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Duplicate data means multiple records of an individual or entity. For example, you could have five records of Mary Jane in your CRM, with each record containing a different email ID or phone number.

While most databases will have 5 – 10% of duplicate data, an excess of that harms business outcomes. Sadly, many businesses have reported more than 30% of their CRM consist of duplicate data, leading to flawed insights and skewed analytics.

For example, a recent customer discovered that 32,000 out of their 74,000 CRM records (nearly 43%) were duplicate data. This revelation gave rise to internal team conflicts, caused the team to shift from their on-going goals to focus on resolving data problems, and had them redefine their internal processes.

You don’t want the same to happen to your business.

So in this guide, we’ll help you understand the common causes of duplicate data, how to resolve and avoid them, and what steps you need to take to ensure duplicate data doesn’t become an organizational crisis.

This guide is for data analysts, developers, and business users who are in a time-sensitive situation and need help to resolve their duplicate data challenges quickly and efficiently.

Watch how this customer discovered they had duplicate CRM data:

WHAT CAUSES DATA DUPLICATION?

Data duplication can happen due to a myriad of reasons, some of the most common being:

❌ Human error: While most businesses now have automated data collection systems, you will still have sales clerks manually enter customer data in POS systems. The clerks are unaware of data quality, and may accidentally enter one customer’s information twice – or – the customer may forget the original email ID they signed up with and give another email to the clerk. Before you know it, the system now has more than one entry of the same customer.

 Disconnected systems: International stores and brands struggle with disconnected systems that cause a serious challenge with duplicate data. You could have that same customer visit the same store in a US branch and a UK branch. They may also use different or temporary phone numbers to register.

Unless there is one centralized system, the business is now recording this customer’s data in two separate databases, under two different phone numbers! Duplicate data like this could cause skewed insights and analytics. You would send out marketing materials to US customers only to lose half of them in return mails caused by the recording of temporary phone numbers and addresses.

❌  Technical errors: Issues such as formatting errors, software updates etc can cause files and records to become duplicates without the user’s knowledge. This applies to businesses that use API integrations to connect multiple databases and platforms. For example, an e-commerce website may end up having duplicate records in its CRM and main database because of a technical API glitch that causes an order to be recorded twice.

These technical issues are silent killers and are never caught until a data quality audit is performed (which is why you must have a data quality audit every three months especially if you have thousands of customer data streaming in every month).

❌  Data migration: Sometimes, duplication occurs when data is migrated from one system to another without following data quality protocols. Most businesses make the mistake of migrating the data as it is without assessing it for data quality challenges. Of course, large enterprises have data migration processes to follow, but a small or mid-tier business would simply extract the data onto a spreadsheet, upload it into their new CRM, only to discover existing duplicates have skewed their analysis. As basic as this sounds, there are companies that struggle with such mundane activities, eventually causing more serious challenges later.

❌  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 or SSNs. In the absence of these identifiers, email IDs, addresses or phone numbers are used, or the database assigns unique serial numbers to the records, but with these attributes, there is always a chance of duplication as one person can have multiple email addresses or phone numbers, so even though they have a unique serial number, it doesn’t necessarily mean they are a unique record.

❌  Poor data quality: The lack of data quality controls, and the 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 are usually non-existent or low. For instance, letting the user type in a country or city instead of a drop-down or auto selection.

It’s no joke. Companies struggle significantly with duplicate data challenges because of seemingly small problems that happen at the root level. Eventually, these problems become bottlenecks in downstream applications of the data, ballooning into a full-blown crisis!

How do we resolve these issues?

Here are three common approaches.

HOW TO DEDUPE DATA : 3 COMMON APPROACH

As the term suggests, data deduplication is the process of removing duplicates. Sounds simple in theory, but not so easy in practice!

There are three ways to fix data deduplication issues:

 Fuzzy Match Algorithms:

Experienced developers or data analysts, use FuzzyWuzzy, a Python language library to match datasets and identify duplicates based on their similarity score. This is a great option if you have a developer on analyst in house who’s main job is to keep track of data quality and resolve for duplicates within batches.

 Be an Excel Pro:

Need to quickly remove basic contact name duplicates? Excel’s VLOOKUP feature does a great job with removing duplicates that share the same characters, such as Catherine and Katherine. However, if the same name is spelled as Kathryn or Kathy, it won’t result in a match! Moreover, you’ll have to spend a good amount of time in manually cleaning the data – which many sales and marketing people end up doing, begrudgingly. It’s a tedious, thankless, hairsplitting process! I mean I love Excel to organize and process my data, but don’t make me use it for deduplication!

 Use a Data Deduplication Software:

Before you run a Google search, let me tell you there are dozens of data deduplication software out there. But you essentially need a software that does data matching well – more specifically, fuzzy data matching.

Why the emphasis?

