DEV Community: Kepha Gitau

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

Kepha Gitau — Sun, 12 Oct 2025 17:25:58 +0000

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.

Is Excel still relevant in the era of Power Bi and Python?

Kepha Gitau — Fri, 03 Oct 2025 19:19:03 +0000

Is Microsoft Excel out-of-dated compare to other data tools?

Well let's dive into it and see how this data tool has being around for decades and it's still in use till date. Over the years, there has been introduction of other tools for example Power Bi and Python that have challenged the sit to the throne but excel never fades away. Why???

Reasons why Excel is far from being irrelevant.

When you think about data in whichever circumstance be it school or business, Excel will come up or be in use. Reason being;

It's widely known and popular worldwide.
Excel is easily accessible for everyone.
This data tool is easy to learn and operate.
Excel is flexible to use in different operations e.g. school, finance.
Excel is user friendly and simplified.
Excel is integrated into our data tools which is a plus for it e.g. Power Bi.

What did the new kids on the block have under their sleeves?

Power Bi and Python came later into use when dealing with data. With them, they brought their own set of capabilities which either matched, complimented or rivaled Excel which was struggling with;
Performance with big data.
Data visualization being rivaled with Power Bi.
Complex data analysis.

So is Excel still relevant?

Excel still being in high use till date is a good indicator that its still relevant and widely accepted. It is simple, quick, user friendly when performing tasks with it.

Final Take.

Excel being in a world that is ever evolving, it still remain a key part for data processing, analyzing and reporting.
As a data analyst, it is essential to learn excel and compliment it with Power Bi and Python so that you choose the right tool for the task.

Apache Kafka in Data Engineering.

Kepha Gitau — Wed, 17 Sep 2025 17:33:20 +0000

Introduction

Apache Kafka is an open source, distributed event streaming platform designed for high performance data pipelines, streaming, analytics, data integration. Think of it as a high-speed message hub for your data. It lets applications publish, store and subscribe to streams of records in real-time.

Key concepts in Kafka

1. Producer

An application that sends messages to Kafka topics.

2. Consumer

An application that reads message from Kafka topics.

3. Topic

It's a category or feed-name to which records are sent. Think of this as a channel.

4. Broker

A Kafka server. Multiple brokers form a Kafka cluster.

5. Kafka Cluster

A group of Kafka servers working together.

Kafka use case

Imagine an e-commerce platform:

Producers- Checkout service, inventory services, payment get way
Kafka - Handles all events.
Consumers - Analytics dashboards, fraud detection systems and email notifications.

Conclusion

Apache Kafka is a backbone for real-time streaming.