Asking the Right Questions and Domain Research
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Data Science

Asking the Right Questions and Domain Research

Basty’s Notebook

Hi, everyone! My name is Basty, and I’m a data scientist and educator from Metro Manila. If there’s something I value so much, that is education. Education has allowed me to not only dive into the amazing world of code and data, but also to encourage and inspire others to do the same. Read more about me here.

Outside of work and school, I love playing video games like Valorant and League of Legends. I also love listening to Broadway musicals (HAMILTON, DEH, TICK TICK BOOM ALL THE WAY!). Lastly, I LOVE watching Friends, New Girl, HIMYM, and The Big Bang Theory.

Now, let’s take a look at my notebook!

March 2023 Notebook entry

With this month’s theme about having the power to make a difference, and my previous article about career shifting—asking questions and researching in the field you are in is important to help you in your career. And so, in this article we’ll talk about the importance of asking the right questions and having the domain knowledge to succeed in your role as an analyst. Let’s get started!

Let’s first establish why it's important to ask the right questions through a short story on how I got to appreciate this skill. When I was still fairly new in my role as a Junior Data Scientist, I was so eager to do projects to stand out as a new hire, without having to understand how these projects could impact the company. 

When I was assigned my first task, I wanted to get my hands dirty as soon as possible by analyzing the data and creating graphs. It took me quite some time to finish this task because of a crucial mistake that I made—not defining the goals and objectives of the task, and not fully understanding why I was doing the task. In the end, I wasted my time cleaning the data for some time, only having to repeat it again because of having the wrong assumptions and mindset towards the task. 

Now don’t get me wrong, I fully recognize the difficulties of asking the right questions—especially if you’re a beginner. What questions do you need to ask? How do you know that these questions are even right? When do I stop asking questions? These are some challenges that many beginners face. I believe that asking questions is easy, but asking the right questions is ambiguous. How will you know that a question is even right, when we ourselves don’t even know what questions to ask? 

I hope by now, you already got a feel of why asking the right questions is important as an analyst, or even in any job. In order to help you apply this in your future or current role, here are some tips and guides on how to ask the right questions.

Defining the problem

Defining the problem is actually the first step in the data science framework/pipeline. Without having a clear understanding of what the problem is, or what you are trying to accomplish, you won’t be able to craft the right questions that are geared towards the completion of the problem. 

  1. Understand the problem

    This will always be your first step in every task you do. What are you trying to solve and why are you solving it? Most of the time, managers just give out tasks without giving you that much context behind it, making it unclear on our end what our actual goal is. So as data analysts/scientists, it is our job to help our stakeholders frame the problem into a data science statement. The only way we can do this is by putting on our detective hats and looking at the problems in different perspectives.

    This is where the term Domain Knowledge also comes in. In order for us to fully understand the problem, we need to research the nature of the problem. Typically, we should ask questions from the stakeholders because they have the domain knowledge needed for the problem. After getting the domain knowledge from them, it is now our job to come up with a data-centric solution that will drive impact in the company.

  2. Define your outcome and actionable steps

    An outcome is a goal or desired end result that you and your stakeholders want to achieve. Now that you know the current state, which is the problem, you now need to come up with an outcome, which represents the desired state.

    After establishing an outcome, what are now the activities and steps you need to take in order to achieve the outcome. Make sure that you have a solid plan on what to do from start to finish, while also making sure that there is room for iteration because data analytics and data science is never a linear path. There will always be roadblocks and insights that will make you look back from your previous steps.

  3. Defining a success metric

    This is essential. No matter how defined your problem statement and steps are, without any metrics to evaluate the success of the project then you’ll be left with only an ambitious project. This metric should be measurable and not something that cannot be quantified. It’s important that you discuss this with your stakeholders as well, and that it should come in at the start of the project, where you are still asking questions.

You are now another step closer to being a data analyst, AKA a detective. Always remember that asking the right questions and having domain knowledge are crucial to success in any role, especially as an analyst. Defining the problem, understanding the problem, defining the outcome and actionable steps, and defining a success metric are important steps to take when asking the right questions. It's also essential to research the nature of the problem and ask questions from stakeholders to gain the domain knowledge needed for the problem. By putting on a detective hat and looking at the problem from different perspectives, analysts can help stakeholders frame the problem into a data science statement and come up with a data-centric solution that will drive impact in the company. 

Asking the right questions and having domain knowledge are skills that can be developed and honed over time, however I want you to also remember that asking questions is not just about getting answers, but it's also about gaining a deeper understanding of the problem at hand. By taking the time to define the problem, understand the problem, and define the outcome and actionable steps, analysts can ensure that they are asking the right questions and that their work is aligned with the goals and objectives of the organization. In addition, asking the right questions is also a collaborative effort. It's important to involve stakeholders and get their input and feedback throughout the process. 

Lastly, congratulations on reaching the end of this blog. I hope it gave you a glimpse of the importance of asking the right questions in order to solve problems using a data science approach as well as to generate insights and impact in the company.

Never stop learning!