How to Clean Excel Data: A Beginner’s Guide for Accurate Analysis

If you’re new to data analysis, you might be wondering, ‘How do you clean data in Excel?’ or ‘What are the best Excel data cleaning tips?’ 

Look no further. This beginner’s guide will walk you through the basics of turning a messy spreadsheet into something much easier to work with. Clean data is the backbone of reliable insights, whether you’re crunching sales numbers, analysing customer trends, or putting reports together. Poor data quality (think duplicates, extra spaces, or missing values) can result in flawed conclusions and costly mistakes. In fact, IBM estimates that bad data costs businesses over $3 trillion in the U.S. alone.

In this guide, we’ll walk you through practical, step-by-step techniques to clean Excel data, making sure it’s accurate, consistent, and ready for analysis. We’ll cover the key steps - from removing duplicates to dealing with missing values. Whether you’re a small business owner, an HR professional, or an aspiring analyst, these Excel data cleaning tips will empower you to make smarter, data-driven decisions.

Why Clean Data in Excel?

Before we look at exactly how to clean Excel data, let’s talk about why it matters.

Messy data (that which is riddled with duplicates, inconsistent formats, or errors) is one of many common data quality issues can sabotage your analysis. Imagine running a marketing campaign with duplicate customer records or analysing sales with missing entries. The results? Wasted resources, skewed insights, and frustrated stakeholders. With clean data, you can ensure:

• Accuracy: Your conclusions reflect reality
• Efficiency: Less time fixing errors, more time analysing
• Reliability: Trustworthy data builds confidence in decisions
• Compliance: Accurate data helps meet regulatory standards, like GDPR

Top 10 Excel Data Cleaning Techniques

Here are 10 beginner-friendly techniques to clean Excel data - methods that address common issues like duplicates, extra spaces, and inconsistent formats, ensuring your data is ready for accurate analysis. 

1. Remove Duplicates

It’s normal for duplicates to sneak into a dataset from multiple sources, inflating counts and skewing results. Excel has a built-in ‘Remove Duplicates’ feature that gives you a quick fix. 

If you have a sales dataset, there might be multiple entries for one customer ID. Removing the duplicates ensures each customer is counted only once, preventing inflated sales figures. 

2. Trim Extra Spaces

Even something small like an extra space can break a lookup or stop a filter from working properly. The TRIM function can be your salvation here. 

Values like ‘ John Doe ‘ become ‘John Doe’ after applying TRIM, making your filtering consistent throughout. You can use Find and Replace to remove specific unwanted characters, like double spaces. 

3. Standardise Text Case

Mixing text cases - like ‘john’, ‘JOHN’, and ‘John’ - makes sorting and grouping harder than it needs to be. Use PROPER, UPPER, or LOWER functions to standardise it.

‘JOHN doe’ thus becomes ‘John Doe’ with PROPER, making customer lists look polished and professional.

4. Handle Missing Values

Gaps in your data can easily lead to misleading results, especially if whole sections like sales figures are missing.

A dataset missing 10% of email addresses can be flagged as ‘Missing’ for follow-up or removed if non-essential, for example. Our data enhancement services can append missing data, like customer demographics, giving you a complete, valuable dataset. 

5. Fix Inconsistent Formats

Inconsistent formats (e.g., dates in MM/DD/YYYY or DD-MM-YYYY) can break calculations. Standardise these using Format Cells.

If you convert ‘12-31-2024’ and ‘12/31/2024’ to a uniform ‘31/12/2024’, for example, you’ll ensure that sorting is accurate. And, as a pro tip, use Power Query for advanced format standardisation across large datasets.

6. Remove Non-Printable Characters

Imported data sometimes carries hidden characters like line breaks or tabs, which can cause errors later. The CLEAN function removes them.

A product description with stray line breaks becomes clean and readable after CLEAN, which can be combined with TRIM for a one-two punch against text issues. 

7. Spell Check

Typos in names or descriptions can lead to mismatches. Excel’s Spell Check catches errors quickly.

Regular spell checks prevent errors from creeping into large datasets. Correcting ‘Custmer’ to ‘Customer’ or something just as innocuous can make a huge improvement in consistency of reporting. 

8. Highlight and Investigate Outliers

Strange values - say a £10,000 sale when most sales are closer to £100 - can throw off your numbers. Use Conditional Formatting to spot them. 

Highlighting that £10,000 sale outlier for review will ensure it’s valid before it’s analysed. You can cross-check outliers with source data to confirm the accuracy rather than deleting them outright. 

9. Split or Merge Columns

Sometimes you’ll need to reshape your data, like splitting full names into first and last names, or combining fields together. By splitting John Doe into two columns, you can improve the sorting and filtering processes.

10. Use Data Validation

You can save headaches later by setting simple rules for data entry; for example, only allowing numbers in an age column.

When you restrict ‘Age’ to 18-99, you prevent invalid entries like ‘ABC’. Combine this tip with our data validation services for enterprise-level rule enforcement. 

Advanced Tools for Excel Data Cleaning

If you’re dealing with bigger datasets or repeat tasks, Excel has advanced tools that can speed things up:

• Power Query: Import, transform, and clean data from multiple sources. You can use it to unpivot messy data, remove totals, or standardise formats. At Sagacity Solutions, we use Power Query in our data management solutions to help streamline workflows
• Flash Fill: This automatically fills patterns (e.g., extracting first names from a column). You can activate via Data > Flash Fill or Ctrl+E
• Macros: Record repetitive tasks (e.g., formatting steps) to automate the cleaning process. Use View > Macros > Record Macro

Practical Excel Data Cleaning Tips

Follow this beginner-friendly workflow to clean your Excel data like a pro:

1. Backup Your File: Save a copy to avoid irreversible changes
2. Profile Your Data: Use Filter or Sort to spot duplicates, blanks, or outliers
3. Apply Cleaning Techniques: Start with duplicates, then tackle spaces, formats, and missing values
4. Validate Results: Check a sample to ensure accuracy
5. Automate with Tools: Use Power Query or macros for repetitive tasks
6. Document Changes: Note steps in a separate sheet for transparency
7. Set Up Data Validation: Prevent future errors with rules

If you’re working at scale, our data quality management blog can help you automate the process and keep your datasets in good shape.

Transforming Excel data step-by-step

You might find yourself daunted about the prospect of cleaning your Excel data. You don’t need to be.

By mastering these Excel data cleaning tips, which range from removing duplicates to standardising formats, you can transform your messy spreadsheets into reliable datasets for accurate analysis. Whether you’re asking ‘How do you clean data in Excel?’ or just wanting to streamline your workflows, this guide will equip you with the necessary practical tools and techniques. 

For advanced needs, we can offer services like data cleansing, data enrichment, and data deduplication to take your data to the next level. Start small, practice these techniques, and explore our Insights Library for more data-driven tips. With clean data, you’re not just analysing, you’re unlocking insights that will propel your business to success.