About SkillsEngine

This reality surfaced in many environments throughout the education, training, and workforce development world. The Texas Workforce Commission, which provides intermediary services matching unemployed job seekers and job postings, found significant incongruities between job posting language and how jobseekers described their skills and work history. This resulted in poor job referrals and declining satisfaction from the business community who did not get the skilled talent they wanted. Not to mention workers who were sent to jobs for which they were not qualified. The lack of a commonly understood skill language to mediate these inconsistencies impeded appropriate referrals.

Similarly, as more Baby Boom generation workers fell on the cusp of retirement, the business community found themselves in need of better transferable skills information to facilitate succession planning, identify areas for internal staff training, streamline promotional ladders, and recruit the next generation of workforce talent. The necessary detailed job analysis was either too expensive for most companies or relied on the same limited, occupational constructs already vexing the job posting and recruiting process. Many companies lamented the lack of tools to appropriately build, cross-train, and allocate talent and connect their internal job descriptions with outputs from the education system.

Finally, the education community found themselves under increased pressure from regulators, taxpayers, employers, and students to improve the alignment between what is taught within various course and program offerings and the skills demanded in the workplace. This misalignment is perhaps the most challenging because it requires business, education, and the occupational labor market at large—each with their own vernacular and organizing taxonomies—to communicate at a detailed level. Missing from this conformation was a common and shared transferable skills language around which each constituency could commune.

At the same time these stakeholder pressures were building, the world of technology opened some new data opportunities. Specifically, electronic data parsing engines facilitated the collection and decomposition of online job postings. The ability to scrape and deconstruct job posting data from the Internet provided a path to look at the labor market from a more granular ‘skills’ level and transcend generic occupational coding structures. But while natural language algorithms facilitated the decomposition of job postings, resume, or education program text, there arose a need for a new organizing concept around which to rebuild the raw text into meaningful constructs that could be embraced by all participating stakeholders.

It should be mentioned that job analysis around the concept of skills is hardly a new discipline. The federal government through an operation known as ONET or the Occupational Skills Network routinely collects job task and work context information for a group of standard occupational titles. This often highly detailed and rigorously collected occupational database provides most job analysts with a rich body of information around which one can build a comprehensive understanding of job requirements and the work values, education, and context for workers in those occupations. However, despite the richness of the ONET database it still suffers the challenges of providing data only for standard, generic occupational titles, and operating under an irregular update schedule.

It was under these circumstances that a partnership formed in Texas of groups attempting to remedy these frictions for their respective constituencies.