Excel vs Power BI – Which is a Better for Decision Making?

In today’s fast-paced business landscape, making informed decisions is essential for the success of organizations. Understanding popular business intelligence tools and their unique features is crucial for harnessing their true potential. Both MS Excel and Power BI offer impressive capabilities regarding data analysis and decision-making. However, determining the best choice depends on specific requirements. This article will delve into the strengths and specific use cases of MS Excel vs Power BI, helping you decide which tool to choose for your business needs.

Excel vs Power BI – Overview

What is MS Excel?

Microsoft Excel is a powerful and user-friendly tool that enables data organization, manipulation, analysis, and visualization. It offers essential functions for data processing, cleaning, and transformation. With built-in features like data analysis tools, pivot tables, and charts, Excel is valuable for data analysis and visualization. It also provides decision-making capabilities through features such as Goal Seek, Solver, Decision Trees, and Sensitivity analysis, allowing users to make informed decisions based on summarized data. Power pivot and query play a significant role in decision-making by facilitating data modeling and transformation. Excel is a versatile tool that empowers users to analyze data and make effective decisions.

What is Power BI?

Power BI is another decision-making table offered by Microsoft capable of competency with Excel. It serves similar functions to Excel, such as data transformation, decision-making, connection to different data sources, integration, visualization, and presentation. Power BI has its characteristics, such as the ability to create dynamic and interactive reports and real-time dashboards. It also involves data modeling, forming relations between different data, and searching for dependencies within the data.

Besides, the data querying through Power Query is an intriguing feature allowing data processing actions such as cleaning. Shaping and transforming data through an intuitive graphical interface. As a Microsoft product, it also provides core features and services as a comprehensive and user-friendly business intelligence tool.

Best Features of Excel

1. Spreadsheet for Organizing Data

1. Sorting and Filtering: Easily organize data by using sorting and filtering functions.
2. Grouping and Outlining: Group rows or columns and collapse/expand them for improved readability.
3. Freezing Rows or Columns: Keep headings static while scrolling for easier comparison.
4. Custom Formatting: Customize cells with different colors, borders, and font styles.
5. Formulas and Functions: Perform data manipulation using a variety of formulas and functions.
6. Data Cleaning and Transformation: Split or merge cells, remove duplicates, and perform tasks like concatenation.
7. Power Query and Power Pivot: Utilize advanced data transformation techniques.
8. Conditional Formatting: Highlight specific data based on defined criteria.
9. What-If Analysis: Analyze different scenarios to explore potential outcomes.
10. Sensitivity Analysis: Evaluate the impact of changing variables on data and decision-making.

2. Data Manipulation Techniques in Excel

1. CONCATENATE: Combines data from different cells into one cell using the CONCATENATE(text1, text2, text3, text4,…) formula.
2. DAYS: The DAYS(end_date, start_date) formula calculates the days between two dates.
3. NETWORKDAYS: Automatically excludes weekends when calculating the number of working days using the NETWORKDAYS(start_date, end_date, holidays) formula.
4. LEN: Determines the number of characters in each cell using the LEN(text) formula.
5. AVERAGEIFS: Calculates the average of multiple parameters using the AVERAGEIFS(avg_rng, range1, criteria1, range2, criteria2,…) formula.
6. COUNTA: Counts the number of non-empty cells in a range using the COUNTA() function.
7. HLOOKUP: Searches for a value in the top row of a table and returns a corresponding value from a specified row using the HLOOKUP() function.
8. VLOOKUP: Searches for a value in the leftmost column of a table and returns a corresponding value from a specified column using the VLOOKUP() function.
9. COUNTIFS: Counts the number of cells that meet multiple criteria using the COUNTIFS() function.
10. IF: Performs logical tests and returns specified values using the IF() function based on the conditions.

Also Read: Learn all the Excel Formulas, Applications and Shortcuts with our Free course

3. Visualization Options in Excel

Best Features of Power BI

1. Data Importing and Transformation Capabilities of Power BI

• Data importing capabilities:
  • Connection with various data sources (cloud services, files, databases, online services)
  • Built-in connectors
  • Data query function for direct connection to data sources
  • Folding query functions for efficient data handling
• Data transformation capabilities:
  • Query editor with a visual interface
  • Filtering, merging, sorting, pivoting, splitting, and transforming data
  • Built-in functions for data manipulation
  • Cleaning tasks (correction, duplicate deletion, etc.)
  • Advanced transformations (custom column creation using M language, appending multiple queries, splitting columns)
  • Shaping and structuring data
• Query dependencies and refresh option:
  • Set the order of query execution
  • Automatic data refresh schedules
  • Specify the transformation method for automated execution.

