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Data Matching

Data Matching Definition

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. 

What is Data Matching?

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. 

Data Matching Techniques

There are two main approaches: equality-based and pairwise comparison.

  • Equality-based: data records are matched if some or all the fields are equal or nearly equal
  • Pairwise comparison: records are matched based on a similarity data match score, which is calculated via a record linkage algorithm. The approaches include Deterministic, Probabilistic, and Machine Learning.
    • Deterministic record linkage: Weights are assigned and similarity scores are calculated based on a set of defined rules.
    • Probabilistic match: In probabilistic record linkage, probability that two records represent the same entity is determined by statistical methods. 
    • Machine Learning data matching: For data matching using machine learning, supervised learning is applied when there is training data, unsupervised learning is applied when there is no training data, and active learning chooses the set of examples which will have labels.

Examples

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: 

  • Healthcare: Data matching is a crucial component when matching medical records with other data points in order to study drug effects and reactions to treatments.
  • eCommerce: Businesses frequently compare products and their prices across platforms. Enterprise data matching helps identify and match identical products even if they don’t have the same description or any common identifiers matching.  
  • Fraud detection: Data matching software breaks down the smokescreen that criminals use to camouflage their data by honing in on areas that are losing money and identifying suspicious activity and anomalies. 
  • Computing: Data matching helps identify and remove duplicate data, which will decrease storage needs and optimize the computing process. 
  • Mailing lists: Business mailing lists are riddled with duplicate and dirty data. Data matching can help with pruning and merging records.

Challenges

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. 

How Does Explorium Improve Data Matching?

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.

Explorium delivers the end-game of every data science process - from raw, disconnected data to game-changing insights, features, and predictive models. Better than any human can.
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