Structured vs. unstructured data: What's the difference?

“Structured” and “unstructured” are terms used to classify data based on its format and schema rules or lack thereof.

Structured data has a fixed schema and fits neatly into rows and columns, such as names and phone numbers. Unstructured data has no fixed schema and can have a more complex format, such as audio files and web pages.

Here are key areas where structured and unstructured data differ:

• Format: Structured data has a strict, predefined data model. Unstructured data does not have a predefined format.
  
  
• Storage: Structured data storage systems have rigid schemas, such as those in relational databases or data warehouses. Unstructured data is often stored in its native format in nonrelational databases or data lakes.
  
  
• Use cases: Organizations can use both structured and unstructured data across artificial intelligence (AI) and analytics use cases. Structured data is often used in machine learning (ML) and drives ML algorithms. Unstructured data is often used in natural language processing (NLP) and is a rich and diverse data source for generative AI (gen AI) models.
  
  
• Complexity: Structured data is easier to manipulate and analyze for general business users with traditional tools. Unstructured data can be more complex and requires specialized skills and tools to parse and analyze.

Continue reading for an extensive review of the definitions, use cases and benefits of both structured and unstructured data.

Structured data is organized in a clear, predefined format. The standardized nature of structured data makes it easily decipherable by data analytics tools, machine learning algorithms and human users.

Structured data can include both quantitative data (such as prices or revenue figures) and qualitative data (such as dates, names, addresses and credit card numbers). For example, a financial report with company names, expense values and reporting periods organized into rows and columns is considered structured data.

Structured data is typically stored in tabular formats, such as Excel spreadsheets and relational databases (or SQL databases). Users can efficiently input, search and manipulate structured data within a relational database management system (RDBMS) by using structured query language (SQL). Developed by IBM® in 1974, structured query language is the programming language used to manage structured data.

Use cases for structured data include:

• AI model training
• Customer relationship management (CRM)
• Business intelligence (BI)
• Inventory management
• Search engine optimization (SEO) rich snippets

The benefits of structured data are tied to its ease of use and access:

• Works well with machine learning: Machine learning can process both structured and unstructured data. However, it can be easier for ML applications to analyze and draw insights from structured data due to its specific and organized architecture.
  
  
• Accessible and easy to use: Understanding structured data does not require in-depth data science knowledge. Due to its standard format and high level of organization, most users find it easy to access and interpret structured data.
  
  
• Abundance of tools: Structured data predates unstructured data, so there are more apps and tools available for use and data analysis. For example, online analytical processing (OLAP), SQLite, MySQL and PostgreSQL, among others.

The challenges of structured data revolve around data inflexibility:

• Limited usage: Structured data has a predefined data model that can only be used for its intended purpose, which limits its flexibility and usability. Mining more insights requires modifications or additional data.
  
  
• Limited storage options: Structured data storage repositories typically have rigid schemas, such as those within a relational database or data warehouse. Changes to data requirements need updating all structured data, which is time and resource-intensive.

Unstructured data does not have a predefined format. Unstructured datasets are typically large (think terabytes or petabytes of data) and comprise 90% of all enterprise-generated data. This high volume is due to the emergence of big data—the massive, complex datasets from the internet and other connected technologies.¹

Unstructured data can contain both textual and nontextual data and both qualitative (social media comments) and quantitative (figures embedded in text) data.

Examples of unstructured data from textual data sources include:

• Emails
• Text documents
• Social media posts
• Call transcripts
• Message text files, such as those from Microsoft Teams or Slack

Examples of nontextual unstructured data include:

• Image files (JPEG, GIF and PNG)
• Multimedia files
• Video files
• Mobile activity
• Sensor data from Internet of Things (IoT) devices

Because unstructured data does not have a predefined data model, it is not easily processed and analyzed through conventional data tools and methods.

It is best managed in nonrelational or NoSQL databases or in data lakes, which are designed to handle massive amounts of raw data in any format.

Often, machine learning, advanced analytics and natural language processing (NLP) are used to extract valuable insights from unstructured data.

Use cases include:

• Retrieval augmented generation (RAG)
• Generative AI (gen AI)
• Customer behavior and sentiment analysis
• Predictive data analytics
• Chatbot text analysis

The benefits of unstructured data involve advantages in data format, speed and storage:

• Flexibility: Unstructured data is stored in its native format and remains undefined until needed. This file format flexibility widens the pool of available data and enables data scientists to use data for multiple use cases.
  
  
• Fast accumulation rates: For most organizations, this type of data is growing at 3x the rate of structured data. Since there is no need to predefine unstructured data, it can be collected quickly and easily, which is helpful for generative AI and large language model (LLM) fine-tuning.²
  
  
• Easy and cheap to store: Unstructured data has more storage options than structured data. For instance, file systems or data lakes allow for massive storage and pay-as-you-use pricing, which cuts costs and eases scalability.

The challenges of unstructured data center on expertise and available resources:

• Requires expertise: Due to its undefined or nonformatted nature, data science expertise is required to prepare and analyze unstructured data. This can alienate business users who might not fully understand specialized data topics or analysis.
  
  
• Specialized tools: Traditional tools such as Excel are not adequate for manipulating unstructured data, and product choices are limited for data managers. Some tools for unstructured data management include: MongoDB, DynamoDB, Hadoop and Azure.

• Data cleanliness: The large volume and nonuniform data structure of unstructured data can introduce inconsistencies, inaccuracies and data quality issues. Data cleaning might be necessary before data processing.

AI can quickly process large volumes of data. This is a key capability for organizations that want to transform massive amounts of unstructured data into actionable insights.

With machine learning and natural language processing (NLP), AI algorithms can sift through unstructured data to find patterns and make real-time predictions or recommendations. Organizations can then incorporate these analytical models into existing dashboards or application programming interfaces (APIs) to automate decision-making processes.

Semi-structured data is the “bridge” between structured and unstructured data. It is useful for web scraping and data integration.

Semi-structured data does not have a predefined data model. However, it uses metadata (for example, tags and semantic markers) to identify specific data characteristics and scale data into records and preset fields. Metadata ultimately enables semi-structured data to be better cataloged, searched and analyzed than unstructured data.

Examples of semi-structured data include JavaScript Object Notation (JSON), comma-separated values (CSV) and eXtensible Markup Language (XML) files. A more commonly cited example is email where some data sections have a standardized format (such as headers and subject lines) but unstructured data content within those sections.