Operational Resource Planning For Information Collection Training

Objectives:
• Define operational resource planning in the context of information collection.
• Identify key components of resource planning.
• Understand the role of resource planning in achieving organisational objectives.
• Recognise the importance of aligning resources with strategic goals.

Topics:
• What is operational resource planning?
• Scope and importance of information collection.
• Overview of resources: human, financial, technological, and informational.
• Linking resource planning to strategic objectives.
• Key terminology and concepts.
• Introduction to the course structure and assessment criteria.

Objectives:
• Explore various information collection frameworks.
• Understand the SAQA unit standard requirements.
• Identify ethical and legal considerations in information collection.
• Apply standards to ensure data quality and integrity.

Topics:
• Overview of information collection models (e.g., intelligence cycle).
• SAQA unit standard 114507 specifics.
• Ethical guidelines and legal compliance (POPIA, etc.).
• Data quality dimensions: accuracy, completeness, timeliness.
• Standard operating procedures for collection.
• Case study: applying frameworks in practice.

Objectives:
• Conduct a needs assessment for information collection.
• Define information requirements based on stakeholder needs.
• Prioritise collection requirements.
• Document requirements clearly and concisely.

Topics:
• Techniques for needs assessment (surveys, interviews, workshops).
• Identifying stakeholders and their information needs.
• Translating needs into specific requirements.
• Prioritisation methods (MoSCoW, ranking).
• Creating a requirements document.
• Practical exercise: conducting a mini needs assessment.

Objectives:
• Identify different types of resources required for information collection.
• Categorise resources into human, financial, technological, and informational.
• Assess resource availability and constraints.
• Develop a resource inventory.

Topics:
• Human resources: skills, expertise, availability.
• Financial resources: budgeting, cost estimation.
• Technological resources: tools, software, hardware.
• Informational resources: existing data, knowledge bases.
• Resource categorisation frameworks.
• Inventory management basics.
• Practical: create a resource inventory for a scenario.

Objectives:
• Estimate costs associated with information collection activities.
• Develop a budget plan.
• Identify cost-saving opportunities.
• Monitor and control expenses.

Topics:
• Cost estimation techniques (analogous, parametric, bottom-up).
• Budget components: personnel, equipment, travel, software.
• Creating a detailed budget plan.
• Cost control mechanisms.
• Budget approval processes.
• Practical exercise: develop a budget for a collection project.

Objectives:
• Plan human resource requirements for collection teams.
• Define roles and responsibilities.
• Develop a staffing plan.
• Understand team dynamics and leadership.

Topics:
• Role definition: collectors, analysts, supervisors.
• Skills matrix and competency mapping.
• Recruitment and selection basics.
• Team structures (centralised, decentralised).
• Leadership styles and motivation.
• Conflict resolution and communication.
• Practical: create a staffing plan.

Objectives:
• Identify technological tools for information collection.
• Evaluate tools based on requirements.
• Plan technology integration.
• Understand data management systems.

Topics:
• Survey tools (e.g., Google Forms, SurveyMonkey).
• Data collection software (e.g., ODK, KoboToolbox).
• Database management systems.
• Data security and privacy tools.
• Tool evaluation criteria.
• Integration with existing systems.
• Practical: compare two tools.

Objectives:
• Develop a realistic schedule for collection activities.
• Use scheduling tools and techniques.
• Manage time effectively across resources.
• Identify and mitigate scheduling risks.

Topics:
• Work breakdown structure (WBS) for collection.
• Gantt charts and critical path method.
• Resource levelling and smoothing.
• Time estimation techniques.
• Scheduling software (MS Project, Trello).
• Risk identification for schedule delays.
• Practical: create a Gantt chart.

Objectives:
• Identify risks related to resource planning.
• Assess risk probability and impact.
• Develop risk mitigation strategies.
• Monitor and review risks throughout the project.

Topics:
• Risk categories: resource availability, budget, technology, human factors.
• Risk assessment matrix.
• Mitigation strategies (avoid, transfer, mitigate, accept).
• Contingency planning.
• Risk register creation.
• Practical: conduct a risk assessment for a scenario.

