DQGuard_D&AI

DQGuard is a GenAI and Agentic AI-powered data quality and profiling solution that operates on Microsoft Azure, utilizing Azure OpenAI and machine learning services. The platform performs comprehensive data profiling—including table and column profiling, pattern recognition, value distribution analysis, correlation analysis, and multi-table comparison. DQGuard also features advanced GenAI and Agentic AI and AI/ML-based anomaly detection, including real-time anomaly identification. Its natural language interface allows users to run data quality checks through conversational queries, generating actionable data quality scores and insights across datasets. DQGuard ensures robust, intelligent monitoring and enhancement of data quality at scale.

Full Description:

• GenAI and Agentic AI-based data profiling insights, including table profiling, column profiling, pattern analysis, value distribution, and correlation analysis
• Multi-table correlation and comparison to assess consistency and relationships across datasets
• LLM-based insights on profiling outputs, providing intelligent summaries and recommendations in natural language
• GenAI and Agentic AI and AI/ML-based anomaly detection, including support for real-time anomaly identification and alerting
• Natural language interface for data quality queries, enabling users to generate and run data quality checks conversationally
• Pre-built connectors for multiple database platforms, ensuring broad integration and easy onboarding

Our Solutions:

TechM’s solution (i.e., DqGuard) comes with various features.

Data Profiling

• Perform comprehensive table and column profiling, including null percentage, data types, min/max values, standard deviation, and distinct counts • Analyse value distribution and pattern detection across columns • Execute correlation analysis to uncover relationships within and across tables • Conduct multi-table correlation and comparison to assess consistency and interdependencies • Automatically identify and classify data types during profiling • Generate LLM-based natural language insights on outputs for smarter analysis and decisions.

Anomaly Detection

• Leverage GenAI and Agentic AI-based anomaly detection to identify outliers, broken patterns, and quality issues • Utilise AI/ML-powered real-time anomaly detection to flag deviations as data flows in

Data Quality Evaluation

• Natural language querying to define, create, and execute data quality rules without coding • Generate runs and scores to evaluate datasets against business-defined standards • Flexible configuration of quality rules and metrics tailored to domain-specific needs

Business challenges

1. Inconsistent and Poor-Quality Data Across Systems

• Disparate data sources result in missing, inaccurate, or inconsistent information.

2. Manual and Time-Consuming Data Profiling

• Traditional assessments require manual effort and expertise, leading to inefficiencies.

3. Lack of Real-Time Anomaly Detection

• Delays in identifying and correcting issues affect decision-making and reliability.

4. High Dependency on Technical Teams for Data Quality Rules

• Business users rely on IT to define and run checks, causing bottlenecks.

5. Challenges in Understanding Profiling Output

• Raw profiling metrics are hard to interpret, limiting usability for non-technical stakeholders.

Benefits

1. Improved Data Quality Through GenAI and Agentic AI-Powered Profiling

• Automates detailed profiling with advanced insights and pattern detection.

2. Faster Decision-Making with LLM-Based Insights

• Converts technical profiling data into easy-to-understand summaries for business users.

3. Proactive Issue Identification with Real-Time Anomaly Detection

• Uses AI/ML to detect and surface issues instantly, reducing risk.

4. Empowers Business Users via Natural Language Interface

• Enables non-technical users to run data quality checks and create rules conversationally.