How to Enforce Data Quality in Salesforce

In part II of this series on data quality, we’ll focus on some practical tips to help you stay ahead of data quality pitfalls and prevent potential problems before they start. If you missed the first article, we invite you to read it and learn what data quality means in Salesforce and how to improve and maintain the quality of your data.

Easy Ways to Prevent Data Quality Problems Before They Start

Correcting data inaccuracies is time-consuming and difficult. If you set aside some time for planning before you start collecting and analyzing data, you will be able to prevent costly mistakes from happening and save valuable time and effort on the backend.

Salesforce offers various tools and features to achieve and maintain data quality. However, the availability of some features may depend on the Salesforce edition you are using. Leveraging the following features will help to prevent entering bad data to your Salesforce Org:

1. Standardize Your Data – This is an easy win! One of the best practices is to always use a drop-down (picklist) field rather than using a Text field. For example, take a field named ‘Best Time to Contact’ – if you use text data for the field, then users may enter 2PM, 14:00 or Afternoon. This would result in a data integrity nightmare. Especially if you are planning to use the field to make important business decisions. So, the best thing to do is to use a picklist with values such as: Morning, Midday, Afternoon, or Early Evening.
   If you choose not to use a picklist, make sure to define naming conventions. Define clear rules for: capitalization, required and optional fields, and so on. This will help you avoid confusion and reporting errors.

Ensuring the data quality in Salesforce is the task of the Salesforce professional, and part of that job is to provide guidelines for Salesforce users. Make sure to communicate the standards you set. Training and clear framework will help you minimize errors and maximize data quality.

2. Define CRED Rights (Create, Read, Edit, Delete) – CRED stands for Create, Read, Edit Delete and is a profile setting that allows users specific permissions for objects they have access to. Every user profile can have a specific CRED for each object, based on how the user will be interacting with the record itself. Configuring CRED rights is one of the keys to preventing data quality problems. So here is a rule of thumb for managing these rights: do not grant create or delete access to each and every user in your organization. Grant edit access as necessary, but with caution. Remember that any user with edit permission can inadvertently modify your data.
   
   
3. Set ‘Required Fields’ – Sometimes, making fields required helps you to improve the quality of your data. For example, by default, a lead’s email and phone is not required. However, if these fields remain ‘not required’, you may end up with hundreds or thousands of leads that you have no way of contacting.
   
   
4. Use Validation Rules – Validation rules are a great way to maintain data quality. They stop users from entering bad data when creating the record. For example: you want to make sure that phone numbers follow a specific format? Set up validation rules for this field. Then, when records are saved, the data is automatically checked to make sure that it follows the format. Validation rules are super versatile. For example, if you want to make a certain field mandatory, you can use a validation rule that checks if the field is blank. You can set up validation rules for phone, credit card, customer ID fields, etc. Validation Rules in Salesforce are more flexible than simply making a field mandatory and can contain more complex logic. For example, when initially creating an Opportunity record, you may not know the value for the opportunity. Making this field mandatory could force the user to guess a value. Using a Validation Rule would enable a more complex logic to be applied, such as allowing the field to be completed at a later stage.
   
   
5. Create Duplicate Rule – Salesforce has some built-in tools to help manage duplicate records. You can both prevent duplicates from being created by warning or prevent users from creating what looks like a duplicate with duplicate matching rules. A duplicate rule tells Salesforce what to do when users are trying to create a record that already exists based on matching parameters. You may also identify and report on existing duplicates so you can correct or merge the duplicates. Having such rules in place is an industry best practice to avoid redundant data. Check out Salesforce Trailhead training module – Resolve and Prevent Duplicate Data.

Monitor Data Quality – Organizations that focus on data quality find it useful to have a data quality indicator dashboard highlighting the data quality KPIs such as contacts or leads with hard bounce, data uniqueness, data completeness, and data consistency, etc. Salesforce offers Data Quality Analysis Dashboards to help you track the quality of the information users enter into the system.

Data Quality Monitoring and Management

So what’s next? How do you make data quality management a more central part of how you manage your Salesforce environment? Here are a few ideas:

Create a clear long-term Salesforce data strategy – establish a clear roadmap for the business data. Start by defining the critical aspect of how management and business teams are planning to use their data to achieve their business goals. By having a future-ready and long-term data strategy in hand, you will be able to monitor and measure the quality of the data and make sure it meets the business goals. 

Stay close to your business stakeholders – The definition of Data Quality may change as the business evolves. Working closely with the business teams will help you understand how data quality may need to change in the future. Perhaps your organization is planning to start a direct mail campaign – suddenly high-quality mailing address info is critical. Or considering opening new branch offices – lead data quality in that geography might suddenly become more important. Your definition of data quality needs to change to respond to the business and provide it what it needs.

Data Backup and Archiving – Even the most comprehensive data management processes and techniques are ineffective unless they are documented and made available to everyone who needs them. Teaching individual users how to manage data themselves and document it independently will help you achieve this goal. This can be accomplished by including helpful texts within a Salesforce program, to guide the user through the process of inputting the information and completing the appropriate task.

Conclusion

In summary, data quality is not something that you can solve by running a project. Good data quality demands a disciplined data governance team, rigorous supervision of incoming data, accurate requirement gathering, etc. Factors such as these help in designing the right solution; putting in place a thorough regression testing for change management; and, setting up careful design of data pipelines. I hope these suggestions provide a solid starting point for creating and maintaining data integrity of your organization. Good Luck!!