GenAI Data Analysis: Google Colab’s Gemini vs. Excel Copilot — Which One Should You Use for Conversational Data Analytics?

I’ve done some basic data analysis over the years on the financial side and assessing the performance of systems under load – it’s always been a manual exercise and then I strayed into the world of Jupyter Notebooks and developed a whole new respect for people who live and breath in the data analysis world. To help with that, AI assistants are now integrated into the data tools you use daily, helping to reveal the stories and patterns in your data with natural language commands. Two of the tools I have been working with lately are Google‘s ‘Analyse with Gemini’ in their Colab Notebooks and Microsoft‘s Copilot in Excel with it’s ‘Advanced Analysis’ prompt. These tools, while on the surface appear similar, do have differences that make them suitable for different types of users.

What is it?

At its core, AI-powered data analysis is about having an intelligent assistant that can understand your data and help you explore it. Instead of manually writing complex formulas or code, you can now ask questions in plain English, like “Show me the quarterly sales trends for the last two years” or “What are the key drivers of customer churn in this dataset?”. These AI tools will then generate the necessary analysis, visualisations, and even predictive models to answer your questions.

Analyse with Gemini is embedded in Google Colab, a Python-based notebook environment. It uses natural language prompts to generate full Python notebooks, complete with data cleaning, visualisations, and even machine learning models. It’s like having a junior data scientist on call.
Copilot in Excel is deeply integrated into Microsoft Excel. It allows users to ask questions in plain English and receive insights, charts, PivotTables, and formulas. It’s designed for business users who want fast, actionable insights without writing a single line of code. It’s ‘Advanced Analysis’ prompt has a similar feel in the output it generates to the Colab Notebook.

For examples, screenshots and experiences, take a look at the end of this article.

What does it mean from a business perspective?

The integration of AI into data analysis tools like Google Colab and Excel’s Copilot has several opportunities for organisations:

Increased Efficiency and Productivity: Repetitive and time-consuming data tasks, such as cleaning, sorting, and creating standard reports, can be automated. This frees up valuable time for your team to focus on higher-level strategic thinking and decision-making.
Speed – Deeper and Faster Insights: AI can quickly identify patterns, trends, and outliers in your data that might be missed by human analysts. This ability to rapidly surface key insights allows for more agile and informed business strategies.
Accessibility – Empowerment of Non-Technical Users: Business analysts, marketers, and other professionals who are comfortable with Excel but not necessarily with coding can now perform more advanced data analysis. This fosters a more data-driven culture throughout the organisation.
Enhanced Data-Driven Decision-Making: With more accessible and understandable data analysis, businesses can move towards a culture where decisions are consistently backed by data, leading to better outcomes and a stronger competitive advantage.
Improved Collaboration: Both platforms offer collaboration features, allowing teams to work together on data analysis projects, whether in a shared Excel workbook or a collaborative Colab notebook.

What do I do with it?

So, how do you navigate this new functionality and choose the right tool for your needs? Here are some concrete actions to consider:

For the Business Analyst and Excel Power User: If your work primarily revolves around spreadsheets (and within the M365 environment) and you need to quickly generate insights, create pivot tables, build charts for reports and do Advanced Analysis, Copilot in Excel is your ideal starting point. Its deep integration within the familiar Excel environment makes the learning curve gentle and the productivity gains immediate.
For the Data Scientist and Python Enthusiast: If your role involves more complex data manipulation, statistical modelling, and machine learning, ‘Analyse with Gemini’ in Google Colab is the more powerful choice. It leverages the full potential of the Python data science ecosystem, offering unparalleled flexibility and customisation. To be honest, this option is that friendly to use, even if you are just interested, it is worth a try – there is no real learning hurdle to get started, you’ll absorb some Python along the way and develop sympathy for Data Analysts.
Start with a Pilot Project: Choose a small, well-defined project to test the capabilities of either tool. This will allow you to understand its strengths and weaknesses in the context of your own data and business questions before a wider roll-out.
Invest in Training and Exploration: Encourage your team to experiment with these new features. Both Google and Microsoft provide documentation and tutorials. A small investment in learning can unlock significant long-term value.

The choice between Gemini in Colab and Copilot in Excel isn’t about which tool is definitively “better,” but rather which tool is the best fit for your specific needs and workflow. Copilot in Excel excels at bringing the power of AI to the familiar environment of the spreadsheet, making advanced analysis accessible to a broader business audience. On the other hand, Gemini in Colab provides a robust, more code-centric platform for data scientists and analysts who require the depth and flexibility of Python for their analyses.

The best way to decide is to start exploring. Pick the tool that aligns with your current workflow and start asking questions of your data (and don’t be afraid of the Colab Notebooks). You might be surprised by the answers you uncover.