Unstructured data is data that isn’t organized in a pre-defined fashion or lacks a specific data model. Meanwhile, structured data is data that has clear, definable relationships between the data points, with a pre-defined model containing it. That’s the short answer on the difference between structured and unstructured data, but let’s take a closer look at the differences between the two types of data.
Structured Data
When it comes to computer science, data structures refer to specific ways of storing and organizing data. Different data structures possess different relationships between data points, but data can also be unstructured. What does it mean to say that data is structured? To make this definition clearer, let’s take a look at some of the various ways of structuring data.
Structured data is often held in tables such as Excel files or SQL databases. In these cases, the rows and columns of the data hold different variables or features, and it is often possible to discern the relationship between data points by checking to see where data rows and columns intersect. Structured data can easily be fit into a relational database, and examples of different features in a structured dataset can include items like names, addresses, dates, weather statistics, credit card numbers, etc. While structured data is most often text data, it is possible to store things like images and audio as structured data as well.
Common sources of structured data include things like data collected from sensors, weblogs, network data, and retail or e-commerce data. Structured data can also be generated by people filling in spreadsheets or databases with data collected from computers and other devices. For instance, data collected through online forms is often immediately fed into a data structure.
Structured data has a long history of being stored in relational databases and SQL. These storage methods are popular because of the ease of reading and writing in these formats, with most platforms and languages being able to interpret these data formats.
In a machine learning context, structured data is easier to train a machine learning system on, because the patterns within the data are more explicit. Certain features can be fed into a machine learning classifier and used to label other data instances based on those selected features. In contrast, training a machine learning system on unstructured data tends to be more difficult, for reasons that will become clear.
Unstructured Data
Unstructured data is data that isn’t organized according to a pre-defined data model or structure. Unstructured data is often called qualitative data because it can’t be analyzed or processed in traditional ways using the regular methods used for structured data.
Because unstructured data doesn’t have any defined relationships between data points, it can’t be organized in relational databases. In contrast, the way unstructured data is stored is typically with a NoSQL database, or a non-relational database. If the structure of the database is of little concern, a data lake, or a large pool of unstructured data, can be used to store the data instead of a NoSQL database.
Unstructured data is difficult to analyze, and making sense of unstructured data often involves examining individual pieces of data to discern potential features and then looking to see if those features occur in other pieces of data within the pool.
The vast majority of data is in unstructured formats, with estimates that unstructured data comprises around 80% of all data. Data mining techniques can be used to help structure data.
In terms of machine learning, certain techniques can help order unstructured data and turn it into structured data. A popular tool for turning unstructured data into structured data is a system called an autoencoder.
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