How Data Science Teams Use ChatGPT Work

ChatGPT Work enables data science teams to accelerate the creation of analysis assets by synthesizing fragmented inputs into structured first drafts. By integrating various data sources, the tool reduces the time required to move from raw data to a validated deliverable.

Streamlining the Analysis Workflow

ChatGPT Work transforms scattered data inputs into usable analysis assets. The tool is designed to ingest multiple types of fragmented information to assemble a comprehensive first draft of a technical deliverable. Supported inputs include:

• Dashboards and Metric Definitions: Utilizing existing visualization and measurement frameworks.
• Data Exports: Processing raw data files.
• Experiment Notes: Incorporating qualitative observations and test results.
• Business Context: Integrating organizational goals and background information.

Deliverable Components and Validation

The output generated by ChatGPT Work is intended to be a "review-ready" asset, providing a foundation that teams can validate before sharing. These generated drafts include:

• Visualizations: Automated creation of charts.
• Contextual Details: Inclusion of caveats and source links to ensure transparency.
• ChatGPT Work also generates review questions to help data scientists identify potential gaps or errors in the analysis during the validation phase.

Platform Integration and Availability

Workflows previously associated with the Codex app have been integrated into the broader ChatGPT ecosystem. Users can now access these data science capabilities through:

• The ChatGPT web interface at chatgpt.com.
• The ChatGPT desktop application.

OpenAI provides additional resources for these workflows via the OpenAI Academy, including on-demand webinars and a dedicated collection of data science use cases.