New 2025 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions - Download Report

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One unified platform for data leaders, engineers, and analysts—offering observability, quality, discovery, and remediation.
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An autonomous, Agentic AI driven capabilities to continuously improve management of data issues.
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Automated data quality, anomaly detection, semantics-driven discovery, and issue resolution for trusted and reliable data.
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Automate data quality with no-code checks, anomaly detection, lineage, and governance for trusted, accurate, and reliable data.
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Discover data with semantics-driven categorization, advanced search, and catalog integration for faster, actionable insights.
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Automated Data Quality Management in Google BigQuery

Overview

BigQuery is a fully managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence. BigQuery's serverless architecture lets you use SQL queries to answer your organization's biggest questions with zero infrastructure management. BigQuery's scalable, distributed analysis engine lets you query terabytes in seconds and petabytes in minutes.

The BigQuery and DQLabs integration makes it easy for organizations to detect, resolve, and prevent data quality issues by running data quality checks on BigQuery tables. Leverage the power of automated monitoring and anomaly detection to manage data quality across the stack and catch bad data before it has a downstream impact. Create incidents to track, manage, and resolve issues early so that every user can use and share reliable, trustworthy data.

Data Quality and Observability for Google BigQuery

Validate Data in Your Production Pipeline

By integrating DQLabs with Google BigQuery, organizations can continuously monitor and validate data in their production pipelines, enabling them to identify, analyze, and correct data issues at both the dataset and individual record levels, ensuring high-quality data flow.

Establish SLAs to Define and Enforce Data Quality Standards

The integration allows businesses to set and enforce data quality SLAs within BigQuery, ensuring that data meets predefined quality standards and is fit for its intended analytical purposes, reducing errors and inconsistencies. The integration also allows organizations to automatically monitor any BigQuery table for data quality issues. DQLabs continuously runs checks on data, detecting inconsistencies, missing values, duplicates, or other anomalies in real time. This automated monitoring reduces the need for manual checks, allowing teams to focus on high-priority tasks.

Ensure High-Quality Data for AI and Analytics

DQLabs automatically detects data issues and understands their root cause, giving you the confidence to trust the data being used in your AI models, analytics, and BI dashboards. This ensures that business decisions, powered by insights derived from data, are based on clean, accurate, and reliable information. By ensuring that only high-quality data enters your analytics pipelines or machine learning models, organizations can improve the accuracy and effectiveness of AI-driven predictions, boosting the overall performance of their analytics and machine learning frameworks.

Anomaly Detection

DQLabs leverages anomaly detection to identify data issues early in the process. By catching anomalies before they have a downstream impact on reports, analyses, or AI models, businesses can prevent costly mistakes or inaccuracies in their data-driven decisions. DQLabs not only identifies data quality issues but also provides insights into the root cause of these issues. This deep understanding enables faster remediation, as teams can quickly pinpoint whether the problem originates from data ingestion, transformation, or any other part of the pipeline.

Proactive Issue Resolution

When issues are detected, DQLabs automatically creates incidents that allow teams to track, manage, and resolve issues. This systematic approach ensures that data quality problems are addressed promptly, minimizing any potential disruption to business operations.

Accurate Insights and Reporting

By ensuring the quality of data in BigQuery, businesses can trust the insights generated from that data, whether it’s for operational reporting or high-level strategic decisions. This leads to better, more reliable decision-making across the organization. The ability to catch and resolve data issues early accelerates the data-to-insight process. With fewer data quality bottlenecks, teams can generate reports, dashboards, and models faster, enabling quicker responses to business needs.

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