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From startups to multinationals, Microsoft Excel is at the core of countless organization’s reporting and financial planning and analysis (FP&A). IDC reveals that 64% of businesses still lean on spreadsheets for their financial operations.
Excel’s appeal lies in its simplicity and cost-effectiveness, but it’s not without limitations. However, more options are available to improve your finance team’s performance.
Learn more about Power BI, Excel, and FP&A solutions, dissecting their roles in optimizing your financial workflows. By contrasting their key features and unique benefits, we aim to guide you in choosing the most suitable tool for your financial needs.
The conventional method of manually copying and pasting data into Excel can be a struggle, especially when used for FP&A tasks that demand access to extensive external data. On average, medium-sized organizations gather data from more than 400 sources for business intelligence. Managing even a portion of these sources in Excel can lead to complexity and errors.
A more effective strategy in such cases is a data model-driven approach. This method uses Excel as a dynamic data container, automatically refreshing data, improving productivity, and providing deeper insights while minimizing errors. This leads to a critical question: Is Excel adequate, or are newer, self-service analysis tools like Power BI more effective?
Power BI stands out as a top choice. Recognized in Gartner’s Magic Quadrant Analytics, FP&A professionals favor it for its comprehensive self-service analytics capabilities. Power BI simplifies financial planning with its built-in features for creating visualizations and reports. Being cloud-based, it enables remote data access and easy sharing, offering a broader range of functions than Excel, especially for larger organizations.
However, some might still prefer Excel over Power BI. Excel’s strength lies in its customizability and flexibility, allowing bespoke formulas and charts. Additionally, Excel’s cost-effectiveness makes it a viable option for smaller businesses and individual users.
We will now dive into these two tools’ distinct features and differences.
In the Excel vs. Power BI debate, it’s essential to recognize that both tools can analyze various datasets and present the information in tables, visual charts, and graphs.
Excel is a powerhouse for creating complex tabular reports and performing ad hoc or quick computations, thanks to its wide range of functions and expressions available. By contrast, Power BI’s strength lies in its more extensive, interactive analytics capabilities.
Although Excel and Power BI share some components, such as the data modeling engine, they differ significantly. As a result, each software is better suited for specific scenarios, depending on the key capabilities they offer users.
Start your reporting or analysis by building a data model that optimizes the data for the analysis process and integrates different sources. The Power Query data modeling feature is available in Excel and Power BI but works with slight differences across the platforms.
One key distinction between Power BI and Excel is in the structure of the data model, which can have a significant impact if you’re working with a single table versus a comprehensive model with relationships between tables.
Excel (with the Excel add-in, Power Pivot) and Power BI (Analysis Service) support a full relational model, commonly called a star schema, where data is organized into a central fact table surrounded by dimension tables, forming a star-like structure. This capability sets Excel and Power BI apart from tools like Google Sheets, which need similar functionality.
The data model tables in Excel are limited by the number of Excel worksheet rows or the limitations of the local data model, Power Pivot, associated with the workbook. The data model in Excel is also limited to use within the workbook itself, without support for client-server functionality, meaning users cannot access the data model outside of the workbook.
For local requirements, Power BI Desktop also uses a local analysis services instance as the data storage engine in import mode. There are limitations similar to Excel in this mode, but in live connect DirectQuery mode, there are no such limits. Power BI Desktop allows users to deploy the data model to the Power BI service, enabling an authentic client-server architecture. This means that the model becomes accessible to other clients, such as other Power BI reports or even Excel.
Designing an effective data model can be a very complex task that requires significant expertise of the modeler, particularly in light of complex enterprise resource planning (ERP) systems and integrating other sources, such as customer relationship management (CRM) software or point-of-sale (POS) solutions, into one data model. Solutions like Acterys Apps offer readily available best practice data models, including Power BI and Excel reports, and can help streamline this process.
Excel has expanded its capabilities beyond simple copy-pasting to offer native connectivity for importing external data. With the Power Query feature, Excel can import data from various sources, including unstructured file formats, SQL and SAP databases, Azure Cloud and Power BI.
