Project Overview

This Power BI dashboard explores global trends in agricultural credit using data from the FAO_IC dataset. The dashboard offers insights into how credit has been distributed across countries and how it has evolved over time. This tool is aimed at data analysts, policymakers, and international development stakeholders.

Purpose of this project

The goal is to answer key questions such as:

• Which countries receive the most agricultural credit?
• How has global credit issuance changed from 2000 to 2020?
• What is the year-over-year growth in agricultural credit?
• How evenly is credit distributed across countries?

Dataset Summary
This project, I worked with a dataset titled FAO_IC.csv, which I sourced from the Food and Agriculture Organization (FAO). At first glance, the dataset included a variety of columns such as Country, Year, Element, Unit Measure, and Credit_USD. While all these fields served a purpose in the original context, I had to take a step back and ask myself: Which of these are meaningful for visual storytelling and trend analysis?

After some exploration, I realized that not every column would be useful for my analysis. For example, the Element column was uniform across all rows; it just stated “Credit,” offering no additional insights or variation. Similarly, Unit Measure was redundant, as all monetary values were already expressed in USD, and this was consistent across the dataset.

To keep things clean, simple, and performance-friendly inside Power BI, I narrowed the focus to the three most valuable columns:

Country — to group and compare credit data geographically

Year — to analyze trends over time

Credit_USD — the actual credit amount being measured and compared

By reducing the dataset to just these three fields, I was able to streamline the dashboard’s design, reduce processing overhead, and focus entirely on generating insights that actually matter, like which countries are getting the most agricultural credit, how those amounts have changed over time, and what global trends are emerging from the data.

Data Cleaning Process

Before diving into any visualizations or DAX formulas, I spent some time cleaning the dataset to ensure it was optimized for analysis in Power BI. One of the first things I did was drop columns that didn’t contribute meaningful value to the dashboard, for example, Unit Measure, which was constant across all rows and simply repeated "USD". Since the dataset already had a column named Credit(USD), it felt redundant to keep this one around.

Next, I checked the data types. The Year column was originally stored as text or a general format, which Power BI doesn't interpret well for time-based visuals or sorting. So, I converted it to a whole number to enable proper timeline analysis. The Credit_USD field also needed a bit of formatting love. I ensured it was set to decimal number format, so that totals and averages would calculate accurately and display in a readable financial format.

Finally, I went through the dataset to remove any null values or duplicate entries. This step was important to make sure my visualizations weren’t skewed by missing or repeated data. After that, I was left with a clean, efficient dataset ready to power a dashboard full of insights.

DAX Measures Created

1.Total Credit Issued (All Time)
Total Credit = SUM(FAO_IC[Credit_USD])

This simple measure calculates the total credit issued globally over all years in the dataset. It’s a great way to get a bird’s-eye view of how much support has been extended in the agriculture sector.

2. Average Credit per Country
Average Credit per Country =
AVERAGE VALUES(FAO_IC[Country]), CALCULATE(SUM(FAO_IC[Credit_USD])))

This measure looks at how much credit each country receives on average. It helps to identify whether funding is evenly distributed or skewed toward specific regions.

3. Credit Issued in the Latest Year
Credit in Latest Year =
CALCULATE(SUM(FAO_IC[Credit_USD]), FAO_IC[Year] = MAX(FAO_IC[Year]))

This one focuses on the most recent year in the dataset (which is 2020 in my case). It filters the data to show only credit issued in that final year, helping stakeholders understand the most current funding activity.

Key Insights

1. Over $38M in agricultural credit has been disbursed globally across all years.
2. Credit volume shows steady growth from 1991 to 2010, with plateaus in recent years.
3. Countries like India, the USA, Germany, and China dominate credit allocations.
4. Some years show dips in funding, which may be linked to economic or policy shifts.
5. On average, countries received around $292K in credit, with a large variance.