Data blending is the act of combining two or more datasets together. Data blending is not just for tabular data but should be universal across any data format and source including databases, CSV files, XML, JSON, text, and a variety of others.
The most often places where you will see data blending is for:
Data blending is often considered synonymous with data integration, although the term data integration has taken a much larger meaning around larger ETL and ELT processes to feed data warehouses or data marts.
A major use case for data blending is within data pipelines that feed downstream analytics. Data blending within a data pipeline would be done in one of three ways:
The final result of the data blending (and other transformation and/or preparation steps) will be contained in a data warehouse, cloud data warehouse, data mart, or fed directly to BI or data science tools (via files and/or native formats).
Regardless of where it is performed, data blending is used to create a larger dataset that offers a more complete, in-depth view. This view can then be used for information purposes within an application, analytics in a report, visualization or dashboard, or data science to feed AI, ML, or other types of models.
A prime example of data blending is Customer 360, where multiple sets of information about a customer are combined together to give a comprehensive view of the customer’s activity, actions, and behavior. This data can then be fed into a CRM or customer service application, used for various forms of customer analytics including customer behavior, or to feed data science models that will predict behavior and/or make recommendations.
There are five uses for data blending:
While combining related datasets was one of the original uses for data blending, using data blending for data enrichment has grown substantially as analytics have gotten more sophisticated and pinpoint. Data enrichment is also highly essential for data science.
Data enrichment comes in two forms:
Early uses of data enrichment were very personalized, with analysts or data scientists combining data on their own. However, with the advent of more comprehensive data integration platforms that offer rich data preparation capabilities, such as Datameer, data enrichment processes can be standardized in data pipelines for greater use across an enterprise and governed more effectively.
Datameer offers a comprehensive set of capabilities for data blending as part of the over 300 graphical functions for almost any form of data transformation and as part of data imports. This includes:
A leading provider of title insurance and property and mortgage-related services had highly complex and diverse datasets including data coming from services partners. The diverse data required a heavy dose of coding to normalize and enrich the data for analytics. Each dataset also needed to be classified in multiple ways enriched with calculated values as it came in.
The customer turned to Datameer to eliminate their dependence on time-consuming, manual SQL coding and share data curation processes between the data engineering teams and analyst community. They were able to take advantage of the rich array of graphical Datameer functions to have data engineering teams normalize and classify data, and have analysts enrich data on their own to their specific analytics needs.
Learn more about Datameer’s data blending capabilities, as well as the remainder of our over 300 comprehensive graphical functions for various forms of data transformation, cleansing, enrichment, and preparation, by scheduling a personalized demo.