ChatGPT for Data Analysts: 35 Prompts to Write Reports, Clean Data Descriptions, and Communicate Insights Faster

Write a 1-page executive summary of a [DATASET_NAME] analysis for [TIME_PERIOD]. Audience: [STAKEHOLDER] (non-technical). Key finding: [describe in plain terms, no raw data]. Tone: direct and business-focused. Format: headline finding, 2 paragraphs of context, 3 bulleted recommendations.

I need to explain why [METRIC] changed in [TIME_PERIOD] to [STAKEHOLDER] without using technical jargon. Here's what happened in plain language: [describe without actual data]. Make it clear, credible, and decision-ready.

Write the opening paragraph of a quarterly business review report for [COMPANY_NAME] leadership. The analysis covers [DATASET_NAME]. Lead with the most important finding, written for executives who have 90 seconds to read it.

Turn these bullet-point findings into a cohesive narrative paragraph for a stakeholder report: [paste plain-text bullets, no raw data]. The tone should be professional but not academic. No hedging language.

Write a 'so what' section for a data analysis on [METRIC] during [TIME_PERIOD]. The audience is [STAKEHOLDER]. They care about revenue impact and operational risk. Connect the finding directly to a business decision.

Draft a short 3-sentence alert memo for [STAKEHOLDER] about an unexpected change in [METRIC] during [TIME_PERIOD]. Include: what happened, why it matters, and what we're doing about it.

I'm presenting [DATASET_NAME] findings to [STAKEHOLDER] next week. Write 5 questions they're likely to ask and strong answers for each, based on this plain-language summary of results: [describe findings without data].

Write a plain-English explanation of what this SQL query does. Assume the reader is a non-technical business stakeholder who needs to understand the logic, not run the query. Query purpose: [describe what it calculates — no proprietary schema details].

Write inline SQL comments for a query that calculates [METRIC] for [TIME_PERIOD]. The comments should explain the purpose of each major section (CTEs, joins, filters, aggregations) in plain English.

Write a README section for a SQL script that generates [DATASET_NAME]. Include: purpose, inputs required, output fields, and any known limitations. Use professional but accessible language.

I need to explain a data transformation logic to a new analyst joining the team. The transformation converts [describe input data type] into [describe output]. Write a step-by-step explanation they can follow without seeing the actual code.

Write a data validation checklist for a pipeline that processes [DATASET_NAME]. Include 8–10 common checks (nulls, duplicates, date range integrity, referential integrity, etc.) formatted as a runnable QA checklist.

Draft a Confluence/Notion page template for documenting a new SQL data model. Include sections for: purpose, source tables, business logic, output fields, refresh frequency, and owner.

Write a plain-English business rule description for a calculated field named [METRIC] in a [DATASET_NAME] report. The formula logic is: [describe in words, not code]. The audience is business stakeholders reviewing the data dictionary.

Write a 90-second opening script for a data presentation to [STAKEHOLDER] about [DATASET_NAME] results for [TIME_PERIOD]. Start with the business problem, not the methodology. End with a hook that makes them want to hear the rest.

I have 5 slides in a data presentation. Write the talking points for each slide. Findings (plain language, no raw data): [describe each slide's point]. Audience: [STAKEHOLDER]. Conversational, not robotic.

Write a 'story arc' outline for presenting [METRIC] trends to a non-technical audience. Use the structure: situation → complication → resolution. Fill in each section based on this plain-language summary: [describe the findings].

Write 3 different ways to phrase this finding for different audiences: [plain-language finding]. Versions: (1) for the C-suite in 30 seconds, (2) for department managers in 2 minutes, (3) for analyst peers in technical terms.

Write the slide headline and 3 supporting bullets for this key finding: [describe in plain terms]. The headline should be a statement, not a label. Each bullet should add a new layer of meaning.

Create a 'what this means for you' closing slide script for a [STAKEHOLDER] presentation on [DATASET_NAME]. Translate the data findings into one clear business action they can take in the next 30 days.

Write a follow-up email to send after a data presentation to [STAKEHOLDER]. Summarize the 3 key findings, the recommended actions, and next steps. Under 200 words. Professional and direct.

Write a data dictionary entry for a field called [METRIC] in the [DATASET_NAME] dataset. Include: field name, data type, description, business definition, source system, calculation logic (described in plain English), and any known caveats.

I'm building a data dictionary for [DATASET_NAME]. Write a one-paragraph overview section that explains what this dataset is, who owns it, what business questions it answers, and how frequently it updates.

Write descriptions for these 5 fields in a data dictionary. For each field, provide a business-friendly definition in 1–2 sentences. Fields: [list field names and a plain-language description of each — no proprietary schema details].

Write a 'known limitations' section for a [DATASET_NAME] data dictionary. Common limitations to address: historical data gaps, sampling methodology, refresh latency, and scope exclusions. Clear and honest tone.

Write a change log entry for a data model update to [DATASET_NAME]. Change summary: [describe what changed, in plain terms]. Include: date, change description, reason for change, and impact on existing reports.

Write a 'how to use this dataset' guide for business users accessing [DATASET_NAME] for the first time. Cover: what questions it can answer, what it cannot, key fields to know, and how to request access or support.

Create a data stewardship RACI matrix template for [DATASET_NAME]. Define roles and responsibilities for: data quality, access management, documentation, and issue resolution. Use plain-language role descriptions.

Help me write a performance review self-assessment for a data analyst role. Key accomplishments this period: [describe in plain terms — no proprietary metrics or client names]. Tone: confident, specific, results-oriented.

I'm preparing for a data analyst job interview. Write 5 behavioral interview questions I might face and strong STAR-format answers for each, based on this project background: [describe a project in general terms].

Write a LinkedIn post about an insight I gained from a recent analysis project. Topic: [describe the general learning — no proprietary data]. Style: thought leadership, direct, valuable, not a humble-brag.

Help me write a proposal to implement a new analytics process at my organization. The process: [describe in general terms]. Include: problem statement, proposed solution, expected impact, and what's needed to start.

Write a 'how I solved this' case study summary for my portfolio about a data analysis project. The project: [describe without proprietary details]. Structure: challenge, approach, key finding, business impact.

I need to explain a complex statistical concept to a non-technical manager. The concept: [name it]. Write a plain-English explanation with a real-world analogy. No formulas, no jargon.

Write an email to [STAKEHOLDER] requesting data access for a new analysis project. Include: the purpose of the analysis, why the specific data is needed, how it will be used, and who will have access. Professional and succinct.