DataValidation
DataValidation is a data quality and validation tool used to ensure accurate and reliable data across databases and applications. It features an intuitive interface for quickly building validation rules, data profiling dashboards, and data cleansing workflows.
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data-validation
data-profiling
data-cleansing
DataValidation: Data Quality and Validation Tool
A data quality and validation tool ensuring accurate and reliable data across databases and applications with intuitive interface and data profiling dashboards.
What is DataValidation?
DataValidation is a powerful data quality and data validation platform used by data teams and IT organizations to improve data accuracy across their business systems. The software allows users to easily build validation rules and data quality checks through an intuitive point-and-click interface using a library of over 200 predefined rules as well as the ability to custom build regex and SQL-based validation checks.
Key features include:
- Centralized data quality hub consolidating validation rules across source systems
- Library of over 200 predefined data validation rules covering common data quality checks
- Intuitive dashboard highlighting overall data quality metrics and trends
- Data profiling for analysis of data types, patterns and statistics to identify quality issues
- Workflow automation for data parsing, standardization, matching and cleansing
- Real-time data monitoring via validation agents connecting to source databases
- Detailed reporting of data anomalies and validation scan results
- Collaboration among data stewards and IT teams for rapid remediation
DataValidation enables data quality teams to ensure reliable data at the source to reduce operational and compliance risks from bad data across the business. It is compatible with both cloud and on-premise environments and connects to most modern data sources including relational databases, NoSQL databases, data warehouses, SaaS applications, files, ERP systems and more.
DataValidation Features
Features
- Intuitive interface for building validation rules
- Data profiling dashboards for analyzing data quality
- Data cleansing workflows for automating data cleaning
- Supports multiple data sources and formats
- Customizable validation rules and alerts
- Collaboration and team management features
- Detailed audit trails and reporting
Pricing
- Subscription-Based
Pros
Improves data quality and reliability
Saves time and resources spent on manual data validation
Provides visibility and transparency into data issues
Scalable and adaptable to growing data needs
Collaborative features enable team-based data governance
Cons
Can be complex to set up and configure for large-scale deployments
Requires a learning curve for non-technical users
May be overkill for small-scale data validation needs
Integration with legacy systems can be challenging
Official Links
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