Structured versus Unstructured Data

In an effort to spread the love on data science, I’m going to try to tackle some of the most common concepts and keywords and re-explain them in my own words, without all the boring jargon. Join me.

You may hear about structured and unstructured data when starting on your data journey. But what does this mean?

At a high level, data comes in these two forms: structured or unstructured. Structured data is what you’re most likely to encounter and use when practicing your data skills. This refers to data that is organized and formatted, such as a table, CSV file, or database. It is generally easier to read and analyze.

Unstructured data, however, is what is most commonly found out in the world and pretty much refers to everything else that isn’t clearly organized. This data comes in various forms, from email messages to video clips, often in large quanities. This complexity makes it more challenging to analyze and process, which is why you mostly deal with structured data when starting out.

In navigating the landscape of data, understanding the differences between structured and unstructured data and knowing which one you are working with will help you determine your next steps.

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