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Data Blending: Processes, Benefits, and Best Practices

Data blending involves merging data from multiple sources to create a unified dataset, which can be analyzed to uncover deeper insights and support decision-making. 

In marketing terms, data blending allows marketers to gain deeper insights into customer behavior and campaign performance across different channels. It enhances multi-channel attribution, helping teams understand the full customer journey and the impact of each touchpoint. 

What Is Data Blending?

Data blending is the process of combining data from multiple disparate sources to create a unified dataset for analysis. This involves integrating data from platforms like CRM systems, web analytics, social media, and email marketing tools. This process allows marketers to consolidate various data points into a single view, facilitating comprehensive analysis and reporting.

Data Blending vs. Data Transformation

Data blending is often mixed up with data transformation. While these two processes are integral parts of the analytics workflow, you need to differentiate between the two to know when to blend data versus when to transform data. 

Data Blending

Data blending focuses on combining data from multiple sources to create a single dataset that can be analyzed together. It is typically used for exploratory analysis and answering specific business questions on the fly.

Data Transformation

Data transformation involves a more extensive process of converting data into a format suitable for long-term storage and comprehensive analysis. This process includes normalizing, aggregating, and enriching data to ensure it is consistent and ready for use in data warehouses or other systems.

Aspect Data Blending Data Transformation
Purpose Quick integration for exploratory analysis Comprehensive preparation for long-term storage and analysis
Process Collecting, cleaning, and joining data from various sources Normalizing, aggregating, and enriching data
Flexibility Highly flexible, ideal for ad-hoc analysis Less flexible, requires detailed planning
Complexity Simpler and quicker More complex and time-consuming
Typical Use Case Merging campaign data with customer feedback for immediate insights Creating a standardized data warehouse for detailed reporting

The Importance of Data Blending

Data blending is pivotal in extracting actionable insights from a vast array of data sources. By integrating diverse datasets, organizations can achieve a holistic view that drives better decision-making and operational efficiency.

For instance, retailers can blend in-store purchase data with online shopping behavior to identify trends and preferences, enabling personalized marketing strategies that enhance customer loyalty and increase sales.

Data blending also supports real-time analytics, which is crucial for responding to market changes swiftly. 

For example, during a product launch, marketing teams can blend real-time sales data with social media feedback to adjust their strategies on the fly, ensuring the campaign’s success.

Streamlining data processes through blending reduces the need for extensive IT support and expensive data warehousing solutions. Businesses can allocate these savings to other strategic initiatives, such as product development or market expansion.

Key Steps in Data Blending

Data blending involves several critical steps that ensure the integration of diverse data sources into a cohesive, actionable dataset. Each step is designed to streamline the data preparation process, ensuring accuracy and efficiency.

1. Data Acquisition

Identify and collect data from various sources such as internal databases, CRM systems, cloud applications, and external data sources like social media platforms and web analytics tools. This might include gathering sales data from point-of-sale systems, customer feedback from social media, and campaign performance metrics from marketing automation platforms.

Improvado provides over 500 pre-built data connectors.
Improvado data extraction capabilities

Streamline the process of data acquisition by integrating automated solutions like Improvado. 

Improvado is a marketing-specific analytics and data management solution. It offers 500+ pre-built API data connectors and supports flat data ingestion, meaning capabilities to gather data from a spreadsheet or Google Disk. Improvado further facilitates data acquisition and automation by offering data extraction templates, up to 5 years of historical data load, and hourly data sync.  

Integrating directly with source systems, Improvado facilitates the continuous flow of up-to-date information, critical for timely analysis and decision-making.

2. Data Cleansing

Aggregated data is still not suitable for analysis. Clean the collected data to ensure its accuracy and consistency by removing duplicates, correcting errors, filling in missing values, and standardizing formats. 

This ensures reliable analysis. Ensuring all customer contact data follows a uniform format and correcting any discrepancies in sales data before blending it with digital marketing metrics can significantly enhance data quality.

3. Joining Data

Combine the cleaned data from different sources using methods such as joins, unions, or relationships, depending on the data structure and analysis requirements. Merging transactional data with customer demographic information can reveal purchasing behaviors across different segments, providing valuable insights.

4. Data Transformation

Transform the data into a format suitable for analysis, including normalizing the data, aggregating it into meaningful metrics, and ensuring it adheres to the required structure. Aggregating daily website traffic data into monthly reports can help identify trends in visitor behavior, aiding in strategic decision-making.

Improvado provides pre-build data models and dashboards for various marketing analytics use-cases
Improvado provides pre-built data pipelines for marketing use cases enabling automated data transformation without any data engineering and SQL

Improvado provides an enterprise-grade data transformation engine that helps marketers to get analysis-ready data without the need for manual intervention, the knowledge of SQL, or custom scripts. 

