Microsoft Copilot Tools for Data Analysis: Report on Excel, Power BI, GitHub, and Microsoft 365 Copilots

TOPICS

• Excel Copilot

  • Getting Started

  • Formula and Chart Generation

  • Data Import and Table Creation

  • Text and Sentiment Analysis

  • Advanced Analysis with Python

  • Key Capabilities Summary

  • Use Cases

• Power BI Copilot

  • Getting Started

  • Ask Data Questions

  • Report Summarization and Narratives

  • Report Page Generation

  • Authoring Assistance

  • Semantic Modeling

  • Use Cases

• GitHub Copilot

  • Getting Started

  • Code Generation

  • Jupyter Notebook Commands

  • Data-Science Workflows

  • Copilot Chat and Explanation

  • Use Cases

• Microsoft 365 Copilot

  • Getting Started

  • Copilot Chat (General)

  • Analyst Agent

  • Researcher Agent

  • Cross-application Insights

  • Use Cases

• Comparing Copilot Tools

• Recommendations

  
  

Microsoft Copilot is an AI-powered assistant integrated across many Microsoft products. It lets you analyze data and generate insights using natural language and code.

The main Copilot tools for data analysis are Excel Copilot, Power BI Copilot, GitHub Copilot, and Microsoft 365 Copilot. Each has different strengths:

Excel Copilot (in Microsoft 365 Excel) helps create tables, charts, formulas and even Python scripts from plain-language prompts.

Power BI Copilot aids in building and exploring dashboards: it can write DAX formulas, suggest report layouts, and generate narrative summaries of your visuals.

GitHub Copilot is an AI coding assistant (now based on GPT-4.1) that suggests code and even entire notebooks for data processing, modeling, and visualization.

Microsoft 365 Copilot (aka Copilot Chat/Agents) provides a cross-app chat interface and specialized agents (like Analyst and Researcher) for higher-level analysis and data aggregation across your documents.

These tools work with natural-language queries and can dramatically accelerate data tasks for users of all skill levels. The sections below explain how to get started with each Copilot, cover advanced techniques, and highlight real-world use cases across domains like finance, marketing, business intelligence, and research.

Excel Copilot

Getting Started: Copilot in Excel appears as a chat pane on the Home ribbon (the Copilot icon) or next to any selected cell. To begin, open your Excel file (on OneDrive/SharePoint) and click the Copilot icon or a cell’s Copilot button. You can then type or speak prompts in plain English. For example, try prompts like “Create a bar graph showing the sales growth between Q2 and Q3.” or “Add a column showing percentage difference between columns A and C.” Copilot will generate the chart or formula and insert it into your sheet.

Formula and Chart Generation: Excel Copilot can autogenerate formulas and charts from language. It “generates formula-column suggestions, shows insights in charts and PivotTables, and highlights interesting data.” For example, typing “Bold the top 3 values in Annual Sales” will apply the formatting, and “Create a pivot table of sales by region” will insert the pivot. Beginner users can rely on Copilot to perform spreadsheet operations without writing formulas manually.

Data Import and Table Creation: Copilot can set up data tables with appropriate headers and formatting. You might say “Create a table for our sales-team data” and Copilot will build a table with columns, formulas, and even conditional formatting. It can also import data from other files or the web: for example, prompting “Import budget details from Budget.xlsx and merge into this sheet” will connect and insert live data via Power Query.

Figure: Excel Copilot summarizing customer feedback in a sheet. Copilot analyzes the free-text comments and lists key positive and mixed-review themes (from a Microsoft blog).

Text and Sentiment Analysis: Excel Copilot can analyze unstructured text. In marketing or customer-service scenarios, you could load survey feedback into a sheet and ask Copilot to “Summarize the feedback in this sheet.” It will group comments into themes and even perform sentiment analysis. For example, Copilot can label each comment as positive/negative and outline major issues and praise. This accelerates qualitative analysis, saving hours of manual review.

Advanced Analysis with Python: For power users, Copilot in Excel now supports Python. In Advanced Analysis mode, you can write prompts like “Forecast sales for the next 4 quarters” and Copilot will generate a Python code block (using pandas, scikit-learn, etc.) in the grid. It can create forecasts, clustering analyses, optimization models, and custom charts (e.g., boxplots or network graphs not natively in Excel). You don’t need to know Python; Copilot writes and explains the code. This opens up Excel to statistical modeling and machine learning without manual coding.

Key Capabilities Summary

• NL-driven formula and chart creation, pivot-table suggestions.

• Importing/merging data via conversational prompts.

• Text analytics: summarization and sentiment tagging.

• Python integration: forecasts, clustering, regressions, etc.

Suitable for spreadsheet-centric tasks and users of all levels (from beginner to Excel power-user).

