Power BI Quick Start Guide

This document offers a comprehensive guide to getting started with Power BI, a leading business intelligence tool. It begins by explaining Power BI’s popularity due to its Microsoft ecosystem integration, cost-effectiveness, and user-friendliness, while also outlining various career roles that utilize Power BI skills. The core of the document provides a step-by-step tutorial on using Power BI Desktop, covering everything from downloading the application and importing data from CSV files to transforming and cleaning data within Power Query, and then building interactive visualizations like bar charts, maps, and tables. Finally, it demonstrates how to publish the completed report to the Power BI service in the cloud, showcasing the platform’s full functionality.

Power BI: From Basics to Business Intelligence Mastery

Power BI is consistently ranked as the number one business intelligence (BI) tool in 2025, leading both the Gartner Magic Quadrant and the Forrester wave for BI platforms, surpassing competitors like Tableau, Clicksense, or AWS’s Quicksight.

Here’s an overview of Power BI basics, drawing from the provided sources:

Why Power BI is Popular:

• Seamless integration within the Microsoft ecosystem: Most large corporations run on Microsoft tools, and Power BI integrates well with them.
• Cost-effective: A Power BI Pro license costs only $14 a month (at the time of filming), which is a bargain compared to competitors like Tableau.
• Extremely user-friendly: If you are familiar with Excel, learning Power BI should not take long due to its similar interface.
• Strong demand for skills: Companies across diverse industries, including finance, healthcare, retail, and tech, use Power BI.

Roles You Can Land with Power BI Skills:

• Power BI Developer: Focuses on designing data models, building dashboards, and optimizing pipelines.
• Power BI Analyst: Main focus is to analyze data, create reports, and deliver insights for business decisions.
• Business Intelligence Specialist: Develops BI strategies, manages data infrastructure, and implements BI solutions.
• Data Visualization Specialist: Creates compelling visualizations and dashboards to tell data stories.

Getting Started with Power BI: A 10-Step Checklist Overview:

1. Download Power BI Desktop: You can download it from the Microsoft Store. Note that Power BI Desktop only works on Windows, not Mac.
2. Explore the Home Screen: Familiarize yourself with options like connecting to a data source, starting with a blank report, and viewing previous files.
3. Connect to Data: In a blank report, go to the Home tab and select “Get data” to connect to various common data sources, such as Excel workbooks or CSV files. The source demonstrates loading three offline CSV files (one fact table and two dimension tables).
4. Transform Data with Power Query: Before loading data into Power BI, it’s crucial to clean it in Power Query.

• Enable View Indicators: Turn on column distribution, column profile, and column quality from the “View” tab for better data understanding.
• Adjust Column Profiling: Change profiling from the default “top 1,000 rows” to the “entire data set” for a complete view.
• Remove Duplicates: Select columns (e.g., product ID to account ID) and right-click to remove duplicate rows. You can easily undo steps in Power Query.
• Extract Date Information: Duplicate date columns and then extract details like day of the week, month, or year from the “Transform” tab.
• Format Text Data: Capitalize each word (e.g., for product groups) or convert text to uppercase (e.g., product size, product type) using the “Format” option in the “Transform” tab.
• Handle Missing Data: For example, untick blank fields in a column to populate all entries.

1. Load Data and Explore Power BI Views: After cleaning, click “Close and Apply” in the Home tab to load changes into Power BI. Power BI has several key views:

• Report View: Where you build your visualizations using filters, visualizations, and data panes.
• Table View: Allows you to see your entire table easily.
• Model View: Power BI often automatically creates relationships between tables based on common columns (e.g., product ID, account ID), but you can also manually create and manage them. This view shows cardinality (e.g., many to one) and filtering direction.
• DAX Query View: An advanced view that is beyond the scope of the basic introduction.

1. Build Your First Visuals:

• Column Chart: Select a column chart, then drag fields like “Date hierarchy” and “Sales” to visualize sales by year, quarter, or month. You can add data labels and use “small multiples” to separate data by categories like product type (e.g., indoor, landscape, outdoor).
• Map Visual: Use a map visual with a location field like “country code” and drag “Sales” to the “Size” field to create bubbles representing sales amounts by country. Add “Country” and “Sales” to tooltips for interactive details.
• Table Visual: Create a detailed table with fields like country, product name, product size, product type, sales amount, quantity, and cost of goods sold. You can sort the table by sales descending.

1. Make Reports Interactive:

• Slicer: Add a slicer visual (e.g., for product size) to filter the data displayed across all visuals.
• Cross-filtering: Clicking on elements within one visual (e.g., a country on the map) will automatically filter the data shown in other visuals on the dashboard, making it highly interactive.

1. Publish the Report: Go to “File,” then “Publish” to publish your report to the Power BI service (cloud). You will save the file and choose your workspace.
2. Access in Power BI Service (Cloud): Once published, your report will be available in your browser (e.g., Google Chrome) and is OS independent. It retains all the functionality, including interactivity, that you built in Power BI Desktop.

