Graham Attwell

Pontydsugu

July 2024

Skills and Skills Intelligence

In the UK, as in other European countries, there is an increasing focus on the importance of skills, to support increases in productivity and GDP, as well as boosting individual employability.

What is Skills Intelligence?

But long before skills took centre stage in policy debates, CEDEFOP defined skills intelligence as the outcome of an expert-driven process of identifying, analysing, synthesising and presenting quantitative and/or qualitative information on skills and labour market.

Cedefop says skills intelligence helps translate megatrends and aspirations of key stakeholders at national, regional, local and sectoral level into labour market trends and skill needs. No single skills anticipation, approach, method or tool can sufficiently capture and comprehensively anticipate labour market needs and skill trends and skill needs. It has become increasingly evident that neither employment forecasts nor any other single approach, method or tool can sufficiently capture and comprehensively anticipate labour market needs and skill trends.

Who needs skills intelligence?

While skills intelligence has always had a wide range of potential beneficiaries, the focus is shifting from developing evidence for experts and policy makers towards shaping and disseminating user-centred information that translates trends and policy aims into actionable learning and skills matching opportunities.

Skills intelligence is important to a wide range of people and organisations.  Young people planning their careers want to know about skills, as do unemployed people considering undertaking a training course, or people thinking about moving for new employment opportunities.

But skills intelligence is also important for local or regional economic planners and for developing local skills strategies.It is essential for shaping feedback loops that effectively transmit labour market signals to education and training systems. Timely and relevant skills intelligence is central to renewing and updating Vocational Education and Training curricula and programmes.

It enables businesses to start thinking about how to manage existing and new staff to be the most productive, and for governments to identify areas of growth, and pursue an industrial strategy.

Where does skills intelligence come from?

At the end of the day, skills intelligence is dependent on both data and experts. Traditional sources of labour market data have been largely based on surveys, on administrative data, and on data from industry bodies. UK Surveys have included the National Survey of Hours and Earnings (ASHE), the Labour Force Survey, the Employer Skills Survey, and the Census. Administrative data includes figures for employment and unemployment and tax data. Industry bodies include The National Health Service and the Construction Industry Training Board. Most labour market data has been classified by the Standard Occupational Classification (SOC), which as its name implies is about jobs and the Standard Industrial Classification (SIC) about industrial sectors. Both these come at varying levels of disaggregation. These data sources are very useful but have limitations when it comes to Skills intelligence:

• frequency of data – for instance the very detailed National Census only takes please once every ten years;
• sample size – even the Annual Survey of Hours and Earning which has a sample of around 180,000 has limitations in significance in smaller occupations;
• collection – for some months the UK National Office of Statistics has been struggling to collect sufficient data for the Labour Force Survey.
• many industry bodies which previously provided data have disappeared in the last 15 or so years;
• limited focus on skills – for instance the Labour Force survey provides data on the highest level of qualification achieved by a person, but not on their overall skills;
• lack of comparability – SOC and SIC are UK taxonomies which is a barrier to comparison with skills development in other countries.

Of course similar issues also occur to a greater or lesser extent in other countries.

In recent years the development of technology around big data and the use of artificial intelligence have led to new approaches based on analysing online job advertisements. Job advertisements can be scraped from the web, the data cleaned and then analysed by algorithms. This approach has been adopted by large international private employment organisations such as Lightcast, by national employment agencies, and also by Cedefop, the European Centre for the development of Vocational Training. A key advantage of this approach is that the data can be provided in near real time and most job adverts include skill requirements. Furthermore, given a large enough sample, it can provide data on at least a regional level.

Of course, there remain issues. Online job adverts tend to be skewed towards managerial and technical jobs and to include less public sector, low paid jobs and jobs in sectors such as agriculture. Cleaning the data, for example to eradicate duplicate adverts and ensure the address of the employment is correct, can be time consuming. With regard to skills, there is a question of what skills taxonomy to adopt. Lightcast has chosen to develop its own proprietary skills classification while Cedefop has integrated the multilingual classification of European Skills, Competences, and Occupations ESCO.

CEDEFOP’s Skills-OVATE provides detailed information on the jobs and skills employers demand based on online job advertisements (OJAs) in 32 European countries. The sources, include private job portals, public employment service portals, recruitment agencies, online newspapers and corporate websites.

To show up-to-date labour market and skills trends, Skills-OVATE presents data for the last four available quarters and is updated four times a year. Yearly averages for key variables are available via Cedefop’s skills intelligence platform. The UK was originally involved in the development of skills-OVATE and the website continues to include UK data and analysis based on research conducted before the United Kingdom’s exit from the European Union on 31 January 2020.

Work is ongoing supported by the UK Future Skills Unit and the National Statistics Office to develop a Labour Market data service for the UK based on scraped data. LMI for All presently provides data on employment based on the Employer Skills Survey. This is reliable data but not as timely as the potential of scraped data

Making Sense of Labour Market Intelligence

It is one thing to develop new data sources; another to make sense of the data and a further leap for that data to be actionable. To be trusted, relevant and usable for policy and in practice, multi-level stakeholder involvement in skills anticipation is crucial. This can include national, regional and local authorities, social partners, sectoral organisations, research institutes, education and training providers, and chambers of trade or commerce (CEDEFOP).

Such a partnership approach is linked to increasing engagement in tackling the skill challenges stemming from digitalisation and the green economy, demographic change as well as the increasingly fluid geopolitical landscape.

Developing skills anticipation capacity at local and regional levels helps regional and local players gain insight into what green innovation means for employment and skills on the ground. Combining anticipation methods and leveraging the expertise of regional and local stakeholders to identify VET and skills policy (implementation) priorities and recognises that the economic, labour, skills and digitalisation, artificial intelligence and green policies may be quite different in different regions, even within the same country.

The crucial role of partnership in turning skills anticipation into regional- and local-level – action, skills intelligence tools need to be easily accessible and adapted to the needs and expertise level of users so they can be used by audiences that need them most. While Lightcast and CEDEFOP have developed advanced graphical visualisiation of their data – and tools such as Tableau facilitate such visualisations  – it remains a challenge to make sense of the data. JISC has experimented running workshops for UK universities for curriculum development based on Labour Market Intelligence. The EU funded Career Pathways project has undertaken similar activities involving vocational colleges in four European regions. It is also likely that in the future the use of artificial intelligence will support sense making and the development of actions based on skills intelligence.  Yet collaboration is still seen as key to effective skills anticipation, with stakeholders from diverse sectors and geographic units working together. But by harnessing the power of skills intelligence and fostering collaborative development is a step towards ensuring that education and training systems effectively prepare individuals and organizations for the future of work