Structured vs. Unstructured Data: 3 Key Differences

In today’s digital economy, data is a company’s biggest asset. Though data comes in many forms, identifying whether structured or unstructured data will meet your business’s needs is of the utmost importance, and ultimately determines which method of analysis to use.

Structured data is quantitative data, such as transaction numbers or dates of birth. It can be stored in relational databases, and analyzed with standard data analysis methods. In contrast, unstructured data is qualitative data that is more difficult to work with, such as social media posts, online reviews and support tickets. This data is often not acquired and analyzed in conventional analytics methods, nor is it generally stored in relational databases, and typically requires a greater level of expertise in order to conduct analysis.

Thanks to the many impressive advancements in machine learning, we now turn to methods of data analysis that involve Natural Language Processing (NLP), which are ideal for working with unstructured data. With approximately 80% of the world’s data being unstructured, there are plenty of invaluable insights available for businesses to understand more about their customers, competition and market that is often untapped due to the complicated nature of the disorganization of the raw data. And now, with the help of NLP, organizations around the world are able to tap into this data to find actionable insights. Businesses now acknowledge the value of unstructured data, however, it is important to highlight that structured data continues to serve a purpose, and plays an important role in an organization’s understanding of its customer experience and other critical key performance indicators. Plus, unstructured and structured data are often utilized together to provide organizations with a full picture on operational health, emerging trends and product development opportunities.

There are three key differences between structured and unstructured data that determine when we choose to collect structured data, or unstructured data, and as a result, which method of data analysis to employ.

Qualitative vs. Quantitative

Unstructured data is often referred to as qualitative data that most analytics software can’t consume, often because it consists of subjective text — survey responses, social media comments, email responses, call transcriptions, business documents, and the like. It is valuable information that can come in all shapes and sizes, providing businesses with a contextual understanding of their customers and brand that looks beyond just the numbers. With unstructured data at their disposal, businesses are able to gauge the true sentiments of their customers with respect to a product or service that would otherwise be lost in purely quantitative data.

Structured data can vary widely depending on a business’s product or service offering, however, it is most often referred to as quantitative data — objective facts and numbers that most analytics software can consume. Structured data uses a predefined format that can come from many different sources, with the common factor being that the fields are fixed, as is the way that it is stored.

A survey question that asks respondents to rate an experience on a likert scale (1-5, 1 = really bad) is collecting structured data. The format is fixed, as are the response choices. A survey question which asks respondents to provide written comments about their experience is collecting unstructured data.


Unstructured data lacks defined data types, such as numbers, percentages and currency. This makes it very difficult to analyze and transform the data into actionable insights. Most of the time, unstructured data must be stored in Word documents or NoSQL databases, analyzed through Search Engines that index raw data such as Elasticsearch or Solr, which are capable of performing search queries for words and phrases. In addition to text, unstructured data can come in many forms to be stored as objects: images, audio, video, document files, and other file formats. The common denominator with all types of unstructured data is that they lack established and expected elements.

As structured data consists of objective facts and numbers — data types whose pattern makes them easily searchable — the data is easier to export, store, and organize with tools like Excel, Google Sheets, and relational databases using SQL. A real-world example of structured data is transactional data from an online purchase. A business can expect each data entry (purchase) to include a timestamp, purchase amount, account information, item(s) purchased, payment information and confirmation number. As each field has a defined purpose, the data is easily stored. The information includes a predefined format for effortless scalability and processing, even if handled manually — this is a key trait of structured data.


As structured data is easily stored, businesses can efficiently examine the information with standard data analysis methods and tools like regression analysis and pivot tables. This is the most valuable aspect of structured data — an average business user with an understanding of the topic to which the data relates is capable of working with structured data. Expertise or deeper knowledge of various different types of data is not necessary when working with structured data.

When it comes to unstructured data, it is typically not stored in relational databases. For that reason, using standard analysis methods and tools such as structured queries, regression analysis and pivot tables is not applicable. This data can be either manually analyzed or analyzed with the help of machine learning such as Natural Language Processing (NLP). However, to use these tools effectively, a high level of technical expertise is critical. Analysis of unstructured data is worth the investment, however, as businesses that can successfully extract insights from it can develop a deep understanding of their customer’s preferences and sentiment toward their brand.

For example, while an increase in social engagement following a product launch might initially suggest that interest in a given product has grown, NLP would look further to analyze what exactly users are saying; are their comments positive or negative? And where on the positive to negative spectrum do the comments fall? Unstructured data analysis digs deeper, beyond just the numbers.

Unstructured and Structured Data Together

While there are specific use cases of unstructured and structured data, this doesn’t mean that the two are mutually exclusive — that is, that a business can only work with one or the other. In fact, businesses that use structured and unstructured data together are better-positioned to gather deeper insights about their customers.

Using a company’s social posts as an example, a business can use structured data to sort posts by any number of metrics, like engagement. From there, further filtering of information can be done by relevant hashtags, eventually leading to analyzing the unstructured data like messaging, type of media, tone of voice, and other elements that might provide further insights.

With the help of machine learning and artificial intelligence, unstructured data analysis is becoming increasingly more automated. Using NLP and other similar techniques, businesses can analyze text for keyword patterns and positive/negative language. The importance of these tools continues to increase at a rapid pace, particularly as big data becomes bigger in a reality where the majority of big data is unstructured.

Harness the power of your organization’s strongest asset — data. Businesses that use data to back their decision-making are best-positioned to thrive in today’s digital economy so there’s never been a better time to make sure that you’re getting the most out of the data that’s right at your fingertips. Learn how sentiment analysis, powered by machine learning, can consume thousands of customer reviews, instantly identify key themes, and uncover actionable insights. Keep going here.