You’ve probably heard of the two main types of data: structured and unstructured. Structured data is data that is organized in a predetermined model. In contrast, unstructured data is data that is not yet modeled. Both data types require different amounts of storage space and are less reliable. Let’s discuss each type in greater detail. Firstly, let’s consider the difference between structured and unstructured data.
Structured data is organized in numerical or text format.
Unlike unstructured data, which is not standardized in structure, structured data is easy to observe and sort. It also allows for calculations and can be easily compared with other data. By contrast, unstructured data, also known as “anything else,” is not categorized into a standardized database and is often difficult to use. So whether it’s an unstructured database or structured data, the key is to understand the difference.
Data that is structured can be alphanumeric, numeric, or both. It is also grouped according to similar values. Sometimes, such data is stored in relational databases. Typically, data is stored as encoded strings manipulated by a program or human to perform a specific operation. In databases, structured data includes financial information, address details, demographic data, star ratings of customers, and machine logs. These are the basic things to know about structured vs. unstructured data.
Unstructured data lacks a predetermined data model.
Unstructured data is a vast domain that cannot be analyzed using traditional tools, such as relational databases. As a result, unstructured data should be stored in non-relational databases or a data lake. Unstructured data represents over 80% of enterprise data, and 95% of companies prioritize their management. Unlike structured data, which a predetermined data model defines, unstructured data is stored in its native format, often undefined until it is needed. This adaptability will allow data scientists to analyze only the data they need to analyze.
Unstructured data is generally text-based and doesn’t have a predetermined data model. It can be used for various applications, such as evaluating marketing campaigns, uncovering buying trends, monitoring policy compliance, etc. The choice between structured and unstructured data is primarily a matter of data type, expertise, and on-read schema. In this article, we’ll examine two methods for analyzing unstructured data.
Unstructured data requires more storage space.
The vast majority of data in a company is unstructured. This data can be in email bodies, video footage, images, and other file types. Moreover, it can also be in the form of social media posts, chat transcripts and even machine sensor data. Unstructured data requires more storage space than structured data because it doesn’t follow a prescribed pattern. Business owners can manage the structure of this data. In contrast, data scientists are responsible for analyzing and transforming it for use by non-technical users. Many large organizations have entire teams dedicated to managing unstructured data.
In the future, this data volume will grow to 35 zettabytes, enough to hold 1 trillion hours of movies – if you watched them all! But even with all this data, figuring out how to process it will take much more. Fortunately, there are now tools that make extracting the information you need from unstructured data easier. More than 16,000 Egnyte customers have benefited from the company’s storage solutions.
Unstructured data is less reliable.
There are many benefits of using unstructured data, including finding new patterns in your business and enhancing your competitiveness. Unfortunately, unstructured data is also more challenging to process and manipulate. It requires more processing power than structured data, and companies may not have the right hardware resources to handle it. If you have a lot of unstructured data, consider using a data management tool that specializes in handling such data types.
Structured data is more accurate and reliable. However, it also takes more time and resources to maintain. For example, suppose you are working with customer data. In that case, you must ensure that all customer details are consistent and accurate. In contrast, unstructured data is less reliable. In addition, unstructured data can be messy, but it is not useless. This is because you can store it in various file formats, including unstructured ones.
Unstructured data is more difficult to analyze
In the past, companies could not effectively analyze unstructured data, so they focused their efforts on structured data that they could count. However, advancements in AI tools have made it possible for companies to search through vast amounts of unstructured data and discover actionable business intelligence. Google, for example, has made tremendous advancements in image recognition technology. AI algorithms can recognize images and automatically identify objects within them.
Most businesses store their unstructured data outside of a traditional database. It can be in the form of a text file, an image, or a video. Since unstructured data lacks the common elements of a database, it’s much more challenging to analyze. Many companies store unstructured data in large, limitless repositories called “data lakes.”
