Passage Technology Blog

The first step in Agentforce implementation is improving data and records across the platform—before AI begins its work.

Without that commitment, users may not achieve, initiatives may fail, and the Admin may need to answer some difficult questions. With it, possibilities open up for the business and the Admin’s career—here’s how.

One of the challenges for optimizing the use of Salesforce^® is that many different users create the records stored in the platform. This creates the potential for wide variances in how the records are created and the information they contain. This lack of uniformity can make it difficult to find and unearth actionable insights across numerous business functions.

A key responsibility of every business’s IT and Analytics department is to establish and execute a comprehensive data governance program. Executive leadership teams view this function as vital to a company’s overall risk management and growth potential—and with good reason. This applies to businesses of all sizes, from small to very large.

A business’s operations, and its resulting success, are highly dependent upon the quality of the data it uses. This truism is universally accepted. Data impacts every facet of the business, and with the growing use of AI, the business must be proactive in its data quality strategy.

Outputs from both Salesforce^® and AI are entirely dependent on the inputs—the quality of the data being analyzed. More than ever, your company needs a data optimization process that leverages all of the stakeholders and not just a select few.

Recently, we launched a new app, Data Quality Helper. It gives Admins and users much more functionality than the default Salesforce data quality tools. As mentioned in our recent blog post, data validation rules and warning messages are now more customizable—something the Salesforce Community has wanted for quite some time.

But that’s only half of the story.

Since July 31, 2007, members of the Salesforce Community have been clamoring for a new feature to be added to the platform—”soft” data validation rules and warnings. On that date, a post was added to the IdeaExchange, and since then it has garnered some 25,000 points and more than 2,500 votes. The people have spoken, and they overwhelmingly support this idea.