What is Structured Data and Why is It Important for You?

Every second, a massive amount of data is produced as billions of people leverage online digital websites and software. This information is gathered by businesses to understand customers and make informed decisions. Today, the significance of data has become so profound that organizations have assimilated the importance of both Structured and Unstructured data for business growth. While both types of data are crucial for companies to enhance business performance, Structured data has been the most popular data type among companies for decades.

Until the recent development in Machine Learning for assessing Unstructured data, insights from Structured ones were the go-to method for all business decisions. While Unstructured data is now gaining popularity, Structured data is still the most widely used within organizations for insights delivery.

In this article, you will understand Structured Data in detail while also comparing it with Semi-Structured and Unstructured data.

What is Structured Data?

Data that primarily fit into rows and columns of a spreadsheet is called Structured data. Being dubbed as the traditional form of data, it shares a close relation with Relational databases. Businesses typically use Relational databases to store it and simplify the data flow for software development and data analytics. In order to work with it for reading, writing, and updating, companies use Structured Query Language (SQL), a programming software language developed by IBM in the 1970s for Relational databases.

Airline reservation systems, inventory control, CRM, sales transactions, and ATM activity are some examples of Relational database applications containing it. Even the data obtained through online forms and surveys are sources for it. Businesses also gather transaction data as Structured information, which might include financial or user behavioral data. Apart from the online or real-time collection of Structured information, organizations create it by manually updating spreadsheets with information gathered from day-to-day business operations.

In Layman’s words, any data that adheres to a data model with a well-defined structure and can be readily accessed and utilized by computer programs is referred to as Structured data. With it, similar items are seamlessly connected in databases to establish relations or classes. Such connections enable companies to find hidden patterns with data analytics for making informed decisions for business growth.

Why is Structured Data Important?

Data is a crucial source of information for businesses in various forms, such as customer feedback, financial records, and social media posts. However, much of this data is unstructured and difficult to quantify, such as emotions and reasons for behavior. Structured Data is crucial because of the following reasons:

• Improved Data Organization: Structured data is highly organized, making it easier to store, search, and retrieve specific information quickly.
• Enhanced Data Quality: With predefined formats, structured data minimizes errors and inconsistencies, ensuring data accuracy and reliability.
• Efficient Analysis: Structured data can be easily analyzed using tools like SQL, enabling quicker insights and decision-making.
• Better Search Engine Optimization (SEO): Search engines favor structured data as it improves the visibility and relevance of content, boosting website SEO.

What are the Alternatives for Structured Data?

1. Semi-Structured Data

A hybrid version of Structured data is called semi-structured data, which does not adhere to the formal structure of data models of Relational databases. Instead, it uses tags or other markers to distinguish semantic elements and impose hierarchies of records and fields. This allows business or data analysts to determine information grouping and hierarchies of the data. Often times it is considered a mixture of Structured and Unstructured forms of data.

One typical example of this is email. While the text is Unstructured, it does contain Structured data such as the sender’s and recipient’s names and email addresses, as well as the time the message was delivered. In addition, it can be found in HTML, XML, and JSON files that organize data either by tags or key-value pairs.

2. Unstructured Data

Data that cannot be stored in rows and columns and lacks a data model are called Unstructured data. Here is the detailed guide on structured vs unstructured data. This data can be textual or non-textual in nature. Unlike Structured data, it isn’t possible to organize it in a Relational database. Therefore, Unstructured information is generally kept in a NoSQL database or a non-relational database. This data is essentially worthless for many businesses until resources are allocated to transforming it into clear, actionable information. It is important to note that while Unstructured data is not pre-defined through data models, it has an internal structure.

Unstructured data is becoming more widely available in complex data forms such as documents, audio, video, text files, digital pictures, navigational details, and different social media postings. It is believed that around 80% of the available data is unstructured in nature. When compared to data contained in Structured databases, the inconsistencies and ambiguities found in these datasets make it harder to comprehend using standard algorithms. As a result, organizations are seeking ways to store and analyze data to make crucial business choices in order to thrive in a competitive market.

Converting it into an easily understandable format is a challenging process. In general, Machine Learning algorithms are employed to automatically and efficiently label and categorize Unstructured data. 

