Qualytics
Helping enterprises proactively manage their full data quality lifecycle through contextual data quality checks, anomaly detection and remediation. Expose anomalies and metadata to help teams take corrective actions. Automatically trigger remediation workflows to resolve errors quickly and efficiently. Maintain high data quality and prevent errors from affecting business decisions. The SLA chart provides an overview of SLA, including the total number of SLA monitoring that have been performed and any violations that have occurred. This chart can help you identify areas of your data that may require further investigation or improvement.
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Data Quality Sense
Data Quality Sense (DQS) is a 100% Salesforce-native data quality app by Tucario. It measures, monitors, and improves the reliability of your CRM data without ever exporting it — everything runs inside your Salesforce org.
DQS scores your data across six dimensions — Completeness, Validity, Uniqueness, Consistency, Timeliness, and PII Detection — producing a weighted Data Quality Score at the org, object, and field level. A no-code Definition Builder (5-step wizard) lets admins define rules, scheduled scans keep scores current, and Insight Studio surfaces trends and field health.
Automatic PII detection (8 pattern types incl. SSN, credit card, IBAN, email, IP, date of birth) helps teams find and protect sensitive data before exposure. With Agentforce and AI on the rise, DQS prepares your Salesforce data for reliable AI — an AI agent is only as good as the data behind it.
Available on the Salesforce AppExchange.
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DQOps
DQOps is an open-source data quality platform designed for data quality and data engineering teams that makes data quality visible to business sponsors.
The platform provides an efficient user interface to quickly add data sources, configure data quality checks, and manage issues. DQOps comes with over 150 built-in data quality checks, but you can also design custom checks to detect any business-relevant data quality issues. The platform supports incremental data quality monitoring to support analyzing data quality of very big tables. Track data quality KPI scores using our built-in or custom dashboards to show progress in improving data quality to business sponsors.
DQOps is DevOps-friendly, allowing you to define data quality definitions in YAML files stored in Git, run data quality checks directly from your data pipelines, or automate any action with a Python Client. DQOps works locally or as a SaaS platform.
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iceDQ
iceDQ is the #1 data reliability platform offering powerful, unified capabilities for Data Testing, Data Monitoring, and Data Observability. Designed for modern data environments, iceDQ automates complex data pipelines and data migration testing to ensure accuracy, integrity, and trust in your data systems. Its AI-based observability engine continuously monitors data in real-time, quickly detecting anomalies and minimizing business risks.
With robust cross-platform connectivity, iceDQ supports seamless data validation, data profiling, and data reconciliation across diverse sources — including databases, files, data lakes, SaaS applications, and cloud environments. Whether you're migrating data, ensuring ETL/ELT process quality, or monitoring live data streams, iceDQ helps enterprises deliver high-quality, reliable data at scale. From financial services to healthcare and beyond, organizations rely on iceDQ to make confident, data-driven decisions backed by trusted data pipelines.
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