4 Differences Between Structured and Unstructured Data

Businesses understand the importance of managing data. Part of that includes understanding what kind of data they have. Data is generated in a multitude of "flavors," but all data falls into two general buckets: unstructured and structured.

Unstructured data makes up about 80% of all the data generated today. It's messy, raw, and diverse. The remaining percentage is structured data, which, on the other hand, is neatly organized and contained, ready for processing and analysis.

Both have advantages and drawbacks. In this article, we'll break down the major differences between the two, lay out how they are used, and the important role they play in business operations.

What is structured data?

Structured data is the organized, well-behaved data sibling. It’s stored in predefined formats like rows and columns, making it easy to search, analyze, and integrate into business systems. Think of a database table with clear labels for names, dates, and transaction amounts.

Because of its predictable structure, structured data powers financial reports, tracks inventories, and informs customer relationship management (CRM) platforms. Its organization makes it valuable for businesses that rely on precise and efficient analysis.

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What is unstructured data?

Comparatively, unstructured data is the creative free spirit. It's difficult to tame and doesn’t conform to predefined formats like rows and columns, making it harder to store, analyze, and manage. Instead, it exists in raw formats, including emails, social media posts, videos, and audio files. These file types need to be transformed and organized before they can be analyzed.

While unstructured data is more challenging to work with, its variety and richness offer opportunities for deeper insights, whether analyzing product reviews for customer sentiment or identifying trends in multimedia content.

More on unstructured data

Structured vs. unstructured data: 4 key differences

How the data is formatted

Structured data is easily organized into predictable formats, like the rows and columns in a spreadsheet or relational database. The information within is consistent with the definitions assigned to it, which makes it easier to query and analyze. Tables store the variables within each definition. For example, customer orders might be tracked in a table that includes these definitions:

• Customer name
• Order ID
• Product ID
• Purchase date

This is not true for unstructured data. Video files, for instance, contain metadata like resolution, location, and duration. The content within, however—spoken words, focus quality, and visual details like colors per pixel—can't be analyzed without advanced tools to transform it into something useful. Social media posts that combine text, images, and hashtags also go into the bucket of unstructured data.

How the data is stored

Structured data inhabits relational databases like SQL Server or MySQL, which use schemas to define how the data is organized. They support defined queries and transactional workflows, making for fast and reliable access.

Data without structure is raw and unorganized. It takes up a lot of space and needs storage systems that are exceptionally flexible and scalable. Environments like data lakes and distributed storage systems, including Snowflake or Azure Data Lake, can handle massive volumes of unstructured data, but the data still needs processing to be analyzed.

Where the data is stored

Since structured data is easier to manage and analyze, it's typically housed in high-performance relational databases, either on-premises or in the cloud, where it can be easily maintained and quickly accessed.

The variety of formats and raw state of unstructured data means it can't be stored in databases. Data lakes and distributed storage systems are better suited because they can be scalable and handle the diversity of the format of the data.

How the data is analyzed

Analyzing structured data is straightforward with SQL-based queries and business intelligence (BI) tools. For example, a retail business could use structured data to quickly calculate monthly sales by region.

Unstructured data requires advanced tools and techniques to analyze. Machine learning (ML) models, natural language processing (NLP), and video analytics software can convert disorganized information like customer reviews, call recordings, or surveillance footage into understandable and actionable insights. These tools give businesses the ability to uncover trends and sentiments that structured data can't show.

Data of all formats needs to be managed properly. Learn how automation can manage data throughout its lifecycle.

When to use each: structured vs. unstructured

The line between when to use structured or unstructured data is pretty well-defined. Structured data is best used for tasks with known parameters, where the information is neatly organized for quick analysis. Unstructured data is used when the formats can't fit within strict definitions but are rife with deep, detailed information. Both forms play an important part in business processes.

Structured data

Analyzing customer behavior patterns and trends: Structured data stored in relational databases makes it easy to analyze customer interactions, such as purchase histories or website visits. Retailers, for example, use this data to segment audiences and tailor marketing strategies.

Processing and recording financial transactions: Financial institutions rely on structured data to process transactions efficiently and maintain accurate records. Structured formats ensure compliance with regulations and streamline tasks like generating reports or auditing accounts.

Analyzing website traffic: Structured data tools like Google Analytics help businesses track metrics such as page views, bounce rates, and conversion rates. This data enables companies to optimize website performance and improve the user experience.

Unstructured data

Forecasting market trends: Unstructured data, such as social media posts, news articles, and blogs, provides rich insights into emerging market trends. Businesses use advanced tools to analyze this data and stay ahead of industry shifts.

Uncovering buying trends: Analyzing unstructured data, like customer reviews or call recordings, helps identify hidden buying preferences and sentiments. For instance, sentiment analysis can reveal how customers feel about a specific product or service.

Data mining for product development: Unstructured data sources like multimedia content, research papers, and user-generated feedback can be mined to inspire product innovations or refine existing offerings.

CData Sync

Whether structured or unstructured, data connectivity is key to gaining the valuable insights that drive your business forward. CData Sync simplifies data integration and replication from any source to any destination, regardless of the structure. Take it for a spin with a free trial.