Power BI vs. Excel: Optimizing Insights with the Right Tool

Power BI vs. Excel is a hot topic.

Picture this: you’re a project manager tasked with examining sales data to discover patterns and make critical business choices. You rely on Excel, a reliable instrument for analyzing data. It is common, adaptable, and widely employed, yet the constraints become apparent as your data sets grow.

This is when Power BI makes an appearance.

The Microsoft Power BI tool is specifically designed to handle big datasets. It goes beyond simple spreadsheet software. It is a powerful business intelligence tool that can quickly analyze and display intricate data.

On the other hand, Excel has been a fundamental tool for analyzing data for many years. Many professionals choose it due to its user-friendly interface and familiarity. Nevertheless, as the amount and intricacy of data increase, Excel’s constraints become apparent. Many individuals discover that they dedicate more time to manipulating data than analyzing it.

So, what distinguishes these two tools? Understanding Power BI vs. Excel is essential to determine which tool is most suitable for your analytical needs.

Let’s explore further the functionalities, capabilities, and real-world uses of these two powerful data tools.

Table of Contents:

1. Why Use Power BI over Excel?
2. Power BI vs. Excel: Key Differences
3. What are Similarities and When to Use Power BI vs. Excel?
4. Power BI vs. Excel: How to Use for Data?
5. Power BI vs. Excel: Which One to Choose?
6. How to Create Visualization in Power BI?
7. What are the Pros and Cons of Power BI vs. Excel?
8. Wrap Up

First…

Why Use Power BI over Excel?

Choosing Power BI over Excel can significantly enhance your data analysis and visualization capabilities. Here are some key reasons to use Power BI over Excel:

• Scalability: Power BI is designed to handle large datasets more efficiently than Excel. It can process and analyze vast amounts of data without the performance issues that often arise with large Excel files. Power BI is a better choice for organizations with extensive data sets.
• Interactive visualizations: Power BI provides a wide range of interactive visualizations that are more advanced and customizable than those in Excel. You can create dashboards with dynamic, drill-down capabilities that make data exploration and insights discovery easier and more intuitive.
• Real-time data analysis: Power BI supports real-time data analysis, allowing you to connect to live data sources and refresh data automatically. This ensures the data is always up-to-date, enabling timely and informed decision-making. Excel requires manual updates or complex setups to achieve similar real-time capabilities.
• Centralized collaboration and sharing: Power BI offers centralized collaboration features that Excel lacks. You can share dashboards and reports with colleagues through the Power BI service. This promotes a collaborative environment and improves decision-making processes within the organization.
• Data modeling and transformation: Power BI’s Power Query and Data Model features include powerful data modeling and transformation tools. These tools allow you to seamlessly clean, transform, and integrate data from various sources. While Excel has some data transformation capabilities, Power BI’s tools are more robust for complex data manipulation.

Power BI vs. Excel: Key Differences

Power BI vs. Excel is a powerful data analysis and reporting tool. But they serve different purposes and offer distinct functionalities. This table highlights the key differences between Power BI vs. Excel.

Aspect	Excel	Power BI
Purpose and Functionality	Primarily used for spreadsheet calculations, data entry, and basic data analysis. It is well-suited for tasks like budgeting, forecasting, and simple reporting.	Designed for business intelligence, data visualization, and advanced analytics. It is better suited for organizations looking to gain deep insights from their data and make data-driven decisions.
Data Handling and Scalability	Handles smaller datasets well but can struggle with very large datasets.	Optimized for handling large datasets efficiently, supporting complex data modeling and transformations.
Visualization and Collaboration	Offers basic charting and graphing capabilities sufficient for simple data visualization needs. However, its interactive features are limited.	Provides advanced, interactive visualizations and dashboards where you can create dynamic reports with drill-down capabilities It provides robust sharing and collaboration features through its cloud service.

What are Similarities and When to Use Power BI vs. Excel?

