What is Data Blending, How it Works, & Tools to Use

Data engineers use structured and unstructured data from different sources, including databases, platforms, and APIs, to make it more accessible for analysis. According to Statista, by the end of 2024, the total amount of data that will be created, copied, and consumed will reach 149 zettabytes. This exponential growth in data underscores the challenge of managing and deriving meaningful insights.

This is where data blending comes in. It allows organizations to consolidate disparate pieces of information from various sources into a cohesive and actionable dataset. This comprehensive view of the data makes it easy to analyze it.

This article will elaborate on the data blending definition, how it works, and why blending data is essential in data engineering tasks.

What Is Data Blending?

Data blending is a technique that involves combining data from varied sources and creating a unified dataset for analysis. The purpose of data blending is to obtain a comprehensive view of business process data. For example, you can combine customer relationship systems and sales performance data to see which products are profitable and attract customer interest.

Importance Of Data Blending In Data Engineering

The following points highlight the significance of data blending:

Strategic Planning

A consolidated view of business data allows you to assess how your business process works. It helps to pinpoint the areas of improvement, make data-driven decisions, and create strategies that align with current conditions and future objectives.

Comprehensive Analysis

Instead of analyzing different pieces of information separately, you can combine all the data together through data blending. The analysis provides a more precise and detailed understanding of your business operations and helps your data teams identify opportunities, address challenges, and set achievable goals.

Ad Hoc Reporting

Data blending allows you to quickly access specific information needed for particular tasks. You can combine data blending tools with a database system and quickly create a customized summary of data without the need for pre-scheduled reports. This flexibility helps you address urgent business needs and generate real-time insights.

How Data Blending Works?

The following steps describe how data blending works:

Data Preparation and Collection

The first step involves preparing and collecting the necessary data. You must determine the information needed to solve the problem and then collect it from relevant sources. Ensure all the data share a common dimension so it can be combined.

There are two types of data sources for the data blending process. The first is primary sources, such as a sales management system, from which you pull direct information. The other is secondary sources, where data is gathered from primary sources for research and analysis purposes. An example is regional sales performance metrics.

Select a Data Blending Tool

The choice of a data blending tool varies depending on the complexity of your datasets and the depth of analysis required. Data blending tools help you automate the process of merging and combining data to support business tasks. Some of the popular data blending tools include Tableau, Alteryx, and Google’s Excel Sheet.

Implement Data Blending Techniques

The common operations supported by data blending tools include:

• Join: This operation helps you merge the datasets that have common field attributes.‍
• Union: Allows you to combine data from different datasets with the same structure.‍
• Aggregation: Used to summarize data, like calculating sums or counts.

Ensure Data Quality

After blending, you must verify the quality of the combined dataset. Check for duplicate values, inconsistencies, or other issues that could compromise the data integrity. This will ensure that the blended dataset is accurate for reliable analysis.

Are Data Blending & Join The Same?

Data blending and data joining are different processes, although they both aim to combine data. Here’s how they differ:

‍Data joining or join, is a fundamental operation that involves merging datasets by identifying and matching common fields to create one single dataset. Using join, you can combine tables within a single database or across different databases, as well as merge sheets in spreadsheets.

On the other hand, data blending extends beyond simple joins. It enables you to combine datasets from multiple and often unrelated sources. The process involves aggregating and harmonizing data, regardless of differing formats or originating systems.

Data Blending Examples

You can apply data blending across various industries to get a comprehensive view of data. Here are some examples:

Marketing Performance Analysis

Data blending can help you optimize marketing strategies based on sales trends and market conditions. For example, you can combine advertising spending data with conversion rates to identify the most effective channels. You can also merge customer engagement metrics with demographic data to create personalized campaigns. This will help your business stay competitive and responsive in dynamic markets.

Inventory and Demand Forecasting

Data blending can help predict future product demand and optimize inventory levels by combining historical sales data, current inventory records, and seasonal trends. This blended dataset enables the creation of more precise inventory management strategies, ensuring your business can efficiently cater to customer demand.

Financial Analysis

With data blending, you get a complete view of your company’s financial health by combining data from sources like accounting software, bank statements, and economic reports. This view will help you identify trends such as revenue growth and currency exchange rates, track KPIs, and prepare for audits and budget planning.

Supply Chain Operations

PepsiCo, one of the leading beverage companies, uses Tableau Desktop to analyze its inventory, logistics, and financial data. The analysis helps the company to effectively understand and utilize big data. With Tableau, PepsiCo has reduced its report production time by 90% and end-to-end analysis time by 70%, strengthening its supply chain operations.

Limitations Of Data Blending

Here are some of the challenges that you can face while implementing data blending:

Data Consistency

When you consolidate data from multiple sources, it gets challenging to create a unified dataset due to differences in data formats, structures, and definitions. Inconsistent data can lead to inaccurate analysis.

