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Structured vs. Unstructured Data: How They’re Different and Why It Matters

Updated: Mar 17, 2022

Data is one of the fundamental foundations of business. Learn how utilizing structured vs unstructured data can help drive better business outcomes.

Nowadays, data makes the world go ‘round. From the tiniest of data nodes in a complex algorithm to every single post a person makes on social media, data is everywhere, and it powers just about everything.

For businesses, good data can be a goldmine—not just of information, but of technological capability that drives new innovation forward.

But all data isn’t created equal. Just like the different types of AI, each type of data has advantages and disadvantages over others depending on the context. Here we’ll go over these different types of data, particularly structured vs unstructured data, to see how they differ and how each supports different use cases in business.

Structured vs unstructured data

Let’s start with the most common form of data: unstructured data.

What is unstructured data?

Unstructured data is data that can’t easily be organized and processed through conventional methods. This type of data is most often qualitative, which means it’s descriptive, rather than based around numerical inputs. Thus, unstructured data can encompass anything from text messages to survey answers, audio files, and more. Because it has no predefined model, this type of data can’t be organized in relational databases. This is why many businesses choose to house their unstructured data either in data lakes or non-relational (NoSQL) databases.

Common use cases

While unstructured data can be harder to process, that doesn’t mean it isn’t useful for businesses. In fact, 80% of all enterprise data is classified as unstructured, and 95% of businesses today are prioritizing the management of that data. To give you an idea of how important this type of data can be, here are some of the most common use cases for unstructured data.

  • Fraud detection: Text analysis of unstructured data within customer claims can help businesses identify patterns of language that are more common on legitimate claims vs fraudulent ones. Through this analysis, suspicious claims can automatically be flagged while legitimate ones can be sent for quick processing.

  • Reputation management: Being able to gather and analyze customer sentiment is key to reputational management. Data from social media comments and reviews can help teams gain insights into what consumers like and don’t like—ultimately help them resolve issues to create a better customer experience.

  • Improved efficiency and productivity: Unstructured data can hold incredible value for businesses. From information on productivity to outlining gaps in processes, being able to tap into this data can yield insights that accelerate efficiency across the board.

How structured data is different

On the other side of the spectrum, structured data is quantitative, which means it’s made up of numbers and/or text. This type of data is highly relational, which makes it much easier to organize, process, and analyze by typical machine learning algorithms. Oftentimes organized in a relational SQL database, this data can be easily searched through and manipulated by data analysts.

Common use cases

Structured data makes up less of the overall data businesses have access to, but it plays a vital role in the organization and management of information. Common applications include:

  • Reservation systems: Restaurants use structured data to keep track of reservation and party information, including time, number of people, and things like dietary restrictions.

  • Web stats: Commonly available on most website building platforms, site data shows statistics for number of unique visitors, time spent on specific pages, and other relevant information that can help businesses optimize their digital presence.

  • Geolocation: Spatial location can be helpful to businesses looking to better understand the geographic distribution of their audience. In terms of marketing, it can also be useful in determining high-traffic areas to place things like DOOH advertising or billboards.

Structured vs unstructured data: pros and cons

Pros of unstructured data

  • Easy storage on data lakes: Unstructured data can easily be stored in almost limitless quantities on data lakes.

  • Fast accumulation: This type of data requires no predefining, which means it can be collected and stored much more quickly.

  • Adaptability: Unstructured data is stored in its ‘native’ format. This means the data can be flexibly adapted to a variety of use cases depending on how it's prepared and organized.

Cons of unstructured data

  • More expertise needed: Knowledge of data science is needed to be able to effectively draw insights from unstructured data.

  • It’s harder to wrangle: Unlike structured data, unstructured data can only be manipulated through specialized tools that some businesses may not have access to.

Pros of structured data

  • Accessibility: Structured data is older than unstructured data, and thus has a wider variety of options for analytical tools.

Cons of structured data

  • Limited use cases: Since this type of data is predefined, it can only be used for its specific purpose or application.

  • Less options for storage: Structured data is most often stored in data warehouses, which can incur massive costs for organizations over time. However, new cloud-based data storage methods are alleviating some of the burden from businesses.

Other types of data

Semi-structured data

Lying at the halfway point between structured and unstructured data, semistructured data attempts to bridge the gap between the two. It doesn’t fit into typical relational databases, but still includes tagging systems that enable data analysts to separate and search through the data. A good example of semi-structured data is smartphone photos. Each photo contains unstructured content (the picture itself), along with time, location, and geospatial data.


Think of metadata as the ‘brain’ that powers a large set of data. It is a master dataset that is able to describe other types of data and provides business analysts extra information that helps drive better decisions. One of the most common examples of metadata is that found on websites, including headlines, image alt-text, meta descriptions, and blog posts like this.

Leveraging all data moving forward

Ultimately, data is foundational to doing good business. Success depends upon a team’s ability to collect, analyze, and make decisions based on data, making it important to have a good understanding of exactly what kind of data is needed.

Fortunately, artificial intelligence is increasingly playing a role in helping businesses parse through and interpret vast quantities of data. If you’re not sure how to take advantage of the data at your fingertips, AI can help. Reach out to us today to learn how AI can solve your business needs.

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