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Writing and customer stories on Salesforce and Dynamics 365 data quality — deduplication, validation, routing, and what breaks when data drifts.

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    Data Quality & Artificial Intelligence

    AI changes how Salesforce data quality gets managed: active learning for deduplication, NLP-based fuzzy matching, predictive cleanup, and automated workflows.

    • Salesforce
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    Causes of Poor CRM Data Quality

    Five root causes explain most of the bad data in a CRM: entry errors, incomplete records, duplicates, outdated information, and no shared data standards.

    • Data quality
    • Deduplication
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    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
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    The Importance of Data Quality

    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.

    • Data quality
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    7 Best Data Quality Practices

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

    • Salesforce
    • Data quality
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    3 Types of Data Quality 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.

    • Salesforce
    • Deduplication
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    What Is a Marketing Tech Stack?

    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.

    • Data quality
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    6 Steps to Implement a New Martech Tool

    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.

    • Data quality

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