Structured Data vs Unstructured Data

What is Structured Data?

Structured data is stored in rows and columns of databases, spreadsheets, CRMs and ERP systems, and other systems of record. Structured data is quantitative, highly organised, and easy to analyse using data analytics software. It has been organised into a formatted repository that is typically a database; stored in a tabular structure with rows and columns. They have relational keys and can easily be mapped into pre-designed fields. Applications such as Microsoft Excel are used to store and organise structured data and can easily be connected to other analytical tools for further analysis.

Structured data is great for basic organisational and quantitative calculations, but must fit into rigid, pre-set parameters. The data points are easily searchable within their set structure and can be cross-referenced with other databases. You could search by customer address to discover which products are most popular in a certain location or find out which products are ordered multiple times by multiple customers.

Unfortunately, structured data isn’t great for uncovering the “How” and “Why”.

What is Unstructured Data?

Unstructured data is information that has no set organisation and doesn’t fit into a defined framework. It has an “open format”, like audio, video, images, and all manner of text like reports, emails, and social media posts.

Finding insights within unstructured data isn’t easy, but when properly analysed, this data can be extremely valuable to extract qualitative results, like customer opinions, or organise business data like customer service tickets, into individual categories to be routed to the proper employee.

Advanced data analysis software use machine learning algorithms to “read” unstructured text, then categorise and analyse it as a human would, but in a fraction of the time and with very high accuracy.

Let’s look at customer feedback as an example. It can come from anywhere: emails, phone calls, surveys and social media posts to name a few. Text analysis programs can perform a variety of automatic tasks to sort this data such as, keyword extraction (finding relevant words and expressions), sentiment analysis (classifying positive, negative and neutral emotions) and more.

Unstructured data can present several problems when attempting to analyse it because the formats and locations can vary widely. However, with the help of text analysis software, unstructured data can be automatically formatted and properly analysed with machine learning.