Data Blending with Looker Studio

Data Blending with Looker Studio

Data blending with Looker Studio involves pulling data with same dimensions from the data sources and blending them according to a common reference point.

By: Mussarat Nosheen | 9 mins read
Published: Jun 7, 2024 8:46:46 AM | Updated: Jun 16, 2024 04:51:25 AM

Have you been wondering about using data from different sources to create a single unified dataset but don’t know how?

Data blending with Looker Studio is the easiest route to achieve unified data. You can use this in further calculations, analyses, and visualizations. 

But first, let us understand data blending. 

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What is Data Blending?

Data blending combines data from multiple sources to create a unified dataset within a single visualization or report. 

This technique allows users to draw insights from a comprehensive view of their data, overcoming the limitations of isolated data sources. 

In Looker Studio, data blending lets you seamlessly merge information from diverse data streams, such as Google Analytics, Salesforce, and SQL databases, into a cohesive and actionable dataset.

With data blending, Looker Studio provides a powerful tool for users to analyze relationships between different data points, identify trends, and make data-driven decisions without complex data warehousing solutions.

What is Data Blending?

If you find data blending or visualization overwhelming, consider hiring a data visualization consultant

Benefits of Data Blending in Looker Studio

Data blending in Looker Studio is a powerful feature that allows users to integrate multiple data sources into a comprehensive view. This capability unlocks numerous benefits for businesses looking to use their data more effectively. 

Benefits of Data Blending in Looker Studio

Here are some of the key advantages:

  1. Holistic Data Insights

Data blending enables the consolidation of different data sets, providing a holistic view of your business metrics.

Businesses can better understand their performance across channels and touchpoints when combining data from multiple sources like Google Analytics 4, CRM systems, and social media platforms. 

  1. Enhanced Decision-Making

With blended data, businesses can create more robust and informative reports highlighting correlations and trends that might not be visible when data sets are analyzed separately. 

This enhanced visibility supports better decision-making by offering a complete picture of your operations and customer interactions.

  1. Time and Resource Efficiency

Blending data in Looker Studio eliminates the need for manual data integration, which can be time-consuming and error-prone. 

Automated data blending saves time and resources, allowing your team to focus on analyzing data rather than preparing it. 

This efficiency is particularly beneficial for businesses dealing with large volumes of data from multiple sources.

  1. Customized Reporting

Looker Studio allows users to create tailored reports that meet specific business needs.

Businesses can use the blended data to customize their reports to include only the most relevant metrics and KPIs. This customization ensures that the stakeholders have access to the information they need, presented in the most useful format.

  1. Improved Data Accuracy

Data blending helps to improve data accuracy by cross-referencing information from different sources. This cross-verification can highlight discrepancies and ensure the reported data is consistent and reliable. 

Accurate data is crucial for making informed business decisions and maintaining trust in your analytics.

  1. Flexibility and Scalability

Looker Studio offers flexible and scalable data blending capabilities, making it easy to adjust and expand your data sources as your business grows. 

Whether you are integrating new marketing platforms, sales tools, or customer feedback systems, Looker Studio can handle the increasing complexity of your data landscape without compromising performance.

  1. Enhanced Collaboration

With centralized blended data in Looker Studio, teams across different departments can collaborate more effectively. 

Shared access to comprehensive data sets fosters better communication and collaboration, as all stakeholders work from the same data foundation. 

This shared understanding is critical for coordinated efforts and unified strategies.

Tools and Resources for Data Blending

Using the right tools and resources for data blending in Looker Studio can significantly enhance your data integration and reporting capabilities. 

Here, we explore the features within Looker Studio and some external resources and plugins that can elevate your data blending efforts.

Looker Studio Features

Looker Studio offers a range of features specifically designed to facilitate seamless data blending:

  • Data Source Blending: This core feature allows you to combine multiple data sources into a single dataset, enabling comprehensive analysis across different data points.
  • Custom Fields: You can create custom fields to calculate new metrics from your blended data, enhancing the depth of your insights.
  • Interactive Dashboards: Looker Studio’s interactive dashboards support real-time data visualization, making it easier to explore blended data through filters, date ranges, and drill-downs.
  • Visualization Tools: A variety of visualization options, such as charts, tables, and graphs, help in presenting blended data in an easily digestible format.

Looker Studio Integrations with other Data Tools

Looker Studio seamlessly integrates with numerous other data tools, enhancing its data blending capabilities:

Integrations with other Data Tools

  • Google Analytics 4 and Google Ads: Direct integration with Google’s suite of tools allows for easy blending of website analytics and advertising data.
  • BigQuery: Connect Looker Studio to Google BigQuery for powerful data analysis capabilities, especially useful for handling large datasets.
  • Cloud SQL: Integrate with Cloud SQL for advanced data management and blending from multiple databases.
  • CRM Systems: Easily connect to CRM systems like Salesforce to blend sales and customer data with other business metrics.

