Organizational Hierarchy Inference from Business Card Data
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Solution Overview
Problem
In multi-tenant database systems, accurately determining organizational hierarchy from contact data is challenging due to inconsistencies and inaccuracies in contact information, which is crucial for sales and marketing efforts but often missing from conventional databases.
Innovation Solution
A method is implemented to infer organizational hierarchy by normalizing title phrases from contact records using a training set, converting words to lowercase, concatenating with underscores, and comparing them to a lookup table to determine rank and department with associated confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional databases rely on individuals to maintain their own contact information, then data storage simplicity is maintained, but data accuracy and consistency deteriorate
Solution Approach 1:
The system automatically maintains contact information by extracting data from publicly available sources like LinkedIn profiles, company websites, and business cards. The MTS provider's system performs self-service data collection, validation, and updates without requiring individual users to manually maintain their contact information, thereby improving accuracy while avoiding complex manual maintenance processes
Solution Approach 2:
The system implements feedback mechanisms where contact information is continuously validated against multiple external sources. When discrepancies or updates are detected in publicly available data, the system automatically retrieves corrected information and updates the database, ensuring ongoing accuracy through continuous verification cycles
2Productivity
If MTS system shares data resources among multiple customers, then resource utilization efficiency is improved, but data accuracy and consistency for each customer deteriorates
Solution Approach 1:
The system applies customer-specific data quality rules and validation criteria to each tenant's contact information within the shared MTS environment. Each customer's data is processed, validated, and updated according to their specific requirements and standards, ensuring local data quality excellence while benefiting from shared infrastructure resources
Solution Approach 2:
The MTS system segments contact data by customer/tenant, maintaining logical separation and independent validation processes for each customer's data while physically sharing the underlying database infrastructure. This segmentation ensures that accuracy improvements for one customer do not compromise another customer's data quality
3Reliability
If MTS provider maintains accurate up-to-date data centrally, then data consistency across the organization is improved, but data collection and validation complexity increases
Solution Approach 1:
The centralized system performs multiple functions including data collection from diverse sources (LinkedIn, company websites, business cards), validation against multiple criteria, automatic updates, and distribution to all customers. This multi-functional approach consolidates what would otherwise require separate systems for each customer, achieving data consistency without proportionally increasing complexity
Solution Approach 2:
The MTS provider acts as an intermediary between public data sources and customer applications. The provider's system collects, validates, and standardizes data from various external sources, then delivers consistent, verified contact information to all customers, simplifying the data maintenance burden for individual organizations
4Reliability
If contact databases include organizational hierarchy information, then marketing effectiveness is improved, but data collection difficulty increases
Solution Approach 1:
The system proactively collects and processes organizational hierarchy information from public sources like LinkedIn profiles and company websites before marketing campaigns are executed. By pre-enriching contact records with rank, department, and organizational structure data, the system eliminates the need for real-time hierarchy detection during marketing operations
Solution Approach 2:
The system replaces manual or complex mechanical methods of determining organizational hierarchy with automated electronic data extraction and analysis. By using algorithms to parse public profile data, company directories, and online presence information, the system automatically infers hierarchical relationships without requiring manual research or complex interpersonal verification
Data Source
AI summary
A system and method for determining organizational hierarchy from contact data. A phrase having multiple terms representing a job title is received and converted to lower case, then concatenated with a symbol to separate the terms. The phrase is compared to a training set of predefined normalized phrases representing known job titles. If a match is found, a data record is created or updated with organizational hierarchy information associated with the matched phrase in the training set. If a match is not found, a term is removed from the phrase and the comparison repeated.