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A guide to customer data cleansing in Excel using DQ for Excel

Cleansing customer data in Microsoft Excel is a critical step to ensure data accuracy and consistency. Clean data is essential for making informed business decisions and maintaining a positive customer experience. Here’s a step-by-step guide to help you cleanse customer data effectively in Excel: 

Step 1: Back Up Your Data

Before you start cleansing customer data, it’s essential to create a backup of your original dataset. This ensures that you can revert to the original data if anything goes wrong during the cleansing process. 

Step 2: Open Excel and Import Your Data

  1. Launch Microsoft Excel.
  2. Open a new or existing workbook.
  3. Go to the “Data” tab on the Excel ribbon.
  4. Select “Get Data” or “Get External Data,” depending on your Excel version.
  5. Choose the source of your data (e.g., from a file, database, or online source) and follow the prompts to import your customer data into Excel. 

Step 3: Examine Your Data

Before you start cleansing, take some time to review your data. Look for common issues such as missing values, duplicates, inconsistent formatting, and spelling errors.

To help you with this DQ for Excel has a generate token capability for both pattern analysis and phonetic (sounds like) record matching.

Additionally, you may wish to Validate, Format, Verify and Authenticate data values to ensure their fitness for use? 

Step 4: Remove Duplicates

To improve on the inherent matching within MS Excel, DQ for Excel™ has the ability to enable better matching.  It does this by normalising and standardising data and generates phonetic representations of names, companies and addresses for better duplicate detection. 

This capability will help you to match and fine duplicate people like: Rob Thompson, Bobby Tomsen, Robert Tompsen, and similar company variants, like: Xerox Inc. and Zerocks Incorporated. 

When you have pre-processed your records with DQ for Excel™, then… 

  1. Select the range of data that you want to check for duplicates.
  2. Go to the “Data” tab.
  3. Click on “Remove Duplicates.”
  4. In the dialog box that appears, select the columns you want to check for duplicates based on and click “OK.” 

Step 5: Handling Missing Data 

Missing data can impact your analysis. Depending on the nature of the data, you can: 

  • Delete rows with missing values if they are not essential. 
  • Replace missing values with placeholders (e.g., “N/A” or “Unknown”). 
  • Use Excel functions like “IF” or “IFERROR” to fill in missing data based on certain criteria. 
  • Use the DQ for Excel™ to fill gaps in your data, you might use: 
  • Our MarketBase, Acquire, Enrich and Refresh capabilities to add and refresh missing data. 
  • Our Derive to add missing data by deriving it from existing data, a common use case is to add Country or ISO Codes, where missing, for better segmentation. 

Step 6: Standardise Data

Standardise data to ensure consistency. Common tasks include: 

  • Capitalise or convert text to lowercase. 
  • Use date formatting consistently. 
  • Format phone numbers, addresses, and postal codes uniformly. 
  • Use the DQ for Excel, standardisation capabilities, referencing over 340,000 data points across 5 spoken languages to improve the consistency and completeness of records

Step 7: Correct Inconsistent Data 

Identify and correct inconsistencies in data such as variations in spelling, abbreviations, or naming conventions. You can use Excel functions like “Find and Replace” or “Text to Columns” for this purpose. 

Use the DQ for Excel format and transform capabilities to standardize emails, phones, addresses, websites and more with ease. 

Step 8: Remove Unnecessary Columns

If your dataset includes columns that are not relevant to your analysis, consider removing them to simplify your data. 

Step 9: Remove Unnecessary Rows

If your dataset includes rows that are not relevant to your analysis, consider removing or de-activating them to simplify your data. 

DQ for Excel™ can help identify, records that have moved, are deceased, or have asked to be removed from your marketing lists and call lists. 

Step 10: Validate Data

Ensure data integrity by using Excel’s data validation features. You can set rules and constraints on data entry to prevent errors and inconsistencies. 

Use the DQ for Excel validate capabilities for phones, emails, URL’s etc to confirm the syntax is correct, before considering Formatting and Authentication, to confirm an email will deliver and a phone number will dial. 

Step 11: Use Formulas for Data Validation

Use Excel’s formulas to validate data. For example, you can use functions like “LEN” to check the length of text entries or “IF” statements to verify that data meets specific criteria. 

Step 12: Check for Outliers

Identify and handle outliers in your data, which may skew your analysis. Use Excel’s statistical functions and charts to visualize and detect outliers. 

To support this analysis, the pattern tokenization can be very useful for clustering of errant data 

Step 13: Document Your Changes

As you make changes to the data, maintain clear documentation. Create a separate sheet or a cell where you note the actions you’ve taken to cleanse the data. This helps maintain transparency and allows others to understand your data cleansing process. 

Step 14: Recheck Your Data

After performing all the cleansing steps, review your data again to ensure that it is consistent, accurate, and ready for analysis. 

Step 15: Save Your Cleansed Data

Save your cleansed data in a separate file or sheet from the original dataset to avoid overwriting the original data. 

By following these steps, you can effectively cleanse customer data in Microsoft Excel, ensuring its accuracy and reliability for your business needs. 

Remember that data cleansing is an ongoing process, and regular maintenance is crucial to keep your data clean and useful. 

If you use the DQ for Excel Add-In regularly, your data will remain up to data and fit for business use. 

DQ for Excel Customer Data Solutions

Designed to improve the quality of your customer data, for optimal business use.

DQ for Excel Data Quality Capabilities

Our comprehensive range of data quality capabilities, all designed to unlock the value in your customer data and guarantee it is fit for its intended use.

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