Google Data Studio Tutorial for Beginners🔥

Have you ever struggled to build custom dashboards fast? We have. It appeared that Google Data Studio is a great solution to represent data. Let’s see how it works. Hi! My name is Sergey and I am a Product Expert here at Railsware. The dashboard market is really fragmented and there are plenty of different solutions. Usually you have to choose between heavy BI tools which allow you to do everything, but the licenses can easily cost you 50 thousand US dollars. Or simpler solutions which are quite limited and can’t cover all of your needs. Thankfully, there are easy to use solutions available as well. And Google Data Studio is a great example. This is a platform that allows you to create and share beautiful reports fast and smart. If you run a small or medium size business or a department in a big corporation, this video is for you. Let’s go and build something great together. This is a Google Data Studio tutorial for beginners. Today we will cover the following topics: Data Studio environment setup How to create and manage Data Sources How to build and share Reports As an example, we will build a Recruitment Dashboard from scratch. You will learn: How to connect GSheets to a report What are the Dimensions, Metrics and aggregation functions How to create tables, charts, and scorecards How to build and manage filters Also we’ll review styling basics and the way you can share your report See the full agenda in the video description below. You can switch to a section you need using time stamps. Don’t forget to ask your questions in comments! Data Studio is a part of Google Marketing Platform and it has its own ecosystem. To get started just visit https://datastudio.google.com and sign-in using your google account. If it’s your first visit you will need to set up Data Studio. Press get started Agree to the terms and conditions Leave the company name blank if you use the platform for personal needs Sign up for different news subscription If you don’t have a firm in Europe just say NO If you do have business in EU - press Yes and fill in the needed info. It just takes longer There are four major concepts in Data Studio - Data Source, Connector, Report and Explorer. Data Sources and Reports have many to many relationships. This means that one report can use multiple sources of data, while the same data source can be connected to many reports. Today we are going to play with simple configuration - one report will use only one data source. Please keep in mind that changes in data source apply to all the connected reports. It’s not that evident from the UX perspective. Connector is an interface of receiving data from various platforms (like BigQuery, Youtube, Google Analytics, Facebook ads, Spreadsheets, etc.). While Data Source is a blueprint or a wrapper of a connector that you can modify. There are connectors provided by Google and created by the community. If you need a custom one, you can either create it yourself, or transfer data to the data storage with an existing connector to it. Explorer is a “what you see is what you get” type of Report, which is in beta stage for now. We will review it in our next videos. When you join Data Studio, you can easily navigate between Reports, Data Sources and Explorers using the top menu. You get a few samples of dashboards; you can open them and see what are the possibilities to build your custom report. Today we will build a Recruitment Dashboard using data from our applicant tracking system. The first step is to prepare our data set. With a well organized data you can build a representative dashboard fast and easy. We use Pipedrive as our ATS(Applicants tracking system). But there is no Data Studio connector for this tool yet. This is why we pull data from Pipedrive to GSheets with the help of Coupler.io - a service that securely transfers data between platforms. And we will use GSheets data source to build our recruitment dashboard. Not to spend too much time we have already prepared needed file. With coupler you can select how often you would like your data to be updated (every hour, day, or by request). In our case, daily update is what we need. All new entries will appear on the list after the next daily import. We pull the data from Pipedrive to Applicants tab. And we use Applicants Filtered tab to filter our data we will use for the Dashboard. If you want your reports to work faster we recommend to minimize your data sets. To avoid issues with data, follow a few simple rules when you shape your dataset in GSheets: Don’t use cell merge Keep each data record in a single row Use the first row as a header Each column should contain the same data type and format Go back to Data Studio and create a Data Source. Pick a GSheets connector. It will offer you a list of Google Sheets shared with or owned by you. We select Pipedrive Data Stream Demo and the Applicants Filtered worksheet. Click “Connect”. If everything is successfully connected, you will see the list of column headers from your spreadsheet. Btw in the video description you will find a link to a Google Sheet Pipedrive Data Stream Demo. So you can repeat all the flow I’m gonna show you today. Metrics and dimensions are the building blocks of your reports. Metrics are marked blue, dimensions - green. Metrics are the numbers contained in your data. In our case, it is Salary and Applicant ID. Dimensions