What is Structured Data?

Structured data refers to data with a standardized format, or predefined schema, that makes it easily readable by both software and humans. Structured data is typically tabular with rows and columns that clearly define data attributes. Computers can effectively process and interpret structured data due to its known, machine-readable format. For example, a structured customer data table with clearly defined relationships can provide valuable insights. You can easily count the total number of customers and the locality with the most customers. In contrast, unstructured data, such as a list of social media posts, can be more challenging for software to ingest and analyze.

Structured data has several notable features.

Predefined format

Structured data is defined in advance, in a schema that defines field names, data types, and constraints on the data. Any new records added to the dataset will follow the same format.

Definable attributes

Structured data has the same attributes for all data values. For example, each booking record in a reservation system could include the attributes: booking name, event name, event date, and booking amount.

Analytical structure

Structured data lends itself well to mathematical analysis. Because the raw data is uniformly organized, you can efficiently measure the frequency of attributes and perform complex operations on categorical and numerical datasets.

Relational attributes

Structured data tables share common values that link different datasets. For example, you can relate customer data with booking data by using customer_id and booking_id fields.

Storage

You can store structured data in relational databases and manage it using Structured Query Language (SQL). SQL lets you define a data model called a schema under which you determine preset rules for your data, such as fields, formats, and values. You can then store structured data in spreadsheets, OLTP systems, data warehouses, or other relational database technology.

Read about relational databases »

Read about Structured Query Language (SQL) »

Structured data examples

Here are common examples of structured data types and sources:

• Spreadsheets
• SQL databases and other relational databases
• Standardized web form results
• Financial ledgers and ERP records
• Internet of Things (IoT) sensor data and fleet telemetry
• Point-of-sale (POS) and inventory control databases
• Electronic health records and medical monitoring devices
• Online Transaction Processing (OLTP) systems
• Airline, hotel, and enterprise reservation systems
• Customer relationship management (CRM) account data
• Product information management (PIM) product directories
• Search engine optimization (SEO) tags in JSON-LD

There are several benefits to using structured data.

Ease of use

Software applications can easily read structured data. Routine database operations are easy to run, for example, querying and updating records. Structured data also enables highly efficient storage because systems can allocate fixed-length memory for specific data values such as CHAR, INT, and DATE.

Scalability

Structured data scales at a known rate. You can add storage and processing power as your data volume increases. Modern systems that process structured data can scale to petabyte-scale volumes.

Analytics

SQL operations, business intelligence tooling, and machine learning algorithms can analyze structured data and identify common patterns. You can use Structured Query Language (SQL) to generate reports, modify, and maintain data. Structured data is also useful for big data analytics.

Data security

Because structured data conforms to a strict, predefined schema, it is straightforward to secure. Database administrators can implement precise row-level and column-level access controls. Users and applications can only access the specific data points they are authorized to view.

Data governance

Structured data systems can provide a reliable history of how information is collected, altered, and moved over time. Systems instrumented with logging features and lineage tooling provide advanced governance. This transparency facilitates enterprise governance and compliance auditing.

While structured data provides many clear operational advantages, its rigid format limits it to specific business applications.

Limited qualitative insights

Structured databases can easily track quantitative information, such as product prices and order volume, but they can’t capture the qualitative context behind those transactions. For example, they can’t extract customer sentiment data. That comes in the form of unstructured data, such as open-ended survey responses, social media comments, or text reviews.

Schema inflexibility

Structured data models must be strictly predefined with a schema. Adding new fields or altering existing relationships within a structured database requires structural changes to the database schema. This process can be costly and requires careful planning to avoid disrupting existing applications and reports.

Unstructured data is information with no set data model, or data that has not yet been ordered in a predefined way. Unstructured data exists without any predefined schema.

Here are common unstructured data examples:

• Plain text documents
• Video files
• Audio files
• Images and scanned documents

Enterprises are creating data at an exponential rate, and much of it is unstructured. This unstructured data requires different technologies and strategies to analyze it effectively. For example, you can handle unstructured data in NoSQL databases and data lakes, which are designed specifically to accommodate it.

Ease of analysis

One advantage of structured data is that both people and computer programs can easily read and analyze it. There are many tools available to analyze structured data and generate actionable business intelligence. It’s significantly more difficult to analyze data without a predefined data model, and far fewer proven tools in the market can do so.

Searchability

Structured data is easy to search as it adheres to predefined rules. By comparison, unstructured data lacks the order necessary to derive business insights using conventional data-mining techniques. Searching and analyzing unstructured data requires advanced analytical techniques, such as natural language processing and text mining.

Storage

Different environments are needed to store structured and unstructured data: data warehouses and data lakes. In general, the vast majority of data is unstructured, and enterprises require more money, space, and resources to store it. In contrast, you can store structured data very efficiently.

Data warehouse

Data warehouses are the most commonly used cloud computing services for storing structured data. Data warehouses pull data from multiple structured sources, including databases, data integration systems, and transactional systems, and serve as a central repository for enterprise data. Organizations mainly use them for data storage, but they can also utilize data warehouses to analyze data and develop business intelligence. They can support large-scale data analysis.

Read about data warehouses »

Data lake

A data lake is a central repository used to store raw, unstructured data at scale. These data repositories apply a schema to data before analysis, but not before storage. A data lake stores relational data from business applications and non-relational data from a wide variety of sources, including mobile applications, Internet of Things (IoT) devices, and social media.

Read about data lakes »

Semi-structured data is not considered fully structured data because it lacks a specific relational or tabular data model. Despite this, it includes self-describing elements that can be analyzed, such as tags, metadata, nesting, and other markers.

Semi-structured data is considered more straightforward to derive information and insights from than unstructured data. However, it does not have the completeness of and adherence to a predefined data model in the same way structured data does.

Common examples of semi-structured data include:

• JSON
• XML
• HTML
• EML email messages
• ZIP compressed files

You can set up, operate, and scale relational databases quickly with Amazon Relational Database Service (Amazon RDS). It’s a collection of managed services that can be hosted in the cloud or, in some cases, managed on premises with AWS Outposts. Services include:

• Amazon Aurora with MySQL compatibility
• Amazon Aurora with PostgreSQL compatibility
• Amazon RDS for MySQL
• Amazon RDS for MariaDB
• Amazon RDS for PostgreSQL
• Amazon RDS for Oracle
• Amazon RDS for SQL Server

You can build web and mobile applications, move to managed databases, improve existing database efficiency, and break free from legacy databases.

Here are some other things you can do with Amazon RDS:

• Migrate without rearchitecting applications
• Spend less time managing databases
• Cut capital and operational spending
• Focus on innovation

Get started with managing and analyzing your structured data by creating a free AWS account today.