10 Google Data Studio Interview Questions and Answers

Google Data Studio is a powerful, free tool for transforming raw data into informative, customizable dashboards and reports. It integrates seamlessly with various data sources, including Google Analytics, Google Sheets, and BigQuery, making it an essential tool for data visualization and business intelligence. Its user-friendly interface and robust features allow users to create dynamic, interactive reports without needing extensive technical expertise.

This article offers a curated selection of questions and answers to help you prepare for interviews focused on Google Data Studio. By familiarizing yourself with these topics, you’ll be better equipped to demonstrate your proficiency in data visualization and reporting, showcasing your ability to turn complex data into actionable insights.

Google Data Studio Interview Questions and Answers

1. Explain the primary purpose and key features of Google Data Studio.

Google Data Studio is a free tool from Google designed for data visualization and business intelligence. It enables users to create interactive dashboards and reports from various data sources, transforming raw data into insights through visual representations.

Key features include:

• Data Connectivity: Supports a wide range of data sources, including Google Analytics, Google Sheets, Google Ads, BigQuery, and third-party connectors, allowing users to consolidate data into a single report.
• Customizable Reports: Offers a drag-and-drop interface for creating reports with charts, tables, and graphs tailored to specific needs.
• Interactive Elements: Includes interactive features like date range controls and filter controls, enabling deeper data exploration.
• Collaboration and Sharing: Facilitates easy sharing and collaboration, allowing multiple users to work on the same report simultaneously.
• Real-time Data Updates: Reports can automatically update with the latest data, ensuring access to current information.
• Templates and Community Visualizations: Provides pre-built templates and community visualization features for custom visualizations.

2. Write a formula to create a calculated field that shows the percentage change between two metrics.

In Google Data Studio, a calculated field can be used to create new metrics from existing data. To calculate the percentage change between two metrics, use the formula:

((Metric2 - Metric1) / Metric1) * 100

Here, Metric1 is the initial value, and Metric2 is the new value. This formula calculates the difference, divides it by the initial value, and multiplies by 100 to convert it to a percentage.

To create this field:

• Go to your data source.
• Click on “Add a Field.”
• Enter the formula above.
• Name your new field, such as “Percentage Change.”

3. List and describe three types of charts available in Google Data Studio and their best use cases.

Google Data Studio offers various charts for effective data visualization. Here are three types and their best use cases:

1. Time Series Chart

• Description: Displays data points at successive time intervals, useful for showing trends over time.
• Best Use Case: Ideal for tracking metrics like website traffic or sales over time.

2. Bar Chart

• Description: Represents data with rectangular bars, with length proportional to the value.
• Best Use Case: Suitable for comparing categories, such as sales by region or product performance.

3. Pie Chart

• Description: Shows data as slices of a circle, each representing a proportion of the whole.
• Best Use Case: Best for displaying the composition of a dataset, like market share or budget allocation.

4. Explain how to blend data from two different sources and provide an example scenario where this would be useful.

Blending data in Google Data Studio involves:

• Open your report.
• Add a chart or table.
• In the data source panel, click “Blend Data.”
• Select the primary and secondary data sources.
• Choose the join key(s) to combine the data, typically fields common to both sources.
• Configure the metrics and dimensions from each source.
• Apply the blend and adjust the chart or table as needed.

An example scenario is a marketing report combining website traffic data from Google Analytics with sales data from a CRM system, showing the relationship between online activity and revenue.

5. Describe how to apply a filter to a chart and explain why you might want to use filters.

In Google Data Studio, filters control the data displayed in charts and reports, allowing you to focus on relevant information.

To apply a filter to a chart:

1. Select the chart.
2. In the properties panel, find the “Data” tab.
3. Click “Add a filter” and choose “Create a filter.”
4. Define the filter conditions by specifying the field, operator, and value.
5. Apply the filter to see changes in your chart.

Filters are useful for:

• Data Segmentation: Segmenting data for specific analysis, like filtering by date range or product category.
• Improved Clarity: Creating focused reports by filtering out irrelevant data, highlighting key insights.
• Performance Optimization: Enhancing report performance by reducing data processing.
• Custom Views: Creating custom views for different stakeholders, each with relevant data.

6. Write a formula to create a calculated field that uses a CASE statement to categorize data based on specific conditions.

In Google Data Studio, the CASE statement creates calculated fields that categorize data based on conditions. It defines multiple conditions and returns values based on those conditions, useful for creating custom segments.

Example of a CASE statement:

CASE
  WHEN Age < 18 THEN 'Minor'
  WHEN Age >= 18 AND Age < 65 THEN 'Adult'
  ELSE 'Senior'
END

This categorizes individuals into ‘Minor’, ‘Adult’, and ‘Senior’ based on age.

7. How would you integrate Google Data Studio with Google BigQuery to visualize large datasets?

To integrate Google Data Studio with Google BigQuery:

• Open Google Data Studio and create a new report.
• Click “Add Data” and select “Google BigQuery” as the data source.
• Authorize access to your Google BigQuery account.
• Select the project, dataset, and table from BigQuery.
• Configure data fields and apply filters or transformations.
• Add the data to your report and start creating visualizations.

This integration leverages BigQuery’s querying capabilities to handle large datasets and visualize them in Google Data Studio.

8. Explain how to use date ranges and comparisons in your reports.

In Google Data Studio, date ranges and comparisons allow users to analyze data over specific periods and compare different time frames, aiding in understanding trends and performance.

To use date ranges, add a date range control to your report, allowing viewers to select the time period. Configure the default date range as fixed or dynamic to keep the report relevant.

Date comparisons enable comparing data from different periods, like the current month’s performance to the previous month. Use the Comparison Date Range feature to specify a comparison period, and Data Studio will calculate and display differences.

9. Describe techniques for troubleshooting data discrepancies in your reports.

To troubleshoot data discrepancies in Google Data Studio:

• Verify Data Sources: Ensure connected data sources are correct and up-to-date.
• Check Data Blending: Verify join keys and blending configurations.
• Compare with Source Data: Cross-check data in Google Data Studio with the original source.
• Review Filters and Segments: Ensure filters or segments are correctly configured.
• Examine Calculated Fields: Verify formulas and logic in calculated fields.
• Check for Data Sampling: Ensure data sampling settings are appropriate.
• Audit Data Transformation: Review transformation processes before loading data into Google Data Studio.

10. Explain the importance of data transformation techniques and provide an example.

Data transformation techniques are important in data analysis and visualization for:

• Data Cleaning: Removing inconsistencies and errors to ensure accuracy.
• Data Aggregation: Summarizing data for a higher-level view, like calculating averages.
• Data Structuring: Organizing data for easier analysis and visualization.

In Google Data Studio, data transformation can be done using calculated fields and data blending. For example, with sales data, you might create a calculated field to aggregate sales by month, helping visualize monthly sales trends.

Example:

Suppose you have a dataset with Date, Product, and Sales columns. Create a calculated field to aggregate sales by month:

• Create a new calculated field named “Month” using: FORMAT_DATE("%Y-%m", Date)
• Create another field named “Monthly Sales” using: SUM(Sales)

This allows visualization of total sales for each month, providing a clearer picture of sales trends.