Issues in forecasting regional needs and targeting university actions to address those needs; particularly from a Nordic perspective.

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