Last Updated on décembre 10, 2025
Having provided merge purge solutions to clients for over a decade, we consider merge purge operations an essential function in business operations such as direct mail marketing, entity resolution , and obtaining single-source versions of truth. However, for many organizations, the merge purge process remains limited to Excel functions and techniques that hardly address increasingly complex data needs.
This guide for IT and business users demystifies the merge and purge process and helps you understand why your teams can no longer rely on merging and purging using Excel. The key takeaways from this guide are:
- What is fusion purging?
- How is purging by fusion traditionally performed?
- Create a well-thought-out merger-and-purge strategy
- Business processes that can be improved with Merge Purge
- Creating the golden record through data survival
- Best practices for data merging and purging
Let’s dive in!
What is a merge and purge function or process?
As the term suggests, merge purging refers to the process of combining multiple data sources while simultaneously removing duplicates and bad records from the data source.
For example, look at the image below:
Notice that you have three duplicate records with multiple data quality issues for a single individual. When a data merge purge function is applied to this record, it transforms it and returns a clean, unique version like the image below:
A new column [Industry] was added to this record, which was stored in another data source. After merging and purging duplicates from the two data sources, the result is a consolidated view of the entity record.
The result of a merge purge function is to create records containing unique names, addresses, and additional information that will serve the data’s business purpose. In this particular case, the above data, once optimized, constitutes a reliable record that marketers can use for their email campaigns.
How has purging by merger historically been carried out?
Today, in most companies, teams still use Excel to manage their records. Business users manually cut, paste, and concatenate multiple columns of data from disparate sources to create accurate records. Days and weeks are wasted merging and purging hundreds of thousands of records. This doesn’t account for human error during the merging or purging process, nor for damaging events such as software crashes.
Besides operational inefficiency, the key factor making Excel counterproductive is the increasing complexity of data. Today, businesses deal with more than just basic contact information. An entity may have additional records such as:
- Securities
- Occupation
- Industry
- Social media accounts
- Multiple email accounts
- Household data information
And so on.
It is virtually impossible to manage all these nuances of data through manual implementation of Excel functions and formulas. It is therefore necessary to move beyond Excel and consider other options that allow for merging and purging complex data while maintaining optimal operational efficiency.
Create a well-thought-out merger-and-purge strategy
Merging and cleaning a database can be a lengthy and error-prone task, which is why it is essential to have a well-thought-out strategy before implementing it.
Here’s a quick, step-by-step guide:
- Integrating data from multiple sources: Merging different databases from various sources (SQL Server, MySQL, Excel, ODBC, etc.) and combining them into a common structure is the first step in the merging process. You will need a merge purge tool like DataMatch Enterprise to import, combine, and export to the most common database formats. Additionally, you can also automatically match similar fields from different data sources.
- Identifying duplicates: Duplicates pose the greatest threat to data accuracy . Vigilance is crucial to prevent duplicates—whether individuals, households, or businesses—from appearing in your database, especially when combining multiple lists for a single mailing. Duplicates are identified through fuzzy matching , acronym identification (e.g., International Business Machines to IBM), data cleaning and garbage removal, data normalization before matching, and the application of libraries for normalization, particularly for first names (e.g., Jon, Jonathan, Johny, etc.). If you use an automated merge purge tool, you don’t need to worry about manually implementing these mechanisms.
- Data matching for merging and purging: Excel isn’t very good at matching data . While it can eliminate certain exact matches, it can’t identify probabilistic records, such as the use of nicknames for an individual. The tools in the merge function have advanced data matching capabilities that allow you to match records even if the first and last names vary. For example, John Smit might be the same person as Johnny S. In cases where spellings and abbreviations are instantaneous, you’ll need to prepare the data first before submitting it to a comparison process.
- Knowing which records to keep: Once you’ve identified duplicate records, cleaned and normalized your data, you can decide which records to keep and which to « purge. » This process, also known as data survival, allows you to create clean and final records of your data.
- Continue to optimize your list: The merge purging activity is not a one-time event. As you acquire data from multiple sources and continue to develop the customer profile, you will need to continue merging and purging your records. Once you have the primary record, simply compare it to records 2, 3, 4, and so on to continue enriching your data .
Merge purging software will help you implement this strategy. However, in our experience, the best results are achieved by defining the records you need in advance and then simply using the tool to perform reconciliation, deduplication , and cleanup . The more clearly defined your merge/purge objectives are, the more quickly and efficiently you can use the tool to achieve them.
