Finished week six with an interesting course in the CXL mini degree program.
The next course in this mini degree program was about Google Data Studio.
It is a free product, a data visualization product perhaps referred to as Google’s version of Tableau. It’s web based. It connects to a lot of different data sources. So it natively connects to a lot of Google data sources, but it also connects to other data sources if you’re a user of something like Amazon eCommerce Data or Adobe Analytics Data. You can use those as well within Data Studio through some third party and partner connections. You don’t have to use Google Analytics or any Google product. You could literally just use Data Studio on top of flat files that you get from some other place or other database systems. It launched as a beta in mid 2016,so it’s only been around a couple of years. It was considered to come out of beta in 2018.Now this last part is kind of important. It is also constantly changing and being updated. Couple of things to be aware of, so when I say it’s free it’s like it’s literally just free. There’s not a paid version or a 360 upgrade sort of similar to the way there is Google Analytics. You don’t have to be say a Google Analytics or a GMP 360 customer to use it. And really importantly because it’s always changing and updating I will bet you a thousand dollars that perhaps by the time you view this course there’s going to be something that I say, “Oh, unfortunately that’s not possible.” And you’re going to be like wait that totally is possible. Because the product will have already added that feature. So I’m choosing to call that really exciting because what isn’t possible today might be possible tomorrow. So our first lesson is going to get us started. So in this lesson you are going to get an overview of the Data Studio interface. You’re going to get an introduction to the types of data that we can use. We’ll also check out how you can quickly explore data and see what’s going on in your data source. Look at just some basic functionality. We’re also going to discuss a fundamental principle of data visualization, that underpins our entire course and a lot of the decisions that we’re going to make when we’re building our visualizations. Before you do anything else, if you don’t have access to another Google Analytics account I would encourage you to go and get access to the Google Analytics demo account. So this is a fully working version of Google Analytics. It’s actually from the Google merchandise store. You’ll be able to use with Data Studio when we go through our training and our exercises. Now if you have your own GA account you’re welcome to use that instead. That’ll work fine, but this gives you for sure something that you have access to. We are going to be using Google Analytics as an example data set that we’re going to work with. There’s lots of others available, but it’s just a really good foundational starting point to teach us how to use Data Studio. So let’s do a quick overview. We are going to take a live look at the UI and see some of the features that you’ll find when you first log in. So here we have the home screen for Google Data Studio. So on the left hand side you’re going to see your quick menu where you can either just go in and create a new report or you can view recent ones. Things that people have shared with you. That works really similarly to Google Drive, where it’s like stuff people shared with you shows under that.
Now we get to talk about the really exciting stuff. In this lesson, we are going to dive into the different visualizations that are available in Data Studio, how you can use different visualizations to convey different messages, you know, get your audience to see what you see in the data, how different chart types might work together to paint a really complete picture of your data, and how we can customize them to ensure that they’re communicating our data well. So let’s build our first chart. For starters, some of the available data visualization in Data Studio include things like tables, scorecards, which essentially just like a single number. Line charts, so you can have, like, time series and other kinds of line charts. Bar, column, area charts, pie and donut charts, we’ll talk about those later. Maps, and then some admittedly I think often lesser-used charts like scatter plots, pivot tables, bullets, tree maps, et cetera. We’re not going to have a chance to go through every single one of these charts. You’ll probably explore some of them, you know, at a later date but we’re going to talk about the ones that are most heavily used and how you can customize them to best convey your data. There are also community visualizations to be aware of so you can explore those in your own time. People can build visualizations if they don’t exist in Data Studio. You can build them and then share them with others, so that’s pretty cool. It’s really important before you just go and build a chart to have a think about what the right kind of chart is. So a good resource for that might be to have a look at Extreme Presentation. They have this kind of chart selector based on the type of data that you’re trying to chart.
It is a relatively easily understood colour scheme. But you do have to do the sanity check. You need to make sure that, if somebody’s colour blind, that they’re still going to be able to understand your visualization, and if something is printed in black-and-white, that it’s also going to come across. Also, if you happen to work for a company that has offices in a lot of different places in the world, there can be regional differences in how people interpret colours that you need to be aware of. So for example, red can be both mourning and celebration. Green can be a environmental colour. It can also be the color of death. So depending on where your data is going, red and green may not just be the default that you want to run to. So in Data Studio we have some options. So we can control our color palette. We can pick what some of our primary colors are. And there are some really good, very intuitive ways that you can just choose to use color that are going to help your end-users. So one is to use brand colors. When we look at a chart, Facebook blue, YouTube red, for example, we immediately understand that we don’t need to necessarily even really have as much of a legend. If you were to flip those and you were to put YouTube in blue and Facebook in red, people are really going to struggle and they’re probably going to misread the chart and think that they’re the other way. Another way I really like to use color in Data Studio is to denote different data sources. So in the example of this report, this report is pulling from a couple of different places, but two main data sources. One of them is Google Analytics and one of them was our back-end data. So what we did was we made it clear, where anything that was pulling from Google Analytics is going to be the blocks that are in orange and anything that was pulling from our back-end data source was going to be in grey. Where they’re on the same line chart, we have grey and orange so that we can easily tell where this data is coming from.
There might be times that I talked about, there’s a data source as a thing in Data Studio. And then there’s like the source of your data, like where it’s coming from.
We are going to talk about how you can share access with your data sources and your reports so that others can either view or edit them. So Data Studio works pretty similarly to Google Drive permissions. If you’ve used Google Drive permissions for other kinds of documents it’ll seem pretty familiar, where you can set things so that they’re view able to whoever is in your organization, if you happen to be a G Suite customer, or you can share specific links or just share with specific individuals. Keep in mind that some of your, if you are a G Suite customer so G Suite being corporate Gmail and Calendar and all of that, some of your organizational settings may affect how Data Studio sharing works. So if there’s something where you’re like, “Oh, I thought that was supposed to be possible, “but it doesn’t seem to be, “it also could be your org settings. But you can share your Data Studio reports and data sources with others so that they’re able to edit it so that they’re able to view them.
In this lesson, we are going to talk about some cool uses ,some creative hacks and some ways to build visualizations that might come in handy for you. The first one that I want to talk about is how we add annotations. Very often, you need a way to annotate or explain your data. Google Analytics has annotations, but you can’t pull them into Data Studio. You could add a text box for example and say, we had this happen on the homepage, but it’s going to become very quickly out of date, it’s just really kind of cluttered, and it’s not going to scale because people are going to be seeing annotations from the first of August when now, we’ve already rolled around to another time period. The way that I like to deal with this is actually by using the Google Spreadsheets Connector and I create a Google spreadsheet that contains my annotations. At bear minimum, you’re going to have the date, what the comment or the context of the annotation is, who added it is typically a helpful thing to include, and then just a dummy metric, a count of one, it doesn’t really matter. A tip here, you can literally select and copy-paste all of your annotations out of Google Analytics, plunk them in a spreadsheet, and that’s that. Then, you make your spreadsheet a data source. You add a data table to your report and you tell that data table that our date field, the date that the annotation took place, that’s our date as far as Data Studio’s concerned. Now, if you have a date selector on your page, it is going to pull the annotation for that time period. If I have a specific month, it is only going to show me users the specific annotations that are relevant for that time period. It’s really, really handy.