How to Prevent Data Duplication

Summary:

• Duplicate data is a common issue for businesses, leading to operational inefficiencies and poor decisions.
• Common causes of duplicate data include manual entry errors, inconsistent processes, disconnected systems, multiple touchpoints, and lack of data validation.
• To prevent data duplication, spot duplicates in real time at the point of entry using AI-native solutions like Tamr.
• Continually review and monitor data to prevent duplicates as data changes over time.
• Rely on user feedback and intuitive GenAI interfaces to identify and resolve duplicate records within existing systems.

Duplicate data is a persistent issue for businesses. But it often goes unnoticed, hiding beneath the surface until the same customer, employee, product, or vendor appears multiple times in a dashboard, causing a ripple effect of misguided decisions.

Whether it’s creating confusion in customer service, skewing marketing analytics, or leading to costly operational errors, the impact of duplicate data can be far-reaching. However, despite advancements in data management technology and practices, duplicate records continue to plague businesses, leading to operational inefficiencies, customer frustration, and poor decisions.

The Root Causes of Duplicate Data

Duplicate data can manifest itself in many ways, causing companies to double count customers, overstate available inventory, and inflate forecasts. But in order to effectively combat data deduplication, it's essential to understand the root causes. Below are five common reasons why your company has duplicate data.

Manual data entry errors: from typos and inconsistent formatting to simply entering the same data multiple times, manual data entry errors are one of the most common ways duplicate records enter your systems.

Inconsistent data collection and data entry processes: duplicates persist when companies lack consistent processes to govern the collection and entry of data across systems and departments. This lack of consistency means that each department may enter the same data in slightly different ways, causing the same customer, vendor, or product to enter your systems multiple times.

Disconnected systems and silos: it's common for companies to have the same data captured multiple times in different systems across their organization. But when companies fail to master their data across these disparate systems, redundancies remain.

Multiple customer touchpoints: customers often interact with companies in multiple channels. And every time they interact, they enter their information, creating similar, yet slightly different, versions of their customer profile. Left unresolved, each entry remains its own record, resulting in numerous versions of a customer.

Lack of data validation: integrating disparate systems and adding new data sources is common practice. But when companies lack the tools to validate the data prior to reaching the point of entry, it's almost inevitable that same data will enter the system multiple times.

Strategies to Prevent Data Duplication

While it's possible to find and eliminate redundant data once it enters your systems, the best way to overcome data duplication is to spot and resolve duplicates in real time at the point of entry. This proactive approach prevents duplicate records from entering the system in the first place, which, in turn, eliminates the need to rationalize data on the back-end.

Using persistent IDs, Tamr's AI-native data management solution identifies duplicates in real time, enabling organizations to capture change and make improvements to the data proactively while it's still in motion. Not only does this approach expedite their ability to onboard new data, but it also gives users confidence in the integrity of the golden records within the system.

Take lead data as an example. Because customers interact with your organization in multiple channels, it's possible that a "new lead" is actually an existing prospect that sales is actively engaging in a sales process. By using a persistent ID to identify in real time that the "new lead" actually exists in the system, you can add the new lead to the prospect's existing record, avoiding unnecessary outreach that would confuse or annoy the customer.

It's also important that organizations continually review and monitor their data to prevent duplicates as data changes over time. Tamr's AI and ML mastering capabilities regularly cleanse the data to identify duplicates as data changes over time. For example, if a merger or acquisition occurs, records that were previously distinct may now need to roll up to the same entity, given the changes to their organizational structures.

Finally, relying on user feedback is key to spotting and resolving duplicate records within existing systems. Using an intuitive, interactive ChatGPT-like interface, data users can ask questions within Tamr, enabling them determine if the record they are viewing is, in fact, a duplicate. By providing this level of explainability in an intuitive way, end users can quickly and easily identify and resolve duplicate records and improve the overall quality of their data.

Preventing data duplication is essential for maintaining the integrity and efficiency of your systems. By implementing proactive measures such as AI and machine learning to identify duplicates in real time at the point of entry and leveraging Generative AI capabilities, your organization can minimize the risk of redundant records, not only improving the accuracy of insights derived from data but also enhancing decision-making, operational efficiency, and customer experience. Tackling the root causes of data duplication and embracing the strategies to overcome it ensures a cleaner, more reliable data ecosystem that supports long-term success.

To learn more about how Tamr can help your organization prevent data duplication, please request a demo.