Talent inventory and workforce agility
More and more organizations understand the value of creating a comprehensive talent inventory. Inventorizing the skills and professions in your organization makes it possible to:
- Identify talent at risk of being poached by competitors by comparing the distribution of jobs in your talent pool to the market demand for the same roles.
- Make data-driven decisions on workforce planning, succession planning and recruitment strategy.
- Account for acquired talent and skills by harmonizing talent data from different sources, such as during mergers and acquisitions.
Business agility and flexibility is derived from a strong understanding of your talent inventory. By developing a strong understanding of your business’s talent needs and comparing to available professions within your current workforce and wider talent pool, you can be much more proactive about closing this loop. This requires the use of a consistent catalogue of jobs, which is exactly what the Professions API enables.
Surface more candidates in your existing database
When leveraging the power of taxonomies in your search and match processes, there’s no need to remember synonyms of professional titles, nor are you required to create complex search Boolean queries to identify them.
Semantic, taxonomy-based search systems understand the intent behind the keywords you type. Because Textkernel’s Professions Taxonomy has multiple layers of hierarchy, it can help recruiters to broaden their search queries and find candidates that wouldn’t have surfaced when searching with keywords only.
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Textkernel’s Multilingual Classification Capabilities
If you have a multilingual candidate pool, the benefits for your organization are even greater. Textkernel’s taxonomy is cross-lingual. What does this mean for you? The Professions API can be set up so that a user can search for professions in one language, and receive results in another. Another possibility is for documents collected across different languages to be normalized and reviewed in another language.
Textkernel’s Profession Taxonomy
The essence of our profession normalization tooling is the ability to classify job titles to the Profession Taxonomy, which is structured as follows.
Normalization means being able to relate job titles to professions in this taxonomy. This entails recognizing synonyms and spelling variants across multiple languages.
The profession taxonomy is characterized by three key features. It is data-driven, cross-lingual and dynamic.
Data-driven. Our taxonomy has been developed based on data, rather than on intuitions by domain experts. Unlike common taxonomies like ISCO, ESCO and O*NET-SOC, our taxonomies are created by extracting job titles from millions of vacancies and resumes, thus reflecting the job descriptions and progressions as they actually appear on the marketplace. In case you need ISCO or O*NET-SOC classifications, you can still leverage the richness of the Textkernel taxonomy and its synonyms through a mapping to those taxonomies.
Cross-lingual means there’s a single taxonomy for multiple languages. This makes it possible to harmonize and compare data from different regions and countries.
Dynamic refers to the fact that the taxonomy is continuously being updated and improved. Based on customer feedback and data mining, we’re always looking to incorporate the latest market data and user feedback.
Professions API – How can I leverage the data within my organization?
Textkernel’s Profession Taxonomy is already embedded in Extract! , Match! and Jobfeed. If you wish to adopt the Professions taxonomy outside the context of these products, you’re in luck. Our profession normalization capacities are now also available through an API.
The Professions API enables the following:
The standardization of job titles from different sources
Do you have one or multiple databases of job titles, which need to be standardized in order to be of value? With a few simple queries to the Professions API, all of your job titles will be classified according to Textkernel’s profession taxonomy. In addition, they will be mapped to international standards such as ISCO and O*NET. Thus, the Professions API enables analysis of job data according to various catalogues and standards, including those used by government institutions around the world.
Example input and output of the Professions Normalization API:
The suggestion of taxonomy entries as you type
The autocomplete functionality of the Professions API can help with selecting the professions from the taxonomy based on partial input. There’s no need to memorize the names of the professions in the taxonomy: you will get suggestions as you type, based on spelling variations, acronyms and synonyms.
Example input and output of the Professions Autocomplete API:
The downloading of the taxonomy
To embed our taxonomy in your processes, you might need to upload the full list of professions into your CRM, ATS or HRIS (for instance, to populate a dropdown list). This no longer needs to be a manual process: with the Professions API, you will always have programmatic access to the latest version of the taxonomy.