The term “unstructured data” refers to digital information that doesn’t have a predefined data model or schema. Also referred to as “qualitative data,” it cannot be processed and analyzed using conventional tools and methods. Because it lacks a consistent internal structure, such as a tabular format, it is managed and stored in non-relational (NoSQL) databases and data lakes to preserve its raw form.
Unstructured data can be textual or non-textual and human- or machine-generated. Its examples include:
Because it is disorganized, indecipherable, and non-formatted, unstructured data was previously underutilized for analytics. However, with the availability of specialized tools and large amounts of storage, many businesses have begun to use unstructured data to make strategic decisions.
The significance of unstructured data is undeniable, accounting for the majority of data collected by enterprises. But why continue to gather and store huge volumes of it? Below are some use cases:
There are many key differences between structured and unstructured data. For one, structured data is categorized as quantitative because it gives an overview of customers, whereas unstructured data provides a deeper understanding of their behavior and intent.
The main differences include:
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