What is Instructured Data?

Written by Lynn Orlando

Written by Lynn Orlando

Published on December 23, 2019

If you’ve ever heard the terms “structured” and “unstructured” data you may be wondering what they are and what the difference is. The two are similar in that they are both forms of data stored in a data center, but the type of data center and the type of data are very different. Structured data is usually machine-generated and stored within a relational database, while unstructured data is typically human-generated and stored in a variety of databases of various types, or stored physically in the case of physical documents.

The most significant difference between the two is in the way we parse and search them, with structured data being straightforward to search and unstructured data typically being more difficult. This difference is due to the way that structured and unstructured data are patterned; structured data is very predictable, and unstructured is often unpredictable.

What Are the Types of Structured and Unstructured Data?

Structured data is usually a string of predictable text or numbers, such as names, phone numbers, or social security numbers. These are easily searchable and follow a set pattern that algorithms can quickly identify. These types of data are usually kept in relational databases, where SQL (Structured Query Language) makes it easy to search for and identify specific strings of data.

Unstructured data is anything that is not structured data and can’t be stored in a relational database. This type of data may be media files like audio and video, scientific data like genome sequencing or space exploration, or social media posts and emails. These types of data are unstructured because they are not predictable and can’t be sequenced the same way that structured data can. Unstructured data is used just as much or more than structured data, but searching it can be difficult due to the unpredictable nature of its contents.

How Is Unstructured Data Used?

Unstructured data is instrumental in many ways, and because there are many types of unstructured data, there are many different uses. For example, a police officer may need a photo or sketch of a suspect to identify them, and a picture is a type of unstructured data. A doctor may also need images such as x-rays or MRIs to diagnose a patient accurately. This photo, sketch, or image is often used in conjunction with structured data, like a patient’s age, weight, or other medical histories that can be stored in a relational database.

A picture is not the only type of unstructured data. A scientist may use genome sequencing to identify a DNA strand in a virus, which is another type of unstructured data due to its complexity. Businesses may also use social media posts and comments about their product to judge the effectiveness of a marketing campaign. This type of data is especially unstructured, as no two social media posts are the same. A business may be able to search for keywords or “mentions” of their product or company to track who is talking about them, but this isn’t as perfect or predictable as searching a database for structured data such as product sales numbers.

How Is Unstructured Data Stored?

Since 80% of data currently in use by businesses and other organizations is unstructured data, all of it needs to be stored somewhere. It can’t be stored in relational databases like unstructured data, but it can be stored in NoSQL databases or other data centers. If you think about what you have on your computer, the majority of it is probably unstructured data. Think about Word documents, photos, or music files that you keep on your hard drive. This type of unstructured data has at least some metadata, such as file names and types that allow you to search for them and locate them on your computer.

Most unstructured data can be stored and searched for in this way because it is semi-structured, which means that it has at least some structure to it that can be filed and organized. This organization helps with storage and searching for data, although not all data has metadata that can help you search for it. However, most unstructured data can still be stored on a computer or in public or private cloud software, while the rest is typically stored physically, such as paper-only documents.

How Is Unstructured Data Managed?

Unstructured Data Management, or UDM, is vital to managing your business or organization successfully. UDM helps you find your unstructured data more quickly and utilize it when it’s necessary to help you solve a problem. The first step to managing your unstructured data is to index it. No matter where it is stored, indexing your data will help you search it faster. Specific programs may index some of your data for you; for example, many email programs will index all the emails you send and receive and archive them to be searched easily. However, other data may need to be indexed manually.

You may also want to API-enable your data, which means that you allow back-end programs to access and search the data for your company. If you API-enable your data, it will allow users of your programs to search the data and find it more easily, even if it’s unstructured or only semi-structured. This search feature also allows you to change where your unstructured data is stored without losing search and access functionality. Whether you move from an on-premises storage system to a private cloud, or from one cloud system to another, this is key to the flexibility when it comes to managing your unstructured data.

Unstructured data is integral to any organization’s success due to the sheer volume of unstructured data that exists both inside of and outside of the organization. Managing and storing this data becomes the critical factor, as data that is not controlled or accessible is not useful. Compared to managing and storing structured data, this can be a tall order, but it is still possible with the use of back-end applications and proper storage, whether physical or digital. Documents like photos, emails, and scientific data are all unstructured or semi-structured data that are important to an organization’s success when indexed and accessed correctly.

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