Identity resolution and data deduplication are two processes that are often confused. While they both involve data matching algorithms, they are used for different purposes. In this article, we'll explain the difference between identity resolution and data deduplication and how they can be used to improve customer data management. Data deduplication is the process of removing duplicate records from a dataset.
It is used to reduce the amount of data that needs to be stored and processed. This process is often used in databases, where duplicate records can take up unnecessary space. Data deduplication is usually done using algorithms such as fuzzy search technology, which helps to identify similar records. Identity resolution, on the other hand, is the process of connecting unique identifiers to create a single, unified customer identity.
This process is used to maintain a strong supply chain by consolidating supplier data into data silos spread across multiple business units, regions, geographies, and categories of parts and materials. Identity resolution is also used to reconcile products, compare their prices, and decide which vendor sells the cheapest. The most fundamental piece of information in the identity graphic is the identification tag associated with the device, account, network, session, transaction, or other anonymous identifier that can interact with your company. Real-world data is scattered, irregular and disordered—exactly the chaos that machine automation is least able to digest.
A corporate service provider can use entity resolution to resolve the names of organizations despite different representations, spelling errors, abbreviations, and typographical errors. Deterministic matching is a more conclusive approach to identity resolution when data security or accuracy are an important requirement. This method uses algorithms such as fuzzy search technology to identify similar records. It is generally cheaper than other methods because the raw data is already yours.
Thanks to modern data processing systems such as business data warehouses in the cloud and customer data platforms, identity resolution allows us not only to understand individual interactions with customers but also to react to them in real time in a way that adapts to each individual customer. In conclusion, identity resolution and data deduplication are two processes that are often confused but have different purposes. Data deduplication is used to reduce the amount of data that needs to be stored and processed while identity resolution is used to create a single unified customer identity. Both processes use algorithms such as fuzzy search technology but deterministic matching is generally more accurate for identity resolution.