Got raw data? Here’s How to Clean Datasets

Data cleaning is a non-negotiable part of analysis. Unclean datasets with formatting errors, whitespaces, and duplicates can hamper your analysis and result in wrong business decisions. That’s why data analysts spend 60% of their time cleaning data. 

But is it necessary to spend more than half of the time on cleaning itself? I disagree.  

In this blog, I share top data cleaning methods that can help you keep your data squeaky clean and how you can speed up the process to spend more time making decisions rather than clean-ups. 

Let’s start, shall we?

What is dataset cleaning? 

Data cleaning, also known as data scrubbing or data cleansing, is the process of identifying eros and inconsistencies in data and removing them so the dataset becomes fit for analytics. The data cleaning process involves removing incorrect, corrupted, or duplicate data to improve its quality.

Why do you need to clean data?

There are several benefits of cleaning data. Here are 4 of them: 

You get accurate data 

In data cleaning, you eliminate errors, inconsistencies, and inaccuracies, leading to more accurate analyses and decision-making.

For example, in a sales database, if there are inconsistent entries for product prices (e.g., one entry in dollars, another in euros). In the process of data cleaning, you would standardize the currency, ensuring accurate and comparable pricing information for sales analysis.

It enhances your decision-making

Clean data enables you to make well-informed choices based on trustworthy information, as it reduces the risk of faulty conclusions.

Take healthcare analytics, for example. Accurate and clean patient records are crucial for making decisions about treatment plans. With data cleaning, you ensure that patient data, such as medical history and test results, is reliable, leading to informed and effective healthcare decisions.

It saves time and resources 

Cleaning data shortens the analysis process as you can focus on extracting meaningful insights rather than dealing with data quality issues.

Suppose you need to analyze customer purchase data for an e-commerce company. While cleaning data, you would remove duplicate entries for the same transaction. It saves you time and resources by preventing you from mistakenly counting the same sale multiple times.

It improves data integration

Clean data is more compatible with other datasets, making it easier to integrate and combine information from different sources. This promotes a more holistic understanding of the data and facilitates comprehensive analyses.

For example, if you’re analyzing data for a multinational corporation, data from various subsidiaries may have different date formats. Data cleaning would involve standardizing date formats. This makes it easier to combine financial information from different areas into one organized dataset for overall financial analysis across the company.

It increases stakeholder confidence

Stakeholders and users are more likely to trust and have confidence in the data when they know that it has undergone a thorough cleaning process. This trust is essential for effective decision-making based on data-driven insights.

For example, a financial institution using data for risk assessment needs clean and accurate data on borrowers' credit histories. Data cleaning ensures that stakeholders, such as loan officers and executives, can trust the information when making decisions about approving or denying loans.

How to Clean Datasets

Data cleaning involves several steps to ensure that the dataset is accurate, consistent, and ready for analysis. Here are the key steps involved in cleaning data, along with brief explanations for each:

1. Handling Missing Data:

In this step, you Identify and decide how to address missing values in the dataset. To tackle missing data, you can remove records with missing values, input values based on statistical methods, or use domain knowledge to fill in the missing information.

For example, if some sales records lack information on the customer's address, decide whether to remove those records, impute the missing addresses based on available data, or use a default value for missing entries.

2. Removing Duplicates:

To ensure that each observation is unique, identify and eliminate duplicate entries or records in the dataset. 

For example, identify and eliminate duplicate entries where the same sale is recorded multiple times, ensuring that each sale is represented only once in the dataset.

3. Correcting Inconsistencies:

You can identify and resolve inconsistencies in data, such as typos, formatting errors, or other discrepancies.

For example, if a dataset contains variations in product names, like "Laptop" and "laptop," correct the inconsistencies to ensure uniformity in naming conventions.

4. Standardizing Data Values:

Standardizing data ensures that your dataset has consistent units of measurement, date formats, and other data elements. 

For example, you can convert "01/15/2023" and "15-Jan-2023" to a consistent format such as "2023-01-15".

5. Handling Outliers:

Outliers are values that are unusually high or low compared to the rest of the data that can distort the results of your statistical analyses. To clean these outliers, you can either remove them from datasets altogether or transform them. 

For example, you can identify unusually high sales amounts that may be errors or anomalies. Decide whether to remove them if they are data entry mistakes or to transform them.

6. Dealing with Inaccuracies:

Data entry errors may lead to inaccuracies. This step is crucial for maintaining the integrity of the dataset.

For example, correct a typo in a product price, where "50$" is corrected to "$50" to ensure that your financial data is accurate.

7. Validating Data:

Validation helps identify issues that may affect the reliability of the data. To validate data, check it against predefined rules or criteria to ensure it meets quality standards. 

For example, you can check if all sales transactions have a valid payment method recorded. This helps you make sure that only accurate and complete transactions are included in the dataset.

8. Transforming Data:

To transform data, you convert it into a standardized format or structure to facilitate analysis. Transformation may involve reformatting, aggregating, or creating new variables based on the existing data.

Imagine you have a sales dataset with columns for "Product," "Quantity Sold," and "Unit Price." Each row represents a different sale. Now, you want to transform this data to understand the total sales for each product better.

For this purpose, you can add a separate column called "Total Sales," which represents the total revenue generated for each product. You can do this by multiplying the "Quantity Sold" by the "Unit Price" for each row.

The "Total Sales" column aggregates the data, providing a clearer picture of the revenue generated for each product.

9. Ensuring Consistency:

Ensuring consistency includes checking spellings, abbreviations, units, names, and formatting for the same category. 

For example, you can ensure that product categories are consistently labeled as "Electronics" rather than having variations like "Electronic" or "Electronix."

10. Documenting Changes:

To document changes, keep detailed records of the changes made during the cleaning process. With correct documentation, you can maintain transparency and allow others to understand what steps you took to clean the data.

For example, keep a log that records all changes made during the cleaning process, including the specific modifications to the data and the reasons behind each change.

Using Gigasheet to clean data

Data cleaning in Gigasheet is super easy. You can find all functions in the taskbar to clean any row or column. 

I had to remove duplicate lead sources from my leads list. Here’s how I did it in Gigasheet. 

I found the “data cleanup” feature.

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Then, I selected the column that I wanted to clean up. And ‘remove’.

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In a single click, Gigasheet removed all duplicate lead sources from my list. 

Similarly, you can remove white spaces, change cases, and combine or split columns to clean data and make it perfect for analysis. 

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Gigasheet offers an easy to use IF Then Builder that can be used to handle inconsistencies in the data to create a new version of the column with clean data. All of this can be done without writing a line of code!

Clean data in seconds with Gigasheet

As I said earlier, you don’t have to spend more than 50% of your time cleaning data. Gigasheet speeds up the data cleaning process so you can quickly move to analysis. Sign up for Gigasheet today and start analyzing data like a pro!