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Common Salesforce Data Quality Issues

1 day ago2 min read

Introduction

When organizations think about data quality issues in Salesforce, duplicate records often come to mind. However, various other data issues can affect Salesforce’s data quality, which is frequently overlooked. This article will discuss common data quality issues and how ActivePrime can help your organization identify these issues effectively.


Common Data Issues in Salesforce

  1. Duplicate Records

Duplicate records, whether in standard or custom Salesforce objects, can affect data quality significantly. Key issues caused by duplicates include:

  • Sales pipeline and forecast inaccuracies that may mislead decision-makers.

  • Inconsistent communications, potentially lead to customer dissatisfaction.

  • Over-contacting or under-contacting prospects and customers, reducing marketing effectiveness.

  • Integration errors with other systems and issues in automated processes.

  • Increased storage costs due to high data volumes and degraded system performance.

  • Potential GDPR compliance issues, such as managing opt-outs, data deletions, and customer updates become challenging.


  1. Formatting Issues

Inconsistent data formats compromise data accuracy and reliability in Salesforce, especially in text fields like names, addresses, and phone numbers. Examples include:

  • Irregular capitalization or abbreviation usage, which complicates reporting and analysis (e.g., “New York” vs. “NY”, “New York” vs “NEW YORK”).

  • Improper special characters or incorrect field lengths can cause integration and process errors with external systems.


  1. Missing Data

Missing data refers to the absence of crucial information within Salesforce, such as contact details, addresses, demographics, or preferences, and can impact customer segmentation, reporting, and targeted marketing efforts. A key part of ActivePrime’s method of identifying missing data is to capture what is visually missing. Most people may add in filtering for NULL, but what about fields with spaces or tabs?


  1. Incorrect Data

Incorrect data involves inaccuracies in Salesforce data such as emails, phone, and addresses and can be very problematic when trying to get in touch with someone.  Examples include:

  • The format of all address fields is correct but the address does not exist.

  • The format of an email address is correct but the domain of the email address does not exist.

  • The phone number looks right, but it isn’t associated with a carrier.


  1. Damaged Data

Damaged data includes corrupted or compromised information like unreadable phone numbers (e.g., 8⃞0-555-1212).  This can affect system stability and data integrity and pose potential security risks.


  1. Junk Data

Junk data encompasses irrelevant, outdated, or unnecessary information within Salesforce.  Examples include:

  • Entries like “Test” or “Mickey Mouse” which are non-actionable.

  • Junk data clutters the system, hindering accurate reporting and efficient analysis.


  1. Misplaced Data

Misplaced data refers to information stored in incorrect fields (e.g., a URL in the Phone Number field), which can result in difficulties in data retrieval, and inaccurate analysis can lead to erroneous reporting and flawed decision-making. Misplaced data can have a negative impact on downstream systems as well.



How Can ActivePrime Help?

Your organization can start with a Data Quality Assessment to gain insights into your Salesforce data across the data issue dimensions mentioned above. The next step is deploying ActivePrime CleanData to resolve the data quality issues. CleanData provides a robust approach to ensure that Salesforce data remains accurate, reliable, and optimized for efficient use.


Request your Data Quality Assessment today from ActivePrime or request one with your Salesforce Account Executive.

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