Data scientists work with hundreds of different types of data. Numbers, images, audio files, and videos are all kinds of data that a data scientist could be working with at any given time. All of those can be categorized into two groups: unstructured and structured. While there are key differences between the two, both can be used to improve any organization.
To begin, unstructured data, in simple terms, is any data that is unorganized. It is more difficult for unstructured data to be stored or organized in any structured database. Things like images, videos, surveys, presentations, audio files, and texts are all considered unstructured data. Because of its wide variety, the lack of structure or patterns can make unstructured data difficult to work with. Although unstructured data may be more difficult to manage, it allows for more flexibility in storing data and can be converted into more manageable formats that make storing and evaluating unstructured data easier.
On the other hand, structured data is data that can be stored in an organized format like rows or columns. Every single kind of structured data can be conformed to a data model, making the lives of data scientists less challenging. Examples of structured data are dates, product numbers, names, addresses, and phone numbers. Structured data is more consistent and has more patterns and sequences, so it can be more easily managed. Also, because structured data can be fit into typical databases, data sets of large magnitudes are easily scalable. However, one downside of structured data is that it is less flexible to work with.
Unstructured and structured data is everywhere. Whether in rows or columns or in the "wild," data scientists can expect to work with all kinds of data. But as the field of data science grows, working with structured and unstructured data can become easier and more efficient.
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