Structured vs. Unstructured Data Comparison

Have you ever wondered why some data seems neat while others look like a jumbled mess? Well, that’s because not all data is the same. Some data are nicely organized and called structured data, while most are all over the place and are known as unstructured data. They’re collected and handled in different ways and live in different types of databases. In this article, I’ll explain two kinds of data. I’ll show you how they are different and how you can use each type effectively. Let’s dive in!

What is Structured Data?

Structured data is organized and fits into specific categories within a record or file. It is usually stored in relational databases, known as RDBMS. This type of data includes both text and numbers. If it follows the RDBMS format, you can collect structured data automatically or manually. Setting up structured data requires creating a data model, which defines the types of data included and how it is stored and processed.

SQL, or Structured Query Language, is the programming language for managing structured data. IBM developed SQL in 1974 to handle relational databases. It is simple to use and does not require advanced programming skills. Examples of structured data are names, addresses, credit card numbers, and information in Microsoft Excel or text files.

What Is Unstructured Data?

Unstructured data is all the data that doesn’t fit into specific categories. Unlike structured data, unstructured data doesn’t follow a particular format. There’s no set model; it’s just stored as it is.

Examples of unstructured data include pictures, words, social media posts, videos, sound recordings, and many other types of files.

Unstructured data is a big part of all the data, even more than structured data. It’s estimated to be about 80% or more of all the data businesses have. And this amount keeps getting bigger. So, if companies don’t pay attention to unstructured data, they might miss out on important insights for their business.

What Is Semi Structured Data?

Semi structured data is a mix of structured and unstructured data. It’s structured, but it doesn’t fit neatly into a database. Instead, it uses tags and markers to organize things, making them easier to search through.

Smartphone photos are a good example of semi structured data. Each photo has the picture itself (unstructured) and tags like time and location (structured). This helps organize the data even though it’s not in a formal database structure.

When it comes to file types, JSON, CSV, and XML fall into the semi structured category. These formats keep things organized, even if they’re not perfect. So, while it could be more tidy than structured data, semi structured data still has some order because of things like tags and markers.

Side-by-Side Comparison of Structured vs Unstructured Data

Key Differences Between Structured and Unstructured Data

Structured data is organized neatly like in a table, while unstructured data is messy, like emails or social media posts. Let’s explore the main differences between them.

Defined vs. Undefined Data

Structured data is neatly organized information kept in rows and columns, and it is easy to understand and access. On the other hand, unstructured data is more like a messy pile of stuff stored in its original form without any clear structure. So, while structured data is well-defined and can be put into databases with specific fields, unstructured data doesn’t have a set model and is all over the place.

Qualitative vs. Quantitative Data

Structured data is like numbers or things you can count, such as what you see in a customer system. It’s quantitative because it’s all about numbers and counting. People studying data can understand it better using special methods like regression, classification, and clustering. They use these methods to find important things for businesses.

Unstructured data is different. It’s more about words and descriptions. This data type comes from customer surveys, interviews, and social media. It’s more complicated to understand than structured data. People studying data must use advanced data mining and stacking methods to understand it. These methods help them find useful information from unstructured data, which is important for businesses.

Ease of Analysis

One key difference between structured and unstructured data is how easy it is to analyze structured data. Structured data is quite simple to search, which is great for data analysts and various algorithms. On the flip side, unstructured data is more complex to sift through and generally needs some processing to make sense of it.

There are many analysis tools for structured data. However, things get a bit trickier when we talk about unstructured data. Most tools, like those based on natural language processing (NLP) and machine learning (ML), that help sort and analyze unstructured data are still being developed. They still need to be more advanced, so there’s a lot of work to be done in that area.

Storing Data in Data Warehouses vs. Data Lakes

Data warehouses and data lakes are two different storage places for business information. In a data warehouse, neat and organized data goes through a process before storage. On the other hand, data lakes are big pools where messy data can be kept as it is or cleaned up a bit.

The data stored in warehouses is usually tidy and takes up less space, while data lakes can hold all sorts of messy information, which might need more space.

For databases, structured data that fits nicely into tables is often stored in one type of database, while messy, unorganized data is stored in a different kind of database.

Predefined Format vs. Variety of Formats

Structured data usually sticks to a common format, mainly text and numbers. It’s all organized based on a data model set up in advance.

However, unstructured data is a whole different story. It can come in many forms, like audio clips, videos, pictures, emails, and even sensor data. There’s no specific data model for unstructured data. Instead, you can store it just as it is, on its own or in a data lake, without needing to change anything.

Why You Should Manage Your Unstructured Data

Managing unstructured data is very important because businesses accumulate more data every year. This data isn’t used after 30 days, which we call “cool” data. This cool data fills up expensive hard drives and raises storage costs.

Unstructured data is especially challenging for companies to handle. It’s difficult to sort and fits poorly in regular XML, key-value, or JSON data databases. Companies usually use a different system to work with this kind of data, which means moving the data around. This takes up more storage space and costs more money.

Some companies ignore managing unstructured data and add more space to their main storage systems. But this method needs fixing. It uses up all the space on the main storage, which is the most expensive kind because it often requires costly flash drives.

Also, businesses must update their storage systems every three to five years and include all their unstructured data. They must consider the costs of moving data and the extra storage needed for backups.

It’s also important for businesses to follow global data laws. These laws require companies to check what’s in their unstructured data, especially if it contains personal information.

By managing unstructured data well, companies can work better and save money. Cloud storage, tapes, or other secondary storage options can make it easier to handle unstructured data. This helps companies manage their data better and keep costs down.

Final Words

As a data expert, I want to summarize our discussion by clarifying the key differences between structured, unstructured, and semi-structured data one last time.

Let’s start with structured data. This type of data includes names, addresses, and credit card numbers. It’s neatly organized in database tables, making it easy for big data programs to process.

Then, we have unstructured data, which is quite different. This includes things like audio files, videos, and surveillance data. It’s stored just as it comes until we need to analyze it. It might be more challenging because it comes in many formats, but paying attention to it is crucial. Believe it or not, it accounts for over 80% of all data businesses use and is growing by 55% to 65% annually.

Lastly, there’s semi structured data. It sits somewhere in the middle. It has some organization, like tags, but needs to fit neatly into a traditional database structure.

In short, while structured data is simpler to analyze, the massive amount of unstructured data contains valuable insights we are starting to unlock with newer technologies. We must use all types of data to make sure we get all the key information that could help make better decisions.