Who’s responsible for an organization’s Salesforce data cleaning strategy? As this article shows, the answer isn’t as simple as you think.
Poor Data Quality
In today’s business environment, poor data quality is created by a variety of sources and has the potential to cause any number of undesirable outcomes – from inconvenient disruptions in the normal flow of business activities, to disastrous consequences for a company’s revenues or reputation.
No matter who creates it or what impact it has, poor data quality in a Salesforce database typically falls into one of these categories:
- Missing data – Empty fields that should contain data. Example: An automated billing process breaks down because a customer’s billing address is missing from the system.
- Wrong or inaccurate data – Information that has not been entered correctly or maintained. Example: Bills come back marked “Return to Sender” because the ZIP Code doesn’t match the billing address.
- Inappropriate data – Data that’s been entered in the wrong field. Example: A promising lead doesn’t appear in regional reports because the city name has been entered in the State field.
- Non-conforming data – Data that hasn’t been entered according to the organization’s naming convention. Example: A critical report on all U.S. clients doesn’t include clients with “USA” or “United States of America” in the Country field.
- Duplicate data – A single Account, Contact, Lead, etc. that occupies more than one record in the database. Example: Sales reps in different regions unknowingly create three distinct IBM Accounts, wasting valuable time, sowing confusion, and causing missed opportunities.
Who Holds the Responsibility for High-Quality Salesforce Data?
There are a lot of stakeholders who rely on clean data in Salesforce. So, who should be responsible for achieving it?
The sales rep who manually enters customer data?
The marketing manager who periodically imports leads en masse?
The senior managers responsible for setting company policy?
Or the Salesforce administrator who manages the database?
The correct answer is “all of the above.”
While it’s easy to assume the responsibility for an organization’s Salesforce data cleaning strategy lies solely with the Customer Relationship Management (CRM) department, this isn’t really the case.
A CRM admin may have primary responsibility for the efforts made to achieve and maintain clean Salesforce data, but every member of the sales team, marketing team, and anyone else involved with the data should share the burden of ensuring its quality is top-notch. For example:
- Sales reps should carefully enter customer data according to naming conventions.
- Marketing managers should do their best to scrub new lists before importing them into the database.
- Senior managers should establish and maintain reasonable company policies regarding data quality.
Salesforce Admins Lead By Example
Of course, as any experienced Salesforce admin knows, mistakes will still be made. Despite a user’s best efforts, some bad data will still make it through. And even with the best company policies in place, the rules won’t always be followed.
This is one area where Salesforce admins can really shine… by championing data quality in their organization. As data quality ambassadors, they develop and enforce a quality standard, train team members in the CRM application, provide educational resources, maintain open lines of communication, and encourage every Salesforce user to do their part.
To help Salesforce admins carry out best practices in data management, we encourage reading our blog and the Salesforce blog regularly, attending Salesforce-related webinars or watching them on-demand, and downloading Validity’s white paper, Overcoming the Challenges of Bad Salesforce Data.
Salesforce Gaming: Really, That’s a Thing!
Finally, in relation to encouraging users to do their part in following the organization’s Salesforce data cleaning strategy, we’ll close with an interesting idea we’ve seen suggested by Bluewolf, an IBM company.
The idea is to incentivize employees to improve the cleanliness of data with game-like challenges.
Bluewolf recommends awarding points based on a Contact record’s completeness. For example, if a record has 10 fields and only four are required, give points for completing the remaining six.
Publishing a league table of average scores grouped by owner allows users to measure their own data and adherence to quality standards against that of their colleagues. And rewarding the star performers and champions of good record capture with prizes and recognition creates further employee engagement and awareness of the importance of data quality.