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

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- DQLabs Platform
One unified platform for data leaders, engineers, and analysts—offering observability, quality, discovery, and remediation.
- Agentic AI Data Management
An autonomous, Agentic AI driven capabilities to continuously improve management of data issues.
- DQLabs Capabilities
Automated data quality, anomaly detection, semantics-driven discovery, and issue resolution for trusted and reliable data.
- Data Observability
Monitor data, pipelines, and usage—detect anomalies, ensure reliability, and optimize the health of your data systems.
- Data Quality
Automate data quality with no-code checks, anomaly detection, lineage, and governance for trusted, accurate, and reliable data.
- Data Discovery
Discover data with semantics-driven categorization, advanced search, and catalog integration for faster, actionable insights.
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Automated Data Quality Management in Oracle

Overview

Oracle Database is a relational database management system (RDBMS) developed by Oracle Corporation. It is one of the most widely used enterprise-level database systems in the world, designed to provide efficient, reliable, and secure storage and management of large amounts of data. Oracle Database offers a variety of features and tools for data management, including support for SQL and PL/SQL programming languages, advanced security features, backup and recovery capabilities, high availability options, and scalability for handling growing data volumes.

Integrating DQLabs with Oracle Database ensures the quality of source data right from the point of ingestion. This integration helps detect errors early, isolate problematic data, and resolve issues before they impact downstream systems. By continuously monitoring data quality, configuring proactive alerts, and maintaining robust data pipelines, businesses can prevent disruptions, minimize downtime, and eliminate the need for reactive data quality fixes ultimately ensuring more trusted data for decision-making and operational efficiency.

Data Quality and Observability for Oracle

Automate Data Quality Checks

Automate your data quality checks using out-of-the-box data measures across multiple categories and perform deep column-level profiling at ease for business validation.

Strengthen Data Trust and Governance

Improve the health of your data and accuracy towards business purposes using auto-discovery features around semantics tagging, business rules, and terms.

Empower AI, ML, and Analytics Initiatives

Data quality is often the primary barrier preventing organizations from embracing advanced data use cases. With DQLab’s platform, Oracle users can enhance data trust and confidence, driving more impactful decision-making and innovation in their AI, ML and analytics use cases.

Automate Data Validation

With this integration organizations can automatically validate data as it enters the Oracle database. This ensures that the data is trusted before it is used in reporting or analytics. For instance, validating data types, formats and ranges can be automated during the ingestion process.

Implement Custom Data Quality Checks

The integration allows you to define and implement custom data quality rules where you can specify the conditions that must be met (e.g., a field cannot be null, values must fall within a specified range, etc.).

Detecting Data Issues During Ingestion

By applying real-time data quality checks during the ingestion process, you can identify and rectify issues (such as duplicates, incomplete records, or invalid formats) before they enter the Oracle Database. This prevents poor-quality data from contaminating your data warehouse, which can otherwise lead to unreliable analytics and reporting.

Proactive Issue Detection and Reduced Risk

Integrating DQLabs with Oracle enables organizations to configure alerts to catch data quality issues early, ensuring that potential problems are identified before they escalate. By setting up automated alerts based on predefined data quality rules, organizations can proactively monitor their Oracle databases for issues such as missing values, data inconsistencies, or duplicates. This early detection empowers teams to address data quality concerns quickly, minimizing the risk of incorrect data affecting analytics, reporting, or decision-making. With real-time alerts, companies can ensure that their Oracle data is always accurate, complete, and ready for business decisions.

Seamlessly Integrate with Your
Modern Data Stack

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