How Mergers and Purges Processing Optimizes Marketing and Direct Sales
Data merging and purging is one of the most important data processing functions, significantly impacting a company’s objectives, tasks, and marketing goals. Clearly, with the increasing complexity of data, you want to optimize your lists and records to maximize your marketing, service, and customer personalization goals.
Over the years, we’ve worked with several Fortune 500 clients to process their data and help them get the most out of a merge/deletion goal. Companies using a merge purge tool can optimize their marketing and direct sales lists in several ways, as outlined below:
1. Segment their lists down to the T
A merge/delete activity isn’t just about combining and deduplicating records. It’s primarily about optimizing lists. You want the ability to test different segments, merge and purge different lists and records, and identify which of your lists are contributing to ROI and your expected marketing objectives through their activity.
For example, you might want to divide your email marketing list into product or service categories. Suppose you have one active list of subscribers interested in the tech products on your e-commerce site, another list interested in related products, and so on. Merge and purge tools allow you to separate, create new records, save old records, and test them without any limitations.
2. Create your own merge rules and matching definitions
Merge rules are instructions that specify whether you want to match duplicates at the individual level (i.e., the same person at the same address), at the household level (people with the same last name and address), or at the address level (everyone at that address, regardless of their last name). Additionally, you can create your own rules if you want to match at different levels, depending on your business objective. For example, some business users want to match their list at a community level, an organizational level (all names within the organization), or even an income level.
By assigning different merge rules and definitions, you make informed decisions rather than throwing a dart in the dark. Furthermore, these rules will also help you understand gaps in your data and allow you to obtain a realistic figure (for example, by discovering that your list may only contain 4,000 names after purging, whereas you anticipated 7,000).
3. Comparison of the lists with the data compliance rules
Data security is one of the main reasons why companies need data merging and purging tools. There are numerous examples of major companies being fined by the government for failing to match their listings with US sanctions lists and other authorized databases.
Furthermore, you may also be bound by GDPR regulations, so if you have a subscriber list that doesn’t want to receive emails or have their cookies stored, you cannot violate these regulations and send them emails. For small and medium-sized businesses, data compliance is of crucial importance.
4. Verify and validate your address details with an authorized database
Address data is one of the most challenging components of a data source. It is imperative to verify your address list against an authorized database (such as the USPS) to ensure the authenticity of your data. Furthermore, it is not uncommon for an entity to have multiple addresses, many of which may be incorrect, unverified, or invalid. Therefore, it makes sense to validate and verify them during the merge and purging process, so that outdated addresses can be discarded and the correct ones obtained.
We also discussed in detail how you can obtain the ideal mailing list , which you can review.
5. Reduce marketing costs and increase efficiency
The ultimate goal of any data processing activity is to reduce costs, increase return on investment, and maximize operational efficiency. Marketing is the department within an organization that generates the most costs—in terms of email campaigns, social media campaigns, newsletter campaigns, direct mail campaigns, and many others. You can significantly reduce the costs of these campaigns by targeting selected lists and eliminating duplicates.
For example, sending three emails to a user at three different addresses, or sending three emails to three users at the same address, is wasteful and significantly increases your operating costs. Returned emails alone will cost you millions of dollars, not to mention the disgruntled customers who don’t appreciate receiving multiple newsletters or promotional emails from the same company.
Data merging and purging are more than just a quick deduplication. To get the most out of your data, you need to use all available tools and plan ahead. The more you plan and link your lists to your goals, the more you’ll benefit from your next merge/purge.
Data survival and the creation of the gold record
The merge purge process allows you to identify and remove duplicates, leaving you with clean records. But how do you know which records should be saved and which should be moved?
For example, how do you know which is the correct address to keep for an entity if it has three different addresses? The answer to this question is « smart, » meaning you need to establish rules. In this particular case, you can establish a « most recent » rule. Whichever address is the most recently recorded for the entity will be updated using the most recent rule.
This part of the data merging is easy.
The biggest challenge is figuring out how to replicate this information across multiple databases, which may contain outdated addresses. How can you ensure your entity’s address is up-to-date in your organization’s CRM, ERP, and other data sources?
Whereas previously this meant another merge/purge cycle, with the availability of data survival options in most merge/purge tools, it has become easy.
