Data Matching for De-duplication

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Farah Kim • January,2023

Duplicate data harms business outcomes. When companies are connected to multiple data sources, have siloed data processing models, and poor data quality practices; duplicate data is inevitable. For example, duplicate customer data may cause inaccurate insights & analytics, misleading a sales or marketing team into believing their strategies worked. Worse, data duplication can lead to security breaches, angry customers (if they receive marketing materials at multiple addresses), and a host of issues that become an unnecessary bottleneck, hampering team morale, revenue, and business operations. 

The solution?

Preventing data duplication at the source by setting data quality standards, investing in data deduplication technology, and training your teams to recognize and resolve duplicate data *before* the data is used for a report, insight, or forecast.

In this data deduplication guide, we’ll explain:

What Causes Data Duplication?

Data duplication doesn’t occur naturally. It is almost always caused by activities or errors at the data collection or data processing stage. Over the years, we’ve discovered, there are five major reasons for data duplication:

 

Human error: Manual data entry mistakes such as inadvertently entering the same information multiple times, or recording multiple instances of the same data in different systems by different people are some common examples of duplication caused by manual entry. For example, a customer support agent may end up recording a new entry for a previous client if they happen to change their phone number or email address.

 

Technical errors: Issues such as formatting errors, software updates etc can cause files and records to become duplicate without the user’s knowledge. For example, a company we worked with used a CRM system to track customer information and sales. To automate the process, they connected the CRM to an automation trigger tool to create a record any time a new sale was made. What they forgot to take into consideration was when the same customer made more than one purchase, the data should be synced. Instead, the automation recorded every new purchase by the same customer as a new entry, assigning it a unique ID.  Because the system was on autopilot no one really paid heed to the duplication. However, a few months later when the sales team noticed their sales figure was not meeting the stats from the CRM’s dashboard, they began to dig deeper into the data. That’s when they realized multiple entries were recorded for each customer that made more than one purchase. The team thought they had amazing growth, while in fact, it was a workflow error!

 

Data migration during mergers: Sometimes, duplication occurs when data is migrated from one system to another without following data quality protocols. Most businesses make the mistake of importing data without assessing the quality of data. For example, post migration, they discover the current system stores records by the order of Last Name and First Name instead of the other way round. In other instances, if the companies target the same kind of customers, chances are the same customer’s information is present in both databases. This type of duplication can create trouble for business operation teams who will be dealing with an influx of duplicate data. Hence, it’s always recommended to spend a good amount of time reviewing the quality of data & identify potential challenges before initiating a migration process.

 

Lack of a unique identifier: When businesses use online sources to scrape data, or when data is protected under certain legal obligations, it does not hold unique identifiers such as registration numbers, ID numbers etc which is unique to each individual. In the absence of these identifiers, email IDs, addresses or phone numbers are used, but with these attributes, there is always a chance of duplication as one person can have multiple email addresses or phone numbers. It’s always important for data managers to ensure there are unique identifiers they can use to prevent the unnecessary occurrence of duplicates.

 

Other minor causes to watch out for includes:

 

File format errors: Converting files into new formats (such as from CSV to Excel), there is a chance that some of the original data may be duplicated in the new file format. This often happens when users create multiple copies or formats of the same data.

 

Automated processes: scripts that run periodically can cause duplication of data if they do not detect existing records before running. As given in the example above, when you want to automate a task, be careful of the rules you set!

 

Too many cooks: When data entry access is given to too many people, there is always a chance for duplication. For example, if a marketing executive, a sales executive, a lead generation executive all has data entry rights they each of them can manually key in entries that already exist. Therefore, user access rights must be carefully monitored to avoid duplication and other data quality problems.

 

Poor data quality: The lack of data quality controls, and absence of data entry standards leads to duplication when users key in data that contains typos, odd characters, and excessive noise. This usually happens when people enter information via web forms where the standards for accurate data entry is usually non-existent or low. For instance, letting the user to type in a country or city instead of a drop-down or auto selection.

How to Resolve Data Duplication Issues?

It costs more to fix a data duplication issue than it is to prevent it. Data duplication can have a significant negative impact on businesses. According to a survey by Gartner, poor data quality costs organizations an average $12.9 million. A research by Black Book states, on average, duplicate records occupy almost 20% of the EHR system of any given healthcare provider. While duplicate records are harmful for any industry, in the medical industry, it could lead to matters of life and death.

 

Data duplication can cause:

 

  •   Unnecessary storage requirements which increase costs;
  •   Increased risk of data corruption;
  •   Loss of vital information;
  •   Difficulty in managing and maintaining the data;
  •   Reduced system performance due to disk space constraints.

 

Resolving duplication issues is not simple, but with the latest data deduplication technology, it’s not impossible to achieve either. Some of the ways you can resolve data duplication can be:

 

  •   Use data matching and cleaning solutions that uses powerful matching algorithms to identify & eliminate duplicates
  •   Train employees handling data with the basics of data duplication, how to identify and prevent duplication. Better data training is the key to resolving most data quality challenges!
  •   Implement quality checks on data entry using business rules, constraints, or triggers
  •   Use unique identifiers whenever possible, even when merging multiple databases 
  •   Regularly audit databases for accuracy and for duplicate entries. Ideally, you must have an automated solution to handle this process. 
  •   Employ standardization techniques such as mapping lists and validations on field values
  •   Regularly backup databases in order to restore it if a mistake occurs in the system
  •   Ensuring that any external sources of data have been verified before importing it into the system

 

Of course, even with your best efforts, data duplication can still occur, but the goal is not to have zero duplicates – the goal is to have significantly less duplicates. It’s impossible to quantify this, but as a rule of thumb, if your data is affecting your projections, chances are your duplicate data is in excess.