The platform provides two options: 

  • Pre-built data pipelines for multiple marketing use cases spanning from data extraction to visualization for various use cases. For instance, if you select a paid ads analytics recipe, the platform will extract the needed data from the ad platforms, automatically map the platform's unique spend structures, and present a dashboard with data on daily campaign performance down to adset, ad level, creative, or placement level.
  • Self-service data transformation engine that has a spreadsheet-like UI and supports over 300 features and functionalities to automate lengthy analytics timelines and facilitate data discovery. 

5. Validation and Refinement

Validate the blended dataset to ensure its accuracy and integrity by checking for anomalies, verifying data relationships, and ensuring that it meets analysis requirements. Cross-referencing campaign performance data with original CRM entries helps maintain consistency and reliability.

Cerebro enhances operational efficiency with structured data governance.
Marketing Data Governance, AI-powered campaign management and data governance solution

Once again, the integration of automated solutions can significantly ease the process of data validation and refinement. Marketing Data Governance is an AI-powered campaign management and data governance solution that automatically validates the consistency of your data and alerts you of any anomalies and data discrepancies. 

6. Integration with BI Tools

Integrate the blended data with business intelligence tools for visualization and analysis, enabling the creation of dashboards and reports that provide actionable insights. Visualizing customer journey data blended with conversion metrics helps optimize marketing strategies and improve campaign performance.

Marketing Data Blending Challenges and Best Practices

To effectively leverage data blending and overcome its inherent challenges, it is essential to adopt strategic best practices that ensure accurate, efficient, and scalable data integration.

Challenge: Data Integration from Multiple Sources

Integrating data from various sources can be complex and time-consuming. Each source often has different data formats, structures, and terminologies, making it difficult to create a unified dataset for comprehensive analysis.


  • Standardize data formats: Implement data standardization practices to ensure consistency in data formats and structures across different sources. Use data transformation tools to normalize data, making it easier to blend and analyze.
  • Use ETL tools: Employ Extract, Transform, Load (ETL) tools to automate the process of extracting data from various sources, transforming it into a consistent format, and loading it into a unified data warehouse. ETL tools streamline data integration and reduce the risk of errors.
  • Data mapping: Develop a comprehensive data mapping strategy to align data fields from different sources. This ensures that similar data points are correctly merged, facilitating more accurate analysis.

Challenge: Data Quality and Consistency

Ensuring data quality and consistency is crucial for reliable analysis. Inconsistent or poor-quality data can lead to inaccurate insights and misguided decision-making.


  • Data cleaning: Implement regular data cleaning processes to remove duplicates, correct errors, and fill in missing values. Use automated data cleaning tools to maintain high data quality.
  • Validation rules: Establish data validation rules to ensure that data entering the system meets predefined quality standards. Automated validation checks can identify and correct inconsistencies in real-time.
  • Continuous monitoring: Set up continuous monitoring and auditing of data quality. Use data quality dashboards to track key metrics and identify issues promptly, ensuring that data remains consistent and reliable.

Challenge: Cross-Platform Data Integration

Integrating data from various marketing platforms and tools, each with its own API and data structure, can be complex and time-consuming.


  • API integrations: Develop robust API integrations to automate data extraction from various marketing platforms. Alternatively, utilize tools like Improvado that already have over 500 API connectors, can build custom connectors on demand, and cover API management.
  • Unified data layer: Create a unified data layer that standardizes and consolidates data from different platforms. This approach ensures that all data is stored in a consistent format, making it easier to blend and analyze.

Simplifying Data Blending 

Improvado streamlines the process of data blinding by automating the entire data preparation cycle, from data extraction to normalization and transformation. The platform ensures that data from various channels is consistent and ready for analysis. This is crucial for organizations that depend on accurate and uniform data to inform their marketing strategies.

Marketing Data Governance by Improvado enhances these capabilities by focusing on campaign data management and data governance. Marketing Data Governance automates compliance and operational checks for marketing data against established standards, ensuring that everything from naming conventions to targeting parameters is consistent across all assets. 

Schedule a demo call with Improvado to get access to timely and accurate performance insights.


What is data blending?

Data blending involves merging data from multiple sources into a single dataset for comprehensive analysis and better decision-making.

How does data blending differ from data transformation?

Data blending quickly integrates diverse data sources for immediate analysis, while data transformation prepares data for long-term storage and detailed reporting by normalizing, aggregating, and enriching it.

Why is data blending important?

Data blending enhances insights, decision-making, and operational efficiency by providing a holistic view of data from various sources.

What are the key steps in data blending?

Key steps include data acquisition, data cleansing, joining data, data transformation, validation and refinement, and integration with BI tools.

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