Consider that you are attempting to prepare a fine dining experience, but your materials are either missing, mislabeled, or spoiled. It is inevitable that the finished dish will suffer regardless of your level of culinary expertise. That is the experience of a data scientist working with unorganized data.
In data science, data cleaning, also known as data cleansing, is the unsung hero. Fixing or eliminating inaccurate, distorted, or insufficient data from your datasets is what it is. It’s not the most glamorous aspect of the work, but it’s vital.
Actually, according to a lot of data scientists, cleansing data might take up to 80% of their time!
The importance of data cleaning, typical forms of contaminated data, a detailed tutorial on data cleaning, and tools and advice that facilitate the process will all be covered in this blog article. By the end, you’ll know how to handle your disorganized datasets with the respect and care they need.
What Is Data Cleaning?
Data cleaning is a comprehensive process of data analysis that involves fixing duplicate, incorrect, inconsistent, or corrupted data within a dataset. This process may include removing duplicate entries, fixing missing values, verifying information, and adding more details to the dataset.
Since data is collected from multiple sources for analysis, it might have inaccurate values or misleading information. This will result in biased or unreliable outcomes and mislead your business decisions. This is why the data cleaning process is performed before analyzing the data.
There is no definite method for data cleaning as the data cleaning processes vary from dataset to dataset. Therefore, it is crucial for a data scientist to prepare an outline of data cleaning and stick to it to carry out the process effectively.
What Is Dirty Data?
Dirty or messy data includes multiple errors such as missing values, inconsistent formatting, outliers, duplicates, typographical errors, wrong data types, irrelevant data, etc.
Identifying these errors within the dataset is the first step to successfully perform the data cleaning process.
Why Is It Important To Clean Unorganized Data?
Let’s understand why you should care about data cleaning.
Accurate Insights
Inaccurate data can lead to inaccurate results! If you use messy data for analysis, the insights you extract from it will be incorrect, too.
For instance, consider you are developing a machine learning model to track customer behaviour on an E-commerce site and recommend products to them. If your dataset contains inaccurate customer details, missing purchase history or duplicate entries, the outcome will be unreliable and can cause losses for the business.
Improved Decision Making
As said in the previous example, using clean data for analysis can give better insights to make fruitful business decisions. Reliable data is what it needs to make accurate business decisions.
Better Efficiency
When the data input is inaccurate, how can you expect good results? But, with clean data, the workflow becomes smoother and faster, eliminating the struggle of data scientists.
Compliance with Standards
It is essential to have accurate data in industries where sensitive data is involved, such as finance and healthcare. When processing datasets of these industries, you can comply with the standards by using clean data.
Efficient Machine Learning Models
The efficiency of machine learning models or algorithms is dependent upon the type of data they are trained on. So, using clean, accurate, and well-organized data can enhance the reliability and performance of machine learning models.
Related: The Data Science Toolkit: 12 Most Used Data Science Tools Every Data Scientist Needs to Know
What is Data Cleaning and Data Transformation?
Data cleaning and data transformation are two techniques used to prepare and improve data for use. Data cleaning is the process that removes duplicates and fixes errors in a dataset. On the other hand, data transformation is the process that converts accurate raw data from one format or another or one structure to another best suited for analysis.
Some businesses merge the data cleaning and data transformation process into one step to save time in the case of small datasets. In this article, we will merely focus on the data cleaning process.
Benefits of Data Cleaning
It is now clear that data cleaning is a crucial process of data analysis, and one must follow it. In addition to the above aspects, clean data also offers various benefits, and those are as follows –
Elimination of Mistakes
Unorganized data doesn’t just affect the data analysis process; it also impacts other tasks wherever this data is used. For instance, an email list with wrong names can cause the marketing team to send personalized emails to the wrong people.
Improved Productivity
Data cleaning removes the anomalousness from the dataset and provides an updated and organized version. Therefore, professionals can easily find what they want within the dataset without looking into the old databases.
Cost Effectiveness
Needless to say, poor data leads to poor business insights, which costs huge amounts of money to the business. But, with clean data, you can easily figure out the error or minor deviation in the dataset and fix it right away. This will save you time and money.
Maintains a Tidy Environment
Businesses usually collect data from various sources and various formats. Keeping this data in one place will be messy and overwhelming. However, regularly cleaning data makes it tidy, organized, and easier to store and retrieve.
How to Clean Data? – A Step By Step Guide
Below is a practical outline of data cleaning followed by most data scientists. This workflow focuses on the essential steps of data cleaning, and it may vary from dataset to dataset.
1. Understand the Dataset
The first step of data cleaning is to understand the dataset. You need to know what type of data is stored, how many columns and rows are there and what they represent, and your goals for this dataset.
After that, you can go a step forward and scan the data to identify errors. Look for quality issues like inconsistencies in text, duplicate values, missing values, unwanted outliers, etc. Identifying these errors will help you perform the next steps efficiently.
2. Remove Duplicate and Unwanted Entries
After understanding and identifying problems in the dataset, you can abolish the duplicate values and unwanted entries. Such components in the dataset will take up storage space and distort the final result. So, it is better to remove these values before proceeding with the next steps.
When data is collected from multiple places or sources and combined into one dataset, the likelihood of duplicate entries increases. So, it is crucial to identify and remove these irrelevant values from the dataset for better efficiency.
3. Fix Typographical Errors
Text inconsistencies or typographical errors are common in datasets and must be corrected before analysis. Issues like spelling mistakes, alternate abbreviations, uneven arrangements, etc., can cause problems in data analysis.
So correcting these errors and normalizing the text will provide you with clean data for analysis.
4. Remove Unwanted Outliers
Outliers can affect your analysis as they differ from other data points in a dataset. They are problematic for certain types of analysis or data models.
