When and How Should You Use Merge Purge Software? by@javeriagauhar

When and How Should You Use Merge Purge Software?

Javeria Gauhar HackerNoon profile picture

Javeria Gauhar

Javeria is an experienced B2B/SaaS writer specializing in writing for the data management industry.

Merging data entries from your company’s database, especially when it is coming in from multiple sources, is enough to ruin an IT team’s day. The average enterprise makes use of at least 464 applications that stream data to your company. This includes CRMs (like Salesforce), POS data stores, Excel sheets, and content marketing platforms (such as Hubsoft). Unless you use a merge purge software, consolidating all that data in one place is easier said than done.

Merge purge is a process that combines two or more lists/files by scanning databases and simultaneously identifying and merging similar records. It also gets rid of unwanted data entries. Ultimately, you end up with a unique record, complete with properly arranged names and addresses.

Why do you need to merge data?

It is not uncommon to come across inaccurate, inconsistent, and duplicate data entries in most databases. This usually happens when different people write a customer’s name and address in different ways. These data entries may also be entered in different sources, be it from billing records, websites, social media advertising information, and more.

As you can imagine, the data is all over the place. If your company decides to migrate to a new system or CRM, you are setting yourself up for a setback. Let’s walk you through one example:

Your Company wants to move to a New CRM Platform to Take Advantage of Automation

Suppose your company is looking to automate processes related to social media, email, lead generation, etc. To accomplish this, you need to move to a CRM platform that supports these processes. Migrating to a new CRM involves transferring customer data from many different departments. Your IT team initiates the data transfer but ends up discovering massive data quality issues that threaten to derail the whole data migration.

You find that most email addresses entered for your customers are either wrong, invalid or just left incomplete. Addresses like [email protected], [email protected], and so on are found in your database. Upon further inspection and verification, it was found that 15% of email addresses were entered incorrectly. And this is just one thing. What if this inconsistency is not limited to email addresses? What if names are spelled wrong, and other information similarly missing or incomplete?

Why does this happen? Perhaps the same customer gave the right address to the billing department, but the wrong one to the online survey team. Perhaps the address contained a typo when it was being manually entered.

As you can see, this mismatched data creates hurdles if the company decides to initiate an email marketing campaign. It’s clear you need to carry out a merge purge before you can do so.

Before you decide to do a merge purge, what do you need to keep in mind? A data quality check process that’s what.

Steps Before Merging Data from Multiple Sources

Assuming your company has data quality issues as described above, you need to take this approach to ready your data for merging.

  1. Data profiling activity

  2. Data quality fixes once done with the profiling process

  3. Roll out a final data profiling check

Data profiling is done to examine your data from existing sources. It takes a look at how accurate, complete and valid your data is. Once done, the software creates a summary for later use. You can now identify data at its source level, with the help of inaccurate formats, null values, even missing information through data matching. Basically, you now know what needs to be fixed in your database.

Once done with the profiling, the next thing you need to do is fix issues related to data quality. Because an enterprise has multiple systems working independently of each other, there is no single source to represent a customer’s data.

By using multiple data entry points, errors related to the difference in names, abbreviations, spellings and more is widely observed. This results in:

  1. duplicate data – multiple entries of the same person, etc.

  2. incomplete data – missing phone numbers, addresses, etc.

  3. outdated information – old home or office addresses, contact info, etc.

  4. inconsistent formats – wrong dates, country codes, zip codes, etc.

Last but not least, a final data profiling check is carried so to confirm if no data was missed during the process. This is necessary because sometimes, you may accidentally end up creating new errors while fixing data.

Now that everything has been done, it is finally time to merge purge your data and create an entirely unique database with unique entries and records. Instead of juggling with a number of different platforms, all your data is neatly nested in one place.