Data Quality Monitor

DataOps DQ Monitor automates the testing of data in motion and data at rest. Business users can monitor the data quality metrics using intuitive Dashboards.

Datagaps Data Quality Monitor

Benefits of DataOps DQ Monitor for Data Quality Monitoring

Build trust in the quality of your enterprise data by leveraging AI-powered Data Observability and Data Quality Dashboards 

Better Decision Making

Reliable data results in better decisions that impact the bottom line for organizations

Risk Mitigation

Improving Data Quality minimizes the risks associated with Compliance and Reputation

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Cost Reduction

Continuous Data Quality Monitoring reduces operational costs associated with rework in data intensive projects

Key Features

Simplify Data Management with AI-driven autonomous data quality empowering Business Users and Data Owners with Trustable data

Data Quality Dashboard

Displays a trend of Data Quality Scores for the enterprise. Users can drill down to review the scores at Data Model, Table, Data Element, and Data Quality Dimension levels.

Data Quality Score is automatically computed based on the Data Quality Rules for data at rest as data in motion (ETL). An easy-to-monitor Data Quality Dashboard, competing with best-in-class Data Quality Tools.

DQ Dimensions

Categorizes data checks into data quality dimensions such as Data Accuracy, Consistency, Unicity, Conformity, Completeness.

Data Quality Score

Computes Data Quality score for data assets and displays trend reports on a Data Quality Dashboard

Data Observability

Profiles data assets and compiles statistics from the past. Predicts expected values and detects deviations ahead of time.

Data Catalog

Crawls data sources for metadata information about Tables, Columns, and changes to them over time.

Business Data Rules

Data rules can be defined centrally by business users and applied automatically to data elements in multiple data sources.

Semantic Data Types

AI-enabled detection of Semantic Data Types classifies data (PII, PHI) and applies data type-specific quality rules.

Common Data Model

A dictionary of enterprise data elements and their definitions that can be mapped to physical tables and datasets.

Reference Data Checks

Validate the list of values in categorical data elements with the reference data to ensure that the values are as expected.

Data Reconciliation

Check for data integrity by matching data from various sources to ensure that data is consistent across systems.

Case Study

Data Governance and Data Quality Collaboration

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FAQ's about Data Quality Monitor

What is DQM?

Data Quality Management (DQM) refers to the processes and technologies involved in ensuring the accuracy, completeness, reliability, and relevancy of an organization’s data throughout its lifecycle.

Why is DQM important?

DQM is crucial because it ensures that data is accurate and trustworthy, which is essential for making informed decisions, maintaining regulatory compliance, and enhancing customer satisfaction.

What are Data Quality Dimensions?

Data Quality Dimensions include accuracy, completeness, consistency, reliability, and timeliness, which collectively help assess the value and effectiveness of data in supporting business processes.

How does the Data Quality Monitor integrate with existing data systems?

Data Quality Monitor seamlessly integrates with various data systems, offering flexibility and ease of use without disrupting your current data processes.

Can Data Quality Monitor handle complex data quality scenarios?

Absolutely. It’s designed to manage complex data quality scenarios, ensuring accuracy across diverse data sets.

What makes Data Quality Monitor stand out from competitors?

Its user-friendly interface, zero-code deployment, and advanced features like automated reporting and notifications set it apart.

How does Data Quality Monitor improve decision-making?

Providing accurate and reliable data quality assessments ensures that business decisions are based on trustworthy data.

Can non-technical users easily navigate Data Quality Monitor?

Yes, its intuitive design and zero-code deployment make it available to users of all non-technical backgrounds as well.

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Data Quality

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

Synthetic Data

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

ETL Testing

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

BI Validation

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.
Products

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DataOps Suite

End-to-End Data Testing Automation

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ETL Validator

Automate your Data Reconciliation & ETL/ELT testing

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BI Validator

Automate functional regression & performance testing of BI reports

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DQ Monitor

Monitor quality of data being Ingested or at rest using DQ rules & AI

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Test Data Manager

Maintain data privacy by generating realistic synthetic data using AI

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