Using Gemini for Data Analytics: Use Cases, Limitations, and Best Practices

Data analysis has undergone a transformation over the last few years thanks to an influx of AI-driven tools. Google's Gemini, is a versatile large language model (LLM), that has emerged as a powerful contender in the AI landscape. With advanced capabilities in understanding and generating human-like text, Gemini offers exciting possibilities for applications across multiple use cases. It integrates with platforms like BigQuery and Google Sheets, allowing users to analyze data through natural language queries. This article focuses on Gemini's potential in data analysis, exploring both its promising capabilities and important limitations.

Gemini is a multimodal AI model designed to process and understand various types of input, including text, images, and data. As part of Google's AI ecosystem, Gemini offers several advantages for data analysis tasks through its integration with Google's suite of tools and services. Gemini can be utilized in several ways, depending on your data environment:

• Gemini in BigQuery: This integration helps with data exploration, SQL query generation, and building machine learning models. You can ask questions like "How do I get started with BigQuery?" or prompt it to generate SQL queries for specific analyses.
• Gemini for Google Sheets: Access it via the "Ask Gemini" feature to automate data entry, create financial models, and generate charts. It's ideal for summarizing data and creating visualizations directly in spreadsheets.
• Standalone Gemini: Upload files (CSV, Excel files, Google Sheets, up to 10 files, 100 MB each) and interact via natural language, making it easy to analyze data without additional tools.
  
  The following table summarizes the key methods and their capabilities:

Gemini brings practical, hands-on benefits to the data analysis process. By combining conversational AI with powerful analytical capabilities, it enables a range of use cases in business intelligence, automation, predictive analytics, and visualization. Here are some of the key ways teams are using Gemini to supercharge their data work:

Gemini allows users to query data using everyday language, turning tools like Google BigQuery, Looker, and Google Sheets into conversational BI platforms​. Instead of writing SQL or navigating complex dashboards, a marketer could ask, “Which campaign had the highest ROI last quarter?” and Gemini will fetch the answer. This natural language interface means insights are no longer limited to those with technical skills – data becomes democratized across the organization​. In practice, Gemini can even generate the SQL queries for you under the hood. Simply describe the data or relationship you want to analyze, and it will produce the appropriate SQL code​, letting non-technical users tap into databases directly.

With its advanced AI capabilities, Gemini can assist in uncovering trends and making forward-looking predictions. It can scan historical data for patterns and answer questions like, “What factors most influenced our Q4 sales, and how might they change next quarter?” By identifying trends in past data, generative models like Gemini (and its peers) help users perform a form of predictive analytics​. For instance, a growth team could ask Gemini to analyze user behavior data to find early signs of churn or to forecast next month's website traffic based on historical trends. While it’s not a full replacement for specialized statistical tools, Gemini brings predictive insights to anyone who can ask a question. It’s like having an analyst that highlights patterns and anomalies you might otherwise miss.

Gemini doesn’t just spit out numbers or text – it can also help visualize data and create compelling reports. If you ask for a chart or graph, Gemini can generate one on the fly, translating your description into a visual (e.g. “Show me a line chart of monthly revenue vs. target”). In fact, one of Gemini’s features is interactive visualization through simple dialogue: you describe what you want to see, and it quickly produces the chart or image for you​. This is incredibly useful for executives who want to see the data trend without fiddling with chart settings. Moreover, Gemini can go a step further by generating slide decks and summaries of your analysis automatically. After analyzing data, it could produce a brief report or presentation highlighting key findings and insights, ready to share with your team. This blend of analysis + storytelling means faster turnarounds from data to decision-making.

To make these concepts more concrete, let’s look at some real-world examples and scenarios where Gemini is being used to drive data insights. Gemini can be effective for various data analysis tasks, with performance depending on data quality and query clarity.

Tools: Gemini Advanced with data upload functionality

Dataset: Marketing campaign performance data with metrics like impressions, clicks, and conversions

When analyzing marketing campaign data, Gemini demonstrates strong capabilities in identifying performance trends and correlations between different metrics. It can effectively compare campaigns across different channels and time periods, providing actionable insights about which strategies are most effective.