Use Cases: Excel Copilot shines in many domains. In finance, it can quickly analyze budgets and trends – e.g., by asking “Compare Q1 and Q2 expenditures by category” or “Analyze forecast vs actual spending” you immediately get charts and tables. One example showed that using Copilot to “Analyze quarterly budget forecasts and compare them with actual expenditures” saved up to three hours per quarter. In marketing, Excel Copilot is ideal for survey analysis and campaign metrics: it can aggregate data, highlight key trends, and even interpret free-text feedback (for customer sentiment). In business intelligence, it speeds up ad-hoc reporting; non-technical users can type questions and instantly get charts and insights. In scientific research, users can process experimental data with Copilot’s built-in stats or Python capabilities. Overall, Excel Copilot is the go-to tool when your data is in spreadsheets and you need quick exploration.

Power BI Copilot

Getting Started: In Power BI Desktop or the Power BI service, Copilot for Power BI provides a chat pane and enhanced query interface. First, ensure Copilot is enabled (requires a Fabric or Premium workspace). Then open a report and click the Copilot button (often on the toolbar or by selecting a visual). You can type questions about your data in natural language. For example, ask “Show total sales by region and product category” or “What are the top trends this quarter?” Copilot will respond either with a created visual or a natural-language summary. Copilot “helps business users get more from your data” by answering questions or summarizing insights in seconds.

Ask Data Questions: You can query your model conversationally. Power BI Copilot can “answer questions with visuals created from your semantic model’s data.” If the data isn’t already visualized, Copilot will generate charts or tables automatically. For example, typing “How did sales in Australia change month over month?” will produce the appropriate chart. This is powered by the same engine behind Power BI Q&A, but with generative AI, so it draws on your model’s fields and measures.

Report Summarization and Narratives: Copilot can generate written explanations of your dashboards. You can ask it to “summarize this page” or even “summarize sales trends for the last 6 months” and it will create a bullet-list summary referencing visuals. It can also insert a summary visual or narrative card on the report itself, translating charts into plain-English insights. This makes reports accessible to non-technical stakeholders.

Report Page Generation: A standout feature is creating entire report pages by prompt. By giving Copilot a high-level request (e.g., “Create a sales-performance report by region”), it identifies relevant tables and fields, generates visuals, and builds a new page. This uses AI to detect trends and relationships in your data, quickly laying out charts that a human analyst would create manually, saving massive time in report prototyping.

Authoring Assistance: For report developers, Copilot can write or explain DAX formulas. In the model-editing view or DAX-query view, you can ask Copilot to generate or document measures. For instance, asking “What is the DAX for year-over-year sales growth?” yields a formula. It can also generate synonyms for Q&A, add descriptions to measures, and suggest semantic-model improvements—automating repetitive BI tasks like documentation and complex calculations.

Figure: Power BI Copilot can automatically generate DAX calculations and write narrative summaries of report visuals.

Semantic Modeling: Copilot assists with building and understanding the data model. It can describe your model (summarizing tables, columns, relationships) to help new developers understand it. It can also help design the model—e.g., by generating measure descriptions or explaining complex relationships. Copilot can “streamline redundant tasks (like generating measure descriptions)” and “explain DAX concepts or generate DAX queries,” which is especially useful in large corporate models.

Use Cases: In business intelligence, Power BI Copilot is ideal for dashboard creation and exploration. An analyst can rapidly prototype reports by asking Copilot to suggest pages or visuals relevant to their dataset. In finance, analysts might ask Copilot to create a cash-flow or P&L dashboard, and Copilot will lay out charts and computations—e.g., writing DAX for key metrics like profit margins and generating a narrative for quarterly changes. In marketing, Copilot can produce campaign-performance dashboards and answer questions like “What was our ROI on campaign X?” In scientific or technical fields, it helps non-technical stakeholders understand complex reports by summarizing them in plain language. Across domains, Copilot for Power BI speeds up both report generation and insight interpretation.

GitHub Copilot

Getting Started: GitHub Copilot is an AI pair-programmer available as an extension in IDEs like VS Code or in GitHub Codespaces. After installing and signing in, Copilot suggests code completions as you type comments or code. It supports many languages; for data analysis, Python and R are common. You can also use Copilot Chat inside the IDE to ask questions or generate larger code blocks. Copilot now uses GPT-4.1 for suggestions, improving its ability to write complex code.

Code Generation: With GitHub Copilot, you can describe your data task and get code instantly. For example, in a Python file you might type a comment like # Load sales data and plot distribution and Copilot will propose the corresponding code (reading CSV, plotting a histogram with matplotlib, etc.). It even recognizes context: if you have a DataFrame df loaded, asking Copilot to “show correlation heatmap of df” will generate the code. Copilot accelerates boilerplate work like data cleaning: prompts like “normalize missing values in dataframe” yield complete code snippets.

