# DQLabs - llms.txt ## About DQLabs DQLabs is an Agentic AI-powered Data Observability and Data Quality company, recognized as a Visionary in the 2026 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions. The platform - branded as PRIZM by DQLabs - serves data engineers, data stewards, data leaders, data scientists, and data architects at mid-to-large enterprises. It automates data monitoring, anomaly detection, semantic discovery, and governance across modern data stacks, including Snowflake, Databricks, Google BigQuery, Azure, and AWS. Core capabilities: end-to-end data observability, AI-driven data quality rules, semantic/auto discovery, agentic AI task automation, data lineage, issue management, and usage analytics. Tagline: "Built for Speed. Designed for Trust." Primary ICP: CDOs, Data Leaders, Data Engineers, Data Stewards at enterprise organizations in Financial Services, Insurance, Healthcare & Life Sciences, Retail/CPG, Government, Energy & Utilities, Manufacturing, and Technology verticals. Key differentiators: Gartner Visionary recognition (2025 & 2026), Everest Group Leader (PEAK Matrix 2024), Gartner Peer Insights Customers' Choice, G2 High Performer, 50+ native integrations, no-code rule builder, AI-driven anomaly detection with reduced false positives, and semantic layer for automated data classification. --- # Core Platform > Important (https://www.dqlabs.ai/): DQLabs homepage - Agentic AI-powered Data Observability and Data Quality platform. Best starting point for any product or company query. > Important (https://www.dqlabs.ai/prizm/): PRIZM by DQLabs - the AI-native flagship platform for data observability and data quality. Overview of architecture, key features, and differentiation. > Important (https://www.dqlabs.ai/platform/): Full platform overview covering all capabilities - observability, quality, discovery, and agentic AI - in one unified view. > Important (https://www.dqlabs.ai/data-observability/): Data Observability product page - real-time pipeline monitoring, anomaly detection, schema change detection, freshness checks, and root cause analysis. > Important (https://www.dqlabs.ai/data-quality/): Data Quality product page - AI-driven rule generation, out-of-the-box checks, no-code rule builder, data lineage, and compliance assurance. > Important (https://www.dqlabs.ai/data-discovery/): Data Discovery product page - semantics-driven auto-discovery, classification, tagging, and business context enrichment across data assets. > Important (https://www.dqlabs.ai/ai-agentic-data-management/): Agentic AI Data Management - autonomous AI agents for quality, observability, and efficiency at enterprise scale. Covers MTTD reduction, automated task handling, and smart rule recommendations. (https://www.dqlabs.ai/dqlabs-capabilities/): Full capabilities listing - anomaly detection, semantic discovery, issue management, usage analytics, and governance features in one reference page. (https://www.dqlabs.ai/unified-data-quality-approach/): Explains DQLabs' unified approach integrating data quality, observability, and discovery for modern data teams. (https://www.dqlabs.ai/data-observability-tool/): Product detail for the AI-powered data observability tool - pipeline monitoring, proactive anomaly detection, and issue resolution. (https://www.dqlabs.ai/data-quality-tool/): Product detail for the AI-driven data quality tool - automated monitoring, validation, and governance for accurate business decision-making. (https://www.dqlabs.ai/ml-based-anomaly-detection/): AI-driven data profiling and anomaly detection - covers automated health monitoring and data quality management. (https://www.dqlabs.ai/semantic-discovery/): Semantic Discovery feature - automated data categorization, classification, and governance to enhance data quality and visibility. (https://www.dqlabs.ai/business-dashboards-and-insights/): Automated data quality dashboards tailored for different user personas - monitor, improve, and gain insights into data quality and governance. (https://www.dqlabs.ai/smart-native-connectors/): Smart Native Connectors - seamless integration across data ecosystems via automated solutions and customizable APIs. (https://www.dqlabs.ai/usage-analytics/): Usage Analytics - query performance optimization, cost reduction, and insights into data consumption trends. (https://www.dqlabs.ai/issue-management/): Issue Management - centralized tracking, alert prioritization, and collaboration tools for accelerating data quality issue resolution. (https://www.dqlabs.ai/improved-governance/): Data Governance features - secure access, role-based controls, and domain-based policies for compliance across