Structured Data vs Unstructured DataAmr Saafan
We generate, share, and store massive amounts of data these days. Large and small businesses use it to run their businesses and gain useful insights. Data is the veins of the twenty-first century.
According to a study conducted by Finances Online, the estimated amount of data consumption in 2021 is 74 Zettabytes. Furthermore, by the end of 2024, this figure will have doubled. A zettabyte of data is equal to one billion terabytes. A zettabyte hard drive is estimated to be capable of storing 60 billion video games.
However, what do most people know about data itself?
They deal with data in the form of numbers, names, or images. In most cases, all information is classified as structured or unstructured. What exactly is the distinction between unstructured and structured data? Continue reading to learn everything you need to know about structured vs unstructured data.
The definition of structured data is straightforward. In a nutshell, it is a type of quantitative data that corresponds to specific criteria. It is simple to organize, sort, and analyze. The primary criterion for structured data is that all records have the same format and are made up of numbers or symbols. Structured data is typically stored in relational databases comprised of rows and columns.
Structured data, as defined by GeeksforGeeks, is a type of data that a human or computer can easily access. SQL is commonly used to manage structured data (Structured Query Language). It enables the management of data in databases through the use of requests that both users and machines can submit.
Data warehouses are commonly used to store information so that it can be easily analyzed. This data has a predefined data model that is not flexible because it always conforms to specific standards. The number of structured data formats is extremely limited.
Source of Structured Data
The primary source of structured data is SQL databases. Machines now collect a large amount of data about users online. According to a recent study, Google receives 1.3 TB of data from Android and iOS devices. All of the data consists of numerous records on how users use their devices, what apps they install, how much time they spend online, and so on.
SKU codes generated by inventory management systems are another example of structured data. Given that each variation of the product’s different colors, shapes, and sizes requires a unique SKU number, the number of SKU codes is rapidly increasing. As a result, the amount of machine-generated structured data is constantly increasing.
Nonetheless, there are numerous sources of structured data that can be generated by users. Spreadsheets, OTPL (Online Transaction Processing) systems, online forms, and server logs, for example, can all be sources of structured data. XML and CSV are the most commonly used file formats for storing structured data.
Structured Data Use Cases
Structured data is an example of a spreadsheet that contains critical client information. It could include columns indicating the name, age, profession, citizenship, nationality, and so on. These pieces of information are made up of numbers and letters. Every cell corresponds to a specific piece of information, allowing it to be easily addressed. Users can use formulas because the information meets specific criteria. They can also edit data in bulk, sort it, or select cells that meet specific criteria with a few clicks.
Furthermore, almost every customer management system only works with structured data. On average, it denotes identified client information. The information is organized by specific needs, payment details, and other factors. For example, it could denote the date of the first purchase, the amount of money spent monthly, or the average response time. Because all of the data is structured, it can be analyzed to yield useful insights.
Advantages of Structured Data
1.Simple access. Indeed, the primary advantage of structured data is that it is easily managed by computers. Because all information has a consistent format, algorithms can quickly process it and provide useful insights. Furthermore, users can access and analyze structured data. It employs the same predefined and consistent patterns as relational databases.
2.This is useful for machine learning. AI (Artificial Intelligence) and ML (Machine Learning) technologies are rapidly evolving these days. Structured data can aid developers in the training of machine learning algorithms. It simplifies information manipulation for algorithms. Algorithms learn to manage unstructured data with the help of structured data.
3.Data mining is simple. When a user needs to find a specific piece of information, structured data can save a lot of time. It speeds up the search and extraction processes. Even if a spreadsheet contains thousands of rows, users can easily find the ones they need.
4.Data management that is secure. A large amount of data used by businesses must be stored wisely. It could refer to sensitive information or credit card information. When all of the data is structured, it is simple to encode and decode. As a result, companies that use structured data can keep customer information secure even in the event of a data breach.
5.Integration is simple. Structured data is easily integrated into other platforms and processed using a variety of tools. An inventory spreadsheet in CSV format, for example, with product names, prices, images, quantity, and other characteristics, can be easily integrated into an online store. As a result, users can create thousands of product cards and run an online store in a matter of minutes.
Disadvantages of Structured Data
1.Flexibility is limited. This type of information has a predefined structure, according to the structured data definition. As a result, in most cases, it is only used for specific purposes. It is difficult to update or convert to be used for different purposes.
2.There are few storage options. Structured data is stored in data warehouses, which are systems with strict schemas. As a result, if any requirements change, users must update all data. It may necessitate a significant amount of computing power and time.
Structured Data Management Tools
Microsoft Excel is a useful tool for managing structured data. It envisions the ability to arrange all data in a specific order, set data types for cells, and modify data in bulk. However, despite its convenience, it is not the only tool used for managing structured data. There are numerous popular tools available for this purpose. The most well-known are listed below.
1.MySQL. It is the most widely used relational database management system for organizing structured data into rows and columns. Users can access and modify data as needed. Furthermore, the tool makes it simple to search for and update any structured data.
2.OLAP. The tool’s name is an abbreviation for Online Analytical Processing. It enables users to efficiently analyze structured data stored in centralized storage. Many businesses use it to run extensive analytical queries.
Unstructured data is another type of data that is used. Qualitative data is difficult to process using standard information processing tools. What exactly is unstructured data? Unstructured data, in a nutshell, refers to all information in any format. Images, videos, songs, speech recordings, and other media can be used. There is no end to the variety of formats.
Non-relational databases are used to store the data because there is no predefined model. All unstructured data is not processed and is stored in its original format. Because it is not organized, users have difficulty processing large amounts of unstructured data. Furthermore, this type of information consumes roughly 80% of all stored data, making the task more difficult.
