Regional needs: forecasting and targeting university actions
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The document outlines the role of regional services in improving cooperation between universities and regional partners to address local needs in skills, innovation, and community interests. It discusses the importance of adaptable solutions and effective data gathering methods to forecast regional development needs and the capabilities required by universities to meet these demands. Additionally, it emphasizes the necessity of leveraging multiple data sources and fostering user engagement to enhance data quality for informed decision-making.
Regional needs: forecasting and targeting university actions
- 1. Regional needs: Forecasting and targeting university actions Director of Regional Services Dr Teemu Ylikoski www.laurea.fi
- 2. 2 Regional Services & the UAS field Improving the depth and width of regional cooperation Partnership management Systematic cooperation Quality management and feedback
- 3. 3 finding adaptable solutions PERSONNEL NETWORK PROJECTS NEEDS PresentationPro coordination one-stop shopping common goals Regional Services
- 4. 4 A win-win-win equation Student Regional cooperation needs to benefit all parties Authenticity; Employability Competitive position in the educational Regional partner Laurea field Improved processes; new skills
- 5. 5 Needs and capabilities L R S uni Capabilities: L = Learning; R = Research; S = Services S I C regi on Needs: S = Skills; I = Innovation; C = Community Mode 1 to 2 Direct relationships to regional nets Regional funding mechanisms Modified from Goddard & Chatterton 1999 What are the capabilities needed for assessing: - The region’s needs in terms of skills gaps, innovation needs, community interests? - The Uni’s ability to produce learning, research and services to match the above needs? Goddard, J. B., & Chatterton, P. (1999). Regional Development Agencies and the knowledge economy: harnessing the potential of universities. Environment and Planning C, 17, 685-700.
- 6. 6 Capabilities in data acquisition: Forecasting regional development needs in the Uusimaa region Country level soc-dem data Matching Proxy for regional needs Total students / projects / etc required Education to occupation match: FNBE workforce demand forecasts by industry: GIER Workforce structural forecasts by industry: GIER Employment forecast: STAT Labor market exit forecast: GIER* Regional socio-dem trends: STAT Legend: GIER = Government Institute for Economic Research (VATT) STAT = Statistics Finland FNBE = Finnish National Board of Education (OPH) *future of forecast uncertain as of 9/14 Extrapolation to regional level Loosely based on the ”Mitenna” forecasting model (FNBE)
- 7. 7 Data acquisition: other perspectives " Towards a dynamic, future-oriented view " Aggregate country level data is updated infrequently and lags 12mths or more " Stiff trends do not allow for agile responses " In terms of emergent regional needs, primary data collection may be necessary " Primary data collection: considerations " Due to the diversity of the population (regional clusters) and actor size dispersion (SMEs), a potential survey faces severe complexities " For certain selected subgroups, other approaches may be available (regional forums, industry panels, associations), however, this will likely result in non-standardized data
- 8. 8 Capabilities in forecasting system deployment " Data interfaces: multiple, non-standard data sources; non-standard update procedures, non-standard update frequencies. Manual entry likely needed. " User interfaces: an open, online platform with low entry threshold likely needed – ensuring low barriers for regional public sector, private sector, third sector " User adoption rate has a positive return loop to data quality " Privacy issues should be avoidable by focusing on an aggregated, semi-public level in the raw data
- 9. 9 An opportunity to learn Best practices benchmarking various approaches comparison of current options New approaches compensating for missing data dynamic capabilities; towards a real-time view
- 10. thank you teemu.ylikoski@laurea.fi | twitter:@teemuy www.laurea.fi