organizations. (https://www.dqlabs.ai/drift-and-behavior-analysis/): Drift and Behavior Analysis - automated data drift detection, metric tracking, and ML-driven anomaly insights. (https://www.dqlabs.ai/measure-data-quality/): Data Quality Scoring - automated dashboards and supervised learning for measuring and improving organizational data accuracy. (https://www.dqlabs.ai/conversational-data-quality-and-observability/): Conversational Data Quality and Observability - GenAI-powered interface for data exploration and quality management. --- # Solutions - By Industry (https://www.dqlabs.ai/data-observability-for-financial-services/): Data Observability for Banking, Financial Services & Insurance - trend tracking, risk mitigation, and compliance improvement. (https://www.dqlabs.ai/data-quality-for-financial-services/): Data Quality for Financial Services - ensuring accurate and consistent financial data integrity. (https://www.dqlabs.ai/data-observability-for-healthcare-and-life-sciences/): Data Observability for Healthcare and Life Sciences - clinical data monitoring for improved patient outcomes. (https://www.dqlabs.ai/data-quality-for-healthcare-and-life-sciences/): Data Quality for Healthcare and Life Sciences - enhancing data accuracy for patient safety and AI readiness. (https://www.dqlabs.ai/data-observability-for-retail-and-cpg/): Data Observability for Retail and CPG - uncovering sales patterns and consumer insights in real time. (https://www.dqlabs.ai/data-quality-for-retail-and-cpg/): Data Quality for Retail and CPG - boosting product data accuracy and consistency across supply chains. (https://www.dqlabs.ai/data-observability-for-government/): Data Observability for Government - transparency, real-time public data monitoring, and compliance. (https://www.dqlabs.ai/data-quality-for-government/): Data Quality for Government - driving reliable public records and service quality. (https://www.dqlabs.ai/data-observability-for-energy-and-utilities/): Data Observability for Energy and Utilities - monitoring operational metrics for safe power delivery. (https://www.dqlabs.ai/data-quality-for-energy-and-utilities/): Data Quality for Energy and Utilities - optimizing utility data accuracy and regulatory reporting. (https://www.dqlabs.ai/data-observability-for-manufacturing/): Data Observability for Manufacturing - tracking production data for continuous process improvement. (https://www.dqlabs.ai/data-quality-for-manufacturing/): Data Quality for Manufacturing - enhancing product data quality and reducing defects. (https://www.dqlabs.ai/data-observability-for-technology/): Data Observability for Technology - real-time visibility into technical operations and data pipelines. (https://www.dqlabs.ai/data-quality-for-technology/): Data Quality for Technology - driving data consistency to accelerate product innovation. --- # Solutions - By Data Platform (https://www.dqlabs.ai/snowflake/): DQLabs for Snowflake - automated data quality and observability natively integrated with Snowflake. (https://www.dqlabs.ai/databricks/): DQLabs for Databricks - data profiling, monitoring, and observability for Databricks pipelines and lakehouse environments. (https://www.dqlabs.ai/aws/): DQLabs for AWS - data quality and observability across AWS data services. (https://www.dqlabs.ai/azure/): DQLabs for Azure - data quality management and observability integrated with Azure data services. (https://www.dqlabs.ai/google-cloud-platform/): DQLabs for Google Cloud Platform - quality and observability for GCP-based data stacks. (https://www.dqlabs.ai/sap/): DQLabs for SAP - data quality management for SAP environments. (https://www.dqlabs.ai/catalogs/): DQLabs for Data Catalogs - integration with catalog tools for enriched data quality and governance. --- # Solutions - By Persona (https://www.dqlabs.ai/data-engineers/): DQLabs for Data Engineers - schema change detection, pipeline reliability, root cause analysis, and anomaly resolution. (https://www.dqlabs.ai/data-leaders/): DQLabs for Data Leaders - organization-wide data health visibility, strategic decision support, and measurable business outcomes. (https://www.dqlabs.ai/data-scientists/): DQLabs for Data Scientists - data drift monitoring, schema consistency, and trustworthy inputs for AI/ML models. (https://www.dqlabs.ai/data-architects/): DQLabs for Data Architects - data integrity across complex systems with automated quality checks and 50+ integration support. (https://www.dqlabs.ai/data-stewards/): DQLabs for Data Stewards - governance enforcement, compliance monitoring, and end-to-end lineage tracking at scale. --- # Integrations (https://www.dqlabs.ai/integrations/): Full integrations directory - 50+ native connectors across databases, data platforms, orchestration tools, catalogs, BI tools, and notification systems. (https://www.dqlabs.ai/integrations/snowflake/): Snowflake integration - automated data quality and observability for Snowflake environments. (https://www.dqlabs.ai/integrations/databricks/): Databricks integration - data profiling, monitoring, and observability for Databricks. (https://www.dqlabs.ai/integrations/google-bigquery/): Google BigQuery integration - real-time data quality monitoring and anomaly detection for BigQuery. (https://www.dqlabs.ai/integrations/amazon-redshift/): Amazon Redshift integration - automated data quality management and issue resolution. (https://www.dqlabs.ai/integrations/azure-synapse/): Azure Synapse integration - automated data quality management for Azure Synapse Analytics. (https://www.dqlabs.ai/integrations/dbt/): dbt integration - data quality checks and anomaly detection in dbt transformation pipelines. (https://www.dqlabs.ai/integrations/apache-airflow/): Apache Airflow integration - real-time monitoring and automated quality checks in Airflow pipelines. (https://www.dqlabs.ai/integrations/tableau/): Tableau integration - data quality monitoring and validation to ensure trusted data in Tableau dashboards. (https://www.dqlabs.ai/integrations/power-bi/): Power BI integration - ensuring only trusted data is visualized for business decision-making. (https://www.dqlabs.ai/integrations/collibra/): Collibra integration - data governance and quality management with real-time insights synced to Collibra. (https://www.dqlabs.ai/integrations/alation/): Alation integration - data quality metrics synced with Alation for unified data management. (https://www.dqlabs.ai/integrations/atlan/): Atlan integration - data quality metrics synchronization and real-time monitoring in Atlan. (https://www.dqlabs.ai/integrations/oracle/): Oracle Database integration - automated data quality management and real-time validation. (https://www.dqlabs.ai/integrations/mssql/): MSSQL integration - automated data quality management and issue resolution for SQL Server. (https://www.dqlabs.ai/integrations/sap-hana/): SAP HANA integration - real-time data quality management and governance for SAP HANA. (https://www.dqlabs.ai/integrations/postgresql/): PostgreSQL integration - automated data quality management and continuous monitoring. (https://www.dqlabs.ai/integrations/ibm-db2/): IBM Db2 integration - AI-driven data quality monitoring and compliance for Db2. (https://www.dqlabs.ai/integrations/ibm-db2-i-series/): IBM Db2 iSeries integration - automated data quality management and regulatory compliance. (https://www.dqlabs.ai/integrations/aws-athena/): AWS Athena integration - advanced profiling, monitoring, and anomaly detection for Athena queries. (https://www.dqlabs.ai/integrations/aws-emr/): AWS EMR integration - real-time data quality management for large-scale EMR data processing. (https://www.dqlabs.ai/integrations/adls/): Azure Data Lake Storage integration - automated data quality management and governance for ADLS. (https://www.dqlabs.ai/integrations/adf-pipeline/): Azure Data Factory integration - data quality monitoring and lineage tracking within ADF pipelines. (https://www.dqlabs.ai/integrations/redshift-spectrum/): Amazon Redshift Spectrum integration - data quality management and anomaly detection for Spectrum queries. (https://www.dqlabs.ai/integrations/s3-select/): Amazon S3 Select integration - AI-driven validation and monitoring for S3-based data retrieval. (https://www.dqlabs.ai/integrations/talend/): Talend integration - real-time data quality monitoring and pipeline control. (https://www.dqlabs.ai/integrations/denodo/): Denodo integration - data quality monitoring and automated alerts for Denodo virtual data layer. (https://www.dqlabs.ai/integrations/slack/): Slack integration - real-time data quality alerts and anomaly notifications in Slack. (https://www.dqlabs.ai/integrations/teams/): Microsoft Teams integration - data quality issue management and alerts via Teams. (https://www.dqlabs.ai/integrations/jira/): Jira integration - automated tracking and resolution of data quality issues in Jira. (https://www.dqlabs.ai/integrations/big-panda/): BigPanda integration - enhanced incident management through real-time data quality monitoring. (https://www.dqlabs.ai/integrations/okta/): Okta integration - data security, governance, and quality management via Okta-controlled access. (https://www.dqlabs.ai/integrations/azure-active-directory/): Azure Active Directory integration - data quality and identity management compliance via Azure AD. (https://www.dqlabs.ai/integrations/ping-federate/): PingFederate