Understanding Structured, Semi-Structured, and Unstructured Data

By | 2020-05-18T07:04:03+00:00 May 18th, 2020|

When we talk about data or analytics, the terms structure, unstructured, and semi-structured data often get discussed. These are the three forms of data that have now become relevant for all types of business applications. Structured data has been around for some time, and traditional systems and reporting still rely on this form of data. However, there has been a swift increase in the generation of semi-structured and unstructured data sources in the past few years. More and more businesses are now looking to take their analytics to the next level by including all three forms of data.

structured semi structured and unstructured data

Image source: BBVA

This blog post will examine the differences between structured, semi-structured, and unstructured data and how modern tools make it possible for us to analyze these different data formats.

What Is Structured Data?

Structured data is information that has been formatted and transformed into a well-defined data model. The raw data is mapped into predesigned fields that can then later be extracted and read through SQL easily.  SQL relational databases, consisting of tables with rows and columns, are the perfect example of structured data.

The relational model of structured data utilizes memory since it minimizes data redundancy. However, this also means that structured data is more inter-dependent and less flexible.

Structured data is generated by both humans and machines.  There are numerous examples of structured data that is generated by machines, such as POS data like quantity, barcodes, and weblog statistics. Similarly, anyone who works on data would have used spreadsheets once in their lifetime, which is a classic case of structured data generated by humans. Due to the organization of structured data, it is easier to analyze than both semi-structured and unstructured data.

What Is Semi-Structured Data?

Your data may not always be structured or unstructured – there lies another category between these two that is partially structured. Such data is defined as semi-structured. This type of data has some consistent and definite characteristics, it does not confine into a rigid structure such as that needed for relational databases. Organizational properties like metadata or semantics tags are used with semi-structured data to make it more manageable, however, it still contains some variability and inconsistency.

An example of semi-structured data is delimited files. It contains elements that can break down the data into separate hierarchies. Similarly, in digital photographs, the image does not have a pre-defined structure itself. Still, if it is taken from a smartphone, it would have structured attributes like geotag, device ID, and datetime stamp. After being stored, images can also be assigned tags such as ‘pet’ or ‘dog’ to provide a structure.

On some occasions, unstructured data is classified as semi-structured because it has one or more classifying attributes.

What Is Unstructured Data?

Data present in absolute raw form is termed as unstructured. This data is difficult to process due to its complex arrangement and formatting. Unstructured data may take many forms, including social media posts, chats, satellite imagery, IoT sensor data, emails, and presentations.

Unstructured data is qualitative, not quantitative, so it is mostly categorical and characteristic in nature. For example, data from social media or websites can be used to figure out future buying trends or to determine the effectiveness of a marketing campaign. Moreover, unstructured data helps in detecting patterns in scam emails and chat, which can be useful for enterprises for monitoring policy compliance.

Differences Between Structured, Semi-Structured, And Unstructured Data

Let’s differentiate between these three types of data structures using an analogy. Assume that there exist three types of job interviews: unstructured, semi-structured, and structured.

In an unstructured interview, the questions asked are completely the interviewer’s choice. He can decide the questions he wants to ask and the order in which they would be asked.

Another type is a structured interview. In this case, the interviewer will strictly follow a script creates by the HR department, and the same script will be used for all applicants.

The third type is semi-structured. In this case, the interview will combine the elements of both unstructured and structured interviews. It would include the quantitative and consistency elements, similar to a structured interview. However, at the same time, it will also have the flexibility of customizing questions according to the situation, which is an aspect of the unstructured interview.

If we analyze this analogy, we can see that structured data is less flexible, more organized, and stored in a defined format. Whereas, unstructured data is more complicated and mostly provides qualitative information, which cannot be mapped to a pre-defined data model. Semi-structured data, on the other hand, includes properties of both types.

Historically, businesses have only focused on extracting and analyzing information from structured data. However, with the growth of semi-structured and unstructured data, businesses now need to look towards a solution that can help them analyze all three types of data.

Enterprise-grade tools, such as Astera Centerprise, can help out with this. Centerprise comes built-in support for structured, semi-structured, and unstructured data formats. The tool allows you to capture data entrapped in disparate system quickly, validate its quality, transform to meet business requirements, and export it to the data analysis layer. The outcome is that you can translate input data from your database, documents, emails, PDFs, and various other formats into a consistent stream of output information that can then be used to make key business decisions.

To summarize, businesses need to analyze all three forms of data to stay ahead of their competition and make the most out of the information they have.

Astera Centerprise is an end-to-end data integration tool that can help extract, transform, and verify all types of data using an easy-to-use interface. Interested in finding more about how it works and what it can do for your business? Download the trial version.