What is meant by "data blending" in the context of BI?

Data blending refers to the process of combining data from different sources to provide a comprehensive analysis. This technique is essential in business intelligence because organizations often have data scattered across various platforms and systems, such as databases, spreadsheets, and cloud services. By blending this data, businesses can create a more unified view, which enables better insights and decision-making.

When data is blended, it is typically merged in a way that maintains its individual characteristics while allowing for combined analysis. This is particularly useful for organizations seeking to understand relationships and trends that might exist across disparate datasets, such as sales data alongside marketing metrics.

The other options focus on different aspects of data management. For instance, integrating data into a single source emphasizes consolidation but does not capture the flexibility and analytical benefits of blending. Cleaning and preprocessing data is about ensuring its quality and readiness for analysis, which is a necessary step but distinct from the concept of blending. Aggregating data for performance tracking pertains to summarizing data points, which serves a different analytical purpose compared to the broader scope of blending.