Good Data Practices to Maximize Salesforce ROI

In the world of Salesforce, where the mantra is ‘Everyone’s an Einstein,’ the critical foundation of good data practices is frequently overshadowed, potentially hindering the full realization of the platform’s capabilities.

This is because Salesforce Einstein is built on data & gets better predictions when it’s fed with good and enough data.

Let’s look at the data quality metrics that Salesforce asks us to reconsider:

A full 100% data quality score to actually measure the results
Einstein needs to learn your Salesforce data for at least 90 days to give results
Hence, Salesforce data storage needs to be relevant & in a tip-top shape

The importance of data quality has even deeper roots. It not only helps your Salesforce Einstein to predict data better but safeguards business data from several unlawful clauses like a violation of data privacy principles, non-compliance to data protection laws, loss of customer trust, and many others scavenges on your return on investment.

Let’s get down to the details with some effective practices that can bring good data health to your organization, ROI generation tips & how can you 10x your Salesforce application performance & efficiency using the right tools that suit your business, and your customization needs.

Data Quality: A Forgotten Principle of Data Privacy

Appropriate uses & disclosures of data also come with the responsibility to keep the data ‌accurate.
Here are some of the checkpoints that you need to consider, for integrating data privacy processes as impact assessments in Salesforce:

At the point of collection
When or where you are processing Salesforce data
(like how Einstein can process data outside your geographical location)
Knowledge of who is your next-level processor using your Salesforce data
Whether the data is accurate when collected from 3rd party
(Where Salesforce data backup recoveries need 100% accuracy)
During data migration cross-platform
(Where activities like org-to-org seeding in Salesforce needs accuracy)
This also amount having data accuracy across business departments

These are the situational pitfalls to avoid. As we understand good data = accurate data, are there ways to distinguish? Let’s get a brief of the characters.

Good Data Vs Bad Data

The Harvard Business Review notes that poor data quality leads to suboptimal strategic decisions in 84% of organizations. Instances of data breaches and compliance failures further underscore the consequences of inadequate data practices and according to experts here is good data becomes stale when the initial checkpoints (as mentioned above) are not met when you are collecting, storing, or managing data in Salesforce.

Good Data Practices for a Viable Data Culture in Salesforce

Maximizing Salesforce ROI (Return on Investment) involves implementing good data practices to ensure the accuracy, completeness, and reliability of your data. Here are some key data practices to consider:

Running a Data Quality Assessment: Using reporting tools in Salesforce to identify and clean up duplicate records, incomplete information, and outdated entries. Implement data validation rules to enforce data quality standards during data entry.
Having a Data Governance Plan for your Salesforce: Data governance procedures are stringent & vary across business practices. Depending on your organization’s structure, define roles and responsibilities and implement access control to have a streamlined data management system.
Ensuring Data Integrity: Integrating Salesforce with proper parent-child relationships ensures seamless data flow across the platform. Leveraging tools like Salesforce Connect or MuleSoft for efficient data integration.
Regular Data Backups: Perform regular backups of your Salesforce data to prevent data loss and ensure business reliance and continuity. Salesforce provides tools like Data Export for this purpose or automates the entire process with DataArchiva for easy backup & recovery.
User Training: Salesforce admins need to provide comprehensive training to users on data entry best practices, data hygiene, and Salesforce functionalities. Educated users are more likely to enter accurate and complete data.

Automation and Workflows: Implement automation rules and workflows to streamline data entry processes. This not only reduces manual errors but also ensures consistency in data formats and structures.
Data Security: Prioritize data security to protect sensitive information. Utilize Salesforce’s built-in security features, including field-level security, record-level security, and two-factor authentication Also, one needs to regularly audit and monitor user access to sensitive data.
Customized Reporting: Building customized reports and dashboards to extract meaningful insights from your Salesforce data. This allows you to make informed decisions and identify areas for improvement. Here is how you can learn how to use standard reports & dashboards with Salesforce Big Objects.
Data Archiving: Implement data archiving strategies to manage large datasets efficiently. Archive or delete redundant data of historical records. In order ensure compliant processes with legal and regulatory requirements, checkout this webinar on next-gen Salesforce data management strategy: storage, performance, compliance, and security brilliance.
Regular Audits: Conduct periodic data audits to identify and rectify any discrepancies. This includes validating data against external sources and ensuring data accuracy.
Stay Informed on Updates: Keep your Salesforce instance up to date with the latest releases and updates. This ensures that you are benefiting from new features, performance improvements, and security patches.
User Adoption: Encourage user adoption by making Salesforce user-friendly and aligning it with business processes. When users find the system easy to use and valuable, they are more likely to contribute accurate and timely data.