DATA MATCH: What Is It and How Does It Work?

Data Match Software
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Data Match refers to comparing and contrasting two or more data sets to identify similarities and differences. Whether or not two “entities” are, in reality, the same “entity” is the issue it attempts to address. There are a variety of approaches to data matching. The procedure is often based on an algorithm or a predetermined cycle in which data from both sets is isolated and compared. Read further to get an in-depth understanding of data Match software.

Let’s dive in!

What Is a Data Match?

Data Match is an algorithm that searches for similarities in large data stores. Some companies with extensive data processing capabilities may have data matching software embedded in their server, database, or other system. They may also employ a third-party data-matching tool that provides additional analytics and business intelligence capabilities. A government entity, for instance, might make use of a data matching tool that compares database records for similarities, such as a shared social security number or driver’s license number. This method reduces the number of identical accounts in the database.

Attribute values, which include descriptors like names, locations, phone numbers, and so on, are used to record and identify real-world items in databases. There is always some room for error when recording this information, whether it is done manually or automatically.

What Industries Use Data Matching?

There are several fields where data matching might be useful, like:

#1. Retail

Data matching can be used by businesses with customer datasets to eliminate unnecessary customer accounts and discover commonalities in consumer behavior across demographic subsets.

#2. Institutions of the State

To facilitate the census and registration processes, government organizations can use data matching systems to consolidate citizens’ personal information into a single online profile.

#3. Expertise in several fields 

Data matching software can help professional service providers spot spam accounts in their customer databases and patterns in user behavior.

#4. Education 

Combining student records from different schools and spotting demographic trends in standardized test scores and high school completion rates are two potential uses of data match software for school districts.

#5. Health care

Administrators often combine patient records from several institutions to create a comprehensive database of a patient’s health status and any known drug allergies through a process known as “data matching.”

How Does Data Matching Work?

Different types of data matching software exist to detect and remove duplicate or similar records from databases. Common procedures in data matching include:

#1. The matching policy is user-defined 

Since databases and servers can store an enormous quantity of information, the first stage in data matching is to establish the criteria for comparison. It is recommended that users provide a data point that is unique to each person, such as a user ID or social security number, if they wish to run a matching procedure to merge duplicate entries. Analytics and data mining matching policies may prioritize shared information between entries, such as a common time or place.

#2. Data matching software finds similar records

The database is then combed through by a data matching software in search of potential matches. A data matching tool may utilize a percentile ranking of potential matches, however, this may vary by data tool and user preferences. If two people in a government database share the same name and birthday but have different social security numbers by just one digit, the tool may conclude that there’s a 50% possibility that they’re the same person but for a typo in one of the numbers.

Database administrators and other professionals who use data matching tools can benefit from the reports the software generates. They may also have merged controls embedded into interactive dashboards. The matching tool may recommend updated postal addresses for databases with location information, which is helpful for customer databases.

 #3. The user evaluates the findings

Data matching software reports can be used to merge duplicates or construct analytics resources, depending on your function and department. A database administrator may open the accounts that were flagged after running a program to discover probable duplicate accounts and merge them if they are clearly the same individual. Accounts with similar details can be automatically merged by some data-matching technologies. When it comes to marketing analytics, some businesses rely on data matching. A marketing coordinator or other professional may then utilize the results of the data match report to better target their email campaigns.

Data Match Software

Data matching software, also known as record linkage or entity resolution software, helps users find and eliminate duplicate records and entries in databases to boost data integrity and accuracy.

#1. WinPureTM Clean & Match

WinPure Clean & Match is a set of data cleansing and matching tools developed by WinPure to improve the quality of customer or corporate records. Cleaning, rectifying, and deduplicating email lists, databases, spreadsheets, and CRMs is a breeze with this software suite. You can save both time and money by using WinPureTM Clean & Match in your company.

Improve the precision of almost any list, spreadsheet, database, customer relationship management system, etc. Windows software is installed locally, eliminating security concerns because all data processing occurs on-premises.

Using the built-in advanced fuzzy and phonetic match algorithms, you may save a lot of time cleaning and deleting duplicate records from your lists or databases. Low-priced licenses with first-rate customer service and education are on offer. Try it out for free and get access to live online classes.

#2. Senzing®

Senzing® entity resolution API software is the industry standard for accurate and simple relationship discovery and data matching. Whenever new information is received, the Senzing program will automatically resolve records into common entities. You may cut expenses and open up new revenue streams by getting a bird’s-eye view of all records pertaining to any individual or organization across all of your internal and external data sources. Businesses use Senzing’s API for resolving entities to present comprehensive and correct profiles of individuals, groups, and their connections. The Senzing entity resolution API can be used in both on-premises and cloud-native settings. Your data stays where it is and never leaves your ecosystem for Senzing to see. In one day, you may run a free proof of concept on AWS or BareMetal. Senzing produces decisions with the intelligence of a human being without being trained or fine-tuned.

#3. OpenRefine 

OpenRefine (formerly Google Refine) is a robust platform for dealing with unstructured data, including cleaning, converting, and extending it using online services and external data. It never exposes your data to anyone but you until you want to do so. Only after you approve can your private information be transmitted from your device. It works by running a small server on your computer, and you use your web browser to connect with it. It makes it simple to investigate massive datasets. Using OpenRefine, you may join your dataset with other web services for additional insights. It can also upload your cleaned data to a centralized database like Wikidata, which is supported by some services. The wiki currently hosts a growing collection of add-ons and components.

