Data360 DQ+

Precisely Data360 DQ+ is a data quality solution that aims to enhance the quality of data in-motion and at-rest. According to the vendor, it offers advanced monitoring, visualization, remediation, and reconciliation capabilities to ensure the accuracy, consistency, and reliability of data. The product is designed to target small to large enterprises and is utilized by business analysts, data scientists, data engineers, as well as professionals in the financial services and insurance industries.

Key Features

Data Quality Monitoring: According to the vendor, the product automates data quality checks across the entire data supply chain, ensuring data quality as transactions move across systems and processes. It validates data elements across multiple sources, tracks timeliness, and ensures totals are within limits.

Business-Driven Reconciliations: The vendor claims that the product can handle complex data quality rules for uncommon data sources, including third-party, text, numeric, financial, and streaming data. It supports business-driven reconciliations and provides case management with workflow tasks for issue remediation. Additionally, it maintains a complete audit trail for data quality activities.

Dashboards & Reporting: The product provides configurable built-in dashboards for personalized insights into raw or curated data. According to the vendor, it generates custom data quality scores aligned with business goals and visualizes data to understand outcomes. It aims to offer clarity into data of any size, from individual files to data lakes and streams at scale.

Shared Rule Library: According to the vendor, the product allows users to share and reuse data quality rules, reducing development time and redundancies. It promotes collaboration and shared understanding across teams, ensuring consistency in data quality metrics and visualizations. The vendor claims that it facilitates annotation on dashboards and captures point-in-time feedback.

User Collaboration: The product enables seamless collaboration on data quality metrics and visualizations. Users can annotate on dashboards and capture point-in-time feedback, promoting communication and shared understanding of data quality issues. The vendor states that it facilitates collaboration across all business levels within the organization.

Semantic Identification: According to the vendor, the product automatically tags data with a semantic type to understand and share its context. It provides a consistent and standardized way to identify and classify data, improving data governance and management practices. The vendor claims that it ensures accurate interpretation and usage of data for enhanced data quality.

Audit Transparency: The product allows access to historical audit information, aiming to decrease the review cycle time of compliance and policy audits. It provides visibility into data quality activities and changes over time, ensuring regulatory compliance and data governance requirements. According to the vendor, it supports transparency and accountability in data quality processes.

Integrated Data Governance: The vendor claims that the product integrates data quality and data governance practices, ensuring data quality as an integral part of the data governance framework. It aligns data quality rules and metrics with data governance policies and guidelines, providing a holistic approach to managing and improving data quality.

Self-Service Options: The product offers self-service capabilities for users to manage and monitor data quality. According to the vendor, users can perform data quality checks and remediation tasks independently, reducing dependency on IT or data quality teams for routine activities. It aims to improve efficiency and agility in data quality management.

Advanced Visualizations: The product provides configurable built-in dashboards for visualizing data quality metrics. It offers interactive charts, graphs, and reports to gain deeper insights into data quality. According to the vendor, users can customize visualizations based on specific requirements, enhancing data storytelling and communication of data quality insights.