 

One basic way of measuring this is by comparing the total number of unique records to the total number of records with duplicates. If there is a large discrepancy (for e.g anything above 5% in a 1,000 record) then it’s time to take action!

What is Data Deduplication & Dow Does It Work?

Simply put, data deduplication is the technology of removing duplicates. As enterprises become data-driven, they have also begun to understand the negative impact of duplicate data.  Many businesses have started investing in solutions that can help them reduce their data footprint by removing redundant copies of data – copies created through years of data collection, mergers, migrations, backups etc.

 

Data deduplication uses advanced data matching algorithms – exact, numeric, fuzzy, phonetic and sometimes platform proprietary algorithms to identify duplicates embedded within or across data sources.

 

The effectiveness of data deduplication technology depends on factors like:

 

  1. The “cleanliness” of the data. For example, the more a dataset is like to contain dirty data such as typos, incomplete fields, inaccurate, or non-standardized data, the more difficult it is to efficiently weed out duplicates. In such instances, cleaning the data is a necessary pre-requisite to identifying duplicates.

 

  1.   How disparate or siloed are data sources. If a company has multiple data sources from various vendors, administrators, and third-party sources, then data deduplication can take more time and resources than usual.

 

  1.   The speed with which data deduplication can happen. While data deduplication technologies today can identify duplicates in a matter of minutes (with no coding required), the human intervention time in reviewing this data requires more time. Users will have to manually review duplicates and make decisions.

 

  1.   The computational resource available. On average, it is estimated that a single processor is capable of processing up to 1 million records per hour. Algorithms for deduping large datasets is computationally intensive, so a native deduplication technology will require additional computing resource.

 

It’s important to note here, that many companies are often tempted to build an in-house deduplication solution, team, or even hack to tackle duplication problems. The results are never favorable, especially if the business is not directly involved in the business of data management.

This is where automated data deduplication solutions step in.

Why You Should Use a Data Deduplication Solution?

Even if you don’t want to turn to third-party sources, you can invest in an affordable solution like WinPure that lets you clean and resolve duplicates without any coding required.

 

To summarize, here are some key benefits of using a data deduplication tool.

 

Benefit Description
Cost savings Data deduplication tools can save cost by reducing resource time in manual deduplication efforts. Depending on the size of the dataset, businesses have reported a savings of anywhere from 10% to 70%, representing thousands of dollars in cost savings.
Improved match accuracy Data matching solutions are designed to accurately weed out duplicates. Manual methods such as scripting algorithms are basic and require multiple iterations to be effective. Even then, they usually have an accuracy score lower than 80%.
Reduced risk of errors & inconsistencies in matching With a data deduplication technology, you can be at ease since you won’t have to code in any rules. A tool like WinPure has built-in templates and rules that you can use or modify as needed. This reduces the risk of errors and the occurrence of false positives or negatives
Significant reduction in time Businesses have reported saving around 300 – 1,400 hours on data deduplication for a million-row data set. The hours can increase depending on all the factors given above. With a data deduplication solution though, you can profile, clean, match, and remove duplicates within an hour of set up!
Improved processes & organizational efficiency With reduced manpower hours, and the resources required to manage data, your organization will be motivated to regularly monitor and audit data! Improved processes and organizational efficiency are big drivers of change and growth.
   

 

How to Find the Right Data Deduplication Tool?

Despite the benefits of data deduplication technologies, finding the right data deduplication tool that meets your specific challenges, requirements, and resources can be challenging. Here are some tips to help you select the best solution:

 

  •   Assess your data’s complexity and scope – different tools may be better suited to different datasets. If you’re a small business, you don’t need a million-dollar solution. If you’re an enterprise with 50> billion revenue/year, then you’ll need a solution that can be integrated within your platforms. Make choices depending on data scope and complexity.
  •   Evaluate customer reviews, pricing and licensing options on channels like G2 and Capterra. Look out for customer support reviews and how the tool is able to solve a problem relevant to your challenges. Review the solution’s online presence and the message they convey.
  •   Test out the demo versions of potential vendors to get a better understanding of their accuracy and flexibility. Always opt for solutions that allow for free demos without any strings attached.
  •   Consider other features such as scalability, integration capabilities, reporting capabilities, etc. You can identify these by speaking to the support or technical team of your tool of choice. Always use your test data set to review the tool’s capabilities.
  •   Review training time and complexity of the tool. Ideally, you’d want the solution to easily be used by non-business users as well, so it’s better to have a zero-code tool rather than one that increases complexities and dependencies.

 

Choosing a solution may be difficult, but once you get the right match, half your data quality woes can be resolved. The key is in partnering with a solution that “gets” your data duplication challenge and helps you resolve it at a grass root level instead of just doing superficial fixes.

To Conclude

Data duplication is a common occurrence, but with disastrous impacts. While you cannot prevent it from happening entirely, you can reduce its impact significantly by investing in the right tool, process, and people. Once you find the right data deduplication technology to resolve the problem, you can save nearly 70% of labor hours and thousands of dollars!

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Farah Kim

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Farah Kim is a human-centric product marketer and specializes in simplifying complex information into actionable insights for the WinPure audience. She holds a BS degree in Computer Science and a MA degree in Linguistics. She is fascinated with data management and aims to help businesses overcome operational inefficiencies caused by ineffective data management practices.

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