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A Quick Guide to Data Merge Purge

A merge purge solution helps companies with disparate data sources unify their data to create a single version of the truth. Companies often use multiple systems to store data. Over time, this data needs to be consolidate to create a master record or the disparity results in skewed insights and inaccurate analysis. Using a merge purge solution, this data is first cleansed, matched, deduped, and finally merged into a master record that serves as the fundamental source of truth for the company.

Traditionally, the merge purge process was a complex operation that was solely handled by the IT team. Business users would have to extract this data into Excel sheets and manually make changes. Not only is this time-consuming, it’s also ineffective as there is no guarantee of accuracy. Today though, technology has changed. It has enabled the rise of self-service merge purge tools that corporate professionals can use to handle their data.

This guide aimed at IT and business users demystifies the merge purge process and helps you understand why your teams can no longer rely on merging and purging using Excel. Key takeaways from this guide will be:

    • What is Merge Purge?
    • How is Merge Purge Traditionally Done?
    • Creating a Thoughtful Merge Purge Strategy
    • Business Processes that Can Be Improved with Merge Purge
    • Creating the Golden Record through Data Survivorship
    • Data Merge Purge Best Practices

Let’s dive in!

What is a Merge Purge Function or Process?

As the term suggests, merge purge refers to the process of combining/consolidating/merging multiple sources of data simultaneously removing duplicates and bad records from the data source. Companies often store useless, obsolete data that take up unnecessary space (also increases costs if you’re using cloud storage), offering no benefit to the organization’s data-driven goals. The purging process allows companies to sort redundant data and remove them as needed.

For instance, take a look at the image below:

merge purge, A Quick Guide to Data Merge Purge

Notice, that there are three duplicated records of one entity. When a data merge purge function is applied to this record, it will transform and return a clean, singular version as the image given below:

merge purge, A Quick Guide to Data Merge Purge

A new column [Industry] was appended to this record, which was stored in another data source. After merging and purging duplicates from two data sources, the result is a consolidated view of the entity record.

The outcome of a merge purge function is to create records that will contain unique names, addresses, and additional information that will serve the business purpose of the data. In this particular case, the above data once optimized serves as a reliable record for marketers to use in mailing campaigns.

How is Merge Purge Historically Done?

In most companies today, teams still use Excel to manage their records. Business users manually cut, paste, concatenate multiple data columns from disparate sources to create accurate records. Days and weeks are wasted in merging and purging hundreds of thousands of records. This does not take into account human errors that happen at the time of merging/purging or of damaging occurrences such as software crashes.

Other than operational inefficiency though, the key factor that makes the use of Excel counter-productive is the increasing complexity of data. Companies today deal with more than just basic contact data. One entity can have additional records as:

And so on.

It’s next to impossible to manage all these various nuances of data through the manual implementation of Excel functions and formulas. Hence it’s necessary to step out of the Excel bandwagon and see other options that allow for complicated data merging and purging while keeping operational efficiency at its best.

Creating a Thoughtful Merge Purge Strategy

Merging and purging a database can be a time consuming and error-prone task which is why it’s essential to have a thoughtful strategy before implementing it.

Here’s a quick step-by-step guide:

  1. Integrating Data from Multiple Sources: Merging different databases with various sources (SQL server, MySQL, Excel, ODBC etc.) and combining it into a common structure is the first step in the merge process. You will need a merge purge tool to import, combine, and export to the most common database formats. Additionally, you can also auto-map similar fields from different data sources together.
  1. Identifying Duplicates: The greatest threat to data accuracy is duplicate data. It takes vigilance to keep duplicates out of your database. Duplicates are identified through the use of fuzzy matching, acronym identification (for e.g, International Business Machines to IBM) cleaning and standardizing data prior to matching and applying libraries for standardization, especially for first names like (Jon, Jonathan, Johny etc.) If you’re using an automated merging purging tool, you need not worry about implementing any of these mechanisms manually.
  1. Data Matching to Merge and Purge: Excel does poorly at data matching. While it can weed out definite exact matches, it cannot identify probabilistic records such as the use of nicknames for an individual. Merge purge tools have advanced data matching capabilities that allow the matching of records even if the first and last names vary. For instance, John Smith can be the same person as Johnny S. In instances where spellings and abbreviations are used, you will need to clean the data first before putting it through a matching process.
  1. Knowing Which Records to Keep: Once you’ve flagged records as duplicates, cleaned up and standardized your data, you can decide which records to keep and which to, ‘purge.’ This process, also known as data survivorship, allows you to create clean, final records of your data.
  1. Keep Optimizing Your List: The merge purge activity is not a one-time activity. As you acquire data from multiple sources and continue to expand on the information, you’ll need to keep merging and purging your records to ensure uniqueness. Once you have the main master record though, you can continue to add more information to that record and enrich your data.

A merge purge software will be instrumental in helping you execute this process. For more effective results, try defining your merge/purge goals. Identify the kind of records you want to keep and the ones you want to remove. For instance, you might want to keep only email records of the past 3 years; the older a record is, the higher its chances of being obsolete.

