Unstructured data is any data that is not in a pre-defined format. This could include text, images, and other unstructured elements. Structured data, however, is formatted in a specific way and consists of numbers, dates, etc. Structured data can be easily searched and studied, while unstructured data is more challenging to access and examine. However, as data-extraction technology progresses, the defining terms “structured” and “unstructured” are becoming more of a spectrum than a binary. Here’s why: not all text strings are created equal. Much unstructured text data (like that produced by automated point-of-sale systems) is random, arbitrary, and unstandardized, making it difficult to organize in a database. But with more advanced technology, natural language (which would have previously been considered unstructured data) is predictable and patterned enough to be organized irretrievably in a Natural Language Processing (NLP) database. So, while it may not have the same format as tabular data, natural language is structured data.

To know more about the blurring differences between the two categories, read on:

What Is Unstructured Data?

Unprocessed data is unstructured: it is in a raw form. This type can be challenging to work with and can cause problems when used in business or scientific projects because it is difficult to organize and even more difficult to retrieve from a database. However, there are methods to deal with unprocessed information and make it easier to use.

What Is Semi-Structured Data?

Semi-structured data is not completely structured. It can be in databases, text files, emails, and other sources. The most common use is in web applications, where it is used to store user information. Semi-structured data does not conform to a traditional model. It is not as well defined as a conventional, tabular model and often contains fields that are not directly related. Creating a database that can store and manage semi-structured information is challenging. However, it can be beneficial for understanding the relationships between different pieces of information.

Structured Vs. Unstructured

Structured data is a specific type organized in a particular way. Natural language is structured data. For instance, Natural Language Processing (NLP) is a method that helps extract value from data by organizing it into a particular form.

Computers often use it to make otherwise semi-structured data easier to comprehend and use. Unstructured information can be more challenging for computers. Structured information comes in a pre-defined format, while the unstructured is in its native, often proprietary form. Structured can be found in databases and tables, while unstructured looks more like text or multimedia.

Structured data is stored in information warehouses, while unstructured information is stored in data lakes. One is easy to query, while the other is difficult.

Examples Of Semi-Structured Data

Markup language XML

XML is a popular choice for information interchange because it is easy to use and flexible. It is a markup language that is useful for defining a data structure. XML files are text documents that use tags to identify the data within them. XML tags are similar to HTML tags, but they are not interchangeable with HTMLs.

Open standard JSON (JavaScript Object Notation)

The IETF (Internet Engineering Task Force) has adopted JSON. JSON is based on a subset of the JavaScript language, so it feels natural to use for Ajax applications. JSON is valid in many modern web applications, including Facebook, Twitter, Google, and LinkedIn.

NoSQL

NoSQL is a database that doesn’t use the traditional table-based structure. This makes it better suited for managing large volumes of information and handling queries that are too complex for conventional databases. NoSQL databases are often distributed, spreading across multiple servers to improve performance.

Conclusion

Unstructured data is more difficult to control and process than structured. However, it can be more valuable because predefined schemas do not limit it. Therefore, businesses should consider the advantages and disadvantages of both before deciding how to store and manage their data.