Source of Unstructured Data
Unstructured data can come from almost anything. A lot of companies nowadays collect a lot of data. However, because they are unable to process the data, it is stored in data lakes. It’s ideal for storing raw data in its entirety. What does unstructured data look like? It could be videos, emails, blog posts, heatmaps, and so on.
The most common sources of unstructured data are social media platforms and messages. People, unlike computers, dislike keeping everything in sync. They create a massive amount of content every second and share it online. According to recent data, users create 2.5 billion gigabytes of content per day. All of the media is unstructured content that is stored on the servers of various tech behemoths.
Unstructured Data Examples
The best way to understand unstructured data is to look at examples of it. It can assist in distinguishing between unstructured and structured data. In a nutshell, it is any information that cannot be structured or analyzed because it does not meet any predefined criteria. As a result, the publication you’re reading right now, as well as all of the media files, are examples of unstructured data.
One of the most intriguing unstructured data examples to consider is data mining. Many businesses today are dissatisfied with the amount of structured data they have. As a result, they use data scraping specialists and AI to analyze a large amount of unstructured data. It enables them to obtain additional structured data that will supplement their existing insights. Unstructured data can be a valuable source of information for businesses at times.
Furthermore, unstructured data aids in the development of chatbots. In most cases, this type of data is used to assist algorithms in understanding the requests of users. As a result, chatbots can direct users to relevant sources of information. They can also share answers to the most frequently asked questions. It aids in increasing the productivity of customer service representatives.
Advantages of Unstructured Data
1.The native format. Unstructured data does not have to meet any specific requirements. As a result, it does not need to be converted. All content created by humans and robots is saved in its entirety.
2.Data collection is completed quickly. Unstructured data is simple to collect because it accounts for roughly 80% of all information on the Internet. Furthermore, because it does not need to be predefined, there are no constraints that may sort out specific pieces of unstructured data.
3.Better understanding. Because raw unstructured data is difficult to analyze, businesses rarely use it. However, it can also be a great source of useful insights that can aid in the acquisition of data about customers or businesses.
4.Scalability is simple. Unstructured data is stored in data lakes and does not have to meet specific requirements. As a result, if new information is added, there is no need to update a specific database structure. To store unstructured data, cloud storage or on-premises servers can be easily scaled.
5.Access is available on-demand. Unstructured data is typically accessed on demand. As a result, storing it is less expensive.
Disadvantages of Unstructured Data
1.Analyzing is difficult. The main disadvantage of unstructured data is that there are an infinite number of formats. It makes it difficult to analyze and gain new insights. Unstructured data, for example, can refer to text, videos, animations, or images. Data science specialists must be hired to analyze the data and extract useful information.
2.Inadequate access to specialized tools. Because there are no standards for unstructured data, only a few tools are capable of managing it. In most cases, different tools are required to operate images, videos, texts, and other data types.
Unstructured Data Management Tools
Even though unstructured data implies a wide range of formats, it must be managed. It is necessary to use specific tools to keep all of the information organized and accessible. The best ones will help you find a tool that will meet your requirements.
1.MongoDB. It is a comprehensive platform that can store a wide range of data types. With the help of this tool, users can, for example, store and use unstructured documents across multiple platforms.
2.Hadoop. It is a tool that allows you to manage and process large amounts of data across multiple networks. Furthermore, the tool lacks the necessary formats. As a result, it can handle any type of unstructured data.
Structured Data vs Unstructured Data: Key Points
It’s difficult to provide a comprehensive answer to the question, “What are some examples of structured and unstructured data?” There are numerous peculiarities to be considered.
The comparison table below, on the other hand, will help you understand the main differences between structured and unstructured data.
|Structured Data||Unstructured Data|
|Formats||A few formats only||Limitless number of formats|
|Data Model||Pre-defined||Not pre-defined|
|Storage||Data warehouses||Data lakes|
|Search||Easy to search||Difficult to search|
|Example||Names, dates, phone numbers, SKU codes, credit card credentials||Documents, photos, transcripts, videos, heatmaps, media files|
Other Types of Data
Over the last few decades, businesses have faced an increasing number of challenges related to data storage and management. As a result, dealing with such large amounts of data as only structured data vs unstructured data options was extremely constrained for developers. As a result, new types of data have been developed to help with this process.
For a long time, many developers worked to bridge the gap between structured and unstructured data. As a result, the semi-structured data format has emerged. It is a distinct format that is unrelated to any of the preceding. It roughly takes 5% of the total data and solves specific problems. It is critical for businesses because semi-structured information is a combination of structured and unstructured data.
In a nutshell, it’s the same as unstructured data. It does, however, make use of metadata, which gives it the ability to be used efficiently. Users, for example, can use this data type to organize unstructured content such as documents, images, and videos. It assists managers in managing large amounts of unstructured data. Furthermore, they can gain additional insights.
To grasp the concept of semi-structured data, consider the markup language XML. It assists both developers and machines in understanding how specific data should be organized. It is a tag-driven programming language that assists developers in updating data structures.
Metadata is a critical component in making semi-structured data searchable and categorizable. It is a type of data that uses tags and semantic markers to define unstructured information. They identify specific types of information and streamline unstructured data management processes.
The alt text that images may have is the best example of metadata. It aids in defining what is shown in a particular image. Unfortunately, Google algorithms are unable to recognize all images in order to search for the correct ones. Robots use alt tags to identify images for further search. Metadata can include details such as the date, location, and technical specifications of the hardware used to create a snap.
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