are attributes of your data. Those are strings, dates, URLs, locations, etc. Usually, you don’t have to change types of entries as Data Studio detects those correctly. However, in our case country was detected as string. So we change it to “Country”. Also we can spot that Salary is detected as the number - let’s change it to Currency USD. At this stage you can also add calculated fields. We will discuss those in our next videos. Name this datasource “Applicants Stream Demo”. Once done, create a report. Select a name for your report. Let’s call it “Recruitment Dashboard Demo”. Here we have our working canvas, properties panel, and a toolbox. On the top you can find control panel with refresh data, share report, and change between view and edit modes. You can create multiple pages for your reports by adding a new page. Use the top left control to switch between pages. You can duplicate and hide pages in the view mode. Property panel reflects the properties of objects. As we don’t have any objects selected, we can see the layout and theme settings for the whole report. Select the header visibility. It is pre-set to “Initially hidden”. If you go to the view mode and want to use the control panel, you need to hover over it. If the header is pre-set to “Always show”, it will be visible at any time. This is a better option for editing. Once done, switch back to “Initially hidden”. In the view mode, you can see the list of your pages in the top left corner. Switch between pages using arrows, or click in the center to select from the dropdown list. You can also select the navigation to be represented on the left so that you can see the list of pages like this. You can also change canvas size for your convenience. Let’s create a table with candidates list, their locations, and salary data. Add a simple table by clicking “add a chart” in the menu and selecting “simple table”. Data Studio is trying to shape a basic table out of random columns. We need to select the correct ones now. Let’s say we want to see the list of candidates’ with their name, position, application date, responsible recruiter, location and stage of the process. Let’s start. In fact, all the fields we’ve mentioned (except for salaries) are Dimensions. So we work with the dimensions field in the properties panel. Use “Add dimensions” to see the list of options. We pull Application Date, Applicant Name, Position, Stage Name and Owner Name to identify the responsible recruiter. The OwnerName appeared without a space + It’s better to call it “Recruiter Name” as we need to keep a consistency in Data Source naming Let’s edit it. Go back to the spreadsheet, find the column, and rename it to “Recruiter Name”. If you changed names of columns in the original file, you need to update the data source. In Data Studio, click “Edit data source” in the Properties Panel. Or Resource -> Manage added data sources -> choose the needed data source -> click edit. You can see a button “Refresh fields”. It shows recently added fields and the missing fields. Click apply. And our table is broken now. This happened because we refer to the Dimension “OwnerName”. Data Studio is not yet that smart to replace one dimension with another. So we need to remove missing dimensions and add the new ones. Data Studio highlights invalid dimensions. Remove them. Now the table is fixed. Add “Recruiter Name”. Edit names without changing Data Source Adjust the size of your table and the width of every column. Click on any vertical border and drag it. But it’s easier to double click on any vertical border, and Data Studio will adjust the column size to fit text automatically. Change the order of columns by dragging and dropping dimensions in the properties panel. As you can see, data studio have automatically added one metric. You might be confused by seeing SUM applied for Applicant ID, as it doesn’t have a lot of sense. We change Applicant ID to Salary Expectations. It still has the SUM stated. This is an aggregation function. We have only 1 salary record for each row. So the SUM and AVERAGE are equal, just as MEDIAN, MAXIMUM, and MINIMUM values. You can choose between these options in case you work with aggregational tables. We will show how it works a bit later today. For now, we select SUM here. You might have only SUM stated in the list and you can’t use other aggregational function. This is because fields editing in reports might be disabled. If you have such a problem, edit data source in the properties panel, and turn on this option. Now you should see all the functions in the list. You can select how many rows you would like to see per page. You can see a summary row, but it doesn’t make sense in our case. You can Sort by metrics or by dimensions. Click on the property and select the dimension to sort by. We will use application date. We want to see the newest applicants first, so let’s select descending order. Let’s also select a secondary sorting dimension. We want to also sort by position. Now we can see the applicants sorted by position and by date. You might want to change the name of columns right in Data Studio for a specific table. Go to dimensions -> select dimension to edit -> change “Salary expectations” to “Salary”, “Recruiter Name” to “Recruiter”. Let’s switch to the view mode. Click 3 dots, here you can sort the table as you want, as well as export this table. To remove row numbers go