Improving Data Quality for AI Reliability

Enhance GenAI performance by ensuring data accuracy and completeness.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course focuses on strategies and best practices to improve data quality for Generative AI projects. Participants will explore techniques for cleansing, validating, and enriching data to boost the reliability, accuracy, and trustworthiness of AI outputs.

Learning Objectives

Participants will be able to:

• Identify key data quality dimensions relevant to GenAI

• Apply data cleansing and validation techniques

• Use augmentation methods to enrich datasets

• Monitor data quality continuously for AI model reliability

• Address challenges like missing, inconsistent, or biased data

Target Audience

• Data engineers and analysts

• AI developers and data scientists

• Data quality managers and stewards

• AI project managers

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Lectures on data quality principles and metrics

• Hands-on workshops for data cleansing and augmentation

• Case studies of data quality impacts on GenAI results

• Group activities on designing quality assurance processes

Materials Provided

• Data quality assessment tools and templates

• Sample datasets for cleansing and enrichment exercises

• Guidelines for continuous data quality monitoring

• Reference materials on bias detection and correction

Outcomes

• Practical skills in improving and maintaining AI data quality

• Understanding of data quality’s impact on GenAI performance

• Ability to design quality assurance workflows for AI data

• Enhanced awareness of data bias and mitigation strategies

Outline / Content

Day 1: Understanding Data Quality for AI

• Dimensions of data quality: accuracy, completeness, consistency

• Impact on AI model outputs

Day 2: Data Cleansing and Validation Techniques

• Methods for cleaning and verifying data

• Handling missing or inconsistent data

Day 3: Data Augmentation and Enrichment

• Techniques for enhancing datasets

• Synthetic data and external data integration

Day 4: Monitoring and Maintaining Data Quality

• Continuous quality assurance processes

• Bias detection and mitigation strategies