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.
Table of Contents
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 Structured Data is 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. In contrast, structured data is organized and can be easily understood and processed by computers, which allows for more efficient analysis and decision-making.
As a business grows or expands into new markets, structured data becomes even more important. It can be used in machine learning and AI to make accurate predictions about future growth and product success. Additionally, structured data is useful for employees as it is easily accessible, manageable, and provides relevant information such as customer details, sales data, and inventory levels.
What are the Alternatives for 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.
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.
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What are the Differences Between Structured Data and Unstructured Data?
|Structured Data||Unstructured Data|
|Can be displayed in rows, columns and relational databases.||Cannot be displayed in rows, columns and relational databases.|
|Numbers, Dates and Strings.||Images, audio, video, word processing files, emails, spreadsheets.|
|Estimated 20% of enterprise data.||Estimated 80% of enterprise data.|
|Requires less storage.||Requires more storage.|
|Easier to manage and protect with legacy solutions.||Harder to manage and protect with legacy solutions.|
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 Benefits of Structured Data?
The inherent nature of Structured data allows users of any knowledge level to comprehend and evaluate the data’s multiple relationships. The well-defined schema facilitates easy data storage and retrieval, thereby allowing stable analytics workflows.
Organizations have been using Structured data for quite a long time now. Hence you already have an abundance of mature tools and models to process this data to get meaningful insights. A lot of tools exist that have been explored, tested, and evaluated for it. In addition, because the patterns within the data are apparent, it is easier to train a Machine Learning system using it.
Due to the availability of superior data analytics tools, a wide range of professionals can leverage Structured data for boosting better decision-making. These assists organizations in building a data culture, where teams within the organization are not always reliant on data analytics or data scientist for obtaining insights.
What are the Limitations of Structured Data?
Since Structured data accounts for about 20% of the enterprise data, it does not provide a comprehensive view of business functions. This means that businesses will be missing out on a lot of information if they only consider and process it. What makes the matter worse is that it can only be used for its intended purpose. Any changes require in reporting or analytics can lead to disrupting the ETL and data warehousing workflows.
While Data Analysis can be mostly streamlined with Data warehousing and No-code analytics tools, it comes at steep costs. It is typically kept in Relational databases or data warehouses, and both are costlier than data lakes, which cater to all the Unstructured data analysis requirements.
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.
Despite Unstructured data gaining more buzz around applications and usefulness, Structured data will always provide the central picture in businesses. It holds a strong foundation in the Big Data community for use cases like Real-time analytics and Data Democratization within organizations. Its simplicity of it will continue to add more value to businesses that have incorporated best practices for data warehousing to enable better decision-making by the end-users of Big Data.
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