Identify Data Patterns with Natural Language Processing and Machine Learning - DATAVERSITY

Discovering, extracting, and analyzing data patterns in textual data from the myriad data sources streaming into modern data-driven organizations is no easy task. Organizations must be equipped with state-of-the art techniques such as Natural Language Processing (NLP) within well-developed Artificial Intelligence (AI) and Machine Learning (ML) platforms, to reliably understand the pulse of their consumers in real time, while also controlling the data deluge that often overwhelms under-prepared organizations. The ability to derive patterns and insights from a plethora of structured and unstructured document types requires the skill to prioritize and understand which pieces of information are most important to act upon first. According to Karthikeyan Sankaran, Director of Data Science and Machine Learning at LatentView Analytics, in a recent DATAVERSITY interview, such an skill requires organizations to have a platform that can "harness the textual data assets so they can then potentially solve interesting and profitable use cases." This is where Natural Language Processing (NLP), as a branch of Artificial Intelligence steps in, extracting interesting patterns in textual data, using its own unique set of techniques.