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-Remediate
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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DQLabs, Inc is proud to be a Top 10 Semi-Finalist in the Snowflake Startup Challenge 2023.

``Snowflake Startup Challenge 2023: DQLabs in Top 10 Semi-Finalist``
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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Recognitions & Partnerships

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DQLabs ranks as a Leader in the Everest PEAK Matrix® Assessment for Data Observability.

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Data Curation 101: Importance, Challenges, Processes and Best Practices
Data Curation 101: Importance, Challenges, Processes and Best Practices
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As organizations generate massive volumes of data, the need to maintain, organize, and validate this information has never been more important. Think of data curation as digital librarianship–it’s the art and science of organizing data so it remains findable, usable, and valuable throughout its lifecycle. While traditionally seen as the responsibility of data stewards and…

Enhance Data Trust and Reliability with Data Quality Dashboards
Enhance Data Trust and Reliability with Data Quality Dashboards
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What went wrong? Despite your preparation, the root cause is glaringly simple yet often overlooked—the lack of visibility into your data’s quality. How can you avoid this? The answer lies in leveraging Data Quality Dashboards! A data quality dashboard provides real-time insights into the health of your data landscape. Without data quality dashboards it becomes…

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What is Metadata Management Gartner defines metadata as the information that describes an information asset to improve its utility throughout its lifecycle and metadata management as the business discipline for managing the metadata. Metadata management, which was once sidelined, is now emerging as a broad-based practice. As a result of which, many data management platforms…

DataOps Success Starts with the Right Data Tools
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DataOps is a collaborative data management framework that focuses on improving the communication, integration, and automation of data flows between data and business teams. DataOps with its emphasis on collaboration and continuous improvement brings together data engineers, data scientists, analysts and other business users. This approach ensures the delivery of high-quality, reliable data to the…

Case Studies

Learn how enterprise customers worldwide leverage DQLabs to improve data quality.

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Upcoming Webinars

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White Paper

Insights on Modern Data Quality Approach using multiple capabilities and enabling reliable, accurate data for better business outcomes.

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CASE STUDIES

Learn how enterprise customers worldwide leverage DQLabs to improve data quality.

Read our Case Study

MDQS recordings available. Watch expert insights anytime.

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WHITEPAPER

The Convergence of Data Quality and Data Observability

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DQLabs, Inc is proud to be a Top 10 Semi-Finalist in the Snowflake Startup Challenge 2023.

``Snowflake Startup Challenge 2023: DQLabs in Top 10 Semi-Finalist``