概要
公開日: 2026-05-15
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原文
May 15, 2026 OpenAI Academy How data science teams use Codex See how data science teams can use Codex to turn questions, dashboards, and raw data into review-ready analysis assets. Download Codex (opens in a new window) Loading… Share With Codex, data science teams can turn scattered inputs into usable analysis assets faster. Starting from dashboards, metric definitions, exports, experiment notes, and business context, Codex helps assemble a first draft of the deliverable—including charts, caveats, source links, and review questions—so teams can validate the work and share it with confidence. Learn more about using Codex for everyday work in our on-demand webinar (opens in a new window) . Top Codex use cases for data science teams Most data science work does not end with the query. It ends with an artifact someone can read, challenge, and act on. Use these prompts to have Codex turn dashboards, exports, metric definitions, and stakeholder context into a first draft of a real deliverable—whether that’s a root-cause brief, impact readout, KPI memo, or dashboard spec. Then apply your judgment where it matters most: validating the evidence, pressure-testing the caveats, and sharpening the recommendation. 1. KPI root-cause analysis Use this when: A key metric moved unexpectedly and the team needs a source-backed brief that explains what changed, why it likely happened, and what to do next. What you bring What Codex returns KPI dashboard, metric definitions, exports, launch or campaign context, segment cuts, and relevant stakeholder threads A root-cause brief with charts, confirmed drivers, hypotheses, caveats, source links, open questions, and recommended actions Suggested plugins: Google Drive, Spreadsheets, Slack, Gmail, Documents How it works Codex reviews the metric definition, dashboard context, source exports, and recent business activity. It breaks down movement by segment, cohort, channel, geography, and product surface where relevant. It creates a review-ready root-cause brief that separates confirmed findings from hypotheses. Starter prompt Investigate why [KPI] changed for [business/product/segment] during [time period]. Use the KPI dashboard, metric definitions, recent launch or campaign notes, customer or usage segments, spreadsheet exports, and collaboration threads I provide. Break down likely drivers by segment, cohort, channel, geography, and product surface where relevant. Create a root-cause brief with charts, caveats, source links, recommended actions, and open questions. Separate confirmed findings from hypotheses. Real-world example Investigate why weekly paid subscriptions changed for Acme Pro and Acme Plus. Use the “Subscriptions KPI Dashboard,” “April Growth Launch Notes,” metric definitions from “Consumer Metrics Glossary,” recent growth-metrics discussion notes, subscription warehouse exports, and any related context I provide. Create an executive root-cause brief with likely drivers, supporting charts, segment cuts, caveats, recommended actions, and source links. Validate the numbers and flag anything uncertain. 2. Business impact readout Use this when: A launch, experiment, or initiative needs a clear readout leaders can use to decide whether to scale, adjust, or stop. What you bring What Codex returns Experiment plan, success metrics, cohort data, dashboard exports, customer signals, and launch notes A business impact readout with lift, guardrails, segment findings, methodology notes, caveats, and a recommendation Suggested plugins: Google Drive, Spreadsheets, Slack, Gmail, Documents, Presentations How it works Codex reviews the initiative plan, success metrics, cohorts, dashboards, and customer signals. It quantifies impact, checks guardrails, and inspects segment-level differences. It creates a decision-ready readout with charts, caveats, methodology notes, and scale/change/stop guidance. Starter prompt Measure whether [initiative/experiment/launch] improved [target outcome]. Use the experiment or