Simply select the columns you want to save, along with the sources to which you want to save the data, and the platform will allow you to overwrite that data in your new records. Data survival using a platform like DataMatch Enterprise has not only become easier, but also saves time and improves accessibility.
This ability to obtain an accurate, complete, and up-to-date view of your customer record is often called the Golden Record and represents the most valuable goal of an organization’s data management objectives. And it has become much easier to make it a reality.
How to merge purge data to create gold records
To learn more, see how we’ve helped companies combine data from multiple sources to create the perfect case.
Best practices for data merging and purging
Regardless of your business, industry, or company size, a merge/delete initiative forms the foundation of your data-driven objectives. Thus, while historically the merge/cleanup exercise was limited to combining and eliminating data, it has now evolved into a powerful mechanism that allows users to delve deeply into their datasets.
Although the process has been largely automated through the use of advanced merge purging tools, users should still maintain best practices. We recommend that our customers:
- Always focus on data quality: Poor-quality data is a challenge. You can’t make sense of your data if it’s riddled with typos, fake IDs, invalid addresses, and messy content. Before even considering a merge and purge, you should always clean and normalize your data. This makes the deduplication process much easier. If you start by inferring data without cleaning it, you’ll be frustrated and disappointed with the results.
- Always have a realistic plan: We’ve mentioned planning several times above, but we can’t help but reiterate this point: you must have a merge/purge plan. If simply merging data isn’t what you’re after, and you understand the limitless possibilities of using a tool to perform even the nth segmentation of a list, then you need to develop a plan that assesses the types of records you want to merge and purge.
- Optimize your model: Generally, after the first round of merges/cleanups, you’ll have a better understanding of your data model. For example, you can learn which names are most often abbreviated or turned into nicknames. You’ll learn about the matching criteria, specifically whether you want to loosen or tighten the matching (e.g., individual matches or matches at the same address). Once you have a preliminary understanding of this model, you can then use this information to create campaign performance indicators, KPIs, and reduce the time spent on the next merge/cleanup activity.
- Keep track of your lists: Purging a list doesn’t mean deleting it entirely. Data merge/purge software and tools allow you to save your records and maintain a database of all the changes you’ve made to your list. For example, in one record, your customer might have had address A, and now they may have moved after getting married. Your records for the same person will show address B. Does this mean you’re deleting address A? No. Instead, you’ll update your records by merging address B into, for example, a new column showing the current address. You can also purge address A and create a new record with address B, while keeping the old record. This growing intelligence can help you understand demographic behavior.
- Trying to maintain a single source of truth: disparate information about your user data will waste time and effort for you and your team. The best use of a merge/delete function is to create a single source of truth or a single view of the customer that contains everything you know about the customer and needs to be kept up to date. You will receive information about your user from multiple data silos, so you will need to ensure that the old and new data are properly aligned.
This single source of truth can be obtained by matching and merging data across multiple datasets, within datasets, and between datasets. In other words, you can match record A [customer name] from dataset 1 [Billing data] to merge with record A [customer name] from dataset 2 [Sales data], or record A [customer name] with record B [customer surname] from dataset 1, and so on.
Using a self-service merge-purge tool
One of the most effective and common solutions for creating the Golden Record is merge purge tools which can help you overwrite old records with new information using a data survival function.
Self-service merge and purge software allows users and business professionals to easily merge and purge their data without requiring intensive learning or programming languages.
This tool is designed to help professional users:
- Prepare the data by evaluating it to detect errors and ensure the consistency of the information.
- Clean and normalize the data according to the defined business rules.
- Matching multiple lists using a combination of proprietary and established algorithms
- Remove duplicates with an accuracy rate of 95 to 100%.
- Create golden records and obtain a single source of truth
and much more.
These tools are becoming the leading solution to a long-standing problem of reliance on complex IT processes to merge and purge data. In an era where automation is key to business success, companies cannot afford this dependence and the resulting delay in data optimization.
Conclusion – Use a merge-purge solution to create the perfect source of truth
Your data is a valuable asset, and like any asset, it needs to be cared for. Today, businesses strive to acquire more data and enrich their « collection, » but if data is inactive and occupies costly storage or CRM space, it needs to be disposed of. You can simplify this complex process by using a single merge purge software that allows you to merge your data sources and create valuable records.
How the best fuzzy matching solutions in their category work: Combination of established and proprietary algorithms
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