Your Guide to AI-Ready Salesforce Data: 10 Steps Every Company Should Take

Imagine a world where your company’s data is pristine, every decision is guided by accurate, actionable insights, and your team operates with AI-ready data at their fingertips. Achieving high data quality in your CRM is no longer optional; it’s essential. High-quality data lays the groundwork for successful AI implementations, like Agentforce, offering a competitive edge and actionable insights that can shape your company’s future.

Here are ten concrete steps you can take now to prepare your Salesforce data for AI, boosting reliability, compliance, and accessibility.

1. Establish a Data Governance Framework with Salesforce Data Cloud

Effective data governance ensures data is well-managed and uniformly controlled, which is critical for quality. A clear framework sets data policies, assigns roles, and creates accountability across departments.

Salesforce Data Cloud can serve as a central hub for your data governance efforts, bringing together data from multiple sources into a unified, compliant platform (more on this later). Assign data stewards within Salesforce to monitor and enforce data quality standards.

• Action Items:
  • Appoint data stewards for each department to monitor and enforce data standards.
  • Define policies for data access, privacy, and quality that all team members can follow.
  • Form a data governance committee that meets regularly to assess data policies and implement updates.
  • Establish guidelines on data usage, modification, and sharing to prevent inconsistencies and data silos.

2. Conduct Data Audits and Assessments with Salesforce Tools

A solid data assessment provides a baseline for data quality, ensuring only high-quality data enters your AI systems.

Salesforce provides tools such as Salesforce Shield and Einstein Analytics for in-depth data auditing and compliance monitoring. Regular audits within Salesforce identify duplicates, inaccuracies, and gaps, giving you a real-time assessment of data health.

• Action Items:
  • Schedule quarterly data audits to measure the accuracy, completeness, and compliance of your Salesforce data.
  • Use data profiling tools to analyze data sets for outliers, duplicates, or missing values.
  • Track audit results and create a remediation plan for recurring issues.
  • Prioritize key data fields for auditing to enhance areas that have the most impact on AI outcomes.

3. Standardize Data Entry Protocols using Salesforce Flow

Standardized data entry reduces human error, ensuring consistency and reliability for AI analysis. Clear guidelines for data input reduce variability in fields like names, dates, and addresses.

Salesforce Flow enables you to create customized, automated workflows that enforce data entry standards across teams. Predefined formats for fields like dates and addresses ensure that your team collects consistent, AI-ready data from day one.

• Action Items:
  • Set standard formats for commonly used data types in your Salesforce org (e.g., dates, addresses).
  • Integrate dropdown lists, checkboxes, and validation to streamline and guide data entry.
  • Train employees on data standards to emphasize accuracy and consistency.
  • Regularly review data entry guidelines and adjust based on frequent errors or feedback.

4. Automate Data Cleansing

Data cleansing tools automate the identification and resolution of inaccurate or duplicate records, preserving data quality at scale. This automation enhances data integrity and frees up valuable time for your team.

You can consult Sikich data experts or use Salesforce AppExchange to find data cleansing applications that integrate with Salesforce.

• Action Items:
  • Choose a data cleansing tool compatible with Salesforce.
  • Set regular cleansing schedules to remove duplicates or outdated entries.
  • Use data-cleansing automation to check for consistency across fields.
  • Implement rules for merging duplicate records to maintain data relevance and accuracy.

5. Achieve Real-Time Data Validation through Salesforce Validation Rules

Implement validation rules in Salesforce to check data in real-time as it’s entered. For instance, enforce correct email formats or address fields to catch errors instantly and ensure only high-quality data enters the system.

• Action Items:
  • Set validation rules for key fields (e.g., email formats, phone numbers).
  • Automate real-time validation to flag incomplete or inconsistent entries.
  • Provide instant feedback to users to address errors immediately.
  • Review and update validation rules regularly to meet evolving business needs.

6. Centralize Data Sources with Salesforce Data Cloud

A centralized data repository eliminates data silos and enables a single source of truth, ensuring consistent, reliable data.

