The True Cost of Data Duplication

data duplication

In the hyper technology era of of Big Data, small, mid-sized, and enterprise businesses face an ever-increasing challenge in the form of data duplication. Redundancy is nearly impossible to avoid, and it is estimated that “dirty data” (including partially or fully duplicated information) may encompass as much as 20 % of any corporation’s database.

What is Data Duplication

Data duplication is one or more sets of records that contain information on a customer that is identical or nearly so to a previous record. This duplication can take place in several ways, including but not limited to:

  • An individual signs up for a free offer with an email address. They later sign up again with the same name but a different email;
  • A person orders something online and enters a shipping address. Later on, they order again, but enter the shipping address in a slightly different format, as “Avenue” instead of “Ave.”;
  • A company buys out another company, and customer records are merged. An individual is a customer of both companies, so they are now listed twice.

Issues With Data Duplication

When an individual is contacted multiple times over the same matter, served duplicated marketing campaigns, and hammered with repetitive contact, they may choose to unsubscribe, request no-contact, or even cease being a customer. Data duplication also saps resources, with extra working hours and data processes required to maintain the replicated entries.

The True Cost Of Data Duplication And How It Can Harm Your Business

While immediate and highly visible expenses associated with duplicated data include unnecessary data storage, marketing, and CRM costs, the true cost of data duplication is both external (in the havoc it wreaks on consumer confidence, brand reputation, and reduced customer lifetime values) and internal (in its effects on productivity, efficiency, and reporting.)

External Costs:

Loss of Confidence

Consider a consumer who feels hammered by repetitive email, direct mail, or phone campaigns quickly losing confidence in the organization contacting them. This individual may conclude that a company that can’t appropriately manage its data should not be trusted to handle other aspects of a business.

Decreased Brand Reputation

Consider a customer who contacted customer service to resolve an issue and was upsold or cross-sold during the experience. If the same individual receives a phone call a day later offering the same cross-sell due to duplicated data, the customer may feel insulted and that the brand is disorganized and not focused.

Reduced Lifetime Value

Consider a customer who always tries new products or services, but notices they are receiving four emails targeting each new item instead of one (again, due to duplicated records). This individual may choose to send subsequent emails directly to spam (missing out on new offers) or self-terminate their status as a customer.

Internal Costs

Diminished Productivity

Time spent trying to clarify which customers are viable and which are not due to duplicated data takes away from other, more critical tasks. Attempts to clean data can be difficult and ineffective, often with inaccurate results.


Marketing efforts are quickly bloated due to replicated data and targeting of the same individual more than once in each campaign. Customers may also end up having their multiple profiles segmented into different demographics or marketing verticals based on inaccuracies with one or more datasets.


When duplicate data is used in a survey, campaign, or analysis, the reporting can become skewed. Responsiveness can be underreported due to customers only replying to one out of three or four queries sent, and sales numbers or website visits made inaccurate due to the uniqueness or non-uniqueness of each visitor not being preserved.

Avoiding Future Data Duplication

The best way to avoid duplicating data is to anticipate the possibility early on and set up safeguards and filters to help prevent additional records being added to the master database. This can include flagging new data based on similarities in name, email, address, and more to an already existing record in the database.

These similar records can then be quickly scanned and one of the following actions taken:

  • Each dataset individually approved as its own unique record
  • Both datasets merged into a single cohesive record
  • Only one set of data saved as the most current update of the existing record

Dealing With Existing Data Duplication

Massive sets of data can benefit from specifically-designed algorithms that can trace all data back to its original entry in the uncompressed stream, match it to other entries, identify unique individual records, and compress these final, updated datasets into an easily storable and retrievable database. Data marked as non-current can be archived for any possible business intelligence it may hold.

Ultimately it is the responsibility of businesses to accurately manage the data provided to them by customers, prospects, and leads. Learning to avoid new data duplication and correct previously duplicated records can increase customer satisfaction, streamline back-office processes, and make both marketing and reporting more cost-effective

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