Salesforce Data Cleansing Best Practices That you Should Consider Implementing Now
Salesforce is a powerful tool that can help your business grow, but it can only reach its full potential when it’s populated with good quality data. As a content management expert, it’s not uncommon for people to tell me that their Salesforce data isn’t quite where they want it to be. If you’re in the same boat, this article is for you. I’ll share information on Salesforce data cleansing best practices that you can adopt to prevent data quality issues occurring and how you can go about fixing the existing quality-related issues that have found their way to your CRM before prevention measures were in place.
The Cost of Bad Quality Data
It’s not unusual for business leaders to underestimate the true cost of bad data for their business. According to Gartner’s Data Quality Market Survey, bad data is really hitting businesses where it hurts, their pockets – to the tune of $15 million on average per year. And setting aside the financial ramifications, the reality is that bad data kills data-driven initiatives. While you may have a substantial amount of data in Salesforce, your data may lack the quality standards required to achieve actionable insights.
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Common Types of Bad Quality Salesforce Data
You’ve heard about bad quality Salesforce data, but what is it and why is it bad for your business?
| Type | Examples | Why it’s Bad |
| Duplicate Records | Multiple lead records for the same contact | Your sales team will not have a centralized view of the prospect, allowing them to carry out any due diligence prior to reaching out. |
| Consistency | Records lack the use of capitalization in the First Name field | For marketing, lack of proper consistency in records, specifically first name, can hamper adding any personalization to email campaigns. Apart from that, sending personalized emails where names are not formatted properly just looks bad. |
| Completeness | Missing field data | Another one for marketing: Missing information prevents marketing teams from segmenting contacts for email campaigns. When your org is hosting an event in a particular territory, let’s say Japan, for example, where do you think they pull the data from? That’s right, Salesforce. |
| Junk Records | Spam email addresses | Junk records are unnecessary clutter in your org that find their way to your marketing automation system and skew open rates for email campaigns. Oh, and they take up precious storage space in your org. |
How to Find and Fix Bad Salesforce Data
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Identify the Source
There are many effective cleansing techniques that you can use to declutter and organize your data to achieve a closed reporting loop for your organization. However, before you can fix the problem, you need to understand how it occurred in the first place so that you can take steps to prevent it happening again.
In my experience, typical cause/s will fall into one of the categories below:
- Poor manual data entry
- Botched imports
- Third-party app integrations
How to Identify the Source/s
To sidestep identifying the source/s manually, consider using reports to isolate bad data. Not only will it provide you with immediate intel on the cause/s, but it can also be used to keep tabs on the health of your records. Take a look at the examples below for inspiration on the types of reports you might want to create.
- Consistency: Look at the variations used in fields such as date, state, and country fields. How many variations are used in fields where there should be single values?
- Completeness: Look at records that are missing values in key fields.
- Junk records: Record data in fields such as first name, last, name, email address for bogus values like [email protected] and [email protected] etc.
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Act: How to Fix Salesforce Data Quality Issues and Prevent Reoccurrence
Armed with the intel on the types of bad data your organization is up against, now you’ll want to look at ways of fixing the records affected by the issues and put measures in place to prevent restarting the cycle.
Prevent Future Missing Field Values and Inconsistent Field Values
It’s at this stage that you should assess the fields the types that your organization uses. By type, I mean: Number, formula, date, etc., Believe it or not, I still see some companies that have used text fields to house dates. Can any of your field types be changed to limit the control users have over the data that can be entered into the field? A practical example would be to utilize picklists for fields that will only ever be populated with a value from a specific set of options.
Going further, you may want to make the fields that house essential information, required fields. In doing so, users will not be able to save records until they have populated all of the required fields. It’s worthwhile noting that while this is an effective solution, it’s only useful for fields that should always have a value, ‘company’ for example. In cases where populating a field is only required when another field contains a certain value, you’ll want to look at implementing validation rules. Validation rules allow you to define standards for your records based on logic.
How to fix the historical missing and inconsistent field values
If you don’t use Salesforce’s Dataloader, now would be a good time to start. It’s one of the free tools that I use regularly to populate missing field data and append field values to address inconsistent data.
One thing to note about the Dataloader is that it’s not what you’d class as a ‘quick fix’, as you need to append the record data in a CSV file prior to using the tool to mass update field values. That said, while it’s time-consuming, it’s definitely the fastest way to mass populate and append Salesforce record values.
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Find and Merge Duplicate Salesforce Records
Salesforce comes with some in-built duplicate management features that you can leverage to detect duplicates.
Potential Duplicates Component (Lightning Experience): Add this component to page layouts so that users can see potential duplicates at a record level and merge.
Merge Contacts Button (Classic Experience): Unlike the potential duplicate component in Lightning Experience, this button comes out of the box and allows users to find and merge duplicate records that are attached to the account.
Matching Rules and Duplicate Jobs: Set up rules to define what your organization would class as a duplicate record then run a duplicate job to assess your data versus the criteria outlined in the matching rules.
Final Thoughts
All in all, Salesforce provides some pretty robust features to manage data quality. However, if your organization is a bit late to the data quality party, it’s extremely likely that you’ve got a bad data and/or duplicate epidemic already in play. In such cases, specifically for duplicate record pandemics, I’d encourage you to take a look at the third-party Salesforce Data Cleansing options to help speed up the initial clean-up exercise. Take it from me, merging duplicates records natively in Salesforce is a drag (sorry, Salesforce!)