Learning Resources:

• DataCamp’s Power BI Fundamentals Track: Recommended for beginners, covering data connection, cleaning, and dashboard building with real datasets.
• Data Analyst in Power BI Track: Designed for job readiness, covering advanced topics like data modeling and business case studies.
• Microsoft Power BI Data Analyst Certification: Completing DataCamp’s job-ready track offers 50% off the official Microsoft Power BI data analyst certification exam.

Power BI: Data Cleaning with Power Query

Data cleaning is a crucial step in Power BI, performed primarily within Power Query before the data is loaded into Power BI Desktop. The purpose of Power Query is to clean and transform your data to ensure it’s in the optimal format for analysis and visualization.

Here’s a breakdown of data cleaning steps and techniques mentioned in the sources:

• Accessing Power Query: After connecting to a data source (e.g., CSV files), instead of directly loading the data, you should select “Transform data” to open Power Query.
• Enabling View Indicators: To better understand your data and identify cleaning needs, it’s recommended to turn on Column Quality, Column Distribution, and Column Profile from the “View” tab in Power Query. These indicators provide visual insights into your data’s structure and potential issues.
• Adjusting Column Profiling: By default, column profiling might be based only on the “top 1,000 rows”. For a complete understanding and accurate cleaning, you should change this to “entire data set”.
• Removing Duplicates: A common cleaning step is to remove duplicate rows from your tables. You can select multiple columns (e.g., from product ID to account ID) by holding Shift, right-clicking, and choosing “Remove Duplicates”. Power Query allows you to easily undo any cleaning step to review its impact. For instance, if you remove duplicates and see the row count decrease, you know duplicates were present.
• Extracting Date Information: From a date/time column, you can extract various components like the day of the week, month, or year using the “Transform” tab. It’s a good practice to duplicate the original date column first before extracting new information, preserving the original.
• Formatting Text Data: Power Query allows for easy text formatting:
• Capitalize each word: Useful for fields like “product group” to ensure consistent capitalization of every word in a text string.
• Uppercase: You can convert entire text fields (e.g., “product size”, “product type”) to uppercase for uniformity.
• Handling Missing Data: For columns with missing values (e.g., blank account IDs), you can untick or filter out blank fields to ensure all entries are populated. This is a very useful feature for data integrity.
• Flexibility of Power Query: A key advantage of working in Power Query is its flexibility. You can undo steps, create new steps, or remove steps in between, allowing for iterative and error-correcting data manipulation.

Once all the necessary data cleaning and transformations are completed in Power Query, you simply click “Close and Apply” from the Home tab to load all the changes, data models, and tables into Power BI Desktop. This prepares your data for building visualizations and reports in the Power BI Report View.

Building Power BI Visuals and Reports

Building visuals in Power BI is primarily done in the Report View, which is one of the key views available after you load your data. This view is where you design your dashboards and reports using the filters, visualizations, and data panes.

Here’s a discussion on how to build visuals, drawing from the sources:

• General Process
• The basic process involves selecting a visual type from the visualizations pane and then dragging relevant data fields from your tables into the appropriate sections of the visual. Power BI aims to be user-friendly, and you can quickly get started by experimenting and doing things yourself.
• Types of Visuals and How to Build Them

1. Column Chart

• To create a column chart, you select the column chart visual.
• Then, you can drag fields like “Date hierarchy” (from your fact table) to the axis and “Sales” to the values. This allows you to visualize sales over time, showing sales by year initially.
• Drill Down/Up: The “Date hierarchy” field is powerful because it enables easy drill-down functionality, allowing you to view sales by year, quarter, or month, and drill back up to the year level.
• Data Labels: To enhance readability, you can add data labels by going to the visualizations pane, formatting the visual, and ticking on “data labels”.
• Small Multiples: A cool feature is “small multiples,” which allows you to separate the same chart information by categories, such as “product type” (e.g., indoor, landscape, outdoor products). You drag the categorical field to the “small multiples” section. You can then adjust the layout (e.g., increasing columns to three and dropping rows to one) to improve the visual presentation.

1. Map Visual

• To create a map visual, select the map visual type.
• You can use a location field like “country code” (from an accounts table) by dragging it to the “location” field in the visual pane. Power BI automatically identifies the countries.
• To represent a metric on the map, drag a measure like “Sales” to the “size” field; this will create bubbles where the size of the bubble corresponds to the sales amount.
• Tooltips: To make the map more interactive and informative on hover, you can add fields like “Country” and “Sales” to the “tooltips” section, providing interactive details when a user hovers over a country.
• Map Controls: You can also add controls like the zoom tool to allow users to interact with the map.

1. Table Visual

• Select the table visual type.
• Drag various detailed fields into the table, such as “country,” “product name,” “product size,” “product type,” “sales amount,” “quantity,” and “cost of goods sold”. You can also include fields like “product group”.
• Sorting: You can sort the table by a specific column, for example, sorting by “sum of sales” in descending order to see top-selling items or countries.
• Making Reports Interactive
• Slicer: Add a slicer visual (e.g., for “product size”). This allows users to filter the entire report data based on the selected slicer options (e.g., large, medium, small products).
• Cross-filtering: Power BI reports are highly interactive. Clicking on an element within one visual (e.g., a specific country on the map or a bar in the column chart) will automatically filter the data displayed across all other visuals on the dashboard. This allows for dynamic exploration of the data. To deselect filters, you can click anywhere on the visual that is not an interactive element.