How Merge Purge Processing Optimizes Marketing & Direct Selling

It’s obvious that with rising data complexities, you’d want to optimize your lists and records to maximize customer marketing, service, and 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 their data. Companies using a merge purge tool can optimize their marketing & direct selling lists in multiple ways as given below:

1. Segmentizing Their Lists Right Down to the T

Audience segmentation is a critical component of digital marketing. You could be using a CRM like HubSpot and Salesforce to segment your audience into multiple lists and records, but you can’t match, combine, dedupe, and clean them. For instance, if you’re selling two products, you’d want to match the customers of Product A with the customers of Product B to know how many customers bought both Product A and B. You’d also want to merge this list so you can run a special discount offer to only those customers who bought two products. This is where a merge purge tool comes in handy. Not only can you match, but also clean and dedupe data in the process.

2. Create Your Own Merge Rules and Match Definitions

Supposing you want to group customers according to their company names. Merge rules refer to instructions that show whether you want to match duplicates at an individual level (i.e, same person at the same address), at a household level (people with same surname and address) or at an address level (all people in that address regardless of surname). Furthermore, you can also create your own rules if you want to match at different levels, relevant to your business goal. For instance, some business users want to match their list at a community level, an organization (all names in the organization) or even at an income level.

By assigning different merging rules and definitions, you’re making informed decisions rather than throwing a dart in the dark. Moreover, these rules will also help you understand the gaps in your data and allow you to get a true figure (for instance discovering your list may have only 4,000 names after the purging, as opposed to your anticipation of 7,000 names).

3. Matching Lists Against Data Compliance Regulations

Data security is one of the key reasons why companies need data merging and purging tools. There are multiple examples of big names being fined by the government for failing to match their lists against US sanction lists and other authorized databases. Financial firms and enterprise-level businesses need to constantly merge/purge their lists to ensure they are following global and local data compliance rules. This is especially important in the case of anti-money laundering cases where companies are required to ensure their customer lists are not marked in a money launderer list. Failure to meet these compliance rules may result in heavy fines and loss of credibility.

4. Verify and Validate Your Address Data with an Authorized Database

Address data is one of the most challenging components of a data source. It’s imperative to verify your address mailing list with an authorized database (like the USPS for example) to ensure the authenticity of your data. Moreover, it’s not uncommon for one entity to have multiple addresses – most of which could be fake, unverified and invalid. It makes sense then to validate and verify them during the merge and purge process so you can get rid of the obsolete ones and obtain the right one for use.

5. Reduce Marketing Costs & Increase Efficiency

The end goal of any data processing activity is to reduce costs, increase ROI and maximize operational efficiency. Marketing incurs the highest cost because of poor data quality. You could lose hundreds of thousands of dollars caused by decayed or obsolete data, duplicates & false or invalid information. For instance, invalid address data can cost a significant surge in return mail costs. Similarly, high email bounce rates caused by obsolete email data affects your revenue goals. All this withstanding, you also have to bear the costs of inefficient processes. For example, your marketing team may be spending days in building a new campaign, only to have it miss the goal by a mile because of poor and outdated data. This inefficiency not only affects ROI but also team morale and productivity.

Using Merge Purge to Create the Golden Record 

The Golden Record is perfection, it’s the ultimate source of truth. When companies talk about refining their data with a merge purge tool, they are mostly looking to get the Golden record.

The Golden Record is the master record that you get after consolidating, deduping, cleaning & merging your data. This record acts as a single source of truth – a record that contains no error or duplicates and is the most accurate, up-to-date version. This golden record can then be stored in a centralized location like an ERP for easy accessibility.

Merge purge tools such as Data Ladder are designed to help companies create the Golden Record and overwrite old records with new information using a data survivorship function.

To know more about golden records and how your business can get one, download our whitepaper & see how we’ve helped businesses combine data from multiple sources to create the perfect record. 

Golden Records Guide

How to Merge Purge Data to Create Golden Records

 

Merging Purging as a Complex Data Process

All of this is easier said than done. We know. Which is why we’ve simplified the process for you.

Data Ladder’s DataMatch Enterprise software allows businesses users and corporate professionals to easily merge/purge their data without requiring a steep learning curve or programming languages.

The tool is designed to help business users:

  • Prepare data by assessing the data for errors and information consistency
  • Clean and normalize data according to defined business rules
  • Match multiple lists using a combination of proprietary and established algorithms
  • Remove duplicates with an accuracy rate of 95 – 100%
  • Create golden records and obtain a single source of truth

and much more. Data Ladder’s flagship tool is the solution to an age-old problem of reliance on complex IT processes to merge/purge data. In an age when automation is the key to business success, businesses cannot afford this dependency and delay in data optimization.

Act now. Reap benefits later.

Conclusion – Use a Merge Purge Solution to Create the Perfect Source of Truth 

Your data is a valuable asset and like every asset, it needs to be nurtured. Companies today are highly focused on acquiring more data and adding to their ‘collection,’ but if the data is lying dormant and taking up expensive storage or CRM space, then it needs to be purged. You can simplify a complex process by using a one-stop merge purge software that lets you merge your data sources and create valuable records.

You can download DataMatch Enterprise free trial and use it with your data set.

Alternatively, you can also book a demo with us and let us walk you through the platform and help you make sense of your data. 

 

Farah Kim is an ambitious content specialist, known for her human-centric content approach that bridges the gap between businesses and their audience. At Data Ladder, she works as our Product Marketing Specialist, creating high-quality, high-impact content for our niche target audience of technical experts and business executives.

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