Data visualization with Google Data studio
Everyone wants to be heard and understood; Nobody likes a gap in communication.
Communication is achieved when the message passed by the sender is received by the recipient. There is a problem when you are trying to pass a message and the receiver is not comprehending, its called a communication gap. A lot of things can cause a communication gap, but one major reason can be using the wrong tool or medium to communicate.
No matter the type of communication — brand, personal, corporate etc — text is gradually moving into “wrong tools” to use, especially on data-driven messages.
Visuals are the “new in”… That’s why you see the rise of video and picture content, cause nobody wants to read text anymore. We all want to get the information in one view or one watch. The attention span of people online is reducing drastically, customer attention is currently one of the greatest and scarcest assets online, any information that does not attract with the first 10 seconds gets booted out. So you can see why it’s difficult for text contents to stand out, not impossible but difficult.
The way out is visual contents, some example of visual contents by Content marketing institute are quotes, infographics, gifographics, Gif, videos, screenshots, Slideshare presentations, photos, illustrations, flipbook, and data-driven visuals.
In this article, we will focus on data-driven visuals. Data-driven visuals can be designed as charts or graphs. It makes it easier for your audience to comprehend your data than with text alone.
Having a good visualization of your data can help pass the story behind the numbers effectively. Instead of showing a bunch of numbers, you can use charts e.g bar chart or line chart to communicate the data trend over time or the relationship between two metrics or dimension.
The goal of data visualization is easy comprehension. Meaning people should not look at your report and still come back to ask you what does this numbers mean, except in some extreme cases. Your report should be easily understood by anyone in your organization or outside your organization.
So 3 crucial things to note when it comes to data analysis; Data collection, Data Storage and Data visualization/report. Google tag manager is one best way to collect data from your websites, apps etc. While Google Analytics is good for storing data and Google Data studio is good for reporting or visualization.
Google data studio is a free data visualization product from Google. This product is amazing because you can connect to tons of different data sources, both for Google products like Google Analytics, Google Ads, Google Search Console etc, and for non-google products like adobe analytics, Facebook ads, constant contact etc. So no matter the software you are using as a business to collect and store data, you can integrate it to google data studio and start reporting those data from there. Google data studio launched its beta in2016 and officially launched in 2018.
Overview of Google data studio
Once you are signed in to Google data studio, you need to first connect your data source. Data source refers to where your data is coming from. For example, if you are carrying out a CPC campaign and you want to report on it, you can use Google ads connector, since data from your campaigns are stored there. Or you are using Adobe analytics instead of Google Analytics to record and store your online and offline data.
In the create section you are given the option to create a report, data source or explore. If you are just starting out, you might see the onboarding information to take you through google data studio and to add your data source. Once you click on add data sources, you will be taken to where you will see the connectors available in google data studio. Add your data source and start creating your report.
Note: You can add more than one data source both in a page or in a report, so you are not only restricted to selecting one connector.
Just like Google analytics, Googe data studio make massive use of dimensions and metrics. Dimensions are basically what you want to show, it can be a number, text, date, true/false, geo, currency etc, examples of dimension are device category, page views, source/medium. While metrics are basically what you want to calculate, it can number, percentage, duration, currency etc, examples of metrics are users, sessions.
Building your first chart
You can build different types of chart in Google data studio, charts available in Google data studio are tables, scorecards, pie and doughnut chart, scatter plots, pivot tables, bullet, treemaps, and time series chart. In GDS you can use from any of these charts for your reporting.
But before you jump into using any chart you need to ask yourself what message do I need to pass and which of these charts will help me communicate the message effectively; for example, you can use a line chart to emphasize on trends, a bar chart is effective in showing the difference between 2 or more dimensions, or when you have limited space in your report you can use sparkling to convey a lot of information with limited space.
So it is ideal to have an understanding of the type of report you want to give before actually building your report in GDS.
Adding colours
One important data visualization principle to note is not to depend too much on colours in differentiation, as 10% of the population is colour blind — they can’t tell difference between the colour red or green — So when you have your report that is differentiating your device category with colours alone and not text, the difference will not be known and your report will not pass the needed information it was created to pass.
Another thing to note when playing with colours is that black and white printers are colour blind, so if your report is printed out in black and white then the colours added to that report will be useless. So make sure you add both colours and text for easy identification.
Ways to use colours
- Create differentiation with brand colours: If you are trying to communicate social media data, you can use social media colours, i.e Facebook as blue, and Whatsapp as green. This can add a nice feel to your reporting and aid in easy readability.
- Call out a data point for extra attention: For example, if you want to emphasize on numbers more than 1,000 or pick out all source that has Google, then you can use colours to differentiate them and call them out. Conditional formatting allows you to call out different things in your data.
Data visualization is very important, so before you dive in, make sure you have all the right information, and that you have an understanding of the kind of message you want to pass through your report. That’s all from this week. See you next week, as we do a more deep dive in data visualization.
