How to establish and maintain Salesforce data hygiene best practices

If you haven’t considered your Salesforce data hygiene in a while—or ever—it might be time to look at your databases and do some digging. A Salesforce study found that the average database consists of 90% incomplete contacts, with an additional 74% of records needing updating.  

Data is one of the most reliable ways to drive new revenue to your business, especially when using a platform like Salesforce. However, you need reliable data to achieve this. 

In this article, we’ll discuss data hygiene, how to prevent bad data, and the six best practices for maintaining clean data.

What exactly is Salesforce data hygiene?

Data hygiene ensures that your data and records are up-to-date, accurate, and complete. Without proper data hygiene, “dirty data”—outdated, duplicate, or incorrect records—can quickly become a problem. 

If you’re not concerned with your data hygiene, dirty data can quickly build up in Salesforce. The outcome? Salesforce becomes less effective and reliable, sales reps stop using it, and your CRM adoption rates drop.

Practicing good data hygiene requires cleaning your data to ensure you don’t have poor-quality data in your CRM. But what is poor-quality, or bad, Salesforce data?

What is considered “bad” Salesforce data?

As mentioned above, having bad customer data in Salesforce can negatively impact your business. Bad or dirty Salesforce data is any form of data that introduces an error into your database. Some of the most common examples of bad Salesforce data include:

• Inaccurate or incorrect data: Records with wrong information. This can occur because of a simple typo that results in a misspelled email address or because the original customer information you received from a contact was incorrect. 
• Inconsistent or inappropriate data: Records with the wrong information inputted into the wrong field. If, for example, their phone number is listed in the First Name field, this can cause an issue when communicating with your customers. No one wants to receive an email addressed to 123-456-7890.
• Duplicate data: Duplicate records occur when more than one identical record is entered into your CRM. 
• Incomplete data: Records that are missing critical information, which prevents sales reps from using the data effectively.
• Outdated data: Records that are not up-to-date. For example, not updating a key contact after your original contact leaves the company. 
• Disparate data: When data is stored and processed using a wide variety of systems, tools, and software that don’t speak to each other or integrate, it can result in inconsistent data, missed opportunities, and more issues for your organization down the road. 
• Siloed data: When customer data is stored in a separate database from the rest of the data sources in your organization, it can lead to a fragmented view of your organization’s data

These types of bad data can introduce errors into your CRM which can cause you to make uninformed business decisions and potentially affect the customer experience you deliver. 

How can bad data affect your business?

It should come as no surprise that bad data leads to poor business outcomes. But you might be surprised to learn just how much of a negative impact it can have on your bottom line. According to Salesforce, poor-quality data can cost a business as much as $700 billion annually—or 30% of its average revenue. That’s a lot of money to leave on the table each year. 

Poor quality data can make as much as 20% of your records useless, with incomplete data (90%), outdated records (74%), and duplicate entries (25%) being the top reasons organizations have bad data. The data becomes unusable while taking up space in your CRM. That ends up costing you even more money. 

Let’s take a deeper look at how bad data can impact your organization.

It can decrease effective decision-making

Imagine you’re working on your sales forecasting for the next quarter but when you go into Salesforce to review the sales team’s progress, you see that the numbers are all over the place. Deals are open that should be closed, and vice versa—you’re left trying to forecast for the next quarter with incomplete or inaccurate data.

Imagine you’re trying to reach out to past customers to re-engage their business with a new offering. However, when you log in to Salesforce, you realize that the customer information for many of your past and current clients is out-of-date, inaccurate, or missing altogether. You’re left with the time-consuming task of chasing down information or reaching out to the clients with complete information, even if they might not be the ideal target. 

These are just a couple examples of how poor data quality makes it challenging to make the right decisions for your organization. When we ignore data quality issues in Salesforce, we force ourselves to make choices without fully understanding what we’re choosing.

It can lead to lost sales opportunities

The importance of data to a good sales strategy cannot be overstated. Poor data can make it difficult to see trends, understand customer needs, and find new opportunities within your portfolio. Without strong, clean data, sales efficiency suffers. Instead, your sales team wastes valuable time chasing down bad leads or missing out on new leads and deals because they don’t have access to all the information they need.

