar.
So now let’s say we want to create a filter for year going to go to my order date show filter.
And I’ll change this to a single value list.
So for the year 2016, this is our first year of data in this second row are percent difference. We’re not seeing a value for January. That’s to be expected because that is our first month of data.
However, if I change my year to 2017, 2018, 2019, I’m still missing that January bar that I should have and this is because the percent difference calculation needs to calculate the difference in January using the data from the prior December which is now been excluded by our year filter. So this will not work for this for this use case in a remove that filter and instead. We’ll use our lookup function.
Which will create a table calc.
Create a new calculated field and I’ll name this one-year display filter.
And the calculation will be look up.
Our year of order date so this calculation is going to return the year value for every month on our x-axis the zero represents the current row or column in a partition as opposed to looking up some previous or next value.
I’ll convert that to discreet.
Let’s also set the format since this will be a year.
I’ll choose number.
No decimals, and we don’t need a thousand separator.
So now instead we will use this for our year filter.
I’ll drag your display filter to the filters card and we’ll choose use all and notice the triangle on the end of that year display filter pill indicating that it is a table calculation and let’s show filter.
And again, I’ll choose single value list, right? So for 2016, of course, we don’t expect to see a January value, but for 2017, we now have January 2018 2019. We have our values returned and this is because we are using a table calculation for our year filter and according to the order of operations cable calculation filters and this case are your display.
Happened after table calculations in this case our percent difference to the filter happens last another common business need is looking at averages for various metrics and average can be created in Tableau, of course by using built-in aggregation gestures or some other calculation techniques that we’ve talked about. But for those methods they give equal weight to each value in the set often when calculating an average. We want to vary the weight of values or our observations based on varying degrees of importance.
So, if you want to wait something recent more heavily in this last demo, we’re going to create a Time series visualization that shows profit compared to a more complex user selected weighted moving average of profit and will leverage our previous value function to do that.
And we’ll name this last one weighted moving average.
Again, I’ll drag order date.
Did the column shelf I’m let’s switch to continuous months.
And will bring profit into the rose and let’s add a filter for a year.
I’ll change this again do single value list.
So first, we’ll create our unweighted.
Average here create a calculated field and we’ll name this Prophet moving average.
And we’ll use our window average function.
Some profit we will use our first function.
And a zero, so this is our unweighted moving average. It tells Tableau to create the moving average in this case using all values from the first and a partition through our current value.
And I’ll set the default number format for this as well to currency.
I’ll drag this calculation to our Prophet axis.
Next we’ll create a parameter to capture the user’s desired weight given to the current value in the partition. So if there were a six-month time period they would choose the weight of months six 50%, 60% whatever is preferred and the remaining percent would be used for the previous month’s moving average. I’ll create a new parameter.
And I’ll name this current value wait.
And we’ll keep the data type set to float for our current value will do 60%.
And will have a range of allowable values the minimum being 1% and the maximum being 100% and let’s show our parameter control.
Now make this type in so now we’ll create our calculation that uses that parameter from our end user.
Create a calculated field.
And we’ll name this one profit.
Moving average waited our calculation will be so this is for current some.
Profit X our current value wait and we’ll add that too.
Our previous value some profit x one minus the current value wait they do we want profit.
I will format this as well as currency.
And let’s drive this now to our value axis.
So notice the weighted moving average is closer to our actual profit then the moving average calculation which gives equal weight to each month of profit data and we have our parameter display. So the end user can easily change the weight given to the current observation as well. So again, just another example of how you can use table calculations to easily accomplish more complex aggregation requirements.
Go over this specific use case all of the previous use cases and many others over 25 really interesting demonstrations in great detail and our new complex aggregations course, so hopefully we’ll be able to connect with many of you for some of those upcoming sessions. And with that. I’m going to hand things back over to Mike.
Great. Thank you very much September as you can see everyone September makes the complex look easy, but you can see some of those coming. The class that we’re offering now is a new Tableau class on complex aggregations presently. It’ll be offered as an instructor-led online training course delivered by September.
Where we consider that kind of a master class or an advanced class where you’ll explore over 25 real-world applications of these various expressions and you can find it over on the Senturus website at that link think the beauty of this class is not only that September and the instructors explain the concepts very clearly and then give you concrete examples and you’ll notice that September actually even did some comparisons.
