Using Power BI and DAX Functions for Data Analysis: A Case Study in the Agricultural Sector.

Introduction to Power BI.

In the era of data-driven decision-making, decision-makers across various sectors are increasingly relying on dynamic reports, such as visualizations, dashboards and interactive data structures to support informed analysis and strategic planning. Being able to transform plain data into relevant insights has become crucial in fields such as business, healthcare, and agriculture, to mention just a few.

To facilitate this, various business intelligence(BI) tools have come up with powerful features for data preparation and cleaning, analysis and presentation. Power BI is an example of these tools, being a powerful data analytics tool which has been broadly embraced and outputs user-friendly solutions. BI allows users to connect to multiple data sources, i.e. Excel, SQL servers. It allows for cleaning and transforming data, applying advanced analytical functions using DAX (Data Analysis Expressions), and presenting the results through interactive dashboards and reports.

Using a case study in the agricultural sector, this article will show examples of how DAX Functions in Power BI can give insights which will support in better decision-making. DAX are categorised into;

1. Mathematical & Statistical Functions.

These functions perform arithmetic operations like addition, subtraction, and multiplication. They are used if the user doesn't want to create a new column, i.e.

• The SUM() function is a mathematical function that returns the total sum of all numeric values in a single column.

Example: Total Revenue = SUM(Kenya_Crops_Dataset[Revenue (KES)]) used to calculate the total revenue in the Revenue column.

• The AVERAGE() function returns the arithmetic mean of all numeric values in a column.

Example:Average Planted Area = AVERAGE(Kenya_Crops_Dataset[Planted Area (Acres)]) used to calculate the average planted area in the Planted Area column.

2. Text Functions.

These functions are used to combine or extract key information for better analysis, and it is used for creating calculated columns, cleaning raw data,i.e.

• The Left() function is used to extract a specific number of characters from the beginning(left side) of a text string.

Example:County Code = LEFT('Kenya_Crops_Dataset'[County Name],3) used to return the first three characters from the left side in the County Name column.

• The Right() function is used to extract a specific portion of a text string from the right side.

Example:County Suffix = RIGHT('Kenya_Crops_Dataset'[County Name],3) used to return the first three characters from the right side in the County Name column.

• The CONCATENATE() function is used to join two text strings into one.

Example:Farmer's County = CONCATENATE('Kenya_Crops_Dataset'[Farmer Name],""&'Kenya_Crops_Dataset'[County]) used to join the farmer and county in one name with a space in between.

3. Date & Time Functions.

These functions are used to manipulate, calculate and format dates and times for time-based analysis.

• The YEAR() function is used to return the specific year.

Example:Harvest Year = Year(Kenya_Crops_Dataset[Harvest Date]) used to show the specific harvest year in the dataset.

• The TOTALYTD() function is used to calculate the Year-To-Date total of a given measure or expression.

Example:Total Revenue (Planting) = TOTALYTD(SUM(Kenya_Crops_Dataset[Revenue (KES)]), Kenya_Crops_Dataset[Planting Date]) used to show the total revenue during the planting period.

• The SAMEPERIODLASTYEAR() function is used to return a table of dates shifted back exactly one year, based on the dates in the current context.

Example:Same Period = CALCULATE(sum(Kenya_Crops_Dataset[Revenue (KES)]), SAMEPERIODLASTYEAR(Kenya_Crops_Dataset[Harvest Date])) used to show the dates exactly a year back.

4. Logical Functions.

These functions are used to evaluate conditions and return true/false values or execute conditional logic. Essential for creating calculated columns, measures based on conditions.

• The IF() function is used to check a condition and returns a value if true, another if false.

Example:High Revenue Area = IF(AND(Kenya_Crops_Dataset[Revenue (KES)]>1000000, Kenya_Crops_Dataset[Planted Area (Acres)]>9.86), "High Value", "Low Value"). This function is used to show a range in the revenue and planted areas.

• The SWITCH() function is used to evaluate an expression against multiple possible values.

Example:Quarter Name = SWITCH(Kenya_Crops_Dataset[Harvest Quarter],1, "Q1-Jan to Mar",2, "Q2-Apr to Jun",3, "Q3-Jul to Sep",4, "Q4-Oct to Dec", "Unknown")This function switches the quarter column to rename to quarter name and range.

Conclusion.

Power BI and DAX Functions can be used to transform raw data into very meaningful insights, and this is a strategic advantage for decision-making. These tools assist in collecting, cleaning and understanding data. By applying this in business and in this case, agriculture, decision makers can apply logical reasoning and visualization to uncover trends, compare performance across time and detect opportunities or issues before they arise.
Analysis can assist in turning numbers into knowledge, which can result in action. With this, it can help businesses to make smarter, faster, and more confident decisions.