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Data Audit 101: Admin Guide to Finding Data Gremlins
Unlock your data audit skills and uncover hidden data chaos with tips and tricks gleaned from real-life (eye-opening) examples of data gone wrong. Learn first steps, take away an audit cheat sheet.
Important: This session is first come, first seated. You do not have a reserved seat.

, Architect/Admin, EKIA

Role: Administrator
Product: Agentforce 360 Platform
Topic: Data
Session Level: Beginner
Session Type: Theater
  • Tuesday, Sep 171:30 PM - 1:50 PM PDT
    (8:30 PM - 8:50 PM GMT)
    Moscone West, L1 Forest, Admin Theater
  • Thursday, Sep 192:00 PM - 2:20 PM PDT
    (9:00 PM - 9:20 PM GMT)
    Moscone West, L1 Forest, Admin Theater
Session Summary by Einstein: The session focused on the importance of conducting data audits in Salesforce to identify and rectify 'data gremlins', which can significantly impact the health of an organization's data ecosystem. By drawing parallels to routine health inspections in restaurants, the speaker highlighted the discovery phase of an audit, where both the 'front of the house'—processes and reports—and the 'back of the house'—objects and fields—are scrutinized. The narrative illustrated the perils of duplicate disorder through a case study, emphasizing the cascading effects of bad data on automation, reporting, and ultimately, business operations. The methodology shared included ranking objects by priority, refining criteria for identifying duplicates, and creating exception reports and dashboards for ongoing monitoring to prevent and remedy data issues.
Key Takeaways by Einstein:

• The 'duplicate disorder' case study underscored the potential financial risks of neglecting data quality, as duplications could lead to erroneous transactions.

• Effective data audits require a methodology that includes ranking data objects by priority, reevaluating criteria to identify data gremlins, and using exception reports and dashboards for continuous monitoring.

• The session provided practical strategies for Salesforce administrators to ensure data integrity, which is critical for reliable automation triggers and accurate reporting.

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