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Challenges of Using Excel for Finding Duplicates: A Users Guide

Introduction

Finding duplicates, whether a duplicate transaction or a duplicated value, is a crucial task for maintaining data integrity, ensuring accurate reporting or preventing financial loss. However, when it comes to identifying duplicates, particularly "fuzzy" duplicates (where you are interested in both "close" matches that are similar enough to be considered duplicates and exact matches), Excel often falls short.

Challenges in identifying duplicates can be created by several factors - the complexity of the duplicate criteria i.e. the duplicate assessment requires consideration of several data fields - like your typical Accounts Payable duplicate payment check (Same Supplier, Invoice Number, Date and Amount), the consistency of the data in the fields being compared, and the sheer size of the data being analysed.

In this blog, we will explore the challenges of using Excel for duplicate detection, discuss some Excel approaches that can help find duplicates, and discuss alternative options to finding duplicates if Excel simply is not cutting it.

The Challenges of Using Excel for Duplicate detection

Excel is a useful tool widely used in the majority of organisations, but it has several limitations when it comes to finding duplicate transactions:

1. Limited to a single data field

The inbuilt Excel (and indeed PowerBi) duplicate detection functionality is limited to assessing duplicate values from within a single data field.  If you are looking for a recurring reference number within a data field - perhaps an employee number or Invoice number, then this is fine.  But often, a more advanced search is required - perhaps you need to undertake duplicate detection using data from several data fields, like your typical Accounts Payable duplicate payment check where you will be looking for payments where the Supplier, Invoice Number, Payment Date and Amount are the same.  This is not possible in Excel (although we will discuss a form of workaround later).

Another challenge with using Excel to find duplicates, is that the output results only indicate that a value appears more than once.  If there are only a few duplicate then this does not present too much of a problem - but if you are working with a large dataset, or there are a number of duplicates, it can (depending on the uniqueness of the value) make it difficult to identify which records are duplicated (matched) against each other.

2. Complexity with Large Datasets

Excel struggles with large datasets, leading to slow performance and possible crashes. As the volume of data increases, the time required to process and identify duplicates becomes impractical - largely driven by the Excel Conditional formatting, which is quite inefficient, as you will know if you have used conditional formatting in Excel.

3. Lack of Advanced Algorithms

Excel’s built-in functions do not support advanced algorithms needed for complex duplicate detection. Techniques like phonetic matching - for finding duplicates that sound the same i.e. Rachel and Rachael, pattern recognition, and similarity scoring (a score to indicate how close a match records are) are beyond Excel’s capabilities without extensive VBA (Visual Basic for Applications) programming, which requires specialised knowledge.

Similarly, Excel lacks advanced fuzzy matching capabilities, making it difficult to identify these nuanced duplicates. Functions like `FUZZYLOOKUP` exist, but they are not natively integrated and require additional add-ins, which can be cumbersome. 

5. Error-Prone and Time-Consuming for Inexperienced Users

Manually setting and entering formulas and conditional formatting rules to detect duplicates can lead to errors. The process is also time-consuming, especially for users who are not proficient in Excel.

Tips to using Excel to Find Duplicates

We've discussed the limitations of Excel and PowerBi in detecting duplicates, as they can only analyze one data field at a time. So, how can we tackle duplicates across multiple data fields?

One effective method is utilizing Excel's `CONCATENATE` function to merge relevant data into a single field for duplicate assessment. While this approach is handy, it's essential to note that the resulting data field, due to its precise nature, may miss duplicates if even a single additional space is present.

Additionally, the `COUNTIF` function proves useful in tallying occurrences of a value or data string within the dataset - any count exceeding one indicates a duplicate.

What if Excel is not enough?

Specialist software like Arbutus Analyzer is designed to address these challenges, offering a more robust and efficient solution for finding duplicate transactions. Here’s how Arbutus Analyzer stands out:

1. User-Friendly Interface

Arbutus Analyzer offers a user-friendly interface that simplifies the process of duplicate detection. Users can easily configure and run analyses without the need for advanced technical skills or programming knowledge.

2. Comprehensive Analytics

The software provides comprehensive analytics and reporting tools, allowing users to drill down into the details of duplicate transactions. This helps in understanding the root causes and implementing corrective measures.

3. Advanced Fuzzy Matching

Arbutus Analyzer excels in fuzzy matching, using sophisticated techniques to identify records that are similar but not identical. This includes phonetic matching, pattern recognition, and similarity scoring, ensuring no potential duplicates are overlooked.

4. Isolation / Removal of Duplicates

Furthermore, Arbutus Analyzer offers a range of options once duplicates are identified, including the ability to isolate them and save them in a new data table or Excel workbook. Users can also summarize duplicate transactions for removal, with the flexibility to choose which duplicate transactions to keep - a feature lacking in Excel's 'Remove Duplicates' function, which automatically retains the first record.

5. Handling Large Datasets

Unlike Excel, Arbutus Analyzer is built to handle large volumes of data without performance issues. It processes data efficiently, making it ideal for organisations dealing with extensive transactional records.

6. Customisable and Scalable

The software is highly customisable to meet the specific needs of different organisations. It is also scalable, capable of growing with the organisation’s data requirements.

7. Automated Duplicate Detection

Arbutus Analyzer automates the process of finding duplicates, significantly reducing the need for manual intervention. Its advanced algorithms can quickly scan and identify both exact and fuzzy duplicates within large datasets.



Conclusion

While Excel is a versatile tool for many data analysis tasks, it falls short when it comes to efficiently finding duplicate transactions, particularly fuzzy duplicates. The manual, error-prone processes and limitations in handling large datasets and advanced matching algorithms make it less suitable for this purpose. 

Specialist software like Arbutus Analyzer offers a powerful alternative, providing menu based, and efficient duplicate detection and removal capabilities. By leveraging advanced fuzzy matching techniques and robust data handling, Arbutus Analyzer ensures that organisations can efficiently identify duplicate records and transactions. For anyone struggling with duplicate transaction detection in Excel, making the switch to a specialised tool like Arbutus Analyzer can be a game-changer.

To see a demonstration of finding duplicates using Data Analytics Click Here

 

To find out more about Arbutus Analyzer, Click Here