To avoid these issues, you can implement regular data-cleaning techniques and correct errors. You can also establish some validation rules to make sure the incoming data meets your system’s pre-defined schema.

Excessive Data Load

Data blending requires extracting and loading large data volumes into a single dataset for analysis. This can be time-consuming and resource-intensive, slowing down overall performance.

To improve the speed of these operations, you can use an automated data integration tool. These tools enable you to create efficient data pipelines that you can use to transfer extensive datasets from source to destination quickly.

Missing Data

When you are blending data from multiple sources, gaps or missing values are common. If these values are not looked after properly, they can distort the overall analysis. The cause of missing data can be due to system errors.

To mitigate this issue, you can use techniques that can help you create a more complete dataset. Examples of these techniques include imputation (filling in missing values based on other data) or data interpolation (estimating missing values based on surrounding data points).

Data Blending Vs. Data Integration Vs. ETL

While data blending, data integration, and ETL processes are related to data management strategies, they have distinct purposes. Here’s a comparison table that lists the differences:

Tools to Perform Data-Blending Operations

You can perform data blending using various tools, from spreadsheet applications to advanced business intelligent platforms. Let’s look at some of the most effective tools.

Spreadsheet Tools

Spreadsheet tools are computer applications that allow you to store, organize, and analyze data in a tabular format. These tools are user-friendly and affordable.

Here are two of the most popular spreadsheet tools:

• Excel: Excel is a robust spreadsheet tool known for its diverse features. Functions like VLOOKUP and XLOOKUP allow you to search table values and retrieve corresponding data. The PivotTable function helps summarize, group, and analyze large datasets. These features make Excel well-suited for performing data blending operations, such as merging sales data from multiple regions.
• ‍Google Sheets: Google Sheets is a cloud-based alternative to Excel. It has the added advantage of real-time collaboration, where multiple users can work on the same document simultaneously. The IMPORTRANGE function enables you to pull data from another Google Sheet file. This makes it easy for you to blend data across spreadsheets.

Business Intelligence (BI) Tools

Business intelligence tools help analyze, visualize, and share data insights. These tools facilitate data blending operations with minimal effort and offer techniques like data aggregation, in-memory processing, and data compression.

Some of the most widely used BI tools are:

• Tableau: Tableau is a robust BI tool known for its data visualization capabilities. It allows you to blend data by adding a secondary data source to a primary data source and displaying them together. This blended data provides a view of aggregate joins. The visualization enables you to analyze data across different levels of detail without creating a physical join.
• ‍Alteryx: Alteryx is an analytics automation platform that simplifies data blending with features like spatial and predictive drag-and-drop building blocks. You can combine internal, third-party, or cloud data for analysis. In addition to this, you can use Fuzzy Match, an AI tool in Alteryx, to match datasets based on related attributes such as names or addresses. It also allows you to save the workflows and repeat them for optimization and further blending or analysis.

These tools also provide visualization features that enable you to create charts and dashboards to understand your blended data better.

Can You Automate Data Blending?

You can automate data blending by setting up workflows that enable you to merge, transform, and analyze data with no or less manual intervention. Here are some methods to automate various aspects of data blending processes:

ETL/ELT Tools for Data Integration

ETL/ELT tools help automate and streamline the data blending process by facilitating smooth data flow. They also allow you to clean and transform the data before blending. For instance, Airbyte is an AI-powered data integration tool that simplifies the process of building pipelines.

It offers 550+ pre-built connectors for different sources and destinations, including databases, APIs, and analytical platforms. Using these connectors, you can create a data pipeline to migrate data between systems quickly. You can also utilize Airbyte's Connector Builder feature to build a custom connector within 10 minutes.

Once you configure the connectors, you can set up a data pipeline and load data from primary and secondary sources into a data blending tool such as Google Sheets. This automation significantly reduces manual work and enhances the accuracy of the data blending operations you perform in the destination system.

Some of the Airbyte features:‍

CDC: The CDC feature helps you capture the changes occurring in the source data and reflect them in the destination. This helps keep track of data updates and ensures data is accurate for performing blending operations.

‍PyAirbyte: Airbyte offers PyAirbyte, an open-source Python library that provides a set of utilities enabling you to use the Airbyte connectors in the Python environment. You can quickly extract data from various sources using these connectors and build robust data pipelines.

Schedule Data Blending Workflows

Many modern BI tools support automation, allowing you to blend data on a scheduled basis. For example, with Alteryx, you can set up your data blending workflow. You can choose a tool from the tool palette and drop it on the workflow canvas. It provides pre-configured preset tool options for data blending operations like Union or Join to link data sources. You can also define the blending logic during setup, following which you can schedule the workflow to run automatically at specific intervals.

Conclusion

Data blending allows you to analyze scattered data by consolidating it into a unified dataset. This approach can be utilized in different industries, from marketing to supply chain and finance. Tools like Tableau and Alteryx simplify the implementation of data blending through robust features and scheduling.