However, one notable difference between Excel and Power BI is the absence of the DirectQuery connection type in Excel’s standard Power Pivot data model. DirectQuery allows users to connect to a data source in real time, accessing the latest version of the data without physically moving it into the data model.
By contrast, Power BI supports connectivity with hundreds of databases, cloud services, flat files, apps, systems and more. It even supports DirectQuery, enabling users to create centralized dashboards that display business KPIs, market trends, forecast projections and other insights more visually and interactively.
While Excel’s Power Query has fewer connectors than Power BI, having a connector doesn’t automatically provide the ideal data model. Regardless of the tool chosen, you’ll still need to invest in designing the appropriate data model, integrating different data sources and establishing calculation logic.
Excel and Power BI use the Data Analysis eXpression (DAX) language to define calculation logic. DAX allows users to create customized formulas and perform aggregations, enabling the implementation of complex logic within and across tables.
The DAX language is a powerful feature shared by both platforms that offers lightning-fast calculation times, thanks to intelligent compression capabilities. This ensures efficient processing of calculations, even when dealing with large amounts of data.
In Excel, standard security features are available to protect files and their contents. This includes file-level protection through encryption with a password, granting read-only access and controlling user actions within a spreadsheet. However, more than these security measures may be required for highly sensitive data, such as personal or credit information.
In practice, it’s rare for appropriate security measures to be adequately implemented for Excel workbooks. If someone gains access to the workbook, they have access to all the data, as there is no concept of granular security. This can pose a significant risk, especially when dealing with large datasets that anyone with access to the workbook can easily access.
In Power BI’s import mode, the security situation is similar to Excel. However, a distinct advantage emerges when deployed on the Power BI Server. Power BI Server allows for implementing granular security rights using Row Level Security (RLS). This means that exact security permissions can be defined, providing more control over data access.
In Direct Query mode, Power BI does not store data internally. Instead, users must authenticate successfully with the underlying data source to access the data. This adds an extra layer of security, ensuring that only authorized users can retrieve the data.
Some companies require time-sensitive data reporting. For example, the NYSE or London Stock Exchange can’t afford to work with outdated data, as the market conditions change by the minute.
Unfortunately, there’s no easy way to refresh data in Excel automatically. One approach is to use Visual Basic for Applications (VBA) and code to automate repetitive processes. This can quickly become time-consuming and overwhelming, especially when large datasets and multiple sources are involved.
Power BI, however, offers built-in features that make real-time data updates easy and manageable. It supports multiple methods for refreshing data, including:
Excel is a valuable tool for storing and analyzing smaller datasets, typically with less than 1 million records or under a 500 MB workbook size. However, when handling larger datasets, Excel’s capabilities become limited. While Power Pivot can take significantly more data, such as hundreds of millions of rows, users may experience instabilities and performance issues, as Excel was not designed to be a big data store.
On the other hand, Power BI is specifically built to handle data far beyond the limits of Excel. Power BI can process billions of records, making it suitable for enterprise environments that operate in fast-evolving and dynamic markets.
Its robust compression algorithms intelligently reduce data size and cache it on import, allowing for efficient storage and processing of large datasets. This adds immense value in enterprise environments where handling big data is crucial for making informed decisions.
While a versatile tool, Excel needs more robust features for comprehensive data security and governance. Though Excel does provide password protection for workbooks and worksheets, it lacks more granular security features. Power BI offers cloud-based security provisions that allow IT administrators to improve organizational oversight. This includes role-based authentication, which enables organizations to control who can view and edit reports, and row-level security, allowing organizations to restrict access to specific reports or data model areas.
Regarding governance policies, Power BI again offers more comprehensive options than Excel. Power BI allows organizations to apply governance policies even when data is exported from the platform. This ensures that sensitive data remains protected outside the Power BI environment.
