Data Quality & Trust in AI Systems

Ensure data is reliable enough for decision-making and GenAI output.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course equips participants with the tools and techniques to evaluate, improve, and sustain data quality in AI systems. Since Generative AI relies heavily on the accuracy and consistency of input data, ensuring trust in data sources is critical. The course emphasizes data validation, quality metrics, root cause analysis, and long-term data governance to establish a trustworthy foundation for GenAI applications.

Learning Objectives

Participants will be able to:

• Identify key dimensions of data quality (accuracy, completeness, consistency, etc.)

• Assess data quality impacts on GenAI outputs and model reliability

• Implement frameworks to monitor, measure, and improve data quality

• Apply data profiling, cleansing, and enrichment strategies

• Establish continuous quality assurance processes for AI systems

Target Audience

• Data governance and compliance teams

• Data stewards, engineers, and analysts

• AI/ML professionals and business stakeholders

• Project leads implementing GenAI systems

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures with real-world examples

• Hands-on labs for data profiling and cleansing

• Group workshops for designing quality management strategies

• Use cases of data failures in GenAI and mitigation methods

Materials Provided

• Data quality assessment frameworks

• Templates for root cause analysis and resolution

• Checklists and tools for ongoing data validation

Outcomes

• Ability to evaluate and enhance data quality in AI workflows

• Increased confidence in GenAI model reliability and outputs

• Improved decision-making supported by trusted data

• Institutional knowledge of sustainable data quality practices

Outline / Content

Day 1: Foundations of Data Quality in AI

• Introduction to data quality and trust in AI

• Key quality dimensions and their relevance to GenAI

Day 2: Profiling and Assessment Techniques

• Tools and techniques for data profiling

• Measuring and scoring data quality across systems

Day 3: Improvement Strategies and Governance

• Data cleansing, enrichment, and standardization

• Roles and responsibilities in data quality management

Day 4: Monitoring, Metrics, and Real-World Challenges

• Setting up quality dashboards and alerts

• Case studies of GenAI failures due to poor data

• Capstone: Developing a quality strategy for an AI project