Before you begin your conversation about data, consider each of the above steps. Think about potential candidates, brainstorm some SMART questions, and get an idea of the information you want to record during your conversation. Then, reflect on your conversation:What SMART questions did you ask? How did these questions tie into the field of the person you chatted with? What insights did you discover during your conversation? How did the SMART framework help you arrive at your conclusions?

Write 5-7 sentences (100-140 words) about data sources discussed during your real-life data conversation.First, consider your data conversation and how it went. Here are some questions to help you get started:Was there anything challenging about getting the conversation started?Were there questions you didn’t get to ask? Did you manage your time effectively?Did you take notes? Are they as detailed as you need them to be?Are you missing any information? Is there anything that you still find unclear or vague?  If you could do the conversation over again, is there anything you would change?Next, turn your attention to your notes and reflect on what you know about the data itself:What are the sources of data available for the project?Which data sources were qualitative and which were quantitative? Explain your answer. What decisions could you make when considering each data source separately? Could you make different decisions about the data if you combined it? If so, give an example.Is there any kind of data that isn’t available, but you would like to find? If so, what is it, and why would you like to know more about it?

What SMART questions did you ask? How did these questions tie into the field of the person you chatted with? What insights did you discover during your conversation? How did the SMART framework help you arrive at your conclusions?Now, write 2-3 sentences (40-60 words) in response to each of these questions

Why is it important to create SMART questions about your datasets? How do these questions benefit your work as a data professional?Why is it important to determine your SMART questions and answers before crafting an SOW?Why is it important to perform data analysis on datasets?Now, write 2-3 sentences (40-60 words) in response to each of these questions.

OverviewNow that you have been introduced to the SMART framework for asking questions, pause to apply what you are learning. In this self-reflection, you will consider the questions you would ask in a specific scenario. This self-reflection will help you develop insights into your own learning and prepare you to apply your knowledge of the SMART question framework to your own data investigations. As you answer questions—and come up with questions of your own—you will consider concepts, practices, and principles to help refine your understanding and reinforce your learning. You’ve done the hard work, so make sure to get the most out of it: This reflection will help your knowledge stick!The scenarioYou are three weeks into your new job as a junior data analyst. The company you work for has just collected data on their weekend sales. Your manager asks you to perform a thorough exploration of this data. To get this project started, you must ask some questions and get some information.SMART questionsAs a refresher, SMART questions are:Specific: Questions are simple, significant, and focused on a single topic or a few closely related ideas.Measurable: Questions can be quantified and assessed.Action-oriented: Questions encourage change.Relevant: Questions matter, are important, and have significance to the problem you’re trying to solve. Time-bound: Questions specify the time to be studied.Next, you will use the SMART framework to ask effective questions about the scenario above. Then, you will reflect on the topics your SMART questions should address.Ask the right type of questionsYou can apply the SMART framework to all types of questions. The type of questions you ask can help you explore deeper with your data. Consider the ways your questions help you examine objectives, audience, time, security, and resources.Some common topics for questions include: ObjectivesAudienceTimeResourcesSecurityThink about how you can ask SMART questions about each of these topics.ReflectionConsider the scenario above:Based on the SMART framework, which questions are most important to ask? How will these questions clarify the requirements and goals for the project?How does asking detailed, specific questions benefit you when planning for a project? Can vague or unclear questions harm a project?Now, write 2-3 sentences (40-60 words) in response to each of these questions

What will help you decide which data points to use in your data story?1 pointKnowing the number of people who will see your data story Creating a narrative first Understanding the questions you want to answerCreating a complex goal