Photo by Stephen Dawson on Unsplash

Data is everywhere, so we have to bring it to life through immersive storytelling. Within this article, I would like to focus on starting data visualization using Data Studio.

Enjoy reading !!

Data Visualisation

Data Visualisation is the presentation of row data in graphical format, which helps in data interpretation and retention by getting meaningful, emotional, and engaging stories to key decision-makers.
When we have a lot of data to analyze, we cannot spend days or weeks analyzing millions of rows in Excel spreadsheets to find hidden trends and insight. We need a tool that allows us to quickly make sense of data and determine patterns and anomalies which are otherwise extremely hard to detect promptly. This is where data visualization tools like Google Data Studio come in handy.

Data Studio

Data Studio is a powerful tool for analyzing data. It’s a cloud-based application proposed by Google: https://datastudio.google.com/.

The first interface of Data Studio

To work with Data Studio, we must keep in mind that we first have to pull the data from a data platform (like Facebook, Google Ads, Google Drive…) or a data source into a spreadsheet (like Google Sheets or Excel) using a connector to manipulate it there. Then use that data in Data Studio to make reports and visualizations.

  1. Data Source Schema :

A data source connector allows to establish the connection between the data source and data set, the dataset is the data source schema that can be transformed into reports

1. Click on Data Sources button
2. Data source schema editor
  1. Click on the Data Sources button
  2. Data source schema editor
  3. Data studio logo: to navigate to the home page
  4. Data studio schema name: double click to change it
  5. Data studio schema credentials: who should be able to access data
  6. Data freshness: how up to date the data in the report is
  7. Community visualization access: to display data from the data schema
  8. Field editing in reports: allows or stops editors from changing data schema fields at the chart level
  9. Copy report: to make a copy
  10. Data source version: view or restore previous versions of the data schema
  11. Share data source: share the schema with other users
  12. Help: getting help on google data studio
  13. Switch account
  14. Create: create a new report from the data source schema
  15. Create a new exploration from the data source schema
  16. Add a parameter: allows to pass user-supplied data to calculated fields or connectors
  17. Add a new calculated field: performs actions to other fields in the data source schema
  18. Filter Data by viewer’s email address
  19. Edit data studio schema connection
  20. List of the data schema fields: dimensions and metrics are all considered as fields in the data studio schema
  21. The types of the data studio schema fields
  22. Default aggregation used for every field: the aggregation is the process of summarizing tabular data (sum/total/average/max/min)
  23. Optional description of the schema fields
  24. Search for fields
  25. List of all fields: dimensions are green
  26. List of all metrics: they are blue
  27. Refresh the data source fields
  28. Number of fields

2. Data Studio Charts

Data Studio provides several chart types, such as time series, bar charts, pie charts, etc. Charts derive their data from a data source. They display one or more axes of information (dimensions) and the actual values contained by those dimensions or metrics. To add charts to our report, we have to click on “add chart” to select the type we want :

  • Time-series: visualize data points at successive intervals of time
  • Pie chart: express portions of a whole, using arcs or angles within a circle |Discrete or categorical data
  • Bar chart: To express larger variations in data, how individual data points relate to a whole, comparisons, and ranking | Discrete or categorical data
  • Google map: To show items on a background that is often, but not always, geographical.
  • Line chart: To express minor variations in data | Continuous data
  • Area: To summarize relationships between datasets, and how individual data points relate to a whole | Continuous data
  • Scatter: To represent values for two different numeric variables.
  • Pivot table: An interactive way to quickly summarize large amounts of data, it’s used for querying large amounts of data in many user-friendly ways.
  • Bullet: To visually track performance against a target, displaying results in a single column.
  • Treemap: A method for displaying hierarchical data using nested figures, usually rectangles.
  • Gauge: use needles to show information as a reading on a dial.

Brief, Data studio is more than a simple data tool, it can make data visualisation easy, effortless, and efficient.



Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store