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

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.

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.

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.).

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.

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.