Power BI and Excel are both powerful data visualization tools, each with distinct features and use cases. Understanding when to use each tool can help maximize their benefits for different business needs. This table highlights the similarities and when to use Power BI vs. Excel:

Aspect	Similarities	When to Use Excel	When to Use Power BI
Microsoft Ecosystem	Both integrate well with other Microsoft products, ensuring seamless data sharing and collaboration.	Use Excel for tasks requiring integration with Microsoft Office applications like Word and Outlook.	Use Power BI when integrating with cloud services and online collaboration tools like SharePoint and Teams.
Data Manipulation	Both offer robust data manipulation capabilities, including sorting, filtering, and performing calculations.	Use Excel for simple data manipulation tasks and when working with small to medium-sized datasets.	Use Power BI for advanced data transformations, cleaning, and handling large and complex datasets.
Charts and Visualizations	Both provide charting and visualization features.	Use Excel for basic charting and when static reports are sufficient.	Use Power BI for interactive, dynamic visualizations and when creating detailed, interactive dashboards.
Formula Language	Both allow sophisticated calculations to analyze data.	Use Excel for straightforward calculations and when familiar with Excel’s formula language.	Use Power BI when complex data modeling is needed and for leveraging DAX for advanced analytical capabilities.

Power BI vs. Excel: How to Use for Data?

Power BI vs. Excel. Each tool has strengths and is suitable for different types of tasks. Understanding how to use each to handle data can help maximize business productivity and analytical capabilities.

Using Power BI for Data:

• Connect to data sources: Power BI connects to a wide variety of data sources, including databases, cloud services, and Excel spreadsheets. This capability enables you to pull in data from multiple locations and create a unified view for analysis.
• Data transformation: Once data is imported, Power BI offers robust tools for data transformation. Power Query lets you clean, merge, and shape data to suit your analysis needs. You can create relationships between different data tables and use DAX (Data Analysis Expressions) to perform complex calculations.
• Visualization and reporting: Power BI excels at creating interactive and dynamic visualizations. You can build dashboards allowing deep data exploration using features like drill-downs, slicers, and interactive charts. These visualizations help uncover insights and make data-driven decisions.

Using Excel for Data:

• Data entry and organization: Excel is often used for direct data entry and organization. You can create tables and spreadsheets to store and manage data. Excel’s user-friendly interface makes manually entering or importing data from various sources easy.
• Data manipulation: Excel provides powerful tools for data manipulation. You can sort, filter, and use formulas to perform calculations on data. PivotTables and PivotCharts are useful for summarizing large datasets and identifying patterns or trends.
• Charting and visualization: Excel offers a range of charting and visualization options. While not as advanced as Power BI, Excel’s charts and graphs are sufficient for many basic to intermediate data analysis tasks. You can create bar charts, line graphs, and scatter plots to visualize data trends and relationships.

Power BI vs. Excel: Which One to Choose?

Choosing between Power BI vs. Excel ultimately depends on your data and analysis requirements.

Power BI is proficient in handling complicated data sets, creating interactive reports, and facilitating collaboration. If the criteria you need encompass:

• Large amounts of data from various origins are subject to frequent modifications.
• The necessity to spot patterns, trends, and irregularities within extensive data sets.
• Interactive visualizations allow for the exploration of various perspectives within your data.
• Communicating information with colleagues within your company.

Power BI is the optimal choice. Its capacity to simulate intricate data sets and generate interactive visual representations will support deeper examination.

Excel is most effective for basic, impromptu examination of mid-sized data sets. If you need:

• Ability to customize the format and style of data to your liking
• A user-friendly spreadsheet interface that is easy to understand and navigate
• Ability to work well with other Office tools

Excel would be the better option. Excel may not have all the advanced analysis features of Power BI. However, it still provides a flexible, customizable, efficient platform for medium-scale data projects.

How to Create Visualization in Power BI?

Here is a step-by-step process of how to create a visualization in Power BI.

Stage 1: Logging in to Power BI

• Log in to Power BI.
• Enter your email address and click the “Submit” button.

• You are redirected to your Microsoft account.
• Enter your password and click “Sign in“.

• You can choose whether to stay signed in.

• Once done, the Power BI home screen will open.

