More about SMART questions

Companies in lots of industries today are dealing with rapid change and rising uncertainty. Even well-established businesses are under pressure to keep up with what is new and figure out what is next. To do that, they need to ask questions. Asking the right questions can help spark the innovative ideas that so many businesses are hungry for these days.

The same goes for data analytics. No matter how much information you have or how advanced your tools are, your data won’t tell you much if you don’t start with the right questions. Think of it like a detective with tons of evidence who doesn’t ask a key suspect about it. Coming up, you will learn more about how to ask highly effective questions, along with certain practices you want to avoid.

Highly effective questions are SMART questions:

Examples of SMART questions

Here's an example that breaks down the thought process of turning a problem question into one or more SMART questions using the SMART method: What features do people look for when buying a new car?

• Specific: Does the question focus on a particular car feature?
• Measurable: Does the question include a feature rating system?
• Action-oriented: Does the question influence creation of different or new feature packages?
• Relevant: Does the question identify which features make or break a potential car purchase?
• Time-bound: Does the question validate data on the most popular features from the last three years?

Questions should be open-ended. This is the best way to get responses that will help you accurately qualify or disqualify potential solutions to your specific problem. So, based on the thought process, possible SMART questions might be:

• On a scale of 1-10 (with 10 being the most important) how important is your car having four-wheel drive?
• What are the top five features you would like to see in a car package?
• What features, if included with four-wheel drive, would make you more inclined to buy the car?
• How much more would you pay for a car with four-wheel drive?
• Has four-wheel drive become more or less popular in the last three years?

Things to avoid when asking questions

Leading questions: questions that only have a particular response

• Example: This product is too expensive, isn’t it?

This is a leading question because it suggests an answer as part of the question. A better question might be, “What is your opinion of this product?” There are tons of answers to that question, and they could include information about usability, features, accessories, color, reliability, and popularity, on top of price. Now, if your problem is actually focused on pricing, you could ask a question like “What price (or price range) would make you consider purchasing this product?” This question would provide a lot of different measurable responses.

Closed-ended questions: questions that ask for a one-word or brief response only

Example: Were you satisfied with the customer trial?

This is a closed-ended question because it doesn’t encourage people to expand on their answer. It is really easy for them to give one-word responses that aren’t very informative. A better question might be, “What did you learn about customer experience from the trial.” This encourages people to provide more detail besides “It went well.”

Vague questions: questions that aren’t specific or don’t provide context

Example: Does the tool work for you?

This question is too vague because there is no context. Is it about comparing the new tool to the one it replaces? You just don’t know. A better inquiry might be, “When it comes to data entry, is the new tool faster, slower, or about the same as the old tool? If faster, how much time is saved? If slower, how much time is lost?” These questions give context (data entry) and help frame responses that are measurable (time).

Self-Reflection: Data analyst scenarios

Overview

Now that you have been introduced to the SMART framework for asking questions, you can 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 scenario

You 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 “deep dive” into this data. To get this project started, you must ask some questions and get some information.

SMART questions

As 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 questions

You 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:

• Objectives
• Audience
• Time
• Resources
• Security

Think about how you can ask SMART questions about each of these topics.

Reflection

Consider 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. Type your response in the text box below.

Explain:

Great work reinforcing your learning with a thoughtful self-reflection! A good reflection on this topic would describe how you applied SMART questions to the scenario.

Here are a few questions you might want to ask:

• When is the project due?
• Are there any specific challenges to keep in mind?
• Who are the major stakeholders for this project, and what do they expect this project to do for them?
• Who am I presenting the results to?

Here are some examples of questions you might ask based on the suggested topics:

• Objectives: What are the goals of the deep dive? What, if any, questions are expected to be answered by this deep dive?
• Audience: Who are the stakeholders? Who is interested or concerned about the results of this deep dive? Who is the audience for the presentation?
• Time: What is the time frame for completion? By what date does this need to be done?
• Resources: What resources are available to accomplish the deep dive's goals?
• Security: Who should have access to the information?

