Structured VS Unstructured Data

Data is an integral part of business decisions. The Big Data Analytics market is projected to grow from $307.52 billion in 2023 to $745.15 billion by 2030, creating an astounding 2.72 million jobs in data science over the next few years.

A company’s vision improves following its ability to gather the correct data, interpret it, and use the lessons derived to influence its operational success. However, the amount of data companies access today is rising and comes in different kinds and formats. This data is grouped into two main divides: structured and unstructured data.

So, what are structured and unstructured data?

Structured data consists of clearly outlined data types that come with searchable patterns. In contrast, unstructured data isn't easily searchable and includes commonly used formats such as video, audio, and social media post content.

Both types of data are essential for companies in the life science industry. Their work requires analysis and visualization to make meaningful discoveries.

What Is Structured Data?

Structured data defines resident data in the form of a fixed field within a record or file. The field stores length-specific data.

Examples of structured data include ZIP codes, phone numbers, and email addresses. Records can be of string and variable length or generated by humans or machines.

Structured data is searchable by humans using generated queries and algorithms using data types and field names such as numeric, alphabetic, date, and currency. Structured Query Language (SQL) is used for querying within relational databases.

This data type is typically stored in a relational database management system (RDBMS). Usually, it consists of text and numbers, which can be sourced manually or automatically within the RDBMS-defined structure.

Structured data examples include the following RDBMS applications:

• ATM activity
• Inventory control
• Student fee payment databases
• Airline reservation and ticketing

Structured Data: Pros & Cons

The table below outlines the pros and cons of structured data:

What Is Unstructured Data?

Unstructured data, also known as qualitative data, is the data type that is stored in its original format and is only processed once the need arises. Sometimes, this type of data has a specific structure, though this isn't predefined.

Unstructured data exists in greater variety and abundance than structured data. Essentially, unstructured data is responsible for at least 80% of all enterprise data, and the stats increase daily.

Consequently, companies that don’t consider unstructured data are missing out on a crucial angle of business intelligence.

Typical unstructured data that is human-generated includes the following:

• Email, which is semi-structured via its metadata
• Websites like Instagram, YouTube, and similar photo-sharing platforms
• Social media channels like Twitter, Facebook, and LinkedIn
• Mobile data through text messages
• Business application data from MS Office and other data processing packages
• Media files, including audio and video file formats

Unstructured data that is machine-generated includes:

• Digital surveillance videos and photos
• Satellite images from weather and landforms
• Sensor data from oceanography and vehicle traffic

Unstructured Data: Pros & Cons

The table below outlines the pros and cons of unstructured data:

The Middle Ground: Semi-Structured Data

Semi-structured data is also nicknamed “data that is self-describing.” This data format has a nature that falls between its unstructured vs. structured counterparts.

It uses semantic markers that store the data as a dataset consisting of records and fields.

Examples Of Semi-Structured Data

A familiar example of semi-structured data is found in photos stored in smartphones. Each photo has an element of location, time, and other structure information that easily distinguishes the photo from others.

Common semi-structured data formats include:

JSON (JavaScript Object Notation), which is structured in name/value pairs, as well as an ordered value list. Its interchangeable nature can be easily transmitted between servers and web applications.

XML is a semi-structured document language. It has a tag-driven structure that can be flexibly used for web transportation, making data structure and storage universal.

To learn more about the application of unstructured data in research, read the full blog here: https://bit.ly/3S2MMTb

Learn More About How We Can Help

To delve deeper into this topic, we invite you to watch our on-demand webinar: Exploring How AI Interacts with Structured and Unstructured Data in Research. The session will provide you with comprehensive knowledge about managing and utilizing these two types of data effectively. Don't miss this opportunity to gain expertise and drive your organization's data strategy forward. Watch on demand today!