Understanding and Troubleshooting Data Quality Concerns

Overview

From time to time, customers raise questions about their contact data quality. Because data flows through multiple systems—discovery tools, enrichment processes, and CRM configurations—these questions often require investigation across the entire workflow.

This article outlines a structured approach to troubleshooting data quality concerns, explains why gathering examples is essential, and highlights common scenarios where perceived data issues stem from downstream processes rather than the 6sense data itself.

Why examples are critical

Data quality or integrity questions cannot be investigated without specific examples.

High-level feedback such as “the data isn’t reliable” or “contacts don’t look correct” does not provide enough context to identify a root cause. In many cases, investigation reveals that:

• Data is accurate in 6sense but altered after syncing

• CRM rules or permissions restrict enrichment updates

• Existing contact data takes precedence over new data

• Field mappings or automations modify values

Concrete examples allow us to trace data from discovery → enrichment → CRM, which is essential for accurate troubleshooting.

Step 1: Clarify the data quality concern

Start by aligning on expectations.

Questions to consider:

• What does “data quality” mean in this case?

  • Inaccurate?

  • Incomplete?

  • Outdated?

  • Not usable for your workflows?

• Which expectations are not being met today?

• What would a successful outcome look like?

Data quality is best evaluated relative to a specific use case or action.

Step 2: Identify the most important attributes

Determine which fields matter most for your teams.

Common examples include:

• Phone numbers

• Email addresses

• Job titles

• Departments

• Company or location data

Questions to consider:

• Which attributes are required for your team to take action?

• Are these fields used in routing, automation, or outreach?

Step 3: Understand who is experiencing the issue

Clarify which teams are raising concerns:

• Sales

• Marketing

• SDRs

• Operations

Questions to consider:

• Is the feedback coming primarily from Sales, Marketing, or both?

• Do different teams have different expectations for the same data?

This context often helps distinguish data issues from workflow or usability issues.

Step 4: Trace how contacts are introduced

Understanding contact origin is key to troubleshooting data integrity.

Questions to consider:

• How are contacts being discovered?

  • 6sense Chrome Extension

  • Other 6sense workflows

  • Third-party tools

• Are the contacts:

  • Net-new?

  • Existing Salesforce contacts being enriched?

• How are contacts added to Salesforce?

  • Automatically

  • Manually by reps

Step 6: Confirm the source of truth

Once a contact exists in Salesforce or another CRM, ownership matters.

Questions to consider:

• Which system is the source of truth post-creation?

• Is enrichment allowed to update existing fields?

• If a field already has a value in Salesforce:

  • Can it be overwritten?

  • Or is updating restricted?

Step 7: Review CRM rules and controls

CRM configurations frequently explain data discrepancies.

Ask about:

• Validation rules

• Deduplication logic

• Field-level permissions

• Enrichment locks or vendor restrictions

Examples:

• Are vendors allowed to update phone or email fields?

• Are non-empty fields protected from updates?

• Have there been cases where data looks correct in 6sense but different in Salesforce?

Note:  You might want to consider adding 6sense specific fields, depending on your current CRM rules. For example, a “contact phone number” field might have a secondary “6sense contact phone number” field.

Step 8: Compare data across systems

Using real examples, compare:

• The contact record in 6sense

• The record as created in Salesforce

• Any changes made after creation

This often surfaces:

• Blocked updates

• Overwritten values

• Permission or mapping issues

Common causes of data quality concerns

• CRM rules preventing enrichment updates

• Existing contact data overriding new enrichment

• Manual edits introducing variation

• Field mappings that exclude certain attributes

• Misalignment on post-sync ownership

Within Sales Intelligence, you will likely see some Contacts to Reach recommendations when reviewing an account. These are based on your existing CRM records, not contact data from 6sense. If the contact details shown in this section feel inaccurate, then the issue is typically with the underlying CRM record.

6sense can support improvements through an enrichment workflow.

What to share with 6sense when raising data quality questions

To enable faster resolution, please provide:

• 3–5 specific examples

• The field(s) in question

• Where discrepancies are observed

• Whether contacts are net-new or pre-existing

• Screenshots or timestamps if available