Statista’s data indicates that the value of the big data market was $7.6 billion in 2011. The market is expected to expand dramatically and reach $103 billion by 2027. This significant increase shows that data is important for driving innovation and progress in many industries.
However, data accuracy will depend on the organization’s ability to collect, examine, and utilize the data.
Every day, more data is created because it is available in an abundance of formats, from your most recent social media post to tightly structured relational databases. All of the data accessible by companies in its various formats can be divided into two groups: structured data and unstructured data.
In this blog, we will evaluate structured vs. unstructured data, its meaning, key differences, the uses of both data types, and the definition of semi-structured data.
What is Structured Data?
Structured data, also known as quantitative data, is information that is well-structured and easy for machine learning algorithms to understand. It is usually kept in a relational database management system (RDBMS) and contains data types such as numbers, texts, and dates.
The programming language used to handle the structured data is structured query language (SQL). By using (SQL), businesses can quickly enter, search for, and deal with structured data.
Structured Data Benefits and Drawbacks
Benefits of structured data:
- Ease of use: Companies with a basic understanding of the topic related to the data can easily access and examine the data without having in-depth knowledge about it.
- Simplicity in applying machine learning algorithms: The most crucial benefit of structured data is that the machine learning algorithms efficiently use it. Due to its ordered nature, structure data can be easily modified and accessed.
- Predictability: Structured data’s predictable format allows for better planning and forecasting, supporting business decision-making.
- Data quality and consistency: Structured data usually follows a strict pattern to ensure its quality and consistency. This reduces errors and makes the data much more reliable
Drawbacks of structured data:
- Minimal flexibility: Structured data limits flexibility and adaptability because it adheres to a predefined structure and can only serve that purpose.
- Lack of data storage: Structured data has limited storage options because it is typically stored in data warehouses, which follow a rigid framework that can be difficult to change.
Example of structured data:
Customer relationship management systems (CRM), Excel files, financial records, address books, and employee records are the most common examples of structured data.
Tools for Structured Data:
Tools for working with structured data include, for instance:
- Amazon Redshift
- MySQL
- Oracle ERP Cloud
- PostgreSQL
What is Unstructured Data?
Unstructured data, or qualitative data, is information that lacks a predefined structure. It is typically stored in its original format and processed only when necessary.
Unstructured data accounts for 80-90% of a company’s data, and its percentage rises daily, emphasizing its significance. Companies gain valuable business data by including unstructured data.
Non-relational databases (NoSQL) most effectively handle unstructured data. Data lakes are another method to maintain unstructured data in its raw form.
Unstructured Data Benefits and Drawbacks
Benefits of Unstructured Data:
- Original format: Since unstructured data is stored in its original format and isn’t processed until needed, it creates a large pool of use cases. Data experts may collect and assess only the necessary data while working with unstructured data.
- Flexibility: Unstructured data is usually stored in its original form without a specific organization, allowing for accessible collection and storage of diverse types of data.
- More affordable: Unstructured data is stored in data lakes, which provide large data capacities and help reduce costs. This approach also allows for flexible processing and analysis of data, offering valuable insights and business intelligence.
Drawbacks of Unstructured Data:
- Unique tools: Recognizing that gathering and analyzing unstructured data presents a unique challenge that requires specialized tools and expertise, which may reduce the variety of choices for data managers when handling this type of data.
- Searching and retrieval challenges: Acquiring precise and reliable information from unstructured data is a complex and time-intensive task. This process demands thorough scrutiny, careful analysis, and insightful interpretation to derive meaningful insights.
Examples of Unstructured Data:
Videos, images, emails, social media posts, audio files, PDFs, and web pages are the most common examples of unstructured data.
Tools for Unstructured Data:
Tools for working with unstructured data include, for instance:
- MongoDB
- Azure Data Lake
- Google AI Platform
- Apache Hadoop
Structured vs. Unstructured Data: Critical Differences
The following are the main differences between structured and unstructured data:
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Structure:
Structured data is arranged in a predetermined manner, usually involving numbers and text. Unstructured data does not have a predetermined structure and is generally in video and audio files or text documents.