Stage 2: Creating a Data Set and Selecting the Data Set to Use in Your Chart

• Go to the left-side menu and click the “Create” button.
• Select “Paste or manually enter data“.

• We’ll use the HR sample data below for this example.

Application Channels	Initial Screening	Conduct Interviews	Employee Onboarding	Total Candidates
Social Media	Short Listed	Final Interview	Hired	32
Social Media	Short Listed	Final Interview	Not Hired	400
Social Media	Short Listed	Knocked Out		800
Social Media	Knocked Out			1100
Company Career Page	Short Listed	Final Interview	Hired	20
Company Career Page	Short Listed	Final Interview	Not Hired	250
Company Career Page	Short Listed	Knocked Out		500
Company Career Page	Knocked Out			900
Events	Short Listed	Final Interview	Hired	5
Events	Short Listed	Final Interview	Not Hired	100
Events	Short Listed	Knocked Out		200
Events	Knocked Out			350
Paper Media	Short Listed	Final Interview	Hired	3
Paper Media	Short Listed	Final Interview	Not Hired	80
Paper Media	Short Listed	Knocked Out		135
Paper Media	Knocked Out			700
Employee Referrals	Short Listed	Final Interview	Hired	10
Employee Referrals	Short Listed	Final Interview	Not Hired	70
Employee Referrals	Short Listed	Knocked Out		80
Employee Referrals	Knocked Out			110
Direct	Short Listed	Final Interview	Hired	25
Direct	Short Listed	Final Interview	Not Hired	150
Direct	Short Listed	Knocked Out		425
Direct	Knocked Out			600

• Paste the above data table in the Power Query Window.
• Select the “Create a dataset only” option.

• On the left-side menu, click “Data Hub“.
• Power BI populates the data set list. (If you have not created a data set, refer to the Error! Reference source not found section).

• Click on the “Create a report” dropdown.

Stage 3: Adding the Power BI Sankey Diagram Extension by ChartExpo

• Creating the Sankey Diagram requires us to use an add-in or Power BI visual from AppSource.
• Navigate to the right side of the Power BI dashboard.
• Open the Power BI Visualizations panel.
• Click the ellipsis symbol (…) as highlighted in the diagram below. This will import the Power BI Sankey Diagram extension by ChartExpo.
• The following menu opens:
• Select the “Get more visuals” option.

• The following window opens.
• Enter “Sankey Diagram for Power BI by ChartExpo” in the highlighted search box.
• You should see the “Sankey Diagram for Power BI by ChartExpo”, as shown in the image below.

• Click the Sankey Diagram option and click the “Add” button.

• You can now see the Sankey Diagram for Power BI by ChartExpo icon in the visualizations list.

Stage 4: Drawing a Sankey Diagram with ChartExpo’s Power BI extension

• Select the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel.
• The following window opens in the report section of your dashboard:

• Select the fields to use in your Sankey chart here.

• One thing is important when you select the fields: you must follow the sequence below:
  • Application Channels
  • Initial Screening
  • Conduct Interviews
  • Employee Onboarding
  • Total Candidates
• You’ll be asked for a ChartExpo license key or email address.

• Add the key under the Visual section.

• After adding the key, you can see a chart for your data.
• You can add the top header text in the chart under the General section as follows:

• After that, you can click on Visual set, the number, and enable the options below:

• You can change the “Node Font Style” as follows:

• You can change the “Level Font Style” as follows:

• You can see the Level Labels as follows:

• You can change the Node color by following the steps below:

• Now we show you “How to change Level 1 color,” and then by following this, you can change the node color of other levels as follows:

• Below is the final look of the HR Dashboard in Power BI using ChartExpo after changing the nodes’ color.

Insights

• Social Media is the top application platform with the largest candidate pool (1100).
• Company Career Page follows with 900 candidates.
• Events and Paper Media have fewer candidates, with 350 and 700, respectively.
• 600 candidates have applied directly.
• Employee Referrals contain 110 potential candidates.
• Social media candidates dominate the shortlist (32 hired, 400 not hired).
• The Company Career Page and Direct applications also receive a large number of shortlisted candidates.

