Taxonomy API Features

Challenge

A global defense contractor needed a unified view of employee capabilities. Skills were collected from resumes stored in different formats, described with inconsistent terminology, and incompatible across systems.

Leadership could not answer questions such as:

• How many employees have aircraft mainframe expertise?
• How many employees have expertise in propulsion system design?
• Which roles require proficiency in flight control systems?

The People Analytics team needed a scalable way to build a consistent skills inventory.

Solution

1. Taxonomy-Driven Tagging The Lightcast skill hierarchy (Skill → Skill Sub-Category → Skill Category) provides a clear structure.

Example: Flight Safety → Air Transportation → Transportation, Supply Chain & Logistics.

2. Skills Inventory Dashboards When each employee is mapped to a consistent Skill ID and hierarchy, analysts can view the workforce by:

• Individual skills (e.g., Pitot Static)
• Sub-categories (Air Transportation)
• Broad categories (Transportation, Supply Chain & Logistics)

3. Resume Skill Extraction Resumes can be processed through the free Skills API, which automatically assigns standardized Skill IDs, reducing manual tagging and improving accuracy.

Challenge

A multinational engineering organization struggled with thousands of inconsistent job title variants, such as “Sr. HVAC Eng.” or “Hydraulics Eng.” These inconsistencies affected reporting accuracy, internal mobility, and cross-regional talent benchmarking.

Solution

1. Type-Ahead Autocomplete The Lightcast library of 75,000 standardized titles is integrated directly into CRM and HRIS title fields. Users select a single authoritative title instead of entering free-text variations.

2. Historical Title Normalization The team used up to 50 free title normalizations per month to clean historical data. The Titles API automatically mapped inconsistent or misspelled titles to the correct Lightcast Title IDs. This automated approach standardized years of records within days.

3. Governance and Reporting Dashboards With each record linked to a unique Title ID, workforce teams can reliably roll up and compare data by discipline (for example, Civil Engineer vs. Electrical Engineer), region, or business unit.

Challenge

Organizations manage job data across systems with overly broad occupation groupings.

Occupations may be labeled differently and lack specificity (e.g., “Analyst,” “Engineer,” “Technician”), making it difficult to distinguish meaningful differences in work.

This makes it difficult to answer questions such as:

• How many employees work in the same occupation across regions?
• How do occupations compare across business units?
• Where do we have gaps in specific occupations?

Solution

1. Normalize occupations Lightcast Occupation Taxonomy brings granularity and cross boarder standards, creating a consistent foundation for analysis.

2. Standardized Occupation IDs Each occupation has a unique ID, eliminating ambiguity across systems.

3. Granular, hierarchical structure Occupations are organized into levels (Occupation → Subcategory → Category), enabling both detailed and high-level analysis.

4. Cross-system alignment Once mapped, occupation data can be used consistently across HRIS, ATS, and analytics platforms.