Data Blending is a method used to extract value from multiple data sources. Data Blending merges several sources of data in a variety of forms; including customer, transactional, social, structured, and unstructured data into a single data pool.
Data Blending is the response to the shortcomings of Extract Transfer Load (ETL) data joining. Data Blending responds to the ongoing growth of data that can be captured as structured and unstructured. Every bit of it can be used to produce analytics that are more focused and precise.
Data Blending should be employed when one has a large amount of records from data sources that don’t natively “talk” to each other. Once data is extracted or read from its source, the data is then cleansed, run through matching to remove duplicates and/or consolidate records. When this process is done, the resulting set of actionable data can be used for analytics via tools such as Tableau, Power BI, or Qlik.