To understand is to err—
For there is no greater teacher than failure.
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Dashboard
Interactive Monitoring Dashboard for Agricultural Interventions in Region 10
Objective
To provide an interactive and data-driven monitoring tool for agricultural interventions across all provinces in Region 10. The dashboard consolidates project costs, beneficiaries, municipalities, and intervention statuses, enabling stakeholders to filter and analyze data by province through slicers.
Scope & Coverage
Provinces: Bukidnon, Camiguin, Lanao del Norte, Misamis Occidental, Misamis Oriental, and Cagayan de Oro City
Agricultural programs covered: Corn, Rice, HVCDP, FMR, Livestock, SAAD, and Organic Agriculture (OA)
Years covered: 2020–2024
Dashboard developed in Excel with PivotTables, Slicers, and Dynamic Charts
Key Features
Dynamic slicers for province-based filtering
Interactive donut and trend charts
Automated Top 5 analysis for project challenges and municipalities
Consolidated project cost, farmer beneficiaries, and intervention status
My Role
Designed and developed the dashboard structure
Cleaned and transformed raw datasets
Built interactive PivotTables, slicers, and visualizations
Automated summaries for quick decision-making
Designed
and developed the
dashboard structure
Monitoring
Agricultural Interventions Monitoring Tool for
Objective
To develop a dynamic monitoring tool that tracks the allocation, distribution, and impact of agricultural interventions at multiple administrative levels. The dashboard aims to improve efficiency in data entry, cleaning, and querying, while supporting better decision-making and reporting.
About the Project
This project started as a self-initiated solution to streamline how agricultural intervention data was being handled. The existing system lacked automation, making data preparation slow and error-prone.
Built over a few days while balancing work and personal commitments.
Created with limited coding background, leveraging online tutorials, peer advice, and AI-assisted experimentation.
Designed as a practical tool for a small team, enabling automation of basic but time-consuming tasks such as data cleaning and preparation for queries.
Serves as a portfolio and learning project — showing technical initiative, problem-solving, and dashboard development skills.
Impact & Intended Use:
Reduced manual effort in preparing and cleaning intervention data.
Provided a replicable framework for monitoring and budget utilization.
Enabled easier querying and reporting for team members with minimal technical skills.
Built a foundation for future automation and scalability (scripts, pivot dashboards, budget analysis).
Disclaimer:
The dataset is mock-up and work-in-progress.
Tracking
Automated Post-Monitoring & Task Tracking System
Objective
The core objective of this project is to create an automated and dynamic system for planning, tracking, and reporting on post-monitoring activities . By leveraging automated formulas and a centralized data structure, the system aims to minimize manual effort, ensure data accuracy, and provide real-time insights into project status and team workload.
Scope & Coverage
The project covers the entire lifecycle of post-monitoring activities, from scheduling and task assignment to final observation and data compilation. It includes managing a team roster, maintaining a comprehensive task tracker, scheduling post-monitoring visits for July to December 2025, and compiling key observations and reminders.
Key Features
Dynamic Task Management: Tasks are automatically filtered from a master list to separate sheets, ensuring instant updates.
Real-time Metrics: A dashboard automatically calculates key performance indicators like ongoing monitoring activities.
Intelligent Prioritization: The system filters and displays municipalities for monitoring based on specific criteria, helping the team prioritize their work.
Automated Reporting: The system automatically highlights tasks that are "In Progress" or have deadlines in the current week.
Centralized Data: Names are auto-referenced from a single roster, ensuring data consistency and simplifying updates.
