Data Quality Navigator: AI Data Quality & Cleansing Tool
Data Quality Navigator helps organizations manage and clean datasets to support ERP implementations and data governance. It is designed for teams in production, logistics, and procurement that need to maintain data accuracy.
At a glance
- Category
- Browse Data & Analytics tools
- Best for
- Enterprise companies, Mid-market companies, Data management teams, Organizations undergoing ERP transformation
- Pricing
- Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
- Key use cases
- ERP Implementation Support, ESG Reporting, Procurement Data Management, Logistics and Production Optimization, Data Governance
- Official website
- Visit data quality navigator official website
Data Quality Navigator is an AI-driven platform focused on the accuracy and completeness of business data. It uses automated rule discovery and a library of validation rules to identify errors, duplicates, and missing information.
The tool is designed for organizations undergoing digital transformations or ERP migrations. It helps these businesses detect data issues and apply corrections using AI-assisted suggestions.
Users can manage data quality through a centralized workbench and track progress via dashboards. This allows teams to assign responsibilities for data cleansing and monitor the health of their datasets over time.
Buyers should confirm how the AI-driven correction suggestions align with their specific industry standards and whether the rule catalog covers their particular data structures.
Key Features
AI-Powered Validation
Uses Reader AI to validate data against trusted sources and documents to detect accuracy issues.
Automatic Rule Discovery
Includes Rule Mining AI to identify validation rules within the data.
Designer AI Rule Editor
A low-code editor that helps domain experts translate business knowledge into custom validation rules.
Automated Data Correction
Sweeper AI provides recommendations to fix detected errors and fill in missing details.
Rule Catalog
A repository of over 2,500 validation rules covering completeness, correctness, and duplication.
Quality Dashboards
Monitoring tools that track data quality development over time and highlight pain points.
Use Cases
ERP Implementation Support
Cleaning and harmonizing data to support migrations to new ERP systems.
ESG Reporting
Validating data to support ESG reporting and compliance.
Procurement Data Management
Supporting spend analysis by improving the quality of procurement and sourcing data.
Logistics and Production Optimization
Identifying errors in production data to help reduce process disruptions.
Data Governance
Creating visibility and accountability for data quality through a centralized tool.
Best For
- Enterprise companies
- Mid-market companies
- Data management teams
- Organizations undergoing ERP transformation
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
FAQ
What does Data Quality Navigator do?
- It is an AI-powered platform that identifies and corrects data errors using validation rules, automatic rule discovery, and automated cleansing suggestions.
Who is this tool best for?
- It is designed for businesses in the defense, logistics, production, and procurement sectors, or those undergoing an ERP implementation.
Does it require technical skills to create rules?
- The platform includes a low-code Designer AI editor that helps domain experts create rules, while providing a more technical editor for advanced users.
Source category: Data & Analytics
Source subcategory: Data Quality
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Categories
Software Type
How AI is used
Data Quality Navigator is an AI-powered data cleansing and validation tool for mid-market and enterprise businesses. It supports data governance and ERP implementation workflows through automated rule discovery and error correction. Buyers should evaluate if the provided rule catalog meets their specific industry requirements.
Pros & Cons
Pros
- Includes a library of 2,500+ pre-built validation rules.
- Low-code rule editor supports non-technical domain experts.
- Automated suggestions for data correction may reduce manual effort.
- Provides visibility into data quality trends through dashboards.
Cons
- Pricing information is not clearly available from the provided evidence.
- Effectiveness may depend on the quality of external trusted sources used for validation.
