# How to Convert Any Business Question into an Analytical One Using 5 Simple Steps

As data analytics professionals, our ability to transform business questions into actionable analytic queries is pivotal. This process ensures that we not only provide answers but also deliver insights that drive informed decision-making. Below is a simple five-step framework to extract key analytical components from any business question.

5 Steps Framework to follow

Step 1: Identify the Measure

A measure is a numerical column that quantifies the primary focus of the business question. Examples include sales revenue, number of customers, units sold, or website visits.

Key Questions to Ask:

• What quantity or value needs to be analyzed?
• Is the required data numerical and measurable?

Example: For the question, “What are the total sales of Product X?” the measure is sales.

Step 2: Define the Dimension

A dimension is a categorical variable used to slice or segment the aggregated measure. Dimensions provide context by breaking down the measure into understandable categories, such as time, geography, product type, or customer demographics.

Key Questions to Ask:

• How should the data be segmented?
• What categories or labels can provide additional insight into the measure?

Example: In “What are the total sales of Product X by region?” the dimension is region.

Step 3: Select the Aggregation Function

The aggregation function determines how the measure will be calculated. Common functions include:

• SUM: Total of all values.
• AVERAGE: Mean value.
• COUNT: Total number of occurrences.
• MIN/MAX: Lowest/highest value.
• MODE: Most frequently occurring value.
• MEDIAN: Middle value in a sorted list.

Key Questions to Ask:

• What type of summary statistic will best answer the business question?
• Is the inquiry about totals, averages, counts, or extreme values?

Example: In “What is the average sales per region for Product X?” the aggregation function is AVERAGE.

Step 4: Determine the Filter

Filters are used to refine the dataset, focusing on a subset of data that aligns with the business question. This step eliminates irrelevant values and ensures the analysis is both accurate and targeted.

Key Questions to Ask:

• What criteria define the desirable subset of data?
• Are there specific conditions or thresholds?

Example: In “What are the total sales of Product X in 2024?” the filter is year = 2024.

Step 5: Confirm the Sort

Sorting organizes the output for easier interpretation. Sorting options include ascending, descending, or custom orders.

Key Questions to Ask:

• How should the results be ordered for clarity?
• Does the business question imply a specific order?

Example: In “What are the top 5 regions by total sales of Product X?” the sorting criterion is descending order of total sales.

Applying the Framework

Here’s how the framework comes together with a practical example:

Business Question:

“What are the top 3 products by total revenue in Q4 2024?”

Analytical Breakdown:

1. Measure: Revenue
2. Dimension: Product
3. Aggregation Function: SUM
4. Filter: Quarter = Q4 and Year = 2024
5. Sort: Descending by total revenue

Using this structured approach, the question is now a clear analytic query: "Calculate the total revenue (SUM of revenue) for each product (Dimension: Product) filtered by Q4 2024 (Filter) and sorted in descending order."

Conclusion

Converting a business question into an analytic one doesn’t have to be overwhelming. By breaking it down into the five elements – Measure, Dimension, Aggregation Function, Filter, and Sort – you can ensure clarity, precision, and relevance in your analyses. Whether you’re working with a simple dataset or a complex business challenge, this framework serves as a reliable guide to deliver impactful insights.