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What is Structured Data?

Structured data refers to data sets with strong and consistent organization. Structured data is managed by structured query language (SQL), by which users can easily search and manipulate the data.
 
What is an Example of  Structured Data?
 
Structured data is organized into rows and columns with known and predictable contents. Each column contains a specific data type, such as dates, text, money or percentages. Data not matching that column’s data type is rejected as an error.
 
Relational database tables and spreadsheets typically contain structured data. A higher semantic level of structure combines master data and historical data into a data model. Data model subject areas include topics such as customers, inventory, sales transactions, prices and suppliers. Structured data is easy to use and data integrity can be enforced. Structured data becomes big data as huge amounts of historical facts are captured.
 
What are the Benefits of Structured Data?
 
Structured data is easily understood by machine learning algorithms, making manipulation simple and fast. It’s also easy to query by users, who are able to access, understand and interpret the data.
 
Structured vs. Unstructured Data
 
While structured data deals with highly organized quantitative data managed with SQL databases, unstructured data deals with qualitative data using no predefined data model, which is best managed in NoSQL databases (such as MongoDB).
 
Teradata Take: All important business processes and decisions depend on structured data. It is the foundation of data warehouses, data lakes and applications. When integrated into a data model, structured data provides exponential business value.