Entity Matching for Digital World: A Modern Approach using Artificial Intelligence and Machine Learning

Entity matching is the field of research solving the problem of identifying similar records which refer to the same real-world entity In today s digital world business organizations deal with large amount of data like customers vendors manufacturers etc Entities are spread across various data sources and failure to correlate two records as one entity can lead to confusion Relationships and patterns would be missed Aggregations and calculations won t make any sense It is a significant data integration effort that often arises when data originate from different sources In such scenarios we understand the situation by linking records and then track entities from a person to a product etc There is appreciable value in integrating the data silos across various industrie

A Primer on the Data Cleaning Pipeline

The availability of both structured and unstructured databases, such as electronic health data, social media data, patent data, and surveys that are often updated in real time, among others, has grown rapidly over the past decade. With this expansion, the statistical and methodological questions around data integration, or rather merging multiple data sources, has also grown. Specifically, the science of the ``data cleaning pipeline'' contains four stages that allow an analyst to perform downstream tasks, predictive analyses, or statistical analyses on ``cleaned data.'' This article provides a review of this emerging field, introducing technical terminology and commonly used methods