Structured data is information that follows a predefined format or schema. It is highly organized and easy to understand by humans and when using machine learning (ML) algorithms — designed to learn patterns from data. Its rigid structure means it is easy to identify data points or relationships within a set.

It is typically organized using fields and categories within a database that utilizes tables, rows, and columns. Each field contains a specific type of data such as text, numbers, and dates. This data could be names, addresses, or dates of birth. Clear fields within structured data make it easier to scan, organize, and analyze large amounts of data.

There are various sources and ways to generate structured data. For example, structured data can be derived from online sources such as surveys, or business applications such as customer relationship management (CRM) systems and accounting software. This makes it useful for tasks such as reporting, compliance, and automation. 

Structured data can also be taken from unstructured datasets by using artificial intelligence (AI) techniques like natural language processing (NLP).

Why is structured data important?

Structured data has various practical applications and is commonly used in business intelligence (BI), analytics, and for effective data management. Its standardized nature makes it easier to understand for data analytics tools, machine learning algorithms, and human users. This allows machine learning models to more easily extract useful features from the dataset.

In the ML training process, structured data enables models to perform tasks such as classification, regression, and prediction. This makes structured datasets useful for organizations looking to gain deeper insight into customer behavior, forecasting market trends, and supporting strategic decisions. It is currently estimated that 20% of existing generated data is structured. 

Consistency within the structured dataset simplifies the analysis process and makes it easier to manipulate. Inputting new data and searching for existing values is more efficient using structured data rather than unstructured data. This makes structured data more accessible to general business users taking advantage of traditional tools such as relational database management systems (RDBMs). 

Key features of structured data

Structured data typically exhibits the following key features.

Defined attributes

Defined attributes refer to named characteristics that are consistent throughout the dataset. For example, when looking at a customer record, this could be name, age, and geographical location. These attributes are specific data fields (type of data) found within the set.

Relational attributes

Common values that link different datasets together are known as relational attributes. These are typically found within relational databases and are defined by a specific data type such as text or dates. Relational attributes help to organize the data and make it more efficient when searching for values.

Quantitative data

Quantitative data consists of numerical values which are useful for mathematical analysis. This makes it easier to organize, group, and perform analysis using specific attributes in the dataset as all values are standardized. Qualitative values are usually converted into numerical values to simplify analysis, making the process more efficient.

Storage

Relational databases are typically used for storing structured data and managed using structured query language (SQL). SQL enables data models to be defined using schema, which utilizes preset rules to organize data such as fields, formats, and values. Data that has been managed using SQL can then be stored in other relational databases or data warehouses.

What are the benefits of using structured data?

Using structured data offers a range of powerful advantages that enhance data management, accessibility, and security across various applications.

Accessibility

In-depth data science knowledge is not required to understand or interpret structured data, making it accessible to a wider audience. It follows a well-defined data model or schema which simplifies tasks such as data retrieval, analysis, and reporting. 

Scalability

It is relatively straightforward to scale systems that manage structured data by using algorithms. This means it is possible to add storage and processing power as the dataset increases in volume. The clarity and consistency of structured data makes the scaling process more efficient and improves reliability.

Reliability and consistency

Schema within structured data enables consistency in how it is stored, allowing for easier comparison and analysis when using different data sources. The datasets are typically more accurate and reliable in outputs due to their well-defined format.

Data security

Structured data has a clear lineage or history, which makes it easy to track any alterations to the data. Access to the data can also be controlled using security protocols. This ensures that the data consistently meets quality and compliance standards, providing an audit trail if required. 

What are the challenges of using structured data?

While structured data offers many benefits, it also comes with notable challenges that can impact storage, flexibility, and overall data quality.

Storage

Rigid schemas are used for storing structured data. This limits the storage options for the data. It can also be fragmented across various data sources, which complicates the storage process and can create data silos. 

Limited use

Structured data is limited in its use, meaning it can only be used for its intended purpose. Any required changes to the data model or schema are also harder to implement. It also lacks additional context, making it more difficult to derive significance from the data.

Lack of flexibility

Rigid schemas can cause adapting the dataset for new data sources, or to introduce new data, to be high in cost and a resource-intensive process. It also struggles to accommodate complex or diverse data sources, requiring strict adherence to the schema.

Data quality

Data can be occasionally missing or incomplete, which affects overall data quality. Some data might not directly fit into the defined schema. This will have a negative impact on task performance or output accuracy.

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