What Is Unstructured Data? A Brief Guide in 2022
What Is Unstructured Data?
Data is crucial today and provides critical information for developers, businesses, and users alike. People, social media platforms, and even robots or devices generate data every second of every day. The size of this data is far beyond estimation. The common characteristic of much of it is that it is unstructured.
What are unstructured data? Where do they come from? What can this data be used for? Can this data be analyzed with artificial intelligence technology? Continue reading our article for answers to all these questions.
Definition of Unstructured Data
Unstructured data accounts for 80 to 90 percent of all data worldwide, according to MIT. But what exactly does this amount of unstructured data mean, and where does it come from?
Unlike conventional data, unstructured data does not exist in any schema or system, is not labeled, and is not parsed. This data accounts for up to 90% of the digital information available today. Unstructured data can exist in many different forms. For example; it can be visual or written, it can be sensor data or video, it can be from social media platforms or personal correspondence and emails, or even in the form of a Word document.
Today, social media platforms and even big business generate huge amounts of unstructured data. To better understand unstructured data, let's simply categorize it using some examples. Our first category is unstructured data generated by humans; unstructured data generated by humans can be found in the following forms:
- Social Media Platforms and Websites: Data from social media platforms such as Facebook, Instagram, Youtube, Twitter, and more, as well as data from different websites and file-sharing platforms.
- Media: Data in different multimedia formats such as digital photos, videos, audio files, etc.
- Email: In fact, emails are also unstructured data, although they fall under the category of semi-structured data. We will explain the difference in the Semi-unstructured data section.
- Text: Again, emails, Word documents, presentations, and digital notes.
- Communications Data: All chat messages, phone records and, text messages, instant messaging on different platforms.
The other category we will use for unstructured data is unstructured data generated by machines. Unstructured data generated by machines can be in the following forms:
- Satellite Images: The various satellite images are not always structured in the same way and are used for the same purposes. A huge amount of unstructured data is generated from satellite imagery.
- Surveillance: Surveillance photos and videos resulting from digital surveillance generate large amounts of unstructured data.
- Scientific Data: Unstructured data comes in many different forms, such as seismic images or atmospheric measurements.
You have seen in this section what unstructured data is and the main types of unstructured data. You have also seen that unstructured data is divided into data generated by humans and data generated by machines. In the rest of the article, we will talk in detail about where unstructured data comes from.
Where Is Unstructured Data Coming From?
Unstructured data makes up the majority, if not almost all, of today's digital data. So let's take a detailed look at where all this unstructured data comes from. Here are the sources of unstructured data with detailed explanations:
Running a business generates a lot of unstructured data. This data can be in the form of presentations, draft contracts, notes, documents, photos, videos, or internal emails used in the workplace. By structuring this data, a lot of information that will, directly and indirectly, affect decision processes will come to light.
Social media is now a very important part of our lives. Billions of people around the world produce content on social media every day. Not only individuals but also governments, municipalities, companies, and small businesses are actively using these platforms. This generates a lot of unstructured data such as images, videos, text, or location.
Websites contain a wide range of content, from videos and images to text. They are also very dynamic and constantly changing. Every website on the internet creates content in a certain order. As content changes frequently, it is not easy to analyze. These experimental websites also create a large amount of unstructured data.
During the day, we communicate through many different platforms, such as text messaging, instant messaging platforms, or video meeting platforms. Moreover, not only individuals but also organizations actively use these platforms on a daily basis. Facebook Messenger, and WhatsApp, for example, are all places where a great deal of unstructured data is generated.
Media platforms, channels, surveillance cameras, and individuals generate incredible amounts of visual data on a daily basis across the globe. While some of this data is structured, the vast majority remains unstructured, i.e., unanalyzed.
AI analytics for some of this data is now available and becoming increasingly common. For example, it is possible to detect facial recognition from security camera footage. Such services are also very common. You can click here for detailed information about facial recognition and human detection services.
Movie reviews, news, and advertisements in different categories across the internet fall into this category. Even though we know what these data are, they appear as unstructured data because the website content is not analyzed and classified in detail.
AI Solutions for Unstructured Data
All data is important. Unstructured data has huge potential and is waiting to be unlocked. To structure and analyze large amounts of unstructured data, the use of artificial intelligence technologies is rapidly expanding.
Before we look at AI solutions for data, let's take a look at why unstructured data matters.
- Unstructured data contains important institutional, financial, or scientific information accumulated over many years.
- Analyzing, i.e., structuring, news on the internet can shape e-commerce and trends.
- Structuring the unstructured business data of organizations can be a savior in decision and business development processes.
- For banks, structuring unstructured data can prevent many illegal transactions.
We have found that unstructured data is extremely important. It can be of many different types, and countless different sources can generate unstructured data, so how does AI offer solutions for managing unstructured data?
Analyzing unstructured data is a very difficult task. Because this data is in many different forms, it requires many different tools and is complex. However, with the development of cloud technology and machine learning, these analyses have become feasible.
Today, different AI companies offer different AI solutions for analyzing unstructured data. Machine learning-enabled technologies are at the forefront of this. For example, emails or written data can be analyzed using NLP or Native Language Processing. After the analysis, all data is stored in a structured database.
Amazon AWS, Microsoft AZUR, or companies like IBM Cloud offer services for these databases.
Semi-structured data is slightly different from unstructured data but still quite complex because it is not fully structured. They are not stored by structured data, or their content has not been analyzed. Nevertheless, there may be some tags or metadata in the data that can provide some small distinctions. The fact that the data is not completely scattered makes it semi-structured rather than unstructured.
Emails, HTML code, or spreadsheets are examples of semi-structured data. For example, the content of emails is not fully analyzed, and they are not in a database. Nevertheless, they have categories and tags that distinguish the data from each other. This is why they are semi-structured data.
Huge amounts of unstructured data are generated by humans and machines. This data is highly valuable and holds great potential for improvement. It is important to use artificial intelligence solutions to analyze unstructured data. For example, instead of leaving your security camera footage unstructured, you can use Cameralyze human detection or facial recognition services.
Click here to access free solutions. Maximize the value of your data!