DQLabs
Observe and manage the health of your data ecosystem with no-code advanced anomaly detection algorithms.
Identify missing data before it impacts your projects and models.
The goal of a data quality program is to build trust in data. However, trust is an expansive, and often…
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The need for high quality, trustworthy data in our world will never go away. Treating data quality as a technical…
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With the growth in data and the adoption of cloud architecture or hybrid with data spread across cloud and on-premise,…
Read MoreLeverage DQLabs’ unified platform with smart capabilities across data observability, quality, and discovery.
Using DQLabs smart connectors that comes out of the box, you can connect to an unlimited amount of data sources in any form, shape and any location.
With DQLabs’ Data Sense™ capabilities, you can automatically enrich semantics for any type of data with or without metadata information.
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.
DQLabs Continuous DQ monitoring uses statistical and machine learning approaches to detect data outliers and anomalies.
Remediate Data Quality Issues by cleaning, enriching and merging good records using ML based Smart Curation and Self Learning
Measure business outcomes and receive valuable insights for recommendations using out of the box dashboards.
Using DQLabs smart connectors that comes out of the box, you can connect to an unlimited amount of data sources in any form, shape and any location.
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Readily available insights on alerts and data issues across your data stack powered with business dashboards and configurable widgets.
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Out-of-the-box advanced anomaly detection algorithm detects anomalies across all metadata and custom measures.
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