Recursos de Melissa Data Quality Suite

Correctly identify inaccurate, incomplete, or duplicated data from a data source.

Utilize deletion, modification, appending, merging, or other methods to correct bad data.

Standardize data formatting for uniformity and easier data usage.

Clean data as it enters the data source to prevent mixing bad data with cleaned data.

Finds duplicates using the fuzzy logic technology or an advance search feature.

Provide follow-up information after data cleanings through a visual dashboard or reports.

Automatically run data identification, correction, and normalization on data sources.

Schedule automated audits to identify data anomalies over time based on set business rules.

Gives a view of the entire data quality management ecosystem.

Allows user role-based access and actions to authorization for specific tasks.

Validate phone numbers accurately including formats, country codes and area codes.

Screen phone numbers against blacklists and blockages to prevent verification attempts from known fraudulent personas.

Scale to handle verification requests to accommodate growth and peak call times without affecting performance.

Protect user data during verification process using encryption and secure transmission protocols.

Offer customizable options for verification workflows to tailor to business specific needs and preferences.

Integrate with existing systems and other verification applications via APIs or SDKs for smooth workflows.

Comply with relevant data protection regulations such as GDPR or HIPAA.

Share reporting and analytics tools to track verification performance, monitor usage of tools and identify areas for improvement.

Makes informed choices based on available data and objectives

Capability to perform complex tasks without constant human input