In this video, you will learn to describe what is meant by structured data, semi-structured data, and unstructured data. So structured data. Structured data is data has been organized into a formatted repository, typically databases, so that it's elements that can made addressable for more effective processing in analysis. Now, that simply means that structured data is a data repository that really has a way to organize all the different data. So I can not only go to a specific piece of data, but I can also search for data and the data structure, and the repository makes it very easy for me to do that without ever having seen it before, I can understand the data structure, the data model, that structured data, and then I can immediately go find whatever data that I need to. Now, structured data typically has a database query languages such as SQL, Structured Query Language, which would allow a database administrator or application that's connecting to database to interact with the database. It's worth mentioning structured data contrasts with unstructured and semi-structured data. The three can be considered to exist on a continuum, with unstructured data being the least formatted and structured data being the most formatted. Another way to say that would be to say that, they exists on a continuum and structured data is the easiest to understand and most organized, and unstructured data would be the least organized and hardest to understand and find what you're looking for. Semi-structured data. The difference between structured data and unstructured data is unstructured data has not been organized into a format that makes it easier access and process. Structured data is data that has not been organized into a specialized repository such as a database, but that nevertheless is associated information such as metadata, that makes it more amenable to processing them raw data. Structured data is basically the opposite of unstructured. It has been reformatted and its elements organized into a daily structure so that elements can be addressed, organized, and accessed into various combinations to make better use of the information. However, structured data can turn to unstructured data. If I was to take structured data from a bunch of different databases and throw it into a new location and all of those different pieces of structured data from those different databases, if I don't take the time to reformat it and organize it into a data structure so that I can understand what all of the different databases were doing, and the different commonalities such as customers, clients, products, etcetera, then it becomes much harder for me to understand what data is in the database and to look for commonalities and really understand the data. Let's move on. Unstructured data is information in many different forms that doesn't hew to conventional data models and thus typically isn't a good fit for mainstream relational databases. One of the most common types of unstructured data is simply text. Unstructured text is generated and collected in a wide range of forms including Word documents, email messages, text messages, PowerPoints, survey responses, transcripts, call center interactions, post from blogs, social media sites, on and on. Other types of unstructured data include images, audio and video files. Even though all of those different types of data are very different, they would all be classified as unstructured data.