to Style -> Table body -> Turn off row numbers. In Style, you can also remove headers, wrap text, change text styles, make headers bigger, change alignment per each column, and adjust styles the way you like. Now we have a simple table representing our data. Let’s create a salary aggregation table with more extensive calculations to calculate the minimum, maximum, and median salaries per position. First, make the current table a bit smaller. Add one more chart -> table with heatmap. As always, Data Studio takes random values to represent here. We want to see stats per position. Select Position in Dimensions. Remove dimensions you don’t need. Here is a simple heatmap. Now it represents sums of salaries that make no sense for us. Let’s start with Average. Rename the column to “Average” and choose the proper calculation. Now we need to calculate Median. For this, add one more metric - it will also be a salary, but represented in a new column. We select median function and rename metric to “Median”. Now, we select salary metric again -> select minimum function -> and rename it as well. We do the same for Maximum. Adjust the table size. We also want to see the number of applicants per each position. Select Application ID in metrics and (->) count function. Now, we can see how many applicants we had per each position. Rename the metric to “Applicants Count”. Sort by count. Now let’s move the count column closer to the position name and sort by this metric I personally don’t like all those colors. So let’s apply a bit of styling. Go to Style. Here you can see that each column has its own color. I choose this color for better readablility, and select it for all columns. Keep a different color for count (as it doesn’t calculate salary metrics, but the number of applicants). Adjust the size of each column to make them more readable. And here are our salary stats! The Calculations table is quite useful, compact, and easy to work. But how to work with the 2nd table? It’s pretty huge and unhandy. We can use filters to navigate through the information. There are different types of filters: Those applied locally to a single table/chart or any other control that represents data And those applied to the whole page. Let’s have a look at these first. In the top menu select the Date range. It can be useful to get stats for a сertain period of time (this month, quarter, year, or a custom timeframe). Click on the date range and drop it on our page. By default, it takes auto date range that we can use. Let’s create a default date range “this quarter” and press apply. This filter has been applied to both tables. We can see less positions listed. In the Data table we can see that in the last quarter 354 applicants applied. Now, let’s see how it looks in the view mode. You can select a different time frame (this year) - and apply. Both tables have been updated. You can use a preset or a specific start and end date. Now, click filter control and drop it to our board. It automatically picks applicant ID (that doesn’t make sense). I would like to change it to country. Select it in the Dimensions field. The filter has also showed a default SUM. We are interested in COUNT of applicants per country. We select Applicant ID metric, Count function, and now we can see the number of applicants. Let’s use the country filter. Say, you want to see candidates from Singapore only. Hover over it and click “Only”. Now you can see all candidates from Singapore and the stats updated according to the specific country and timeframe (remember that both filters work together!). Let’s add one more filter to see how filters work together. We change dimension to Recruiter Name, rename it, select COUNT right away in metrics. Go to the view mode, and see the Recruiter filter. You can see that Leslie Winkle had this number of applicants. If you apply Poland, you’ll see that the total number of applicants per recruiter has changed. So now you can have an intersection of a specific Recruiter and the country Poland. You can choose a specific timeframe, and see that the number of Polish applicants processed by Leslie Winkle has changed as well. Imagine you need to see applicants who are now at the Profile Review stage. Select the table, go to the properties panel, select “add a filter”, name it “Profile Review Stage” -> pick “Include” -> select dimension “Stage” -> equal to -> “Profile review” -> Save. Keep in mind that this filter is not applied to any other charts and tables, it works only for this table. Use the OR logic and combine 2 or more values to match. Or switch to “in”, and write a list of options separated by commas. You can use the AND operator as well. You can make filter interaction even cooler. Filter the whole page by making a certain table behave as a filter. It makes sense to use this Calculations table as a filter. Select the table, scroll down in the properties panel, and tick “apply filter”, go to the view mode, now click on each position in the aggregation table, and you’ll see that the second table changed. It works together with other filters we have just set up for the whole page. I like when things look cool, so let’s add styling. Add a rectangle area, place it in the top to mark the filters area, send to back. To make filters visible - color the text white and remove a border shadow. Now it looks fancier. We can select multiple properties and change style for all