Data Matching for De-duplication

Data duplication is caused by activities or errors at the data collection or data processing stage. Having worked with over 4,000 businesses, we’ve discovered, there are five common 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 can manually key in entries or make changes to the existing data. 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.

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.

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;
•    Unreliable insights, flawed foresights and predictions 

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 on the basics of data duplication including 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 to prevent duplication caused by transposition errors and misclassified data.

✅Create master records and build a 360 customer view to unify disparate, disconnected data sets.

It is important to remember the goal is not to have perfect data. The goal is to ensure your data is not affecting your projections, downstream business processes, and revenue. If more than 10% of your collective data is duplicated, you must take immediate action! 

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:

✅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.

✅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.

✅ 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.

✅ 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.

When faced with a data quality challenge, such as duplication, IT teams often resort to manual resources to dedupe the data. They end up spending days using Excel and coded scripts to match, dedupe, and verify the data. With this approach, your team is wasting precious time, while attempting to take on an uphill task.  

This is where automated data deduplication solutions are required. 

Data deduplication solutions work by identifying and removing duplicate data blocks, and then storing only a single copy of each block. This can result in significant savings in storage costs, as well as improved performance and security.

Some of the specific benefits of using a data deduplication solution include:

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 and business sizes. If you’re a small business, a million-dollar solution may be costly and unnecessary. Regardless of size and scope, a data deduplication solution should be able to pre-process, match, and deduplicate data within hours. 

✅ 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. 

✅ 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.

Data duplication affects business processes, which is why it is imperative to resolve duplicates before using the data for business goals. With the right technology partner,  Once you can save nearly 70% working hours, allowing your team to focus on strategic development rather than mundane data matching or deduplication tasks.