Understanding the Contrast Between Structured and Unstructured Data

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Big data is classified into structured and unstructured data, and data professionals must comprehend the differences between these two types.

Structured data is organized, easy to store in a database, and can be managed efficiently. On the other hand, unstructured data is difficult to organize and doesn't conform to a specific data model. Understanding the collection, sourcing, and real-world applications of structured and unstructured data is vital. In this article, we'll provide you with a comprehensive guide to these two data types, including the tools used to manage them and the professions that work with them every day. Let's dive in!

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Is Structured vs. Unstructured data really that different?

Structured data, consisting of easily defined and searchable information such as dates, phone numbers, and product SKUs, is distinct from unstructured data, which is much harder to categorize or search. Unstructured data includes photos, videos, podcasts, social media posts, and emails, which account for the majority of the world's data.

	Structured data	Unstructured data
Main characteristics	Searchable Usually text format Quantitative	Difficult to search Many data formats Qualitative
Storage	Relational databases Data warehouses	Data lakes Non-relational databases Data warehouses NoSQL databases Applications
Used for	Inventory control CRM systems ERP systems	Presentation or word processing software Tools for viewing or editing media
Examples	Dates, phone numbers, bank account numbers, product SKUs	Emails, songs, videos, photos, reports, presentations

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What is structured big data?

Structured data is a type of quantitative data that is organized and easily searchable. Relational databases use the popular programming language Structured Query Language (SQL) to input and search within structured data. Common examples of structured data include names, addresses, credit card numbers, telephone numbers, customer star ratings, bank information, and other data that can be easily searched using SQL. With these tools, you can easily access and manage structured data with confidence.

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Structured Data Examples

In the real world, structured data could be used for things like:

• Booking a flight: Flight and reservation data, such as dates, prices, and destinations, fit neatly within the Excel spreadsheet format. When you book a flight, this information is stored in a database.

• Customer relationship management (CRM): CRM software such as Salesforce runs structured data through analytical tools to create new data sets for businesses to analyze customer behavior and preferences.

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Pros and cons of structured data

Pros	Cons
It’s easily searchable and used for machine learning algorithms.	It’s limited in usage, meaning it can only be used for its intended purpose.
It’s accessible to businesses and organizations for interpreting data.	It’s limited in storage options because it’s stored in systems like data warehouses with rigid schemas.
There are more tools available for analyzing structured data than unstructured.	It requires tabular formats that require rigid schema consisting of predefined fields.

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Structured data tools

Structured data is typically stored and used with relational databases and data warehouses supported by SQL. Some examples of tools used to work with structured data include:

• OLAP

• MySQL

• PostgreSQL

• Oracle Database

• MSSQL
  

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What is semi-structured data?

So, what’s in between? Semi-structured data is a mix of both types of data. A photo taken on your iPhone is unstructured, but it might be accompanied by a timestamp and a geotagged location. Some phones will tag photos based on faces or objects, adding another element of structured data. With these classifiers, this photo is considered semi-structured data.

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What is unstructured data?

Unstructured data is every other type of data that is not structured. Approximately 80-90% of data is unstructured, meaning it has huge potential for competitive advantage if companies find ways to leverage it [1]. Unstructured data includes a variety of formats such as emails, images, video files, audio files, social media posts, PDFs, and much more. Unstructured data is typically stored in data lakes, NoSQL databases, data warehouses, and applications. Today, this information can be processed by artificial intelligence algorithms and delivers huge value for organizations.

Examples of unstructured data

In the real world, unstructured data could be used for things like:

• Chatbots: Chatbots are programmed to perform text analysis to answer customer questions and provide the right information.

• Market predictions: Data can be maneuvered to predict changes in the stock market so that analysts can adjust their calculations and investment decisions.

Pros and cons of unstructured data

Pros	Cons
It remains undefined until it’s needed, making it adaptable for data professionals to take only what they need for a specific query while storing most data in massive data lakes.	It requires data scientists to have expertise in preparing and analyzing the data, which could restrict other employees in the organization from accessing it.
Within definitions, unstructured data can be collected quickly and easily.	Special tools are needed to deal with unstructured data, further contributing to its lack of accessibility.

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Unstructured data tools

Unstructured data is typically supported by flexible NoSQL-friendly data lakes and non-relational databases. As a result, some of the tools you might use to manage unstructured data include:

• MongoDB

• Hadoop

• Azure

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Data-focused professions

Jobs that would typically work with either structured or unstructured data include most types of data-related careers. Here are a few common roles that work with data

• Data Engineer: Data engineers design and build systems for collecting and analyzing data. They typically use SQL to query relational databases to manage the data, as well as look out for inconsistencies or patterns that may positively or negatively affect an organization’s goals. 

• Data analyst: Data analysts take data sets from relational databases to clean and interpret them to solve a business question or problem. They can work in industries as varied as business, finance, science, and government.
  

• Database administrator: Database administrators act as technical support for databases, ensuring optimal performance by performing backups, data migrations, and load balancing.

• Data Architect: Data architects analyze an organization's data infrastructure to plan or implement databases and database management systems that improve workflow efficiency.

• Data scientist: Data scientists take those data sets to find patterns and trends, and then create algorithms and data models to forecast outcomes. They might use machine learning techniques to improve the quality of data or product offerings.