Consider you've been asked to make an important decision based on data from a spreadsheet. You feel confident until you open the file and find duplicate entries, typos, and missing values. 

Unfortunately, this messy data can lead to a wrong conclusion that could negatively impact your business. While this scenario might seem extreme, it’s a common challenge many organizations face when conducting data analysis. 

The good news is that there are data-cleaning techniques to help you fix those errors and get back to your analysis with a clean dataset. In this guide, we’ll start by defining data cleaning before exploring three practical data cleaning examples you can use in Google Sheets and Excel.

Numerous ‘spreadsheet AI tool’ can help you achieve your objectives quickly and easily. This tool automates data cleaning so you can swiftly fix errors in messy data and get back to your analysis all within your existing spreadsheet. 

Table Of Contents

• What is Data Cleaning?

• 3 Practical Examples of Fix Messy Data in Google Sheets and Excel

• Common Data Cleaning Challenges and How to Overcome Them

• Make Decisions At Scale Through AI With Numerous AI’s Spreadsheet AI Tool

What is Data Cleaning?

Data cleaning is identifying, correcting, and removing errors, inconsistencies, and inaccuracies from a dataset to ensure that data is complete, accurate, and properly formatted. It is an essential step before using data for analysis, reporting, automation, or decision-making. Messy data can come from multiple sources, including: 

Manual data entry errors

Typos, extra spaces, or incorrect formatting. 

Inconsistent data formats

Different date formats, uppercase vs. lowercase variations, or multiple spellings of the same value. 

Duplicate records

The duplicate entry appears multiple times, causing inflated counts in analysis. 

Missing data

Empty fields where values should be, making reports incomplete. 

Misclassified information

Data appearing in the wrong column or field leads to misinterpretation.

Why Businesses Rely on Data Cleaning

Many industries rely on clean data to ensure their decisions are based on accurate insights. Data cleaning is crucial: 

1. Enhances Decision-Making Accuracy 

Companies use data to track performance, analyze trends, and forecast future outcomes. If the data is incomplete or inaccurate, businesses risk making poor decisions based on faulty insights.

Example: An eCommerce business analyzing sales data might see an incorrect spike in revenue due to duplicate order records in its dataset. This mistake could lead to over-ordering inventory or inaccurate financial reports without proper data cleaning. 

2. Improves Efficiency in Workflow Automation 

Messy data can break automated workflows, leading to errors and inefficiencies. Many businesses use spreadsheet automation tools like Numerous to process large datasets, but automation only works if the underlying data is structured correctly. 

Example: A marketing team using AI-powered analytics in Google Sheets or Excel might struggle to generate accurate customer insights if customer names, email addresses, or purchase histories are inconsistently formatted. Fixing these issues manually takes hours—but with tools like Numerous, data cleaning can be done in seconds. 

3. Reduces Errors in Financial and Operational Reports 

Businesses rely on spreadsheets for budgeting, forecasting, and financial planning. Errors caused by missing or duplicate data can result in incorrect financial projections, causing firms to overestimate or underestimate revenue and expenses. 

Example: A finance team might accidentally count expenses twice due to duplicate records in an Excel budget sheet, leading to misallocation of funds and budgeting mistakes.

Common Issues Found in Messy Datasets

If you work with Google Sheets, Excel, or any data-driven tool, chances are you’ve encountered some of the following issues: 

1. Duplicate Data 

Duplicate records occur when the same entry appears more than once in a dataset. This can skew reports, inflate totals, and cause confusion in analysis. 

Example: A sales team tracking customer purchases in Google Sheets might accidentally count the same transaction twice due to duplicate rows, leading to inaccurate revenue calculations. 

2. Inconsistent Formatting 

Different formatting styles can make it challenging to analyze and filter data properly. 

Example

• Date formats – Some entries appear as "01/02/2025," while others appear as "January 2, 2025". 

