Make sure to do these 5 data cleaning tips when handling datasets.
Are you struggling to handle big data sets? Do you need a checklist so you know you have covered it all? With a background in data analysis, I have been handling data for more than 9 years (including my uni days) and have compiled data cleaning methods that are simple to follow.
This article will introduce the 5 things to look for when cleaning data, including the importance of data cleaning, examples of data cleaning tools, and more.
This post is all about data cleaning tips business analysts need to know.
Importance of Data Cleaning
What is data cleaning? Data Cleaning is the process of identifying and correcting errors and inconsistencies in data to improve its quality.
Here are some key reasons why data cleaning is important and should not be skipped:
- Ensure accuracy and reliability
Cleaned data ensures that everything (ie analysis, insights, business decisions) that was derived based on the data can be trusted.
- Save Time
Ensuring that data are cleaned right at the beginning means that time and resources can be saved because you don’t need to correct errors in the analysis process. You can focus on analysis rather than data collection.
- Improved Data Integration
Having clean data also means that you will have an easier time if you are looking to import the data into various platforms, or combine multiple datasets.
- Better ROI
Clean data also means that business decisions made are of high quality and can drive meaningful results.
- Efficient ML Models
If you are looking to use the data to train effective machine learning models, ensuring data quality should be at the top of your list as it will impact the performance and accuracy of the models.
Regardless of whether you are using data to train models, run analysis projects, or make important business decision, data cleaning is a step that you absolutely will not want to skip.
5 Data Cleaning Tips
In this section, I have consolidated the 5 most common data cleaning techniques that you should at least look through before moving on.
#1 Remove duplicates
Duplicate records can skew analysis and lead to inaccurate insights. Removing duplicates ensures that each data entry is unique, providing a clearer and more accurate dataset for analysis.
Imagine you have a list of friends to invite to your birthday party. If you accidentally write the same friend’s name twice, you will end up sending them two invitations.
Removing duplicates is like checking your list and making sure each friend’s name is only there once.
#2 Handle missing values
Missing values can lead to incomplete analysis and biased results. Handling missing data might involve
- Removing records with missing values,
- Imputing missing data using statistical methods (mean, median, mode), or
- Using algorithms that support missing values
Choose the method that best fits the context of your data and analysis requirements.
Suppose you are collecting your friends’ favorite ice cream flavors, but some of them forgot to tell you. You could either leave those spots blank, guess based on what you know (like if they always eat chocolate, you might guess chocolate), or ask them again.
Handling missing values is like making sure you have everyone’s favorite flavor so you can buy the right ice cream.
#3 Standardise namings
Inconsistent naming conventions can cause confusion and errors during analysis. Standardizing names, such as formatting dates uniformly or ensuring consistent use of terms (e.g., “NY” vs. “New York”), helps maintain data integrity.
For instance, if you wrote down your friends’ favorite colors, but sometimes you wrote “Blue” and other times “blue” or “BLUE,” it might get confusing.
Standardizing namings means writing “blue” the same way every time, so it’s easy to see how many friends like blue. In the field of data analysis, “Blue”, “blue”, and “BLUE” are all considered different so make sure to standardise them.
#4 Fix outliers
Outliers can distort analysis and lead to misleading results. Identifying and assessing outliers helps determine if they are data entry errors or genuine but extreme values. Depending on the context, you might correct, transform, or remove these outliers to maintain the accuracy of your data analysis.
Imagine measuring your friends’ heights for a game, and you accidentally write that one friend is 10 feet tall instead of 5 feet.
Fixing outliers is like checking to see if any heights look very different from the others and correcting mistakes so everyone’s height makes sense.
#5 Remove irrelevant data
Irrelevant data can clutter your dataset and complicate analysis. Evaluating your data to identify and remove any information that does not contribute to your analysis goals helps streamline the dataset, making it more manageable and focused on the relevant variables and metrics.
To give you an example: If you are making a list of who wants to play different games at your party, and you accidentally write down your cat’s name, that won’t help you plan the games.
Removing irrelevant data is like erasing your cat’s name from the list so you only have your friends’ names and can plan the games properly.
Data Cleaning Tools
When it comes to data cleaning, selecting the right tools can make the process much more efficient and effective. Here are 3 widely-used tools that can help you clean your data:
Tool 1: Excel
Excel is a powerful and accessible tool for data cleaning, especially for smaller datasets.
- Basic Cleaning Functions: Excel offers built-in functions like Remove Duplicates, Find and Replace, and Text to Columns to clean and organize your data.
- Data Validation: You can set up rules to restrict the type of data entered into cells, reducing the risk of errors.
- Formulas and Functions: Use formulas like TRIM, CLEAN, and SUBSTITUTE to correct and standardize your data.
- PivotTables: Summarise and analyse data quickly to identify and correct inconsistencies.
Resources for you: Top 8 Excel Data Cleaning Techniques to Know | 10 Super Neat Ways to Clean Data in Excel Spreadsheets
Tool 2: Python
Python is a programming language with powerful libraries for data cleaning, suitable for larger datasets and more complex tasks.
- Pandas Library: Pandas is the go-to library for data manipulation in Python. It provides functions to handle missing data, remove duplicates, and standardize data formats.
- NumPy: Use NumPy for numerical data cleaning tasks such as handling outliers and performing mathematical operations.
- Regular Expressions: Python’s ‘re’ module allows for sophisticated string manipulation, making it easier to clean and format textual data.
- Automation: Write scripts to automate repetitive cleaning tasks, saving time and reducing errors.
Resources for you: Pandas – Cleaning Data | Pythonic Data Cleaning with pandas and NumPy | Guide to Data Cleaning in Python
Tool 3: R
R is a statistical programming language well-suited for data cleaning, particularly in research and academic settings.
- tidyverse: The ‘tidyverse’ package in R includes ‘dplyr’ and ‘tidyr’, which provide a cohesive set of functions to clean and manipulate data efficiently.
- Data Inspection: Functions like ‘summary()’ and ‘str()’ help you understand the structure and summary statistics of your dataset, making it easier to spot anomalies.
- String Manipulation: Use ‘stringr’ for consistent and powerful text data cleaning.
- Handling Missing Data: R provides robust methods for dealing with missing data, such as ‘na.omit()’, ‘na.locf()’, and imputation techniques.
Resources for you: An introduction to data cleaning with R | Data Cleaning in R
This post highlights the 5 basic data cleaning tips.
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