Simple Solutions for Complex Salesforce Data Quality Issues



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Does your organization spend too much time fixing data problems? Consider these easy-to-implement tips—and then share your experiences to help others.

“Every year, poor data quality costs organizations an average $12.9 million. Apart from the immediate impact on revenue, over the long term, poor quality data increases the complexity of data ecosystems and leads to poor decision making.” — 2021 research from Gartner*

As a Salesforce® Admin, you likely understand this challenge as well as anyone. You are often tasked with resolving data quality issues encountered by various teams within your organization—and also making sure the same problems don't happen again.

Sometimes it may feel like you are fighting a losing battle. However, depending on the type of data issue, a number of solutions are readily available to help save time and money, for you and your users.

Manual Data Quality Correction

In some situations, records may have missing or invalid data in important fields. By implementing a combination of required fields (at the field level or page-layout level) and validation rules, users will be notified about invalid data inputs or missing data when they create or update records. However, while this solution helps with newly created records, it does not automatically fix the problems in existing records. To fix these cases, a report can be created to identify those records needing updates.

Another difficult data challenge is duplicate records in the Salesforce environment. This creates confusion for users about which record is valid. Once again, you can create another solution—this time configuring duplicate rules and matching rules. Users will be notified as they create or update potential duplicate records. But also, once again, this approach does not automatically solve the issue with pre-existing records. It will take a good amount of time to dedupe those records created prior to the implementation of the rules. If you have the Performance or Unlimited version of Salesforce, duplicate jobs can help speed up the process of merging those duplicate records.

The Promise of Automation

Automation not only makes it easier for your users (reducing time and effort needed to populate and maintain the data), but it also ensures the populated data is correct at all times.

Many bad data quality scenarios can be resolved by putting automation in place to regularly populate field values or remove old, unused records. For example:

  • The Rollup Helper app can help populate important aggregated field values by calculating and using field values in child records. Without that type of automation in place, users may forget to populate that information, miscalculate the value, or simply choose not to manually enter it because it takes too much time. As a result, users then waste time creating reports or list views to get at the information or, worse, have no way to get the information until a technical resource is involved.
  • The Storage Helper app may be used to delete old unused records, which makes it easier for your users to find relevant information in Salesforce.
  • Record-triggered flows or formula fields are standard Salesforce tools that automatically populate a record's field value based on other field values for that record or its parent records.

Other common issues encountered include records with incorrect phone numbers, emails, addresses, or other outdated information. An effective, although often expensive, option to consider is working with a data provider vendor to leverage up-to-date third-party data and incorporate it into your Salesforce environment. This can be a one-time-only effort, or an ongoing automation.

Implementing Changes to Processes

Sometimes missing or incorrect data can be traced to an enterprise’s overly complex process used for creating records or updating information. Generally speaking, the more complex the process, the more likely data will be missing or invalid. Using a screen flow can break these complex processes into multiple simple-to-follow steps. A screen flow ensures the user follows all steps in the process, eliminating the chance a user doesn’t complete one of the required steps.

In other cases, it can be due to the lack of a functional data governance model or ownership over data problems. Usually, unless someone has data quality in their job description, it is not viewed as a problem that person has to solve. So ownership must be clearly determined. Alternatively, a committee of individuals could be assembled on an ongoing basis to bring subject matter experts together with more technical stakeholders to solve data issues. 

What Data Quality Challenges Are You Facing?

Each organization's needs are unique, so it is difficult to cover every data quality situation in a single blog post. Still, the tools and solutions highlighted above offer a good starting point. They can be easily incorporated into your Salesforce environment to help solve some of the data quality issues your organization faces every day.

Data quality is important to everyone in the Salesforce community. And it also is important we share our knowledge for solving this challenge. Do you have examples of how your organization has effectively addressed this issue? Or are you wrestling with problems still unsolved? Please share your thoughts. You can leave a comment below or, even better, participate in this short data quality survey.

We’ll report the results at a future date—so make sure your voice is heard today!


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