Data Cleaning Fundamentals: Preparing Data for Analytics and AI

Poor data quietly undermines reporting, decision-making, and business performance, and its impact becomes even more critical as organizations invest in analytics and AI. In this course, you’ll learn why data quality matters, how to recognize the most common types of dirty data across sales, marketing, finance, and procurement, and what to do about it. You’ll be introduced to the COAT framework (Consistent, Organized, Accurate, Trustworthy) and shown how to apply it to assess and improve the data you rely on every day. Through practical Excel techniques, you’ll learn how to identify, clean, standardize, and structure messy datasets so they become usable and reliable. By the end, you’ll be able to build a simple, repeatable approach to data cleaning that supports better reporting, stronger decisions, and AI-ready data.

Learning objectives
Explain why data quality matters and how poor data impacts reporting, decision-making, business performance and AI.
Recognize the most common types of dirty data across sales, marketing, finance, and procurement datasets.
Apply the COAT framework (Consistent, Organized, Accurate, Trustworthy) to assess and improve the quality of your data.
Use Excel techniques to identify, clean, standardize, and structure messy datasets.
Create a practical, repeatable approach to cleaning data that supports analytics, automation, and AI readiness.