Reliable, error-free data is a cornerstone of growth for any instagram database company. Your data impacts customers at every point in the customer journey. It affects the marketing materials they receive, how tailored their sales conversations are, their post-sale support experience, and much more.
Internally, low-quality data impedes essential business processes, slowing them down, and creating manual data maintenance tasks that eat away at morale and time. Bad data’s effects ripple up the food chain to the C-suite, making reporting less accurate, and causing decisions to be based on faulty premises.
But fixing those issues is often easier said than done. There are dozens of potential data problems inside any CRM, across all of your fields, and pinpointing them is a daunting task. Many companies just don’t know where to start.
In this article and the downloadable Data Quality Checklist Cheat Sheet below, you’ll find a list of common data quality issues that should be a part of any data cleanup effort to improve customer data quality.
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Here are 15 key data issues to target when conducting a data cleanup project or designing a data management plan.