Once you have built your visuals and made them interactive, you can then publish the report to the Power BI service (cloud) for wider access.

Power BI: Dynamic Report Interactivity and Data Exploration

Report interactivity in Power BI is a fundamental aspect that allows users to dynamically explore and filter data within a dashboard or report, providing a more insightful and user-driven analytical experience. The goal is to make the dashboard or report interactive, going beyond static displays of information.

Here’s how report interactivity is achieved and functions within Power BI, based on the sources:

• Purpose: To enable users to filter the data displayed across all visuals on a dashboard, allowing for dynamic exploration and focused analysis. Power BI reports are designed to be highly interactive.
• Key Interactivity Features:
• Slicers:
• A slicer is a visual type that acts as an interactive filter for the entire report.
• To add a slicer, you select the slicer visual and then drag a relevant categorical field (e.g., “product size”) into it.
• Users can then click on options within the slicer (e.g., “large,” “medium,” “small” for product size) to filter all other visuals on the report accordingly.
• Multiple options can be selected by holding down Control while clicking.
• The effect of the slicer is immediately visible, for example, if “large” is selected, the table will only display large products.
• Cross-filtering (Visual Interactions):
• Power BI reports are built with inherent cross-filtering capabilities.
• This means that clicking on an element within one visual will automatically filter the data displayed across all other visuals on the same dashboard.
• Examples:
• Clicking on a specific country on a map visual (e.g., China) will filter all other visuals to show only the sales and related data for that selected country. This allows users to see “Chinese sales” when China is clicked.
• Similarly, clicking on a bar or section in a column chart would filter the other visuals based on that selection.
• To deselect or undo a filter initiated by clicking on a visual element, you can simply click anywhere on that visual that is not an interactive element.
• Interactive Data Exploration:
• The combination of slicers and cross-filtering allows for a powerful and dynamic way to explore data.
• For instance, a user could select “large products” from a slicer and then click on “China” on the map; the detailed table would then update to show only large products sold in China.
• The interactivity extends to drill-down functionalities in certain visuals, such as column charts with a “Date hierarchy” field, allowing users to drill down from year to quarter or month and then drill back up.
• Persistence in Power BI Service:
• Once visuals are built and made interactive in Power BI Desktop, the report can be published to the Power BI service (cloud).
• The published report retains all the functionality used in the desktop version, meaning the slicers and cross-filtering capabilities remain active for users viewing the report in their web browser. While there might be a slight loading delay, the interactivity is preserved.

Publishing Power BI Reports to the Cloud Service

Publishing reports in Power BI is the final step in making your created dashboards and reports accessible and shareable beyond the Power BI Desktop environment. Once you have completed building your visuals and ensured their interactivity, you can publish the report to the Power BI service (cloud).

Here’s a discussion on publishing reports:

• Purpose of Publishing: The primary purpose of publishing a report is to make it available for viewing and interaction in a web browser, allowing for wider access and collaboration. It moves the report from the local Power BI Desktop application to the cloud-based Power BI service.
• Publishing Process:
• The process is described as “super easy”.
• From Power BI Desktop, you navigate to “File” and then select “Publish”.
• You will then choose to “Publish to Power BI”.
• Before publishing, you will be prompted to save any unsaved changes to your report file.
• You can then name your file (e.g., “plants dashboard test”).
• Finally, you will select a workspace within the Power BI service where the report will be published. Power BI will then proceed to publish the dashboard onto the Power BI service.
• Accessing Published Reports (Power BI Service):
• After publishing, the report becomes available in the Power BI app within the cloud, which can be accessed through a web browser (e.g., Google Chrome).
• This makes the report OS independent, as it runs within your browser.
• Users can navigate to their selected workspace in the Power BI service to view the published report.
• Retained Functionality and Interactivity:
• A key aspect of published reports is that they retain all the functionality that was used in Power BI Desktop.
• This means that the slicers and cross-filtering capabilities that were set up for report interactivity in Desktop will remain active and usable for users viewing the report in their web browser.
• While there might be a slight delay in loading certain elements (like a map visual) in the browser, the interactivity is preserved. For instance, if you click on a “small product size” slicer option, the report will filter accordingly, just as it would in Desktop.

In summary, publishing a report is the step that transitions your analysis and visualizations from a development environment to a shareable, interactive, cloud-based platform for broader consumption and data exploration.

By Amjad Izhar
Contact: amjad.izhar@gmail.com
https://amjadizhar.blog

Affiliate Disclosure: This blog may contain affiliate links, which means I may earn a small commission if you click on the link and make a purchase. This comes at no additional cost to you. I only recommend products or services that I believe will add value to my readers. Your support helps keep this blog running and allows me to continue providing you with quality content. Thank you for your support!