Excel Data Cleaning

Data cleaning includes repairing or deleting data that is incorrect, corrupted, poorly formatted, repetitive, or incomplete within a dataset. In my line of work as a data scientist, there is no one-size-fits-all method for prescribing exactly what to do in the data cleaning process. This is because Excel data cleaning techniques differ from dataset to dataset.

However, it is important to create a template for your data cleaning process so that you can ensure you perform it correctly every time. Hence, to help you out, I have curated this beginner-friendly guide on data cleaning with Excel. 

Note: This tutorial is meant for learners who already know their way around Excel. If you have no idea how to navigate this software, consider going through an easy Excel tutorial and make sure you get lots of practice!

Common Data Cleaning Process

Some of the most common processes and techniques of data cleansing are:

• Dealing with incomplete information
• Standardizing the procedure.
• Ensuring data accuracy
• Removing redundant data
• Handling structural flaws
• Removing unwanted observations
  

How To Clean Data in Excel?

Excel provides some techniques to easily clean data. The most widely used techniques are:

Delete repetitive or unrelated observations

Delete any repetitive or irregular observations, as well as any unwanted observations. In most cases, duplicate observations occur most frequently when you're collecting data.

For example, if you integrate data sets from various sources, scraped data, or obtained data from clients, your sheet will end up with duplicated data. Whenever I encounter something similar, I use re-duplication.

Irrelevant observations only occur when you discover data that do not apply to the specific topic you're trying to analyze.

Data cleansing tools in Excel help improve analysis efficiency and minimize distractions from your core goal, as well as offer a more manageable and perfromant dataset.

Correct structural flaws

These are errors that occur during data transfer, measurements, and other challenges resulting from poor data management. The most frequent issues with structural errors include inconsistent typos, punctuations, and mislabeled classes.

If you see such errors, it means you need to use Excel data cleaning techniques to clean your data.

Delete unwanted deviations

If you use Excel as your main source of data gathering, you will often run into one-time observations that don't appear to fit into the data that you're examining.

If there is a valid cause to delete an outlier, such as inaccurate data entry, it will help you improve the dataset you're working with.

Remember that just because an outlier shows up doesn't mean that it is incorrect. You need to filter unwanted outliers to determine the accuracy of the data value. I recommend deleting the outlier if it is unrelated to the observations or is just incorrect.

Managing missing data

You simply can't overlook missing data since many algorithms reject missing values. However, there are a few approaches to dealing with missing data; neither is ideal, but you should take a look at them

1. As a first alternative, you can delete observations with missing values, however, this can lead to loss of data, so keep this in mind before using Excel data cleaning techniques.

2. As a second alternative, you can input the missing values based on other observations; however, this option comes with a risk of losing data integrity as you would be working with assumptions instead of actual observations.
   

Verify and assess

As part of the Excel data cleaning techniques, you should be able to validate the following questions at the end of the cleaning process

• Is the data meaningful?
• Does the data adhere to the principles of its discipline?
• Does it corroborate or challenge your initial hypothesis, or does it present fresh insights?
• Are there discernible patterns within the data that could inform a new hypothesis?
• If not, is the an issue with the quality of the data?

In many cases, wrong conclusions because of inaccurate or bad data can influence bad decision-making and business strategy. Incorrect conclusions might be awkward as you will learn that your data doesn't pass the scrutiny.

I recommend that you establish a culture of excellent data in your business. To accomplish this, you need to document the tools you can use to promote this culture and understand how you tackle data quality.

Importance of Excel Data Cleaning

Data quality is a vital essence for any business that relies heavily on data to function. For example, when dealing with multiple clients, you have to ensure that accurate invoices are emailed to the correct client. 

You can boost your brand's value by prioritizing data quality to make the best use of client data.

The following are some benefits of Excel data cleaning techniques:

Avoid expensive mistakes

Excel data cleaning is the most effective option to cut down on costs incurred due to processing mistakes, fixing wrong data, or troubleshooting in general.

Increase customer acquisition

Businesses that maintain their databases in good shape can create prospect lists based on accurate and updated data. This can help you improve customer acquisition and reduce costs.

Interpret data from several channels

Excel data cleaning allows you to smoothly manage multi-channel client data and allows your business to identify potential marketing campaigns and new ways to reach your targeted customer base.

Improve decision-making

If you want to improve your decision-making process, you should stick with clean data. Accurate and updated data will help you enable your business intelligence and analytics. Clean data offers businesses resources for better decision-making.

Boost output from employees

When working with Excel, it is important to manage clean and well-maintained databases. It will help you increase your staff productivity by helping them with various methods, such as client acquisition and resource planning. Businesses that proactively increase the accuracy and consistency of data also see an increase in revenue and response rate.

Excel Data Cleaning Challenges

Data cleansing tools in Excel offer a great way to clean data, but they do come with various challenges. I have outlined them below:

Backing up data

Data backup is the process of copying data from one location to another to secure it in case of accidents and cyber thefts. These days, most small businesses rely significantly on data. As a small business owner myself, losing any amount of useful data can severely affect and cause issues in daily activities.

For example, with a large customer base and didn't back up the data on a different server or machine, I would risk losing every client detail if I fell victim to a malicious online attack. In case of redundant data, you can address them during the Excel data cleaning process.

However, if you can't find any effective backup plan, you might lose your data. Hence, backing up your data is a challenge during the data cleaning.

Various data formats

As you might already know, data comes in various forms, and one strategy to help facilitate Excel data cleaning techniques is to ensure that you use specific data cleaning techniques for specific data types. For example, text-based cleansing won't work when working with visuals, and vice versa.

As a result of this, you might find yourself spending a lot of time trying to segregate data based on the data-cleaning technique applicable to them.

Final Words

Excel data cleaning is a vital feature as it can help you fix various errors, such as an incorrect date, a typo, or even a minute detail, like punctuation. Data cleaning helps maintain the integrity of your data sheet and helps prevent expensive mistakes. 

Speaking from experience, it ultimately improves vital decision-making processes and can, in the long run, add to brand growth. Speaking of experience, if you are here trying to get an idea of data cleaning, you are probably at the threshold of starting your career. Here’s a pro-tip. Try taking up a professional data science course from upGrad. 

Frequently Asked Questions

1. Can you automate data cleaning in Excel?

Yes, you can automate Excel data cleaning by using the Power Query tool.

2. What is the data cleaning add in for Excel?

There isn't a specific "data cleaning add-in" for Excel, but there are third-party add-ins available for Excel that specialize in data cleaning tasks. They make cleaning data easier by offering tools for removing duplicates, parsing text, and formatting cleanup.

3. What are the data cleaning techniques?

Excel data cleaning techniques include removing duplicates, removing irrelevant data, fixing errors, converting data types, and more.

4. How do I clear data from Excel?

You can clear data in Excel by selecting the cells that you need to clean and click “Clear All.”

5. How do I run a clean function in Excel?

You can run a clean function in Excel by choosing your preferred cell and typing ‘CLEAN.’ Then, provide a text argument to use the function.

6. What is automated data cleaning?

Automated data cleaning is exactly what it sounds like; you can automate data cleaning in Excel using software or Data cleansing tools in Excel.

7. What are the 5 concepts of data cleaning?

The answer to this question has been discussed thoroughly in this tutorial.

8. What is data cleaning with an example?

Data cleaning is the process of fixing inconsistencies in your datasheet. For example, fixing typos, fixing syntax errors, or fixing wrong numerical entries.