Data Extract Feature Documentation
Overview
The Data Extract feature is designed to enable users to efficiently retrieve relevant information from a wide variety of data sources. It acts as a bridge between raw data and actionable insights by organizing, filtering, and formatting data in ways that best serve analytical and reporting purposes.
By utilizing AI models such as GPT-4, this feature intelligently interprets user instructions to extract precisely the data needed, thereby simplifying complex queries and reducing the manual effort traditionally involved in data preparation.
Feature Highlight
This feature supports flexible and efficient data handling by allowing users to specify exactly what information they want, and how it should be presented. Whether you need visualizations, detailed reports, or concise summaries, the Data Extract feature tailors the output format to meet diverse analytical requirements.
Key capabilities include:
- Extraction of targeted data subsets based on natural language prompts.
- Support for multiple output types: Charts, Reports, and Summaries.
- Rich instruction input to guide the AI in data selection and formatting.
- Seamless integration with multiple data sources for comprehensive analysis.
Data Extract Form
The Data Extract form provides an intuitive interface to specify all parameters for your data extraction task. It is divided into several main components:
Components
Model
- Displays the AI model used to process the data extraction request, typically GPT-4 or equivalent.
- Allows users to provide optional instructions to customize how the AI interprets and executes the extraction.
- Examples of instructions might include focusing on recent data, emphasizing certain metrics, or applying specific filters.
Name
- A text input field to assign a descriptive name to the data extraction task.
- Naming conventions aid in organizing multiple extraction requests and referencing them in subsequent workflows.
- Example names: “SalesPerformanceQ1”, “CustomerChurnSummary”, or “WebsiteTrafficReport”.
Type
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Dropdown selection for output format, enabling users to specify how they want the extracted data presented.
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Supported types include:
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Chart: The AI generates visual representations such as bar charts, line graphs, or pie charts to illustrate trends, comparisons, or distributions.
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Report: Produces detailed textual or tabular reports that list raw or processed data with explanatory content.
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Summary: Creates brief, high-level overviews highlighting key insights, trends, or metrics from the data.
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Choosing the right type depends on your audience and use case: charts for presentations, reports for detailed review, and summaries for quick decision-making.
Prompt
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A rich text editor where users can enter detailed instructions or criteria for the data extraction.
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Supports advanced text formatting including:
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Bold, italic, underline, and strikethrough to emphasize key points.
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Bulleted and numbered lists for structured instructions.
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Text alignment options to organize content clearly.
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Embedding links and images to provide additional context or references.
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Users can specify filters, date ranges, comparison parameters, aggregation functions, or any other requirements necessary to refine the data output.
Actions
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OK Button: Initiates the data extraction process based on the configured parameters. The system processes the input and returns results in the chosen format.
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Cancel Button: Exits the form without saving or submitting, allowing users to abort if changes are not desired.
Usage Instructions
To effectively use the Data Extract feature, follow these steps:
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Name your extraction task: Provide a clear and descriptive name that reflects the purpose or content of the extraction.
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Select output type: Choose between Chart, Report, or Summary depending on how you want to view or use the extracted data.
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Enter detailed instructions: Use the prompt editor to describe exactly what data you want, including any conditions, filters, or formatting preferences.
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Submit the extraction request: Click OK to start the AI-powered extraction. Review the results when returned.
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Cancel if needed: Use the Cancel button to discard the request if you wish to make changes or abandon the task.
Best Practices
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Be specific in your prompts: More detailed instructions lead to more accurate and useful data extraction.
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Match output type to purpose: Use charts for visual summaries, reports for comprehensive data listings, and summaries for high-level insights.
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Leverage rich formatting: Structure your prompts using lists and emphasis to guide the AI effectively.
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Iterate and refine: Review initial outputs and adjust your prompts or parameters to hone in on desired results.
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Maintain consistent naming: Use standard naming conventions for easier task management and traceability.
Technical Notes
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The AI model interprets natural language queries and maps them to structured data queries internally.
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Integration with data sources may include databases, APIs, spreadsheets, or cloud storage systems.
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Output formats are designed to be compatible with common presentation tools and data analysis software.
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Security and privacy protocols ensure that sensitive data is handled according to organizational policies.
Tips
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If the extracted data seems inaccurate or incomplete, check and refine your prompt for clarity.
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Use filters and conditions in your prompt to exclude irrelevant data.
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For large datasets, consider narrowing the query scope to improve performance and relevance.
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Confirm that the AI model selected is appropriate for your data type and complexity.
Summary
The Data Extract feature provides a powerful and flexible way to retrieve, organize, and present data from diverse sources. By combining user-friendly interfaces with advanced AI processing, it transforms complex data retrieval tasks into efficient, automated workflows. Whether your goal is detailed reporting, visual analysis, or quick summaries, this feature adapts to your needs, accelerating data-driven decision-making.