These questions can help you focus on techniques and analyses that produce results of interest to stakeholders. They also clarify the deliverable’s due date, which is important to know so you can manage your time effectively. When you start work on a project, you need to ask questions that align with the plan and the goals and help you explore the data. The more questions you ask, the more you learn about your data, and the more powerful your insights will be.

Asking thorough and specific questions means clarifying details until you get to concrete requirements. With clear requirements and goals, it’s much easier to plan and execute a successful data analysis project and avoid time-consuming problems down the road.

Self-Reflection: Ask your own SMART questions

Overview

Now that you have learned more about SMART questions, you can pause for a moment and ask your own. In this self-reflection, you will consider your thoughts about the SMART question framework.

This self-reflection will help you develop insights into your own learning and prepare you to apply the SMART 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!

Asking real-world questions

In this activity, you will have a data conversation with someone you know. This can be in person, over the phone, or in a video chat.

Choose someone in your life who uses data to make better decisions. This might be a family member who runs a small business, a friend who leads a committee for the Parent Teacher Association, or a neighbor who teaches piano lessons. All of these people turn to data in some way to be more effective in their roles.

Let them know you're training to be a data analyst, and would like to have a chat about data to practice your skills asking questions. By the end of this conversation, you'll end up with some useful insights that will benefit both of you.

Plan for the conversation

First, decide who you will speak with and how they might use data. Your goal is to plan for a successful conversation. Think about how much time you need and how you will use it. For this step, review the following advice:

• Prioritize your questions: Prepare to ask the most important and interesting questions first.
• Make your time count: Stay on subject during the conversation.
• Clarify your understanding: To avoid confusion, build in some time to summarize answers to make sure you understood them correctly. This will go a long way in helping you avoid mistakes. For example, in a conversation with a teacher, you might check your understanding with a statement like, “Just to double check that I understand what you’re saying correctly, you currently use test scores in the following ways…”

Depending on the field they are in, the person you chat with may not be comfortable sharing detailed data with you. That's okay! Be sure to respect what they are willing to share during your conversation.

Create questions

Now, come up with questions to help you understand their business goals, the type of data they interact with, and any limitations of the data.

Use the SMART question framework to make sure each question you ask makes sense based on their field. Each question should meet as many of the SMART criteria as possible.

As a reminder, 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.

For instance, if you have a conversation with someone who works in retail, you might lead with questions like:

• Specific: Do you currently use data to drive decisions in your business? If so, what kind(s) of data do you collect, and how do you use it?
• Measurable: Do you know what percentage of sales is from your top-selling products?
• Action-oriented: Are there business decisions or changes that you would make if you had the right information? For example, if you had information about how umbrella sales change with the weather, how would you use it?
• Relevant: How often do you review data from your business?
• Time-bound: Can you describe how data helped you make good decisions for your store(s) this past year?

If you are having a conversation with a teacher, you might ask different questions, such as:

• Specific: What kind of data do you use to build your lessons?

• Measurable: How well do student benchmark test scores correlate with their grades?

• Action-oriented: Do you share your data with other teachers to improve lessons?

• Relevant: Have you shared grading data with an entire class? If so, do students seem to be more or less motivated, or about the same?

• Time-bound: In the last five years, how many times did you review data from previous academic years?

• If you are having a conversation with a small business owner of an ice cream shop, you could ask:

• Specific: What data do you use to help with purchasing and inventory?

• Measurable: Can you order (rank) these factors from most to least influential on sales: price, flavor, and time of year (season)?

• Action-oriented: Is there a single factor you need more data on so you can potentially increase sales?

• Relevant: How do you advertise to or communicate with customers?

• Time-bound: What does your year-over-year sales growth look like for the last three years?

