About Prompt
- Prompt Type – Dynamic
- Prompt Platform – ChatGPT, Grok, Deepseek, Gemini, Copilot, Midjourney, Meta AI and more
- Niche – Specific AI Prompts
- Language – English
- Category – Data & Analysis
- Prompt Title – AI Data Analysis Prompt for Business Intelligence
Prompt Details
**Prompt Objective:** To perform in-depth data analysis for business intelligence purposes, adaptable to various datasets and business questions.
**Prompt Structure:**
“`
## Business Intelligence Data Analysis
**1. Data Source & Connection:**
* **Data Source Type:** [Specify the data source type, e.g., CSV file, Database (MySQL, PostgreSQL, etc.), Cloud Storage (AWS S3, Google Cloud Storage), API endpoint, etc.]
* **Connection Details:** [Provide specific connection details like file path, database credentials, API key, etc. Ensure security best practices are followed. Do NOT include sensitive information directly in the prompt. Refer to securely stored credentials or use environment variables.]
* **Data Format:** [Describe the data format, e.g., comma-separated values, JSON, XML, Parquet, etc.]
* **Data Size:** [Estimate the data size, e.g., number of rows, file size. This helps the AI understand the scale and choose appropriate methods.]
**2. Data Description & Preprocessing:**
* **Data Schema:** [Provide column names, data types (e.g., integer, float, string, date), and a brief description of each column. If a data dictionary or schema file exists, provide a link or reference.]
* **Data Cleaning Instructions:** [Specify any necessary data cleaning steps. Examples include handling missing values (imputation or removal), outlier detection and treatment, data type conversion, removing duplicates, etc. Be specific about the methods and thresholds.]
* **Data Transformation:** [Describe any data transformations required, such as feature scaling, normalization, encoding categorical variables, creating new features from existing ones, aggregation, etc.]
**3. Analysis Goal & Business Question:**
* **Business Question:** [Clearly state the business question you want to answer with this analysis. Be specific and avoid ambiguity. Examples: “What are the top 3 factors contributing to customer churn?”, “How does marketing campaign performance vary across different demographics?”, “Predict sales revenue for the next quarter.”]
* **Analysis Type:** [Specify the type of analysis required, e.g., descriptive, exploratory, predictive, prescriptive, diagnostic. This guides the AI in choosing appropriate techniques.]
* **Key Metrics & KPIs:** [Define the key metrics and KPIs relevant to the business question. Examples: customer lifetime value, conversion rate, average order value, click-through rate.]
**4. Output Requirements & Visualization:**
* **Output Format:** [Specify the desired output format, e.g., table, chart, summary statistics, predictive model, JSON, CSV.]
* **Visualization Type:** [If visualization is required, specify the chart type (e.g., bar chart, line chart, scatter plot, heatmap) and any specific visualization preferences like color scheme, labels, etc.]
* **Insights & Interpretation:** [Request the AI to provide insights and interpretations based on the analysis results. Explain how these insights can be used to address the business question.]
* **Code Generation (Optional):** [If you need code for further analysis or implementation, specify the programming language (e.g., Python, R) and the desired libraries.]
**Example Data Snippet (Optional):** [Provide a small sample of the data to give the AI context. This is particularly helpful for complex data structures or when demonstrating specific cleaning/transformation requirements.]
“`
**Dynamic Aspects:**
* **Data Source & Connection:** Adapts to various data sources and connection methods.
* **Data Description & Preprocessing:** Handles different data formats, schemas, and cleaning/transformation needs.
* **Analysis Goal & Business Question:** Addresses diverse business questions and analysis types.
* **Output Requirements & Visualization:** Flexible output formats and visualizations tailored to the specific analysis.
**Best Practices:**
* **Be Specific and Unambiguous:** Clearly define all aspects of the analysis, avoiding vague or general statements.
* **Provide Context:** Include background information and context about the data and the business question.
* **Iterative Refinement:** Start with a simple prompt and refine it based on the AI’s initial output.
* **Security Considerations:** Do not include sensitive information directly in the prompt. Use secure methods for handling credentials and data access.
This dynamic prompt template provides a structured and adaptable framework for leveraging AI for business intelligence data analysis across different platforms and datasets. By clearly specifying the data, analysis goals, and output requirements, you can effectively guide the AI to generate valuable insights for informed decision-making.