• Text inconsistencies – Some names might be in uppercase ("JOHN DOE"), while others are lowercase ("John Doe"). 

• Currency variations – Some prices are listed as "$100.00," while others appear as "100 USD" or "100.0". Businesses struggle with sorting, filtering, and analyzing their data without standardizing formatting. 

3. Missing Data 

Empty cells create gaps in datasets, making analysis incomplete. 

Example: A survey spreadsheet might be missing customer email addresses, making it impossible to send follow-up emails. 

4. Misclassified Data and Column Errors 

Data can be placed in the wrong column or field, making it hard to extract meaningful insights. 

Example: A customer’s phone number appears in the "Email Address" column, making it impossible to send automated emails. Product descriptions are mixed with product prices, breaking eCommerce reports.

Why Manual Data Cleaning is a Bad Idea

Many professionals manually clean data in Google Sheets or Excel by

• Manually searching for duplicates and deleting them. 

• Using Find & Replace to fix formatting errors. 

• Writing complex formulas to fill missing values or correct misclassified data. 

While manual data cleaning is possible, it is

• Time-consuming – Fixing thousands of rows can take hours. 

• Prone to human error – Correcting data increases the risk of missing mistakes. 

• Difficult to scale – Manual cleaning becomes impractical as datasets grow more extensive. 

How Numerous Makes Data Cleaning Effortless

Numerous is an AI-powered tool that automates data cleaning and formatting in Google Sheets and Excel. With a simple prompt, users can Automatically remove duplicates and standardize formats. Fill missing data intelligently based on existing patterns. 

Detect and correct misclassified data with AI-powered insights. Instead of spending hours cleaning messy spreadsheets manually, Numerous automates the entire process, allowing businesses to focus on analyzing and using data efficiently.

Related Reading

• Data Cleaning Process
• How to Validate Data
• AI Prompts for Data Cleaning
• Data Validation Techniques
• Data Cleaning Best Practices
• Data Validation Best Practices

3 Practical Examples of Fix Messy Data in Google Sheets and Excel

1. Remove Duplicates, Standardize Formatting, and Fix Errors in Your Data

Duplicate values distort reports, cause over-counting in analysis, and lead to errors in decision-making. Additionally, inconsistent formatting makes it difficult to sort and analyze data correctly. For instance, mixed date formats, inconsistent capitalization, or numerical values stored as text can all cause serious issues. 

Example

An eCommerce store owner tracks product sales in Google Sheets. However, the same customer appears multiple times in the dataset due to data entry errors. Additionally, some entries have different capitalization styles (e.g., "John Doe" vs. "john doe") and date formats that don't match.

Manual Fix: Using Google Sheets or Excel Tools

Step 1: Removing Duplicates in Google Sheets  

Select the data range where duplicates exist (e.g., Customer Name column). Go to Data > Data Cleanup > Remove Duplicates. Choose which columns to check for duplicates and click Remove Duplicates.  

Step 2: Standardizing Capitalization  

Use the formula =PROPER(A2) to change names to Title Case (e.g., "john doe" → "John Doe"). Use =UPPER(A2) for all uppercase and =LOWER(A2) for all lowercase formatting. Drag the formula down to apply formatting to the entire column.  

Step 3: Standardizing Dates  

Select the column with mixed date formats. Then click Format > Number > Date to choose a uniform date format for all entries.  

Automated Fix: Using Numerous for One-Click Data Cleaning

Instead of doing multiple manual steps, you can automate duplicate removal and standardization using Numerous: 

• Remove Duplicates Instantly – With a simple AI prompt in Numerous:   

"Find and remove all duplicate customer names in Column A."  

• Standardize Capitalization – Use Numerous AI functions to auto-correct inconsistencies across a dataset.  

• Fix Date Formatting Automatically – A simple prompt like "Convert all dates in Column B to MM/DD/YYYY format" fixes date issues in seconds.  

Why This Matters  

By automating Numerous tasks, businesses can save hours of manual work and ensure accurate, structured data for reporting and automation.  