What are the Pros and Cons of Power BI vs. Excel?

Power BI vs. Excel are powerful tools within the Microsoft ecosystem, each with distinct advantages and disadvantages. Choosing the right tool depends on specific needs and capabilities. Here’s a detailed comparison of the pros and cons of Power BI vs. Excel.

Pros and Cons of Power BI:

Pros:

• Advanced data visualization: Power BI offers a wide range of interactive and dynamic visualizations, enabling you to explore data deeply and uncover insights.
• Data modeling capabilities: It excels in data modeling, allowing easy creation of complex relationships and transformations.
• Scalability: Power BI is designed to handle large datasets efficiently, making it suitable for businesses with extensive data needs.
• Integration and connectivity: Power BI integrates seamlessly with various data sources and other Microsoft products, facilitating real-time data analysis and collaboration.

Cons:

• Learning curve: Power BI’s learning curve is steeper than that of Excel. This is especially true for its use of DAX for advanced calculations and data modeling.
• Cost: Power BI requires a subscription for full functionality, which can be a significant expense for some businesses.
• Resource intensive: Power BI can be resource-intensive, requiring substantial processing power and memory to handle large datasets and complex models.

Pros and Cons of Excel:

Pros:

• Ease of use: Excel is known for its user-friendly interface, making it accessible to users of all skill levels.
• Versatility: Excel is highly versatile. You can use it for a variety of tasks, from simple data entry to complex financial modeling.
• Integration with Office Suite: Excel integrates with other Microsoft Office applications, such as Word and Outlook, enhancing productivity and ease of use.

Cons:

• Limited scalability: Excel can struggle with performance issues when dealing with extensive datasets, leading to slower processing times.
• Data integrity: Manual data entry and manipulation in Excel can lead to errors, compromising data integrity.
• Version control: Managing multiple versions of Excel files can be challenging, leading to potential inconsistencies and difficulties in collaboration.

FAQs

Is Power BI going to replace Excel?

Power BI is not going to replace Excel. Each tool has unique strengths. Power BI excels in advanced data visualization and large-scale analysis. Conversely, Excel is ideal for data entry, basic analysis, and financial modeling. Both tools complement each other.

Should I learn Excel first or Power BI?

Learn Excel first. It provides foundational skills in data manipulation and analysis. Excel’s user-friendly interface makes it easier to understand basic concepts. Once proficient, learning Power BI will be easier, enhancing your data visualization and advanced analytics skills.

Is Power BI better than Excel?

Power BI is better than Excel for advanced data visualization and handling large datasets. It offers interactive dashboards and real-time data analysis. However, Excel is superior for data entry, basic analysis, and financial modeling. Each tool has its strengths.

Wrap Up

Power BI vs. Excel differ in their distinct strengths and capabilities. Each caters to different aspects of data analysis and visualization.

Power BI excels in advanced data visualization. It offers interactive dashboards and dynamic charts, allowing deep data exploration and insight discovery. Its ability to handle large datasets efficiently makes it ideal for businesses with extensive data needs.

On the other hand, Excel is preferred for its versatility and ease of use. It remains a go-to tool for data entry, basic analysis, and financial modeling tasks. Excel’s familiar interface and wide range of functionalities make it accessible to users of all skill levels.

Power BI provides robust data modeling capabilities and supports complex transformations and calculations. Excel offers powerful tools for data manipulation and organization. Its Pivot Tables and formulas are valuable for summarizing and analyzing data in a tabular format.

Power BI seamlessly integrates with various data sources and Microsoft products, facilitating real-time data analysis and collaboration. Excel integrates well with other Microsoft Office applications, enhancing productivity and ease of use within the Office suite.

Ultimately, the choice between Power BI vs. Excel depends on the specific needs of the task. Power BI is preferred for advanced data analysis and visualization, especially when handling large datasets requiring interactive dashboards. Conversely, Excel remains indispensable for simpler tasks, data entry, and basic analysis.