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Structured vs. Unstructured Data: What's the Difference?

If you’re unsure what’s the difference between structured and unstructured data and how they can be used, this article will give you an introduction to both. They will also be compared side by side to highlight the main differences.
by Josephine Loo · September 2023


    Two fundamental categories of data, structured and unstructured, play distinct roles in creating an efficient software/system. Understanding how they differ is essential. In this article, we will explore the difference between structured and unstructured data, their usage in various industries, and the tools used to work with them.

    By gaining a deeper understanding of structured and unstructured data and their respective characteristics, you will be better equipped to choose the appropriate methodologies and technologies to manage and apply them.

    What is Structured Data

    Structured data refers to well-organized data with definable attributes. This type of data is commonly found in tables, spreadsheets, and relational databases where there are rows and columns. It follows a fixed schema where the structure of the data is pre-defined and consistent. As each piece of data is associated with a specific attribute, storing and retrieving data is straightforward and the database becomes easier to manage.

    The structured format also makes identifying the relationship between different data attributes and retrieving information from them easy. As datasets can be linked via common values, you can easily query and aggregate data from various datasets. For example, you can relate and link a customer’s personal information and purchase orders from two separate tables using customerId and orderId.

    Structured Data Usage Examples

    Due to the organized nature that facilitates efficient data manipulation and reporting, structured data is widely used in industries and applications like finance, customer relationship management (CRM), reservation systems, enterprise resource planning (ERP), and e-commerce.

    Here are some examples of its usage:

    • Financial Transactions : Transaction records, customer details, bank account balances, and more are stored in the form of structured data. Business users are able track and manage financial activities using SQL.
    • E-commerce/Inventory Management : Retail businesses use structured data to track product SKUs, quantities, prices, and suppliers. This helps businesses to optimize inventory levels, predict demand, and manage supply chains.
    • Human Resources : Human resources departments often manage employee details like names, positions, salaries, and attendance using structured databases, which simplifies workforce planning, payroll processing, and performance evaluation.
    • Customer Relationship Management (CRM): Structured data is also used in CRM systems, where customer details, interactions, purchase history, and preferences are stored.
    • Reservation Systems : Hotels, airlines, and restaurants use structured data to manage reservations, bookings, availability, and customer preferences.

    🐻 Bear Tips: Structured data is commonly used on websites to store details of a piece of information. For example, a job listed on a job board has details like job title, salary, job scope, etc. You can scrape them from the website easily using a browser automation tool like Browserbear.

    Structured Data Tools

    There are several tools you can use for working with structured data. These tools provide functionalities such as storing are querying structured data. Here are some popular ones:

    Relational Database Management Systems (RDBMS)

    • MySQL: MySQL is an open-source relational database management system (RDBMS) known for its performance, scalability, and ease of use. It is used by top companies including Facebook, Twitter, Booking.com, and Verizon to power their high-volume websites, business-critical systems, and packaged software.
    • PostgreSQL: Another powerful open-source RDBMS that offers advanced features like support for JSON, spatial data, and more. It also supports high-tier programming languages like C/C+, Java, Python, etc.
    • Oracle Database: Oracle Database (Oracle DB) is a commercial RDBMS from Oracle Corporation and is known for its robustness and scalability. It offers various types of RDBMS depending on your requirements for performance, scalability, data model, etc.
    • Microsoft SQL Server: Microsoft SQL Server is a comprehensive RDBMS with strong integration with Microsoft technologies such as Windows and Azure. You can run the SQL Server on on-premises infrastructures or Azure, and even extend SQL to IoT devices.

    Cloud-Based Database Services

    • Amazon RDS: Amazon RDS is a managed relational database service offered by Amazon Web Services (AWS). You can use it for all the RDBMSs mentioned above (MySQL, MariaDB, PostgreSQL, Oracle DB, and Microsoft SQL Server).
    • Google Cloud SQL: Similar to Amazon RDS, Google Cloud SQL is a fully managed relational database service for RDBMSs including MySQL, PostgreSQL, and Microsoft SQL Server. It seamlessly integrates with other Google Cloud services to help developers build and deploy applications with ease.
    • Azure SQL Database: Microsoft Azure SQL Database is a managed cloud database provided as part of Microsoft Azure services. It eliminates the complexity of configuring and managing database tasks with a fully managed SQL database.

    What is Unstructured Data

    As opposed to structured data, unstructured data refers to data that lacks a fixed schema or organized structure. It is usually categorized as qualitative data while structured data is usually categorized as quantitative data. Due to its qualitative and ambiguous nature, unstructured data is difficult to be processed and analyzed. It requires more advanced tools as compared to structured data.