integration - secure data access and identity governance for data quality management. (https://www.dqlabs.ai/integrations/ibm-saml/): IBM SAML integration - secure, compliant data access via centralized SAML authentication. --- # Customer Proof - Case Studies > Important (https://www.dqlabs.ai/case-studies/): Case studies index - customer success stories across industries demonstrating measurable outcomes with DQLabs. (https://www.dqlabs.ai/case-studies/leading-american-bank-improves-regulatory-data-accuracy/): Leading American Bank improves regulatory data accuracy by 75% using DQLabs on Azure. (https://www.dqlabs.ai/case-studies/leading-community-bank-achieves-regulatory-compliance/): Leading community bank achieves 100% regulatory compliance and trusted reporting with DQLabs and Azure. (https://www.dqlabs.ai/case-studies/leading-global-insurance-provider-cuts-underwriting-time/): Leading global insurance provider cuts underwriting time by 25% using DQLabs. (https://www.dqlabs.ai/case-studies/global-consumer-goods-leader-accelerates-product-innovation/): Global consumer goods leader accelerates product innovation by 30% with DQLabs. (https://www.dqlabs.ai/case-studies/global-toy-entertainment-leader-cuts-compliance-risks/): Global toy and entertainment leader cuts compliance risks by 40% using DQLabs. (https://www.dqlabs.ai/case-studies/global-industrial-tech-leader-boosts-engineering-productivity/): Global industrial tech leader boosts engineering productivity by 30% with DQLabs. (https://www.dqlabs.ai/case-studies/leading-waste-management-company-improves-data-quality/): Leading waste management company achieves 10x improvement in data quality on Snowflake with DQLabs. (https://www.dqlabs.ai/case-studies/tacoma-public-utilites-improves-data-discovery/): Tacoma Public Utilities leverages DQLabs and Snowflake to unlock high-quality data assets and improve data discovery. (https://www.dqlabs.ai/case-studies/city-of-spokane-achieves-data-trust/): City of Spokane delivers trusted data for public safety and community planning using DQLabs and Azure Databricks. (https://www.dqlabs.ai/generate-capital-creates-trust-in-data-with-dqlabs-and-snowflake/): Generate Capital creates trust in data using DQLabs and Snowflake for scalable, reliable business processes. --- # Analyst Reports & Recognitions > Important (https://www.dqlabs.ai/2026-gartner-magic-quadrant-for-augmented-data-quality-solutions/): DQLabs named a Visionary in the 2026 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions - download the report. Key credibility anchor for enterprise evaluations. (https://www.dqlabs.ai/2025-gartner-magic-quadrant-for-augmented-data-quality-solutions/): DQLabs named a Visionary in the 2025 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions. (https://www.dqlabs.ai/2024-gartner-peer-insights-voice-of-the-customer-for-augmented-data-quality-solutions/): DQLabs recognized as a 2024 Gartner Peer Insights Customers' Choice for Augmented Data Quality Solutions. (https://www.dqlabs.ai/reports/everest-group-data-observability-technology-provider-peak-matrix-assessment-2024/): DQLabs recognized as a Leader in Everest Group's 2024 PEAK Matrix® for Data Observability Technology Providers. (https://www.dqlabs.ai/forrester-data-quality-solutions-landscape-q3-2025/): DQLabs included in Forrester's Q3 2025 Data Quality Solutions Landscape report. (https://www.dqlabs.ai/reports/dqlabs-named-a-high-performer-in-the-summer-2025-data-quality-grid-report/): DQLabs named a High Performer in the Summer 2025 G2 Data Quality Grid® Report. (https://www.dqlabs.ai/reports/dqlabs-named-a-high-performer-in-the-winter-2025-data-quality-grid-report/): DQLabs named a High Performer in the Winter 2025 G2 Data Quality Grid® Report. (https://www.dqlabs.ai/industry-recognitions/): Full industry recognitions page - all analyst awards, G2 badges, Gartner and Everest Group acknowledgments. --- # Resources - White Papers (https://www.dqlabs.ai/white-papers/): White papers index - research and thought leadership on modern data quality and observability. (https://www.dqlabs.ai/white-paper/data-quality-roi-calculator/): Data Quality ROI Calculator white paper - how to calculate the measurable return on data quality investment. (https://www.dqlabs.ai/white-paper/the-convergence-of-data-quality-and-data-observability/): White paper on the convergence of data quality and observability in modern data management. (https://www.dqlabs.ai/white-paper/how-to-get-ai-ready-with-data-quality-and-data-observability/): White paper on preparing for AI with data quality and observability - practical steps and governance