Emily Clarke

What is Data Cleaning?

Modern businesses rely on enormous datasets for both everyday operations and long-term strategizing. Companies can collect troves of data through in-depth analysis to capture insights and discover opportunities. While data scientists try to use as much relevant data as they can, incorrect data can muddy the works.

Data cleaning refers to a process that involves removing so-called "garbage data." That includes duplicate, incorrect, corrupt, improperly formatted or incomplete data.

Why is Data Cleaning Necessary?

Ultimately, data cleaning is important because it helps maintain the quality of datasets. You want datasets to be as accurate and error-free as possible. That's especially true when your organization uses tools like a no code SQL generator to maximize data accessibility.

Junk data is inevitable. It happens when you're pulling information from multiple sources. The problem is that incorrect or corrupted data can lead to inaccurate insights. It taints the dataset, and algorithms become unreliable.

Insights and analysis are only effective when using high-quality data; cleaning makes a big difference.

A Guide to Data Cleaning

There are many ways to clean data, and the steps an organization takes to maintain quality will vary based on numerous factors. However, there are a few simple steps to eliminate most garbage data.

The first is to remove duplicate or irrelevant observations. These errors often occur when pulling information from several sources. For example, you might use a no code SQL generator to learn about a specific segment of your customers. But duplicate or irrelevant data with nothing to do with the target segment may appear. Removing it will lead to more efficient analysis.

Next, teams can fix structural errors and missing values. Structural errors refer to improper naming conventions or simple typos. One of the most common examples is "N/A" and "Non-Applicable" appearing in the same dataset.

Missing values are observations without information. Algorithms can't function with missing values, so inputting that data from other observations or dropping them is important.

After these steps, teams can move on to filtering out outliers and validating the data. Outliers may not fit within the data you're analyzing, resulting in inaccurate results. After validating and quality assurance, you should have a much cleaner dataset. However, it's wise to revise cleaning processes periodically for maintenance.

Author Resource:-

Emily Clarke writes about business software and services like spreadsheets that automatically generate Python code and transform your data with AI etc. You can find her thoughts at Python workbook blog.