Information architecture, and more especially the field of structured data, is the process of methodically sorting, classifying, and archiving data in a consistent and orderly way so that it may be more easily retrieved and processed using algorithms. SQL databases, CSV files, Microsoft Excel sheets, and Google Sheets are a few common instances of structured data.

Structured data is made up of rows and columns that represent discrete entries and properties, respectively, and each unit corresponds to a specific attribute ascribed to an identifiable subject. Because of its inflexible structure, structured data is very easy to query and analyze, especially with the abundance of easily available data analytics tools and libraries.

In the industry, structured data is essential, especially for business intelligence and decision-making processes. Organizations may improve consumer experiences, streamline processes, and find undiscovered markets by using the insightful patterns, trends, and correlations that are shown by analyzing structured data. Structured data is widely used in corporate settings due to its logistical simplicity of transmission, exchange, and integration across a variety of IT ecosystems.

Unstructured data, which is characterized by text documents, image archives, audio files, and videos, lacks clear taxonomies and conventions, in stark contrast to its ordered brother. Recent developments in Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV) have opened the door to profound discoveries hidden inside seemingly random blobs of unstructured data, despite the challenges associated with parsing, mining, and extracting meaning from these chaotic streams. However, distinguishing and isolating information from noise in unstructured data continues to be an unresolved problem and a busy field of study.