Power BI: From Data Cleaning to Interactive Dashboards

Annah Okero — Sun, 28 Jun 2026 19:19:31 +0000

Imagine you are an analyst handling messy data and you need to build a dashboard for the team to understand the relationship between the numbers. The journey begins with data cleaning in Power Query, where inaccurate, duplicate, or missing values are corrected.

Managing missing values is a critical step in data cleaning, as it improves data quality and ensures accurate analysis. Here is the trick:

Text columns: Replace missing values with "N/A" or "Unknown" to indicate that the information is unavailable while maintaining consistency in the dataset.
Numeric columns: Missing values can either be left as blank (null) or replaced using appropriate statistical measures such as the mean, median, or mode, depending on the nature of the data and the analysis being performed. Numeric fields are generally the only columns where leaving blanks is acceptable without affecting data integrity.
Rows with excessive missing data: If a row contains approximately 90% missing values, it is often best to remove it, as it contributes little or no meaningful information and may negatively impact the quality and reliability of the analysis.

Data Modeling

Once the data has been cleaned, the next step is data modeling, where the data is organized into a logical structure that supports efficient analysis and reporting. Data modeling involves defining relationships between tables, creating calculated columns and measures, and organizing the data to improve report performance. A well-designed data model reduces redundancy, enhances query speed, and makes it easier to build accurate and interactive dashboards.

During data modeling, it is important to understand the two main types of tables used in Power BI:

Fact Tables: These contain measurable business data or transactions, such as sales, orders, revenue, or inventory movements. Fact tables typically include numeric values that can be aggregated and analyzed.
Dimension Tables: These provide descriptive information that gives context to the facts, such as customers, products, employees, locations, and dates. Dimension tables help categorize and filter the data for meaningful analysis.

Relationships in Power BI

Relationships define how tables are connected within the data model using a common field. They are created using Primary Keys (PK) and Foreign Keys(FK), allowing Power BI to filter and analyze data across multiple tables without duplicating information. Power BI may detect relationships automatically, but understanding and creating them manually ensures an accurate data model.
Common relationship cardinalities include One-to-Many (1), Many-to-One (N:1), One-to-One (1:1), and Many-to-Many (N), with One-to-Many being the most commonly used in Star Schema models.

Joins in Power BI

Joins are used in Power Query to combine data from two or more tables based on a shared column before the data is loaded into the model. Unlike relationships, which connect tables without merging them, joins physically merge the data into a single result. The main join types are Inner Join, Left Outer Join, Right Outer Join, Full Outer Join, Left Anti Join, Right Anti Join, and Cross Join. Selecting the appropriate join type ensures the correct records are returned and improves data quality for analysis.

Another important concept is the Star Schema, where a central fact table is connected to multiple dimension tables. This design is preferred because it is simple, fast, and optimized for analytical queries. In contrast, the Snowflake Schema normalizes dimension tables into additional related tables. While it reduces data redundancy, it introduces more relationships, making the model slightly more complex but useful for highly structured datasets.

The final step is dashboard creation, where visualizations such as charts, KPIs, maps, slicers, and tables are combined into an interactive report. The real value of Power BI isn't just building reports rather it is telling a story that drives action.

If you could build one Power BI dashboard to solve a real-world problem, what would it be and why?

How Excel is Used in Real-World Data Analysis

Annah Okero — Mon, 08 Jun 2026 17:22:16 +0000

Microsoft Excel is one of the most widely used tools for working with data. It allows users to organize, clean, analyze, and visualize information in a simple spreadsheet format. Whether you are tracking sales, managing inventory, or analyzing survey results, Excel provides practical tools that help turn raw data into meaningful insights.
When analyzing data in the real world, Excel is crucial. It aids managers in keeping track of inventory levels and making wise purchases. Excel is used in finance to create reports, track spending, and examine revenue patterns. Excel is also used by marketing teams to monitor client involvement, assess campaign performance, and spot trends that can enhance future tactics.

‎‎Excel Features and Formulas

One feature I have learned is Data Validation, which helps reduce data entry errors by creating predefined lists and rules. This ensures that data remains accurate and consistent. Another useful feature is Conditional Formatting, which automatically highlights important information such as low stock levels, duplicate values, or overdue tasks as well as outliers. This makes it easier to identify issues at a glance.
I have also learned several formulas that support data analysis. The SUM function calculates totals quickly, while COUNTIF counts records that meet specific conditions. The DATEDIF function helps calculate the number of days, months, or years between two dates, making it useful for tracking inventory age, project timelines, or customer activity periods.

In Conclusion…

Learning Excel has changed the way I see data and its role in everyday decision making. I now understand that data is not just something to be stored, but something that can be used to monitor performance and guide actions. For example, a business owner can use Excel dashboards to track inventory levels, monitor sales trends, and identify fast-moving or slow-moving products. Instead of manually reviewing hundreds of records, Excel presents key information in a clear and visual format, making it easier to decide when to restock products and manage inventory effectively. This has helped me appreciate how data can be transformed into practical insights that support real-world business operations.