A well-structured data management plan ensures data is organized, consistent, and easily accessible, enabling an organization to maximize the value of Salesforce and AI. This section provides key concepts and tools for developing such a plan.
Busting Salesforce Data Myths
Think you know Salesforce data management? Let’s tackle some common myths!
Myth: “Validation Rules slow down my workflow.”
Fact: Properly implemented validation rules streamline workflows by reducing errors and rework, saving time in the long run.
Myth: “Duplicate Management tools don’t work well.”
Fact: Salesforce Duplicate Management tools, when paired with strong matching rules, can prevent a significant amount of duplicates at the point of entry.
Myth: “Data security is only for sensitive industries like healthcare or finance.”
Fact: Every business benefits from field-level security and role hierarchies, as they reduce risk and maintain customer trust and prepare the organization for AI to be limited to data access.
Takeaway: Don’t let misconceptions hold you back from using Salesforce to its fullest potential and to prepare for AI!
The Rosetta Stone?
What does a 2,200-year-old artifact have to do with Salesforce data management? Maybe this is a stretch (or maybe not!):
The Rosetta Stone was the key to unlocking ancient Egyptian hieroglyphs. Its value lay in offering the same text in three scripts: Greek, Demotic, and Hieroglyphic. This consistency enabled scholars to decode a lost language.
Similarly, consistency in Salesforce data (naming conventions, formats, and validation rules) acts as your organization’s Rosetta Stone. It helps to translate raw data into actionable insights, enabling your team to decode customer needs and market trends. As we layer on AI, this becomes even more important as humans take 1 more step away from the data source and rely on AI to do the heavy lifting.
Lesson: A well-structured data management plan is your modern Rosetta Stone, unlocking the full potential of a Salesforce org with AI!
The Data Management Huddle
Step into a recent team meeting where a data issue is resolved using best practices preparing the organization for using AI to .
Admin: “We’ve noticed with account names being abbreviated like Buffalo Wild Wings being put in as BWW. Well, we already have a Buffalo Wild Wings account so now there are duplicates! This is already hurting our reporting, but now that we’re prepping for AI we’ve got to get this resolved.”
Sales Rep: “I know my name’s on that records, but I really don’t think that was me! Maybe the new intern got creative?”
Manager: “What intern? Anyway, yes, it makes automation and reporting unreliable. Let’s enforce a naming convention.”
Data Steward: “Agreed. Admin, can you create a validation rule and set up a quick training for the team?”
Admin: “I don’t think that’s the right tool from our Data Management Plan, but let me look into Potential Matching Rules and see if there’s a good solution there.”
Outcome: These are the types of conversation we need to have to prepare for AI to be taking actions in our organizations!
AI Fundamentals Podcast
Episode 16: The Data Management Plan
Key Concepts for Data Management in Salesforce
What are the key concepts for a Data Management Plan. Open each of the element below to read about the concept, why it’s important, best practice, and examples for what that means for data in the org.
Why It’s Important: A consistent naming convention makes it easier to track, manage, and retrieve data in Salesforce.
Best Practices:
Use clear, descriptive names for records, fields, and reports (e.g., “Account_Created_Date” instead of “ACD”).
Implement a standard for abbreviations and capitalization to ensure uniformity.
Leverage prefixes for custom objects, fields, and flows (e.g., “Custom_Lead_Status__c” for custom fields).
Example: Instead of “ACD”, use “Account_Created_Date” to improve clarity and tracking.
Why It’s Important: Formatting ensures that data is stored in a consistent manner, improving data usability and accuracy.
Best Practices:
Standardize date and number formats (e.g., “YYYY-MM-DD” for dates).
Set field types according to data needs (e.g., use percentage fields for rates, picklists for predefined choices).
Example: Use “2024-12-31” instead of “31/12/2024” for consistent date formatting.
Why It’s Important: Understanding the workflow stages a record undergoes from creation to deletion helps ensure efficient data flow and processing.
Best Practices:
Map out key stages for records like Leads, Accounts, or Opportunities (e.g., for Leads: New → Qualified → Converted).
Use Salesforce automation tools (Flows, Workflow Rules) to ensure records move through these stages seamlessly.
Example: Create a flow to automatically update Lead Status to “Converted” when an Opportunity is created.
Why It’s Important: High-quality data ensures accurate insights and efficient AI use.
Key Metrics & Standards:
Completeness: Ensure all required fields are filled in.
Consistency: Data should be free from contradictions (e.g., contact details match across platforms).
Timeliness: Keep records up to date with the latest information.
