Entity Resolution With TigerGraph

Modern enterprises face a critical entity resolution problem: the proliferation of various enterprise systems has created multiple, conflicting records for the same customers, products, and business entities. Every client-facing and back-office system—including CMS, CRM, ERP, and marketing automation platforms—maintains separate customer profiles with overlapping but inconsistent information.These fragmented entity records include transaction data such as orders, policies, payments, and claims, as well as interaction data from customer service calls, website visits, and physical store engagements. The core entity resolution challenge emerges when the same customer appears as “John Smith” in one system, “J. Smith” in another, and “Jonathan Smith” in a third, each with slightly different contact information or account details.

The lack of effective entity resolution software has created a data nightmare where master data elements like names, addresses, and phone numbers exist in multiple, conflicting versions across systems. 

The business impact of unresolved entity resolution problems follows the 1-10-100 rule: it costs $1 to verify an entity record as it’s entered, $10 to resolve entity conflicts later, and $100 if entity resolution issues are left unaddressed. 

Entity resolution is the process of identifying and linking records that refer to the same real-world entity across different data sources. Traditional master data management systems are built upon relational databases, which store information such as account, contact, lead, campaign, and opportunity in separate tables, one for each type of business entity.

These relational databases are good tools for indexing and searching for data, as well as for supporting transactions and performing basic analysis. However, conventional entity resolution software is poorly-equipped to deal with the deluge of data that results in multiple data entries for a single real-world entity, particularly when real time entity resolution is required.

In this environment of ambiguous data, analysts need to join a number of large tables to run the queries and collect the data for analysis. Such queries could take hours or even days to run, rendering any meaningful analysis of the patterns practically impossible and making real time entity resolution nearly unattainable with traditional approaches.

Graph databases are purpose-built for entity resolution solutions, making them superior to traditional approaches. Merging customer data from multiple data sources is not always easy with conventional systems. One challenge is the entity resolution process itself—deciding when multiple entities from different data sources actually represent the same real-world entity and then merging them into one unified record.

Consider an example where there are three data sources containing the following types of customer information:

• Source1 (SSN, Email, Address)
• Source2 (SSN, Phone, Name, Age)
• Source3 (Email, Phone, Gender)

Let’s assume that SSN, Email, and Phone are each sufficient to uniquely identify an individual (that is, they constitute PII, personally identifiable information). The problem is that the different sources use different identifiers, and that individual records might be missing some information. Over time, missing PII of a customer may show up later in another data source. The goal is to use whatever PII we have about a customer to find all information (attributes) of a customer across all data sources and build a unified record with the following attributes: Customer (SSN, Email, Phone, Name, Age, Gender, Address).