A Practical Guide to Structured vs. Unstructured Data

A crucial distinction exists between structured and unstructured data—each with its own significance and complexities. Businesses worldwide strive to harness these diverse data forms, aiming to convert them into actionable insights to drive strategic decisions. Our guidance will explain what structured and unstructured data is and explore the differences between them.

Key questions to determine these differences:

• What type of data are you collecting?

• Who will be using the data?

• When does the data need to be prepared? Before storage or when used?

• Where will data be stored?

• How will the data be stored?

What is structured data?

Structured data is data that has been formatted to a specific structure before being stored. For example, it may have been sorted into predefined categories in a relational database, which is set up to make establishing the relationships between the data straightforward. Having the ability to easily access information in this way allows organizations to analyze it more effectively and gain in-depth insights.

It can take the form of numbers or text. Examples of structured data include names, addresses, and credit card numbers, among others.

Structured data sources

What is unstructured data?

Unstructured data is data stored in its native format until it needs to be used. There are many formats it can take, including social media activity, video and audio files, Internet of Things (IoT) data, and even surveillance images. It is often managed in a non-relational database. A non-relational database is optimized for the type of data being stored in it, instead of using rows and columns.

Unstructured data is more common than structured data, making up between 80% and 90% of the data organizations generate and collect. It helps organizations to gain a clearer idea of the issue they are trying to address, leading to better understanding and better business decisions.

Unstructured data sources

What is semi-structured data?

Semi-structured data sits somewhere between structured and unstructured data. It is easier to store than unstructured data but more complex than structured data and it does not fit into a relational database. Metadata is used to catalog semi-structured data into specific categories so it is easy to search and analyze.

An example of semi-structured data is a photograph taken with a smartphone camera. The image is unstructured data, but information like the time and location is structured.

Semi-structured data sources

What is the difference between structured and unstructured data?

A comparison of structured vs unstructured data

Structured data

Unstructured data

What are the advantages and disadvantages of unstructured data? 

Advantages

• Unstructured data offers more freedom and flexibility. It is not defined until you need to use it, which means it is quick and easy to collect, can have multiple use cases, and be adapted to suit your requirements.

• Only the data you need has to be prepared and analyzed, which saves time.

• Analyzing unstructured data enables organizations to identify patterns and trends, whether that’s customer behavior or suspicious activity.

• Because unstructured data is stored in its native format it can be collected relatively quickly, and your organization has more data to use.

• Unstructured data is often stored in data lakes, which allow for vast amounts of data to be stored, and pay-as-you-use pricing reduces the amount you need to spend on storage.

Disadvantages

• You need a data scientist to prepare and analyze unstructured data. They must have a thorough understanding of the data’s topic and how it can be useful to the business. Your organization must have the resources to accommodate this.

• You need specialized tools in order to manipulate unstructured data. Most of the tools for analyzing unstructured data are still being developed.

• Generally, unstructured data is more difficult for organizations to manage, with the majority acknowledging it as a challenge.

Use cases for unstructured data 

• Chatbots can analyze text and direct customers to the right person to handle their query.

• Predictive data analytics forecast significant changes in a business’s industry so the organization can plan accordingly.

• Social media and news monitoring enables organizations to identify customer sentiment and the way their business is perceived, then adjust their strategy accordingly.

• Text mining allows organizations to look at consumer behavior, sentiment, and buying patterns, then make changes to their operations to accommodate it, hopefully resulting in greater ROI.

Unstructured data tools

• NoSQL DBMS stands for non-SQL, or not-only-SQL database management system. A NoSQL DBMS stores data in a format that is not relational tables. Each system will have its own features, such as horizontal scaling and flexible schemas.

• Azure offers multiple data services, including storage. Its Blob Storage solution is a cloud-based way of storing large volumes of unstructured data.

• Hadoop is specifically designed for enormous volumes of data, allowing organizations to store and analyze all the information available to them so they can get as much insight as possible.

• MongoDB is an open source, non-relational database management system. Organizations can store different types of data and distribute them across various systems.

What are the advantages and disadvantages of structured data?

Advantages

• It is easy for machine learning to use, manipulate, and question structured data because it is so well organized.

• It is easy for most business users to utilize structured data, if they have an understanding of the topic the data covers.

• There are more tools available far use with structured data because it has been in use for longer than unstructured data.

Disadvantages

• Structured data can only be used for its intended purpose, resulting in a lack of flexibility.

• It is normally stored in data warehouses, which have rigid schemas, and require you to update your structured data every time the requirements change. This can be expensive and time-consuming. The impact of this can be reduced by using a cloud-based data warehouse.

Use cases for structured data

• Accounting organizations or departments use structured data for their financial transactions.

• Business intelligence can be acquired using structured data, allowing organizations to accurately forecast what the most successful path would be.

• Customer relationship management (CRM) software analyzes structured data to find patterns and trends in customer behavior.

• Online booking data is organized via the rows-and-columns format typical of structured data.

• Spreadsheets are by their very format a type of structured data, regardless of how simple or complex they are.

Structured data tools

• RDBMS stands for "relational database management system." An RDBMS gives organizations ways of interacting with a relational database and is a way of managing structured data. 

• MySQL is a database management system that can handle large volumes of data. It is open source, which means anyone can access the software and make changes to it. 

• OLAP stands for "online analytical processing." It is a technology used to query or analyze large amounts of stored data at high speed.

• OLTP stands for "online transaction processing." Economic or financial transactions are recorded and secured so the organization can access the information.

• PostgreSQL is an open-source database management system that can store and scale large volumes of data, including structured data.

• SQLite is a C-language library that can be built into mobile phones, computers, televisions, games consoles, cameras, watches, and countless other devices used by people all over the world.

The future of structured and unstructured data 

The amount of data that exists is increasing exponentially. As technology has developed, the amount of data businesses and individuals generate every day has grown too, thanks to the Internet, Google searches, social media, apps, emails, texts, services, digital photographs, and many more things that are part of daily life. It’s estimated that in 2024 alone, 147 zettabytes of data will have been generated. 

With this in mind, it’s essential that organizations are proactive about managing and storing their data, especially unstructured data. Some questions to ask include:

• What types of unstructured data will be useful for your organization?

• Where can your organization find the unstructured data it needs?

• Does your organization have the resources and ability, internally or externally, to collect unstructured data, store it, and access it when it is required?

• Is your organization using the best storage method for its unstructured data? 

• Is there any reliable third-party data your organization can use? 

Overcome unstructured data challenges with Quantexa

Unstructured data can be challenging to deal with, as you need a data scientist to prepare and analyze it. However, unstructured data also provides considerable value across multiple areas, from finding bad actors and understanding credit-risk decisions to improving customer service.

Quantexa helps organizations bring data together from any source, revealing relationships and insights to enable informed and confident decision-making. Quantexa News Intelligence performs best-in-class natural language processing (NLP) on over 1.3 million unstructured news articles every day, transforming them into structured news data, enriched with 26 data points for each article including: category and entity tagging, sentiment, and rich metadata.

By utilizing the bulk of an organization’s data that is often left unused, the efficiency and effectiveness of your processes can be improved, and you’ll benefit from much greater levels of accuracy.

Gain control of your data

Get a true, connected view across all your data assets from internal and external sources. Improve data quality and build applications.

Request a demo