Advancements in communication technology have changed the way businesses and customers interact. According to research from Teradata, over 50 percent of organizations are now using seven or more channels to reach their customers. The more channels your customers use to interact with you and the more channels your business uses to collect information from them, the more you run the risk of duplicates and other data errors entering your customer relationship management (CRM) database. Thankfully, advancements in data integrity solutions make it possible to prevent these errors. Read on to learn how you can improve your Salesforce data quality in just a few steps.
Taking responsibility for data quality
Although everyone needs to do their part to ensure CRM data quality, it’s often the Salesforce administrator’s responsibility to lead the way by combining the right data quality tools with the right action plan for implementing them.
Generally, this involves developing and enforcing quality standards, training users in working with the CRM, consistently monitoring and cleansing data to maintain its integrity, and keeping the lines of communication open. Use the following 10 tips to help you craft a straightforward, effective plan of action.
Top 10 expert tips to improve your Salesforce data quality
Anyone who’s maintained even a small database will agree that data quality degrades very quickly and exponentially. While updating existing records, users sometimes replace valid information with erroneous data, or simply change/delete information by accident. New records, whether entered manually or imported, invariably contain a certain number of problematic fields, despite an administrator’s best preventive efforts. Continually monitor the database to identify and correct erroneous data.
Every data administrator should develop a quality standard that defines “bad” data in their database, as well as “good” data. The standard is really a collection of rules or tests that, when applied to the database, identify bad data and, in some cases, automatically fix it. But the standards also prevent bad data entry, such as preferences for abbreviations and rules for formatting names, addresses, states, ZIP Codes, etc. After establishing an initial set of standards, you should continually seek to improve and update those standards so that quality remains at a high level no matter how quickly the database evolves or the business changes.
Duplicate records—dupes—create confusion, waste time, and make it more difficult for users to get a complete view of a customer relationship. Use a duplicate prevention tool to regularly search for dupes in the database and create scenarios that determine what to automatically do with those dupes, such as merge them, remove them, or send alerts.
Having defined a quality standard for your database, identify and implement proven data cleansing tools to help you achieve it. Leverage third-party solutions to search the database for records that don’t conform to the quality standard and fix them. To consistently maintain a high level of data quality, automated searches and merges should be conducted on a set schedule—perhaps daily or weekly, depending on how quickly data changes in your organization.
Even if users could enter or import 100 percent clean data, it wouldn’t change the fact that the world is a dynamic place. Companies grow, people change jobs, businesses move or merge. Contact information that was valid just three months ago could now be out-of-date. Implement processes that help ensure data is timely, check the data against credible outside sources whenever possible, and leverage an email verification solution to ensure email addresses are valid.
After performing the remedial tasks listed above, you should then verify that records have been properly updated and the database does indeed conform to the required quality standard. Once this has been completed, employees across the organization can make informed decisions, knowing the data they are using to make them can be trusted.
Seize every opportunity to educate users and managers about the importance of data quality, championing the cause of data integrity to the organization, as well as to individual employees who rely on that data.
Enforcing the standards you create, ensuring the proper training is provided to users, and restricting access to data when necessary is key. Design schemas with data quality in mind. They should:
- Define required fields.
- Use automatically populated default values whenever possible.
- Create field dependencies and workflow rules (e.g., if A and B exist, then C must exist).
- Control object creation: which users are allowed to create Accounts, Contacts, Leads, etc.?
- Implement validation rules to ensure that data is entered correctly.
- Impose restrictions on Web-to-Lead data.
As the Salesforce administrator, you are generally responsible for answering questions and notifying users of changes to the CRM user interface (e.g., the addition of new fields or entire screens), revisions to naming conventions, updates to policies and standards, and anything else related to the user experience. Fostering a supportive atmosphere where users are comfortable asking questions, discussing problems, and suggesting improvements will promote user adoption and adherence to guidelines.
- Get a helping hand
To achieve and maintain a consistently high level of CRM data quality, recognize that you need to move beyond the standard toolset and adopt advanced third-party applications with features that are so effective, they become your best friend. Even by following our first nine expert tips, there’s only so much you can do to improve data quality without the help of specialized software applications.
Meeting the challenge of improving Salesforce data quality
By leveraging the power of Validity Connect, a feature-rich suite of data integrity solutions that includes DemandTools, PeopleImport, DupeBlocker, and BriteVerify, you can consistently achieve and maintain high quality Salesforce data everyone in your organization can trust.