Blending

TL;DR

Combine multiple datasets into a single, cohesive dataset for analysis or downstream use.
Common methods include concatenation and joins (inner, left, right, full outer).
Used in data visualization, machine learning, data analysis, and data integration.

Definition

Blending data refers to the process of combining multiple data sources in order to create a comprehensive, cohesive dataset.

Explanation

Blending is the act of merging two or more datasets so they can be analyzed together. Techniques vary by goal and include:

Concatenation: merging datasets by appending one dataset to the end of another.
Inner join: combining datasets by matching values in a common field or key; results include only records with matching keys in both datasets.
Left join: combining datasets by matching keys while including all records from the left dataset regardless of matches in the right dataset.
Right join: combining datasets by matching keys while including all records from the right dataset regardless of matches in the left dataset.
Full outer join: combining datasets by matching keys while including all records from both datasets, regardless of whether matches exist in the other dataset.

Blending produces a single dataset that can be used for visualization, modeling, statistical analysis, or integration across applications.

Examples

Concatenation

For example, if we have two datasets, one containing customer information and the other containing sales data, we could concatenate the two datasets to create a single, combined dataset that includes both customer and sales information.

Inner join

For example, if we have two datasets, one containing customer information and the other containing sales data, we could use an inner join to combine the two datasets by matching the customer IDs. This would create a single dataset that includes only those records that have matching customer IDs in both datasets.

Left join

For example, if we have two datasets, one containing customer information and the other containing sales data, we could use a left join to combine the two datasets by matching the customer IDs. This would create a single dataset that includes all records from the customer dataset, even if there is no corresponding sales data for a particular customer.

Right join

For example, if we have two datasets, one containing customer information and the other containing sales data, we could use a right join to combine the two datasets by matching the customer IDs. This would create a single dataset that includes all records from the sales dataset, even if there is no corresponding customer data for a particular sale.

Full outer join

For example, if we have two datasets, one containing customer information and the other containing sales data, we could use a full outer join to combine the two datasets by matching the customer IDs. This would create a single dataset that includes all records from both datasets, even if there are no corresponding records in the other dataset.

Use cases

Data visualization: Blending multiple datasets enables more comprehensive visualizations. For example, blending customer information and sales data and using a tool like Tableau or PowerBI to create a scatterplot or line chart that shows how customer characteristics (e.g. age, income, etc.) are related to sales performance.
Machine learning: Blending datasets can produce more robust training data. For example, blending customer information and sales data and applying algorithms like decision trees or random forests to predict customer churn or sales trends.
Data analysis: Blending datasets supports more complete statistical analyses. For example, blending customer information and sales data and using tools like R or SAS to explore relationships between customer characteristics and sales performance to identify trends and patterns.
Data integration: Blending creates a single dataset usable for multiple purposes. For example, blending customer information and sales data to serve as the basis for customer segmentation analysis, predictive modeling, or data visualization projects.

Concatenation
Inner join
Left join
Right join
Full outer join
Tableau
PowerBI
Decision trees
Random forests
R
SAS
Customer segmentation
Predictive modeling