Passage Technology Blog

The decisions people make are the key inputs to business AI output. Choose wisely.

Have you ever stared at your Salesforce^® org, scratching your head over a jumble of phone numbers that look like they came from different planets? One's got parentheses, another's dashed like a secret code, and some might have non-numerical notes for extensions and such. If this sounds familiar, you're not alone.

Having to engage in an audit of any kind is not usually seen as one life’s most enjoyable activities. Some audits (like tax audits) can be especially frightening, but as a general rule doing any type of audit, at minimum, can be a chore. Auditing the quality of your Salesforce^® data is no exception to this general rule.

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.

Dear PT, 

Managing data and data hygiene in Salesforce^® is a constant struggle at our company. Sometimes, incomplete data can be as damaging as bad data. With different people doing the data entry, it can be difficult to make sure the information is complete every time. For example, Opportunities may not have the key roles for the Contacts properly documented. Is there any easy way to identify and fix these data gaps?

Val I. Dayshon from Roll, AZ