SavvyIQ

Handling Messy Data

Overview

Real-world business data is rarely clean. Entity names arrive embedded in legal documents, mixed with addresses, concatenated with tax IDs, or scattered across multiple languages and jurisdictions. This guide showcases examples of complex data scenarios that SavvyIQ successfully processes—cases that typically require manual research or cause traditional systems to fail.

Why Messy Data Is Challenging

Traditional entity resolution systems struggle with unstructured, real-world data because entity names are embedded within metadata, location data varies widely, and each case requires distinct investigation strategies. SavvyIQ is designed to handle these complexities automatically.

Examples of Messy Data We Handle

Legal Name Variations

Name: "Federal Realty OP LP f/k/a Federal Realty Investment Trust"
Location: "909 Rose Avenue Suite 200 Rockville, MD 20852"

Entity names with "formerly known as" references requiring historical research to connect current and previous legal structures.

Concatenated Data Fields

Name: "Meridian 1674, LLC 1111 Brickell Ave Suite 2175 Miami, FL 33131"
Location: "1111 Brickell Ave Suite 2175 Miami, FL 33131"

Company names and addresses mixed together in single fields that need parsing and validation.

Mixed Identifiers and Metadata

Name: "ARAMEX BAHRAIN W.L.L. VAT NO : 200000724100002 P.O.BOX 26951"
Location: "BAHRAIN"

Entity names mixed with tax IDs, registration numbers, and partial addresses requiring jurisdiction-specific knowledge.

Contextual Business References

Name: "Fastenal HQ"  
Location: [none provided]

Generic entity names requiring contextual understanding (headquarters vs. branch locations) with missing location data.

International Entities with Local Identifiers

Name: "FORD MOTOR COMPANY CHILE SPA R.U.T.-.C.L. 787039103"
Location: "Chile"

Cross-border entities with local tax identifiers requiring jurisdiction-specific knowledge and global corporate structure mapping.

Multi-Lingual Entity Names

Name: "株式会社ソニー (Sony Corporation)"
Location: "Tokyo, Japan"

Entity names in multiple scripts requiring cross-language matching and verification.

Complex Multi-Jurisdictional Structures

Name: "Toyota Motor Europe NV/SA"
Location: "Brussels"

Legal entities with complex structures indicating operation across multiple jurisdictions.

Common Data Patterns We Process

SavvyIQ handles these frequent messy data patterns:

  • Legal naming conventions: "fka", "dba", etc
  • Concatenated data fields: Name + metadata
  • Geographic variations: Address formats, postal codes, regional identifiers
  • Mixed languages: Native scripts with English translations

Best Practices

Optimize Your Input

Try to include country information where possible. In the example below, the agent won't know if you're searching for a California entity or a Canadian entity.

{
  "name": "Acme Inc.",
  "location": "CA" 
}

Better:

{
  "name": "Acme Inc.",
  "location": "CA, US"
}

Next Steps:

Questions about your specific data scenarios? Contact our support team.