#4. Match2Lists 

When it comes to matching, merging, and de-duplicating data, Match2Lists is your best bet. Data from numerous sources can be combined into actionable insights in a couple of minutes after duplicates have been removed. Maximizing customer satisfaction through successful pairings is their top priority. Their company’s history was in data visualization and analytics prior to the formation of Match2Lists. We also employed the most advanced “fuzzy” matching software available. To counteract this problem of low match outcomes, they invested ten years in developing the most cutting-edge data matching logic available. They also want to minimize wasted time. They’d rather that our clients invest less time in manual data preparation and more in strategic analysis and deployment. The most efficient in-memory cloud computing architecture was used to accomplish our sophisticated matching logic. In just 30 seconds, it can find matches among 200,000,000 records.

Benefits of Data Matching

The use of a data matching tool can boost an organization’s efficiency in the following ways:

#1. The Data Is Gathered in One Place

If you use a data matching tool on a regular basis to find and merge duplicate accounts or entries, you may reduce the likelihood of errors occurring in your database. It can also make it so that all relevant details about a customer, supplier, or product are in one easily accessible location. Merging duplicates can help organizations boost customer interactions and speed the sales process since they consolidate data from multiple sources.

Database administrators can use a data matching tool to consolidate accounts that share contact information such as email addresses, company names, and phone numbers. That way, the sales and support personnel may quickly and readily access any and all data pertaining to the potential transaction.

#2. Offers Data Mining and Business Analysis

To facilitate data analytics, a data matching tool can standardize data entry formats. Finding patterns in massive datasets is made much easier using analytics tools, however, in many cases, this software requires the user to first standardize the data. Dates, names, and places may be written in a variety of ways if many people at a company manually enter information into a CRM or other database. Using a data matching tool, a database administrator or other clerical expert can instantly standardize the format of hundreds or even thousands of database entries.

#3. Boosts Sales Strategies

Using data matching tools can help you better understand your audience and adjust your marketing strategies accordingly. User profiles can be supplemented with demographic information by using data matching tools, which compare a company’s CRM (customer relationship management) data with a third-party collection of information. It’s common practice for salespeople to ask prospects for their contact details and areas of interest during initial phone conversations. Additional details such as the caller’s residence, age, and occupation may be gleaned from third-party data match software.

#4. Guarantees Conformity

Data such as vendor and client contracts and internal approval forms are commonly stored in databases for the purposes of regulatory compliance. To keep their databases up-to-date and in compliance with various accounts, businesses might benefit from data matching services. These tools assist administrative employees in operating more efficiently by spotting duplicate accounts and accounts with similar specifications and then automating compliance activities.

Read Also: LOGICMANAGER: Feature, Review, Pricing & Competitors 2023

Major Data Matching Challenges

Most businesses do employ pricey data analysts to utilize Excel for data matching, but as datasets grow in size and complexity, it becomes a nightmare to manually match and clean data. If you were to ask any of your data engineers, they could say that data matching is the most tedious and difficult aspect of their work. 

When you try to data match by hand, you may encounter problems like: 

#1. Data variation 

Data heterogeneity is the fancy word for this variation in data that arises from things like different data sources, data structures, and data formats. Customers’ names, addresses, and other contact details might be stored in one database, while products’ names, stock-keeping unit (SKU) numbers, and prices might be stored in another. Standardizing the data so that the same fields appear in each record helps bring these two databases closer together. This isn’t always fast and usually requires some sort of human interaction.

#2. Correcting mismatched data 

Data matching is difficult because of the possibility of false positives and false negatives. Both false positives (when a record is matched incorrectly to the same master record) and false negatives (when a record is not matched despite belonging to the same entity record) are possible. 

#3. Privacy and the lack of data

Privacy regulations make it extremely difficult to acquire data for most data matching methods. Experts have done a lot of research on the best way to access and match data when privacy is a concern. One of the most popular models is the One-way hash encoding, which takes a string value (like “peter”) and turns it into a hash-code (like “51dc3dc01ea0”), so that if you only have the hash-code, it’s almost impossible to figure out what the original string value was with the technology we have now. Secure multi-party computation (SMC), phonetic encoding, bloom filters, and many more are just a few examples of the many additional models available. 

What Is a Data Match?

The technique of determining whether or not two or more records pertain to the same entity by comparing and computing the similarities between them is known as data matching, record linking, or entity resolution.

What Is An Example of Data Matching?

If two people in a government database share the same name and birthday but have different social security numbers by only one digit, the tool may conclude that there’s a 50/50 chance that they’re the same person and that one of the entries has a typo.

What Are the Advantages of Data Matching?

The advantages of data enrichment can be used thanks to data matching. Which requires incorporating information from reliable external sources into the current internal database. Businesses can benefit from streamlined marketing, sales, production, and other operations if their customer data is of higher quality and consistency.

Final Thoughts

We’ve shed light on everything you need to know about Data Match. We do hope this article was helpful for your needs. Let’s hear from you in the comment section below!


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