Objectives:
• Define quality standards for information collection.
• Implement quality assurance processes.
• Conduct quality control checks.
• Address non-conformance issues.

Topics:
• Quality planning: defining criteria.
• QA vs QC: roles and differences.
• Data validation and verification techniques.
• Auditing collection processes.
• Corrective and preventive actions.
• Practical: design a quality checklist.

Objectives:
• Plan logistics for field collection activities.
• Manage equipment and supplies.
• Coordinate transportation and accommodation.
• Ensure safety and security.

Topics:
• Logistics planning: equipment, materials, consumables.
• Transportation and route planning.
• Accommodation and welfare.
• Safety protocols and emergency procedures.
• Inventory management for field supplies.
• Practical: develop a logistics plan.

Objectives:
• Identify key stakeholders and their communication needs.
• Develop a communication plan.
• Create progress reports.
• Manage stakeholder expectations.

Topics:
• Stakeholder analysis (power/interest grid).
• Communication channels and frequency.
• Report templates: status, progress, final.
• Presentation skills for reporting.
• Handling feedback and changes.
• Practical: draft a progress report.

Objectives:
• Understand legal requirements for information collection.
• Apply ethical principles.
• Ensure compliance with relevant legislation.
• Handle sensitive information appropriately.

Topics:
• POPIA and data protection laws.
• Informed consent and confidentiality.
• Ethical dilemmas in collection.
• Institutional review boards (IRBs).
• Compliance monitoring.
• Case studies: ethical breaches and lessons.

Objectives:
• Plan for data storage and retrieval.
• Ensure data security and backup.
• Develop data management policies.
• Implement data archiving strategies.

Topics:
• Data storage solutions (cloud, on-premise).
• Data security measures (encryption, access control).
• Backup and disaster recovery.
• Data retention policies.
• Metadata and documentation.
• Practical: create a data management plan.

Objectives:
• Identify opportunities for resource optimisation.
• Apply lean principles to resource planning.
• Use techniques to maximise efficiency.
• Monitor resource utilisation.

Topics:
• Lean thinking in resource planning.
• Eliminating waste: overcollection, duplication.
• Resource utilisation metrics.
• Continuous improvement techniques.
• Benchmarking best practices.
• Practical: conduct a resource audit.

Objectives:
• Align resources with selected collection methods.
• Match resource capabilities to method requirements.
• Plan resource allocation for mixed-method approaches.
• Evaluate resource-method fit.

Topics:
• Collection methods: surveys, interviews, observations, secondary data.
• Resource requirements per method.
• Balancing quantitative and qualitative methods.
• Case study: resource allocation for a multi-method project.
• Practical: map resources to methods.

Objectives:
• Develop monitoring indicators for resource plans.
• Evaluate resource plan effectiveness.
• Use feedback for adjustments.
• Conduct post-project reviews.

Topics:
• Key performance indicators (KPIs) for resources.
• Monitoring tools and dashboards.
• Evaluation frameworks (e.g., logic model).
• Lessons learned documentation.
• Continuous improvement cycle.
• Practical: design a monitoring dashboard.

Objectives:
• Explore emerging technologies in information collection.
• Assess potential for innovation in resource planning.
• Plan for technology adoption.
• Consider future trends.

Topics:
• AI and machine learning for collection.
• Mobile data collection advancements.
• Internet of Things (IoT) sensors.
• Big data analytics.
• Innovation adoption models.
• Practical: evaluate a new technology for a scenario.

Objectives:
• Analyse real-world case studies.
• Identify best practices in resource planning.
• Apply lessons to own context.
• Develop a best practice guide.

Topics:
• Case studies from various sectors (government, NGO, private).
• Success factors and pitfalls.
• Best practice compilation.
• Group discussion and analysis.
• Practical: create a best practice checklist.

Objectives:
• Synthesise all course content into a coherent plan.
• Prepare for final assessment.
• Present a comprehensive resource plan.
• Receive feedback and finalise.

Topics:
• Review of key concepts.
• Integration exercise: develop a full resource plan.
• Presentation and peer feedback.
• Assessment criteria overview.
• Q&A session.
• Course evaluation.