Enhancing Data Analysis in Data Science: Analytical Critical Thinking skills

Imagine delivering a presentation to a group of coworkers. You've developed a way to manufacture the company's signature product at half the cost. In the middle of your presentation, someone interrupts with a question: "Where did you get those figures?" How would you react to this question? In some organizations, this would be seen as confrontational. Usually, these types of questions come from skeptical or critical supervisors.

In Sidney Finkelstein's book Why Smart Executives Fail: And What You Can Learn from Their Mistakes, he points out that many executives accept good news without question. They save their questions for bad news or when they disagree, so asking questions takes on a negative tone. As a result, people in the organization become reluctant to ask questions.

However, when people stop asking questions, the organization is prone to repeating its mistakes. They're susceptible to groupthink and blind spots. If you follow the news, you can readily see that many failures in the public sector are due to crucial questions that were never asked.

Taking the Criticism out of Critical Thinking: Skills for Data Scientists

Asking interesting questions is a key component of critical thinking— the objective analysis and evaluation of an issue for the purpose of forming a judgment. It shouldn't be used or perceived as criticism— negative or disapproving judgments or comments. Organizations that want to encourage employees to ask questions need to take the criticism out of critical thinking. Critical thinking should be embraced by the entire organization as part of a collaborative quest for knowledge and insight.

Many organizations complain that their people don't think critically. These same organizations have nothing in place to facilitate and encourage critical thinking. Even worse, some organizations stifle it without even realizing what they're doing. Employees are rewarded for setting goals and objectives, planning, executing, and achieving their agreed upon objectives. They are not rewarded and are sometimes punished for challenging assumptions, questioning authority, trying new approaches, or proposing new ideas. How many managers have said or thought of saying to an employee, "You weren't hired to think."?

An organization that wants to be more innovative, creative, and collaborative needs to change its culture from strong top-down management (hierarchical) to a more team-oriented arrangement. In addition, it needs to encourage, facilitate, and reward critical thinking that leads to discovery and innovation.

Organizations can begin this transition by starting with the data science team. Even if everyone else in the organization is focused on objectives, plans, execution, and hitting their milestones, the data science team should be focused on asking questions, exploring the data, and building a growing body of organizational knowledge and insight. The rest of your organization may live in a world of statements and assumptions, but your data science team needs to operate in an environment of uncertainty, arguments, questions, and critical thinking.

Tips for Encouraging and Facilitating Critical Thinking in a Data Science Project

Critical thinking does not always just happen. You need to encourage and facilitate it, especially if the data science team has been recently formed. Here are a few suggestions for getting the ball rolling:

• Allocate sufficient time for thinking. If the data science team is overwhelmed with tasks and deadlines, team members will be focused on meeting objectives instead of thinking. They need time and space to think.
• Create a question board and encourage everyone in the organization to post questions, concerns, and challenges for the data science team. Questions from outside the data science team inspire questions within the data science team while exposing the team to different perspectives.
• Invite others from across the organization to share their perspectives with the team. Guest team members increase the team's knowledge of and insight into the organization. This knowledge and insight inspire questions and ideas.
• Get the data science team involved at the top levels. When executives or managers encounter a challenging problem or question, they should consult the data science team and then listen to what the team has to say. Rejecting the team's input outright discourages the team.
• Encourage team members to challenge assumptions. Assumptions are beliefs that are treated as facts. If the assumption is false, any conclusions drawn from that assumption are questionable. Unfortunately, because people accept assumptions as fact, they have no reason to question those assumptions. Whenever the team encounters a fact, it should ask the question, "Is this a fact or an assumption?" and then, if it is an assumption, "Is this assumption valid?"
• Formulate and test hypotheses. On a data science team, it's okay to guess — to formulate a hypothesis — but then the team needs to follow up by testing that guess. Organizations that aren't data driven, often guess without testing. Leadership comes up with what it believes is a great idea and then implements that idea without testing it first, which often results in costly failures. Data science teams, on the other hand, guess, test, analyze the results, and then propose a course of action.