2. Fill In Missing Data and Fix Errors: An Example

Incomplete data can make reports inaccurate, affect calculations, and cause automation errors. Missing values in customer records, financial reports, or inventory sheets can disrupt workflows and result in bad decisions. 

Example

A marketing analyst works on an Excel customer database but notices several rows are missing email addresses. Additionally, some names are entered incorrectly due to typos.  

Manual Fix: Using Excel’s Find & Replace and IFERROR

Step 1: Identifying and Highlighting Missing Values  

Select the dataset and go to Conditional Formatting > New Rule > Format only blank cells. Apply a highlight color (e.g., red) to identify missing data quickly.  

Step 2: Filling in Missing Email Addresses (If Available Elsewhere)  

If email addresses exist in another sheet, use =VLOOKUP(A2, Sheet2!A: B,2, FALSE) to pull missing values from another dataset. If not, manually enter missing values.  

Step 3: Fixing Name Typos Using Find & Replace  

Press Ctrl + H (Find & Replace) and replace common misspellings (e.g., "Jonh" → "John"). Use Excel’s Spell Check (F7) to identify potential name errors.  

Automated Fix: Using Numerous to Fill in Missing Data and Fix Errors

Numerous missing data can be filled in intelligently in seconds:  

• Find and Fill Missing Email Addresses—Numerous can use AI to predict missing values based on pattern recognition.  

• Auto-Correct Name Typos – A simple AI prompt in Numerous, such as "Correct all name misspellings in Column A," fixes common spelling errors instantly.  

• Detect and Highlight Errors – With automatic alerts, numerous can flag missing or incorrect data.  

Why This Matters 

Instead of spending hours manually correcting spreadsheets, Numerous AI automates the process, ensuring error-free, structured datasets for smooth reporting and automation.  

3. Split and Merge Data for Better Organization: An Example 

Data is sometimes stored in a combined format, making it challenging to analyze. A common issue is having full names in a single column instead of separate "First Name" and "Last Name" columns. Similarly, fragmented data might need to be merged for clarity. 

Example

A customer database stores names in a single column, but a sales report requires separate first and last name columns for personalized marketing campaigns.  

Manual Fix: Using Text-to-Columns in Excel & Google Sheets

Step 1: Splitting Full Names into Separate Columns  

Select the column with full names (e.g., "John Doe"). Go to Data > Split Text to Columns (Google Sheets) OR Text to Columns Wizard (Excel). Choose Space as the delimiter to separate first and last names into different columns.  

Step 2: Merging First and Last Names (If Needed)  

Use =A2 & " " & B2 to combine first and last names into a single column. Use =CONCATENATE(A2,", "B2) for more structured merging.  

Automated Fix: Using Numerous to Split and Merge Data Instantly

• Splitting Full Names – A simple AI prompt in Numerous, like "Split Column A into First Name and Last Name," instantly structures the data correctly.  

• Merging Fragmented Data – Using Numerous AI a command such as "Merge First and Last Name columns into one" ensures proper formatting without writing formulas manually.  

Why This Matters

Manual splitting and merging take time and require knowledge of formulas. Numerous AI do this instantly, making data organization smooth and efficient.  

Numerous is an AI-Powered tool that enables content marketers, Ecommerce businesses, and more to do tasks many times over through AI, like writing SEO blog posts, generating hashtags, mass categorizing products with sentiment analysis and classification, and many more things by simply dragging down a cell in a spreadsheet. With a simple prompt, Numerous returns any spreadsheet function, simple or complex, within seconds. 

The capabilities of Numerous are endless. It is versatile and can be used with Microsoft Excel and Google Sheets. Get started today with Numerous.ai so that you can make business decisions at scale using AI in both Google Sheets and Microsoft Excel. Learn more about how you can 10x your marketing efforts with Numerous’s ChatGPT for Spreadsheets tool.