Jupyter Notebook Commands: Copilot integrates tightly with Jupyter notebooks via the Copilot Chat pane. Special slash commands let you manage notebooks. For example, typing /newnotebook my_analysis.ipynb creates a new notebook; /import pandas,matplotlib imports libraries; and /plot can generate a plot from your data. You can simply describe a visualization (e.g., /plot histogram of 'Revenue') and Copilot inserts the plotting code and output, making interactive analysis fast.

Data-Science Workflows: Copilot helps with all steps of analysis. It can suggest code for data preprocessing (scaling, encoding, splitting datasets) and for model training (fitting regression or classification models). For example, it might complete code to train a scikit-learn model when you write a comment placeholder. It also helps with evaluation: writing a comment like “plot ROC curve” will generate that code. In short, tasks like feature engineering, model tuning, and visualizing results can be done via descriptive prompts.

Copilot Chat and Explanation: Using Copilot Chat (the chat-based assistant in the editor), you can debug and refine your analysis. Ask it to explain a piece of code, or to write tests for a function. If you encounter errors, Copilot can suggest fixes. It also automatically documents your code: a /doc command generates docstrings for functions. This is useful in research, where documenting data transformations is important for reproducibility.

Use Cases: GitHub Copilot is best for programming-centric analysis. In scientific research, researchers often use Python or R for statistical modeling; Copilot can generate code for complex analyses (e.g., regression analysis on experimental data, or processing genomic data) with a simple prompt. It can create publication-quality plots (via matplotlib, Seaborn, or Plotly) and even markdown reports. In finance, developers building algorithmic-trading models or risk simulations can ask Copilot to write back-testing code. In marketing or business analytics, Copilot can script ETL pipelines (e.g., pulling campaign data from APIs, cleaning it, and performing cohort analysis). Across domains, GitHub Copilot turns high-level requests into runnable code, dramatically speeding up development of data pipelines and ML models. It requires some coding skill but is invaluable for experienced data professionals.

Microsoft 365 Copilot

Getting Started: Microsoft 365 Copilot is the umbrella for AI features across Office. It includes Copilot Chat (available in Teams or a browser) and specialized agents like Analyst and Researcher. To use it, you typically need a Copilot license. In Teams or the Copilot web app, click the Copilot icon to open the chat interface. You can type any request, and the system will draw on your Microsoft 365 data (emails, documents, spreadsheets) plus the web. Copilot Chat runs on advanced models (GPT-4o) and can call agents that perform specific tasks.

Copilot Chat (General): For analysis, you can engage Copilot Chat like a research assistant. Ask questions in natural language, upload spreadsheets, or share snippets of data. For example, you could paste a small table and ask “What are the top trends here?”, or ask “Summarize our last quarter’s sales from these files.” Copilot Chat is designed for business and education, promising “better answers, greater efficiency, and new ways to get work done through chat and agents.” Unlike Excel or Power BI Copilot, this interface is a free-form chat—great for exploratory queries or when you need to juggle data from multiple files. (For detailed spreadsheet tasks, Excel Copilot might be more precise.)

Analyst Agent: The Analyst agent is tailored for data analysis. Microsoft describes Analyst as thinking “like a skilled data scientist,” using chain-of-thought to iteratively solve problems. You can point Analyst at data (by uploading an Excel file or connecting to a dataset) and pose complex tasks. Analyst can run Python under the hood and even show you the code it runs. For instance, you might say “Use Analyst to forecast next year’s demand based on the sales figures in these spreadsheets.” Analyst will combine the spreadsheets, perform forecasting (possibly using statsmodels or scikit-learn), and return charts and numbers. It can turn raw data across multiple sheets into a revenue projection or customer-purchasing visualization.

Researcher Agent: The Researcher agent helps with information-gathering tasks. It can ingest documents and web info to produce strategies or reports. For example, ask Researcher to “Develop a go-to-market strategy using our internal sales data and industry trends.” It can pull in external market data and your company’s files to write a plan. Researcher complements data work by collecting relevant background or summarizing literature.

Cross-application Insights: Microsoft 365 Copilot can integrate content across Office apps. For instance, you could ask Copilot Chat about data in an Excel file, a PowerPoint presentation, and an email thread all at once. It can summarize a dataset and draft a report, combining textual analysis and numbers. The Copilot Pages feature (in SharePoint) lets you create reports that mix text, charts, and analysis from Copilot interactions.

Use Cases: Microsoft 365 Copilot (especially Analyst) targets situations where you need insights without building models yourself. In finance, a CFO might use Copilot Chat to ask for a consolidated financial forecast across divisions. Analyst could automatically crunch the numbers and provide a projection with minimal manual effort. In marketing, a manager might upload survey results and have Copilot Chat generate an executive summary of customer sentiment. In business intelligence, Copilot Chat can answer high-level questions like “What were our biggest changes in the last year?” by scanning multiple dashboards. In scientific research or operations, analysts can offload data exploration to Copilot Chat or the Analyst agent—e.g., by giving it a CSV of experimental data and asking for cluster analysis. The key advantage of Microsoft 365 Copilot is that it can see across your company’s data (emails, databases, documents) and automate complex reasoning steps.

