Skip to main content

Did you know that 40% of admins spend an average of 4+ hours cleaning their CRM data? And nearly 60% do this once per month while 30% do it once per week?

As a revenue operations leader managing a backlog of support tickets and developing tasks that will pioneer your Salesforce org forward, I’m sure you can think of better ways to use your Salesforce admins’ time. 

That’s where data standardization in Salesforce can be extremely helpful. Data standardization is the process of uniformly formatting existing data in a way that can be used by all departments across all critical business tools. 

Let’s take a closer look at why data standardization is important, four steps for implementing it into your Salesforce, and examples of fields where you can easily standardize data within your org. 

The Importance of Data Standardization

Proactively standardizing data in Salesforce is so important because it helps nurture and manage future growth. In fact, companies lose 12% of their potential revenue on average due to bad data standardization. 

What reasons contribute to that percentage of revenue loss? Here are just a few:

  1. Misaligned Teams: Not implementing data standardization in Salesforce can potentially cause confusion among everyone across your GTM team. This makes mistakes within your Salesforce Org more likely to occur, consequently creating more frequent and severe downstream errors that can stop your team in their tracks.
  2. Wasted Resources: If your team is looking at the wrong data, then they’re making misinformed decisions across the GTM team, which can ultimately lead to wasted time and dollars.
  3. Poor Customer Service: A lack of data standardization could eventually affect external communications, creating a poor client experience. This can lead to questionable credibility that damages current and future business with potential and return clients. Unfortunately, it  only takes one big mistake. Studies show that 32% of customers will stop doing business with a brand they love after just one bad experience.
  4. Increased Tech Debt: While developing a data standardization process for your Salesforce org does require time and implementation, the cost of not doing it will ultimately be paid in tech debt. Knowing the warning signs of tech debt hurting your organization is critical, but preventing it through data standardization in the first place is even more important. 

The bottom line is,  if you don’t carve out the time to build out a data standardization plan for your Salesforce org, the result will be countless hours in wasted time and decreased quality in client communications. This will not only cut deeply into profit… It will make your job as a RevOps leader a lot more difficult in the long run.

How To Standardize Data

Standardizing data the first time is tedious, but it is a key part of keeping everyone in your Salesforce org on the same page. Companies that are well aligned and on the same page grow revenue 58% faster and are 72% more profitable, so it is without a doubt worth it! In this section we are going to walk you through the step by step process for standardizing your data. 

1: Identifying the required data & clean it

Before you can begin the data standardization process, you will want to create transparency across your organizational teams by discovering how data is being collected, where it’s being stored and what data is available.

To do this, you’ll want to follow these steps:

Step 1: Identify who relies on your Salesforce data:
Start by touching base with the head of each department using Salesforce and see how and where each department is using data. From there, develop a data dictionary that will serve as a common repository that will provide your team a host of benefits– some of which include aligning teams on metadata terms, increasing visibility across your teams to avoid data errors or worse, tech debt, and so much more. 

Step 2: Map out where data is coming from:

Using a data dictionary, you can keep track of data entry points from all third party integrations and managed packages. Take note of common input formats in order to better understand common inbound data formats from the start, and then go from there.

Step 3: Clean and consistently format the data:
As you analyze your data, you will notice that user entry can be inconsistent. So you will want to note the commonalities and clean the data.

Also as you sift through the data, you will more than likely run into other issues, so this is a good time to be on the lookout for any other errors or inconsistencies in raw data that are slowing down your processes. Set aside some time in this first step to make sure raw data is properly formatted, correct, and verified.

2: Implement your change management strategy

Now that you have taken some time to take a closer look at inbound data formats and clean your data in Salesforce, it’s time to prioritize what needs to be addressed and build a change management strategy to effectively build and manage field changes.

The easiest way to pinpoint and prioritize your most important data standardization issues without a third party software is to evaluate the impact of each field change on the organization.

Start by make a prioritization chart such as the one in the example below: 


Downstream impact of data quality issue Priority level
Revenue loss High
Break in daily functions/business process Moderate
Data interpretation Moderate
Lack of productivity  Moderate
Tech adoption resistance High

Think through the overall downstream impact of your current data quality issues. Rank the priorities of particular field changes based on the outcome they will deliver from highest priority to lowest priority. 

For example, if adding a picklist to a particular field could improve revenue– it should be prioritized over a field that may be causing slight production loss. While both are important, your hours are limited and this is the key to prioritizing your admins time.

3: Automate standardization where you can

The best way to keep your Salesfroce org clean is to keep bad data from coming into the picture in the first place. This means putting tools in place to automatically validate, format and cleanse data as it enters your CRM. 

Here are a few recommended tools to add to your revenue operations tech stack: 

Data Standardization Tool Description
DemandTools Fully automate your data standardization strategy such as setting quick and uniform specified values and a large library of data transformation functions for standardization. 
Insycle A customizable tool that enables small to medium sized Salesforce Orgs to automate data formatting, remove duplicates, and more.
RingLead (now Zoominfo) Streamlines routing, data deduplication and compliance management on a large scale in a centralized platform.
Sonar Gives your admins a holistic view of metadata and where it’s being used across your entire org, allowing them to make break-free changes regularly and confidently.

4: Run regular maintenance checks

Preparing to standardize your plan into action and implementing your strategy is just the beginning in terms of keeping your Salesforce org data clean. It’s important to remember that while data standardization tools will make your life a whole lot easier, ongoing maintenance will be required, including regular Salesforce clean up projects and data dictionary updates.

Examples of Data Standardization

Inconsistent data formatting is just a product of having a CRM. Prospects will inevitably fill out forms with lowercase names. And GTM team members might add phone numbers into text fields without spaces while some add them with spaces. While it’s the name of the game, it can also make your revenue operations team’s life more difficult when integrating data or building reports.

Some examples of fields that could benefit from data standardization include:

  • First names: Should be capitalized without any spaces or extra characters.
  • Phone numbers: Should follow a consistent format (Examples: 1-281-0065 or 1-(281)0065
  • States: Should also always follow a consistent format (Example: Georgia or GA).
  • All emails should be in a standard format (Example:
  • URLs: Should always include the full address (Example: “”, not “” or “”)   
  • Job titles: Should be spelled out abbreviated consistently in each field. (Examples: Chief Technology Officer or CTO, V.P. of Sales or Vice President of Sales)

Learning to spot these data quality issues and set up processes to avoid them in the future will be key to enhancing your revenue operations program.

Conclusion: Streamline Your Data Standardization Success with Sonar

While data standardization does require a strategy and time to implement, it will save your company in both time and dollars in the long run. By implementing the right tools, not only can you automate a lot of the data standardization process, but you can empower your RevOps teams to make metadata changes confidently. 

Using a software like Sonar will help you keep track of changes, sort customer data, and keep that data dictionary up-to-date. Sonar delivers the Ops toolkit you need to make Salesforce changes with precision, see accurate forecasts for the impact of manual changes in advance, and allows you to spend less time troubleshooting and more time delivering high-growth projects. Sounds too good to be true? We get it… seeing is believing. Try Sonar free today.