Structured vs Semi-structured vs Unstructured data

BI & Data Science Analyst at 10 Senses

Big Data is no longer just a sexy catchphrase to throw at a cocktail party.

Truth be told, the Big Data market has been on the rise, along with increasing demand from businesses worldwide. These days, companies generate loads of qualitative or quantitative data, and the volume is increasing at an accelerating pace.

In fact, it is estimated that by the year 2027, the Big Data analytics market will reach a value of around $103 billion.

What is more, although numerous businesses still don’t grasp the importance of data and still don’t understand the power of Business Intelligence tools, in 2019, according to New Vantage Partners, 97% of companies were investing in both Big Data and Artificial Intelligence (AI). 

When we talk about Big Data, it is based on the 3V rule:

  • Volume of data,
  • Velocity of data,
  • Variety of data.

Nevertheless, data comes in different flavors, and it is important to understand various data formats to use appropriate tools for data management and analytics.

Once you read this article, you will know:

  • What is Structured data,
  • What is Semi-structured data,
  • What is Unstructured data,
  • What are the key differences – Structured vs Semi-structured vs Unstructured data,
  • Why is the distinction between Structured, Semi-structured, and Unstructured data important?

What is Structured data?

Structured data is schema-dependent data that has been formatted and transformed into a predefined data model.

Structured data can be created by humans or generated by a machine. It can be a:

  • simple spreadsheet created by a data analyst,
  • data entries created based on events, or
  • inventory control systems.

Therefore, it has a high level of organization and usually resides in relational databases (RDBs), such as MySQL, PostgreSQL, Db2, or data warehouses.

When it comes to Structured data, versioning bases upon tuples, rows, and tables. You can manage the data using structured query language (SQL). Data concurrency is available and usually preferred for multitasking processes.

Currently, Structured data is considered the most user-friendly source of insights as it is already organized into a formatted repository. Therefore, you can use it for data visualization, analytics, and machine learning without pre-processing.

Use cases for Structured data 

In fact, use cases for Structured data are related to the data storage and access methods, rather than specific examples of data. You can store the same data as Structured, Semi-structured, or Unstructured, so the key aspect here is what we need to achieve in terms of performance and flexibility.

Structured vs Semi-structured vs Unstructured data -  relationships are easy to spot in Structured data

The benefits of Structured data are primarily results of the ease of manipulating and querying. Data is in tabular form. As a result, the structure is predefined, which guarantees:

  • better quality of data,
  • data correctness,
  • the ability to use relational operators.

Data Engineers and others working with relational databases may input, search, and manipulate Structured data using a relational database management system (RDBMS), like:

  • MySQL,
  • PostgreSQL,
  • MariaDB,
  • Microsoft SQL Server,
  • Oracle Database.

Nevertheless, an inflexible structure can also result in data recording being a time-consuming and constraining process. You cannot process data quickly you need to check everything and you are not able to record any data that doesn’t meet the predefined requirements.

Let’s sum up the pros and cons of Structured data.

Pros and cons of Structured data

Pros of Structured data

First of all, Structured data is universally understood as it doesn’t require a deep understanding of data types to be analyzed. As a result, data analysts with basic SQL skills can manage Structured data. On top of that, in many cases, business users are able to perform Structured data analytics by themselves to make data-driven decisions.

Secondly, a fixed schema with tables and fields enables machine learning algorithms to crawl data easily, which simplifies querying and creating data models. A predefined schema also makes the data processing efficient and quick.

Finally, Structured data guarantees high accessibility of data, as there are multiple tools to access, manage, and modify it, and easy integration with other systems, such as databases and applications.

Cons of Structured data

One of the main disadvantages of Structured data is its limited flexibility, due to a predefined structure. It means it won’t be suitable for all types of data.

What is more, a predefined, rigid schema enforces a specific way of storing data. Usually it is stored in data warehouses, which are built for high querying performance and can be difficult to change. As a result, it can be less scalable under certain circumstances.

What is Unstructured data?

Unstructured data is a type of data that is neither organized in a predefined format, nor has a predefined data model. Consequently, it cannot reside in relational databases, and it is not obvious right away how to analyze unstructured data. You cannot process such data until the moment you need it – such a concept is schema-on-read.

Unstructured data needs alternative platforms for storing and managing it. It usually resides in data lakes within an organization, or in non-relational databases such as:

  • NoSQL, 
  • Redis, 
  • MongoDB,
  • Apache Cassandra,
  • Couchbase,
  • Apache HBase.

In fact, you need technology knowledge to manage Unstructured data. As a result, it is mostly used in businesses by data, machine learning (ML), and Artificial Intelligence (AI) scientists or data engineers that use different techniques to handle Unstructured data and extract meaning out of it.

Use cases for Unstructured data

These days, in analytics, Unstructured data can be used, for example, for classifying images and sounds using deep learning, sentiment analysis using natural language processing (NLP), or as an input to predictive models.

Unstructured data examples include:

  • audio files,
  • text and text files,
  • media logs,
  • social media posts,
  • PDFs,
  • videos.

Unstructured data can come in different formats

Pros and cons of Unstructured data

Pros of Unstructured data

First of all, Unstructured data usage is highly versatile as it is stored in the native format in which it was created and remains undefined until needed (schema-on-read).

Moreover, since it doesn’t need any specifications prior to storing, the process of data extraction is quick and straightforward.

Finally, Unstructured data enables massive storage and cost efficiency with pay-as-you-go pricing in the case of storing data in data lakes (which usually occurs).

Cons of Unstructured data

Firstly, Unstructured data requires expertise and data knowledge to be analyzed due to the non-fixed schema. Moreover, it doesn’t have any specifics or attributes, which hinders the analysis of Unstructured data.

