Data Quality Management with AI

Improve business decisions and AI outcomes by learning practical data quality management, from rules and profiling to cleansing, governance, and AI-assisted monitoring.

Key Features:

- Assess datasets using accuracy, completeness, validity, uniqueness, timeliness, and consistency

- Build data quality rules, profiling checks, and cleansing workflows with ChatGPT and Copilot

- Create governance-ready reports, dashboards, and monitoring plans for trusted data products

Book Description:

When reports, automation, and AI systems depend on unreliable data, you lose time, trust, and confidence in business decisions. Data Quality Management with AI gives you a practical path to find, measure, fix, and prevent data issues before they affect downstream outcomes. You will learn how to turn data quality from a vague business concern into a repeatable process you can apply across analytics, governance, and operational data work.

Written by Georgi Ivanov, a Udemy Instructor Partner with 400K+ learners and 89K+ reviews, this book offers a practical approach to data quality management. You will start by understanding data quality and evaluating datasets across accuracy, completeness, validity, uniqueness, timeliness, and consistency. Then you will define data quality rules, profile datasets, standardize fields, resolve identities, link duplicate records, cleanse errors, enrich records, and monitor quality over time. Examples show you how to use ChatGPT, Microsoft Copilot, validation templates, and AI-assisted workflows to speed up analysis, create data dictionaries, summarize findings, and prepare quality reports. You will connect quality work to governance roles, ownership, root-cause analysis, tool selection, dashboards, and continuous controls. By the end, you will build a data quality plan supporting trusted reporting, master data management, regulatory confidence, and AI-ready data.

What You Will Learn:

- Understand data quality fundamentals and their measurable impact on business outcomes

- Master the core data quality dimensions to accurately measure and monitor data assets

- Create data quality rules, validation logic, and dataset scoring methods

- Implement data profiling, parsing, and standardization workflows using industry tools

- Leverage AI tools like ChatGPT and Copilot to automate quality rules and documentation

- Design an end-to-end data quality process integrated with corporate governance frameworks

Who this book is for:

This book is for data analysts, business analysts, BI developers, data engineers, data stewards, data quality analysts, data managers, and governance professionals who need practical methods to assess, clean, validate, and monitor organizational data. It is also useful for managers who want to understand data quality roles, metrics, tools, and AI-enabled workflows. No prior data quality experience is required, but basic comfort with spreadsheets, databases, or reporting tools will help you follow the examples.

Table of Contents

- Understanding Data Quality

- Measuring Data Quality

- Designing and Implementing Data Quality Rules

- Improving the Quality of Data

- Data Cleansing

- Matching, Linking, and Resolving Data

- Monitoring and Managing Data Quality Over Time

- Data Quality for AI Systems

- People and Responsibilities in Data Quality

- Building an End-to-End Data Quality Program

- Data Governance and Data Quality

- Data Quality Tools and Implementation Strategy

- Data Quality Best Practices and Emerging Trends

- A Practical Data Quality Project

- Building a Career in Data Quality