August 9, 2025
2 min read
Identity resolution, defined as the process of connecting and unifying data from multiple sources to create a single, accurate profile of an individual or entity, has become central to data management practices in both academia and industry (Smith et al., 2021). The primary challenge lies in accurately identifying, matching, and consolidating disparate data points—such as names, emails, phone numbers, and behaviors—especially when data is incomplete, inconsistent, or distributed across various systems.
The process operates through several critical steps:
Results from recent studies demonstrate that deterministic matching ensures high precision but often at the cost of recall, as exact matches can miss legitimate links due to data variations (Jones & Patel, 2020). Conversely, probabilistic methods improve recall by identifying likely matches based on patterns but may introduce false positives if not carefully calibrated (Lee & Chen, 2019). Combining both approaches typically yields optimal accuracy.
Furthermore, deduplication significantly improves data quality by reducing noise; however, aggressive deduplication risks merging distinct entities if identity matching is insufficiently accurate. Enrichment processes contribute additional context, which enhances the robustness of profiles and supports better decision-making in applications such as personalized marketing and fraud detection (Wang et al., 2022).
In summary, identity resolution hinges on balancing precision and recall through tailored matching techniques, rigorous deduplication, and systematic enrichment. The synthesis of these steps creates a reliable, unified identity framework that underpins effective data-driven strategies.
