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Data empowers us to run smarter, more engaging sales and marketing strategies. But if it isn’t managed properly, it can quickly become unwieldy. Bad data is the cause of many of the ills that plague go-to-market organizations. Not only does it stifle revenue generation, it actually causes the business to lose money. In a Gartner survey, respondents said they estimated that bad data cost them, on average, $15 million per year.

A clean Salesforce database, on the other hand, sets the foundation for effective automations, accurate personalization, and better planning, all of which translate to better operations and higher revenue. High-performing operations teams perform proactive, regular Salesforce data cleaning. And that enables sales, marketing, and customer success teams to spend more time engaging with leads and customers, and less time on workarounds. Keeping data clean and organized using tools such as the Salesforce Data Dictionary from Sonar can help admins with this process.

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What is Salesforce data cleaning?

Salesforce data cleaning is the process of removing and repairing outdated, duplicate, or incorrect information from your Salesforce database. 

Over time, prospects change jobs, companies go through mergers and acquisitions, and contact information changes. Plus, when salespeople or leads enter data, mistakes happen. Inconsistencies and duplicates accumulate, and leads are entered with critical fields missing. And when Salesforce admins are forced to implement change on the fly, they create technical debt that can come back to bite as the organization scales.

The goal of data cleaning is to remedy these issues so your database is in the best condition to support your processes. 

Salesforce data cleaning processes

To meet the goal of building a high-performing database, Salesforce data cleaning may include:

  • Deduping, seeking, and removing duplicate records
  • Data appending, completing missing fields
  • Normalization, standardizing how data is structured across the database
  • Data validation, verifying the accuracy of data

The data cleansing process is also a good time to evaluate your users and their access levels. Are there users in the system who’ve left the company or changed roles? Does everyone with admin access need it? Have users created reports or test campaigns that need to be removed? This clutter can hinder your processes and complicate your reporting, and should be considered as part of your data cleanup process.

Salesforce data cleaning tips

Tackling bad data can be a big job, and it’ll have consequences across your tech stack. Use these tips to manage your data cleaning processes effectively.

Choose the right data cleaning tools

There are quite a few tools today on the market to use for data cleaning processes. Here are some of the types of tools you may use:

  • Auditing software to identify broad errors
  • Third-party contact data to correct errors and missing fields
  • Deduplication tools to remove duplicate records

Set a data cleaning schedule

Data cleaning isn’t a one-and-done effort. It needs to be a part of your regular processes. Some tasks, like deduping and data validation, can be performed monthly. Other tasks should be performed quarterly, every six months, or annually. 

Create standards for data entry

While you’re normalizing your data, you need to set standards to ensure you’re treating all data the same. Should company names be listed as “ACME, Inc.” or simply “ACME?” Should a company be listed under the “Oil & Gas” industry, or “Oil/Gas/Utilities?” These kinds of discrepancies lead to duplicate records and gum up your reporting. Once you’ve established your standards, deploy them throughout the organization to prevent future errors.

Identify root causes

As you’re identifying errors, perform a root cause analysis to determine the factors causing your data quality issues. They may be caused by a lack of training, integration errors, or the standardization issues discussed above. Once you’ve identified the causes, you can remedy them in your next release.

How to start data cleaning

The first step to cleaning your Salesforce database is to perform an audit. Before you can remove and repair bad data, you need a process for identifying and documenting it. There’s no shortage of Salesforce audit tools on the market, and you also can choose the do-it-yourself route. Here’s what every audit should encompass, whether you use an auditing tool or perform your own.

Review your requirements

Before diving into the audit, take a step back and look at the core motivations of the users operating out of your CRM. Use this as your measuring stick for the following steps in your audit. As you assess your database, ask yourself what you need from your data in order to meet your CRM requirements.

Document your data entry points

Knowing where data comes from will help you identify the causes of errors. Document the entry points for data in your Salesforce, such as manual entry from sales, integrations from other systems, event attendee list uploads, etc. As you perform your audit, you may start to notice patterns in where certain errors are coming from.

Search for duplicates, missing fields, invalid data, and inconsistencies

At this stage, you can use Salesforce data cleaning tools to identify errors, or you can run your own reports. This is the meat and potatoes of the cleanup process, but it doesn’t have to be overwhelming. If you’re auditing your data regularly, you can run these processes pretty quickly, and making change shouldn’t be overwhelming. Using a change intelligence platform like Sonar lets you see how your changes will impact your entire tech stack, so you can make sure you don’t create another mess in the process of cleaning up.

If you’re facing a big Salesforce data cleaning project, you may be overwhelmed by the scale of the project. But the sooner you get started, the sooner you can get rid of bad data. With higher quality data, go-to-market teams can spend more of their time on tasks that drive revenue.