However, while Gemini generally provides accurate interpretations of straightforward metrics, its recommendations sometimes lack depth and specificity to particular business contexts. The suggestions tend to be generic rather than tailored to specific industry benchmarks or company goals.

Tools: Gemini Advanced with Python code generation

Dataset: Financial statements and market performance data

Gemini shows impressive capability in financial data analysis, particularly when generating code for more complex calculations. It can effectively calculate key financial ratios, identify trends in revenue or profit margins, and compare performance against industry benchmarks.

A notable strength is Gemini's ability to generate Python code that leverages libraries like Pandas and Matplotlib for more sophisticated analysis. However, users must verify all calculations, as occasional computational errors can occur, particularly with complex formulas or large datasets.

Tools: Gemini Advanced with text processing capabilities

Dataset: Customer reviews and feedback from multiple channels

Gemini excels at sentiment analysis and theme extraction from customer feedback. It can effectively categorize feedback into topics, identify common complaints or praise, and track sentiment changes over time.

Its capability to handle mixed structured and unstructured data is particularly valuable for customer feedback analysis. However, like other LLMs, Gemini occasionally misinterprets nuanced sentiment or sarcasm, highlighting the need for human oversight when analyzing subjective content.

In general, Gemini performance is strong when integrated with Google tools, but users should verify results, especially for complex analyses, as accuracy can vary, and AI is prone to hallucinations.

While Gemini offers exciting possibilities, it’s not a magic wand – there are important limitations and challenges to be aware of when using AI for data analysis. Here are some of the key issues and risks:

• Accuracy and Hallucination: Like other LLMs, Gemini can produce AI hallucinations - generating plausible-sounding but incorrect information. When analyzing data, this can result in erroneous interpretations or conclusions that appear convincing but are factually wrong.
• Data Constraints: Limits on file uploads (10 files, up to 100 MB) can restrict analysis of large datasets. Large datasets may need to be summarized or sampled before analysis, potentially missing important details or patterns present in the full dataset.
• User Input Dependence: The quality of analysis depends on how well queries are phrased, with ambiguous questions leading to suboptimal results.
• Limited Analytical Sophistication: While Gemini can perform basic statistical analysis, it lacks the depth and sophistication of dedicated statistical software. Complex statistical methods, hypothesis testing, and advanced regression techniques are better handled by specialized tools.
• Context and Domain Knowledge: Gemini lacks deep domain-specific knowledge unless explicitly provided in the prompt. This limits its ability to interpret data within specialized business contexts or apply industry-specific benchmarks and standards.
• Lack of Data Integrations: Unlike dedicated BI platforms, Gemini doesn't offer direct connections to data sources like databases, APIs, or business applications. Data must be manually prepared and uploaded, creating extra steps in the analysis workflow.

Performance: Both Gemini and OpenAI’s ChatGPT belong to the new wave of generative AI that can assist with data analysis, but there are key differences. ChatGPT became famous for its conversational abilities, and with the ChatGPT Plus subscription, it introduced an Advanced Data Analysis (formerly Code Interpreter) feature that allows data file uploads and analysis​. In practice, ChatGPT has shown it can generate insights, write Python or SQL code, and even create charts for users​.  OpenAI’s GPT and reasoning models typically top many LLM benchmarks, demonstrating strong logical reasoning and broad knowledge coverage. Gemini, while competitive in some tasks, sometimes lags behind in tasks requiring intricate reasoning, code generation, or highly specialized domain knowledge.

Integration: Gemini is directly integrated into Google's ecosystem, which gives it a unique edge. It plugs into Google Workspace and Cloud services (like BigQuery, Sheets, Looker) rather than being a standalone chat on a website​. This means Gemini can natively access your data in those platforms and output results where you work, whereas with ChatGPT you often copy-paste or use plugins. Gemini is also multimodal (handling text, images, etc.) and tuned for data tasks, backed by Google’s latest research updates​. 

Accessibility: Gemini’s basic features are free to anyone with a Google account, while ChatGPT requires a paid plan for advanced data analysis. Gemini offers a premium tier (Gemini Advanced at ~$20/month) to enable file uploads and more power, with a 30-day free trial available​.

