Data can be split into two sections: structured and unstructured. As the names suggest, structured data is highly organized and easy to use. Whereas unstructured data is raw and disorganized, making it more difficult to analyze.
In today’s article, we’re going to dive deeper into the differences between both types and the challenges associated with them. We’ll explain why structured data is so vital and give some examples of both data types. Also, we’ll take a look at the definition of the semi-structured data format.
Let’s dive right in!
To see how the structured and the unstructured data formats compare, let’s take a quick look at the definitions of each one.
Structured data is highly-specific, well-organized, searchable, and to the point. Before it’s stored,which usually is done in a data warehouse, this data is predefined and formatted. Typically, structured data comes in letters or numbers and is arranged in rows and columns of a table.
Unstructured data doesn’t have a predefined format; therefore, it’s difficult to store and manage such data in relational databases. This data is stored in its native format – data lakes – and it’s usually text-heavy and vast in quantity.
Now, let’s break down the main differences:
Qualitative data vs. quantitative data. Structured data is often referred to as quantitative data since it can be expressed in a numerical value. It answers questions like “How many?”, “How often?”, etc. At the same time, unstructured data is also known as qualitative data, making it rather characteristic and categorical, at times, open for interpretation. This data helps us to understand the “why?” and “how?” behind the structured, numerical data.
Unstructured data takes up a lot of space. Normally, structured data doesn’t require a ton of storage space, meanwhile, unstructured data does.
Unstructured data is harder to read. Because unstructured data isn’t stored in relational databases, both humans and bots find it harder (or impossible, even) to read it. Meanwhile, structured data is fairly easy to read, interpret, and analyze.
Both are kept in different storage types. Structured data is typically stored in relational databases and data warehouses. On the other hand, unstructured data is kept in storage repositories called data lakes in order to preserve its raw format for further data analysis.
Different data analysis methods. Structured data is analyzed using regular statistical tools or SQL (Structured Query Language).
Meanwhile, working with unstructured data requires specific technologies: machine learning, natural language processing, artificial intelligence, and other advanced tools.
Before we move on, it’s important that we take a look at the definition of the semi-structured data term.
As the title suggests, semi-structured data is partially structured. Just like unstructured data, semi-structured data doesn’t come in relational databases and neatly-organized tables. However, what separates semi-structured data from unstructured, is that it contains tags or other markers that separate elements, creating a hierarchy. An example of semi-structured data could be an email, a zipped file, data integrated from several sources, and so on.
Structured data is formatted to a set structure before being placed in data storage. As we briefly mentioned, organizing your data makes it easier to analyze, but let’s take a closer look at the pros of structured data:
Easy for ML algorithms to use. One of the main reasons why structured data is important is the fact it’s easy for machine learning algorithms to work with it. Due to the organized and specific nature of structured data, it’s easier to train ML algorithms to work with this type of data.
Business professionals can easily utilize it. If you’re a typical business professional without any technical data background, you may find it hard to read, analyze, and understand unstructured data.
On the other hand, if the data is structured, you can analyze it even if you don’t have in-depth data knowledge. In other words, a business professional can access and use the data themselves without involving data scientists or similar teams, paving the way for proper analysis and business intelligence.
Wider tool choice. The structured data format has been around for longer compared to the unstructured one. With that, there are far more tools adept at processing and analyzing structured data. This way, you have more flexibility when it comes to choosing a tool.
Easy to parse. Since it’s documented and labeled, structured data is easy to parse or break down into separate parts. This way, it takes little effort to extract the exact information you require.
Let’s take a look at some real-life examples of structured and unstructured data. You may be surprised to find out that structured data is a part of everyday life for many people.
|Structured data examples||Unstructured data examples|
|Date and time||Social media posts|
|Barcodes||Audio and video files|
|Social security numbers||Product reviews|
As you can see, the real-life examples clearly reflect the definitions of both data categories: structured (a.k.a., quantitative) data is numerical and factual, while unstructured data is contextual – hence the qualitative data name.
Both types of data have unique challenges associated with them. Starting from structured data, the main issue associated with it is the lack of flexibility.
Since it relies on a strict organizational model, structured data is less flexible. The column (or field) configuration depends on the database’s schema. That said, the data must be in its dedicated column. Although it allows processing and searching data more easily, all records have to meet the strict schema requirements.
Another common structured data issue is limited storage options. As you already know, structured data is typically stored in data warehouses that have fixed schemas. If you make any changes in the storage settings, all the information in the data warehouses can be automatically refreshed. As a result, you may need to spend a lot of time and resources reconstructing large volumes of data.
As for challenges associated with unstructured data, the main one is the fact that you’ll need data science expertise. To work with unstructured data, a person needs to really understand the topic (or area) of that data. A regular business person simply won’t have the specific knowledge of a data scientist required to analyze unstructured information in data lakes.
As we’ve mentioned earlier, there are many tools for working with structured data. Those tools are fairly easy-to-use if you don’t have a data science background. The situation is not the same when it comes to unstructured data – it requires working with specific tools, some of which are still in the early stages.
Whether it’s structured or unstructured data you’re after, you may find it difficult to acquire it in the first place; especially when it comes to automated large-scale data mining operations.
Nowadays, many websites employ several anti-bot measures to prevent malicious actors from harvesting their data.Even if your actions are legal and ethical, these measures may affect you when web scraping. As a result, you may not be able to collect data successfully or even get your IP address banned.
Luckily, there are several tools and methods to avoid that and carry out public data mining activities without failure. For instance, you can use Oxylabs’ Scraper APIs for public data scraping without having to worry about infrastructure maintenance, IP blocks, CAPTCHAs, and other matters. With our Scraper APIs, you’ll be able to get structured data in JSON or unstructured data in HTML, all up to your preference and use case.
We hope you found our structured vs. unstructured data comparison clear and useful. The main takeaway is that both data types have great value in different scenarios: structured data is accessible to many types of business professionals; meanwhile, unstructured data gives more flexibility to work with it.
About the author
Senior Content Manager
Roberta Aukstikalnyte is a Senior Content Manager at Oxylabs. Having worked various jobs in the tech industry, she especially enjoys finding ways to express complex ideas in simple ways through content. In her free time, Roberta unwinds by reading Ottessa Moshfegh's novels, going to boxing classes, and playing around with makeup.
All information on Oxylabs Blog is provided on an "as is" basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website's terms of service or receive a scraping license.
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