What’s the Difference Between Structured Data and Unstructured Data?
Structured data and unstructured data are two broad categories of collectible data. Structured data is data that fits neatly into data tables and includes discrete data types such as numbers, short text, and dates. Unstructured data doesn’t fit neatly into a data table because its size or nature: for example, audio and video files and large text documents. Sometimes, numerical or textual data can be unstructured because modeling it as a table is inefficient. For example, sensor data is a constant stream of numerical values, but creating a table with two columns—timestamp and sensor value—would be inefficient and impractical. Both structured data and unstructured data are essential in modern analytics.
Key differences: structured data vs. unstructured data
You can model structured data as a table with rows and columns. Each column has an attribute (such as time, location, and name), and each row is a single record with associated data values for each attribute. Unstructured data doesn’t follow any predetermined rules.
The following are more differences between structured data and unstructured data.
Data format
Structured data must always comply with a strict format, known as a predefined data model or schema. Unstructured data doesn’t fit a schema. The prescribed format of unstructured data might be as simple as requiring all meeting recordings to be in MP3 format, or that all system events must be collected in a certain store.
Data storage
Both structured data and unstructured data can reside in various types of data stores. The choice of correct storage type depends on the inherent qualities and attributes of the data, the reason for collecting the data, and the types of analysis required.
Examples of structured data stores include relational databases, spatial databases, and OLAP cubes. Large collections of structured data stores are called data warehouses. Examples of unstructured data stores include file systems, digital asset management (DAM) systems, content management systems (CMS), and version control systems. Large collections of unstructured data stores are called data lakes.
Some data stores that you typically use for structured data can also store unstructured data and the other way around.
Read about relational databases
Data analysis
Typically, it’s easier to organize, clean, search, and analyze structured data. When data is strictly formatted, you can use programming logic to search for and locate specific data entries, as well as create, delete, or edit entries. Automating data management and analysis of structured data is more efficient.
Unstructured data doesn’t have predefined attributes, so it’s more difficult to search and organize. Typically, unstructured data requires complex algorithms to preprocess, manipulate, and analyze.
Technologies: structured data vs. unstructured data
The type of technologies used with both structured data and unstructured data depend on the data storage type used. Typically, structured data stores offer in-database analytics, and unstructured data stores don’t. This is because structured data complies with known and repeatable rules for manipulation thanks to its format, and the format of unstructured data is more diverse and complex.
There are various technologies used to analyze both types of data. Queries of the data using structured query language (SQL) is the fundamental basis of structured data analysis. You can apply other techniques and tools, such as data visualization and modeling, programmatic manipulation, and machine learning (ML).
For unstructured data, analysis typically involves more complex programmatic manipulation and ML. You can access these analytics through various programming language libraries and specifically designed tools that use artificial intelligence (AI). Typically, unstructured data requires preprocessing so that it fits in a specific format.
Challenges: structured data vs. unstructured data
The challenges of using structured data are usually minimal compared to those of unstructured data. This is because computers, data structures, and programming languages can more easily understand structured data. Conversely, to understand and manage unstructured data, computer systems must first break it down into understandable data.
Structured data
In any complex organization or group, structured data becomes difficult to manage when the number of relations in a relational database grows significantly. With so many links between databases and data points, developing queries for the data can become quite complex. Other challenges include:
- Data schema changes
- Making all real-world associated data fit into a structured format
- Integrating multiple different structured data sources
Unstructured data
Unstructured data typically poses two big challenges:
- Storage because the data is typically larger than structured data
- Analysis because it’s not as straightforward as analyzing structured data
Although you can do some analysis by using techniques such as keyword search and pattern matching, ML is often associated with unstructured data, such as image recognition and sentiment analysis.
Other challenges can include:
- Preprocessing to extract structured or semi-structured data
- Multi-format processing
- Processing power required for analysis
When to use: structured data vs. unstructured data
Both structured data and unstructured data are collected and used extensively across industries, organizations, and applications. The digital world runs on both forms of data, which is then analyzed and used in surfacing answers, decision-making processes, predictions, reflections, generative applications, and more. Although structured data is typically used for quantitative data and unstructured data is used for qualitative data, this isn’t always the case.
Structured data
Structured data is particularly useful when you’re dealing with discrete, numeric data. Examples of this type of data include financial operations, sales and marketing figures, and scientific modeling. You can also use structured data in any case where records with multiple, short-entry text, numeric, and enumerated fields are required, such as HR records, inventory listings, and housing data.
Unstructured data
Unstructured data is used when a record is required and the data won’t fit into a structured data format. Examples include video monitoring, company documents, and social media posts. You can also use unstructured data where it isn’t efficient to store the data in a structured format, such as Internet of Things (IoT) sensor data, computer system logs, and chat transcripts.
Semi-structured data
Semi-structured data sits between structured data and unstructured data. For example, a store of videos might have associated structured data tags for each file, such as date, location, and topic. Metadata on multimedia files means that these are, by nature, semi-structured data. The blend of structured data and unstructured data types is what makes the data semi-structured. The use of semi-structured data instead of raw unstructured data can make analysis of the underlying unstructured data faster and easier.
Summary of differences: structured data vs. unstructured data
| Structured data |
Unstructured data |
|
| What is it? |
Data that fits in a predefined data model or schema. |
Data without an underlying model to discern attributes. |
| Basic example |
An Excel table. |
A collection of video files. |
| Best for |
An associated collection of discrete, short, non-continuous numerical and text values. |
An associated collection of data, objects, or files where the attributes change or are unknown. |
| Storage types |
Relational databases, graph databases, spatial databases, OLAP cubes, and more. |
File systems, DAM systems, CMSs, version control systems, and more. |
| Biggest benefit |
Easier to organize, clean, search, and analyze. |
Can analyze data that can’t be easily shaped into structured data. |
| Biggest challenge |
All data must fit in the prescribed data model. |
Can be difficult to analyze. |
| Main analysis technique |
SQL queries. |
Varies. |
How can AWS help with your structured data and unstructured data requirements?
Amazon Web Services (AWS) data analytics and storage solutions are among the most innovative and powerful in the world. These solutions are commercially available for organizations of all sizes across all industries. AWS offers a complete range of advanced modern storage, transformation, and analytics solutions, alongside workflow, integration, and management tools for both structured data and unstructured data. Solutions are modular and designed for hybrid and multi-cloud architectures. For example, you can use:
- Amazon Athena for serverless, scalable analysis of operational databases, data warehouses, big data, ERP, multi-cloud data, and Amazon Simple Storage Service (Amazon S3) data
- Amazon Aurora as a high-performance cloud-native MySQL and PostgreSQL-compatible database
- Amazon EMR to run and scale Apache Spark, Presto, Hive, and other big data workloads
- Amazon Redshift for data warehousing, and to analyze structured data and semi-structured data such as transactions, clickstream, IoT telemetry, and application logs
- Amazon S3 with AWS Lake Formation to create data lakes for analysis
- Amazon Relational Database Service (Amazon RDS) for cloud-based relational database storage operations and scalability
Get started with structured data and unstructured data management on AWS by creating an account today.
Next Steps with AWS
Learn how to get started with Unstructured Data on AWS