AI-Powered Entity Resolution
Take control of entity resolution. Tamr helps you match and connect records with confidence, fueling trusted master data and better business outcomes.
Smart Entity Resolution in Real Time
Entity resolution is the process of identifying and matching records across data sources. Tamr’s AI-native approach automates every step of the process—eliminating duplication, connecting records, and delivering accurate, scalable, and explainable results across your enterprise data landscape.
Initial Scanning
Tamr rapidly identifies potential matches by eliminating obvious non-matches and highlighting likely candidates using advanced, patented matching techniques. This initial scan efficiently narrows down the dataset for deeper analysis.
Smart Comparison
For each potential match, Tamr applies sophisticated entity resolution techniques, enrichment, and transformations, including on-the-fly data extraction, field standardization, and calculation of relevance scores—enhancing the quality and comparability of data attributes.
Labeling and Scoring
Tamr assigns descriptive labels (e.g., "Strong Name Match") to explain the reasoning behind each suggested match. Each match is also given a confidence score (e.g., High, Medium, Low), enabling automated actions through configurable thresholds.
Intelligent Ranking
Tamr intelligently ranks results to prioritize the most critical connections. It supports diverse ranking strategies, ensuring that users focus on the most impactful matches first.
Golden Record Creation
Tamr clusters matched records into unified golden records. Confidence thresholds guide automated merges, while uncertain cases are flagged for human review, ensuring trusted results without sacrificing control.
Tamr’s AI-Native Advantage in Action
In the startup world, change is constant. But one thing remains consistent: our values. We believe there’s a strong link between happy people and healthy startups, and we’re committed to building a safe and nurturing environment for our team. We do this through:
Scalable Accuracy
Tamr's AI-driven approach ensures high accuracy in entity resolution, even as data volumes grow, by continuously learning and adapting to new data patterns.
Explainable Results
With transparent labeling and scoring, Tamr provides clear explanations for match decisions, fostering trust and facilitating governance.
Fast Time-to-Value
Tamr's pre-trained models and automated workflows reduce the time and resources needed to achieve high-quality entity resolution outcomes.
Entity Resolution FAQs
Entity resolution, also known as entity linkage, record matching, or identity resolution, is a data management technique that identifies and matches records across multiple data sources to create a golden record of key business entities.
Entity resolution helps organizations overcome challenges with duplicate data, data integration, data quality, and regulatory compliance by reconciling records across and within datasets. For example, customer entity resolution identifies, matches, and unifies records that refer to the same customer across systems to create a holistic 360-degree view.
Yes, Tamr’s AI-native approach and scalable SaaS architecture enable the platform to handle large-scale matching as the amount of data and number of systems continue to grow.
Tamr uses advanced AI incorporating probabilistic methods to automate every step of the matching process, from initial scanning and smart comparison to labeling, scoring, and intelligent ranking. Further, LLM-based AI agents surface edge cases for curator review and suggest edits to golden records based on change events (e.g., records created, updated, etc). As a result, Tamr eliminates duplication, connects records, and delivers accurate, scalable, and explainable results for customer entities.
AI-based entity resolution combines highly tuned AI/ML models and automated workflows to make the matching process faster and more efficient. And because these models are always learning and adapting, they can scale and improve as the data grows and evolves. In contrast, traditional approaches rely on rules, which are difficult to scale and expensive to maintain.
Guide to Entity Resolution with AI-Native MDM
Solving Real-world Entity Resolution at Scale with AI
What is Entity Resolution?
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