
5 Ways to Improve Data Quality
Five practices improve CRM data quality in practice: cleansing and deduplication, profiling and auditing, governance, master data management, and defined metrics.
- Salesforce
- Deduplication

Five practices improve CRM data quality in practice: cleansing and deduplication, profiling and auditing, governance, master data management, and defined metrics.

Poor data quality costs businesses hundreds of billions of dollars a year. This chapter covers five concrete ways clean CRM data pays for itself, from the customer journey to revenue.

Seven practices keep Salesforce data reliable over time: standards, regular checks, continuous improvement, training, collaboration, measurement, and the right tools.

Data quality tools fall into three categories: cleansing and deduplication, data quality management software, and data profiling. Here's what each one actually does.

A marketing tech stack is every tool that runs your marketing, sales, and support work: CRM, CMS, email, analytics. This chapter covers what belongs in one and why data quality decides its success.

Choosing a SaaS tool is half the job. This chapter covers implementing it: standardizing processes first, protecting data quality through the migration, training staff, and testing before rollout.

Ten metrics across sales, marketing, and customer success — from win rate to CLV — and why none of them mean anything if the data behind them is wrong.

Millions of SaaS tools claim to be the best. Here's how to define your actual requirements, check governance and security, research your shortlist, and compare finalists before you commit.

Sales blames marketing for weak leads; customer success can't renew without context from the deal. Five steps to align sales, marketing, and customer success around one shared view of the customer.