what is AI-ready Salesforce data model ? How you design an AI- ready data model on salesforce platform

fcscloud
Dec 16, 2024
4 min read

Designing a data model on the Salesforce platform with AI benefits in mind requires strategic planning and attention to how data is structured, stored, and maintained. An AI-ready Salesforce data model focuses on clean, complete, and well-structured data, with clearly defined relationships and historical tracking. It aligns closely with your business goals and AI use cases, enabling Salesforce Einstein and other AI tools to provide actionable insights and predictions. Investing in a strong foundation today will unlock powerful AI capabilities tomorrow.

1. Understand the AI Use Cases

Define the AI use cases and outcomes you want to achieve upfront. Examples include predictive sales forecasting, customer churn prediction, or lead scoring.
Identify the specific data points required to support those use cases.

Example: If the AI will predict customer churn, ensure your data model tracks customer interactions, purchase history, service cases, and satisfaction scores.

2. Use Standard Objects Where Possible

Salesforce provides many standard objects like Accounts, Contacts, Leads, Opportunities, and Cases. These objects are already optimized for Einstein AI and integrate seamlessly with Salesforce's AI tools.
Leverage these standard objects to ensure compatibility with Salesforce Einstein and other predictive analytics tools.

Example: Using the standard "Opportunity" object for tracking deals ensures Einstein can analyze pipeline trends and provide actionable insights.

3. Ensure Data Completeness

Design validation rules and required fields to ensure critical data is always captured. Missing or incomplete data reduces the effectiveness of AI models.
Use picklists, dependent picklists, and controlled values to standardize inputs and reduce errors.

Example: Require fields like "Industry," "Annual Revenue," and "Lead Source" for Accounts and Leads to enable better segmentation and AI-driven scoring.

4. Structure Relationships Clearly

Define clear relationships between objects using master-detail or lookup relationships. These relationships help AI systems understand how data is connected.
Use junction objects for many-to-many relationships where necessary.

Example: Connect "Opportunities" to "Products" and "Accounts" to enable AI to identify sales trends for specific industries or product lines.

5. Track Historical Data

Ensure the data model supports capturing historical trends, as AI models require historical data for training and prediction.
Use Salesforce’s Field History Tracking, Big Objects, or custom solutions to log changes in key fields.

Example: Track changes to "Opportunity Stage" and "Close Date" over time to predict pipeline slippage.

6. Incorporate External Data

Use Salesforce Connect or APIs to integrate external data sources into your Salesforce instance. AI models often benefit from enriched data like market trends, customer demographics, or competitive insights.

Example: Enrich lead data with third-party intent signals or social media insights to improve Einstein’s predictive lead scoring.

7. Optimize for Einstein AI

Use fields and objects that align with Einstein AI’s requirements for features like Einstein Prediction Builder or Einstein Discovery.
Ensure your data model has enough volume and variety to meet Einstein’s training needs.

Example: For Einstein Opportunity Insights, ensure fields like "Last Activity Date" and "Opportunity Amount" are accurately populated.

8. Maintain Data Quality

Implement data quality checks, deduplication rules, and data cleaning processes to keep your Salesforce org clean and consistent.
Use tools like Salesforce’s Duplicate Management and Data.com for deduplication and enrichment.

Example: Duplicate customer records can confuse AI models, leading to incorrect segmentation or insights.

9. Leverage Custom Metadata and Hierarchies

Use custom metadata types and hierarchical relationships to represent additional data configurations or business processes that may enhance AI predictions.

Example: Custom metadata can define scoring models or weights for different sales territories, aiding in territory-specific AI insights.

10. Enable Data Segmentation

Structure your data model to allow segmentation by key criteria, such as region, industry, or customer tier. Segmentation improves the accuracy and relevance of AI predictions.

Example: Use a custom field like "Customer Tier" on Accounts to group data for AI-driven upselling strategies.

11. Prepare for Scalability

Design the data model to handle increasing volumes of data, as AI benefits from large datasets. Use Salesforce’s Big Objects for high-volume data storage.

Example: Log customer interactions across all channels in a scalable way to support omnichannel AI insights.

12. Incorporate Feedback Loops

Include fields or processes to capture user feedback, which can be fed back into AI models for continuous improvement.

Example: Add a "Reason Lost" field on Opportunities to capture why deals were lost, which can refine future sales predictions.

13. Enable Time-Based Data

AI models often require time-based data for trend analysis and time-series forecasting. Capture timestamps for key events and milestones.

Example: Use time-stamped fields to track when opportunities move stages or when customers open support cases.

14. Ensure Security and Privacy

Structure your data model to comply with security and privacy regulations (e.g., GDPR, CCPA). Use Salesforce’s Shield Platform Encryption for sensitive fields.
Protect Personally Identifiable Information (PII) while still enabling anonymized insights for AI.

Example: Encrypt sensitive fields like Social Security Numbers while keeping anonymized data for AI analysis.