Collecting and combining data from multiple sources

Data blending involves pulling data from different sources and creating a single, unique, dataset for visualization and analysis. To illustrate, you may have data spread out across multiple spreadsheets like Excel or Sheets, business intelligence systems, IoT devices, cloud systems, and web applications.

1. What is combining multiple sources of data called?
2. Why is it important to collect data from multiple sources?
3. How can you assure that data from multiple sources is compatible?

What is combining multiple sources of data called?

Data blending is the process of combining data from multiple sources into a functioning dataset. This process is gaining attention among analysts and analytic companies because it is a quick and straightforward method used to extract value from multiple data sources.

Why is it important to collect data from multiple sources?

To get truly powerful insights, you need to pull in data from multiple sources. The more complex and diverse your datasets, the more surprising and potent the insights they'll produce. Additional data sources increase your chances to inform actions, fueling top-line and bottom-line growth.

How can you assure that data from multiple sources is compatible?

You can choose the ELT data integration method to ensure your multiple-sourced data is compatible with the database. ELT involves extracting raw data streams, loading them on a processing server, and then transforming them to normalize the data according to database compatibility.