Salesforce Data Cloud consolidates data from multiple departments and third-party sources, providing AI models like Einstein and Agentforce a comprehensive view for generating accurate insights.

• Action Items:
  • Integrate disparate data sources into Salesforce Data Cloud.
  • Use integration tools to ensure continuous data flow to Data Cloud.
  • Implement a single-source-of-truth policy that dictates data access and storage.
  • Regularly update and document integration processes to support seamless data flow.

7. Enhance Metadata Management within Salesforce

Metadata provides essential context for data, making it more interpretable by users and AI systems. Robust metadata management increases data traceability and transparency, which supports AI-driven insights.

Salesforce’s built-in metadata management tools let you document data attributes such as origin, format, and owner within the platform. This improves data traceability and enhances AI models’ understanding of the data’s context and relevance.

• Action Items:
  • Establish a clear metadata schema for your data assets, covering origin, format, and usage.
  • Utilize Salesforce’s built-in metadata management tools to organize and centralize metadata.
  • Assign metadata owners to maintain accuracy and consistency across data sets.
  • Review and update metadata to reflect changes in data sources or structure.

8. Prioritize Data Security and Compliance using Salesforce Shield

Data quality includes safeguarding data against breaches, which is especially critical for sensitive information. Adherence to privacy and security standards (such as GDPR, CCPA) is essential to AI trustworthiness.

Salesforce Shield offers encryption, field audit trails, and access control features, making it easier to meet data compliance requirements and protect sensitive data. Adhering to these standards strengthens your AI’s trustworthiness.

• Action Items:
  • Implement encryption and access controls with Salesforce Shield to secure sensitive data.
  • Regularly audit data practices to ensure compliance with relevant privacy regulations.
  • Provide staff training on data security best practices to prevent errors.
  • Leverage compliance tools for tasks like data anonymization and audit trails.

9. Track Data Quality KPIs with Salesforce Analytics

Tracking KPIs specific to data quality allows you to monitor the health of your data, identifying areas for improvement. Consistently high KPIs ensure the data feeding your AI models remains accurate and actionable.

Salesforce’s Einstein Analytics and custom dashboards help you monitor KPIs such as accuracy, completeness, and timeliness.

• Action Items:
  • Define KPIs for accuracy, completeness, timeliness, and consistency.
  • Use Salesforce Einstein or custom dashboards to track data quality KPIs in real time.
  • Set alerts for KPI levels that fall below thresholds, triggering corrective action.
  • Adjust KPIs as necessary to keep them relevant to business and AI objectives.

10. Incorporate Feedback Loops with Salesforce Case Management

Continuous feedback from data users uncovers hidden data quality issues and opportunities for improvement. This feedback loop ensures data remains relevant and adaptable, especially for AI applications.

Collect data quality feedback from users by setting up a feedback loop within Salesforce Service Cloud. Use cases or surveys to capture and track issues reported by employees, and route these insights to your data stewards for continuous improvement.

• Action Items:
  • Implement a feedback system using Salesforce Service Cloud to capture data quality issues across departments.
  • Set up a review process where feedback is addressed and acted upon by data stewards.
  • Use feedback insights to inform future data quality initiatives and policy adjustments.
  • Encourage a data-aware culture where employees feel empowered to report issues.

By following these steps and leveraging Salesforce’s powerful data management tools, your company can create a strong, reliable foundation for AI. High-quality data not only powers better insights but also ensures that your AI investments yield the best possible results. From establishing a robust data governance framework to automating data cleansing and real-time validation within Salesforce, these actions lay the groundwork for a future where data-driven decisions lead to business growth and innovation.

However, achieving this level of data quality and AI-readiness can be complex, and many companies find they need additional expertise to maximize Salesforce’s capabilities. Our team specializes in helping businesses harness the full potential of Salesforce to enhance data quality and prepare for AI integration. Don’t let data challenges hold your company back—contact us today to see how we can support your journey to high-quality, AI-ready data.

Let’s work together to make your data a powerful asset that drives actionable insights and sustainable growth.