Delve into Data Management

Learning Objectives

After completing this unit, you’ll be able to:

Explain the importance of data quality in Salesforce.
Understand how clean data improves user trust, adoption, and the effectiveness of AI-driven tools like Agentforce.
Identify and implement effective data backup and recovery strategies for Salesforce data and metadata.
Develop and apply best practices for data cleansing.
Create and maintain sandboxes as ‌best practice.
Create and maintain a comprehensive data dictionary.

Learn Data Management Best Practices

Data is only as valuable as its quality. Incomplete or outdated records can discourage users, causing them to seek alternatives, and eroding trust in your Salesforce org. That’s why good data management is important. This unit covers several best practices for managing data.

AI and Emerging Technologies

In the age of AI and Agentforce, clean data is paramount for achieving optimal outcomes. Agentforce, the Salesforce AI-powered assistant, relies heavily on accurate and reliable data to effectively automate tasks and provide insightful responses. Agentforce uses custom actions to interact with Salesforce data, enabling users to complete tasks using natural language.

Bad data—such as duplicate, inaccurate, incomplete, stale, or hoarded information—can significantly hinder Agentforce performance and lead to inaccurate results. For instance, if Agentforce attempts to summarize a customer record riddled with inconsistencies or missing fields, the generated output is unreliable,‌ leading to poor decision-making and frustrated users. Maintaining clean data is essential for unlocking the full potential of Agentforce and ensuring the success of AI-driven initiatives within Salesforce.

Review and Maintain a Backup

It’s true that no technology is foolproof, and even a robust platform like Salesforce requires a reliable backup solution. There’s always a potential for data loss due to several factors, including:

User error: This is the most likely cause of data loss and can stem from mistakes during mass imports, incorrect automation or integration deployments, and accidental overrides or deletions by users or admins.
Sandbox refreshes: Refreshing a sandbox essentially creates a copy of the production org, which can overwrite weeks or even months of work done in the sandbox environment.
Rare Salesforce downtime: While Salesforce servers are highly secure, there’s always a small chance of data loss during infrequent downtime incidents.

To mitigate these risks, implementing a robust backup strategy is crucial. Salesforce offers native backup solutions—including Data Export Service, Data Loader, and Report Export for data backup, and Change Sets, Sandbox Refresh, and DevOps Center for metadata backup. However, these tools primarily focus on data export and don’t offer automatic data recovery.

Salesforce has a paid backup and recovery tool, and it’s worth exploring third-party backup providers available on the AppExchange, such as:

OwnBackup: A leading provider with a comprehensive suite of products for data backup, security enhancements, sandbox seeding, and data archiving.
Gearset: A prominent DevOps platform for Salesforce that also offers a comprehensive solution for data and metadata backup.
AutoRABIT: Another leading Salesforce DevOps platform that includes a product called Vault, specializing in building products for regulated industries like financial services.
Oda: Offers an enterprise-proven Salesforce data management solution encompassing data backup, data privacy, and compliance tools.

When deciding on a backup solution, consider these factors.

Business dependency on Salesforce: The higher the dependency, the more critical a robust and readily available backup solution becomes.
Data and metadata loss restoration process: It’s crucial to understand the steps involved in restoring data and metadata in case of loss, including the time it takes and the potential impact on business operations.
Frequency of Salesforce deployments: Frequent deployments increase the risk of data or metadata loss, necessitating a backup solution that aligns with the deployment schedule.

Regularly scheduled data exports are a fundamental aspect of a sound backup strategy. The built-in export feature in Salesforce enables scheduled backups on a weekly or monthly basis. It’s crucial to store downloaded backup files securely in a safe location, such as a SharePoint server or another secure storage solution. And consider including all data in the exports, including documents and images, to prevent missing references and ensure comprehensive data recovery.

Cleanse Data

Clean data means fewer duplicates and better user experiences. Configuring matching and duplicate rules reduces duplication and ensures consistent records. Using custom reports, like ”clean your room” dashboards, helps track and resolve data gaps such as missing contact information. For creating duplicate reports, define your target objects and fields. This provides clarity on necessary data cleanups and maintains consistency across your org. Don’t stop at data—review reports, dashboards, list views, email templates, and metadata like roles, profiles, and permissions. Regularly cleaning up your org streamlines processes and enhances efficiency.

Review and Refresh Sandboxes

Keeping sandboxes fresh is a key best practice for successful Salesforce implementations. Refreshing sandboxes after each production deployment helps mitigate the risk of configuration errors and ensures alignment between different environments. This practice is particularly crucial because sandboxes serve as isolated environments for development, testing, and training, allowing admins and developers to work on configurations and customizations without impacting the live production org.

Minimize Configuration Errors

When changes are deployed to production, the production environment’s configuration evolves. If sandboxes aren’t refreshed, they can retain outdated configurations, leading to inconsistencies and potential errors when new developments or tests are conducted. Refreshing sandboxes brings them up to date with the latest production configuration, reducing the risk of these issues.