of them at once. You can sort information by specific columns by clicking on the headers. To get back to default, click on the arrow up here. Let’s make our report even more informative. Clone this page. Go to Page -> Duplicate page. Let’s rename the first page to “Candidates”, and the second one to “Geography”. In the Geography page, remove the table with data and add a chart -> Geomap. Adjust the size of the map. You can see that Data Studio has correctly picked the dimension - country (as we identified it from the very beginning). Select the COUNT function for Applicant ID metric. Now, we can see countries highlighted according to the number of applications received in a selected timeframe. Try to use filters. Select “United States only”. And you’ll see the US highlighted only. You can hover over each country and see the metrics. As for any other table or chart, all the filters apply in a similar way. If you select “frontend developer”, you will see countries where we got applications for this position. Apply filter on map, click on the US, and you’ll see stats only for the US. Click again to get back to all stats. You can use zoom area (World, Continent, or Subcontinents). For example we can select America and see America only. Let’s go to the list of pages, duplicate Geography page, and rename it to “Charts and score cards”. Here we remove map, but leave the calculations table. Add a chart -> Scorecard Scorecards show one aggregated metric based on the filtered results. Let’s show the number of applicants on the scorecard, and compare it to the previous period. Select Applicant ID, COUNT function, rename to “Applicants”. You can apply a comparison date range (let’s choose for previous period). If we have this month selected, the comparison range will be the previous month. Let’s filter this year, and we’ll see that we had more applicants compared to the previous year. Let’s tune the style a bit. If you don’t want to see percents, but absolute changes - go to Style and tick absolute change. Align in the middle. Change the color of comparison numbers. I like when scorecards have borders, so I use colors and border styles to change them. Now we have a beautiful scorecard. Let’s see the proportion of applications per position from the total number of applications. Let’s use the Treemap. Select Position in Dimensions and COUNT for Applicant ID in Metrics. Let’s add a 2nd Dimension - Recruiter Name, and turn on “drill down” option. Select 1 level to show. Go to the view mode -> Select Data Analyst, and click the arrow down to drill down the number of applications processed by each Recruiter. We can see that most of the Data Analyst applications were processed by Leonard. Now, let’s use a bar chart to see how many new applications we received per day. Choose the bar chart. Dimension - Application Date. Metrics - Applicant ID with COUNT function. Sort by Application Date (select ascending order). Even if we have a larger date range selected, the chart shows only 10 bars. To change this, go to Style -> Amount of bars -> clear number to see ALL the bars for the selected time range. Back to Data, and let’s select a breakdown dimension - Position. Now, we have a separate bar for each position per date. However, we want all the bars to be stacked. Go to Styles and tick stacked bars. Now, all applications per specific date are divided by position and appear in a single bar. Let’s select a shorter time range - for example 1 month. When you hover over a field - you can see the number of applications per position. Let’s adjust the size of this chart. In the view mode, when you click on the position, the bars with applications for this position will get marked. We can also add a simple line chart to see the overall number of applicants per day. Don’t forget to change the naming of dimensions! Select Application Date in Dimensions and Applicant ID with COUNT function in Metrics. When you select a filter, it will be applied to all the graphs, tables, and scorecards. Let’s take a quick look at the objects that can make your reports even more impressive. Add a new page. In Data Studio, you can type simple text and stylize it You can Insert images (logo) Or you can embed a web page right in your report (Visit railsware.com). You can even scroll the website right in Data Studio. Change the Theme to Simple Dark. Or get back to the light theme. Data Studio has many more helpful features to use. Today we showed the very basics. For more tutorials about Data Studio subscribe to our channel. If you found this video helpful - press like. Visit railsware.com to learn more about our services and products. We help startups to build great data-driven software solutions. This is all for today. Thank you for your attention and see you next time!

Channel: Railsware Product Academy

Views: 330,769

Keywords: google dashboard, google data studio, data studio, data studio google, google data studio tutorial, google sheets dashboard, data studio tutorial, what is data studio, data studio google analytics, data studio dashboard, data studio connectors, data studio report, google data studio stacked bar chart, data studio geo map, data studio examples, data studio map, data studio filter control, data studio google sheets, data studio filters, data studio charts, railsware, coupler.io

Id: 1qGsjmmHiu8

Length: 27min 51sec (1671 seconds)

Published: Tue Oct 08 2019