Related Reading

• Machine Learning Data Cleaning
• Automated Data Validation
• AI Data Validation
• Benefits of Using AI for Data Cleaning
• Challenges of Data Cleaning
• Challenges of AI Data Cleaning
• Data Cleaning Checklist
• Data Cleansing Strategy
• Customer Data Cleansing
• Data Cleaning Methods
• AI Data Cleaning Tool

Common Data Cleaning Challenges and How to Overcome Them

1. Handling Large Datasets Without Slowing Down Performance

When working with thousands or millions of rows in Excel or Google Sheets, performance slows down, making it difficult to clean and process data efficiently. Large datasets can cause formulas to lag, crash spreadsheets, and take longer to filter or sorting. 

Manual Solution

• Use Filtered Views Instead of Deleting Rows: Instead of permanently deleting data, you can apply Filters (Data > Create a Filter) to manage large datasets without affecting performance. 

• Turn Off Automatic Calculations in Excel: Excel may recalculate in large datasets with complex formulas every time a cell changes. To fix this, Go to Formulas > Calculation Options and set it to Manual instead of Automatic. 

• Split Data into Multiple Sheets: Instead of working with a single massive spreadsheet, break it into more miniature sheets and use VLOOKUP or INDEX MATCH to reference data as needed. 

Automated Solution Using Numerous

Numerous large datasets can be processed instantly without slowing down performance. By running AI-powered automation, users can: 

• Find and remove duplicates across massive datasets in seconds. 

• Apply formatting rules to thousands of rows instantly. 

• Analyze large spreadsheets without crashing or lagging. 

Why This Matters

Instead of waiting hours for Excel or Google Sheets to load, Numerous speeds up data processing with AI-powered automation, making large datasets easier to manage. 

2. Fixing Inconsistent Formatting Across Multiple Columns

Data collected from multiple sources often contains inconsistent formats, such as: 

• Dates are stored in different formats (e.g., "01/02/2025" vs. "January 2, 2025"). 

• Text inconsistencies (e.g., "USA" vs. "U.S.A." vs. "United States"). 

• Currency or number formats that don’t match across columns. 

Manual Solution

• Use Find & Replace for Common Formatting Errors: (Ctrl + H) to standardize text values (e.g., replacing "USA" with "United States"). 

• Apply a Uniform Date Format: Select the date column, then go to Format > Number > Date in Google Sheets and choose a consistent format. Use Text to Columns to convert dates into a single format in Excel. 

• Use Text Functions to Standardize Formatting: Apply formulas like: =UPPER(A2) to convert all text to uppercase. =PROPER(A2) to capitalize the first letter of each word. 

Automated Solution Using Numerous

With a simple AI prompt in Numerous, users can fix formatting errors across an entire spreadsheet instantly: 

• "Standardize all dates in Column B to MM/DD/YYYY format." 

• "Convert all country names in Column C to 'United States.'" 

• "Ensure all product prices in Column D are formatted as currency." 

Why This Matters

Instead of manually fixing each inconsistency, Numerous automates the process, ensuring a clean and standardized dataset in seconds. 

3. Dealing with Missing Data and Blank Cells

Missing values lead to incomplete reports, incorrect calculations, and broken formulas. Blank cells in crucial columns can disrupt financial projections, customer databases, and sales reports. 

Manual Solution

• Identify and Highlight Missing Data: Use Conditional Formatting to highlight blank cells (Format > Conditional Formatting > Format cells if empty). 

• Use IFERROR to Handle Missing Values in Formulas: If a dataset includes #N/A errors due to missing data, use: =IFERROR(VLOOKUP(A2, Sheet2!A: B,2, FALSE), "Not Found") to return "Not Found" instead of an error. 

• Use INTERPOLATE for Missing Numeric Data: If numerical data is missing (e.g., in time-series analysis), use interpolation to estimate values. 

Automated Solution Using Numerous

Numerous AI can intelligently fill in missing data based on patterns: 

• "Fill all missing email addresses in Column C based on customer records in Sheet 2." 

