About Us

Our passion for what we do at Skills4Industry derives from our core belief that a good society is one that cares about the balance between the 3Hs - head, heart, and hands.

Since our first Skills4Industry program for at-risk youth in the London Borough of Lewisham in 2001-2003, we set our sights on using cutting-edge technology to help individuals navigate pathways to careers from cradle to retirement. Skills4Industry’s first program took place through a partnership with Deloitte, and a continuation by the UK government under eSkills4Industry in 2003 following the programs' global success that has seen hundreds of thousands at risk youth acquire work skills, resulting in gainful employment. The program used CD ROM to capture tutorials of work skills to support students' learning, later eLearning through the Internet and today, we use artificial intelligence to ensure the curricula and pedagogy represent the current requirements of employers.

Experience taught us that individuals need integrative competencies to succeed in the 21st century. This knowledge resulted in developing a Gold Standard (Skills4Industry or higher order thinking skills) consisting of subject-matter designed to link academic curricula to work skills. Advances in technology meant this Skills4Industry could be used to simulate work skills real-time to help individuals and machines develop mastery of competencies valued by employers. To support these goals we embarked on extensive data collection to establish work titles and profiles of competent employees required by businesses for different job positions. With official titles, we embarked on functional analysis that is premised on the purpose of the job role captured by the title to two entities - the industry, sector, trade and domain (ISTD) it represents, and society. This exercise resulted in the definition of over 3.6 million job tasks, which established benchmarks of performance, and the competencies (works skills, academic knowledge, relational and contextual domain) required to perform each task. We used the degree of difficulty in the performance of each task to establish ten levels of hierarchies mimicking real-world organization charts represented by (y), while the ISTD is represented by (x).

Although the resulting standards was a novel approach to classification of occupations and competencies, for application in learning curricula and pedagogy. While it was popular with the education and workforce development sectors, including the manufacturing industry, but resisted by other industries and sectors, because:

Manual updates of occupational standards based on new trending jobs and competencies mean they are not current and cannot be used to make proactive talent management decisions.

They do not capture the entire input and output picture of occupations for product, service, and process quality judgments.

They are not robust enough to capture the complete spectrum of academic, work and relational skills required to do a job to measurable levels of performance benchmarks.

The performance benchmarks cannot be universal, because the skills used in the performance of a back office job, with that of a surgeon and a dancer cannot be measured using the same set of principles.

They did not reflect the particular nature of the different domain contexts under each job performance.

Further decomposition and cleansing of the data for artificial intelligence informed the descriptive analysis of occupational standards, using visualization techniques. These Excel features came to our rescue in building assumptions and hypothesis that resulted in ranking the functional tasks based on competencies requirement using a scoring function.

Classification and clustering methods assigned labels and generated specific patterns from numerical data, to establish a discrete set of possibilities. Such as, determining the competencies required to perform a job task. With this, for example, we uncovered the frequency of occurrence of each independent competency variables (work skills, relational, academic, experience, and specific domain conditions), and their dependent variables work tools (search, word, equipment, threads, and needles). We were able to aggregate the complexity of competencies essential to each career pathway.

Forecasting the frequency of occurrence for each work tool as a component of competency (a numerical quantity), we were able to capture the combined work tools required to perform each task. While aggregating the quantities across functional domain and levels, we forecasted the future value of the numerical functions to optimize productivity based on previous values and other relevant features. Forecasting an individuals’ future career pathway given similar values by a couple planning to have children, will produce forecasts for career pathway for their unborn child. The skill gaps analytics produces recommendations of gaps closing coursework combining academic, work and soft skills. Businesses can optimize their productivity based on clear and aligned job titles, as well as functional tasks. Using Questions Classification and Syntatic Maps, businesses can make strategic automation and digitization decisions, as well as establish the expert knowledge required to implement artificial intelligence, data acquisition, and analytics.

We used labels to write our signature across the entire ISTD at each level of performance based on vectorization that is often used for text mining by keyword, to reveal patterns in our large size data for several purposes. For example, in analyzing rural skills, a Skills4Industry level 1.1 we discovered that K-nearest neighbor could be used to inherit specific attributes of intermediate skills at Skills4Industry level 1.8 to 2.3. We carefully defined problems based on how long individuals have been out of work as an approach for lifelong learning, with the data cleansed, parsed and filtered. These labels now support Skills4Industry signature patterns, which can be merged or moved around different domains or hierarchies — for example, moving an automotive line assembly technician to auto sales or neighborhood grocery store.

How can we help you to make better education, workforce development, and employment programs decisions today?

Would you instead employ education, workforce development, and employability program researchers whose final product informs your community that lack of connection is responsible for their young adults landing low quality and low paying jobs? Or

Would you instead have data scientists with the infrastructure to prescribe solutions based on the exact subject matter? Including where the learning should take place, the type of experiences, when and costs based on learning hours and learning time that is required to close skill gaps using well-defined questions supported by wisdom based on years of experience?

Contact Us today to learn more!