Show Notes:
Poor-quality Sugar data poses significant risks to
marketing campaigns and sales initiatives by causing duplicated entries, missing data, inaccurate information, and/or outdated information that reduces engagement with prospects and customers. Without effective cleansing, these challenges escalate, hindering personalization in campaigns and perpetuating
issues throughout the customer lifecycle. In this episode, we review four best practices you can implement now to prevent or correct “bad” data.
 
Gartner research: How to Create a Business Case for Data Quality Improvements
Econsultancy: Cost of Bad Data Stats
Harvard Business Review: Bad Data Cost the US $3 Trillion Per Year
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