Keep in mind that critical thinking is not easy. Think about the last time someone or some experience challenged you. Having to face the fact that you could be wrong or that a certain behavior is unacceptable can be uncomfortable and even psychologically painful. Those are growing pains. In the same way, a data science team needs to break free of its own comfort zone and challenge the organization to step out of its comfort zone in order to grow. The means by which it accomplishes that goal is critical thinking.

Frequently Asked Questions

Why is critical thinking in data analysis important for data science?

Critical thinking in data analysis is vital because it enables data scientists to thoroughly evaluate data sources, understand potential biases, and make well-informed decisions. By using critical thinking, data scientists can ensure the integrity and reliability of their analytic results, leading to better business insights.

What role does critical thinking in data science play in problem-solving?

Critical thinking in data science plays a key role in problem-solving by enabling data scientists to systematically analyze data, identify patterns, and draw accurate conclusions. Strong critical thinking skills help in breaking down complex business problems and finding innovative solutions.

What tools and techniques are essential for developing critical thinking in data science?

Essential tools and techniques for developing critical thinking in data science include data visualization tools, statistical software, machine learning algorithms, and techniques for data cleaning and preprocessing. These tools help in analyzing raw data and spotting trends and anomalies that require critical evaluation.

How can business problem definition improve analytical critical thinking in data science?

Clear business problem definition can significantly enhance analytical critical thinking in data science by providing a focused objective. It helps data scientists to frame their analysis within the context of the business problem, ensuring that the data analytics efforts are aligned with the desired business outcomes and reducing the chances of misinterpretation.

What are some key skills for improving your critical thinking in data analysis?

Key skills for improving critical thinking in data analysis include data literacy, the ability to evaluate and clean data, proficiency in data visualization, and strong problem-solving skills. Continuous learning and practicing these skills can improve your critical thinking and analytical capabilities over time.

How does data quality impact the critical thinking process in data science?

Data quality is crucial for the critical thinking process because poor quality data can lead to incorrect conclusions. By ensuring high data quality, data scientists can trust their analysis and minimize biases, making their critical thinking process more effective and reliable.

What importance does data collection and data cleaning have in critical thinking in data science?

Data collection and data cleaning are fundamental steps in critical thinking in data science. Accurate data collection ensures that the data set truly represents the real-world scenario being studied, while thorough data cleaning eliminates errors and inconsistencies. Together, these steps are essential for producing valid and reliable analytical results.

How can one develop critical thinking skills specifically for data science?

To develop critical thinking skills for data science, engage in continuous learning through courses and practical experience, practice analyzing varied data sets, participate in problem-solving activities, seek feedback, and collaborate with other data professionals. These activities enhance your ability to critically evaluate and interpret data.

Can you explain the importance of critical thinking as a soft skill in data analytics?

Critical thinking is an essential soft skill in data analytics because it allows data professionals to assess data integrity, draw meaningful insights, and communicate findings effectively. It bridges the gap between raw data and actionable business strategies, ensuring data-driven decisions are sound and impactful.

What separates a critical thinker from a regular data analyst?

A critical thinker goes beyond just analyzing data; they question assumptions, evaluate the validity of data sources, identify potential biases, and thoroughly interpret data within its context. This deeper level of analysis ensures more accurate, meaningful, and actionable outcomes compared to standard data analysis.

This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or AI, incorporating insights from the history of data and data science. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and data ethics.?

This newsletter is 100% human written ?? (* aside from a quick run through grammar and spell check).

More sources

1. https://magnimindacademy.com/blog/how-data-scientists-can-improve-data-skills/
2. https://www.tableau.com/learn/articles/data-science-skills
3. https://www.simplilearn.com/analytical-skills-for-a-successful-data-science-career-article
4. https://tiffanyperkinsmunn.com/critical-thinking/
5. https://www.institutedata.com/blog/enhancing-data-science-skills-8-essential-competencies-and-methods-for-mastery/
6. https://digirupt.io/the-role-of-critical-thinking-in-data-science/
7. https://www.dataversity.net/10-ways-to-enhance-your-data-analysis-skills/
8. https://www.appsilon.com/post/top-7-data-science-skills-2022
9. https://www.techrepublic.com/article/critical-thinking-must-have-skill-analytics/