External Resources and Plugins

Several third-party tools can extend the data blending functionality of Looker Studio:

  • Supermetrics: Supermetrics simplifies the data extraction and blending from various marketing platforms such as Facebook Ads, LinkedIn Ads, and HubSpot.
  • Funnel.io: Funnel.io automates data collection from multiple sources, enabling efficient blending and reporting in Looker Studio.
  • Stitch: Stitch is an ETL tool that helps to move and blend data from various sources into Looker Studio seamlessly.
  • Google Sheets Integration: Use Google Sheets as an intermediary to blend data from unsupported sources, utilizing the flexibility of spreadsheet functions for advanced data manipulation.

How to Blend Data in Looker Studio?

Blending data in Looker Studio allows you to create comprehensive reports by combining multiple data sources into a unified view. 

Here’s a step-by-step guide to help you through each stage.

Preparing Your Data

Before we go for data blending in Looker Studio, we must ensure that all the data sets are compatible and noise-free. 

Ensure Data Compatibility

Before blending data, it’s crucial to ensure that the data from different sources is compatible:

Prepare your data to blend in Looker Studio

  • Verify that the data types (e.g., dimensions, dates, numbers, text) are consistent across the sources you plan to blend.
  • Ensure that the data is at the same level of granularity. For instance, sales data aggregated by month should be blended with marketing data aggregated similarly.
  • Use unique identifiers (like customer IDs or product codes) to match data across sources accurately.

Cleaning and Transforming Data

Data cleaning and transformation are essential steps to prepare your data for blending:

  • Ensure there are no duplicate records in your data sets.
  • Address any missing values by filling them with appropriate defaults or removing incomplete records.
  • Make sure date formats, currency symbols, and other elements are standardized across all data sources.

Creating a Data Blend with Looker Studio

This section discusses the various steps in creating a data blend in Google’s Looker Studio. 

Step-by-Step Guide to Setting Up a Data Blend

  • Log in to Looker Studio and open the report where you want to blend data.
  • Click “Add Data” and select the data sources you want to blend.

Step-by-Step Guide to Setting Up a Data Blend

  • Select the charts to blend, then go to the menu and click “Blend Data” to begin a new data blend.

  • Determine the fields that can act as a common reference point (e.g., date, product ID, user ID).
  • Configure the join by selecting the common fields from each data source. 

Join configuration in Looker Studio for data blending

  • Looker Studio supports various join types including: 
  • Left Outer Join: Combines data from the leftmost data source with data from the right sources sharing the same key values. If the primary metric has gaps, include an additional metric to populate missing days with zeros.
  • Right Outer Join: Combines data from the rightmost data source with data from the left sources sharing the same key values. If the right data source lacks data for certain values, the blended data will also lack those values.
  • Inner Join: Reports only the rows with matching values in the left and right data sources. Rows without a match in both sources are excluded. Use this join when you need data strictly present in both sources. 
  • Full Outer Join: Includes all matching rows from left and right data sources, ensuring no data is left out, whether it matches or not. Use this join for a comprehensive view of all data. 
  • Cross Join: Produces a Cartesian product of the left and right data sources, combining every row from the left source with every row to the right. Every possible combination of rows from both data sources is reported resulting in a huge data set. 
  • Finally, set the joining conditions. These are common fields or sets of fields in each table, used for connecting data. And you have successfully blended data.
  • For example, to visualize customers, orders, and items in a single chart; identify the common fields like customer IDs, order numbers, or product IDs, in every table; to establish the join conditions that link the data across these tables.

Blended data in Looker Studio

Using the Blended Data 

Now that you have blended data at your disposal it is time to put it to good use. 

Creating Charts and Tables with Blended Data

Once your data blend is set up, you can use it to create informative charts and tables:

  • Click “Add a Chart” in Looker Studio and select a suitable chart type that best represents your data (e.g., bar chart, line chart, table).
  • Select your newly blended data source for the chart.
  • Choose the appropriate dimensions and metrics to visualize your blended data effectively.

Best Practices for Visualization

Here are a few best practices to make the best of your data visualizations using blended data. 

  • Keep it simple and avoid cluttering your visualizations with too much information. Focus on key metrics and trends.
  • Ensure all chart labels and legends are clear and easily understandable.
  • Use a consistent color scheme to differentiate between data sources or categories.

Example Visualization and Dashboards from Blended Data

Here are a few examples of how the blended data can be visualized and used in custom dashboards:

  • Sales and Marketing Dashboard: Combine sales data from your CRM with marketing spend data to visualize the return on investment (ROI) across different marketing channels.
  • Customer Journey Analysis: Blend web analytics data with customer support data to understand the customer journey from acquisition to retention.
  • Financial Performance Dashboard: Integrate revenue and expense data to create a comprehensive financial performance dashboard.

