Prompt

Using the raw data provided below, generate a clear analysis summary that highlights key insights. The data pertains to [brief description of data context, e.g., monthly sales figures by product]. Identify important trends (such as increases or decreases over time), notable outliers or anomalies, and any significant correlations you observe. Present the findings in a concise narrative form, explaining what the data means in practical terms. If applicable, include actionable recommendations based on the insights (for example, suggest actions if a particular metric is underperforming).

Insert raw data or a summarized form of the data here

How to Use

1. Define Your Inputs: Gather the data that you want analyzed. This could be a table of numbers, a spreadsheet, or a set of statistics. Make sure you understand the basics of the data (what each column or metric represents, the time period, etc.). If the dataset is large, consider providing a summarized version or key highlights due to input size limits. For example, you might calculate summary stats (averages, totals) or list data in a condensed form if you can’t show it all.

2. Customize the Prompt: Fill in the prompt with a brief description of the data context and then include the data itself (or the important parts of it). For instance: “data pertains to monthly sales by region for the past 2 years” and then perhaps provide a few data points or trends, like “North: 500→600→700…, South: 450→400→500…”, etc., or simply paste a small table. Specify what you want out of the analysis — e.g., “focus on year-over-year growth and any seasonal dips” if those are important to you. If you expect recommendations, state that in the prompt (though the base prompt already invites them if applicable).

3. Optional Add-ons: You can request a particular format for the output. For example, ask for bullet-point insights versus a paragraph narrative, if that suits your needs. If you have multiple sets of data to compare, you could instruct “compare the trends between Product A and Product B”. Additionally, if you want the AI to hypothesize reasons for trends (which can be speculative), you might add “and suggest possible reasons for these trends”. Just be cautious with speculative analysis — ensure you label it as such if you use it.

4. Run the Prompt: Input the prompt (with your data included) into the AI. The AI will analyze the data provided and generate a summary of insights. It will likely highlight patterns (e.g., “sales peaked in Q4 each year”, “product X consistently outsells product Y by 20%”), point out anomalies (“except in May, where sales dropped significantly below the trend”), and potentially end with recommendations (“focus marketing on the Northern region which shows highest growth”).

5. Review & Select: Read the AI’s summary and compare it against the data to ensure accuracy. While AI is good at summarizing, it might misinterpret if the data was ambiguous or if there were any copying errors in input. Verify each key point: does the trend described actually exist in the numbers? If something is off, you may need to clarify the data or correct the AI. Also, consider the recommendations critically – do they make business sense, and do they follow from the data? If needed, refine the prompt to be more specific or ask follow-up questions (like “elaborate on why May was an outlier”).

6. Expected Outcome: You will get a concise yet informative analysis of your raw data, written in plain language. Instead of poring over spreadsheets, you’ll see the main takeaways highlighted for you – for example, “Overall, sales increased 15% from Q1 to Q4, with the Eastern region growing the fastest. An notable anomaly occurred in May with a sharp dip, possibly due to supply issues. It’s recommended to investigate that dip and to allocate more resources to the Eastern region, which showed a 25% year-over-year growth.” This allows you to quickly understand and act on the data insights without doing all the analysis manually, making your decision-making process faster and more data-driven.