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What is Structured Data?

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  • Difficulty Level : Easy
  • Last Updated : 24 Feb, 2023
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Structured data refers to data that is organized and formatted in a specific way to make it easily readable and understandable by both humans and machines. This is typically achieved through the use of a well-defined schema or data model, which provides a structure for the data.

Structured data is typically found in databases and spreadsheets, and is characterized by its organized nature. Each data element is typically assigned a specific field or column in the schema, and each record or row represents a specific instance of that data. For example, in a customer database, each record might contain fields for the customer’s name, address, phone number, and email address.
Structured data is highly valuable because it can be easily searched, queried, and analyzed using various tools and techniques. This makes it an ideal format for data-driven applications such as business intelligence and analytics, as well as for machine learning and artificial intelligence applications.

Examples of structured data formats include relational databases, XML, and JSON. In contrast, unstructured data, such as text documents or images, do not have a predefined schema or structure, and can be more difficult to analyze and interpret.

Structured data is the data which conforms to a data model, has a well define structure, follows a consistent order and can be easily accessed and used by a person or a computer program. Structured data is usually stored in well-defined schemas such as Databases. It is generally tabular with column and rows that clearly define its attributes. SQL (Structured Query language) is often used to manage structured data stored in databases. Characteristics of Structured Data: 

  • Data conforms to a data model and has easily identifiable structure
  • Data is stored in the form of rows and columns Example : Database
  • Data is well organised so, Definition, Format and Meaning of data is explicitly known
  • Data resides in fixed fields within a record or file
  • Similar entities are grouped together to form relations or classes
  • Entities in the same group have same attributes
  • Easy to access and query, So data can be easily used by other programs
  • Data elements are addressable, so efficient to analyse and process

Sources of Structured Data: 

  • SQL Databases
  • Spreadsheets such as Excel
  • OLTP Systems
  • Online forms
  • Sensors such as GPS or RFID tags
  • Network and Web server logs
  • Medical devices

Advantages of Structured Data: 

  • Structured data have a well defined structure that helps in easy storage and access of data
  • Data can be indexed based on text string as well as attributes. This makes search operation hassle-free
  • Data mining is easy i.e knowledge can be easily extracted from data
  • Operations such as Updating and deleting is easy due to well structured form of data
  • Business Intelligence operations such as Data warehousing can be easily undertaken
  • Easily scalable in case there is an increment of data
  • Ensuring security to data is easy


  • Easy to understand and use: Structured data has a well-defined schema or data model, making it easy to understand and use. This allows for easy data retrieval, analysis, and reporting.
  • Consistency: The well-defined structure of structured data ensures consistency and accuracy in the data, making it easier to compare and analyze data across different sources.
  • Efficient storage and retrieval: Structured data is typically stored in relational databases, which are designed to efficiently store and retrieve large amounts of data. This makes it easy to access and process data quickly.
  • Enhanced data security: Structured data can be more easily secured than unstructured or semi-structured data, as access to the data can be controlled through database security protocols.
  • Clear data lineage: Structured data typically has a clear lineage or history, making it easy to track changes and ensure data quality.


  1. Inflexibility: Structured data can be inflexible in terms of accommodating new types of data, as any changes to the schema or data model require significant changes to the database.
  2. Limited complexity: Structured data is often limited in terms of the complexity of relationships between data entities. This can make it difficult to model complex real-world scenarios.
  3. Limited context: Structured data often lacks the additional context and information that unstructured or semi-structured data can provide, making it more difficult to understand the meaning and significance of the data.
  4. Expensive: Structured data requires the use of relational databases and related technologies, which can be expensive to implement and maintain.
  5. Data quality: The structured nature of the data can sometimes lead to missing or incomplete data, or data that does not fit cleanly into the defined schema, leading to data quality issues.

Overall, structured data offers many advantages in terms of ease of use, consistency, and security, but also presents some limitations in terms of flexibility and complexity that need to be carefully considered when designing and implementing data management systems.

Note: Structured data accounts for only about 20% of data but because of its high degree of organisation and performance make it foundation of Big data To read Differences between Structured, Semi-structured and Unstructured data refer the following article –

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