Take good notes

It is important to take good notes during your conversation. Your notes should be comprehensive and useful. To help you capture meaningful notes, you should stick to a process of asking a question, clarifying your understanding of their response, and then briefly recording it in your notes.

Remember: If a question is worth asking, then the answer is worth recording. Commit yourself to taking great notes during your conversation.

Helpful aspects of your conversation to note include:

• Facts: Write down any concrete piece of information, such as dates, times, names, and other specifics.
• Context: Facts without context are useless. Note any relevant details that are needed in order to understand the information you gather.
• Unknowns: Sometimes you may miss an important question during a conversation. Make a note when this happens so you can figure out the answer later.

For example, if the previous SMART questions led the ice cream shop owner to propose a project to analyze customer flavor preferences, your notes might appear something like this:

• Project: Collect customer flavor preference data.
• Overall business goal: Use data to offer or create more popular flavors.
• Two data sources: Cash register receipts and completed customer surveys (email).
• Target completion date: Q2
• To do: Call back later and speak with the manager about the location of survey data.

The notes you will take will differ greatly based on the data conversation you have. The important thing is that your notes are clear, organized, and concise.

Now you are ready to have a great conversation about data in real life.

Reflection

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?

Now, write 2-3 sentences (40-60 words) in response to each of these questions. Type your response in the text box below.

Explain: Great work reinforcing your learning with a thoughtful self-reflection! A good reflection would describe how you created relevant SMART questions and what insights they helped you gain.

Coming up with SMART questions for data-driven conversations is one of the most important tools in a data analyst’s arsenal. As you practice, you will feel more comfortable interacting with others about data and asking meaningful questions during those interactions. Going forward, you can also practice asking yourself SMART questions to help you manage and measure your own goals.

What your questions revealed

You recently created SMART questions to help you learn how a friend or family member uses data in their career or personal life. Now, consider the questions you asked:

• Which questions provided the most useful and interesting responses? Why were they successful?
• Which questions led to less insightful answers? Why do you think that was?

Share a response of two or more paragraphs (150-200 words) about what you learned regarding SMART questions as a result of this activity. Then, visit the discussion forum to read what other learners have written, and engage in discussion about at least two posts.

Test your knowledge on crafting effective questions

Question 1

A data analyst uses the SMART methodology to create a question that encourages change. This type of question can be described how? A. Results-focused

B. Stimulating

C. Action-oriented

D. Motivational

The correct answer is C. Action-oriented. Explain: In the SMART methodology, questions that encourage change are action-oriented.

Question 2

A time-bound SMART question specifies which of the following parameters?

A. The topic or subject of the analysis

B. The metrics or measures related to the analysis

C. The desired change the analysis should produce

D. The era, phase, or period of analysis

The correct answer is D. The era, phase, or period of analysis. Explain: A time-bound SMART question specifies the era, phase, or period of analysis.

Question 3

A data analyst working for a mid-sized retailer is writing questions for a customer experience survey. One of the questions is: “Do you prefer online or in-store?” Then, they rewrite it to say: “Do you prefer shopping at our online marketplace or shopping at your local store?” Describe why this is a more effective question.

A. The first question is closed-ended, whereas the second question encourages the respondent to elaborate.

B. The first question contains slang that might not make sense to everyone, whereas the second question is easily understandable.

C. The first question is vague, whereas the second question includes important context.

D. The first question is leading, whereas the second question could have many different answers.

The correct answer is C. The first question is vague, whereas the second question includes important context. Explain: Vague questions do not provide context. The second question clarifies that the data analyst wants to learn exactly how and where customers prefer to shop.

Question 4

A data analyst at a social media company is creating questions for a focus group. They use common abbreviations such as PLS for “please” and LMK for “let me know.” This is fair because the participants use social media a lot and are likely to be technically savvy. True or False?

A. True

B. False

It is false statement. Explain: Fairness means asking questions that make sense to everyone. Even if a data analyst suspects people will understand abbreviations, slang, or other jargon, it’s important to write questions with simple wording.