Salesforce teams are facing new challenges as AI-driven automation like Agentforce becomes more integrated into their CRM strategy. Data inconsistencies, unclear field definitions, and security risks can lead to poor AI recommendations, compliance issues, and operational inefficiencies. Without a clear understanding of how data is structured, classified, and used, AI models like Agentforce may pull from duplicate, inaccurate, or sensitive fields, leading to unreliable results and potential data exposure.

A real-time data dictionary solves these problems by providing centralized visibility, automated tracking, and governance over your Salesforce metadata. It ensures teams can confidently manage data access, security, and AI readiness without relying on outdated spreadsheets or manual documentation.

This blog is a recap of our recent webinar, “Data Dictionary in the Agentforce Era,” where Brian Olearczyk and Brad Smith explored the critical role of a data dictionary in managing Salesforce data and AI readiness. Watch the full webinar here to dive deeper into the discussion.

What Is a Data Dictionary?

A data dictionary is a structured repository that catalogs all data fields, objects, and relationships within Salesforce. It provides technical and non-technical teams with a single source of truth for understanding and governing data.

Key Functions of a Data Dictionary

• Stores metadata for Salesforce objects, fields, and relationships
• Tracks changes to Salesforce configurations and data structures
• Provides business and security context for data usage
• Enables collaboration between RevOps, IT, security, and business teams

Historically, software engineers have used tools like Alation, Collibra, or Secoda to maintain structured data documentation. However, Salesforce admins, RevOps teams, and IT leaders require a solution tailored to the unique challenges of CRM data.

Sonar’s data dictionary is designed specifically for Salesforce users, offering real-time visibility, automated change tracking, and clear ownership of data fields.

Why a Data Dictionary Matters in the Agentforce Era

Agentforce represents Salesforce’s AI-driven automation framework, enabling AI-powered workflows, intelligent prompts, and automated decision-making. However, AI is only as effective as the quality of the data it processes.

Challenges Without a Data Dictionary

1. Data Inconsistencies – AI models may pull from inaccurate or duplicate fields, leading to unreliable outputs.
2. Security & Compliance Risks – Sensitive data could be exposed to AI-driven tools without proper governance.
3. Inefficient AI Workflows – Poorly documented data may lead to irrelevant or misleading AI recommendations.
4. Operational Silos – Different teams may use conflicting data definitions, reducing cross-team collaboration.

A well-maintained data dictionary ensures that AI tools, including Agentforce, receive structured and validated data inputs, minimizing errors and maximizing efficiency– not to mention provide numerous other benefits to your organization. 

The Risks of Running Agentforce Without a Data Dictionary

As businesses embrace AI-powered automation with Agentforce, the importance of structured, well-documented data cannot be overstated. There’s numerous reasons your organization needs a data dictionary— and without a centralized one, your organization risks introducing inefficiencies, compliance gaps, and misaligned automation. AI models depend on clean, well-defined data to function effectively, and any inconsistencies or security oversights can lead to unintended consequences.

Brad Smith outlined several key pitfalls of implementing Agentforce without a structured data dictionary:

1. Unintended Data Exposures

• AI models could pull from incorrect or non-compliant fields, increasing security risks.
• Lack of documentation may lead to exposing personally identifiable information (PII) or sensitive business data.

2. Data Duplication and Inconsistencies

• Many Salesforce orgs contain duplicate fields (e.g., “Annual Revenue” vs. “ARR”).
• Without proper field definitions, AI models may use conflicting or outdated information.

3. Lack of Cross-Team Understanding

• Different departments (RevOps, Sales, IT, Security) require different levels of data context.
• Without a centralized data dictionary, teams may misinterpret key data fields.

4. Difficult Troubleshooting and Auditing

• AI-driven processes can fail due to missing or misconfigured fields.
• Diagnosing issues becomes significantly harder without a documented data lineage.

What Makes a Great Salesforce Data Dictionary?

