See how data science teams can use Codex to turn questions, dashboards, and raw data into review-ready analysis assets.
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 launch plan, success metrics, relevant dashboards, cohort or assignment data, customer signals, and launch notes I provide. Quantify the lift or movement, check guardrail metrics, inspect segment differences, and explain whether the team should scale, change, or stop the initiative. Return a business impact readout with charts, methodology notes, caveats, source links, and a clear recommendation.
Real-world example
Measure whether Acme’s April onboarding experiment improved activation. Use the “April Onboarding Experiment Plan,” experiment results export, onboarding funnel dashboard, customer cohort table, launch notes, and related team discussion context. Write a business impact readout with lift, guardrail metrics, segment differences, whether to scale or change the experiment, and the analysis steps used. Separate confirmed results from interpretation.
3. Analytics request agent
Use this when: A stakeholder ask is broad, ambiguous, or underspecified and needs to become a scoped analysis asset.
| What you bring | What Codex returns |
| Stakeholder request, business context, metric glossary, source exports, dashboard links, and request threads | A scoped analysis plan plus stakeholder-ready answer with charts, caveats, source links, validation notes, and open questions |
Suggested plugins: Google Drive, Spreadsheets, Slack, Gmail, Documents
How it works
- Codex reviews the request, business question, metric definitions, available data, and surrounding context.
- It scopes the analysis, identifies missing inputs, and runs a first pass using the provided data.
- It creates a stakeholder-ready analysis asset with charts, caveats, validation notes, and analyst review questions.
Starter prompt
Turn this analytics request into a scoped analysis: [paste request or link to source context]. Identify the business question, required metric definitions, source exports, relevant dashboards, and recent product or business context. Draft an analysis plan, run a first-pass analysis using available data, validate the outputs, and prepare a stakeholder-ready answer with charts, caveats, source links, and open questions for analyst review. Do not assume definitions or join logic that are not provided.
Real-world example
Turn the enterprise trial conversion request into a scoped analysis. Use the original request thread, metric definition, source-of-truth table exports, related dashboards, recent launch context, and any relevant notes I provide. Create an analysis plan, run the first-pass analysis, validate outputs, and draft a stakeholder-ready answer with caveats, charts, source links, and open questions for analyst review.
4. Executive KPI review
Use this when: A recurring KPI review needs to become a leadership-ready memo focused on what changed, why it matters, and who should act.
| What you bring | What Codex returns |
| Latest KPI dashboards, exports, metric definitions, prior review, owner notes, and planning context | An executive KPI memo with charts, material changes, anomalies, risks, assumptions, data-quality checks, and owner follow-ups |
Suggested plugins: Google Drive, Spreadsheets, Slack, Gmail, Documents, Presentations
How it works
- Codex reviews current KPI materials, prior reviews, owner notes, and planning context.
- It identifies material changes, anomalies, likely drivers, risks, and data-quality issues.
- It creates an executive KPI memo with source-backed charts, assumptions, and owner follow-ups.
Starter prompt
Prepare the [weekly/monthly] business review for [team/business]. Use the latest KPI dashboard, revenue or usage tables, metric definitions, last review, owner notes, and planning context I provide. Identify what changed, why it matters, anomalies to inspect, risks, and owner follow-ups. Create an executive memo with charts, assumptions, data-quality checks, source links, and a short leadership summary. Cite a source for every material number.
Real-world example
Prepare Acme’s May weekly business review. Use the “Self-Serve KPI Dashboard,” revenue and usage exports, metric definitions, last week’s WBR, owner notes, and any relevant planning context. Create an executive memo with what changed, why it matters, anomalies to inspect, owner follow-ups, and charts suitable for leadership. Include assumptions and data-quality checks.
5. Dashboard builder and monitor
Use this when: A team needs a dashboard spec or first-pass dashboard plan that clarifies metrics, owners, quality checks, and the decisions the dashboard should support.
| What you bring | What Codex returns |
| Strategy brief, workflow context, metric definitions, source exports, dashboard examples, and stakeholder feedback | A dashboard spec with KPI hierarchy, chart specs, filters, QA checks, owner handoffs, monitoring plan, and publication risks |
Suggested plugins: Google Drive, Spreadsheets, Slack, Gmail, Documents, Presentations
How it works
- Codex reviews the workflow, strategy brief, metrics, source data, dashboard examples, and stakeholder feedback.
- It defines the KPI hierarchy, chart specs, filters, QA checks, owners, and monitoring plan.
- It creates a dashboard spec or first-pass dashboard plan and flags gaps before publication.
Starter prompt
Design a decision dashboard for [business workflow or funnel]. Use the strategy brief, metric definitions, source tables or exports, existing dashboard examples, stakeholder notes, and current feedback I provide. Define the KPI hierarchy, chart specs, filters, data-quality checks, owner handoffs, and monitoring plan. Create a dashboard spec or first-pass dashboard plan, and flag data gaps, unclear definitions, or publication risks before anything is shared.
Real-world example
Build a decision dashboard spec for Acme’s enterprise onboarding funnel. Use the “Enterprise Onboarding Metrics Brief,” source data exports, existing dashboard examples, activation definitions, stakeholder notes, and current dashboard feedback. Define the KPI hierarchy, chart specs, filters, QA checks, owner handoffs, and monitoring plan. Create the dashboard spec and flag data gaps before publication.
More resources
Keep exploring Codex for work with these resources:
- Codex for work hub
- Codex for everyday work on-demand webinar(opens in a new window)
- Top 10 uses for Codex at work
https://openai.com/academy/codex-for-work/how-data-science-teams-use-codex/
