1. Structured data is clearly defined and searchable types of data, while unstructured data is usually stored in its native format. 
  2. Structured data is quantitative, while unstructured data is qualitative.
  3. Structured data is often stored in data warehouses, while unstructured data is stored in data lakes.
  4. Structured data is easy to search and analyze, while unstructured data requires more work to process and understand.  
  5. Structured data exists in predefined formats, while unstructured data is in a variety of formats. 

Data is fundamental to business decisions. A company's ability to gather the right data, interpret it, and act on those insights is often what will determine its level of success. But the amount of data accessible to companies is ever increasing, as are the different kinds of data available. Business data comes in a wide variety of formats, from strictly formed relational databases to your last tweet. All of this data, in all its different formats, can be divided into two main categories: structured data and unstructured data. 

In this article, we'll take a closer look at these concepts and the differences between them. 

Table of Contents

  1. What is Structured Data?
  2. What is Unstructured Data?
  3. What is Semistructured Data?
  4. Structured vs Unstructured Data: 5 Key Differences
  5. Conclusion

What is Structured Data?

The term structured data refers to data that resides in a fixed field within a file or record. Structured data is typically stored in a relational database (RDBMS). It can consist of numbers and text, and sourcing can happen automatically or manually, as long as it's within an RDBMS structure. It depends on the creation of a data model, defining what types of data to include and how to store and process it.  

The programming language used for structured data is SQL (Structured Query Language). Developed by IBM in the 1970s, SQL  handles relational databases. Typical examples of structured data are names, addresses, credit card numbers, geolocation, and so on.

What is Unstructured Data?

Unstructured data is more or less all the data that is not structured. Even though unstructured data may have a native, internal structure, it's not structured in a predefined way. There is no data model; the data is stored in its native format. 

Typical examples of unstructured data are rich media, text, social media activity, surveillance imagery, and so on. 

The amount of unstructured data is much larger than that of structured data. Unstructured data makes up a whopping 80% or more of all enterprise data, and the percentage keeps growing. This means that companies not taking unstructured data into account are missing out on a lot of valuable business intelligence.

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What is Semistructured Data?

Semistructured data is a third category that falls somewhere between the other two. It's a type of structured data that does not fit into the formal structure of a relational database. But while not matching the description of structured data entirely, it still employs tagging systems or other markers, separating different elements and enabling search. Sometimes, this is referred to as data with a self-describing structure.

A typical example of semistructured data is smartphone photos. Every photo taken with a smartphone contains unstructured image content as well as the tagged time, location, and other identifiable (and structured) information. Semi-structured data formats include JSON, CSV, and XML file types.

Structured vs Unstructured Data: 5 Key Differences

1) Defined vs Undefined Data 

Structured data is clearly defined types of data in a structure, while unstructured data is usually stored in its native format. Structured data lives in rows and columns and it can be mapped into pre-defined fields. Unlike structured data, which is organized and easy to access in relational databases, unstructured data does not have a predefined data model.

2) Qualitative vs Quantitative Data

Structured data is often quantitative data, meaning it usually consists of hard numbers or things that can be counted. Methods for analysis include regression (to predict relationships between variables); classification (to estimate probability); and clustering of data (based on different attributes). 

Unstructured data, on the other hand, is often categorized as qualitative data, and cannot be processed and analyzed using conventional tools and methods. In a business context, qualitative data can, for example, come from customer surveys, interviews, and social media interactions. Extracting insights from qualitative data requires advanced analytics techniques like data mining and data stacking.

3) Storage in Data Houses vs Data Lakes

Structured data is often stored in data warehouses, while unstructured data is stored in data lakes. A data warehouse is the endpoint for the data’s journey through an ETL pipeline. A data lake, on the other hand, is a sort of almost limitless repository where data is stored in its original format or after undergoing a basic “cleaning” process.

Both have the potential for cloud-use. Structured data requires less storage space, while unstructured data requires more. For example, even a tiny image takes up more space than many pages of text.

As for databases, structured data is usually stored in a relational database (RDBMS), while the best fit for unstructured data instead is so-called non-relational, or NoSQL databases

4) Ease of Analysis

One of the most significant differences between structured and unstructured data is how well it lends itself to analysis. Structured data is easy to search, both for humans and for algorithms. Unstructured data, on the other hand, is intrinsically more difficult to search and requires processing to become understandable. It's challenging to deconstruct since it lacks a predefined data model and hence doesn't fit in in relational databases. 

While there are a wide array of sophisticated analytics tools for structured data, most analytics tools for mining and arranging unstructured data are still in the developing phase. The lack of predefined structure makes data mining tricky, and developing best practices on how to handle data sources like rich media, blogs, social media data, and customer communication is a challenge. 

5) Predefined Format vs Variety of Formats

The most common format for structured data is text and numbers. Structured data has been defined beforehand in a data model.

Unstructured data, on the other hand, comes in a variety of shapes and sizes. It can consist of everything from audio, video, and imagery to email and sensor data. There is no data model for the unstructured data; it is stored natively or in a data lake that doesn't require any transformation.

In Conclusion

There are mainly two categories of data: structured data and unstructured. Structured data resides in predefined models and formats, while unstructured data is stored in its native format until it's extracted for analysis. There is also semistructured data; a category that falls between the other two. It refers to data that has some kind of tagging structure but still doesn't fit into the formal structure of a relational database. 

In this article, we've looked at five important differences between structured and unstructured data:

  1. Defined vs Undefined Data 
  2. Qualitative vs Quantitative Data
  3. Storage in Data Houses vs Data Lakes
  4. Easy vs Hard to Analyze
  5. Predefined format vs a variety of formats

While structured data is much easier for Big Data programs to process, it's paramount not to forget about the unstructured and semistructured data. Analyzing unstructured data does present a more significant challenge. But considering that more than 80% of all enterprise data adheres to this category, leaving it out will create large blind spots. Luckily, as technology evolves, the insights that are hidden in unstructured data are becoming more accessible.

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How Xplenty Can Help

We believe that everyone should be able to manage their data, regardless of their tech experience. That's why we offer no-code and low-code options so that you can add Xplenty to your data solution stack with ease.

Xplenty offers a complete toolkit for building ETL data pipelines, making it easy to implement an ETL or ELT solution to extract unstructured data and transform it into the format you need. 

With Xplenty's workflow engine, you can orchestrate and schedule data pipelines. With our rich expression language, you can implement complex data preparation functions and integrate with other data repositories and applications.

With Xplenty, you can spend less time processing your data, so you have more time for analyzing it. Schedule a demo and learn how our low-code platform can help you turn your unstructured data into valuable business intelligence!