);position:absolute;right:0;width:10px;top:calc(50% + 0px);transform:translateY(-50%)}}@media (min-width:1200px){.dqlabs-header nav ul>li>a{font-size:18px}}@media (min-width:992px) and (max-width:1365px){.dqlabs-header .logo img{max-height:32px}.dqlabs-header nav ul>li{font-size:14px;margin-right:25px}}@media (max-width:1199px){.dqlabs-header{padding:5px 0!important}.dqlabs-header .container{position:relative;display:flex;justify-content:space-between;align-items:center;padding:0 20px}.dqlabs-header .menu-container{width:auto!important}.dqlabs-header .logo{margin-top:2px}.dqlabs-header .logo img{max-height:32px}.dqlabs-header nav,.dqlabs-header .btns-right{display:none!important}}@media (max-width:679px){.dqlabs-header .logo img{max-height:35px}.dqlabs-mega-menu{border:unset}}.dqlabs-card{width:100%;box-shadow:0 1px 10px #74747421;border-radius:20px;background-color:var(--white)}.dqlabs-card .dqlabs-card-body{padding:30px}body .col-md-12 .dqlabs-card{flex-direction:row}body .col-md-12 .dqlabs-card figure{width:100%;margin-bottom:0}body .col-md-12 .dqlabs-card .dqlabs-card-body{width:100%}.dqlabs-footer-links aside ul li a{color:#fff}.dqlabs-footer-links aside ul li{margin-bottom:10px;font-size:16px}div#eut-theme-wrapper{padding:123px 0 0 0}@media (max-width:480px){div#eut-theme-wrapper{padding:63px 0 0 0}}.initial-message-bubble{height:35px}body .popup{position:fixed;top:50%;left:50%;transform:translate(-50%,-50%);max-width:100%;background-color:#FFF;color:#fff;padding:50px 30px;box-shadow:0 4px 12px rgb(0 0 0/.6);flex-direction:column;z-index:1000;display:none;background-image:url(/wp-content/themes/corpus/images/popup/gartner-mq/dqlabs-named-a-visionary-bg.png);background-size:cover;border-radius:3px}body .popup-content-wrapper{display:flex;flex-direction:row;align-items:center;justify-content:center}body .popup img{width:43%;height:auto;object-fit:cover}body .popup .popup-content{width:60%}body 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(max-width:768px){body{font-size:16px;font-family:Arimo;font-weight:400;line-height:30px}h1{font-family:Inter;font-weight:600;font-style:normal;font-size:36px;text-transform:none;line-height:53px}h2{font-family:Inter;font-weight:600;font-style:normal;font-size:32px;text-transform:none;line-height:46px}h5{font-family:Inter;font-weight:800;font-style:normal;font-size:20px;text-transform:none;line-height:26px}h6{font-family:Inter;font-weight:600;font-style:normal;font-size:18px;text-transform:none;line-height:28px}button{font-family:Inter;font-weight:400;font-style:normal;font-size:13px!important;text-transform:uppercase;letter-spacing:.5px}a{color:#999}button{background-color:#e60000;color:#fff}#eut-body{background-repeat:no-repeat;background-size:contain;background-attachment:fixed}a{color:unset}.s0{fill:#000}.s0{fill:#000}.s0{fill:#000}.s0{fill:#000}.s0{fill:#000}.s0{fill:#000}.s0{fill:#000}.s0{fill:#000}}
in the 2025 Gartner® Magic Quadrant™ For Augmented Data Quality Solutions
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Ensure reliable, compliant, and secure data for public programs, policy decisions, and transparent regulatory reporting.
Book a DemoDQLabs empowers government agencies to tackle data issues with comprehensive observability for trusted operations.
Gain end-to-end visibility and control across your data landscape with data, pipeline, usage, and cost observability.
Government agencies must adhere to strict regulations such as FISMA, FedRAMP, GDPR (where applicable), and local government-specific policies governing data privacy, security, transparency, and reporting. Data observability helps by providing continuous monitoring, automated data lineage, and audit-ready documentation, ensuring agencies deliver accurate, traceable, and compliant data for regulatory reporting and public accountability.
Reliable, up-to-date data is critical for effective public services and policy decisions. Data observability platforms continuously monitor data freshness, volume, and schema integrity to detect stale, missing, or inconsistent data early. This enables government teams to maintain program accuracy, reduce service delays, and build public trust through timely and dependable information.
Transparent data lineage tracks data from its source through transformations to final reporting, helping agencies clearly demonstrate data integrity and compliance during audits. Automated lineage combined with pipeline observability simplifies error resolution, supports regulatory inspections, and fosters trust by providing a clear, auditable trail of how data is used in public decision-making.
DQLabs offers granular pipeline observability by tracking runs, jobs, tasks, and tests with linked charts and real-time alerts. This continuous visibility allows government agencies to quickly detect, prioritize, and remediate pipeline failures or bottlenecks that could disrupt critical services or reporting, ensuring smooth and uninterrupted operations.
Let our experts show you the combined power of Data Observability, Data Quality, and Data Discovery.
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