Structured and unstructured data are the two main categories of data. Since we live in an age of information, data is the most necessary thing in our daily lives, and every decision we make is based on data in one way or another.
In this article, we will discuss structured data, unstructured data, and the differences between them.
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What Is Structured Data?
Structured data refers to data that is structured to a predefined model, or organized in a predefined manner. According to Google, “Structured data is a standardized format for providing information about a page and classifying the page content.” Structured Query Language (SQL) is used to manage structured data within relational databases. Initially called SEQUEL, the language was developed at IBM by Donald D. Chamberlin and Raymond F. Boyce in the early 1970s.
Users can easily access and interpret structured data with only a basic understanding of the topic.
Via SQL, users can easily access and interpret structured data with only a basic understanding of the topic. For instance, the specific architecture of structured data eases the manipulation and querying of machine learning (ML) algorithms. For example, in search engine optimization (SEO), structured data is the markup that helps search engines understand how to interpret and display the content.
Structured data is usually housed in a relational database management system (RDBMS). Common applications of a relational database with structured data include ATM activity, airline reservation systems, and sales transactions. Further, the ways to protect structured data are readily available and understandable. Databases offer access control tools and techniques that improve structured data security.
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What Is Unstructured Data?
Unstructured data refers to the data that is either not structured to a predefined data model, or not organized in a predefined manner. This type of data can be human- or machine-generated and possess an internal structure. Unstructured data may include documents, books, metadata, health records, images, audio, video, files, email messages, web pages, etc.
There are several ways to house unstructured data, such as data lakes, NoSQL databases, and data warehouses.
In the late 2000s, the emergence of Big Data led to a heightened interest in the applications of unstructured data in fields like root cause analysis and predictive analytics. According to a prescient 2011 report from Computerworld, more than 90% of all data in organizations might be unstructured by 2021. Indeed, IDC and Seagate predicted that the global Data Sphere will grow to 175.8 zettabytes by 2025 compared to the growth rate of about 26% from 2015; the majority of this data will be unstructured.
According to a report for the 2013 IEEE Conference on Systems, Process & Control, there are several ways to house unstructured data, such as data lakes, NoSQL databases (non-relational), and data warehouses. With the growth in this space, many tools and platforms have been developed especially for unstructured data use, management, housing, and security, such as Amazon DynamoDB, MonkeyLearn, and MongoDB Atlas.
Structured Data vs Unstructured Data
Both structured data and unstructured data can be human- or machine-generated, but there are some obvious differences between them. In particular, unstructured data’s irregularities and ambiguous behavior make it difficult to understand using traditional programs.
Easy to access
Easy to analyze
Is not clearly defined
Difficult to access
Difficult to analyze
Natural language processing
Inventory control systems
Credit card numbers
With the development and invention of modern technologies, it has become easier to analyze and gain new insights from unstructured data. Converting data from unstructured into structured can make it easier and more effective to use, manage, store, and secure.
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