Data blending involves mixing data from multiple sources to create a data set for analytics. As a result, organizations can generate valuable business insights about sales, marketing, and other functions. Data blending combines data from sources such as CRM systems, spreadsheets, business systems, cloud applications, and more.
Stages of Data Blending
The data blending process passes through the following steps:
Combining Data Sources
An organization combines data from a primary source and one or more secondary sources. These sources include:
- Business systems
- CRM systems
- Cloud applications
- ERP systems
- Web analytics
The organization cleanses the data, transforming it into a readable and usable format. It corrects and removes corrupt or damaged data.
With clean, readable, functional data, the organization identifies correlations between data relationships. It makes logical connections between data elements and generates organizational insights quickly.
Note Data blending differs from data integration. The process is considerably faster — too fast for data scientists to intervene.
Uses of Data Blending in Enterprise
An organization with various data sources might experience situations that require data blending:
- The organization requires deep intelligence that comes from blending multiple data sources.
- The organization wants to respond to data flows.
- The organization wants to make smarter data-driven decisions.
- The organization needs to share data insights with analysts, investors, etc.
Data blending exists in all sectors. A medical research company might execute this process to analyze effective treatments, for example, or a finance company might want to analyze economic histories. In both examples, data moves from its original source, combines with at least one other source, undergoes a transformation, and ends up in a final destination, almost always for analytical purposes.
Data Blending Advantages and Challenges
Data blending doesn't involve data scientists or specialists, making it a lucrative method for businesses with limited resources. Sales and marketing departments often use data blending with limited intervention from specialists.
Each data source has its own set of measures and dimensions, so an organization requires the right tools to facilitate the process. Otherwise, there could be unexpected values and field changes, as well as potential data loss.
Extract, Transform, Load (ETL), in particular, plays a critical role in data blending, allowing an organization to connect to multiple data sources for extraction, move data through pipelines for transformation, and load data into an analytics tool. Extract, Load, and Transform (ELT) won't provide the same benefits because it doesn't transform data before the loading stage.