Data deduplication is the process of removing copies of repeated data and merging them into a single record. Deduplication is one of the primary objectives for data quality teams to ensure that databases maintain accuracy and integrity.  

The process of data deduplication is gaining in prominence as more businesses strive to deliver seamless omnichannel customer experiences. For example, if somebody visits a company website, and then calls, uses the mobile app, or emails, each of the channels should recognize them and deliver a personal experience.

In order to offer efficient omnichannel experiences, systems need to match a customer to a record. However, what if a customer sets up two accounts because they can’t remember a password, or a sales agent accidentally adds them again rather than updating an existing record? These situations cause database duplication that can potentially cause chaos with your business strategy.

Problems with duplicate data

There are several reasons as to why businesses need to have an effective deduplication strategy. The top five consequences of not implementing deduplication are:

  1. Cost – duplicate records mean double the storage, sending marketing campaigns twice, or double printing. As well as poor customer experience, your return on investment takes a hit.
  2. No single customer view – if customers have duplicate accounts, it is hard to see a holistic picture of them as data is in disparate places.
  3. Brand reputation – Duplicate records mean you run the risk of calling, mailing, or emailing customers more than once, impacting brand reputation.
  4. Service – without deduplication, customer records exist more than once, making identity checks more challenging than necessary.
  5. Reporting – a duplicate record can impact reports either positively or negatively. Either way that can lead to misinformed business decisions.

Businesses should take time to deploy deduplication processes that solve these common data quality problems. 

Data deduplication

One of the primary causes of duplicate data is human error. For example, perhaps a custome4r makes a purchase using the email address john.doe123@gmail.com but next time makes a typo and enters john.doe133@gmail.com. The two records will forever be separate without deduplication.

Quality control measures like email verification can help to mitigate duplicate records at source,  negating future data issues. In an offline environment, validation measures and input masks can be added to system fields, helping to avoid similar errors.

Fuzzy matching

One of the most popular data deduplication methods is known as fuzzy matching. A fuzzy match looks for similarities between elements of data. Rather than looking for an exact match, fuzzy logic finds records that infer a likely relevance. In the case of human error like typos, fuzzy matching is the most effective form of deduplication.

Data algorithms can identify records that appear to belong to the same customer, like the example below.

Source: Data Science Central

Although the approach can take time to set up, front-end applications of fuzzy deduplication can resolve the potential threat at source, ensuring a return on investment. In data science programs like Python, in-built libraries exist to automate deduplication processes.

Summary

Data deduplication is a critical part of your data quality and governance strategy. By eliminating repeating records, a business can reduce storage costs, improve customer experience, gain an accurate 360-degree view of the customer, and enhance the integrity of reporting. Try StrategicDB’s de-duping tool to de-dupe your data.

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