The Main Differences Between Structured, Semi-Structured, and Unstructured Data

Scraping Robot
May 11, 2023

When researching business intelligence, you will come across structured, unstructured, and semi-structured data. These three types of data are widely used in business intelligence, marketing, and other applications.

Table of Contents

Traditionally, businesses used structured data to create business intelligence reports. However, an increasing number of companies are using semi-structured, structured, and unstructured data in machine learning and other use cases. Examples of these data types in healthcare include patient demographics and lab tests (structured data), delimited files (semi-structured data), and clinical reports and notes (unstructured data).

Read on to learn the differences between structured, unstructured, and semi-structured data. We’ll cover each data type’s pros, cons, and use cases and briefly discuss how to scrape all three data types with web scraping tools and application programming interfaces (APIs).

What Is Structured Data?

What Is Structured Data?

Structured data is in a standardized and well-defined format. It is usually tabular, with columns and rows clearly defining data attributes. Examples of structured data include:

  • Excel files
  • Inventory control
  • Point-of-sale data
  • Search engine optimization (SEO) tags
  • SQL databases
  • Reservation systems

Computers can efficiently process structured datasets for insights due to their standardized and quantitative nature. For instance, a structured eCommerce data table with columns for names, phone numbers, and addresses can provide vital insights. These can include the total number of clients and which products are the most popular with specific demographics.

Pros and cons of structured data

There are several advantages to using structured data, including:

  • Easy to understand and use: Any user can quickly understand and access structured data, making storing, updating, and fixing structured data simple.
  • Easy to analyze: Structured datasets’ standardized predefined parameters make analysis easier for machine learning programs.
  • Compatible with more tools: Structured data has been collected for a longer time than other types of data. As such, there are more tools specifically designed for structured data analysis. Common structured data tools include OLAP, MySQL, PostgreSQL, and SQLite.

As with all things, structured data also comes with several drawbacks. These include:

  • Fewer use cases due to predefined file formats: Structure can help machine learning models analyze big datasets faster. But, it can also limit the number of use cases for data models. This is because structured data can only be used for its intended purpose. For instance, booking system data can show you booking popularity and system finance data. However, it doesn’t reveal which marketing campaigns were more effective in attracting more bookings without further modifications.
  • Format issues: Structured datasets have strict schemes and formats, which means users will have to restructure datasets to meet new requirements when circumstances change. Consequently, restructuring can be costly and resource-intensive.

Structured data use cases

Common structured data use cases include:

  • Customer Relationship Management (CRM) systems: CRM systems store customer information and allow users to derive insights about customer behavior patterns. Marketers, C-suite executives, and others can use CRM systems to create their ideal customer profiles and pinpoint characteristics they can target in marketing campaigns.
  • Financial records management: Companies in the financial industry work with a plethora of data. Accordingly, they can greatly simplify the data management and filtering process by storing records in structured databases.
  • Inventory control: Inventory management requires structured databases for the same reasons as financial record management.

What Is Unstructured Data?

What Is Unstructured Data?

Unstructured data refers to any dataset that lacks predefined formatting. This type of data is usually created by humans, although an increasing amount of unstructured data is now machine-generated. Examples of unstructured data include:

  • Social media posts
  • Open-ended survey responses
  • Business documents such as reports and legal contracts
  • Data from satellite imagery and sensors
  • Chat messages

Unstructured datasets contain information in their native formats, giving businesses more context for insights. They can’t be analyzed or processed with conventional data tools and methods. Instead, they are usually managed in non-relational (NoSQL) databases or data lakes.

Pros and cons of unstructured data

The benefits of using unstructured data include:

  • Easy storage: Because unstructured data doesn’t have to be predefined, you can store it quickly and easily.
  • Data lake storage: Data scientists can store unstructured data in data lakes, which are massive and offer pay-as-you-use pricing. As a result, you can cut costs and ease scalability.
  • Competitive advantage: The amount of unstructured data is only growing, and an increasing number of companies are using unstructured data to derive insights. Companies that leverage unstructured data effectively may gain an edge over their competitors.

