Summary: Each data format is categorized in two different types: Structured and Unstructured data. But what are these? Let’s discuss the difference between structured and unstructured data and their examples for a better decision-making process.
We are in an age where data is overloading- everything from regional databases to your last Instagram story, every piece of information has become like a lifeblood for many businesses. However, not all data is created equal, and each data format is categorized into two different types broadly: Structured and unstructured data.
In this article, I will walk you through structured vs unstructured data, explore the differences between these two types of information, and check their examples for data-driven decision-making.
Let’s get to it!
Structured data is the type of big data that is highly organized and easily interpreted by machine learning algorithms. All the information is organized in rows and columns, just like spreadsheets. These types of data are managed by Sequel Query Language (SQL). Structured data often includes quantitative data; such as age, contact details, address, etc.
Because structured data is quantitative in nature, it is super easy for big data applications to collect and sort these data types. Some examples of structured data are:
Also Read: 7 Best Free SQL Software for Windows and Mac
Unstructured data is categorized as qualitative data, and it can’t be directly analyzed by conventional data software or methods. This type of data is available in various forms, such as emails, social media posts, images, videos, audio files, and documents.
Some of the examples of unstructured data are:
Now that you have understood what is structured and unstructured data, let’s talk about their differences. I have also provided a chart for Structured versus Unstructured data.
Now, let’s take a look at the comparison chart between structured and unstructured data. Here, we will measure the difference between both data types based on characteristics.
Characteristics | Structured Data | Unstructured Data |
Nature | Quantitative in nature | Qualitative in nature |
Format | Fixed and predefined format | No predefined format or organization |
Technology | It is based on relational database | Based on binary and character data |
Processing speed | Faster processing due to organized data | Slower processing as it requires advanced algorithms for analysis |
Use cases | Online booking, inventory control, CRM, etc. | Sentiment analysis, social media analysis, OCR, etc. |
Ease of analysis | Easy and straightforward with standard querying (e.g., SQL) | Challenging as it requires advanced techniques (NLP, ML) |
Examples | Databases (customer info, financial records) | Text documents, images, videos, social media posts |
Apart from structured data and unstructured data, there is another data type called semi-structured data. This data type is not completely structured or unstructured and includes the characteristics of structured data, and also contains unstructured information that does not follow any specific format or schema. Semi-structured data includes inherited information like location, time, email address, or device ID stamp.
To add structured data to your website, follow the steps below:
As we are about to conclude our topic on the difference between structured and unstructured data, here are few points to consider:
Yes, structured data is quantitative. It's often displayed as numbers, dates, values, and strings.
Semi-structured data are data types that do not comply with a data model but it have some structure.
The two examples of unstructured data XML files, images, emails etc.
Unstructured data is a type of raw data and it can be found in file systems or data lakes.
You can store unstructured data in applications, data lakes, NoSQL databases, and data warehouses.
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