For years, analysts have relied on Excel for basic data analysis with its familiar formulas, pivot tables, and straightforward charts. But in today's big data era, an Excel spreadsheet just can't keep up. Analysts now need tools that allow deep data exploration, foster collaboration, and make it easy to visualize large, complex datasets.
If you're ready to break free from Excel's limitations, this guide will help you navigate the crowded data analytics market, identify the features that matter most, and embrace a modern business intelligence solution. Dive in to discover the top Excel alternatives for data analysts and their stakeholders.
What to retain: Excel’s core strengths
Many analysts stick with Excel for their primary data analysis because it's simple and familiar. While it lacks the advanced analysis capabilities and intuitive, user-friendly dashboards of modern tools, it does have some advantages:
- Simplicity and Customization: Excel is a simple, no-frills tool that a single analyst can customize to their own workflows.
- Accessibility: Every analyst understands how to use Excel, and if they don’t, the learning curve is shallow for the most common use cases.
- Compatibility: Pretty much every enterprise application allows CSV imports and exports from Excel.
- Adaptability: Analysts can emulate more advanced software by creating homegrown solutions in Excel, as long as the use case isn’t too complex.
What to rethink: Excel’s major limitations
When you compare Excel to other spreadsheet programs like Google Sheets, Zoho Sheets, or Apache OpenOffice Calc, the differences are minor. However, when you measure it against today’s top solutions for advanced data analysis, Excel's limitations become glaringly obvious.
- Data Handling: Excel can’t handle large datasets or combine different data types and formats.
- Advanced Analysis: Excel doesn’t allow for real-time analytics, machine learning, or any cutting-edge analysis.
- User Interface: It’s hard to track changes and comments in Excel’s user interface.
- Data Integrity: Analysts can’t build in guardrails that keep data clean and standardized.
- Security: Excel workflows aren’t secure enough for the modern tech stack.
Excel is still a go-to for quick calculations, personal use, and project management—it's accessible to nearly everyone. But for data-driven companies, Excel is becoming obsolete as an analysis tool.
Is Excel still relevant for data analysis?
The short answer is, kind of. Excel lingers in slower-moving industries or small, siloed teams of analysts. But for companies with data teams dedicated to understanding their operations, driving performance, and hitting KPIs, Excel is just a fallback tool. It's often used alongside business intelligence platforms, R and Python notebooks, and ETL solutions.
Excel vs Google Sheets: Collaboration and convenience
Google Sheets is one of the closest alternatives to Excel, offering a spreadsheet-based tool for storing, visualizing, and presenting data. It enables collaboration and sharing, and it’s compatible with Microsoft Excel files, including many keyboard shortcuts.
Sheets boasts nearly all the features of Excel and excels in real-time collaboration. However, it’s resource-intensive, consuming significant RAM, and offers fewer formatting options than Excel.
Excel vs SQL: Stepping up your data game
An effective entry-level alternative to Excel is to move data into a database and start using SQL for queries. Transitioning from Excel to SQL is straightforward; the language is simple and designed for querying relational databases, the same structure Excel uses.
Making this shift unlocks a new realm of mid- and enterprise-grade data solutions. With SQL, analysts can query much larger datasets, write and save complex queries, and create highly customizable visualizations for more robust reporting.
Excel vs Python: Power and flexibility
Excel power users, and their counterparts in Google Sheets and OpenOffice, know the program’s syntax inside and out. But to truly master Excel, you need to learn Visual Basic (VBA) for writing macros and performing advanced tasks. Even then, it falls short compared to what even basic Python can achieve.
Python is a powerful programming language that allows analysts to automate data workflows at scale, transform and normalize data across applications, and eliminate the tedious work of manually managing spreadsheets. It’s invaluable for connecting different systems and formats. Python is a cornerstone of the modern analyst’s toolkit, supported by a thriving community and used for tasks far beyond Excel’s capabilities, including machine learning, data mining, and developing applications and backend operations.
Due to its flexibility and widespread use, analysts often work in shared Python notebooks and reporting solutions, enabling their teammates to review their analyses and collaborate on data projects seamlessly.
Excel vs R: The statistical powerhouse
R, a language and environment for data analysis, is similar to Python in that it’s used to clean, model, visualize, and manage data at scale. It has a more narrow use case—R isn’t used to build whole applications like Python is—but is a gold standard for statistical analysis and a powerful tool for experimenting with regression models, neural networks, and other advanced machine learning techniques.
Together, Python and R cover everything an analyst might do in Excel and more.
Excel vs Business Intelligence (BI): The modern approach
BI tools are ideal for Excel users who don’t want to program or build complex models but need to track business KPIs across multiple departments and systems. These tools eliminate the need to manually collect, combine, and normalize data using Excel, and then import or paste charts into reports for stakeholders.