Efficiency: Conventional methods (SQL queries, Excel/Sheets pivot tables) remain the gold standard for control and transparency. Generative AI can automate repetitive tasks, but human oversight is crucial to ensure data integrity. Traditional data analysis often involves a patchwork of tools like Excel, SQL databases, and programming notebooks. A data analyst might manually write queries, export CSV files, build pivot tables, and produce charts by hand. This process not only requires technical skill but can be slow and labor-intensive. Gemini fundamentally changes this workflow by handling many of those steps through conversation. Instead of writing 10 lines of SQL and Excel formulas to figure out a trend, an executive could simply ask Gemini a question and get the answer with relevant numbers. This saves time and reduces the chance of human error, since the AI automates the execution of the steps.

Reliability: While human error still exists in traditional workflows, AI “hallucinations” can be less predictable. Gemini users should cross-check outputs against known data or established dashboards—particularly if the analysis is business-critical.

Accessibility: team members who aren’t fluent in SQL or statistics can still query the data. In essence, Gemini takes what used to be a multi-step, technical process and turns it into a one-step ask-and-get model. However, it’s worth noting that traditional methods still have their place, especially for complex, sensitive analyses that require deep expertise and validation. Gemini should be seen as augmenting human analysts, not completely replacing the rigors of traditional analysis when they’re needed.

Flexibility: Business intelligence (BI) platforms like Tableau, Power BI, or Google Data Studio are common in many companies for tracking KPIs and creating dashboards. These tools are powerful for visualization and routine reporting, but they often require a skilled analyst to set up data models and design the dashboards. Plus, they are usually limited to pre-defined views of data. Gemini approaches BI from a different angle – instead of static dashboards, it offers dynamic, on-the-fly analysis. For example, if a growth team wants to explore a new question that isn’t on the dashboard (“Which region had the fastest growth in online sales this week?”), traditional BI might require an analyst to create a new report or edit the dashboard. With Gemini, the team member can just ask that question in plain language and get an immediate answer or visualization.

‍User Interface: Gemini can provide explanations and context in natural language alongside the numbers, something typical dashboards don’t do. That said, BI tools are proven and trusted for standardized reporting, whereas Gemini’s insights might vary based on how questions are asked. In fact, specialized AI analytics solutions like Narrative BI can offer deeper, more tailored insights than a general chatbot​.

Gemini offers a transformative approach to data analysis, particularly through its integrations with Google tools, making it accessible for non-technical users. However, its limitations, such as potential inaccuracies and data constraints, necessitate human oversight, especially for critical tasks. To maximize Gemini's effectiveness for data analysis, consider these best practices:

1. Provide Context: Include detailed information about your data, including column definitions, business context, and specific questions you want answered.
2. Validate Outputs: Always verify Gemini's interpretations against the raw data, especially for critical business decisions, to mitigate hallucination risks.
3. Prepare data: Ensure data is well-prepared and queries are clear to maximize accuracy.
4. Use Code Generation: For more complex analysis, ask Gemini to generate Python or R code that you can review, modify, and execute in a controlled environment.
5. Combine with Specialized Tools: Use Gemini alongside dedicated data analysis platforms rather than as a complete replacement.

Iterate on Prompts: Refine your queries based on Gemini's responses to extract more valuable insights.

The integration of AI models like Gemini into data analysis workflows represents an evolving approach to business intelligence. While dedicated analytics platforms offer more robust infrastructure for enterprise-scale data analysis, Gemini provides accessibility and natural language interaction that can democratize data insights.

Google continues to enhance Gemini's capabilities, and future iterations will likely address current limitations. The most promising path forward appears to be integration between specialized analytics platforms and LLM interfaces, combining the strengths of both approaches.

Gemini offers significant value for:

• Quick exploratory analysis of small to medium datasets
• Generating starter code for more complex analysis
• Analyzing mixed media formats (text, tables, and images together)
• Making data analysis more accessible to non-technical team members

However, for mission-critical analysis, regulated industries, or working with very large or complex datasets, Gemini should be viewed as a supplementary tool rather than a replacement for dedicated analytics solutions.

For business-critical decisions, consider specialized data analytics platforms with AI capabilities like Narrative BI, where features like automated monitoring, hallucination mitigation and direct source connections provide significant advantages over general-purpose LLMs like Gemini.