This section outlines the core functionality of LakeFusion's Match-Merge process—a critical feature that resolves duplicate or related records and consolidates them into a single, accurate, and actionable Golden Record.

What is the Match-Merge Process?

The Match-Merge process is a two-step, AI-driven approach to data unification, designed to:

• Identify duplicate or related records (Matching): Detect redundancies within a single dataset or across multiple data sources.

• Consolidate matched records (Merging): Combine records into a single, trustworthy version known as the Golden Record.

By leveraging advanced Large Language Models (LLMs), vector embeddings, and fuzzy logic, LakeFusion ensures this process is accurate, scalable, and efficient—even for complex, large-scale datasets.

Identifying Duplicate or Related Records (Matching)

The matching phase focuses on detecting similar or semantically related records across structured and unstructured datasets.

How Matching Works

AI-Powered Algorithms

• Utilizes LLMs and vector search to semantically interpret variations across fields (e.g., name, address, email).

• Identifies context-aware similarities between records, even when text differs in form or format.

Fuzzy Matching

• Accounts for typographical or naming variations using fuzzy logic.

• Example: Recognizes “John Smith” and “J. Smith” as likely the same entity.

Rule-Based Logic

• Applies deterministic logic for exact matches based on critical attributes such as unique identifiers, account numbers, or primary keys.

Embedding Models

• Converts textual fields into vector representations using embedding techniques.

• Vectors are compared to measure similarity with unmatched precision.

Custom Matching Rules

• Users can configure field prioritization (e.g., prioritize email matches over names in customer records).

• Rules can be tailored to suit business-specific entity models.

Threshold Configuration

• Users define minimum similarity scores for what constitutes a match.

• Allows fine-tuning of precision and recall based on business risk tolerance.

Consolidating Matched Records (Merging)

Once records are matched, LakeFusion automatically merges them into a unified Golden Record by applying survivorship logic and automated resolution workflows.

How Merging Works

Survivorship Rules

• Conflict resolution is managed using predefined rules:

  • Most recent value wins

  • Trusted source overrides others

  • Aggregated values (e.g., average, max) based on business logic

Automatic Conflict Resolution

• AI resolves overlapping data autonomously using the configured survivorship policies.

• Example: If multiple phone numbers exist, the number from a verified CRM takes priority.

Manual Review (When Needed)

• High-priority or ambiguous records are flagged for review.

• A user interface allows data stewards to:

  • Inspect conflicting values

  • Compare confidence scores

  • Override AI decisions with manual input

This ensures both automation and governance—delivering clean and consistent records while keeping humans in the loop where necessary.

Why It Matters

The Match-Merge process addresses several core data challenges:

• Eliminates duplicates and redundancies across enterprise systems

• Enhances data reliability for analytics, reporting, and operational systems

• Reduces manual cleanup efforts and ensures governance compliance

• Enables a single, unified source of truth—critical for high-stakes domains like healthcare, finance, and customer engagement