Semi-structured data is data that contains some organizational properties such as tags or metadata, but does not conform to a rigid schema like structured data in relational databases. Semi-structured data sits in between structured data and unstructured data. Semi-structured data has some level of metadata tagging to identify information that gives context to what data points are about. But, like unstructured data, it’s not collected in accordance to a particular data model, or schema.
Semi-Structured Data vs. Unstructured Data: What’s the Difference?
For example, an image file may be considered unstructured data. But, adding image ALT tags associated with the image that provides some information on what the image is about, transforms the file into semi-structured data.
Semi-structured data is the largest growing area of data. This is due to the increase of meta tagging across documents, images, and video to help classify and categorize the content for search engine optimization and organization. As organizations build out their data pipelines, handling semi-structured formats efficiently becomes critical to downstream analytics.
What are the Different Types of Semi-Structured Data?
Different types of semi-structured data includes:
- Compressed Files
- Emails (unstructured body text, but with structured data like subject line and send date)
- Images (that include metadata)
- Webpages
How Does a Customer Data Platform Manage Semi-Structured Data?
Data collection needs to be standardized in order for data integration to succeed. Whether the destination is a data warehouse or a unified customer profile, that data is often fractured and residing in disparate silos. Theright technology solution can help gather that data and combine it together in a standardized fashion.
A customer data platform (CDP) is able to integrate, unify and deliver structured, unstructured and semi-structured to the right teams across the organization. Organizations are also using CDPs to ensure that data is secure and compliant with emerging global data privacy regulations, supported by strong data governance frameworks.
With data that is standardized and integrated into unified profiles, enterprise businesses can de-silo different departments and work together using single source of truth for all customer data. Transforming semi-structured data into structured data with a CDP can be the differentiator brands need to stay ahead of the competition and stay relevant to their customers.
Related Terms
- Data Modeling — Defines the schemas that give semi-structured data more formal organization
- Data Lakehouse — Storage architecture that handles semi-structured formats alongside structured data
- ETL and ELT — Processes that transform semi-structured data into queryable structured formats
- Data Validation — Ensures semi-structured data meets quality standards before downstream use