2. Building Interactive Dashboards in Power BI

Building dashboards involves the creation, customization, and organization of visualization. The design follows enhancing interactivity through the addition of slicers and filters. It must follow a drill-through configuration to ease the navigation to detail reports. Remember the addition of bookmarks before testing and validation. The critical aspects of building interactive dashboards, except for visualizations, include data binding by selecting appropriate fields and measures. The drill-through actions, along with cross-filtering and highlighting, are also significant.

Building reports in Power BI begins with report pages where each page contains the same or different visualizations for data structure presentation. The layout and formatting must be customized to add variety and uniqueness by adjusting the design, color, fonts, backgrounds, and schemes to match the aesthetics. Power BI allows report interaction, drill-through actions, buttons or links for additional report navigation, and effortless and time-saving report formation.

3. Advanced Analytics Features in Power BI

Benefits and Limitations of Excel for Decision Making

Familiarity and accessibility due to Excel’s extensive usage are among the most common perks of its use. It couples with effortless data organization, analysis, and visualization through a user-friendly interface and a categorized options display. The customization features, collaboration, real-time editing option, and sharing with import facility make it the preferred tool.

However, the lack of data integrity and version control limits the check for accidental overwriting, unauthorized changes, and data entry errors. Handling massive datasets and limitations in analytical capabilities for advanced and very complex operations are also problematic.

Benefits and Limitations of Power BI for Decision-making

Power BI aids individuals in decision-making through interactive data visualization options and self-service analytics that eliminate the dependency on IT or data analysts. The user-friendly interface, real-time data monitoring, data consolidation, and mobile access are among the top benefits of using Power BI for decision-making.

Yet, the limitations of the tool remain. The advanced features require learning and training, which challenges non-technical users. The data connectivity is not universal and hence requires custom connectors at some points. It does not have in-built data security. Instead, it is dependent on organizations. The refresh can be automatic, but the larger datasets or complex transformations are time-consuming, hindering the functionalities. It requires paid licensing for advanced features.

Key Differences Between Excel vs Power BI

Scalability and Handling of Large Datasets

Excel is preferred for regular or small datasets, while Power BI is specifically designed for scalability and efficient data analysis. It comprises an in-memory analysis engine, partitioning options, support for DirectQuery, and data compression techniques, making it superior to Excel. Excel depends on system memory, and calculation speed is relatively slow for larger datasets.

Real-time Data Connectivity and Automated Data Refresh

Real-time data connectivity is more efficient in Power BI as the former requires manual refreshing. Power BI is directly connected to real-time data sources and supports real-time streaming. It also provides scheduled refresh options suitable for routine datasets compared to complex ones.

Interactive Visualizations and Drill-down Capabilities

Excel provides numerous depictions for charting and interactivity through lines, bars, pie charts, and plots. The interactivity and manipulation of chart elements and drill-down functionality for summarization are also possible. But, Power BI is curated with specialized and comprehensive feature sets, provided through cross-filtering, drill-down and drill-through, tooltips and data exploration, and Q&A Natural Language Query.

Collaboration and Sharing Options

Excel allows file sharing through email, file-sharing platforms, and network drives. It also allows multiple user access for writing and edition. There are track changes and co-authoring features for better usability. Power BI allows sharing of dashboards and reports via a cloud-based platform. Similar to Excel, it permits workspace collaborations, however, with roles like administrators, viewers, and contributors. The tool is also curated for version control or tracking the changes, along with comments and discussion features.

Integration with Other Data Sources

Excel allows importing different file formats such as CSV files, web services, etc. The integration with online platforms is limited to SharePoint and OneDrive. Power BI provides data connectivity options with SQL Server, Google Analytics, Salesforce, Axure, and SharePoint. The integration here is possible with Azure, Dynamics 365, SharePoint Online, and other Microsoft products.

