Enrichment
Process of improving raw data quality with supplemental information to enable more accurate and insightful AI models.
Enrichment, in an AI context, is the practice of augmenting raw data with additional information to boost the quality and depth of insights gained through AI and Machine Learning (ML) models. It involves integrating data from multiple disparate sources, which can enhance the accuracy of predictive models, decision-making systems, and more. It can even unlock new analytical opportunities. For instance, a basic customer database could be enriched with social media activity data, demographic information, or purchasing history, providing a richer profile for targeted marketing campaigns or personalized service offerings.
While the concept of data enrichment has been around as long as databases themselves, it gained special relevance with the rise of big data in the late 2000s and early 2010s. When AI and ML began to be widely used for business insights and decision-making, data quality issues became a significant constraint, making data enrichment a vital process.
Though it is difficult to attribute the practice of enrichment to specific individuals due to its generic nature, significant tech companies such as IBM and Google have advanced its methods. They have developed powerful algorithms and sophisticated software that perform data enrichment tasks. Equally, researchers in the field of database management and data mining continue to contribute dramatically to its ongoing evolution and standardization.