• "Highlight all rows with missing sales data in Column D." 

• AI-powered predictions allow Numerous to suggest possible values based on previous data trends. 

Why This Matters

Manually filling in missing data takes time and increases human error, but Numerous automates this process, ensuring accurate reports. 

4. Removing Extra Spaces, Special Characters, and Unwanted Symbols

Sometimes, data contains unnecessary spaces or special characters that interfere with sorting, filtering, and calculations. Examples include: 

• Extra spaces before or after the text (" John Doe " instead of "John Doe"). 

• Special symbols ("$100.00" stored as text instead of a number). 

• Non-printable characters (e.g., imported data from PDFs or CSV files). 

Manual Solution

• Use TRIM to Remove Extra Spaces

• Apply =TRIM(A2) to clean up text fields. 

• Use CLEAN to Remove Non-Printable Characters

• Use =CLEAN(A2) to remove hidden special characters. 

• Convert Text-Based Numbers into Usable Numbers

• Use: =VALUE(A2) to convert numbers stored as text into actual numbers. 

Automated Solution Using Numerous

Numerous AI can clean text fields instantly: 

• "Remove all extra spaces in Column A." 

• "Convert text-based numbers into proper numeric values." 

• "Delete all non-printable characters from Column B." 

Why This Matters

Instead of manually scanning every row for formatting errors, Numerous automates cleanup, ensuring error-free data is ready for use. 

5. Merging or Splitting Data for Better Organization

Sometimes, datasets store information incorrectly—separate columns need merging, or combined values need splitting. 

Manual Solution

• Splitting Data into Separate Columns: In Google Sheets, select the column and go to Data > Split text to columns. In Excel, use Text to Columns under the Data tab. 

• Merging Two Columns into One: Use =A2 & " " & B2 to combine first and last names into an entire name column. 

Automated Solution Using Numerous

Numerous AI can merge and split data instantly with a simple prompt: 

• "Split names in Column A into First Name and Last Name." 

• "Combine city and state in Column C into a single address field." 

Why This Matters

Manually restructuring data is time-consuming, but Numerous automates it, making reports more straightforward to manage. 

Transform Your Data Cleaning Process with Numerous  

Numerous is an AI-Powered tool that enables content marketers, Ecommerce businesses, and more to do tasks many times over through AI, like writing SEO blog posts, generating hashtags, mass categorizing products with sentiment analysis and classification, and many more things by simply dragging down a cell in a spreadsheet. With a simple prompt, Numerous returns any spreadsheet function, simple or complex, within seconds. 

The capabilities of Numerous are endless. It is versatile and can be used with Microsoft Excel and Google Sheets. Get started today with Numerous.ai so that you can make business decisions at scale using AI in both Google Sheets and Microsoft Excel. Learn more about how you can 10x your marketing efforts with Numerous’s ChatGPT for Spreadsheets tool.

Make Decisions At Scale Through AI With Numerous AI’s Spreadsheet AI Tool

Numerous is an AI-Powered tool that enables content marketers, Ecommerce businesses, and more to do tasks many times over through AI, like writing SEO blog posts, generating hashtags, mass categorizing products with sentiment analysis and classification, and many more things by simply dragging down a cell in a spreadsheet. With a simple prompt, Numerous returns any spreadsheet function, simple or complex, within seconds. 

The capabilities of Numerous are endless. It is versatile and can be used with Microsoft Excel and Google Sheets. Get started today with Numerous.ai so that you can make business decisions at scale using AI in both Google Sheets and Microsoft Excel. Learn more about how you can 10x your marketing efforts with Numerous’s ChatGPT for Spreadsheets tool.

Related Reading

• Data Cleansing Tools
• AI vs Traditional Data Cleaning Methods
• Data Validation Tools
• Informatica Alternatives
• Alteryx Alternative
• Talend Alternatives