Therefore, it is a good practice to spot them and remove or replace them wherever necessary.
5. Deal with Missing Data
Missing data can occur due to several errors, such as system issues, user error, or incomplete values. You can deal with missing data using various ways.
First, you can remove missing data from the dataset. However, removing this data can take away important information, so you should be careful when doing this.
Second, you can fill in missing values based on the available data. Here, the problem is that you may lose the nobility of data as you will put these values by guesswork, which can alter the final result.
Apart from these two, there is another way to handle missing data. You can flag those data points as missing or 0. This way, you will consider the missing data as information for your analysis.
6. Fix Data Types
After removing the unwanted items and correcting errors in the dataset, you need to ensure that the values are stored with the right data type. For example, numbers as numerical data, text as text input, currency as currency values, etc.
This way, you can store and analyze data appropriately.
7. Standardize The Data
Standardizing your data refers to fixing structural errors and ensuring that every cell is following the same rule. For example, you can format the values to all uppercase or all lowercase and follow the same rule throughout the dataset.
This will be better for understanding and analysing the data.
8. Validate and Document
The final step of data cleaning is validating the clean data before using it for analysis. This involves cross-checking the data for quality and accuracy to make sure that the data is ready for analysis.
You can validate your data using validation rules or existing datasets to check its reliability. If you find any error in this step, you have to fix it right away before proceeding with the next step.
This way, data scientists clean dirty data before analysis to ensure reliability and correctness. As you can see, data science involves multiple steps, and the successful completion of each step is necessary to get clean data for analysis. So, there is no wonder why data scientists spend a lot of time cleaning data.
Best Practices of Data Cleaning
You learned the importance of data cleaning and, step by step, how to clean data effectively. Now, keep these data cleaning best practices in mind when performing the data cleaning process.
Understand the Data
Before making any changes to the dataset, ensure that you understand the dataset properly. Learn the purpose and context of the data to avoid making mistakes in data cleaning.
If you proceed with data cleaning without understanding the dataset, there is a chance of making irrevocable mistakes or removing valuable information from the dataset.
Backup Original Dataset
Always backup your data set before cleaning so that you can refer to the original data if any mistake happens.
Identify and Handle Missing Data Thoughtfully
As discussed above, you can handle missing data in three ways, and the challenge is to decide the appropriate method to apply on a dataset. You should do it carefully, as mishandling missing values can affect your analysis.
Deal with Outliers Mindfully
All outliers are not worthless; some can contain valuable information. So, detect outliers with statistical methods and remove only erroneous or irrelevant outliers.
Document Everything
Don’t forget to document what you changed in the dataset and why you have done it. This will add transparency to your cleaning process and will be beneficial for collaboration, debugging, and reproducibility.
Visualize the Data During Cleaning
Visuals will help detect errors, outliers, and patterns in data with minimal effort. So, try visualizing your data to find errors.
Automate Tasks When Possible
You can automate repeated tasks using reusable functions or cleaning pipelines. This will save time and reduce human error.
Validate the Cleaned Data
After the cleaning is completed, you can recheck the data to ensure that there are no duplicate values or errors. If you find any, fix it before going for analysis.
Practice Ethical Cleaning
Always keep transparency and integrity in your data cleaning job, and don’t try to manipulate data to meet expectations. This will help maintain your professional reputation and thrive in real-world scenarios.
Challenges of Manual Data Cleaning
Despite its occasional necessity, manual data cleansing may be somewhat tedious, especially for novices and even experienced data professionals. Attempting to untangle a massive skein of yarn with one hand tied behind your back is equivalent to this. Here are the main difficulties with manual data cleaning, described in an approachable and straightforward manner.
Time Consuming and Tedious
Manually detecting and fixing errors and formatting issues in a dataset will take too much time. With large datasets, it can take many hours to even days to clean. Still, one can’t guarantee that the final result will be error-free.
Instead of data cleaning, you can use your energy and time on data analysis, modelling, and storytelling.
Prone to Human Error
As mentioned above, human data cleaning can’t be treated as reliable (not always) even after spending long hours in the process. One wrong formula, mismatched character, or deletion of a row can cause big errors.
Difficult to Reproduce or Audit
As I mentioned above, documenting every step of your data-cleaning process is crucial. If not, you won’t have proper scripts or logs to explain what was changed. Also, it will be hard to debug and reproduce such datasets. It loses trust and collaboration opportunities.
Hard to Scale
It is not easy for a human to clean a large dataset within a deadline and without errors. To handle big datasets effectively, fast and automated options are crucial for a business.
Limited Consistency
Manual cleaning is not consistent. What you cleaned today may not want to work on that tomorrow. And multiple people working on the same dataset can cause a conflict of logic and create inconsistent insights.
Not Suitable for Complex Logic
It is not ideal for humans to use complex logic for data cleaning. It may open doors to problems in data. Hence, you should opt for appropriate tools to handle such tasks accurately and efficiently.
Low Data Integrity
Data integrity is critical in data analysis and is at risk with human data cleaning. Human data cleaning can cause issues like deleting valuable information accidentally, breaking links between datasets, etc.
Popular Data Cleaning Tools
Here is a list of popular data cleaning tools.
| Tools | Purpose |
|---|---|
| Pandas | Cleaning and manipulation |
| OpenRefine | Clean Tabular Data with GUI |
| Excel | Spreadsheet based cleaning |
| Tidyverse | Data cleaning using R |
| Trifacta | Big data preparation |
| Great Expectations | Validating and testing data |
Final Thoughts
Clean data is vital, unseen, and frequently taken for granted in the field of data science, much like oxygen. As a data scientist, you will gain strength and confidence the more you embrace this aspect of the workflow.
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