What does an LOD fixed look like roughly compared to what you might do and say Cognos or Power BI so there’s a lot of knowledge there in terms of understanding it from the perspective of may be coming from a different environment or as you’re trying to learn the tool and really master it so you can apply it to your specific use case.
So stick around for the next few slides and we will answer all of your questions here to the best of our abilities with the time we have remaining. At Senturus we concentrate our expertise solely on business intelligence with a depth of knowledge across the entire BI stack. It’s all we do all day every day. Our clients know us for providing clarity from the chaos of complex business requirements disparate data sources and constantly moving targets.
We made a name for ourselves because of our strength at Bridging the Gap between it and business users and deliver solutions that give you access to reliable analysis ready data across the organization so you can quickly and easily get answers at the point of impact in the form of the decisions you make and the actions you take. Our consultants are experts in the field of analytics with years of pragmatic experience advancing the state of the art. Folks like September who have over 15 years of experience doing exactly this from dashboards reporting in visualizations to data prep and modern data warehousing, data warehouse migrations migration of BI environments to the cloud or mixed environments including security etc. software to enable bimodal BI and platform migrations and various other services all related to business intelligence. We’re so confident in our team and our methodology. In fact that we back our projects with an industry unique 100% money back guarantee.
And we’ve been doing this for a while. I’ve been doing this for about almost two decades at this point. You can go to the next slide September. We work across the spectrum from Fortune 500 clients to the mid-market solving business problems across many different Industries and functional areas, including the office of finance sales and marketing manufacturing operations. HR and IT. Our team is both large enough to meet all of your business analytics needs yet small enough to provide personalized attention.
We encourage you to visits senturus.com/resources. You can read it where we have tons of resources are from webinars like this one where the recording in the deck etc. will be on all things BI and our famous up-to-the-minute fabulous easily consumable blogs.
We have some great upcoming free events including what’s new in Cognos 11.1.6 that will be delivered by one of our partner IBM offering managers that’s on Thursday, April 16th at the usual time 11:00 a.m.
Pacific and then we’re doing Tableau prep webinar giving you an overview of self-service data prep for Tableau on Thursday April 23rd at the same time along with that great Tableau prep class that we just added to our quiver we offer complete BI training across the three major platforms that we support IBM Cognos Analytics, Power BI and Tableau that includes corporate training in person, group sessions, personal mentoring which is really helpful in particular with tools like Tableau and Power BI where once you get off the course materials. It’s really helpful to have an expert like September to be able to coach you through very specific examples that you find in your environment to the aforementioned.
And instructor-led online courses as well as self-paced learning and you can always go to Senturus.com for a lot of great resources ranging from unbiased product reviews to tech tips the aforementioned blog and you can learn more about all things BI we have some live product demos dashboard comparisons across Tableau Power BI and Cognos for example, and you can always see what’s coming up. That’s new and interesting and exciting here at Senturus.
So now that brings us to the Q&A section of the presentation. If you have any questions you want to put into the question pane go ahead and we’ll try to get all to all those in the time we have remaining so September, hopefully you had a chance to maybe take a glance at those but there was one question here where they were asking in your exhibit one of your first examples where when you were doing the LOD.
He’s asking why the ship mode is used as an attribute and not a measure.
So let’s actually go. I’m going to go back to that one.
You’re seeing that the aggregation type is that is an attribute. Yeah, so when we drag that in automatically that’s going to create an attribute that’s not something that we had to designate or anything like that. It is a measure it is a measure but the aggregation type is a tribute. So you’ll notice that whenever you connect to data by default Tableau will set the default aggregation to some you can always change that to average or something else.
Like that, but when you are using level of detail calculations, the default aggregation type is at is attribute and attribute is actually an aggregation type.
I also noticed there’s another questions about could we have used another way of getting a total sales value and the answer is absolutely for a lot of the things that you’ll that you’ll be able to use level of detail or even table calculations for there could be other ways of accomplishing the same thing. And so the beauty of taking the class is will actually show that oh, we could we might have accomplished this in this other way. However, the level of detail gives you this additional flexibility or maybe the level of detail calculation is going to perform better because it’s at the database level, but absolutely there will be alternate ways of accomplishing the same thing.
Let’s see if there’s another someone says hopefully the class will go more slowly than a demonstrates. Absolutely. So right and the webinar, we really wanted to just give you a flavor of what’s possible. And what’s going to be in the class during the class will go really slow step by step. We’ll talk through everything. If there are questions. We’ll talk through all of that. So absolutely the class will go quite a bit slower. Yeah.
