As an #AwesomeAdmin, your org’s data quality relies on your company’s data stewardship efforts. Managing duplicate records, fixing data errors, and entering missing data helps you and your company improve the quality of data.
At Dreamforce ’18, our friends from Traction on Demand, an independent Salesforce consulting and application development firm, presented a high-level view of the increasingly important topic of data quality.
In their session, Jerry Schweiger and David Jenkins discussed:
With accurate data, you can better target your customers and analyze your results without second-guessing. Consider artificial intelligence (AI). According to Jenkins, AI is dumb with bad data. Without having quality data, AI features such as Einstein predict the wrong things. The phrase “garbage in, garbage out” is especially relevant today with the powerful AI tools that Admins have at their fingertips.
Salesforce found that CRM systems contain about 15% of duplicates for sales and service records. Additionally, roughly 20% of records contain useless or dead data that nobody has taken the time to remove. A good data stewardship process addresses the problems that contribute to these statistics and builds confidence among your sales and service teams. A win-win scenario!
Although keeping data clean costs time and money, the amount you spend to prevent bad data from entering your system is a bargain compared with the alternative of cleaning it up later—or worse, doing nothing. Consider the model below:
The topic of data quality is timely because Salesforce is working toward providing a 360-degree view of your team’s customers by connecting your Salesforce products with an easy-to-use user interface. Plus, data quality is the foundation for giving the teams you support a meaningful, unified view of their customers.
Data governance defines the rules and practices around how to keep data clean. Think of it as the blueprint for ensuring your data’s usability, quality, and compliance with the policies that matter to your company.
It’s important to note that data governance isn’t a binder of rules that you stash away and forget about. It’s an evolving process and a living document similar to many other practices Admins facilitate.
Data stewardship, on the other hand, is “… the feet on the streets. The people doing the work to facilitate keeping the data clean,” said Jenkins. It’s a specific role and set of activities, such as running duplicate checks and archiving inactive records. Stewardship also involves policing the rules and making sure that everyone plays fairly.
Keep in mind that data stewardship isn’t a small group of workers trying to wrangle data quality. It’s everyone’s responsibility.
Data governance and data stewardship work hand in hand. And when done correctly, your priorities and processes evolve. Try these best practices to keep data clean:
An effective data stewardship practice doesn’t fall on one person’s shoulders. It’s important to get buy-in from everyone who works with your customers’ data. Consider these tasks below to help you get started, or continue your momentum:
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