Written business reports, legal documents, and presentations are often printed on paper, in PDFs, or even hand-written, and some may contain spreadsheets, images, or XML files. First, though, letвЂ™s take a look at different unstructured data types. Later, weвЂ™ll explain how you can get more from your unstructured and semi-structured data with SaaS unstructured data analysis solutions like MonkeyLearn. There is also semi-structured data, which is also text-heavy data but loosely organized into categories or вЂњmeta tags.вЂќ This information can be easily broken into its individual groups, but the data within these groups is itself unstructured.Д®mail is a good example of this: you can search your email by Inbox, Sent, and Drafts, but the email text within each category has no pre-set structure. The framework of structured data models is designed for easy data entry, search, comparison, and extraction. ItвЂ™s organized in a pre-defined structured format, such as Excel and Google Sheets, where data is added to standardized columns and rows relating to pre-set parameters. Structured data, on the other hand, is often numerical and easy to analyze. First, specific bits of information must be extracted and categorized, then analyzed to gain usable insights. But this information is difficult to process in bulk. Social media posts, for example, might contain opinions, topics that are being discussed, and feature recommendations. While all three types of data can offer incredible insights, itвЂ™s important to know which data type to collect and when, and which one to analyze for the insights youвЂ™re hoping to gain.ДЄlthough it contains figures, statistics, and facts, unstructured data is usually text-heavy or configured in a way thatвЂ™s difficult to analyze. Structured, unstructured and semi-structured data all fall under the umbrella of вЂbig dataвЂ™.

Unstructured data analysis methods