There are massive amounts of data that can be analyzed to uncover helpful insights across all businesses and fields. It’s growing by leaps and bounds every day.
Some data is structured, fitting tidily into spreadsheets and databases. Some of it, often in the form of text, audio, or images, is unstructured data, with no predefined construction or systemization.
Estimates say that just 20% of data is structured, while unstructured data accounts for 80-90% of data regularly generated so implementing AI-equipped business intelligence tools to manage and analyze this data efficiently is essential.
Learn more about the differences between unstructured and structured data, and how you can start making the most of your data using AI tools.
Both types of data are collected, processed, and analyzed in different ways, yet, with the same goal of extracting information to make data-driven decisions. Let’s take a closer look at the differences between unstructured and structured data.
Structured data is quantitative, highly organized, and easy to analyze using data analytics software. It’s formatted into systems that have a regular design, fitting into set rows, columns, and tables.
Structured query language (SQL) is the standard language used to communicate with a database and is particularly useful when handling structured data. Used to search, add, update, and delete data, among other uses, SQL makes it easy to organize structured data.
Think of a hotel database, where you’re able to search guests by name, phone number, room number, etc. Or bar codes used to organize and classify products at the production, distribution, and point-of-purchase level.
Structured data is generally contained in relational databases (RDBMS). The information within the databases could be entered by humans or machines and is easily searchable by manually entered queries or algorithms.
Highly methodical programs like Excel, are also used to store and organize structured data, and can easily be connected to other analytical tools for further analysis.
Structured data is great for basic organization and quantitative calculations, but must fit into rigid, preset 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 truly granular insights: the “Hows” and “Whys” of data analytics.
Unstructured data is information that has no set organization and doesn’t fit into a defined framework. It is “open format” data, like audio, video, images, and all manner of text: reports, emails, social media posts, etc.
Finding insights within unstructured data isn’t easy, but when properly analyzed, text data can be extremely valuable to extract qualitative results, like customer opinions, or organize business data, like customer service tickets, into individual categories to be routed to the proper employee.
Advanced data analysis software, like MonkeyLearn, uses machine learning algorithms equipped with natural language processing (NLP) to “read” unstructured text, then categorize and analyze it as a human would, but in a fraction of the time and with total accuracy.
Let’s take customer feedback as an example. It can come from anywhere: emails, phone calls, surveys, even social media posts.
Text analysis programs can perform a variety of automatic tasks to sort this data:
Unstructured data can present several problems when attempting to analyze 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 analyzed with machine learning.
There is also semi-structured data, which contains mostly unstructured text, but is loosely categorized with “meta tags.” An example of this would be email, which you can search by Inbox, Sent, Drafts, etc. Or social media that may be categorized as Friends, Messages, Public Posts, Private Posts, etc.
Semi-structured data can be easily broken down into its predefined categories, but the information within these categories is, itself, unstructured.
When analyzing emails, intent classification can be helpful to automatically read business emails for the intent of a customer to tell you if they are responding to a query with genuine interest or not.
Whether structured or unstructured, data should be at the heart of every business decision.
Structured data provides a view into individual customer habits or quantitative trends, but when you learn to properly organize and analyze unstructured data, the insights increase exponentially. You’ll see how qualitative data results can provide much more useful information.
Go beyond mere numbers and statistics to actual keywords, accurate classifications, and full-blown opinions. Follow your brand status regularly, in real time, and over time. Find out what’s working and what’s not for product releases and marketing campaigns, and perform competitive analysis.
Request a demo from MonkeyLearn and discover how you can use this AI-equipped business intelligence software to analyze and transform unstructured data into useful business insights.
August 26th, 2020