"A functioning data economy requires an organisation's data capabilities, the ability to collaborate within ecosystems, and scalable data sharing."
"The Datatalouskasvattamo project aims to provide knowledge, training and support for the utilization of data in the agricultural sector."
About
The Datatalouskasvattamo project aims to provide knowledge, training and support for the use of data in agriculture. Within the agricultural data ecosystem, different roles can be identified, such as data producers, holders and users. The project gathers information on data economy use cases and business models, fair data economy practices, and technological solutions for implementing data ecosystems and their applications for data producers, processors and users.
Mission
In the data economy, data collection and utilization are central activities. A functioning data economy requires an organisation’s data capabilities, the ability to collaborate within ecosystems, and scalable data sharing. Many agricultural processes generate large amounts of data, but so far this data is often siloed in different systems, making transfer between them difficult. Our goal is that data produced in primary production becomes usable across the entire food sector.
Why
Good data management and sharing support innovation and AI solutions. In the project’s demos you can try data sharing in an experimental data space environment. The Datatalouskasvattamo ecosystem includes typical agricultural applications and service providers that use data. With model datasets compiled by research organisations you can safely explore the roles of data owners, holders and users.
"Data spaces enable secure and fair data sharing across trusted networks and sectors."
What they are
Data spaces are decentralized networks that allow organizations and individuals to share and use data responsibly. They operate under shared standards, ensuring interoperability, trust, and fairness. Data remains with its owner and is shared only when necessary, maintaining full control and sovereignty.Why they matter
By connecting data across sectors like health, energy, transport, and agriculture, data spaces fuel innovation and collaboration. They promote transparency, fair use, and consent-based data exchange supported by neutral intermediaries, or “data operators,” who ensure secure and compliant data flow.
TRAINING
Needs Assessment
- Identify skill gaps through surveys, interviews, performance reviews.
- Align training with strategic goals, regulatory requirements, and future competencies.
- Segment needs by role, department, and seniority level.
Learning Objectives
- Define clear, measurable goals for each training program.
- Use SMART criteria: Specific, Measurable, Achievable, Relevant, Time-bound.
Training Methods
- Choose a mix of formats based on audience and content:
- Workshops & Seminars
- E-learning modules
- On-the–job training
- Mentoring & Coaching
- Simulations or role-playing
- Peer learning / Communities of Practice
Curriculum Design
- Structure content into modules or learning paths.
- Include introductory, intermediate, and advanced levels.
- Integrate practical exercises, case studies, and assessments.
Delivery Plan
- Schedule training sessions (e.g., quarterly, annually).
- Assign trainers (internal experts or external providers).
- Ensure accessibility (language, format, location).
Evaluation & Feedback
- Use pre- and post-training assessments.
- Collect feedback via surveys or interviews.
- Track KPIs: completion rates, performance improvement, ROI.
Continuous Improvement
- Regularly update content based on feedback and changing needs.
- Monitor industry trends and integrate new learning technologies.
- Encourage a culture of lifelong learning.