Advanced Techniques in Data Blending

Mastering data blending in Looker Studio can take your data analysis to the next level. Advanced techniques allow for handling complex data scenarios, creating custom metrics, and resolving common issues. 

Here, we explore some advanced data blending techniques to help you get the most out of your data blending capabilities.

Handling Complex Data Blends

Here are a few advanced data blending techniques used for calculations and visualizations in Looker Studio. 

Combining More Than Two Data Sources

Blending more than two data sources can provide a richer and more comprehensive view of your data:

  • Sequential Blending: Looker Studio allows sequential blending, where you can blend two sources first and then blend the result with a third source. This process can be repeated to combine multiple data sources.
  • Key Selection: Ensure you have common join keys across all sources. For example, if blending sales, marketing, and customer support data, use customer ID as the common key across all three datasets.
  • Field Mapping: Carefully map fields from each data source to avoid conflicts and ensure the data joins correctly. Use unique and consistent field names to maintain clarity.

Handling Complex Data Blends in Looker Studio

Managing Large Datasets

Large datasets can pose performance challenges. Here’s how to manage them effectively:

  • Data Aggregation: Aggregate data at a higher level (e.g., monthly instead of daily) to reduce the volume of data to process.
  • Pre-filtering: Apply filters at the data source level to limit the data required to import into Looker Studio.
  • BigQuery Integration: Use Google BigQuery to handle large datasets. BigQuery can process massive datasets efficiently and integrates seamlessly with Looker Studio.

Using Calculated Fields

Now that we understand the advanced data blends, let us use them for advanced calculations in Looker Studio. 

Creating CustomMetrics and Dimensions

Calculated fields in Looker Studio allow you to create new metrics and dimensions tailored to your specific analysis needs:

  • Basic Calculations: Use basic arithmetic operations to create new metrics. For instance, calculate profit by subtracting costs from revenue.
  • Conditional Expressions: Utilize functions like CASE-WHEN statements to create new dimensions based on conditions. For example, you can categorize customers into segments based on their purchase behavior.
  • Date Functions: Use the date functions to create time-based calculations. For example, calculate year-over-year growth by comparing current and previous year sales.

Examples of Calculated Fields

Let us dive right into some real-world examples of using custom-calculated fields. 

  • Profit Margin

(Revenue - Cost) / Revenue * 100

This formula calculates the profit margin percentage.

  • Customer Lifetime Value (CLV)

AverageOrderValue * PurchaseFrequency * CustomerLifespan

This calculation estimates the lifetime value of a customer based on average order value, purchase frequency, and customer lifespan.

  • Churn Rate

CASE 

  WHEN CustomerStatus = 'Churned' THEN 1 

  ELSE 0 

END

This field identifies churned customers, which can then be aggregated to calculate the overall churn rate.

Troubleshooting Common Issues

As you create exciting and useful blends and complex calculations, you are likely to hit some roadblocks. We discuss some below. 

Troubleshooting Common Issues

Identifying and Resolving Data Blending Errors

Data blending can sometimes lead to errors. Here’s how to troubleshoot them:

  • Join Key Mismatches: Ensure that the join keys used for blending are correct and consistent across all data sources. Mismatches in join keys are a common source of errors.
  • Data Type Inconsistencies: Verify that the data types of the join keys and other fields used in blending are consistent. Convert data types if necessary to ensure compatibility.
  • Missing Data: Address any missing data in your sources. Missing values can disrupt the blending process, leading to incomplete /inaccurate results.

Best Practices for Maintaining Data Integrity

Maintaining data integrity is crucial for accurate analysis:

  • Regular Data Audits: Periodically audit your data to ensure accuracy and consistency. Look for discrepancies and rectify them promptly.
  • Version Control: Use version control for your data sources and blended datasets. This practice helps track changes and revert to previous versions if issues arise.
  • Documentation: Document your data blending process, including the join keys, calculated fields, and any transformations applied. Clear documentation ensures that others can understand and replicate your work.

Conclusion

This blog is about data blending with Looker Studio. 

Data blending is a powerful method for integrating data from multiple sources into a unified, comprehensive dataset, enabling robust analyses, insightful visualizations, and improved decision-making. 

Key advantages include holistic data insights, enhanced decision-making, time and resource efficiency, customized reporting, improved data accuracy, flexibility, scalability, and better collaboration. 

Looker Studio's features, such as data source blending, custom fields, and interactive dashboards, further enhance these capabilities, while integrations with external resources and plugins extend its functionality.

Mastering data blending involves ensuring data compatibility and cleanliness, configuring the blending process, and choosing the dimensions and metrics.

Advanced techniques, such as blending multiple data sources, and using calculated fields, custom metrics, and dimensions can add more depth and meaning to the data. 

Following our guidelines and best practices can help transform disparate data sources into actionable insights, driving your business forward with Looker Studio.

 

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