In the first part of our Structured vs Unstructured Data conversation, we talked about Defined vs. Undefined Data and Qualitative vs. Quantitative Data. In our second installment, we discuss differences in formats, data storage, and ease of analytics.

Predefined Format vs. Variety of Formats

The most common format for structured data is text and numbers. Structured data has been defined beforehand in a data model.

Unstructured data, on the other hand, comes in a variety of shapes and sizes. It can consist of everything from audio, video, and imagery to email and sensor data. There is no data model for the unstructured data; you store it natively or in a data lake that doesn’t require any transformation.

Data Storage in Data Warehouses vs. Data Lakes

Businesses often store structured data in data warehouses and unstructured data in data lakes. A data warehouse is an endpoint for the data’s journey through an ETL pipeline. A data lake, on the other hand, is a sort of almost limitless repository where you store data in its original format or after undergoing a basic “cleaning” process.

Both structured and unstructured data have the potential for cloud use. Structured data requires less storage space, while unstructured data requires more.

As for databases, structured data is usually stored in a relational database, while the best fit for unstructured data instead is so-called non-relational, or NoSQL, databases.

Ease of Analysis, But Not For Much Longer

Structured data is easy to search, both for data analytics experts and for algorithms. Unstructured data, on the other hand, has been intrinsically more difficult to search and requires processing to become understandable.

With the advent of a wide variety of AI-driven tools like natural language processing (NLP) and machine learning algorithms (ML) for mining and arranging unstructured data are leveling the playing field to the point where this is not a key difference.