Lightcast global data can solve problems across a multitude of geographies. Our postings data (demand data) has information on over 150 countries for reference and consulting, while our global software products come ready with data on 20 nations.
Demand data is collected daily from a variety of global sources. Lightcast tries to capture as many primary sources - such as applicant tracking systems and direct postings by employers on job boards - as possible, which we augment with high quality secondary sources.
Employers use all kinds language and definitions to attract talent. Our various global teams, including our linguistics team, address this challenge by cleaning and enriching job postings with our taxonomies. In addition to collecting, we deduplicate global postings daily and add them as incremental updates to the data daily. We refresh all of our demand data with latest enrichments bi-weekly. This entire process unifies data between countries and markets which helps the data speak the same language across geographies.
Skills are a foundational translation layer that enables a standardized way to compare and evaluate job requirements across different languages and cultures, allowing for accurate talent matching and identification of skill gaps.
In order to accurately analyze global job postings, it's important to understand language to avoid misinterpretations or missing important details, and to address language barriers that can impact analysis. We do not translate non-English job postings and use the native language of the job posting to find and tag the relevant skills.
The use of skills to analyze labor demand data supports granular and contextual data-driven decision-making.
Our demand data goes through a process of capturing and normalizing company names. Once the data is cleaned and tagged, it becomes a valuable resource for identifying which companies are advertising job vacancies in various locations.
By using this facet, you can investigate the hiring strategies of key competitors and track changes in posting intensity based on company and geography. The normalized company information derived from this process is highly beneficial for accurately classifying and comprehending international demand data.
We currently tag our global demand data with the Lightcast Occupation Taxonomy, our proprietary occupation taxonomy. The taxonomy identifies roles that are the same, across employers and geographies, regardless of job title. This is especially important in emerging fields, when job titles can evolve quickly. It is composed of four different levels (Career Area, Occupation Group, Lightcast Occupation and Specialized Occupation).
Advertised wages in job postings can provide valuable insights into understanding a particular geography or field, as well as the skills and qualifications that drive wage growth. The salaries advertised can be a signal for what employment is seen as most valuable.
Advertised wage should be analyzed on a very granular level to ensure that local context has a chance to shine through. Different cultural norms, expectations and conventions are hard to capture in a single number.