Digitalization, innovation and network effects in rural and mountainous regions: a study of the status quo and opportunities for the IMS

(PROJECT)

The IMS project was completed in 2022 with support from the Eastern Norway Research Institute (a division of the University of Inland Norway) and SINTEF, one of Europe largest independent research institutions. The project was managed by an affiliate of VentureNet, which VentureNet acquired in 2023, and the project received co-financing from the Research Council of Norway and Innlandet County through the FORREGION program.

Background: small-and-medium-enterprises (SMEs) need help with data management.

Problem: Limited availability and access to private sector (business) data.

• High cost of generating business data and analysis for startups and SMEs.
• High cost of harmonizing business data using Big Data and artificial intelligence (AI).

Opportunities: make business analysis more cost-effective for SMEs using Big Data.

• “Core” business data points are reused in sales, fundraising and other processes.
• Gather existing “core” business data using Big Data to save time and cost for SMEs and advisors.
• Exchange “core” business data across digital platforms to save cost and generate revenue for SMEs, advisors, customers, investors, making SME data more valuable and cost-effective to analyze.

Challenges required advanced knowledge of regional dynamics and data science.

• Lack of digital platforms designed to make use of business data exchange and virtual networking.
• How to build a network of businesses and advisors that benefit from enriching and exchanging data.
• Technical bottlenecks in Big Data related to data harmonization, enrichment and exchange.

Project objective

Inform design of a main research project to develop new machine-learning and data exchange models for Big Data and digital B2B platforms that accelerate digitalization, innovation and network effects for SMEs in rural areas.

Hypotheses for lower growth rates in rural and mountainous regions:

• The Lemon Problem: lower trust in SMEs due to a lack of verified data (“Information Asymmetry).
• Lower access to capital and specialist expertise in non-metropolitan areas (“Agglomeration Effects”).
• Lower growth ambition? The Inland region of Norway has a high proportion of micro-enterprises.
• Is this due to a perception of lower access to private capital?
• Possible lower access to specialist/M&A expertise?
• Other factors, such as cultural factors, systemic inequalities or biases, or lower competitiveness?

Project Results

Key results from the University of Inland Norway

• Statistical analysis of ICT companies in the Inland region using publicly available data from Proff.
• Use of Centralization Index as a basis for future ethnogeography studies and statistical analysis.
• Mapping of advisory services and financial services available for companies in Innlandet.
• Questionnaire templates to evaluate knowledge and utilization of digital tools and advisory services.
• Other relevant suggestions from HINN colleagues for use in future research projects:
  • Social network analysis and gender study methods to analyze cultural and systemic factors linked to growth.
  • Analysis of industry clusters and network effects to identify best practice and factors linked to value creation.

Key results from SINTEF

• Clarified that the IMS has an innovative approach to data enrichment, which can be used to develop advanced machine-learning models with Humans-In-The-Loop, a current challenge for data scientists.
• Identified opportunities to reduce time and cost of data entry by using machine-learning techniques for data harmonization and enrichment.
• Identified areas for further development: Data Fusion, Dynamic Data Enrichment, Data Exchange.
• Delivered a toolbox and recommendations for potential suppliers and partners to enhance the IMS:
  • Several leading data sets providing broad access to available business data in Norway and Europe.
  • Leading ontologies and vocabularies for harmonizing and enriching data with machine-learning.
  • Advanced methods and software for data mining, harmonization, and enrichment.

Project Outcomes

Clear path for development of advanced Big Data solutions (software, methods and data).

• Data Harmonization/Fusion: integrate multiple data sources to dynamically produce structured data.
• Data Enrichment: develop HITL machine-learning models to dynamically enrich missing/incomplete data.
• Data Exchange: re-use “core” business data securely and cost-effectively across platforms and users.

Scalable frameworks and templates for further research and studies into regional dynamics.

Plan and partnerships to build a user ecosystem and test platform for data exchange.

Clear project opportunities and expressions of interest from leading research institutions.

• SINTEF invitation to join the EU Horizon “EnrichMyData” industrial research project with OECD.
• Dialog with SINTEF and NTNU-NorCIS to establish NRC Collaboration Project in cybersecurity.
• Private sector consortium of digital platforms in NRC Innovation Project (IPN) for data exchange.
• Assisting INN to establish “Inn-the-Loop” NRC Social Impact Project (KSP) in artificial intelligence.
• Dialog with INN and IESE Business School to apply for EU Horizon Expanding Investments Ecosystems.

Want to learn more?

Contact VentureNet for more information about the project results and collaboration opportunities.