frameworks. (https://www.dqlabs.ai/white-paper/modern-data-quality-requires-a-rethink/): White paper on why modern data quality demands a fundamentally new approach to meet AI and analytics needs. --- # Resources - Solution Briefs (https://www.dqlabs.ai/solution-briefs/): Solution briefs index. (https://www.dqlabs.ai/solution-brief/the-high-cost-of-data-preparation-in-healthcare/): Solution brief on the high cost of data preparation in healthcare and how DQLabs reduces it. (https://www.dqlabs.ai/solution-brief/data-quality-issues-in-banking-and-financial-services/): Solution brief on data quality challenges in banking and financial services - compliance, risk, and accuracy. (https://www.dqlabs.ai/solution-brief/data-quality-on-a-sled-budget/): Solution brief on affordable data quality for State, Local, and Education (SLED) organizations. --- # Resources - eBooks & Guides (https://www.dqlabs.ai/ebooks-guides/): eBooks and guides index. (https://www.dqlabs.ai/data-observability-for-snowflake/): eBook on Data Observability for Snowflake - pipeline visibility, health monitoring, and best practices. (https://www.dqlabs.ai/data-observability-for-databricks/): eBook on Data Observability for Databricks - pipeline health, data engineering visibility, and AI readiness. --- # Resources - Competitor Comparisons (https://www.dqlabs.ai/dqlabs-vs-others/): DQLabs vs. competitors comparison page - evaluation guide for buyers assessing data quality and observability tools. --- # Resources - Webinars (https://www.dqlabs.ai/webinars/): Webinars index - full library of on-demand and upcoming webinars on data quality and observability. (https://www.dqlabs.ai/webinars/the-future-of-data-quality-and-observability-with-agentic-ai/): Webinar on the future of data quality with agentic AI - proactive issue detection and automated remediation, led by DQLabs CEO Raj Joseph. (https://www.dqlabs.ai/webinars/conversational-data-quality-and-observability-powered-by-genai-and-semantics/): Webinar on Conversational Data Quality and Observability powered by GenAI and semantics. (https://www.dqlabs.ai/webinars/modern-data-quality-for-snowflake/): Webinar on modern data quality for Snowflake - live demo of DQLabs profiling and observability capabilities. (https://www.dqlabs.ai/webinars/going-beyond-data-observability-deep-profiling/): Webinar on deep profiling and advanced data quality beyond basic observability. (https://www.dqlabs.ai/webinars/out-of-the-box-data-quality-scoring-for-databricks/): Webinar on automated data quality scoring and observability for Databricks. (https://www.dqlabs.ai/webinars/effective-unstructured-data-quality-management-with-the-dqlabs-platform/): Webinar on managing unstructured data quality with DQLabs - automation and key metrics for AI environments. (https://www.dqlabs.ai/webinars/life-sciences-pharmaceuticals-data-trust-for-ai/): Webinar on data trust for AI in life sciences and pharmaceuticals. (https://www.dqlabs.ai/webinars/ease-of-use-dataquality-made-simple-with-dqlabs/): Webinar on simplifying data quality management with DQLabs' AI-driven, no-code interface. (https://www.dqlabs.ai/webinars/unified-approach-metadata/): Webinar on unified metadata management for enhanced data governance and observability. (https://www.dqlabs.ai/webinars/data-quality-contracts/): Webinar on data quality contracts - collaboration between data producers and consumers via SLAs. --- # Resources - Blog (Featured Posts) (https://www.dqlabs.ai/blog/): Blog index - insights, trends, and expert opinions on data quality and observability. (https://www.dqlabs.ai/blog/what-is-data-observability/): Foundational explainer: what is data observability, its key pillars, and why it matters for modern data teams. (https://www.dqlabs.ai/blog/data-observability-vs-data-quality-key-differences-why-you-need-both/): Compares data observability and data quality - definitions, differences, and how they work together. (https://www.dqlabs.ai/blog/what-is-data-quality-management/): Comprehensive guide to data quality management - importance, components, and best practices. (https://www.dqlabs.ai/blog/how-to-evaluate-data-observability-tools/): Guide for buyers on how to evaluate data observability tools - key criteria and decision factors. (https://www.dqlabs.ai/blog/understanding-data-drift-and-why-it-happens/): Explains data drift, its causes, and how to detect and address it in production pipelines. (https://www.dqlabs.ai/blog/importance-of-data-quality-in-healthcare/): Explores the importance of data quality in healthcare in 2025 - patient safety, AI, and regulatory compliance. (https://www.dqlabs.ai/blog/what-is-modern-data-quality/): Explains modern data quality - decentralized ownership, AI-driven