    Unstructured data does not fit neatly into traditional rows and columns like structured data, and it is best stored and managed in non-relational databases (NoSQL). This type of data can come in various forms including text, images, audio, videos, social media posts, emails, and more. As social media usage continues to surge, unstructured data has gained immense importance, accounting for over 80% of the data on the internet.

    Unstructured Data Usage Examples

    Unstructured data is used across different industries and applications, especially those that involve media files like images, videos, and audio files. Here are some examples:

    • Social Media Analytics : Unstructured data collected from social media platforms is used to analyze public sentiments, trends, and opinions. The insights gathered help businesses understand customer preferences and tailor their marketing strategies accordingly.
    • Speech Recognition and Voice Analysis : Audio is another common form of unstructured data and can be used for speech recognition in developing virtual assistants, transcription services, and voice commands.
    • Image and Video Analysis : Image and video data are often used to train machine learning models for object detection, facial recognition, and generative AI. They can be used in industries such as surveillance, healthcare, software, etc.
    • Text Mining and Natural Language Processing (NLP): Text data can be mined for insights, sentiment analysis, and topic modeling. This helps companies to understand customer feedback and market trends, and also develop chatbots to answer customers’ inquiries.
    • E-commerce Product Recommendations : Unstructured data such as user reviews and browsing behavior can be used to provide personalized product recommendations to customers on e-commerce stores and platforms.

    Unstructured Data Tools

    Unlike structured data that are stored in SQL databases with rows and columns, unstructured data are stored in NoSQL databases that provide more flexible schema or data models.

    NoSQL Databases

    • MongoDB: MongoDB is a document database designed for ease of application development and scaling. Every record stored in MongoDB is a document. It is similar to a JSON object and the field value may include arrays, other documents, or even arrays of documents.
    • Amazon DynamoDB: Amazon DynamoDB is a fully managed, serverless, key-value NoSQL database designed to run high-performance applications at any scale. You can integrate with AWS services to perform analytics, extract insights, and monitor traffic trends using the built-in tools.
    • Apache Cassandra: Apache Cassandra is an open-source NoSQL distributed database that provides linear scalability and high availability. It is tested on clusters of >1,000 nodes to ensure reliability and stability.
    • Redis: Redis is an open-source, in-memory data store that can be used as a database, cache, streaming engine, and message broker. As a data structure server, it supports various data types including strings, hashes, lists, sets, sorted sets, streams, and more.
    • Couchbase: Couchbase is an award-winning NoSQL document-oriented database software package. The distributed multi-model database package is optimized for interactive applications serving many concurrent users and yet keeps the cost low.

    Cloud-Based Database Services

    All NoSQL databases listed above are also available on the cloud.

    Differences Between Structured Data and Unstructured Data

    Now that you have learned what is structured data and unstructured data, let’s compare them side by side.

      Structured Data Unstructured Data
    Schema Adheres to a fixed schema or data model that defines the structure and relationships of the data. Data can only be added to the corresponding rows and columns. Does not have a fixed schema that requires data to be stored in rows and columns. Any data can be added, modified, or removed without adhering to a predefined structure.
    Data Types/Formats Has specific data type for each attribute in the dataset and only data with the valid data type can be added. It can be an integer, string, date, boolean value, etc. Comes in diverse formats such as text, images, audio, video, etc.
    Data Source Comes from applications that require organized data such as customer relationship management (CRM), reservation systems, enterprise resource planning (ERP), etc. Can be collected from social media posts, emails, documents, video footage, audio recordings, etc.
    Storage Typically stored in tabular formats in RDBMSs. Historical data that is not used or data from multiple applications can be also stored in data warehouses, which act as central repository for the data. Typically stored as files or objects in NoSQL databases. Similar to structured data, the data can be stored in a central repository (data lake) but in a raw form.
    Searchability/Ease of Use Simple to search using SQL as it adheres to a fixed schema or data model. Requires a higher level of expertise depending on the NoSQL database and nature of data as it lacks order and consistency.
    Usability Can only be used for its intended purpose due to the predefined structure, which limits its flexibility and usability. As its stored in its native format and remains undefined until needed, it is more adaptable.


    For developers, understanding the differences between structured and unstructured data is essential for designing efficient systems. Whether you're working with structured databases or unstructured data, it is crucial to choose the appropriate tools, technologies, and frameworks that align with your project's requirements to implement the most effective data management strategies.

    About the authorJosephine Loo
    Josephine is an automation enthusiast. She loves automating stuff and helping people to increase productivity with automation.

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    Structured vs. Unstructured Data: What's the Difference?
    Structured vs. Unstructured Data: What's the Difference?