Soft skills for Data Scientists
How Do You Build Up Your Critical Thinking?
Data scientists, you need this skill.
Critical Thinking uses logic to connect ideas and evaluate facts to develop a belief about a situation. In other words, a critical thinker logically uses the information to build a better understanding and solve a problem. It is an essential asset of any profession where the goal is to deduce objective information and shape realistic solutions.
"It is the mark of an educated mind to be able to entertain a thought without accepting it." – Aristotle
Knowing that about ~200 cognitive biases are identified in the human mind reinforces the need to develop this skill.
But why is it so crucial for Data professionals?
Data professionals are the link between insightful data points and the business, bringing both responsibility and opportunity to steer changes and drive impacts.
"With great power, come great responsibility – Peter Parker"
Too good to be true
According to the WHO, data integrity is necessary and ethical to ensure trustworthiness, transparency, and quality. Critical thinking helps you step back and challenge the integrity of the data in your hands. A great example you probably have already experienced:
How many times did you start working on a dataset right away? VS Pausing and asking yourself: Do the data actually make sense for solving the problem?
Assuming the data is clean, correctly recorded, or even relevant to the problem can lead to wrong conclusions and results that often are too good to be true. Use critical thinking to challenge the reliability of the data itself and avoid making embarrassing erroneous decisions.
Stay focused on what brings value
I’m sure you have already heard about the rabbit hole.
It describes this moment where we get so lost in little details that we fail to look at the overall picture and miss delivering what the business wants. Critical thinking implies that you interrogate data from a wider variety of perspectives to form a more objective analysis of the problem and a deeper understanding of what it is you are trying to solve before shaping an opinion.
The must-have skill
So, as a data professional, critical thinking can help you ask the right questions and focus solely on facts while keeping your gut feeling aside. It encourages you to broaden your perspective while collecting data and challenge the relevancy of it for the problem you are solving. Last but not least, critical thinking will help you constantly remain curious, which can lead to more innovative solutions.
All right. So, how to work on it?
Be willing to be wrong.
The difficulty with critical thinking is that it requires accepting being wrong or, at least, a certain level of skepticism about your truth. It implies a willingness to fail, and let’s be honest: nobody likes that. At best, it’s uncomfortable. At worst, it’s painful.
It does NOT require from you any particular talent. Anybody can be wrong.
What you need is to grow your acceptance of being vulnerable and a more profound sense of humility. It’s not fun or enjoyable, but it’s essential for your personal growth. Take that path, and you will surely and shortly feel incredibly empowered!
Give yourself time and space.
Perhaps you could start giving yourself time and space to be with your thoughts and digging into the "why" with the 5 "whys" technique to get to the root cause instead of focusing on the symptoms of a problem.
I like to meditate to give myself space and time. Some prefer to practice physical activity or do journaling. But it can also be something as simple as decluttering your calendar from all the non-essential meetings and events or sitting for 30 min with a cup of coffee. Whatever works for you!
Surround yourself with a diversity of minds.
Don’t isolate yourself with your perspective for too long, or you might fall into the well-known confirmation bias. Instead, I would recommend surrounding yourself with people with various backgrounds and asking them to do their best to rip apart your logic. Actively diversifying the sources of information and logic will reinforce not being caught up in one single perception of the world.
Be ready; it’s going to hurt. Don’t take anything personally. This is not about you. This is about openness and seeking a higher version of the truth. So keep your mind flexible, and you will gain perspectives you might have never considered. And that is growth.
More about confirmation bias and what to do about it in this video from Cassie Kozyrkov – 1st Chief Decision Scientist at Google, Keynote Speaker, and Creator of Making Friends with Machine Learning
Own your failure.
Sharing your successes is excellent for inspiring and building self-confidence, as does share your failure! By sharing our learning experiences, a.k.a mistakes, we normalize that it’s okay not to know everything. We allow more collaboration and trust because we know that we can ask for help and that if something is wrong, we can work together on fixing it. Research has shown that learning about other people’s challenges improves global performance and efficiency.
"You must not bury your failures but talk about them openly and analyze what went wrong so you can learn new rules for decision making" [Stephen Scharzman]
Let’s be honest; sharing failure is hard! Because failing at something is demonstrating a form of incompetence that runs against our self-esteem. But it can also be relieving and fun! Don’t take my word for it; attend an event organized by Fuckup Nights or watch their videos online. This is incredibly cathartic and inspiring.
Seek a safe environment.
Therefore to grow this ability, you need an environment normalizing failure, open for experimentation and reflection. It is a safe place where you can take a step back from what you consider true and challenge it. A space where your teammates have permission to call your attention to your blind spots or your personal inclination. Most companies struggle with that, as they are hyper-focused on the result. Studies suggest that to generate this sense of safety, workplaces should promote more values centered around learning and transparency, starting with leaders, who should be more open about their fallibility. Because what is best, than teaching by example?
✌️ Watch this TEDx video on "How building a psychologically safe workplace?":
Expand your knowledge.
Another way of expanding your perception and challenging your inner truth is by reading as widely as possible. A classic error is over-extrapolating from our reasonably limited experience. One way to widen your experience is by reading about various topics from various authors. Anything of quality that can help to broaden your perspective is valuable.
And if you want to read more about how to grow your critical thinking, I will highly recommend you these books:
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Thinking, Fast and Slow by Daniel Kahneman This book brings your attention to your thinking process: the good, the bad, and the ugly. It brings awareness which eventually helps you to make more informed decisions.
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Never Stop Learning: Stay Relevant, Reinvent Yourself, and Thrive by __ Bradley Staats This book is a great starting point for accepting that you don’t know everything and becoming a better learner. It can help you to own your mistakes and see others as a source of growth instead of competition.
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Thinking in Bets: Making smarter decisions when you don’t have all the facts by Anne Duke At the time I’m writing this article, I’m still reading this book and enjoying it a lot. It’s about embracing uncertainties in your decision-making process with the best possible outcome. I believe this can be highly relevant while doing data analysis or building a model.
Here you are, with some tools and ideas to grow your critical thinking. If there is one thing I’d like you to take from this article, it’s that:
Critical thinking require openness.
Openness to different versions of the truth, openness to being wrong and vulnerable, openness to your failure. So give yourself some time; it’s a journey, not a goal!
As always, leave me your comments and messages; I’ll be happy to hear from you and improve this article. Follow me on Medium and discover more content about Data Science explained for all.
This article has been inspired by a post from Jordan Morrow and comments from experts and peers: Koo Ping Shung, Susan Walsh, Don Gannon-Jones, Ivanna Jurkiv, Brian Willett, Joel Shapiro, Megan S., Amber Toro-Keech, Dan Manning, Hana M., Risa Mish, Heather MacDonald.
Jordan Morrow on LinkedIn: #dataliteracy #leadership #data | 59 comments
