Semantics Discovery and Enrichment

Data quality measurement without semantics or understanding of the business context of the data does not help drive better business practices. With DQLabs’ Data Sense™ capabilities, you can automatically enrich semantics for any type of data with or without metadata information. Now you can not only automate the process of discovering, inventorying, profiling and tagging using a simplified form of metadata management but also auto discover rules as well as sensitivity classification in alignment with your organizational landscape.

With semantic identification and extended integration into data catalog or data governance systems, now you can derive end-to-end views of your data assets for governance, privacy, compliance and data quality. This not only allows the data stewards to search and discover metadata but also understand the data quality associated with each attribute.

Check out our Semantics Discovery Features

Leverage DQLabs’ Semantics Discovery for any type of data source with built-in support and integration for simplified metadata management and automate the process of discovering, inventorying, profiling, and tagging data.

Auto Discover Semantics

Auto Discover Semantics

Discover and extract semantics from various enterprise data warehouses, operational databases, enterprise applications, cloud data stores, nonrelational data stores, and many more with just a few clicks using out-of-the-box connectors.

Identify True Data Type

Ignore formatting, locale and culture and identify the true data type at attribute level to find relevant data quality rules.

Identify True Data Type
Automatic Sensitivity Classification

Automatic Sensitivity Classification

Configure at ease your own sensitivity levels per your data governance programs and automatically identify the sensitivity footprint andclassification at each attribute level.

Auto Discover Relevant Rules

With enriched semantics and business context for each attribute, let the platform discover all relevant data quality measurement rules for you.

Auto Discover Relevant Rules
Auto Detect Necessary Remediations

Auto Detect Necessary Remediations

Measurement without remediation does nothelp to improve data quality. With enriched metadata and semantics, now you can enjoy remediation libraries that can perform smart curation at every attribute or dataset level.

Search and Discover with Relevance

Perform semantic searches across dataset and find the most relevant datasets by various metrics such as data quality score, drift level, sensitivity classification etc., all within one platform.

Search and Discover with Relevance
Auto tagging and Classification

Auto-tagging and Classification

Includes classification per the most up-to-date data privacy and security compliance regulations — such as the EU General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA).

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Modules

Leverage an out of the box Data Quality platform with measurement, monitoring and remediation.

Measure Data Quality

Scan various types of data sources and data sets in real-time and generate a trustable DQScore™ with the ability to track, manage and improve data quality over time.

Monitor Drift and Behavior Analysis

DQLabs Continuous DQ monitoring uses statistical and machine learning approaches to detect data outliers and anomalies.

Remediate and Improve Data Quality

Remediate Data Quality Issues by cleaning, enriching and merging good records using ML based Smart Curation and Self Learning


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