What is more, data processing requires specialized analytics tools, which limits the pool of available options. It is also more resource-intensive and time-consuming when looking at a structured data vs unstructured data comparison.

Finally, lack of standardization or consistency makes data quality and accuracy more difficult.

What is Semi-structured data?

This type of data is a hybrid between Structured and Unstructured data. It doesn’t have a rigid schema, like Structured data, but it is not completely unstructured, like unstructured data.

Consequently, it doesn’t conform to a data table or relational database structure. Nevertheless, it has internal semantic tags, metadata, or markings that allow it to define a partial structure or hierarchy.

In this type of data, data transactions are adapted from a database management system (DBMS), and data concurrency can pose problems.

Use cases for Semi-structured data

Semi-structured data allows for the integration of data from multiple sources and the exchange of information between various systems that evolve with time. Working only on Structured data would result in the need to change the database schema every time there is a small change.

This type of data allows you to capture any data in any structure or data format without modifying the database schema or coding. New data arriving, or redundant data you remove from the system won’t impact functionality or dependencies.

Examples of such data can be:

  • XML, a markup language used to sort data in a hierarchical form,
  • HTML, a markup language used to create web pages and display images or text on the screen,
  • JSON, an interchange and language-independent open-source text format,
  • Avro, a data serialization network using JSON format to organize data in a binary format,  
  • Parquet, a columnar binary format,
  • ORC (Optimized Row Columnar), a file format used to efficiently store Hive data, 
  • emails,
  • zipped files,
  • data integrated from different sources,
  • server logs.

Persistent Staging Area

A persistent staging area (PSA) is a concept particularly useful for handling Semi-Structured data, which often requires multiple iterations to extract meaningful information.

In brief, PSA is a dedicated space where you can gather data before processing and transformations. Consequently, it serves as:

  • buffer ensuring data integrity during the transformation process,
  • reference point for audit trails,
  • helper in data quality checks.

Pros and cons of Semi-structured data

Pros of Semi-structured data

Firstly, Semi-structured data is more flexible than Structured data in regards to data storage and management (no need to fit into a predefined schema). Nevertheless, semi-structure allows for easy integration with both Structured and Unstructured data.

Secondly, it usually contains more contextual information than Structured data, like metadata or tags, which facilitate the analysis.

Moreover, not only SQL-skilled users can analyze and extracted it. Data can be queried in a flexible way, so the data processing is less time-consuming.

Cons of Semi-structured data

Nonetheless, Semi-structured data is more difficult to handle due to the lack of a fixed schema and to interpret the relationship between data due to the separation of schema and data.

Moreover, there is a limited pool of software to work with this type of data, and it can be challenging to find the most appropriate technologies. Data security may also cause difficulties due to problems with identification and protection of sensitive information from unauthorized access.

Finally, Semi-structured data includes a wide selection of formats, tags, and metadata, which can result in complex data management and processing.

What are the key differences – Structured vs Semi-structured vs Unstructured data?

Let’s sum up all the key differences between Semi-structured, Structured vs Unstructured data:

Structured vs Semi-structured vs Unstructured data - key differences

Why is the distinction between Structured, Semi-structured and Unstructured data important?

As you can see, the distinction between Structured, Semi-structured, and Unstructured data lies in the extent of data organization To add value to data, businesses need to be able to manage all types of data, without fixating on any type.

The degree of data organization, whether it is qualitative data or quantitative data, is important for numerous reasons, but the primary ones are:

  • Implications for data storage,
  • Machine-readability,
  • Structured vs. Unstructured data volume,
  • Implications for analytical robustness.

Data storage 

First, the distinction is important as it impacts data management and storage. Structured data resides in relational databases (data warehouses) and unstructured data in non-relational data warehouses or data lakes, while, as for Semi-structured, it depends.  

Relational databases can store Semi-structured data by mapping it to the relational schema, but there also exist non-relational databases that natively allow the storage of Semi-structured data. What’s more, you can store this type of data through Object Exchange Model (OEM).

As a result, it is important to choose the right data storage systems among mature analytics tools to effectively manage Structured, Semi-structured and Unstructured data in a financially optimal way.


If data has an organized structure, like in an Excel spreadsheet, without any deviations, it is easily machine-readable. Consequently, you can easily analyze large datasets of Structured data using computer power.

On the other hand, if the data is Unstructured, data analysis is more difficult and requires expertise. In fact, Semi-structured and Unstructured data may be simple to follow by humans, but not by machines. As a result, it will be more difficult to harness computer power to start analyzing Semi-structured and Unstructured data.

Nevertheless, there are technologies being developed that aim to allow machines to read Semi-structured and Unstructured data (for example, Large Language Models – LLMs).

Structured vs. Unstructured data volume

Although Structured data is the easiest type of data to manage, only 20% of all data is structured, while unstructured data accounts for 80%. 

Historically, businesses have been focused on extracting and analyzing Structured data. Nevertheless, there is four times more Unstructured than Structured data, which businesses should properly store, analyze, and utilize to gain valuable insights.

Consequently, Data Engineers should be able to manage Unstructured data and choose appropriate technologies (like data lakes) and tools, which entails: 

  • capturing data from disparate systems quickly,
  • validating its quality,
  • transforming it to meet business requirements,
  • exporting to a data analysis layer.

Summing up, it is essential for modern companies to understand the differences between Structured vs Semi-structured vs Unstructured data.

Each type of data has its own specifics, and there are different factors to consider in the process of data mining, data transformation, data analysis, or data storage. With the assistance of data engineers (click here to read about the Data Engineering), they need to be able to analyze all three types of data to stay competitive in the market and make the most of the information they have.

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