Data matching, also known as record linkage, refers to the process of comparing two sets of collected data, typically via advanced machine learning algorithms or by programmed loops. The processes sequentially compare each individual data point in a set to each individual data point in another set, or compare each data string in a set to each data string in another set.
The purpose of data matching is to identify and compare data in different sets in order to identify the ways in which data points or strings correspond. The goal is to find the data that refer to the same entity. It enables us to identify key links between data sets, detect duplicate records within a database, and identify patterns and irregularities. The result is more precise and accurate searches, more advanced data analysis, and more reliable results. Record linkage tools are increasingly important as the formats, sources, and amounts of data continue to grow exponentially.
There are two main approaches: equality-based and pairwise comparison.
Data matching is used in a wide range of industries and applications to help improve things like accuracy, efficiency, and compliance. Some popular use cases include:
Modern data is big, and it’s only getting bigger, making manual data matching an extremely inefficient, tedious, outdated practice. Data comes in widely varied formats and is riddled with inconsistencies and duplications. Things like spelling variations, name changes, differing date formats, and standardization against official address lists all present endless challenges in data comparisons.
The process is lengthy, time consuming, and expensive. First data must be standardized. Then attributes that are likely to be consistent must be identified. Data is then sorted into blocks and matched via probabilities. Record matches are assigned a value, and then summarized to get the total weight. The algorithms must be constantly fine-tuned to maintain accurate results.
Machine learning for data matching has produced a wealth of advanced technologies that significantly improve the process. Modern software platforms streamline this process by automatically detecting data matches, outdated data, data errors, duplicates, inefficiencies, and anomalies.
Explorium automates the data matching process. Explorium’s External Data Platform addresses a multitude of challenges in the data pipeline, including data cleansing, combining, organizing, preparing, and matching. Explorium provides data scientists and analysts with the tools they need to easily integrate and match data from disparate data sources so that they can create more effective and efficient data pipelines and workloads.