In this post, you will learn how to ask effective questions as a data analyst using the SMART framework and fairness. You will also understand why stakeholder expectations are important for your work and see some examples of stakeholder needs in a project.
• Stakeholders are the people who have invested time, interest and resources in the projects you do as a data analyst. You need to understand their needs and communicate with them effectively.
• To ask effective questions, you need to follow the SMART framework, which means that the questions must be specific, measurable, action-oriented, relevant and time-bound.
• To ask specific questions, you need to focus on a single topic or a few related ideas. For example, instead of asking "Do children get enough physical activity today?", ask "What percentage of children achieve the recommended 30 minutes of physical activity at least three days a week?".
• To ask measurable questions, you need to be able to quantify and evaluate the answers. For example, instead of asking "Why did our recent video go viral?", ask "How many views did our video get on YouTube the first week it was posted?".
• To ask action-oriented questions, you need to encourage change. For example, instead of asking "How can we get customers to buy our products more often?", ask "What marketing strategies will increase our customer loyalty?".
• To ask relevant questions, you need to focus on what matters and has meaning for the problem you are trying to solve. For example, instead of asking "Why does it matter that the polar bear started disappearing?", ask "What human activities affected the Arctic habitat of the polar bear between 2010 and 2020?".
• To ask time-bound questions, you need to specify the period to be studied. For example, if you want to study the period between 2010 and 2020, limit your questions to that time span.
• To ask fair questions, you need to avoid bias, which is the preference in favor or against a person, group or thing. Bias can influence data results in a systematic way. To avoid bias and ensure fairness, you need to follow these guidelines:
• Don't use biased questions that influence answers in a certain way. For example, instead of asking "These are the best sandwiches ever, right?", ask "How do you rate these sandwiches on a scale of 1 to 5?".
• Don't use closed-ended questions that can be answered with a yes or no. Use instead open-ended questions that allow for more detailed and nuanced responses. For example, instead of asking "Did you enjoy growing up in Canada?", ask "What was your experience growing up in Canada?".
• Don't use vague questions that lack context or clarity. Use instead specific and precise questions that make sense to everyone. For example, instead of asking "Do you prefer chocolate or vanilla?", ask "What is your favorite flavor of cake?".
• Don't use questions that make assumptions or exclude certain groups of people. Use instead questions that are inclusive and respectful of diversity. For example, instead of asking "What do you love most about our exhibits?", ask "How accessible are our exhibits for people with disabilities?".
Asking effective questions is a crucial skill for any data analyst. It helps you understand the goal of a project, communicate more effectively across your team, and build trust in your work. By using the SMART framework and ensuring fairness, you can craft questions that lead to valuable insights and solutions.
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