Because most software use basic data matching algorithms to highlight duplicates based on exact character count. They are similar to Excel (except with the ability to process larger datasets and provide cool visualizations), but they are not designed to capture naunced data fields. Using the example above, basic data match software won’t be able to identify Catherine or Katherine is the same person – but one that has powerful fuzzy match capabilities will be able to detect and allow you to build a library of such naunced characters and names for future use.

WHY A DEDUPLICATION SOFTWARE IS A BETTER SOLUTION THAN CODE OR EXCEL

Your preferred method to dedupe data depends on four key factors:

✅The “cleanliness” of the data. If more than 10% of your data is incomplete, invalid, and duplicated, a data deduplication software with a data cleaning module can do the job faster.

✅How disparate or siloed are data sources. If a company has multiple data sources from various vendors, administrators, and third-party sources, data deduplication can take more time and resources. You will need to integrate these different data sources into one one platform so you can have a consolidated view of different data sources and also be able to run a match across them. If you have to flip between different Excel sheets and files, you will lose valuable time and increase chances of errors.

✅ The urgency of your data deduplication project: Need to submit a report in a week’s time? A data match software is the fastest, most efficient way to resolve duplicates within a week’s time. Even if urgency is not a problem, a software is more likely to help you dedupe the data more accurately and efficiently than the other two options!

✅ How much money are you willing to spend? On average, a skilled data analyst can cost you $120K/annual, and their job would primarily be a data janitor! With AI/ML-based technologies, is it a good decision to hire a talented individual to just deduplicate or clean data? A better approach would be to hire a data specialist who is comfortable using a combination of tools and technologies (a data match solution starts from as low as $1500) to clean and match data. This way you are able to merge technology + talent to get optimal results. A data match tool save your data analyst’s valuable time, it will also give them the chance to spend more time on strategy and improving processes!

 

Here’s a brief breakdown of what you can get if you opt for a software instead of using Excel, outsourcing to a consultant, or using scripts (this applies to developers as well. You don’t always need to manually build algorithms to solve for data problems!).

Benefits How
Cost savings Data deduplication tools can save cost by reducing resource time in manual deduplication efforts. Depending on the size of the dataset, businesses can save anywhere from 10% to 70%, representing thousands of dollars in savings.
Improved match accuracy Data matching solutions come with built-in advanced matching algorithms that can identify complex duplicates without requiring any additional coding. 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.

 

If you’re ready to use a software, you also need to know how to choose the right one.

In the next section, we’ll give you tips and advice from the customer feedback we’ve obtained where they share with us exactly what they look for when making a decision to choose a data deduplication solution.

Read on!

CHOOSING THE RIGHT DATA DEDUPLICATION SOLUTION

There are many data deduplication software, but how do you know which one’s right for you?

Here are some of the factors that our customers told us they prioritized when choosing a software.

✅ Ease-of-use: This remains the top-most selection criteria for most customers. Data deduplication is already a complex process. It beats the purpose of resolving a data quality problem if your users have to take a training, and be qualified to use a software. Ideally, a good data deduplication or data matching tool should be simple enough even for non-tech users to use.

✅ No-Code: Most developers and Python-trained data analysts would scoff at no-code, but the fact is, it does save you a lot of time! You don’t want to spend hours figuring out why a coding glitch in a script is preventing you from resolving a fuzzy match result. You could just use a software, perform multiple match iterations, use pre-defined libraries to weed out duplicates within minutes.

✅ Customer support: Always opt for tools that prioritize customer support. Use Gartner, G2 and other review sites to gain insights on the company’s customer support efforts.

✅ Integration capabilities: You can identify these by speaking to the support or technical team of your tool of choice. Ideally, the software should allow for easy integration with your CRM, Snowflake database, SQL and other data sources.

✅ Scalability: Can the software handle larger datasets up to a million records or more? Are there any extra credits to more records? Do factor in scalability when choosing a software. You don’t want to pay $5,000 only to discover you can process only 10,000 records!

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 time-sensitive challenges without asking too much from you in terms of resources and capabilities.

TO CONCLUDE – WIN THE DATA DUPLICATION STRUGGLE WITH THE RIGHT SOLUTION

Yep! Data duplication is a fairly common problem that affects most database, but no one really pays attention until it causes a business crisis – such as angry emails from customers, poor business decisions based on poor insights and so on.

You don’t want to waste months to solve a data duplication challenge. That’s when a data deduplication software can do the job faster, and better than other traditional methods like coding or using Excel. Choosing the right solution though is imperative to the success of your project. Opt for solutions that are easy to use, have great customer service and require minimal training.

 

Written by Farah Kim

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, followed by two post-grad degrees specializing in Linguistics and Media Communications. She works with the WinPure team to create awareness on a no-code solution for solving complex tasks like data matching, entity resolution and Master Data Management.

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