El Convertidor de Datos Brutos a Informe Ejecutivo: Convierte Números Desordenados en un Análisis de 4 Etapas Listo para Actuar
Por qué importa este prompt
Raw data doesn't drive decisions on its own — a specific, numbers-backed narrative built from that data does. Analysts who hand over spreadsheets or generic dashboards without translating numbers into business implications routinely watch their findings get ignored, because executives rarely have time to do that translation themselves. An analyst who can't produce a clear, defensible recommendation from the data gets treated as a report generator rather than a strategic partner, and their insights get deprioritized behind whoever tells the more compelling story — even when that story is built on weaker data.
Para qué lo usamos
You're a data analyst at a mid-size e-commerce company. Q3 sales data just landed in a spreadsheet with dozens of columns across 5 regions, and your VP of Sales wants the 3 things that matter by end of day — not a dashboard, not a slide deck, just the insights.
Prompt
Role: You are a data analyst and business intelligence consultant with deep experience translating raw datasets into board-level insights for [YOUR INDUSTRY].
Context: I have the following raw data: [PASTE DATA HERE — CSV, table, or list of numbers]. This data covers [TIME PERIOD / SCOPE, e.g., "Q3 2026 sales by region and product line"]. The audience for this analysis is [AUDIENCE, e.g., "the VP of Sales, who has 10 minutes and no patience for raw numbers"].
Task: Analyze this data in four stages: (1) identify the 3-5 most significant trends, (2) flag any anomalies or outliers with a plausible explanation for each, (3) translate the findings into business implications, (4) recommend 2-3 specific actions ranked by expected impact.
Constraints: Do not simply describe the data ("sales went up") — explain why it matters and what changed. Every claim must reference a specific number from the data, not a vague trend. Flag any data quality issues (missing values, suspicious outliers, inconsistent units) before drawing conclusions from them. Keep the entire output under [MAX WORD COUNT, e.g. 400] words — this is for an executive, not a report.
Output Format: Structure as four labeled sections — Trends, Anomalies, Business Implications, Recommended Actions. Use bullet points, not paragraphs. Each bullet must be a single sentence a reader could act on without re-reading the raw data.Resultado
Filled in for: Q3 2026 e-commerce sales data by region (North America, EMEA, APAC, LATAM, MEA), covering revenue, units sold, and return rate, prepared for a VP of Sales.
Trends
• North America revenue hit $4.2M this quarter, up 18% QoQ — the largest absolute contributor to overall growth, driven mainly by the new bundle pricing launched in July.
• APAC grew 41% QoQ to $1.9M, the fastest percentage growth of any region, off a smaller base than North America or EMEA.
• EMEA revenue was flat at $2.8M (+3% QoQ), suggesting the region has plateaued after two consecutive quarters of double-digit growth.
• Overall units sold grew 22% company-wide, faster than the 15% revenue growth — average order value is declining slightly as growth skews toward lower-priced SKUs.
Anomalies
• LATAM revenue dropped 12% QoQ to $640K while units sold were flat — this is a price/mix problem, not a demand problem, and warrants checking whether a currency shift or a promotional discount is compressing margin.
• MEA grew 65% QoQ, but on a base of only $310K — statistically eye-catching, practically small; do not present this figure without the base number attached, or it will be misread as a major trend.
• LATAM's return rate is 22%, nearly triple the company average of 8% — this is worth flagging as a data quality or fulfillment issue before it's read as a demand signal.
Business Implications
• The July bundle pricing change is doing real work in North America and should be considered for EMEA, where growth has stalled at a similar starting point to where North America was before the change.
• The declining average order value means the 22% unit growth is not translating proportionally into revenue — worth investigating whether promotional activity is training customers toward lower-value purchases.
• LATAM's combination of falling revenue and elevated returns suggests a fulfillment or product-fit issue specific to that region, not a broader demand weakness the other numbers would support.
Recommended Actions
• Highest impact: pilot the North America bundle pricing model in EMEA next quarter, given the similar market conditions before North America's change.
• Medium impact: audit LATAM's fulfillment process and return reasons before the next planning cycle — the 22% return rate is large enough to be actively eroding the region's contribution margin.
• Lower impact but low-cost: hold MEA's growth rate out of headline reporting until the region crosses a materiality threshold (e.g., $1M quarterly), to avoid resourcing decisions based on a statistically noisy small-base number.
La mayoría de los prompts de "analiza estos datos" producen un resumen competente de lo que dicen los números, que no es lo mismo que un análisis sobre el que un ejecutivo pueda actuar. Un modelo al que se le pide "analizar estos datos" describirá las tendencias con precisión y se detendrá ahí, porque nada en una instrucción vaga lo obliga a hacer el trabajo más difícil de interpretación.
Por qué cuatro etapas explícitas en lugar de un "analiza" abierto
Separar Tendencias, Anomalías, Implicaciones Comerciales y Acciones Recomendadas en etapas distintas obliga al modelo a completar realmente cada tipo de razonamiento en lugar de mezclar descripción y recomendación en un solo párrafo que suena bien pero no se sostiene.
Por qué la sección de Restricciones hace la mayor parte del trabajo real
El requisito de que "cada afirmación debe referenciar un número específico" es lo que evita que el modelo recurra a lenguaje vago como "mostró un fuerte crecimiento". La instrucción de marcar problemas de calidad de datos antes de sacar conclusiones existe porque los modelos, de otro modo, tratan cada número en un conjunto de datos como igualmente confiable.
Por qué las Implicaciones Comerciales son una etapa separada de las Tendencias
Este es el paso que la mayoría de los análisis generados por IA omiten por completo, y es el que realmente importa al lector. "Los ingresos en la Región X crecieron 41%" es una tendencia. "El crecimiento de la Región X provino de una base más pequeña, así que no le asignes recursos de la misma manera que asignarías a un aumento del 41% en tu región más grande" es una implicación.
Cómo aprovechar esto al máximo
Pega datos reales, no una descripción de los datos — el modelo necesita los números reales para detectar anomalías como el aumento en la tasa de devolución del ejemplo de salida.