An overview of structured and unstructured data, their key differences, and which format will work best for your business.

Not all data is created equal. Although some data is structured, most of it is unstructured. Sourced, collected, and scaled structured and unstructured data are handled differently, and each is stored in a different type of database.

We'll explore both types of data in this article so you can make the most of your data.

What is structured data?

Data that is structured - commonly referred to as quantitative data - is highly organized and can easily be analyzed by machine learning algorithms. Structured query language (SQL) was developed by IBM in 1974 and is used to manage structured data. Using a relational (SQL) database, business users can input, search, and manipulate structured data quickly.

Structured data: pros and cons

Structured data includes dates, names, addresses, credit card numbers, etc. Data inflexibility is their liability, as their benefits rely on ease of use and access:

Pros

• Easily used by machine learning (ML) algorithms: The specific and organized architecture of structured data eases manipulation and querying of ML data.
• Structured data is easy to use since users do not have to know how different types of data work in detail. A user can easily access and interpret the data if he or she understands the topic in relation to the data.
• Accessible by more tools: Since structured data predates unstructured data, more tools are available for analyzing and using structured data.

Cons

• Limited usage: Predefined structures can only be used for their intended purposes, limiting their flexibility and usability.
• Structured data is typically stored in data storage systems with rigid schemas (e.g., "data warehouses"). Changes in data requirements necessitate an update of all structured data, which consumes a significant amount of time and resources.

Structured data tools

• OLAP: Provides high-speed, multidimensional data analysis from centralized, unified data sources.
• SQLite: Provides a serverless, zero-configuration, transactional relational database engine.
• MySQL: Integrates data into software applications, especially mission-critical, heavy-load production systems.
• PostgreSQL: Supports SQL and JSON querying as well as high-tier programming languages (C/C+, Java, Python, etc.).
• Use cases for structured data
• CRM: CRM software analyses structured data using analytical tools to create datasets that reveal customer behavior patterns and trends.
• Online booking: Hotel and ticket reservation data (such as dates, prices, destinations, etc.) follows the predefined data model's "rows and columns" format.
• A financial company or department processes and records financial transactions using structured data.

What is unstructured data?

• Data that is unstructured, or qualitative, cannot be processed and analyzed using conventional tools and methods. Due to the lack of a predefined data model in unstructured data, it is best managed using non-relational (NoSQL) databases. To preserve unstructured data in its raw form, data lakes are another way to manage it.
• Unstructured data is becoming increasingly important. Over 80% of all enterprise data is unstructured, and 95% of businesses prioritize unstructured data management.

Pros and cons of unstructured data

Data that is unstructured includes text, mobile activity, social media posts, sensor data from the Internet of Things (IoT), etc. Benefits include advantages in format, speed, and storage, while liabilities are related to expertise and resources:

Pros

• The native format of unstructured data remains undefined until it is needed. As a result of its adaptability, a wider range of file formats can be stored in the database, enabling data scientists to prepare only the data they need to prepare and analyze.
• Easily accumulating data: Since there is no need to define the data in advance, it can be collected quickly and easily.
• Data lake storage: Allows massive storage and pay-per-use pricing, which reduces costs and enables scalability.

Cons

• Requires expertise: Due to its undefined/non-formatted nature, data science expertise is required to prepare and analyze unstructured data. This is beneficial to data analysts but alienates unspecialized business users who may not fully understand specialized data topics or how to utilize their data.
• Specialized tools: Specialized tools are required to manipulate unstructured data, which limits product choices for data managers.

Unstructured data tools

• MongoDB: Uses flexible documents to process data for cross-platform applications and services.
• DynamoDB: Delivers single-digit millisecond performance at any scale via built-in security, in-memory caching and backup and restore.
• Hadoop: Provides a simple programming model for processing large data sets and does not require formatting.
• Azure: Provides cloud computing services to help develop and manage apps through Microsoft data centers.

Use cases for unstructured data

• Data mining: Used by businesses to better understand customer behavior, product sentiment, and purchasing patterns by using unstructured data.
• Predictive data analytics: Alert businesses of important activity ahead of time so they can properly plan and accordingly adjust to significant market shifts.
• Chatbots: Analyze text to route customer questions to the appropriate answers.

What are the key differences between structured and unstructured data?

Unstructured (qualitative) data provides a deeper understanding of customer behavior and intent than structured (quantitative) data. Below we will discuss some of the key differences and their implications:

• Sources: Structured data is sourced from GPS sensors, online forms, network logs, web server logs, OLTP systems, etc., whereas unstructured data sources include email messages, word-processing documents, PDF files, etc.
• Forms: Structured data consists of numbers and values, whereas unstructured data consists of sensors, text files, audio and video files, etc.
• Models: Structured data has a predefined data model and is formatted to a set data structure before being placed in data storage (e.g., schema-on-write), whereas unstructured data is stored in its native format and not processed until it is used (e.g., schema-on-read).
• Storage: Structured data is stored in tabular formats (e.g., excel sheets or SQL databases) that require less storage space. It can be stored in data warehouses, which makes it highly scalable. Unstructured data, on the other hand, is stored as media files or NoSQL databases, which require more space. It can be stored in data lakes which makes it difficult to scale.
• Uses: Structured data is used in machine learning (ML) and drives its algorithms, whereas unstructured data is used in natural language processing (NLP) and text mining.

What is semi-structured data?

Semi-structured data (e.g., JSON, CSV, XML) is the “bridge” between structured and unstructured data. It does not have a predefined data model and is more complex than structured data, yet easier to store than unstructured data.

Semi-structured data uses “metadata” (e.g., tags and semantic markers) to identify specific data characteristics and scale data into records and preset fields. Metadata ultimately enables semi-structured data to be better cataloged, searched and analyzed than unstructured data.

• Example of metadata usage: An online article displays a headline, a snippet, a featured image, image alt-text, slug, etc., which helps differentiate one piece of web content from similar pieces.
• Example of semi-structured data vs. structured data: A tab-delimited file containing customer data versus a database containing CRM tables.
• Example of semi-structured data vs. unstructured data: A tab-delimited file versus a list of comments from a customer’s Instagram.

The future of data

Recent developments in artificial intelligence (AI) and machine learning (ML) are driving the future wave of data, which is enhancing business intelligence and advancing industrial innovation. In particular, the data formats and models covered in this article are helping business users to do the following:

• Examine digital communications for compliance: Pattern recognition and email thread analysis software that can search email and chat data for possible noncompliance.
• Identify online threats and monitor marketing campaign results with text analytics and sentiment analysis of high-volume customer conversations.
• With ML analytics tools, businesses can analyze massive amounts of data quickly and learn more about their customers.

By utilizing data formats and models intelligently and efficiently, you can achieve the following:

• Understanding your customers' needs at a deeper level will allow you to better serve them
• Improve marketing campaigns by making them more targeted and focused
• Keep track of current metrics and create new ones
• Enhance product opportunities and offerings
• Reducing operational costs
• IBM and structured and unstructured data

No matter how experienced or inexperienced you are, being able to handle all types of data is essential to your success. By utilizing structured, semi-structured, and unstructured data options, you can benefit from optimal data management.

Visit IBM Cloud Databases to learn more about the types of data you can store there.