In extreme cases, poor data quality can even result in lost sales. For example, if a customer’s information was inputted incorrectly, you may never be able to reach them. Incorrect data can also lead to frustration and confusion, causing the customer to go elsewhere.  

It can hinder business operations

Your business operations can be negatively affected when your CRM is full of duplicate, inaccurate, and outdated data. Poor data quality will also affect team members across your entire organization. From support and marketing teams to customer service and revenue teams, they all need accurate data to do their jobs effectively. 

Dealing with poor-quality data can lead to errors, frustrations, and inefficiencies across the organization. If you want your business to operate effectively and efficiently, it is important to monitor and rectify any data quality issues in Salesforce.

It can decrease customer satisfaction

When your customers interact with your business, they expect a certain level of professionalism. Whether it’s an email, a phone call, or an in-person meeting, your customers hold you to a standard. 

When your data is of poor quality, it can impact customer satisfaction, leading to errors, confusion, and ultimately damaged client relationships. This can discourage your customers from working with your company. Keeping your data clean and up-to-date can help ensure your customers get the best possible service. 

It can decrease employee productivity

When your data is of poor quality, employees will be forced to spend time cleaning up data and tracking down missing information. This leads to decreased sales productivity and, most likely, frustration for your team members. 

According to a 2024 Affinity survey*, 69% of sales leaders said their team already spends 4+ hours a week updating their CRM. Add to that the time spent cleaning up old records, and it’s easy to see why so many employees give up on their CRM. And when adoption rates drop, data becomes even more siloed and disparate across your organization. 

When you prioritize data quality, you can help ensure your team can work efficiently while increasing productivity and CRM adoption.  

What are the benefits of good data hygiene?

More than half (53%) of the world’s top-performing companies use data to drive revenue. You can get the most out of your data with good data hygiene practices. Many benefits come with keeping your data clean, including:

• A more productive workforce: When your team has all the information and data they need at their fingertips—and that data is up-to-date and accurate—they can spend their time more effectively using that data to close more deals. 

• Less errors in data reporting: When your data is clean, your reporting will be more accurate and result in fewer errors, saving time and money and making the decision-making process more manageable.

• Better decision-making abilities: When data is clean, you have all the information needed to make informed business decisions. 

• Increased customer satisfaction: By cleaning up your data and using good data in your organization, you can deliver the best customer experience.

• Minimizing the impact of mistakes: Mistakes can cause loss of revenue, unsatisfied customers, and even more issues in your organization. When you invest in good data hygiene practices, you can minimize mistakes and their impact on your business.

• More valuable and practical insights from your data: Clean data gives you a clear picture of your organization. You can make better decisions using your data because you’ll have access to deeper insights. With tools that add additional information and context to your data, you can learn even more from it. 

How to conduct a Salesforce data cleanse

If you’re worried there is bad data in your Salesforce environment, it’s worthwhile to undertake a CRM data cleanse with the help of some of your Salesforce users. To get rid of bad data, follow these three steps.

1. Perform a data audit 

To fully understand what you’re dealing with, you’ll need to start with a data audit. You can’t clean your data if you don’t know what to clean. Start by looking at various Salesforce list views to filter for missing or inaccurate data. This will give you a quick overview of any incomplete or incorrect records and allow you to add missing data. 

Once you’ve analyzed your databases, take it one step further and analyze data sources and systems that depend on Salesforce data. This will give you a map of your organization’s data network and a baseline for your data cleansing undertaking.  

2. Find the right data cleansing tools 

If you have a large amount of data, it can be overwhelming to go through it by hand. That’s where data cleansing tools can come in handy. By analyzing your Salesforce data at a granular level, these tools can identify discrepancies, update records, validate data, and pick up on any business rule deviations. 

3. Clean poor data

Now that you’ve identified the bad data in your database, you can undertake the task of getting rid of the poor data. 

Next, search for records with duplicate information and merge them into one record. The process of deduplication, or deduping, requires deleting multiple duplicate records so that there is only one record with all the information in Salesforce. 

Start by removing all irrelevant or duplicated data. The process of deduplication, or deduping, requires deleting multiple duplicate records so that there is only one record with all the information in Salesforce. Next, fix any inconsistencies so you can start maintaining standardization across your data. 

