If you are a business owner, looking into structured vs unstructured data will be crucial when deciding which form best suits your business needs. But why is that important? In recent years, there’s been quite a buzz about big data. After all, big data is the lifeblood of every business. In fact, the ability of a company to gather data, interpret it and act on the insights often determines its level of success.
However, as the amount of data that companies interact with increases, so does the variety of data formats. While the two main categories of data are structured and unstructured, data in all its forms is highly valuable to every enterprise.
And learning how to handle all data efficiently will significantly minimize errors and boost your productivity. That said, this article will break down the unique differences between structured and unstructured data.
Structured vs Unstructured Data: Brief Definitions
What Is Structured Data?
Structured data is a well-organized and accurately formatted form of data that you can easily decipher by machine learning algorithms. In essence, structured data is managed by a programing language developed by IBM in 1974- the Structured Query Language (SQL).
It occurs in numbers or text and you can source it either manually or automatically. This form of data exists in relational databases (RDBMS), meaning it comes in corresponding rows and columns. Since it fits within the RDBMS structure, it’s easy to search for specific information and single out the relationship between each piece of information.
What Is Unstructured Data?
Unstructured data, on the other hand, is raw data that often lacks a predefined format. This kind of data is too text-heavy, large in quantity, and stored in its native format. For that reason, unstructured data requires large storage spaces making it hard to keep secure. And since it occurs in non-rational databases, it’s hard for both humans and computers to decipher. Unfortunately, this kind of data accounts for up to 80% of enterprise data. That’s why every organization must learn how to perfectly analyze unstructured data.
Structured vs Unstructured Data: What are the Key Differences?
Aside from the notable difference in definitions, structured vs unstructured data are almost two worlds apart. Unstructured data is in its native format, while structured data is in a predefined arrangement. In both data sets, the information offered once the content is analyzed can be helpful to your business. That said, let’s explore some key differences between structured and unstructured data.
i. Data Models
Structured data comes in a predefined data model, which relies on strict organization. Therefore, it’s less flexible. It highly depends on the database schema and the data types in the columns. However, that kind of dependency on schema has its pros and cons. No doubt you can quickly process the data, but it has to follow strict schema requirements.
Unlike structured data, unstructured data offers more scalability and flexibility. While this data is subjective and harder to work with, the fact that it has no predefined purposes makes it very flexible. On top of that, you can store the information in a wide range of formats.
ii. Data Formats
Structured data comes in fewer formats, mostly numbers, and texts. The most common ones are XLM and CSV, which are standardized and user readable. In structured data, the data format is already pre-determined.
On the other hand, unstructured data formats come in various shapes and sizes. It has no predefined data model since it’s in its native format. It can either be in audio files, video files, images, PDF documents, social media posts, emails, or surveyed data.
iii. Storage Databases
As we mentioned, structured data is stored in relational databases or RDBMS. The information is then organized into tabular formats like SQL databases and excel sheets. For that reason, structured data requires less storage space and is easier to scale as it lives in a data warehouse. Also, since the SQL syntax is just like the English language, structured data is easy to read, write and interpret.
On the other hand, you can store unstructured data in non-relational databases. These kinds of databases have a wide range of data models and store the data in a non-tabular way. Common types include; graphs, documents, key-value and wide columns. Unlike relational databases, NoSQL databases can also process huge data volumes as they have better scalability and flexibility.
iv. Data Nature
Typically, structured data is more or less qualitative, while unstructured is quantitative. Structured data contains specific textual elements and precise numbers. More importantly, the analysis methods are straightforward and accurate regarding structured data.
Unstructured data contains subjective elements that are hard to interpret using traditional methods. To process such information, you’ll need cutting-edge analytics tools.
v. Ease of Analysis
Another significant difference between these data structures is the analysis process. Structured data is easy to interpret and search for both algorithms and humans. However, unstructured data is a bit more difficult to process and search. To understand this data, you need to first analyze it
Currently, there’s a wide array of tools that can analyze structured data, but that’s not the case with unstructured data. Typically, without the proper tools to analyze unstructured data, you might not utilize this data type’s full potential.
In Summary: Structured Data Vs Unstructured Data
There you have it! You now understand the complex difference between structured and unstructured data. Both data types are essential to the growth of your business. But as we’ve established, it’s not easy to analyze and utilize unstructured data.
But thanks to Speak Ai, you can now comfortably analyze your unstructured data to make insightful decisions about your business. Join 20,000+ individuals and teams who are relying on Speak Ai to capture and analyze unstructured language data for valuable insights. Start your free trial or book a demo to streamline your workflows, unlock new revenue streams and keep doing what you love.