Data matching is a process that is used to match records from different sources and identify duplicate records in a database. It is an invaluable tool for businesses and other organizations, providing them with the ability to ensure that their records are accurate and up to date. Data matching is a process that can be used for a variety of purposes, from improving customer service to reducing risk, making it a powerful tool for any organization. In this blog post, we’ll be taking a closer look at the key concepts and benefits of data matching, and how it can be used to maximize efficiency and accuracy. We’ll explore the various data matching algorithms that are out there, as well as the differences between them. Additionally, we’ll cover practices and best practices that businesses and organizations should be using when it comes to data matching. Finally, we’ll discuss how data matching can be used to enhance customer experience, reduce risk, and save time and money.
What is Data Matching?
What industries use data matching?
Many different industries, including the following, can benefit from data matching:
What is data matching?
A technological procedure called data matching compares various sets of data to look for matches. A data matching tool may be integrated into a company’s server, database, or other systems that process a lot of data. Additionally, they might use a different data matching program with analytics and business intelligence capabilities. A government organization might, for instance, have a data matching tool that examines each entry in its database and compares entries that share the same social security number or driver’s license. The number of duplicate accounts in the database is decreased through this process.
How does data matching work?
Numerous data matching software tools are available that can examine database data and highlight duplicate or similar entries. Here are common steps in a data matching process:
1. User defines the matching policy
Determining the parameters you want to analyze is the first step in the data matching process because databases and servers have access to enormous amounts of data. A user may select a data point that is specific to each individual, such as a user ID or social security number, to run a matching process on in order to merge duplicate entries. The parameters that multiple entries have in common, such as location or timestamp, may be the focus of matching policies for analytics or data mining.
For instance, the administrator of a customer database might select the client’s phone number as the primary parameter for the matching procedure. The tool can identify accounts that might belong to the same person even if they use different names or email addresses because customers frequently use the same phone number to contact businesses. The database administrator can merge these duplicate accounts into a single account that records all activity pertaining to that customer by spotting these duplicate accounts.
2. Data matching tool identifies matches
The data matching tool then examines all the data in the database to look for matches. A data matching tool may rank potential matches by percentages depending on the type of data tool and the customized settings the user applies. For instance, the tool may determine that two entries in a government database with the same name and different social security numbers by one digit have a 50% chance of being the same person due to a typo in one entry’s social security number.
For the database administrator or another professional using the program, data matching tools frequently produce reports. They might also feature interactive dashboards with built-in merging controls. The matching tool may make updated addresses for databases with location data by consulting postal data, which is helpful for customer databases.
3. User analyzes the results
Depending on your department and role, you might use data matching tools reports to merge duplicates or create analytics resources. A database administrator may open the flagged accounts and merge them if it is clear that they belong to the same person after running a program to find potential duplicate accounts. Some data matching tools have automatic merging features that let the program combine accounts with the same data. Some companies use data matching for marketing analytics. When that occurs, a marketing coordinator or other expert may segment emails using the data from a data match report.
Benefits of data matching
Using a data matching tool can help an organization perform better in the following ways:
Centralizes information
Your database can run smoothly if you frequently use a data matching tool to find and merge duplicate accounts or entries. Additionally, it can guarantee that employees can access information about a customer, vendor, or product in a single location. Due to the fact that many businesses have multiple sources of information, combining duplicates can enhance customer relations and speed up the sales process.
As an illustration, three different employees from a small retail business might get in touch with a tech company to inquire about financial services software. Despite the fact that they are all accounts within the same company, if they speak to different sales representatives, they may have three different accounts. Similar email addresses, business names, and phone numbers can be found using a data matching tool, and the database administrator can link the accounts together using the report that is provided. In this manner, all the data about the potential deal can be easily accessed by the sales and customer service teams.
Provides analytics and business intelligence
A data matching tool can help in the data analytics process by converting data entries into the same format. In order to find trends in massive amounts of data, analytics software is a powerful tool, but many of these programs demand that the user standardize the data first. Dates, names, and locations may be entered manually by multiple employees into a CRM or other database in a variety of formats. A database administrator or other administrative staff member can use a data matching tool to automatically reformat data in hundreds or thousands of database entries.
Improves sales tactics
To improve your comprehension of a target audience and to help you hone your marketing strategies, you can use data matching tools. Data matching tools can link user profiles to demographic data to build a more complete profile of a current or potential customer by comparing a company’s CRM (customer relationship management) data to a third-party collection of information. A sales representative may obtain the caller’s contact information and interests when a prospective customer calls a business. Applications used by third parties to collect data may offer additional details, such as the address, age, and occupation of the caller.
For instance, a parent interested in tutoring services for their teenager might speak with a sales representative from a tutoring company. The representative might enter the parents’ zip code and phone number into the business’ CRM system. The data matching tool can provide details about the student’s school district and nearby rival businesses by comparing this data to the information the company buys from a data collection agency. This knowledge can assist the salesperson in crafting a targeted sales presentation for the potential client.
Ensures compliance
Many businesses keep compliance data in their databases, including contracts with clients and vendors and approval forms. Companies can maintain their databases and make sure they’ve complied with compliance requirements for various accounts by using data matching programs. These programs can automate compliance tasks by identifying duplicate accounts and accounts with similar parameters, which can improve the productivity of administrative staff.
For instance, school districts might demand particular approval before allowing outside vendors to work in system schools. Information on hundreds of potential vendors who have inquired about providing services or selling goods in their schools may be found in a school system database. The database administrator can use a program to find and merge duplicate company accounts. In this way, they are able to identify the vendors who have valid approval forms on file.
FAQ
What is data matching used for?
Data matching is the process of comparing and matching two different sets of data against one another. Finding data that refer to the same entity is the process’ goal. The data frequently have no common identifiers and come from two or more different sets of data.
What does it mean to match data?
Abstract. In the context of epidemiology, record linkage refers to the combination of data (records) on the same person that may have been recorded on various occasions and included in various data collections.
What is matching in data quality?
Disambiguating records that correspond to real-world entities across and within datasets is the task of entity resolution (ER). Entity resolution has a wide range of uses, especially for federal and public sector datasets related to law, finance, health, transportation, and counterterrorism.
What is data matching algorithm?
Data matching can be done for various types of data mining or to eliminate duplicate content. Finding a significant connection between two data sets is the goal of numerous data matching efforts, which may be used for marketing, security, or other practical purposes.