Power BI also enables the addition of sensitive content labels to critical data, providing an extra layer of security. It also includes session monitoring, allowing organizations to track user activity in real-time and identify suspicious or unauthorized actions.
Excel provides live collaboration and sharing capabilities, such as Excel Online in the web browser and integration with Microsoft Teams. While these features enable real-time collaboration, they can become challenging to manage in an enterprise setting. With numerous Excel files scattered across cloud storage platforms like SharePoint, extracting insights and effectively collaborating on projects can be difficult.
On the other hand, Power BI offers comprehensive collaboration options, mainly through the Power BI Service. Power BI Desktop and the ability to publish to the web are free, but they may not be ideal from a security perspective. The Power BI Service is the recommended solution for secure sharing with granular security controls.
The Power BI Service allows users to publish data to their desired destination, whether it’s single reports or dashboards. These can be shared within dedicated Workspaces, enabling a group of people to access and collaborate on the content simultaneously. The added benefit is that once you’ve published your content, the service will refresh the data automatically, saving you from resharing it repeatedly.
The Power BI Service also allows for embedding dashboards on online portals and websites. This feature provides viewers with interactive charts and graphs, allowing them to drill down into the data just as they would within the Power BI app.
Data written back in Excel is stored in the workbook by default, which isn’t optimal for analytics, reuse in other reports and tools or for security and data sharing purposes. Instead, a central database is a much better option. While Power BI also doesn’t offer write-back options by default, both solutions can be extended with add-on solutions like Acterys.
Acterys provides automatically generated data models from commonly used accounting, ERP, and SaaS solutions. It also offers tools to easily create data models from any data source. This data is stored in a standard SQL database in the cloud, which is already the standard in many organizations. Extensive planning, forecasting, and collaborative write-back options are available with eight custom visuals in Power BI or an add-on that works in the Excel Desktop version and Excel Online.
The key to success in today’s hypercompetitive markets is the ability to react to change and do it fast. Power BI allows setting notifications and alerts when specific metrics or KPIs go below, above, or hit a set threshold. These notifications can be received through the Power BI app and via email, enabling users to proactively address issues before escalating.
For example, if you want to cap a marketing campaign’s spending at $10,000, you can use Power BI to set up an email alert that notifies you when spend nears or reaches that threshold. This allows you to closely monitor your budget and evaluate areas where spending is high, enabling you to optimize your expenses accordingly.
By contrast, Excel doesn’t offer this feature, making it more challenging to monitor and manage critical KPIs and metrics proactively. Users may miss essential changes or exceed thresholds without timely intervention and setting notifications and alerts.
As a part of the Microsoft Office suite for decades, Excel has a significant advantage regarding user familiarity and ease of use. With multiple generations of users already accustomed to its interface, Excel offers a straightforward and intuitive experience. Users can quickly implement processes such as using mathematical formulas on datasets, building pivot tables, and creating graphs.
Power BI provides a drag-and-drop interface that simplifies building dashboards and reports. This intuitive interface makes it easy and enjoyable for users to create appealing visualizations.
However, to unlock the true power of both tools, you’ll need to dive deeper into advanced features, such as Power Query, setting up different visuals, and assessing the data refreshing and sharing processes.
The battle of Microsoft Excel vs. Power BI for FP&A teams is all about what your organization needs most. Power BI offers a range of built-in features that simplify financial planning for teams that prioritize high-level analytics and visualizations. Its cloud-based nature allows easy data access and sharing, making it suitable for recurring FP&A tasks.
However, Excel provides greater customization and flexibility, allowing financial teams to create customized formulas and charts. It’s also more accessible across platforms and affordable for small businesses and individuals.
To find the right solution for your business, consider factors such as the need for specialized financial functions, interactive analysis features, data governance, and KPI tracking. The most important choice is whatever helps your FP&A team unlock the power of streamlined financial planning and analysis.
If you want to automate data models from ERP, CRM, and SaaS systems or add write-back and planning capabilities to Power BI and Excel, request an onboarding session with our solution experts.
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