automation, and continuous monitoring. (https://www.dqlabs.ai/blog/why-data-observability-matters-for-ai-readiness/): Why data observability is critical for effective AI implementation and model reliability. (https://www.dqlabs.ai/blog/what-is-data-quality-and-why-is-it-important-for-ai-readiness/): Explains data quality's role in AI readiness - model accuracy, bias reduction, and decision-making. (https://www.dqlabs.ai/blog/4-pillars-of-modern-data-quality/): The four pillars of modern data quality: business context, product management, observability, and governance. (https://www.dqlabs.ai/blog/how-data-lineage-enhances-data-quality/): How data lineage improves data quality by providing visibility into data flow and transformations. (https://www.dqlabs.ai/blog/data-quality-automation/): Guide to data quality automation - AI-driven techniques for accuracy, efficiency, and reliability. (https://www.dqlabs.ai/blog/what-is-data-anomaly-detection/): Explains data anomaly detection - types, benefits, and use cases for proactive data quality management. (https://www.dqlabs.ai/blog/impact-of-data-quality-on-model-performance/): How data quality directly affects AI and ML model performance - challenges and best practices. (https://www.dqlabs.ai/blog/what-is-data-profiling/): Explains data profiling - techniques, importance, and how it supports data quality and analytics. (https://www.dqlabs.ai/blog/integrating-data-quality-checks-in-data-pipelines/): How to integrate data quality checks into pipelines to prevent bad data propagation. (https://www.dqlabs.ai/blog/how-to-improve-data-quality-a-complete-plan-for-data-teams/): Complete data quality improvement plan for data teams - strategies, challenges, and best practices. (https://www.dqlabs.ai/blog/what-is-dataops/): Explains DataOps as a collaborative framework for better data management, integration, and automation. (https://www.dqlabs.ai/blog/data-pipeline-observability/): Best practices for data pipeline observability - quality, performance, and reliability in modern data stacks. --- # Resources - Events (https://www.dqlabs.ai/events/): Events index - upcoming and past events where DQLabs presents or sponsors. (https://www.dqlabs.ai/modern-data-quality-summit-2024/): Modern Data Quality Summit 2024 - DQLabs' annual industry event on data quality, analytics, and AI. (https://www.dqlabs.ai/modern-data-quality-summit-2024-on-demand/): Modern Data Quality Summit 2024 on-demand - full session library including AI readiness, data observability, and governance tracks. --- # Conversion & CTA Pages > Important (https://www.dqlabs.ai/request-demo/): Book a demo - primary conversion page for prospective customers to schedule a personalized platform demo. > Important (https://www.dqlabs.ai/contact-us/): Contact DQLabs - inquiries, meeting scheduling, and platform information requests. (https://www.dqlabs.ai/resource-library/): Full resource library - white papers, reports, case studies, webinars, and guides in one place. --- # Company (https://www.dqlabs.ai/about-us/): About DQLabs - mission, team, and platform overview. DQLabs is on a mission to help organizations improve data trust. (https://www.dqlabs.ai/partners/): Partner program - tools, training, and resources for system integrators and technology partners. (https://www.dqlabs.ai/news/): News and announcements - product updates, industry insights, and thought leadership. (https://www.dqlabs.ai/careers/): Careers at DQLabs - open roles and culture overview for prospective employees. (https://www.dqlabs.ai/industry-recognitions/): Industry recognitions - Gartner, G2, Everest Group, and other analyst acknowledgments. (https://www.dqlabs.ai/how-can-dqlabs-drive-new-business-with-system-integrators-large-and-small/): DQLabs for System Integrators - how SIs can build data quality practices on top of the DQLabs platform. (https://www.dqlabs.ai/news/dqlabs-appoints-dave-casillo-as-chief-revenue-officer-to-drive-strategic-growth/): News: Dave Casillo appointed Chief Revenue Officer to lead DQLabs' go-to-market growth. (https://www.dqlabs.ai/news/renowned-gartner-analyst-ankush-jain-joins-dqlabs/): News: Gartner analyst Ankush Jain joins DQLabs as Head of Marketing and Product Strategy. --- # Legal (Low Priority) (https://www.dqlabs.ai/privacy-policy/): DQLabs privacy policy - data collection, use, sharing, and user rights. (https://www.dqlabs.ai/privacy-and-legal/website-terms-of-use/): Website terms of use - legally binding terms governing use of dqlabs.ai. (https://www.dqlabs.ai/privacy-and-legal/dqlabs-end-user-license-and-services-agreement/): End User License and Services Agreement - terms for software and cloud services usage.