Finally, you’ll need to address all missing or incomplete records by inputting the missing information. You can use tools to help move this process forward in an efficient manner.

Once complete, you’ll have a Salesforce environment full of clean data to help you quickly close more deals. 

Data hygiene best practices to maintain clean data in Salesforce

Now that you’ve cleaned your data in Salesforce, it’s time to set your organization up for success by implementing best practices to help you maintain clean data. Data hygiene best practices can help ensure you always have the best quality data possible in your CRM. 

Here, we share the top five best practices to maintain high-quality data. 

1. Follow a data cleansing schedule 

According to Salesforce, 70% of data deteriorates and becomes inaccurate annually. Data cleansing should be a recurring event that you schedule throughout the year. Think about your business and how much data is introduced each week, month, and year. This will help you determine whether you should be on a week, month, quarter, or annual cleansing schedule. 

When you make data cleansing a part of your business, it won’t be as extensive as your first data audit because you’ll already be working from a clean database. 

2. Implement data validation

Data validation is one way to ensure that your scheduled data cleansing isn’t too arduous. By creating validation rules within Salesforce, you can ensure that only clean data is entered into the CRM. 

Whether it’s the format of phone numbers or the validity of an email service provider, data validation is the filter that keeps your data clean. 

3. Enforce data quality standards 

Data quality standards are another way to ensure the data that enters your CRM is clean. You can define required fields to avoid incomplete records, create drop-down options or auto-populated fields, and create workflow rules and programming dependencies to ensure data stays accurate, up-to-date, and complete. 

4.  Train your sales team on how to effectively input CRM data

Even with excellent data validation and quality standards, it’s essential to train your employees on how to input data effectively in Salesforce. Your team is responsible for maintaining data cleanliness, so it’s important to ensure they have an up-to-date understanding of the system in place. 

5. Automate data entry 

What if your organization didn’t need to rely heavily on your employees for data hygiene? What if you could automate data entry using the places where data often comes from like emails, calendars, and events? 

That’s what you can do with Affinity for Salesforce. With Affinity for Salesforce and activity tracking, you can automate the process of creating, updating, and enriching records, saving more than 200 hours of manual CRM work per user annually. It also helps to lower the risk of introducing manual errors into Salesforce. 

6. Invest in data enrichment tools

Finding effective ways to add more information to your CRM while keeping it clean is a great way to get more from it. With Affinity for Salesforce, sales teams have access to up-to-date biographic, firmographic, and experience data alongside valuable relationship intelligence that help uncover warm introductions to decision-makers, as well as discover and engage with new opportunities.

Relationship paths are displayed alongside deal-relevant Salesforce data, giving you the complete history of engagement with a prospect or client. All these insights are automatically updated and available in Salesforce, so you always have the information you need at your fingertips.

{{a4s-request-demo="/rt-components"}}

Salesforce data hygiene FAQs 

What is data hygiene?

Data hygiene is the ongoing process of keeping data clean. To keep data “clean,” you must ensure that your entire database is accurate, up-to-date, and free of errors. 

How do you maintain data quality in Salesforce?

To maintain data quality in Salesforce, you must clean your data and ensure no “dirty” data enters your CRM.

Here are seven steps you can take to maintain data quality:

1. Set a data cleaning schedule
2. Use data validation to help ensure only clean data enters Salesforce.
3. Enforce data quality standards to ensure only clean data enters Salesforce.
4. Use a data cleaning tool to stay on top of your data.
5. Train your team on how to input data into Salesforce properly.
6. Automate the data entry process.
7. Invest in tools that help you enrich your data.

Why is it important to clean data in Salesforce?

It’s important to clean your Salesforce data because over half of the world’s top-performing companies use data to drive revenue, but each year, 70% of data deteriorates and becomes inaccurate. 

Cleaning your data is the only way to keep your data usable. There are other benefits of keeping your data clean:

• Keep your employees productive. 
• Fewer errors in data reporting.
• Better decision-making abilities.
• Increased customer satisfaction.
• Minimize the impact of mistakes.
• Gain more valuable and useful insights from your data.

* Data from Affinity’s 2024 survey of 250+ business leaders across investment banking, media and communications, real estate, professional services, healthcare, financial services, manufacturing, and enterprise technology.