Consistent Refresh Schedule

A consistent refresh schedule fosters better collaboration and predictability within teams. Admins and developers can anticipate when their sandboxes will be refreshed, allowing them to plan their work accordingly and minimize disruptions. This also ensures that everyone is working in the most up-to-date environment, leading to more efficient development and testing processes.

Salesforce Sandbox Types and Refresh Intervals

Salesforce provides different sandbox types, each catering to specific needs and offering varying refresh intervals.

Developer Sandboxes: Designed for individual development and testing, these sandboxes can be refreshed daily, ensuring developers always have a fresh environment to work with.
Developer Pro Sandboxes: Offering a larger data set than Developer Sandboxes, they’re suitable for more complex development and testing and can be refreshed every few days.
Partial Copy Sandboxes: These contain a subset of production data, making them ideal for user acceptance testing and integration testing, with refresh intervals typically ranging from a few days to a week.
Full Sandboxes: Replicating the entire production environment, including data and configurations, they’re suitable for performance testing and staging, with refresh intervals typically monthly due to their size.

Sandbox Ownership and Coordination

Designating owners for each sandbox is vital for smooth coordination before any refresh. This practice allows for a clear communication channel and responsibility for managing the sandbox, ensuring that in-progress work is preserved and any potential conflicts are addressed before the refresh. The sandbox owner can notify users of the upcoming refresh, allowing them to back up any crucial data or configurations not yet ready for deployment.

Alignment and Deployment Readiness

Regularly reviewing and refreshing sandboxes keeps configurations aligned with production, ensuring that the developed features and customizations are ready for deployment without disrupting ongoing work. This practice contributes to a smoother and more reliable release process.

Refreshing sandboxes, however, may not always be a straightforward process, especially when dealing with substantial customizations or large data volumes. For specific guidance on complex sandbox refresh procedures, consult Salesforce documentation or engage with Salesforce support for expert assistance.

Maintain and Improve Your Data Dictionary

A data dictionary is a vital tool for effective collaboration and management within a Salesforce environment, especially when dealing with IT and integrations. Maintaining a data dictionary helps minimize miscommunication, errors, and rework while enhancing collaboration.

Data Dictionary: A Comprehensive Blueprint

A well-maintained data dictionary serves as a central repository of information about your Salesforce org’s data structure. This blueprint includes details about objects (equivalent to tables in a database) and their attributes (fields), capturing crucial information such as:

Data types: The kind of data stored in each field (text, number, date, picklist). This information helps ensure data integrity and consistency within the system.
Sample results: Illustrative examples of data values for each field, providing clarity and understanding for users and developers. These examples can aid in data validation and testing.
Integration details: Documents how each field interacts with external systems or applications, particularly crucial for managing integrations effectively.
Field usage: Details about where and how a particular field is used, including page layouts, Apex classes, Visualforce pages, and workflows. This information helps identify unused fields and assess the impact of potential changes.
Data source: The origin of the data for each field, whether it’s manually entered, derived from an automation, or populated through an integration. This is vital for understanding data flow and dependencies within the system.
Security and access levels: Documents the field-level security settings, specifying who has access to view, edit, or modify the data in each field. This is crucial for ensuring data privacy and security compliance.
History tracking: Identifies which fields have history tracking enabled to audit and analyze data changes over time. This is particularly useful for troubleshooting data-related issues and ensuring data integrity.
Page layouts and field placement: Specifies the page layouts where each field appears and its position within the layout. This helps identify redundant or unused fields and optimize page layouts for user experience.
Questions and notes: Provides a space for recording questions or observations about specific fields, facilitating clarification and communication among team members. This fosters a collaborative approach to data management.

Benefits of a Data Dictionary

Enhanced collaboration: A data dictionary acts as a single source of truth for all stakeholders, including admins, developers, business analysts, and IT professionals. This shared understanding of data structure facilitates effective communication, reduces misunderstandings, and promotes informed decision-making.
Error reduction and reduced rework: By providing clear definitions and guidelines for data, a data dictionary helps prevent data entry errors, inconsistencies, and misinterpretations. This, in turn, reduces the need for data cleanup, rework, and costly fixes down the line.
Streamlined integrations: A well-documented data dictionary is indispensable for managing integrations effectively. Clear understanding of data structures, relationships, and integration points simplifies the integration process, minimizes errors, and facilitates seamless data flow between systems.
Improved data quality: By setting standards for data types, validation rules, and data sources, a data dictionary helps maintain data consistency and accuracy. This, in turn, improves the reliability of reports, dashboards, and analytical insights.
Facilitated data audits: A data dictionary provides a framework for conducting data audits, helping to identify data quality issues, such as duplicate records, mismatched data, incomplete records, and corrupted data. This enables proactive data cleansing and improvement initiatives.

Maintaining a Data Dictionary

Keeping a data dictionary up-to-date is important for any admin. While a weekly update schedule might be suitable for organizations with frequent changes, the optimal frequency depends on your organization’s specific needs and the pace of development. The key is to ensure that any changes to data structures, fields, or integrations are quickly reflected in the data dictionary.

By incorporating these insights, you can effectively use a data dictionary as a powerful tool for collaboration, data management, and integration within your Salesforce environment.

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