In any professional environment, understanding data is a crucial step toward making informed decisions. Whether it’s tracking business performance, summarizing survey results, or identifying trends across multiple projects, data analysis often requires time and effort. This is where the Analyst agent in Microsoft 365 Copilot Chat can help. This preconfigured agent is designed to assist you in exploring, interpreting, and summarizing your business data using natural language instructions.

The Analyst agent acts as your personal data assistant. It can analyze data contained in files you have access to—such as Excel spreadsheets, Word documents, and Power BI dashboards—and provide useful summaries, charts, and relevant insights based on your questions. For example, if you ask, “What are the key trends in this sales report?” or “Summarize performance by region,” the Analyst agent responds with a clear and concise synthesis, often including visuals or summaries that you can use directly in reports or presentations.

In any professional environment, understanding data is a crucial step toward making informed decisions. Whether it’s tracking business performance, summarizing survey results, or identifying trends across multiple projects, data analysis often requires time and effort. This is where the Analyst agent in Microsoft 365 Copilot Chat can help. This preconfigured agent is designed to assist you in exploring, interpreting, and summarizing your business data using natural language instructions.

The Analyst agent acts as your personal data assistant. It can analyze data contained in files you have access to—such as Excel spreadsheets, Word documents, and Power BI dashboards—and provide useful summaries, charts, and relevant insights based on your questions. For example, if you ask, “What are the key trends in this sales report?” or “Summarize performance by region,” the Analyst agent responds with a clear and concise synthesis, often including visuals or summaries that you can use directly in reports or presentations.

Let the Analyst Agent Do the Heavy Lifting with Your Data

The Analyst agent thinks like an experienced data scientist, allowing you to go from raw data to insights in just minutes. It’s optimized for advanced data analysis using chain-of-thought reasoning, progressing iteratively through problems. It performs as many steps as needed to refine its reasoning and deliver a high-quality response that reflects human analytical thinking. It can execute Python code to handle your most complex data queries, and you can view the code it runs in real time and verify its work. For example, you can use the Analyst agent to transform scattered raw data across multiple spreadsheets into a demand forecast for a new product, a visualization of customer purchasing habits, or a revenue projection.

The Analyst agent is especially useful for recurring tasks such as preparing business reviews, tracking performance indicators, or identifying outliers in your data. It can also help you spot inconsistencies or gaps that might otherwise go unnoticed. You don’t need to know advanced Excel formulas or complex BI tools to get started. The Analyst agent understands common business language and can interpret your intent. This makes it particularly helpful if you’re not a data expert but still need to work with numbers and reports. It also saves time by automating tasks that might otherwise take hours, such as creating a pivot table or a chart.

One of the most powerful features of the Analyst agent is its ability to connect data across files you’ve recently worked on in Microsoft 365. If you’ve collaborated on a shared Excel sheet or viewed a PowerPoint presentation containing embedded tables, Analyst can reference that content to answer your questions more completely. It also respects your file permissions, accessing only the data you’re authorized to view.

Sample Queries to Use with the Analyst Agent

Here’s a list of example queries you can try with the preconfigured Analyst agent in Microsoft 365 Copilot Chat. These queries are designed to be natural and task-oriented, helping users gain insights from their data without advanced technical skills.

Understanding and Summarizing Data

• “Summarize the key trends in this Excel file.”
• “What are the top five revenue-generating products?”
• “Can you identify anomalies in this sales data?”
• “Give me a quick overview of our Q1 performance.”

Creating Visuals

• “Create a bar chart showing monthly sales by region.”
• “Visualize customer churn rate over the past year.”
• “Generate a pie chart of expenses by category.”

Comparison and Filtering

• “Compare this year’s revenue to last year’s.”
• “Filter this data to show only North America and EMEA.”
• “Which departments had the highest cost increases?”

Time-Based Analysis

• “Show me the sales trend over the last six months.”
• “What was our average weekly revenue in Q2?”
• “Highlight seasonal trends in this dataset.”

Predictive and Hypothetical Scenarios

• “Based on current trends, what will our revenue look like next quarter?”
• “What happens if we increase marketing spend by 10%?”
• “Forecast customer growth for the next six months.”

Limitations of the Analyst Agent

While the Analyst agent is a powerful tool for data analysis and insight generation, it has some limitations to be aware of:

• Requires clear data structure: The agent works best when data is well-organized and structured, such as in tables or spreadsheets. If your data is messy, incomplete, or requires significant cleaning, you may need to prepare it before using the agent for in-depth analysis.
• Does not replace human expertise: Although it can generate insights from data, it doesn’t replace domain-specific expertise. For specialized analyses or nuanced interpretations, consulting an expert is recommended.
• Limited handling of non-tabular data: The agent excels with structured data like tables. For unstructured data (free text, images, raw notes), it may be less effective without prior transformation into a structured format.
• Requires human validation for complex insights: For critical decisions, the agent’s results should be considered as support. Human review is necessary for final interpretation.
• Limited customization for specialized queries: While it can handle many standard tasks, highly specific queries may require manual adjustments or additional analysis.
• Does not process real-time data: The agent can handle static datasets but is not designed to integrate with real-time or streaming data sources unless specifically configured.
• Does not automatically detect all inconsistencies: It can identify obvious gaps or inconsistencies, but not all—especially subtle or contextual ones. Manual inspection may be needed.
• Does not replace advanced BI tools: While capable of advanced analysis, it doesn’t replace BI tools like Power BI or Tableau for enterprise-scale reporting, dashboards, or visualizations.

Next Unit: Exercise – Using the Analyst Agent