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Guide

Stop Default Values from Sabotaging Salesforce Data

Salesforce Data Quality Guide

Default values offer convenience and help streamline data entry for your users. However, default values can also inadvertently discourage users from entering accurate data. This skews analytics, creates a distorted picture of reality, and results in faulty decision-making. When the duplicate value primarily signifies inaccurate data, it’s time for cleanup.

Are Default Values Deceiving You?

Excessive reliance on default values can exaggerate data trends, magnifying problems and complicating solutions. Consider these scenarios:

  • After a new product launch, the majority of cases are categorized as “high” severity. Is there a major issue with the new product or is the support team overlooking the Priority field?
  • Customers who return an order overwhelmingly cite late delivery as the reason, but delivery records show on-time delivery. Do you need to look for a new shipping vendor or update your return form with a notice “Your shipment appears on time” to encourage more accurate data entry?
  • Based on registration records, all event attendees are gluten-free. Should you request a 10% budget increase for special meals, or is it possible that a default value has corrupted the data?

Identify Fields with Questionable Default Usage

Cuneiform for CRM’s statistical analysis can quickly separate fields with legitimate default value usage from those with potential abuse. To quickly zero in on the most actionable list of fields, look beyond the list of fields with a default value specified. Instead, seek to understand default value usage relative to other distinct values.

Focus on Disproportionate Default Value Usage

In a field with healthy usage, you should expect a distinct value distribution representative of your business process. For example, if 70% of your customers are based in the United States, you can expect approximately 70% of your Billing Country and Phone Country Code fields to also include a US-based value. In other cases, you may expect a more uniform distribution of distinct values.

As a general guideline, start with fields where the default value appears 3-5 times more frequently than the average of other options in that field.

Identify overused default values in Salesforce fields.
Use field value distribution to identify excessive use of default values in Salesforce fields.

Prioritize Field Cleanup Based on Data Quality Impact

While many field types can have default values, not all have the same impact on improving data quality. You will realize the greatest improvements by focusing on picklist values and string fields. Likewise, prioritize fields used in automation or reporting first. Inaccurate data in these fields can cause glitches in your automated processes and generate misleading reports.

As with identifying unused fields, it’s important to analyze default value usage within the context of the business process. Comparing default usage in different business scenarios or with different user groups will unearth behavioral differences impacting data quality. For example, if one team consistently generates the most records with default values, it might be time for retraining.

Cuneiform for CRM’s profiling insights makes it easy to report on fields with default values and relative value frequency. You can also quickly see any reporting or automation dependencies.

How to Clean Up Salesforce Fields with Excessive Default Value Usage

A one-time data cleanup will be necessary. But it’s even more critical to prevent the ongoing collection of bad data in Salesforce. Prioritizing this will resolve data capture issues and reveal essential business logic to streamline the batch cleanup.

Start by engaging your data owners and data stewards to understand what constitutes correct usage of the default value. Are the purpose and data standards for the field(s) clear? How can they tell if the default value is correct for a given record? Take note of what other fields on the record the data stewards evaluate to decide correctness.

Next, conduct a spot-check of random records to look for patterns on proper and invalid use of the default value. Based on the record’s information and related records determine if the default value is appropriate and accurate. These insights will guide what system changes to make to improve data quality.

Stop the Ongoing Collection of Bad Data

To prevent the continued use of incorrect default values, refine the data capture experience of the field. Here are options to improve data quality:

  • Replace Default Values with Formula Fields: If you can consistently determine the correct value from the record’s information, convert the field to a new formula field. This will ensure complete and accurate data for reporting as the values will be populated consistently and automatically.
  • Use Validation Rules or Flows to Improve Data Quality: Consider a validation rule or flow to prevent users from saving invalid data. For example, you have a Purchase Order Required field with a default value of “no” because historically, customers haven’t needed one to make purchases. However, as the company starts serving more enterprise clients, missing POs have caused unexpected sales delays. Implementing a validation rule that prompts sales reps to verify if an enterprise customer will require a purchase order before advancing to the negotiation stage can streamline the sales process and improve data accuracy.
  • Remove the Default Value: If neither of the above options is feasible, consider turning off the default value. Depending on your business process a null value may be permissible and more accurate than leaving the default value.

Clean Up Incorrect Default Values

Finally, complete a one-time cleanup of historical data. By combining insights from your spot-checks with the logic from refining the data capture you can build a report to pinpoint all incorrect records. You will likely be able to update some fields in bulk, while others may have exceptions or need to be resolved record by record. Regardless, make sure you have a backup of the original data.

Collaborate with your data stewards to decide on the course of action when it’s unclear what the correct value should be. For customer records, put in place a practice where you encourage data verification or correction by your end users who are more likely to know the right answer.


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