What Is Matching, and Why Is It Important?
Matching refers to the process of pairing or aligning items based on specific criteria or similarities. The term is used in several contexts, from social dating to data analysis.
In data analysis, matching is the process of combining or aligning datasets based on common identifiers. This is particularly useful when dealing with large sets of data, where inconsistencies, duplicates, and errors are common. It is crucial for integrating and consolidating data from multiple sources to better organize data, identify relationships, conduct more comprehensive analysis, and generate insights for better-informed decision-making.
There are many reasons to properly resolve the identity of business data, ranging from risk mitigation and regulatory compliance to marketing and sales operations.
Take this use case. The finance team has a customer record that they want to match with a corresponding record from the sales team in order to consolidate information. The organization can match company name to company name. Now let’s make it a little more complicated; the finance team spelled the company name differently than the sales team did or the addresses don’t match up. There may be other attributes, such as the business address field, that can be used as well to match these two records for a common view of the customer across the business.
But consider the magnitude of matching hundreds, thousands, and millions of records with multiple parameters of varying completeness and quality. This is where the pedal hits the metal and a data-matching exercise comes into play.
How Matching Works
Data matching compares and matches data from the client’s source data (for example, their customer data) to a more robust third-party external source (for example, the Dun & Bradstreet Data Cloud.) The goal is to identify records that represent the same entity or have a high likelihood of being a match. This is accomplished by aligning the business’s internal datasets with the third-party data through a series of attributes.
Benefits of Matching: Optimizing Your Data
Matching is a critical step to take in optimizing our data by keeping it up to date, complete, and relevant.
Matching allows an organization to find out more about the companies it does business with. Incomplete fields can be corrected. Inconsistent formatting can be addressed. Additional data fields can be appended to the data, and related data can be linked, whether to give a sales team more context or a finance team a bigger picture of the customer. Additionally, duplicate records can be discovered and merged for more efficiency and less confusion.
Understanding Matching as a Tool
Think of matching like the facial recognition on your mobile device. If it only unlocks when focused on only one acceptable angle or expression of your face, then you will have limited success. The benefits of using facial recognition for security lie in its effectiveness to identify you – and only you – as the user, but through your many dimensions of facial expressions and angles. Your master data is no different. Matching, also commonly referred to as identity resolution, is the process whereby multiple sources, datasets, and versions are used to resolve the identity of targeted entities. Let’s consider these examples.
Business name – These are likely to have many iterations, such as the legal business name, local language, a dba, trade style, etc. There will also be many permutations of how this attribute can manifest. These may live in many different record sets throughout the enterprise data ecosystem.
Address or location – Address data also comes in many forms. A singular business entity could have a legal address, a secondary address, a mailing address, a vanity address, etc. It also could have an alternative address if the entity is large enough to occupy multiple streets on the block.
Previous version – Entities may change locations, change names, face divestitures (mergers and acquisitions), or go out of business. Master data is alive, so to speak. Which version do you have? Perhaps what you have is a combination of those above.
Digital presence – For starters, we’re talking about web domains and IP addresses, and just like with the other examples, there can be multiple versions of these. These digital attributes can be found as visitor data at your digital front door. It’s another “facial angle” or “facial expression” to consider.
Make the Most of Your Data
Matching is a critical workflow component for many organizations. The downstream impacts of missing or improper resolution can be significant throughout the business.
Read the Dun & Bradstreet Break-It-Down Guide: The Basics on Data Matching. This clear, easy-to-understand guide explains how Dun & Bradstreet’s proprietary processes can improve the quality of your business data to enhance operational efficiency and growth opportunities.