Modern business intelligence solutions excel where Excel falls short. They integrate data from all sources and solutions, presenting it in real-time, customizable reports and dashboards. Built for today’s massive datasets and enterprise workflows, BI platforms seamlessly connect with tools like Salesforce, Zendesk, Slack, and Jira, transforming data with various ETL solutions.
With data centralized in a single warehouse, platforms like Sigma enable deep analysis, data sharing across the organization, and self-serve capabilities for non-analysts to find answers without creating a backlog for the data team.
While some analysts might still rely on Excel, there’s no way to effectively connect and learn from business data without moving beyond spreadsheets for good.
The best Excel alternatives for advanced data analysis
Sigma
Sigma is a cutting-edge business intelligence platform that empowers teams to quickly uncover insights and answer questions using raw or modeled data. It boosts self-service analysis and data maturity, helping teams extract maximum value from their data stack.
Sigma pros and cons:
- Advanced analysis: Sigma offers a high technical ceiling, enabling data teams to perform advanced analysis while allowing business teams to use trusted data curated by experts. This creates unparalleled decision support for tackling complex questions.
- Ease of use: Sigma is user-friendly and backed by excellent customer support. It also provides a free, comprehensive SQL tutorial covering basic to advanced functions.
Sigma's top features:
- Easy deployment: Sigma is incredibly easy to deploy. Unlike other BI platforms, you can be up and running within minutes. Ninety percent of users choose Sigma over competitors for its ease of setup.
- Centralized data: Sigma provides a central hub for accessing useful, governed, and easy-to-interpret data, transforming organizational knowledge into a competitive advantage.
- Self-service reporting: Sigma empowers everyone to find their own answers, with data teams curating trusted data that inspires confident self-serve reporting.
Sigma's top benefits:
- Data literacy: Sigma enhances data literacy across the organization, enabling all users to explore data through collaborative analysis.
- Rapid insights: Gain rapid insights into complex business challenges with ad hoc analysis, adding query writers across the organization.
- Interactive dashboards: Create interactive dashboards, reports, and custom internal tools in hours or days.
- Scalability: Unlike legacy BI tools, Sigma offers a scalable platform that grows with your team, reducing the cycle time between data and business teams as your company expands.
Tableau
Tableau is a BI tool with a strong emphasis on data visualization. Companies use it to uncover straightforward business insights and share them with stakeholders.
Tableau pros and cons:
- Data visualization: Tableau excels at data visualization, making it easy for business users to navigate. However, while it provides simple tools for business users, it falls short in meeting the critical needs of data teams. Instead of offering reliable, dedicated support, Tableau users must depend on the community for assistance.
Looker
Now part of Google, Looker is a self-service BI tool that centralizes business and customer insights. With Looker, you can create dashboards and reports that tell compelling data stories, though its modeling layer can slow teams down. Looker’s SQL Runner feature allows analysts to explore data and export reports to non-technical teams.
Looker customer feedback:
- Data models: Looker offers a robust tool for creating data models. You can connect Looker to a SQL database and automatically generate LookML models.
- Reports and visualizations: Business users can create reports and visualizations, but their teams need to create and maintain LookML models for each table and run everything through the modeling layer.
Metabase
Metabase is an open-source business intelligence tool with an easy-to-use interface that lets users query databases without needing SQL knowledge. It enables organizations to effortlessly create dashboards and share insights.
Metabase pros and cons:
- User-friendly interface: Metabase’s intuitive design makes it simple for users to create queries and visualize data with minimal training.
- Open Source: As an open-source tool, Metabase offers flexibility and customization options for organizations.
- Limited advanced features: While it’s great for basic queries and visualizations, Metabase lacks the advanced features found in other BI tools, making it less suitable for complex data analysis.
Power BI
Power BI is Microsoft's business analytics tool, designed to transform data into meaningful insights through interactive visualizations and robust business intelligence capabilities.
Power BI pros and cons:
- Integration with Microsoft products: Power BI seamlessly integrates with other Microsoft products like Excel and Azure, making it a convenient choice for businesses already within the Microsoft ecosystem.
- Robust data analysis: Power BI offers powerful data analysis features, including the DAX language for complex calculations.
- Learning curve: While feature-rich, Power BI can have a steep learning curve for new users, particularly when navigating advanced features and the DAX language.
Moving beyond Excel
If your organization is still relying on Excel for anything beyond simple, small-scale tasks, it’s time to move on. Excel's limitations in collaboration, data integrity, and security make it unsuitable for unlocking your data's full potential.
Modern BI platforms like Sigma offer advanced capabilities that enable your team to connect, understand, and utilize data more effectively—whether they’re SQL experts or newcomers. Discover how Sigma can transform your organization’s data strategy by requesting a demo today.
