How do you define research questions for a new data analysis project?

1 Identify the problem

The first step is to identify the problem that you want to solve or the question that you want to answer with your data analysis. This can be based on your own curiosity, a business need, a gap in the literature, or a client request. The problem should be clear, specific, and relevant to your data analysis goals. For example, you might want to find out how customer satisfaction affects retention rates, or how different marketing channels influence sales conversions.

2 Review the literature

The second step is to review the existing literature on your problem or question. This can help you understand the background, context, and previous research on your topic. It can also help you avoid duplicating or contradicting existing findings, and identify gaps or opportunities for further exploration. You can use online databases, journals, books, reports, or other sources to find relevant literature. You should summarize and synthesize the main points, methods, and results of the literature that you review.

3 Formulate the main question

The third step is to formulate the main question that you want to answer with your data analysis. This is the overarching question that guides your data analysis process and defines your expected outcome. The main question should be concise, focused, and answerable with data. You can use the PICO framework to help you formulate the main question. PICO stands for Population, Intervention, Comparison, and Outcome. For example, your main question might be: How does customer satisfaction (outcome) differ between online and offline customers (population) who receive different types of incentives (intervention and comparison)?

4 Break down the main question

The fourth step is to break down the main question into smaller sub-questions that address different aspects or dimensions of the main question. These sub-questions help you narrow down your data analysis scope and specify your data analysis methods. They also help you organize your data analysis results and structure your data analysis report. You can use the SMART framework to help you break down the main question. SMART stands for Specific, Measurable, Achievable, Relevant, and Time-bound. For example, some of your sub-questions might be: What are the levels of customer satisfaction for online and offline customers? How do different types of incentives affect customer satisfaction? How does customer satisfaction relate to retention rates?

5 Determine the data sources

The fifth step is to determine the data sources that you will use to answer your sub-questions and main question. These data sources can be primary or secondary, quantitative or qualitative, structured or unstructured, or a combination of these. You should evaluate the availability, reliability, validity, and relevance of the data sources that you choose. You should also consider the ethical and legal implications of using the data sources. For example, you might use customer surveys, transaction records, web analytics, or social media data as your data sources.

6 Plan the data analysis methods

The sixth step is to plan the data analysis methods that you will apply to your data sources to answer your sub-questions and main question. These data analysis methods can include data cleaning, data transformation, data visualization, data modeling, data testing, or data interpretation. You should select the data analysis methods that are appropriate for your data type, data size, data quality, and data analysis objectives. You should also document your data analysis methods and assumptions. For example, you might use descriptive statistics, inferential statistics, regression analysis, or cluster analysis as your data analysis methods.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?