Not all data dictionaries are created equal. Simply cataloging fields in a spreadsheet is not enough—especially in an era where AI-driven automation like Agentforce relies on structured, high-quality data. A great data dictionary should do more than just document fields; it should provide real-time updates, ensure data integrity, enhance collaboration across teams, and align with security and compliance standards.

The most effective data dictionaries share these key characteristics:

1. Real-Time Updates

• A dynamic data dictionary automatically syncs with Salesforce metadata.
• Avoids reliance on outdated spreadsheets.

2. Persona-Based Views

• Different teams need different levels of detail.
• Custom views for Finance, Security, Admins, and RevOps improve efficiency.

3. Data Sensitivity & Compliance

• Classifies data based on risk level, ownership, and compliance requirements.
• Ensures alignment with GDPR, CCPA, SOC 2, and other regulations.

4. AI & Automation Insights

• Tracks how Agentforce and AI-driven tools interact with Salesforce data.
• Helps businesses define which fields should or should not be processed by AI.

5. Easy Accessibility

• Embeds directly into Slack, Confluence, Notion, or other collaboration platforms.
• Reduces dependency on manual data requests.

6. Change Tracking & Audit Logs

• Provides a historical record of data changes for governance and security audits.
• Helps teams diagnose errors, anomalies, and unauthorized changes.

How Sonar Automates the Data Dictionary

Maintaining a manual data dictionary in spreadsheets is time-consuming and prone to errors. Sonar automates the process by:

1. Syncing Salesforce metadata in real time to ensure accuracy.
2. Tracking field usage across flows, reports, and integrations for visibility.
3. Identifying risks and potential compliance violations before they occur.
4. Providing AI-driven insights into data relationships and governance needs.

Case Study: How 6Sense Transformed Its Data Dictionary with Sonar

Before implementing Sonar, 6Sense relied on a manual, spreadsheet-based data dictionary, requiring four team members to spend 20+ hours per month tracking Salesforce metadata updates. This process was not only time-consuming but also prone to human error, causing inefficiencies, data inconsistencies, and limited visibility into their Org’s metadata.

By adopting Sonar’s automated Data Dictionary, 6Sense eliminated manual effort, gained a holistic view of metadata ownership and changes, and ensured better compliance with sensitive data and PII regulations. With Sonar, their team can now track all objects and fields in real time, reduce operational overhead, and focus on higher-impact initiatives.

For the full case study, read more here.

Next Steps: Future-Proof Your Salesforce Data Strategy

As Salesforce continues to evolve with AI-driven automation like Agentforce, the need for structured, well-governed data has never been greater. A dynamic, real-time data dictionary is beyond a best practice– it’s a necessity for ensuring data integrity, optimizing AI performance, and maintaining compliance. Without it, organizations risk data inconsistencies, security vulnerabilities, and operational inefficiencies that can slow down business growth.

To stay ahead, businesses must take a proactive approach to Salesforce data governance. Here’s how to get started:

1. Start Documenting Your Data – Establish a structured data dictionary to improve AI readiness.
2. Align Data Governance with AI Initiatives – Define which fields Agentforce can access and which should remain restricted.
3. Leverage Automation – Replace static spreadsheets with a real-time data dictionary solution.
4. Educate Your Teams – Provide training on data governance best practices and AI-driven automation.
5. Engage with the Community – Join Wizard of Ops and Salesforce user groups to stay ahead of best practices.

Conclusion: Strengthen Your Salesforce Strategy with a Dynamic Data Dictionary

As Salesforce and Agentforce evolve, maintaining structured, secure, and AI-ready data is more important than ever. Watch the full webinar to explore how a real-time data dictionary helps prevent data inconsistencies, compliance risks, and inefficiencies that can slow business growth.

With Sonar’s automated Data Dictionary, you can:

• Ensure AI models use accurate and compliant data
• Reduce operational risks and inefficiencies
• Improve visibility and collaboration across teams

Request a demo to see how Sonar simplifies Salesforce security, governance, and automation.