Data is a valuable asset for businesses, but large databases often contain errors that make the data hard to use. One of these errors is data duplication.
Duplicate records mean having the same stuff repeated in a dataset. This happens when multiple entries represent the same thing, like having two identical customer profiles or repeated sales figures. They show up for various reasons—maybe someone made a mistake when combining info from different systems or during updates and changes.
Duplicates can be exact copies or slightly different versions, which makes finding and fixing them a real puzzle. These repeated entries don't just clutter up databases; they mess with the reliability and accuracy of the data.
This article will show you how to solve the problem of duplicate records.
Consequences of Data Duplication
Duplicate data causes several problems for businesses and data management. First, it messes up the interaction with customers, reports and analyses that companies use to make decisions. When there are copies of the same information, the data looks weird and can lead to wrong conclusions, making it hard for businesses to execute and plan well.
Having extra copies of data also takes up extra room in the computer systems and costs more to store and manage. Plus, when people use this duplicated info, they can confuse and make mistakes. Imagine having two different addresses for the same customer—it might lead to sending things to the wrong place.
When a company sends multiple messages or offers to the same person because of duplicated info, it creates a bad customer experience. They might feel annoyed or think the company isn't organized.
In some industries like fintech, IT, or medicine, having messy data can even get a company in trouble with the law if it does not follow the rules about keeping information correct and secure.
Factors Affecting the Choice of Duplicate Removal Method
Choosing the best way to eliminate duplication from a dataset depends on a few things. Here's what you should think about.
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Factors
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Description
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Size and Complexity
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The more data you have and the trickier it is, the more it can influence the method you'll need to remove duplicates.
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Types of Data
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Different data needs different ways to clean out duplicates. Some methods are great with words, pictures, or sounds, while others handle structured or messy data better.
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Accuracy Needs
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The level of precision needed for duplicate detection dictates the choice of methodology. Manual or mixed methods might be best for really accurate results. Automated methods might do the trick if you are more focused on efficiency.
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Scaling Up and Keeping Going
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If you plan to manage data for a long time, pick a method that can grow with your data or adapt to changes. For instance, machine learning methods might be more suitable in the long run.
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Rules and Laws
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If you're dealing with sensitive or private information, make sure your chosen method is compliant. It's about keeping data safe while getting rid of the extra stuff.
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Money Matters
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Consider the costs involved in choosing a method. Doing it by hand might be cheaper for small sets, but tech-powered methods might be worth the higher initial costs for bigger sets.
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Ways to Get Rid of Data Duplication
Don't worry if you find duplicate data in your repositories. There are ways to fix it, and you can pick what suits your company best. Check out these methods to resolve the problem.
Manual Removal of Duplicates
Manually removing duplicate records means finding and removing them from the data by hand and or linking to the correct version. This method is good for small sets of info or when you need things to be super accurate.
If you have a small amount of duplicate data, doing it manually might be quicker. Also, when your data is super important or sensitive, this hands-on approach might be necessary to ensure every duplication is gone. And for certain data types like images or sounds, doing it by hand might be the only way to spot duplicates.
First, you look at the data to spot possible copies. You can do this by eye or use special tools to help. Then, check to be sure these possible duplicates are the same. This might mean looking at more data or asking experts. Once you're confident, you take out the duplicates from the data.
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Pros
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Cons
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High Accuracy
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Time-Consuming
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Flexibility
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Not Scalable
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Control
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Prone To Human Errors
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Automated Duplicate Removal
Automated data deduplication means special computer programs find and delete copies of the info. This method works really well for big data sets and is super fast.
It would take forever if you had to go through tons of data by hand. Hopp software speeds things up a lot. It clean your data during a migration to prevent your new system inherit the problems with duplicates. With Hopp software you can create validation rules to identify and manage duplicates.
Hopp can look through all the data to find the duplications. When it thinks it has found copies, it double-checks to ensure they are duplicates. After that, it gets rid of the copies by putting info together or removing the extras.
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Pros
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Cons
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Efficiency
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Initial Setup
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Scalability
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False Positives
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Cost-effectiveness
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Data Sensitivity
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Mixed Method For Data Deduplication
The mixed method for eliminating duplicates combines the best of both worlds—using computer tools and human smarts. This way ensures super accurate detection and removal of duplicates by combining the efficiency of tools with human accuracy.
This method is perfect for those who need top-notch data accuracy. If your data is sensitive and needs a close look to find duplicates, this mixed method is for you. Also, a mixed approach works well if your data combines different types of info, like structured or messy.
How Mixed Method Duplicate Removal Works:
- Software like Hopp scans the data using specific rules to find potential duplicates.
- Human experts review and verify these possible duplicates for accuracy, especially in tricky cases.
- The rules are refined based on the experts' findings to improve duplicate detection.
- This process iterates, with the refined rules scanning for duplicates again, and human verification continues until the desired accuracy is achieved.
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Pros
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Cons
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High Accuracy
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Requires Expertise
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Adaptability
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Time and Resource Investment
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Scalability
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Complexity
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