DEV Community: Mary Ngure

PowerBI; Relationships, Schemas & Dashboards

Mary Ngure — Mon, 29 Jun 2026 06:06:38 +0000

When I started learning Power BI, I thought dashboards were mostly about choosing the right charts.

Then I discovered data modeling.

Everything clicked.

I realized that dashboards aren't powered by visuals, they're powered by relationships.

The Mistake I Used to Make

Coming from Excel, I was used to keeping everything in one large table.

It worked... until the data became larger and more complex.

Duplicate information increased.

Performance slowed down.

And maintaining the dataset became frustrating.

Power BI introduced me to a completely different way of thinking.

Instead of one giant spreadsheet, data is organized into related tables.

Understanding Relationships

Relationships allow tables to communicate using common fields, often called keys.

For example:

A Sales table stores transactions.
A Products table stores product information.
A Customers table stores customer details.

Rather than repeating product names thousands of times, Power BI connects the tables using Product IDs.

This keeps the model cleaner, faster, and easier to maintain.

Discovering Star Schema

One concept that completely changed my understanding was the Star Schema.

Instead of random tables connected everywhere, the model is organized into:

Fact Table

Contains measurable business events such as:

Sales
Revenue
Quantity Sold
Profit

Dimension Tables

Contain descriptive information like:

Customers
Products
Locations
Dates

The fact table sits in the center, while the dimension tables surround it—forming a star.

It's surprisingly elegant.

And it's one of the reasons Power BI performs so efficiently.

Why Good Models Create Better Dashboards

Once relationships were configured correctly, dashboard creation became much easier.

Instead of manually combining data for every visual, Power BI automatically understood how everything connected.

Slicers filtered multiple visuals correctly.

Measures returned accurate results.

Cross-filtering worked naturally.

It felt like the model was doing half the work for me.

Dashboard Design Is More Than Pretty Colors

Building dashboards taught me another important lesson.

A dashboard isn't a decoration.

It's a decision-making tool.

Now, whenever I design one, I ask:

What question should this answer?
What KPI matters most?
What action should someone take after seeing this visual?

Those questions have become more important than choosing between a bar chart or a line chart.

My Growing Power BI Workflow

As I continue learning, my process has become surprisingly consistent:

Understand the business problem.
Clean the data.
Build relationships.
Design a star schema.
Create measures.
Build visuals.
Refine the dashboard until it tells a clear story.

Each step builds on the previous one.

Skipping one almost always creates problems later.

Final Thoughts

Power BI has taught me that great dashboards aren't created in the report view.

They're created through thoughtful data modeling.

The visuals may be what users notice first, but relationships are what make those visuals meaningful.

I'm still learning, but every project reinforces the same lesson:

Clean data is important.

Well-designed models are powerful.

And dashboards are simply the final chapter of a much bigger story.

Learning Power BI; Where it all begins

Mary Ngure — Mon, 29 Jun 2026 05:59:39 +0000

When I first opened Power BI, I thought the exciting part would be building beautiful dashboards. I was wrong. It is such a powerful tool.

The real work and arguably the most important work starts long before creating charts. It starts with messy data.

As someone transitioning deeper into data analytics, I've realized that data cleaning isn't the boring step people make it out to be. It's where you develop the mindset of an analyst. Every duplicate, missing value, incorrect data type, or inconsistent entry tells a story about the quality of your data.

And if the data is wrong, your dashboard will simply visualize bad decisions.

My First Encounter with Power Query

The first thing that surprised me was that Power BI separates data preparation from visualization through Power Query.

Instead of editing the original dataset, Power Query records every transformation as a reusable step. This means that when new data is added later, I don't have to repeat the cleaning process manually.

That alone completely changed how I thought about data preparation.

Cleaning Data Isn't Just Removing Errors

One lesson I quickly learned is that cleaning data is about making it analysis-ready.

Some of the common tasks I performed included:

Removing duplicate records
Changing incorrect data types
Renaming columns for clarity
Replacing missing values
Splitting and merging columns
Creating calculated columns
Filtering unnecessary rows
Standardizing inconsistent text values

Each transformation made the dataset a little more trustworthy.

Functions That Made My Life Easier

As I continued practicing, I became more comfortable using functions inside Power Query.

Some of my favorites include:

Text functions for cleaning inconsistent names
Date functions for extracting months and years
Conditional Columns for categorizing records
Replace Values for fixing repeated errors
Group By for summarizing data quickly

These small tools eliminated hours of manual work.

The Biggest Lesson

Before learning Power BI, I assumed analysts spent most of their time creating reports.

Now I know that a significant portion of analytics happens before a single chart is built.

Clean data creates confidence.

Confidence creates better decisions.

What's Next?

Now that I'm becoming comfortable cleaning data, I'm moving into one of the most interesting parts of Power BI, building relationships between tables and designing dashboards that tell meaningful stories instead of simply displaying numbers.

Because data isn't valuable until someone can understand it.

And that's exactly where Power BI starts to shine.

How Excel is used in Real World Data Analysis

Mary Ngure — Sun, 07 Jun 2026 08:05:09 +0000

I’ve always known Excel as a tool for creating tables and performing simple calculations. However, after spending a week learning its fundamentals, I now understand why Excel remains one of the most widely used tools in data analysis.
Microsoft Excel is a spreadsheet application that allows users to collect, organize, clean, analyze, calculate, and visualize data. Its user-friendly interface and powerful features make it a valuable tool for individuals and organizations across different industries.
One way Excel is used in real-world data analysis is in business decision-making. Companies collect large amounts of data on sales, customers, and operations. Analysts use Excel to sort and filter this data, helping managers identify trends, monitor performance, and make informed decisions. For example, a retail business can sort products by sales volume to identify its best-selling items.
Excel is also widely used in financial reporting. Businesses use it to track expenses, calculate profits, prepare budgets, and generate financial reports. With formulas and formatting tools, financial data can be organized in a way that is easy to understand and analyze.
Another common application is marketing performance analysis. Marketing teams collect data from campaigns, websites, and social media platforms. Excel can be used to analyze campaign results, compare performance metrics, and identify which strategies are generating the best outcomes.
Throughout this week, I learned several Excel features and formulas that are useful in data analysis. The first is filtering, which allows analysts to display only the data that meets specific criteria. This is useful when working with large datasets and looking for particular information. I also learned about data validation, which helps maintain data quality by restricting the type of information users can enter into cells. This reduces errors and improves data accuracy.
In addition, I learned functions such as SUM(), AVERAGE(), and COUNT(). SUM() helps calculate totals, AVERAGE() finds the mean value of a dataset, and COUNT() determines how many numerical values exist within a range. These functions make it easier to summarize and understand data quickly. I also found text functions such as TRIM() and PROPER() useful for cleaning and standardizing data before analysis.
Learning Excel has changed the way I see data. Before, I saw data as a collection of numbers and text. Now, I see it as information that can tell a story and support decision-making when properly organized and analyzed. Excel has shown me that effective data analysis begins with understanding how to clean, structure, and explore data. As I continue my journey in data science, I can already see how these foundational Excel skills will support my learning of more advanced tools and techniques.