Working with messy datasets in Google Sheets can be frustrating. Data issues like inconsistencies, formatting problems, duplicate rows, and missing values make analysis difficult. However, Google Sheets provides powerful built-in tools to clean up messy data and prepare it for analysis. This article outlines best practices and step-by-step instructions for standardizing and normalizing messy datasets in Google Sheets.
Table of Contents
- Why Standardize and Normalize Data
- Best Practices for Data Cleaning
- Google Sheets Tools for Data Cleaning
- Example: Standardizing a Messy Dataset
- Key Takeaways
Why Standardize and Normalize Data
Standardizing and normalizing data refers to transforming raw data into a consistent and standardized format. This process ensures:
- Consistent formatting and data types in each column
- Removal of duplicates, errors, and inconsistencies
- Data is scaled appropriately for analysis
Standardized and normalized data is easier to analyze and visualize. It also reduces errors in analysis since the data has a uniform structure.
Best Practices for Data Cleaning
Follow these best practices when cleaning messy datasets:
- Make a copy of the original raw data before transforming it
- Set data types and formats appropriately for each column
- Create a data dictionary detailing what each column represents
- Use validation lists to limit data entry to specific values
- Filter and sort to identify inconsistencies and errors
- Break down transformation steps instead of using complex formulas
Documenting your data cleaning helps with transparency and reproducibility.
Google Sheets Tools for Data Cleaning
Google Sheets provides several useful tools for data cleaning:
1. Cleanup suggestions
The “Cleanup suggestions” tool under the Data menu identifies common data issues like duplicates, inconsistencies, and formatting problems. It provides suggestions to fix these issues.
Cleanup suggestions demo
2. Find and replace
Find and replace lets you batch edit data by replacing text. This is useful for standardizing inconsistent values like country names or product codes.
3. Split column
The “Split column” tool splits column data into multiple columns based on a delimiter like a comma or space. This helps break up columns with multiple values into a normalized format.
4. Pivot tables
Pivot tables summarize and restructure data into a tall format. This can help normalize wide datasets into a format better suited for analysis.
Custom formulas help transform data beyond what built-in tools allow. Useful formulas include
Example: Standardizing a Messy Dataset
Let’s go through an example of standardizing a messy dataset in Google Sheets:
1. Import the raw data
We’ll import a spreadsheet containing data on historical stock prices in an inconsistent format.
2. Make a copy
First, we’ll make a copy to preserve the original raw data.
3. Set column data types
We’ll set appropriate data types – date, number, text etc.
4. Run cleanup suggestions
The cleanup tool identifies several inconsistencies we need to fix, including:
- Inconsistent date formats
- Numeric data formatted as text
- Leading/trailing spaces
5. Standardize data formats
We’ll fix date and number formatting, trim extra whitespace, standardize text case using
6. Split column with multiple values
One column contains the stock name and exchange separated by a dash. We’ll split this into two columns for better normalization.
7. Remove duplicates
Some rows are duplicated, so we remove them.
After following these steps, we have clean, standardized, and normalized data ready for analysis and visualization!
- Standardizing and normalizing data in Google Sheets is essential before analysis.
- Built-in tools like cleanup suggestions and find/replace help automate cleaning.
- Custom formulas help transform data beyond what tools allow.
- Document and preserve raw data, and break down steps for transparency.
- Cleaned, normalized data leads to more accurate analysis and visualization.
Following these best practices will save you headaches and help you get the most out of your data!