When we talk about data or analytics, the terms structured, 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, due to the rise of Big Data. As a result, more and more businesses are now looking to take their business intelligence and analytics to the next level by including all three forms of data.
- Structured Data vs. Semi-Structured Data vs. Unstructured Data
- Differences Between Structured, Semi-Structured, And Unstructured Data
This blog post will examine the differences between structured vs unstructured data, and how modern tools allow us to analyze and process these different data formats.
Structured Data vs. Semi-Structured Data vs. Unstructured Data
Let’s get down to the basics:
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 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 this data format utilizes memory since it minimizes data redundancy. However, this also means that structured data is more inter-dependent and less flexible. Now let’s look at more examples of structured data.
Examples of Structured Data
This type of data is generated by both humans and machines. There are numerous examples of structured data from 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?
You may not always find your data sets to be structured or unstructured. Semi-structured data or partially structured data is another category between structured and unstructured data. Semi-structured data is a type of data that has some consistent and definite characteristics.
It does not confine into a rigid structure such as that needed for relational databases. Businesses use organizational properties like metadata or semantics tags with semi-structured data to make it more manageable. However, it still contains some variability and inconsistency.
Examples of Semi-Structured Data
An example of data in a semi-structured format 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 but has certain structural attributes making them semi-structured. F
or instance, if you take a photo from a smartphone, it would have some structured attributes like geotag, device ID, and DateTime stamp. After you save them, you can assign tags to images such as ‘pet’ or ‘dog’ to provide a structure.
On some occasions, unstructured data is classified as semi-structured data because it has one or more classifying attributes.
What is Unstructured Data?
Unstructured data is defined as data present in absolute raw form. This data is difficult to process due to its complex arrangement and formatting.
Unstructured data includes social media posts, chats, satellite imagery, IoT sensor data, emails, and presentations. Unstructured data management takes this data to organize it in a logical, predefined manner in data storage. Natural language processing (NLP) tools help understand unstructured data that exists in a written format.
In contrast, the meaning of structured data is data that follows predefined data models and is easy to analyze. Structured data examples would include alphabetically arranged names of customers and properly organized credit card numbers. After understanding the definition of unstructured data, let’s look at some examples.
Examples of Unstructured Data
Unstructured data can be anything that’s not in a specific format. This can be a paragraph from a book with relevant information or a web page. An example of unstructured data could also be Log files that are not easy to separate. Social media comments and posts are also unstructured.
Here is an example of unstructured data from a log file.
Wed Sep 23 2020 05:21:01 GMT+0500
Unstructured data is qualitative, not quantitative, so it is mostly categorical and characteristic in nature. For example, data from social media or websites can help predict future buying trends or determine the effectiveness of a marketing campaign. Another unstructured data analytics example is detecting patterns in scam emails and chat, which can be useful for enterprises in monitoring policy compliance. That’s why businesses extract and store unstructured data in data warehouses (also called data lakes) for analysis.
Differences Between Structured, Semi-Structured, And Unstructured Data
Let’s understand the difference between structured vs. unstructured data vs. semi-structured data using an analogy of interviews. We can do this by looking at some structured and unstructured data examples in the real world. Assume that there exist three types of job interviews: unstructured, semi-structured, and structured interviews.
In an unstructured format interview, the questions asked are completely the interviewer’s choice. He can decide the questions he wants to ask and the order in which he will ask them. Popular examples of unstructured questions include “Tell me about yourself” and “Describe your ideal role.”
Another type is a structured interview. In this case, the interviewer will strictly follow a script created by the HR department and will use the same script for all applicants. Likewise, structured vs. unstructured data follows an organized format with a less flexible schema.
The third type is semi-structured data. In a semi-structured interview, the interviewer 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, like semi-structured data, structured interviews will have the flexibility of customizing questions according to the situation. To reiterate, the main difference between unstructured and semi-structured data is that unstructured data follows no pre-defined format, while semi-structured data is only partly unstructured.
The following points highlight the differences between structured data vs. unstructured data vs. semi-structured data:
- Organization: Structured data is well organized. Therefore, it has the highest level of organization. Semi-structured data is partially organized; hence the level of organizing is lesser than structured data but higher than that of unstructured data. Lastly, the latter category is not organized at all.
- Flexibility and Scalability: Structured data is relational database or schema dependent, therefore less flexible and difficult to scale, while semi-structured data is more flexible and simpler to scale than structured data. However, unstructured data doesn’t have a schema that makes it the most flexible and scalable out of the other two.
- Versioning: Since structured data is based on a relational database, versioning is performed over tuples, rows, and tables. On the other hand, in semi-structured data, tuples or graphs are possible as only a partial database is supported. Lastly, in unstructured data, versioning is likely as a whole data as there’s no database support.
- Transaction Management: In structured data, data concurrency is available and, therefore, usually preferred for the multitasking process. In semi-structured data, the transaction gets adapted from DBMS, but still, data concurrency isn’t available. Lastly, in structured data, neither transaction management nor data concurrency is present.
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 for a solution that can help them analyze all three types of data.
Simplify Unstructured Data Management With Astera
Enterprise-grade data tools, such as Astera Centerprise, can help out with this. Centerprise comes with built-in support for structured, semi-structured, and unstructured data formats. The tool allows you to capture data entrapped in a 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 managers can use to make key business decisions.
To summarize, it is essential for businesses to understand the difference between structured, unstructured data, and semi-structured data. They need to analyze all three forms of data to stay ahead of their competition and make the most out of their information.
Astera ReportMiner is an end-to-end data extraction tool that helps with the extraction of structured, semi-structured, and unstructured data. It also converts unstructured data to structured format in an easy-to-use interface.
Interested in finding out more about how it works and what it can do for your business? Try it out for 14 days, free of cost, or contact us for tailored advice.