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Storage:
Structured data is stored and managed in relational databases (RDBMS) or data warehouses.
Unstructured data is stored and handled in non-relation databases (NoSQL), data lakes, or file systems.
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Analysis:
Structured data is more accessible to analyze and examine using traditional tools and techniques.
Unstructured data is complex and requires specialized tools and techniques like NLP, image recognition, and machine learning algorithms.
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Flexibility:
Structured data is less flexible because a specific format organizes it and cannot be easily changed.
Unstructured data is more flexible because it can support a variety of formats and features.
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Nature:
Structured data is quantitative, consisting of real numbers and items that can be counted.
Unstructured data is qualitative and cannot be assessed and examined using traditional tools and techniques.
Use Cases for Structured Data
1. Customer Relationship Management (CRM):
CRM software uses powerful analytical tools to process structured data, enabling businesses to gain valuable insights into customer behavior and purchase patterns.
2. Financial Systems:
Accounting firms use structured data to keep track of financial transactions, account balances, and financial reporting.
3. Human Resources:
Human resource departments also use structured data to store employee records, payrolls, and attendance data.
Use Cases for Unstructured Data
1. Social Media Analysis:
Companies use unstructured data to analyze comments, posts, and images better to understand public actions and trends.
2. Chatbots:
Chatbots analyze text to deliver helpful and valuable answers tailored to the customers’ questions. It’s like having a professional assistant at your service 24/7!
3. Email Management:
Another use case for unstructured data involves sorting, categorizing, and searching through massive volumes of email data to manage and retrieve information efficiently.
What is meant by Semi-Structured Data?
Semi-structured data falls somewhere between structured and unstructured data. It is more complicated than structured data and does not follow a predefined format, but it is much easier to store and manage than unstructured data.
Analyzing semi-structured data is faster and easier than analyzing raw unstructured data. This type of data is also known as “self-describing data.”
Semi-structured data is excellent for when you want the freedom to work with various data formats, like when you’re dealing with web exchanges, using APIs, or storing documents. This type of data strikes a nice balance—it’s not too chaotic since it has some organization, but it’s flexible enough not to box you in. This blend makes storing, handling, and sifting through easier than completely unorganized data.
But here’s a downside: OCR data extraction of semi-structured data is pretty hard to handle.
Example of Semi-Structured Data:
Some common examples of semi-structured data include JSON files, XML files, email with metadata, HTML pages, and log files.
Use Cases for Semi-Structured Data
Web Data Exchange:
Web services and APIs commonly use JSON and XML to exchange data between clients and servers. These formats enable a flexible structure to represent complex data hierarchies, making them well-suited for data interchange on the web.
Client Feedback:
Surveys and feedback forms often mix structured elements, like ratings and categories, with unstructured comments. This approach helps businesses measure customer satisfaction and gather helpful insights from comments.
Document Storage:
Unstructured content exists alongside structured tags in HTML documents. This structure allows search engines to effectively index and obtain pertinent information and web browsers to render material appropriately.
Summary
In a society where data is more critical than ever, understanding the complications between structured and unstructured data is essential for businesses looking to use data effectively.
Structured data, with its organized nature, allows users to search and decipher it easily using traditional tools and techniques. It provides ease of use, higher data quality, and the ability to apply standard tools, making it priceless for applications like customer relationship management (CRM), human resources, and financial systems.
On the other hand, most of the data we create isn’t in a neat format, but it’s beneficial. It lets us better understand social media, talk with chatbots, and organize emails. Unstructured data, which makes up most of the data produced today, provides excellent flexibility and has a wide range of uses. These include analyzing social media, interacting with chatbots, and managing emails.
We also discussed that semi-structured data is a type of data that falls somewhere between structured and unstructured data. It is easier to manage than unstructured data but more complicated than structured data.
VisionX provides generative AI and machine learning services that improve how structured and unstructured are managed and used. These services let companies get insightful information from diverse data types, driving better strategic planning and operational efficiency.
