What is Power BI?
Power BI is a data visualization and analysis tool that helps users better understand information—whether it’s numbers, tables, or trends. Instead of relying only on raw tables for analysis, Power BI transforms data into interactive charts, maps, and reports. It also allows users to build dynamic dashboards for real-time, ad-hoc insights into performance and trends.
Why is this useful?
Imagine you're a farmer in Kenya growing corn, beans, and coffee. You collect all kinds of data: monthly harvest amounts, market prices, and weather patterns. Power BI helps you turn that data into useful insights, such as:
- When does corn grow best?
- In which month is coffee the most expensive?
- Which crop gives the highest profit?
- Which regions produces the most coffee in a year?
- How was the corn production in the year 2022 compared to 2025?
What is DAX?
Data Analysis Expressions (DAX) is a formula language designed to analyse data in Power BI. It allows you to calculate, filter, and compare data, enabling a deeper analysis compared to programmes like Excel.
DAX enhances Power BI's ability to analyze, visualize and report data.
Examples of DAX Functions
Mathematical Functions
- CALCULATE(): Applies a calculation with specific filters.
Example:
TotalRevenue_Nairobi = CALCULATE(SUM(Kenya_Crops_Dataset[Revenue (KES)]), Kenya_Crops_Dataset[County] = "Nairobi")
Text Functions
- EXACT(): Compare two Strings and returns “True” only if 100% identical.
Example:
ExactCropMatch = EXACT(Kenya_Crops_Dataset[Crop Type], "Beans")
Date and Time
- TOTALYTD(SUM(Sales), Date) – Calculates sales from the beginning of the year up to today.
Example:
Total Revenue(Planting) = TOTALYTD(SUM(Kenya_Crops_Dataset[Revenue (KES)]), Kenya_Crops_Dataset[Planting Date])
Logical Functions
-SWITCH(): evaluate an expression against a list of values and return a result that corresponds to the first matching value.
Example:
SWITCH(Product, "Corn", "Group A", "Coffee", "Group B", "Other").
Why is this important?
With Power BI and DAX, companies and farmers can:
- better plan when to sell,
- Identify which product generates the most profit,
- respond quickly to problems like bad weather or falling prices.
Conclusion
Power BI and DAX very valuable because they help turn a lot of numbers into smart decisions. Farmers in Kenya (or companies here) can save money, plan better harvests, and spot risks early.
It’s like a “compass” – it helps you find the ideal path without getting lost.
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