The Ultimate Salesforce Data Cleanup Checklist Guide

Bad data isn’t just an operational challenge anymore. It’s a revenue problem. In fact, a Gartner report reveals that bad data costs businesses $15 million annually. Adding to the problem, data quality is deteriorating over 2.1% per month, making it less useful for companies.?

Over 150,000 businesses rely on Salesforce to manage their customer data. If you are one among them and struggling to maintain your data quality, here’s a quick and effective checklist to keep your Salesforce data clean and up-to-date at all times.

STEP 1: Data Quality Assessment

• Identify data entry points including manual data inputs, imports, and integrations.
• Perform an initial audit to assess the types of bad data
• Generate data quality reports to identify different data quality issues
• Outline a data cleaning strategy with specific actions, processes, and timelines.

STEP 2: Data Cleanup

Data Standardization

• Review and remove fields that are not being actively used to prevent data clutter and errors.

Data Validation

• Establish a clear naming convention for your rules to maintain organization.
• Use validation rules for essential data components, such as email addresses and mailing addresses.
• Validate email addresses to ensure they are correctly formatted and valid.
• Verify mailing addresses for accuracy and standardization.

Data Deduplication

• Define criteria for identifying duplicate records specific to each object (e.g., leads, contacts, accounts).
• Configure and enable duplicate and matching rules to identify duplicate records
• Define duplicate merging criteria to merge duplicate records.?

Note: Choose the most accurate and complete record as primary and merge others into it to eliminate redundancy.

Data Updation

• Determine what specific data attributes you need to enrich to support your sales and marketing efforts effectively.
• Avoid data hoarding by only enriching records with data that serves a specific, well-defined purpose.
• Focus on data accuracy by verifying the quality and reliability of enriched data.
• Identify and rectify any discrepancies or inaccuracies in the enriched data.

Export Data

• Export the duplicate report to a CSV file, clean up the data, and reimport the updated data.
• Address duplicates and inconsistencies in child objects and progress to parent records.
• Use excel or spreadsheets to automatically reassign orphaned contacts to appropriate parent records.

Third-party Tools

• Evaluate third-party deduplication tools that offer more advanced merging capabilities and flexibility. Here’s a list of third-party tools and applications to enhance your Salesforce data quality:

1. Duplicate Check by Plauti
2. Dedupe by PowerRouter
3. Zoominfo SalesOS
4. Demand Tools by Validity?
5. Cloudingo
6. Datagroomr?

• Establish a routine data cleanup schedule to maintain data accuracy based on your specific needs and preferences
• This schedule can be set to recur monthly, every three months, or on a quarterly basis

STEP 3: Training and Awareness

• Develop training materials, including guides, videos, and presentations.
• Address common data entry errors and how to avoid them.
• Teach users how to implement and adhere to data validation rules.
• Train users on identifying and resolving duplicate records in Salesforce.
• Provide guidance on data cleanup procedures, including merging and updating records.
• Show users how to perform data audits and make necessary corrections.

STEP 4: Dashboards and Reports

Data Quality Audit Reports

• Develop a report summarizing scheduled data quality audits.
• Include audit schedules, findings, and recommended actions.
• Generate a report that evaluates the effectiveness of data cleanup efforts. Measure the reduction in duplicate records and data errors.
• Create a dashboard to analyze data quality trends over time - note the improvements or deteriorations in data quality.
• Build a report that provides a historical view of data audits and corrections - Include details of data audits performed and actions taken.

Data Quality Metrics Reports

• Establish a report to measure key data quality metrics, including data completeness, accuracy, and timeliness - Include charts or visualizations for easy interpretation.
• Track KPIs like data error rates, data completion rates, and adherence to validation rules.

Data Accuracy Reports

• Generate a report that highlights data accuracy issues, such as incorrect values or outdated information.
• Create a dashboard to track data completeness by key fields or objects.
• Develop a report that captures common data entry errors - include fields or objects with high error rates.

STEP 5: Continuous Monitoring and Improvements?

• Conduct periodic data quality audits.
• Implement ongoing training programs for users.
• Establish a feedback mechanism for users to report data quality issues.
• Regularly review and update data validation rules.
• Set up automated alerts for duplicate records.
• Keep documentation and standard operating procedures (SOPs) up-to-date.