We try to convey a lot of information in these and certainly the person who asked this question mentioned there they’re new to Tableau. If you’re new to Tableau, this could certainly be a little overwhelming. So you’d want to start back with one of the easier classes, but we kind of one of our other instructors uses the term. We’re like the Marines where we have no man left behind. So we make sure that everybody gets through the material and understands all the concepts, but this is definitely top of that pyramid in terms of the complexity, but it’s also in terms of the power and the questions you can answer beyond basic questions in Tableau.
This really enables you to go way beyond what you can kind of do out of the box and September makes it very clear. But yeah, we do go slower much slower in the in the class.
So how do you know when to use a load? He’s from a best practices perspective.
So I would say a lot of it is like anything. It depends if you’re if you are working through Performance challenges, you might choose an LOD because the database is doing the work that said table calculate you can create things using an LOD or a table calculation in some cases and we talked about this a lot in the class as well.
But you might use a table calculation because the set of data that you’re bringing back is so small that even it’s computed locally. It’s going to be more efficient and you don’t have to worry about speed of other things that are going on with the database and those sorts of things. So it really it depends on your specific use case. But also you might use an LOD just because it’s more flexible instead of creating ten calculations that are that are various grains of aggregation. You could use an LOD just to simplify things. It’s just a lot more flexible.
So a lot of that will depend on what you’re trying to accomplish swoosh overall with your workbooks that’s a great answer. There’s another question is more specifically about that using that same ship mode the youth. I think you could use the exclude to arrive at the black labels. Oh at I guess those are the labeled the marks should you indicate this so that the user knows what those labels indicate?
Yes, 100% any time you are doing anything that’s slightly out of the box and unless it’s something that your users are incredibly familiar with. They already know because of other visualizations that you tend to put totals at the top. It’s always best to indicate in some way. What those values are and by default you get tooltips for any anything basically that you hover over so you could use a tool tip to indicate that you could have some sort of footnote.
You could have a separate sheet with notes, but absolutely it’s best to always indicate what those what those values are make it really Yeah, we have a lot of on the resource site. We have a ton of webinars and among them are dashboard best practices. And there’s the three second rule where your audience or the consumer of your information should be able to understand what your visualization is saying within about three seconds. Otherwise, you need to go back and simplify it or add more instructions to it. That would certainly fall under that. Can you add comments to your calculations for Developers?
That’s another question. We have you can so you can do a few different things so you can include a calculation in or a comment in the calculation itself. So I could go in and edit this and have some sort of text underneath that explains. You know what this calculation does.
So absolutely I can add comments to a calculation and in addition to that you can add as a fault property you’ll notice that there’s a comet so this comment is actually a tool tip for the calculation, which is really nice. So I might have something previous let them know that I’m using the previous value function for instance something like that. And now when I hover I don’t even have to open the calculation I can see that tool tip pop up when I just hover over that so there are a couple different ways that you can notate. That’s great. Yeah and Tableau is also they’re adding interesting new.
Analogy to the stack including Tableau catalog which will allow you to look at your data and see where it came from. So though you folks familiar with Cognos. You’ll know the Cognos lineage. For example So that’s its akin to that and you’re seeing things like explain data where it will explain the tool explain to you in English terms what the analysis looks like. So you’re seeing more of the AI and machine learning being integrated into these tools.
I’ve seen a lot of you asking for about the recording and the demos and stuff like that. So the recording will be posted to the website the recording takes us a few days to edit it a little bit but we do definitely put it up there along with the deck so you can follow along and most of these courses are available as self-paced or e-learning that you can go through at your own pace. And we even offer all-access passes for say Cognos, or if you want all of our Tableau classes or all of our Power BI class.
Has or all of all of the classes so those are great kind of options for those of you who might be working from home and looking to sharpen the sword or keep your skills up while you’re while you’re stuck at home. So those are all the questions that I see in there were coming towards the top of the hour. So with that I want to thank first and foremost September for a tremendous webinar.
It’s a lot of great info there and thank all of you for taking time out during the challenging times to spend some time with us, and hopefully you learned a lot here today. I hope you’re all being safe and staying healthy and washing your hands thoroughly and frequently, and thanks for joining us, and we look forward to seeing you on the next senturus knowledge series presentation. Thanks a lot and have a great rest of your day.