As an analyst, you will always begin your analysis with raw data that is often messy, inconsistent, incomplete and scattered across multiple systems. To be able to create value from this raw data, you will have to first clean, model, standardize and finally transform your raw data into insights that guide strategic data driven decision making.
PowerBI is one of the most powerful business intelligence tools analysts use to bridge the gap between messy data and actionable business intelligence.
This article explores how we can use PowerBI to do data cleaning, data modelling, DAX (Data Analysis Expressions) and in creating dashboard designs.
Data Cleaning and Transforming Messy Data in PowerBI
Data cleaning is the process of identifying and correcting errors, inconsistencies and inaccuracies in data sets.
Data cleaning is very crucial because it enables us to work with clean, structured and relevant data in our analysis unlike using uncleaned data that is poor quality data which translates to incorrect analysis and decisions.
Power Query in PowerBI allows analysts to clean and transform raw data through;
- Removing duplicates
- Handling missing values and errors
- Fixing datatypes
- Standardizing formats
- Splitting or merging columns
- Filtering irrelevant records
Structuring data with proper modelling
After data cleaning, it is important to build relationships between tables using proper data modelling techniques.
Data modelling is basically setting up tables, relationships, calculations and access for analysis scenarios
Good data modelling in PowerBI involves incorporating a Star Schema which improves performance, the accuracy of calculations, flexibility of reporting and scalability for future analysis.
Proper Modelling often involves:
- Creating fact tables
- Creating Dimension tables
- Designing a Star Schema
- Defining relationships between the tables.
Turning Data into Insights with DAX (Data Analysis Expression)
DAX is the formula language used in PowerBI to create custom calculations. We use DAX to transform structured data into meaningful business insights.
DAX enables us to calculate New measures and New columns to compute key metrics such as Total Revenue, Profit margins, growth rates among others.
For example, DAX allow you to create a New Measure for Total Revenue as shown below;
Total Revenue = SUM(Kenya_Crops_Cleaned_Final[Revenue (KES)])
We can also create a New Column as shown below;
Expected Selling Price = Kenya_Crops_Cleaned_Final[Market Price (KES/Kg)]*Kenya_Crops_Cleaned_Final[Yield (Kg)]
We use New Measure if you expect a single output while we use New Column if the expected outpu is multiple rows corresponding to the data.
Designing Dashboards in PowerBI
A dashboard is not just a collection of charts but a clear visual representation of our analysis that supports effective decision making.
An effective PowerBI dashboard must:
- Highlight Key Performance Indicators(KPIs)
- Use appropriate graphs to draw attention to critical metrics
- Present insights clearly and concisely
- Enable filtering
Here is an example of a PowerBI dashboard with KPIs, Slicers and Charts
Conclusion
PowerBI is more than just a visualization tool. It is a platform that enables us as analysts to transform messy data into reliable insights that drive real business decisions.
We use Power Query to clean the data, Data modelling to organize the data , DAX enables us perform dynamic calculations and the dashboard for visualizations and insights.
We are able to use PowerBI to find out what happened, why it happened and what will happen through data cleaning, data modelling, DAX calculations and interactive dashboards.
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