The Modern Data Quality Platform

Deliver reliable and accurate data for better business outcomes.
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Modern Data Quality Platform leveraging observability

A Modern Data Quality Platform delivers the data that matters.

A centralized platform that brings both the data minds and business minds together to not only observe, measure but also visualize, remediate and collaborate on Data Quality issues in their own ways with a focus on direct outcomes and measurable business value.

The DQLabs Platform harnesses the combined power of Data Quality and Data Observability to enable data producers, consumers, and leaders to achieve decentralized data ownership and turn data into action faster, easier, and more collaboratively.

Data Observability

Data Observability

Remediation-centric

Data Relevance

Decentralized Data Ownership

Enhanced Data Collaboration

AI/ML-enabled Semantic Data Discovery

Fast, Reliable, and Scalable

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Trusted By Modern Data Teams Worldwide

Observe-Measure-Discover
the data that matters

The DQLabs platform harnesses the combined power of Data Observability, Data Quality and Data Discovery to enable data producers, consumers, and leaders to turn data into action faster, easier, and more collaboratively.

Data Quality

Agile quality-focused assessment for known issues across domains for data leaders, producers, and consumers.
Data Discovery

Automatic identification and classification of business terms across your data for standardized quality checks and improved governance.

Unified Data Quality Approach

Regardless of your role, you need a simple, augmented data quality platform that delivers relevant data that meets your unique needs.

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Data Reliability

Improve data trustworthiness for critical decisions using automated data monitoring and notifying data engineers of reliability issues.

Business Quality

Improve confidence in consumption using business outcome-focused checks across processes, process owners, and data stewards.

Fully Automated

Modernize your data infrastructure in minutes using ML-powered, no-code data quality checks.

Performance Delivered

Deliver millions of checks across petabytes of data across lakes and warehouses in seconds.

Security Compliant

SOC Type 2 compliant platform in a highly secure infrastructure with no customer data retrieval.

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Our Customers

Modern Data Quality Approach

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Join DQLabs at Data + AI Summit 2023
 Join DQLabs at Data + AI Summit 2023 750 416 DQLabs

DQLabs is a Bronze Sponsor at the Databricks’ Data + AI Summit 2023, the world’s largest data and AI conference returns June 26-29, 2023, at San Francisco. Visit our booth #12 to discover how Modern Data Quality Platform delivers reliable and accurate data for better business outcomes. Attend either in-person or free virtually to discover…

Modern Data Quality Approach
 Modern Data Quality Approach 750 416 DQLabs

An organization with 1000 employees, in 2022, has an average of 177 SaaS applications. Most of these applications store data relevant to their needs, However, in order to perform cross-organizational analysis, this data needs to be aggregated, enriched and integrated. This process vastly increases the scope of data quality initiative from the past days, when…

Top 4 Benefits of Modern Data Quality
 Top 4 Benefits of Modern Data Quality 750 416 DQLabs

The goal of a data quality program is to build trust in data. However, trust is an expansive, and often ill-defined term that can include many topics that control and manage data. Trusted data is possible when all the components of the metadata management platform work as a single unit. For example, without accurate data,…

4 Pillars of Modern Data Quality
 4 Pillars of Modern Data Quality 750 416 DQLabs

The need for high quality, trustworthy data in our world will never go away. Treating data quality as a technical problem and not a business problem may have been the biggest limiting factor in making progress. Finding technical defects, such as duplicate data, missing values, out of order sequences and drift from expected patterns of…

Case Studies

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