Data normalization is the process of organizing data into tables in such a way that the results of using the database are always unambiguous and as intended. Such normalization is intrinsic to relational database theory. It may have the effect of duplicating data within the database and often results in the creation of additional tables. In a nutshell, data normalization is the act of organizing data in a database. This interesting information came to us from Dataconomy in their article, “Is Your Data “Normal” Enough?”

Businesses today are collecting more data than ever. However, many companies are struggling to make the most out of the information that keeps piling up. In many cases, insights are hiding right below the surface.

Normalization significantly enhances the usefulness of a data set by eliminating irregularities and organizing unstructured data into a structured form. Data may be more readily visualized, insights may be obtained more efficiently, and information can be updated quickly due to data normalization.

At the end of the day, data needs to be findable and, that happens with a strong, standards-based taxonomy. Access Innovations is one of a very small number of companies able to help its clients generate ANSI/ISO/W3C-compliant taxonomies and associated rule bases for machine-assisted indexing.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the world leader in taxonomies, metadata, and semantic enrichment to make your content findable.