Manually checking for errors and anomalies in datasets is a time-consuming and error-prone process that is not scalable, leading to delayed insights and diminished trust in the data.
Fragmented Data Sources
Organizations struggle to integrate and standardize data from disparate sources, leading to inconsistencies, duplication, and incomplete datasets.
Lack of Real-Time Data Monitoring
Errors or issues in data are often discovered too late, resulting in costly corrective actions and delayed decision-making processes.
Pipeline Failures
Schema changes, unexpected values, or pipeline delays disrupt workflows, introduce errors, increase data downtime and make it challenging to maintain reliable data flows across systems.
Monitor Data Quality
Continuous Monitoring for Accurate, Trusted Data
DQLabs continuously monitors data health, detecting and resolving inconsistencies instantly to ensure accurate and reliable insights for decision-making.
Trace Data Lineage
Complete Transparency Across Pipelines
Get full data lineage visibility with DQLabs, enabling fast issue resolution, reduced downtime, and maintained data integrity from source to destination.
Proactive Alerting and Notifications
Faster Fixes, Less Downtime
DQLabs uses advanced AI/ML techniques to identify outliers in real time, preventing analytical errors and enabling proactive issue resolution to reduce downtime.
Simplify and Streamline Data Quality the Smart Way with DQLabs
Data Quality Automation & Monitoring
Ensure Reliable Data with Automated Monitoring and Pre-built Quality Checks
50+ Pre-built Checks: Ensure data accuracy across key dimensions (Accuracy, Completeness, Consistency, Uniqueness, Validity, Timeliness).
Customizable Rules: Tailor data quality checks with flexible queries and conditional logic for specific use cases.
Advanced Data Profiling: Analyze data at basic and advanced levels with built-in profiling measures.
Real-Time Monitoring: Detect unexpected values, nulls, schema changes, and other issues in real time.
Anomaly Detection & Alerts
Proactively Identify and Resolve Data Issues with AI-Driven Anomaly Detection
Real-Time Anomaly Detection: Instantly flag unusual data patterns, outliers, or errors as they occur.
Customizable Thresholds: Set custom thresholds for anomaly detection based on specific business rules or data characteristics.
Automated Root Cause Analysis: Automatically identify potential root causes of data anomalies, reducing troubleshooting time.
Proactive Alerts & Notifications: Receive real-time notifications via email, SMS, or integrated platforms like Slack, ensuring prompt action.
Data Lineage & Governance
Gain Full Transparency and Control with Comprehensive Data Lineage and Governance
Data Lineage Mapping: Visualize data flow from upstream to downstream systems for quick issue identification.
Granular Access Control: Set role-based access and usage permissions for better data governance.
Bi-Directional Catalog Integration: Sync with data catalog tools to enhance data quality automation.
Audit Trails: Maintain a detailed history of data access, modifications, and usage for governance and regulatory requirements.
GenAI-Driven Data Quality
Simplify Data Quality Management with AI-Powered, Conversational Interfaces
Conversational Interface: Enable seamless interaction for both technical and non-technical users.
Simplified Queries: Easily request data profiles, explore quality metrics, and run checks with natural language.
User-Friendly Data Exploration: Easily explore and interact with data, reducing the need for complex queries or technical know-how.
Contextual Data Understanding: The AI provides contextual information to help users interpret data quality metrics and results.
Platform Flexibility & Usability
Seamless Integration and Flexible Deployment for All Data Environments
Flexible Deployment: Available on-premises, PaaS, SaaS, or in the cloud (public or private).
Wide Connectivity: Integrate with major data warehouses, lakes, ETL tools, catalogs, and issue management platforms.
Broad Audience Support: Designed for Data Leaders, Analysts, Engineers, Stewards, Scientists, and Citizen Users.
Low-Code/No-Code Interface: Empower business users to manage data quality without needing coding expertise, simplifying workflows across departments.
DQLabs Platform
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.
Detect unknown reliability issues across your data ecosystem to resolve any data issues faster than ever before for both data at rest and in motion.
Data Observability
Monitor and resolve your data issues before they affect your downstream operations.
Data content: Track data accuracy, completeness, and integrity. Detect anomalies, errors, and schema changes automatically.
Data flow and pipeline: Monitor data pipelines, resolve bottlenecks, and catch broken pipelines or failed jobs in real time.
User, usage and utilization: Identify data usage patterns, track user access, and spot unusual activity for optimized security and performance.
No code, automated business quality-focused checks for known issues across domains to ensure data is fit for purpose.
Data Quality
Ensure data accuracy and trust
50+ OOB DQ checks: Ensure data accuracy and trustworthiness with DQLabs out of the box DQ checks for all dimensions - Accuracy, Completeness, Consistency, Uniqueness, Validity, and Timeliness.
Customizable DQ checks: Customize data quality checks with flexible query mechanisms and conditional logic for specific use case requirements.
Data profiling: Out-of-box measures to analyze the data to profile at basic and advanced levels depending on one's use case.
Auto-discover rules and leverage standardized checks on business terms for improved governance and stewardship.
Data Discovery
Simplify the way you find and understand your data
Self-service data discovery: A user-friendly platform that provides End-to-end visibility of business terms and quality metrics across your entire data ecosystem.
Semantic layer: Ability to define semantic terms, tags, applications, dimensions, and fields for effective data categorization.
Catalog and governance integration: Bi-directional integration with leading catalog and governance tools for enhanced data cataloging, automated data quality, and Increased trust in data.
Augmented, plus GEN AI enabled remediation combined with the power of semantics.
Anomaly Detection and Alert Prioritization
Timely issue detection with smart alert prioritization
AI/ML powered anomaly detection: AI/ML-powered anomaly detection to continuously monitor data flows, comparing current values against historical benchmarks.
Alerts prioritization: DQLabs Automatically prioritize alerts by deviation severity—high, medium, or low—so you can focus on the most critical data issues first.
Issue management: Integrate with Jira, Slack, and more to streamline issue resolution, enhance collaboration, and drive faster decisions.
What Our Clients Are Saying
City of Spokane Enhances Public Safety with DQLabs
“DQLabs helped us deliver a consistent source of high-quality data to address high-risk populations’ concerns and improve public safety and community planning.”
Tacoma Public Utilities Enhances Data Discovery with DQLabs
8x
Improvement in data discovery and issue resolution
“By using DQLabs, We automated the end-to-end business process and were able to continuously monitor the critical data elements with better productivity..”
Seamlessly integrate with your Modern Data Stack
Recognitions & Partnerships
Let our experts show you the combined power of Data Quality, Data Quality and Data Discovery.