What are the Differences Between Structured Data, Unstructured and Semi-Structured Data?

Attribute	Structured Data	Unstructured Data	Semi-Structured Data
Data Format	Organized in rows and columns (tables)	No predefined format (text, images, videos)	Contains tags or markers to separate data elements (JSON, XML)
Storage	Relational databases (e.g., MySQL, SQL Server)	File systems, NoSQL databases, or cloud storage	NoSQL databases, XML/JSON files
Data Organization	Highly organized and easily searchable	No consistent structure or organization	Some structure, but more flexible than structured data
Example	Customer information (name, address, phone)	Emails, social media posts, videos, images	JSON, XML files, log files
Processing	Can be processed using SQL queries	Requires advanced tools (e.g., NLP, ML) to analyze	Can be processed with both structured and unstructured methods
Flexibility	Low flexibility due to strict schema	High flexibility due to lack of structure	Moderate flexibility with some predefined structure
Data Consistency	High consistency, rules enforced by schema	Low consistency, varied formats	Medium consistency, defined structure with flexibility
Use Cases	Transaction processing, financial systems	Multimedia storage, web content, social media	Web data exchange, log files, configuration data

What are the Characteristics of Structured Data?

No matter how the data is kept or what the information is about, good structured data will have a variety of qualities. Structured data:

• Possesses a distinguishable structure that complies with a data model.
• Is shown in columns and rows, similar to a database.
• Consists of fixed fields in a file or record that are arranged so that the definition, format, and meaning of the data are clearly known.
• Has data from related groups gathered into classes.
• The characteristics of data points in the same group are the same.
• Access to and searching for information is simple for both people and other programes.
• Elements can be addressed, allowing for effective processing and analysis.

What are the Limitations of Structured Data?

• Rigidity: Structured data relies on a fixed schema, making it difficult to accommodate changes or new data types without altering the schema. This can make it inflexible for dynamic or evolving data.
• Limited Data Types: It is designed to handle only well-defined data types (e.g., numbers, strings, dates), which makes it unsuitable for more complex data like multimedia, text, or unstructured content.
• Complexity in Scaling: As the volume of data grows, scaling relational databases can become complex and resource-intensive, especially when handling large, distributed datasets.
• Lack of Context: Structured data doesn’t capture the context or meaning behind data, making it less suitable for analyzing qualitative or ambiguous data like sentiments, behaviors, or multimedia.

What are the Use cases of Structured Data?

Some common use cases of structured data include:

• Search Engine Optimization Tool: Website owners can change the HTML of their site to add a set of HTML tags known as microdata that search engines can use to describe a webpage. Microdata tags assist search engines better comprehend a website, increasing the likelihood that it will show up in search results. A group called Schema.org develops, upholds, and supports common microdata vocabularies that can be used to mark up websites. Structured data is acting as metadata in this situation.
• Machine Learning: Structured data is used by programmers to create and improve supervised learning-based machine learning algorithms. Structured data tends to apply to the machine’s rules more readily than unstructured data, hence well-labeled training data is used to train machines in supervised learning.
• Data Management: To track fundamental data like client contact details, login passwords, and financial transactions, business intelligence software may use SQL databases or Excel files. Online analytical processing, MySQL, and PostgreSQL are a few of the tools used to store structured data.
• ETL: This entails extracting data from its original sources, cleaning it up, and then transforming and loading it into a larger data repository, such a data warehouse.

Conclusion

In conclusion, structured data plays a crucial role in organizing information in a highly organized and accessible format, making it perfect for applications like transactional systems, data analysis, and reporting. Its predefined schema ensures consistency and accuracy, allowing for efficient querying and easy integration with tools like databases and business intelligence platforms. However, while structured data excels in many areas, it can become rigid and challenging to scale when dealing with more complex, dynamic, or unstructured data types.

For businesses looking to manage and migrate data seamlessly, Hevo offers an ideal solution. Hevo effortlessly integrates and migrates structured, unstructured, and semi-structured data from over 150+ sources, ensuring a smooth, automated data pipeline process. Whether you’re dealing with simple transactional data or complex datasets from various platforms, Hevo can save you time and resources. Try Hevo today and streamline your data workflows like never before!

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