Comparing Copilot Tools

Aspect / Tool	Excel Copilot	Power BI Copilot	GitHub Copilot	Microsoft 365 Copilot (Chat/Agents)
Interaction Style	Chat pane or cell-level prompts in Excel; natural language in the ribbon or Copilot button.	Copilot chat pane or Q&A visual in Power BI; prompts targeting report pages or data model.	Code suggestions or Copilot Chat in IDEs/notebooks; prompts in comments or slash commands.	Free-form chat interface (Teams/browser); use of specialized agents (Analyst, Researcher) via chat commands.
Input Format	Plain-English queries, sometimes via sample prompts (e.g., “Summarize column X” or chart requests). Can also accept pasted images or tables.	Natural-language questions about reports or data (e.g., “show me a bar chart of…”). Also structured prompts for report/page creation.	English comments or prompts in code (e.g., “# calculate correlation matrix”) or special slash commands in notebooks.	Natural language in chat (e.g., “Forecast next year’s sales based on our data”), plus file uploads. Agents can be invoked (e.g., “/use Analyst”).
Analysis & Modeling	Spreadsheet operations (formulas, pivot tables, charting). Data import/cleanup via Power Query commands. Advanced: Python analytics (regressions, ML, new visual types).	Semantic modeling (tables, relationships, measures) and visualization. Generates DAX for new measures; creates charts and entire report pages based on data.	Full programming: data cleaning, statistical analysis, ML model building (scikit-learn, TensorFlow, etc.), custom visualizations in code.	Multi-step reasoning: Analysts can write/run Python behind the scenes. Researcher can gather data from files/web. Chat can leverage connected data sources (Outlook, SharePoint) for analysis.
Outputs	Excel grids: new columns, filled formulas, inserted charts, pivot tables. Can output code in Python cells or plain-text summaries.	Power BI visuals and report pages (charts, tables, narrative cards). Also natural-language text summaries (narrative visuals).	Code (Python, R, etc.), complete scripts or notebook cells, and inline documentation. Also test code or explanations (via Chat).	Textual answers or reports (in chat or documents), integrated with charts/images. Can output Python/R code (Analyst shows code) and whole documents/strategies (Researcher).
Skill Level	Beginner-friendly for spreadsheet users; no coding required. Intermediate users benefit from Python features.	Intermediate analysts and BI developers (comfortable with data models and DAX). Non-technical users can still get insights via chat, but model-building tasks may need more skill.	Advanced: designed for developers/data scientists who code. Copilot accelerates their workflow but assumes familiarity with coding concepts.	Varies: Analyst and Chat make advanced analysis accessible to non-coders; still powerful for data-savvy users. Researcher handles strategic tasks. Little/no coding needed for end users.
Best For	Quick analysis of spreadsheet data, ad-hoc reporting, formula automation, exploratory data work in Excel.	Building or exploring interactive dashboards, generating data-driven reports, answering analytical questions in a BI context.	Writing and iterating data pipelines, custom analyses, machine-learning models, and visualizations in code.	Enterprise-level Q&A and reporting across apps; generating high-level insights or complex forecasts from multiple data sources, even for non-technical users.

Recommendations

• Choose Excel Copilot when your data lives in spreadsheets and you need fast results without coding. It’s ideal for finance/budgeting tasks and beginner-to-intermediate users. A junior analyst can ask Copilot to create charts or do regressions without writing formulas.

• Choose Power BI Copilot if you’re building dashboards or working with large models. Power BI Copilot helps data professionals quickly prototype reports and dive deep with DAX, making it suited to BI or analytics roles who need to present insights via visuals.

• Choose GitHub Copilot for code-heavy data work. Data scientists and engineers use it to accelerate Python/R scripting and notebook analysis. It requires coding knowledge, but it can dramatically speed up tasks like cleaning data or training ML models.

• Choose Microsoft 365 Copilot (Chat/Analyst) when you want an AI assistant across tools. Non-technical managers can get forecasts, summaries, or research done by simply chatting. Analysts can let the Analyst agent run complex computations. Use it when you need to integrate data from multiple files or ask nuanced questions without diving into code.

In practice, many teams will use multiple Copilots together. For instance, you might use Excel Copilot to clean data, Power BI Copilot to build a report, GitHub Copilot to write a predictive model, and Microsoft 365 Copilot to summarize the findings for leadership. The key is to match the tool to the task and your comfort level. Beginners can start with the GUI-based Copilots (Excel and Power BI), while advanced users leverage code generation (GitHub Copilot) and agent-driven analysis (Microsoft 365 Copilot).