REAL-TIME DATA QUALITY

Control customer data at the point of entry

Real-Time Data Quality validates, standardizes, and controls customer data the moment it enters your systems. Prevent bad data at the source and watch as duplicates decrease, matching improves, and your customer data foundation gets even stronger.

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The benefits of Real-Time Data Quality

Stop bad data at the source
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Validate customer data as it is entered so inaccurate, incomplete, and inconsistent records do not spread across systems.
Reduce duplicates and improve matching
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Strengthen duplicate prevention and matching accuracy by improving data consistency before records are stored or synchronized.
Increase operational efficiency
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Reduce the time and cost spent fixing downstream errors, cleansing data later, and reconciling inconsistent customer records.
Support better customer experiences
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Give sales, service, and marketing teams cleaner customer data to support smoother interactions and more reliable engagement.
Improve downstream analytics and AI outcomes
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Feed analytics, automation, and AI-driven workflows with more accurate and consistent customer data from the start.

What’s included in Real-Time Data Quality

Real-time validation at the point of entry
Validate customer data as it is created in forms, CRM systems, and other operational workflows.
Automated quality checks and rules
Apply validation rules, standards, and controls automatically to improve consistency and reduce errors.
Duplicate detection and prevention
Use matching logic during data entry to help identify and prevent duplicate customer records before they spread.
Data standardization and formatting
Normalize entered data, so records are more accurate, usable, and easier to match across systems.
Integration with CRM, ERP, and surrounding systems
Connect real-time data quality checks into Salesforce, HubSpot, and other systems that create and use customer records.

How Real-Time Data Quality works

Real-Time Data Quality, a part of Customer Experience Data Cloud, helps turn inconsistent customer input into accurate, standardized records. That way, your teams can improve downstream matching, operational efficiency, and customer data quality across the business.

1. Capture data during entry

Capture customer data in CRM systems, forms, operational systems, and other points of entry.

2. Validate against rules, formats, and sources

Check data against validation rules, required formats, and available reference sources to identify issues immediately.

3. Flag or correct issues in real time

Surface errors as they happen so users can correct them before bad data enters the system.

4. Prevent duplicates with matching logic

Use matching and duplicate-prevention logic during entry to reduce the chance of fragmented or repeated customer records. 

5. Store clean, consistent records

Ensure downstream systems receive more accurate, standardized customer records from the moment data is created.

Real-Time Data Quality in action

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Nets built a more complete view of customer-related data

• Improved data quality consolidating master data

• Built a complete view of customer contracts, products, and locations

• Identified new business opportunities

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A global insurance provider improved record completeness and reduced duplicates

• 25% uplift in record completeness

• 33% of records deduplicated

• Unified 1.3 million customer records across regions and systems

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Bennet made customer data more accurate and consistent across touchpoints

• Centralized and standardized customer information

• Created a more accurate customer view

• Improved operational efficiency

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Real-Time Data Quality FAQ

What is real-time data quality?

Real-time data quality is the process of validating, standardizing, and controlling customer data as it is entered, so inaccurate, incomplete, and inconsistent records do not spread across systems.

 

Why is data quality important at the point of entry?

Improving data quality at the point of entry helps prevent bad data from being created in the first place, reducing downstream cleanup, duplicate records, and operational friction.

How does real-time validation prevent duplicates?

Real-time validation helps prevent duplicates by standardizing and checking records during entry, making matching logic more effective before data is stored across systems.  

How does Real-Time Data Quality integrate with CRM systems?

Real-Time Data Quality is designed to connect with CRM systems, forms, and other operational entry points where customer data is created and updated.  

How does Real-Time Data Quality improve customer data quality?

It improves customer data quality by validating records at the source, applying quality rules in real time, and ensuring cleaner, more consistent data enters the business from the beginning.

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