Why Data Needs a Cleanup
Explore the importance of cleaning messy data by identifying common issues like errors, duplicates, and inconsistencies. Understand key data quality dimensions and learn practical techniques to clean data effectively, ensuring accurate and trustworthy analysis outcomes.
We'll cover the following...
Data collected from real-world sources is seldom neat or ready-to-use. It’s usually messy, with missing pieces, mixed-up labels, odd formats, and even duplicates. It’s like trying to read a book with pages out of order and smudged words. We need to clean it up before it can truly make sense.
...
Data professionals devote a significant portion of their time to cleaning and preparing data. While it may not be the most appealing part of data analysis, it’s absolutely essential because even small errors can undermine the integrity of an entire analysis.
In this lesson, we’ll explore the main sources that make data dirty, the key dimensions of data quality issues we encounter, and different techniques to resolve them.
What makes data dirty?
Dirty data isn’t just an abstract concept; it refers to information that fails to accurately or consistently reflect reality. Understanding the sources of dirty data is the first step toward addressing them.
1. Human entry errors
Many datasets originate from manual inputs, ...