Structured Data vs Unstructured Data in Data Science and Market Research

Structured Data 

Structured data is most often categorized as quantitative data, which is objective facts and numbers that most analytics software can collect through the adhering pre-defined data model. Structured data fits neatly in a tabular format with numbers or words packed in a database. Common examples of structured data include names, dates, addresses, credit card numbers, stock information, geolocation, and more.

The most attractive feature of structured data is that it depends on the existence of a data model where each file is discrete and can be accessed by running it through data analysis methods and tools like regression analysis and pivot tables.

As the programming language, SQL was developed by IBM to manage structural data in the early 1970s and since the earliest versions of DBMS were able to store, process and access it, structural data is considered the most traditional form of data. particularly useful for handling relationships in databases.

Unstructured Data

Unstructured Data, mostly known as qualitative data, is usually subjective opinions and judgments of your brand in the form of emails, videos, audio files, web pages, and social media messages., which most analytics software can’t collect. As this makes the unstructured data difficult to analyze, it is often stored in Word documents or non-relational (NoSQL) databases, like Elasticsearch or Solr, which can perform search queries for words and phrases.

If you can successfully extract insights from unstructured data, though, you can develop a deep understanding of your customer’s preferences and their sentiment toward your brand.

Analyzing Unstructured Data

Today, we see stronger statements that indicate the rule of thumb cited by Merrill Lynch in 1998. Over 90% of all the human knowledge accumulated since the beginning of time, is unstructured data. This includes text, images, audio, and video. We can think of the other 10% as numbers in tables (structured), which is the primary result of any quantitative market or marketing research.

Other than reading, listening to, or viewing unstructured data, artificial intelligence is another way to understand their meaning, especially when one needs to understand the information hidden in mega-, Giga-,tera-, Peta- or n-ta-bytes of data. With machine learning, a discipline that produces A.I., one can create models that can process large files of text or images in seconds and annotate sentences, paragraphs, sections, objects or even whole documents with topics, sentiment and specific emotions. Sentiment and semantic analysis are the two most popular ways to analyze and understand unstructured data with the use of machine learning or a rules-based approach. When the unstructured data to be analyzed is in text format, the discipline falls under Computer Science (not linguistics funnily enough) and is called Natural Language Processing (NLP) or Text Analytics.


In a world where Google Analytics can spit out every metric under the sun, there seems to be a lot of perceived complexity in using artificial intelligence or other engineered approaches to analyze text and images in an automated way. However, qualitative data, like customer feedback, is just as crucial for informing your marketing strategy as web metrics. Without unstructured data, you won’t have a clear understanding of how your customers actually feel about your brand. And that’s crucial for every marketer to know.