Learning Curve and Ease of Use

Excel is highly familiar and provides a user-friendly interface and a vast library of formulas and functions. The learning curve here is steep, which ease representing easy to start and learn advanced features. Power BI requires time to learn the data modeling concepts and create interactive reports and dashboards. The drag-and-drop interface provides more accessible functionality. The user-friendly interface is available for data transformation and cleaning; however, advanced features require additional learning and practice.

Excel vs Power BI Use Cases and Examples

Let us use some scenarios for better understanding the difference between power query and power pivot available in Excel vs. Power BI:

Excel Case Study

Let us assume you are a sales manager who wishes to analyze sales data and make informed decisions concerning sales strategies and forecasting. You will begin with data analysis of data obtained from different regions. Open the same in Excel and use the sorting and filtering functions to find the top-performing productions, sales representatives, sales, or other specific detail. You will have an insight into sales trends and patterns.

One can use power pivot vs. power query to create pivot tables for summarisation into categories followed by analysis via parameters such as period, customer segment, or product category. You can also use pivot tables. Go on for representation via charts and graphs. Subsequently, use forecasting functions to predict future sales according to historical data. Experiment with different scenarios and impacts using Goal Seek or What-if analysis. Combine the insights from each action, look for the improvement areas, resource allocation, alteration in sales strategies, and setting realistic sales targets for exponential business growth.

Power BI Case Study

Let us assume a retail company that aims to analyze sales data and improve inventory management decisions using Power BI. The problem is excess inventory levels, stockouts, and inefficient replenishment properties. The company should begin by importing data from the ERP system, inventory management, and sales database. Post this, they must create interactive dashboards to gain real-time insights with organized and easily interpretable visualizations.

They must begin the action by combining the sales data with inventory levels to recognize the high-demand products, slow-moving items, and potential stockouts. This will guide in identifying the patterns, decoding product demand, and optimizing inventory levels. Next, analyze the supplier performance, identify the delivery times and the reason for the delay or quality problems, and make data-driven decisions. Further, use Power BI for forecasting, demand planning, and drill-down function to analyze data at different granularity levels.

Factors to Consider in Choosing the Right Tool

Aiding in finding the difference between Excel and Power BI, we compare the factors to be considered in choosing the right tool for decision-making:

1. Nature and Volume of Data

Excel can handle small to moderately-sized databases with tabular nature. Power BI is created to handle voluminous data of any complexity. It can also take up data from multiple sources.

2. Decision-making Requirements and Complexity

The data requiring fundamental analysis and simple decision-making will be easily performed in Excel. Power BI is more suitable for advanced analytics, numerous categories of data requiring automated and updated visualization with forecasting and other functions.

3. Team Collaboration and Sharing Needs

Excel allows multiple sharing options along with real-time writing and editing options. It also provides tracking change options available under different Power BI names. Excel is more system friendly than Power BI, which is also suited for phones. The sharing options on Power BI are, however, limited.

4. Integration with Existing Systems and Data Sources

Considering Excel vs. Power BI, both can integrate with existing systems and data sources. Excel will require manual import and transformations, while Power BI will require a one-time setup for automatic integration and transformation functions.

5. Budget and Cost Considerations

Between Excel and Power BI, Excel is cost-effective due to the lack of requirement for additional licensing. Power BI provides limited features in the free version. Hence, the advanced features require other payments.

6. User Preferences and Familiarity

Users with Excel prefer the same when confused between Excel and Power BI. The learning curve here is steep compared to Power BI, where technical expertise is pre-requisite to handle the advanced functionality.

Conclusion

Excel vs Power BI is both business intelligence tools suited for decision-making. Though similar in numerous aspects, their capabilities differ in the availability of functionality and complexity. The prime usage of each is dependent on different factors such as complexity, volume, and nature of data. The type of analysis, complex or advanced or regular, also plays a crucial role in decisions concerning the same. Ultimately, it is wiser to analyze the objective and components of data for decision-making and leverage the strengths of both tools as per their suitability.

We hope you find Excel vs Power BI article helpful. Let us know your views in the comment section below. If you want to become a master of data visualization without writing a single line of code, then No Code AI program is the right choice for you. With this course, you can leverage Data Science and AI applications to make better decisions at work.

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