Limited data visibility
Understand data structures, completeness, distinct values, and potential PII.
Discover, validate, score, and trace enterprise data quality—all in one connected platform.
meta-lumiq helps data teams understand data quality, apply consistent validation rules, investigate issues, and trace data dependencies across supported databases.
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Data quality issues can undermine reporting, analytics, and operational decisions. Teams need a consistent way to discover data, validate it, investigate failures, and understand dependencies.
Understand data structures, completeness, distinct values, and potential PII.
Apply defined validation rules and investigate failures by severity.
Use configurable scores, history, and trends to assess data quality.
Explore data lineage to understand upstream sources and downstream dependencies.
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meta-lumiq combines data quality, observability, lineage, and governance, with planned AI capabilities to help teams build greater trust in enterprise data.
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Apply repeatable quality checks to data used in analytics and business reporting.
Profile customer datasets and identify missing values, patterns, and potential PII.
Apply a common validation approach across supported database environments.
Review validation failures and trace dependencies to understand potential impact.
Current focus: scheduled and batch data quality operations.
Why meta-lumiq?
meta-lumiq combines data quality validation, explainable scoring, lineage, and enterprise controls today—with planned AI capabilities intended to help teams identify anomalies, investigate potential causes, and explore relevant recommendations.
Apply defined validation rules across supported databases and investigate failures through a connected workflow.
Track configurable quality scores, history, and trends to understand data quality performance over time.
Explore table-level and column-level relationships to investigate dependencies and assess potential downstream impact.
Planned
Planned capabilities to help identify unusual data patterns and potential quality anomalies that warrant investigation.
Planned
Planned AI-generated suggestions to help teams investigate potential causes and identify possible corrective actions.
Planned
Planned recommendations to help teams understand compliance considerations and address risks associated with PII.
Available today
Planned capabilities
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meta-lumiq is a data quality and trust platform that helps teams discover, profile, validate, score, and trace enterprise data in one connected workflow.
meta-lumiq currently supports PostgreSQL, Oracle, MySQL, Snowflake, and Databricks, with a focus on scheduled and batch data quality operations.
Yes. Teams can define validation rules once and run them as scheduled or batch checks across supported databases, then investigate failures by severity.
AI-assisted anomaly detection is a planned capability, intended to help identify unusual data patterns and potential quality anomalies that warrant investigation.
Suggested resolutions are planned: AI-generated suggestions intended to help teams investigate potential causes and identify possible corrective actions.
meta-lumiq profiling helps identify potential PII today, and audit controls are available. Compliance and sensitive-data guidance is a planned capability.
Not currently. meta-lumiq's current focus is scheduled and batch data quality operations.