The disadvantages of using unstructured data include:

  • Requires expertise: Data science expertise is needed to prepare and analyze unstructured data because it is non-formatted and undefined. This means that companies must hire data scientists to use unstructured data.
  • More vulnerable to cyberattacks and unauthorized access: Due to its lack of consistency, organizations often do not have an easy way to identify and classify unstructured data. As a result, sensitive data in unstructured datasets are more often at risk than in structured data sources.
  • Requires specialized tools: Data scientists must use specialized tools to manipulate unstructured data. These include MongoDB, Hadoop, DynamoDB, and Azure.

Unstructured data use cases

Companies can use unstructured data in the following use cases:

  • Chatbots: Using unstructured data, data scientists can perform text analytics and link customer questions to relevant sources of information.
  • Data mining: Companies can analyze unstructured datasets such as social media posts to understand consumer behavior and spot potential improvement opportunities.
  • Predictive data analytics: Companies can also perform predictive data analysis on unstructured datasets to predict future events and trends.

What Is Semi-Structured Data?

What Is Semi-Structured Data?

Semi-structured data lies in between structured and unstructured data. It is more complex than structured data and does not have a predefined data model, but is easier to store than unstructured data. Examples of semi-structured data include:

  • Delimited files (sequential files where each line represents a single company, book, or item, and each line has fields separated by a comma or another delimiter)
  • XML and other markup languages
  • TCP/IP packets
  • Web pages
  • Data integrated from different sources

Semi-structured data uses metadata to identify unique data characteristics and scale data into preset fields and records. Metadata is a set of data that gives information about other data, such as semantic markers and tags. Because semi-structured data use metadata, it can be better cataloged, analyzed, and searched than unstructured data.

Pros and cons of semi-structured data

Semi-structured data provides the following benefits:

  • Scalable: Semi-structured data and its schema are usually easy to scale since they don’t fit into pre-made structures. As a result, there are few to no limits on the amount of semi-structured data a business can store and analyze.
  • Portable and easy to store: Semi-structured data is much more portable and storable than unstructured data. Portability refers to how easy it is to access, transfer, share, and organize data. Computers have more ways to analyze semi-structured data compared to unstructured data, so it is relatively easy to transfer semi-structured data from one location to another.
  • Relatively versatile: Semi-structured data lets you change the schema. However, the data and schema are frequently tightly linked, so you often have to know what data you’re looking for when conducting queries.

Drawbacks of using semi-structured data include:

  • More difficult to use than structured data: Semi-structured data is easier to use than unstructured data, but it still creates challenges. Fortunately, teams can use text analysis designs and other machine learning technology to break down and analyze semi-structured data for powerful insights.
  • Harder to store than structured data: Semi-structured data’s lack of rigid, fixed schema makes it more difficult to store than structured data.
  • Less secure than structured data: Semi-structured data can be more challenging to secure than structured data since it may contain sensitive information in less-visible or unstructured parts of the data. This can make it harder to spot and protect sensitive information from unauthorized access.

Semi-structured data use cases

Businesses can use semi-structured data in the following ways:

  • Sentiment analysis: Businesses can use semi-structured data to perform sentiment analysis. This process tracks social media activities to analyze customer opinions.
  • Natural language processing (NLP) algorithms: A lot of important information is shared via semi-structured data. Accordingly, NLP algorithms must have the ability to process semi-structured data and interpret it for accurate predictions.
  • Predictive analysis: Like unstructured data, semi-structured data can be used to make future predictions.

Scraping and Using Structured Data, Semi-Structured Data, and Unstructured Data

Scraping and Using Structured Data, Semi-Structured Data, and Unstructured Data

If search engines are your main sources for discovering electronic healthcare records (eHR Go) and other types of structured, semi-structured, and unstructured data for electronic health records, consider using an API or web scraping tool. Both tools can greatly simplify the data extraction process.

A web scraping API builds an automated data pipeline between you and a specific part of the target website, allowing you to pull data manually or on an automated schedule. Meanwhile, a web scraper or bot extracts all of the content from a website, including text, videos, and images. It then stores the extracted content as a data file.

To try APIs and web scrapers today, check out Scraping Robot’s no-code web scraper and API. Our scraper handles proxies, metadata parsing, and much more, so you can focus on the most important task: getting valuable metadata. Our Postman documentation also lets you plug and play with our API.

Create a Scraping Robot account today to receive 5,000 free scrapes. You will also receive access to all of our features.

The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.