Use validation rules and automation to maintain quality (e.g., validate email formats or enforce mandatory fields).
Example: Ensure all phone numbers are in the format “+1-123-456-7890” for consistency.
Why It’s Important: Establishing clear ownership over data ensures accountability and proper data management.
Best Practices:
Define data stewards or owners for different data objects (e.g., Account Owner, Lead Owner).
Use Salesforce roles and profiles to assign ownership and ensure accountability for data accuracy and updates.
Example: Assign “Account Owner” field to responsible team members to maintain accountability.
Why It’s Important: Protecting sensitive data ensures that only authorized users have access.
Best Practices:
Use Salesforce’s Role Hierarchies, Profiles, and Permission Sets to control access.
Apply field-level security to restrict visibility of sensitive fields.
Enable two-factor authentication (2FA) for high-security access.
Example: Restrict “Salary” field visibility to HR roles using field-level security.
Why It’s Important: Regularly monitoring data helps catch errors early and ensures continuous data integrity.
Best Practices:
Use reports and dashboards to monitor data health and integrity.
Set up data validation rules and process builder workflows to flag incomplete or inaccurate data.
Example: Create a dashboard highlighting incomplete or outdated data for weekly review.
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Implementing Data Management in Salesforce
To implement a solid data management plan in Salesforce, you need to plan to leverage built-in tools to ensure data is accurate, secure, and structured. Let’s brainstorm the platform elements we can use with our interactive desk space. Work through each of the available elements by adding the sticky notes to the table, and review how you plan to use them.
Required Fields
Validation Rules
Flows (Automation)
Page Layouts
Dashboards
Data Enrichment
Duplicate Management
Custom Field Types
Great work! So this is the basis for our Data Management Plan:
Required Fields
Enforce essential fields (e.g., First Name, Last Name, Email) to ensure critical data is always captured.
Use page layouts to make fields required for specific record types.
Validation Rules
Set up validation rules to ensure data integrity (e.g., validate email formats, enforce specific number ranges).
Example: A rule ensuring that the “Close Date” for an Opportunity is not in the past.
Flows (Automation)
Automate key data processes such as updating field values, sending notifications, and creating related records using Flows.
Example: A flow that automatically updates the status of a Lead when a task is completed.
Page Layouts
Customize page layouts to control which fields are visible and editable for users.
Tailor layouts based on roles to provide different views for sales reps, managers, or service agents.
Dashboards
Use dashboards to visualize key data metrics, such as data completeness and accuracy.
Create data quality dashboards that highlight fields missing required data.
Data Enrichment Tools from AppExchange
Enrich your Salesforce data with third-party tools (e.g., Data.com, Clearbit).
These tools add missing information such as firmographic details for Accounts or demographic data for Leads.
Duplicate Management
Use Salesforce Duplicate Management to prevent and handle duplicate records.
Set up matching rules and duplicate rules to block duplicate entry at the source.
Custom Field Types
Leverage custom field types to structure data effectively (e.g., formula fields for calculated values, picklists for standardizing inputs).
Example: Create a picklist field for “Lead Source” to categorize where the lead came from (e.g., Email, Event).
A well-executed data management plan ensures that your data in Salesforce is reliable, accessible, and secure. With the right naming conventions, validation rules, roles, and tools like Flows and AppExchange apps, you can maintain a clean and efficient data environment that supports your organization’s AI and analytics initiatives.
Now Drop In To Focus
Why are naming conventions important in Salesforce?
Consistent naming conventions make it easier to track, manage, and retrieve data. Use clear, descriptive names, standard abbreviations, and prefixes for custom objects and fields.
How should I format data in Salesforce?
Standardize date formats (e.g., “YYYY-MM-DD”), use appropriate field types, and ensure consistent input standards across records.
What are the key record lifecycle stages?
Map stages like New → Qualified → Converted for Leads, and use Salesforce automation tools to ensure smooth transitions.
How do I ensure high data quality in Salesforce?
Focus on completeness, consistency, accuracy, and timeliness. Use validation rules and automation to maintain data quality.
How can I manage roles and ownership effectively?
Assign clear ownership of data objects using Salesforce roles and profiles. Define data stewards to maintain accountability.
How can I secure sensitive data in Salesforce?
Use Role Hierarchies, Profiles, and Permission Sets. Apply field-level security and enable 2FA for high-security access.
How can I monitor data health effectively?
Use reports and dashboards to monitor data integrity. Set up validation rules and workflows to flag errors.
What tools can help with data enrichment?
Use AppExchange tools like Data.com or Clearbit to enrich data with missing information like firmographics or demographics.
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