Teaching Data Literacy. Why We Need to Focus on Soft Skills

Key Insights

• Kevin Hanegan emphasizes the importance of soft skills in data literacy, stating that it is not just about technical skills but also about critical thinking and communication.
• With easy access to vast amounts of information, critical thinking skills are becoming increasingly important in determining the validity and potential misinterpretation of insights.
• Soft skills such as challenging assumptions and critical thinking are crucial for data literacy.
• Relying solely on machine learning models for decision-making without questioning their ethical implications can lead to harmful outcomes.
• "What's getting in our way is we have an incorrect assumption, we have an outdated mental model, we have someone that doesn't understand correlation doesn't equal causation."
• Using relatable examples and simple solutions can be an effective way to engage people in learning about data and technology.
• "Challenge your assumptions, understand the brain has biases, and think about reflecting at the end of the day."

Summary

• 00:00 Educational programs should teach critical thinking and problem-solving skills alongside technology to effectively work with data and make informed decisions.
  • Kevin Hanegan, Chief Learning Officer at Qlik, interacts with customers, partners, and prospects to fulfill his unique role at the company.
  • Educational programs need to focus on teaching critical thinking and problem-solving skills in addition to technology in order to effectively work with data and make informed decisions.
• 01:44 Data literacy is more than just industry knowledge; it requires critical thinking, diverse perspectives, and the ability to ask the right questions and interpret insights accurately to avoid misinterpretation and incorrect actions.
  • Without critical thinking and diverse perspectives, insights can be easily misinterpreted and lead to incorrect actions, making it crucial to know what questions to ask and how to interpret insights accurately.
  • Understanding data literacy goes beyond just having industry knowledge, as it involves recognizing that correlation doesn't equal causation and avoiding misinterpretation of data, which is essential in today's non-technical world.
• 03:50 Soft skills like critical thinking, challenging assumptions, and getting diverse perspectives are crucial for data literacy, and advancements in technology may help us overcome the limitations of understanding complex systems.
  • Soft skills like challenging assumptions, mitigating cognitive bias, asking the right questions, critical thinking, and getting diverse perspectives are crucial for data literacy.
  • Many people lack the skill of understanding complex systems and thinking holistically, but advancements in technology, such as large language models, may help us overcome this limitation and achieve a faster and more holistic view.
• 05:14 Relying solely on technology for decision-making can be problematic, as it lacks the human element and critical thinking skills necessary for ethical decision-making.
• 06:24 Challenge assumptions, develop data literacy skills, and understand that correlation does not equal causation to overcome incorrect assumptions and outdated mental models; customers can be excited about data-driven decision-making through interactive workshops and demonstrations.
  • Challenge assumptions, avoid confirmation bias, and develop data literacy skills to overcome incorrect assumptions and outdated mental models, and to understand that correlation does not equal causation.
  • Customers need to be excited and fully committed to data-driven decision-making, and this can be achieved through interactive workshops and demonstrations that simulate real-life scenarios.
• 08:05 People may dismiss data literacy as a buzzword, but when they see its practical applications and benefits, they become more interested and willing to learn, such as using the iterative process of data analysis to uncover hidden insights and improve product features.
  • If people are not challenged and shown the potential of data literacy, they may dismiss it as a buzzword, but when they are given the opportunity to see the practical applications and benefits, they become more interested and willing to learn.
  • The iterative process of data analysis involves starting with familiar data, then pivoting to the customer's domain to uncover hidden insights and improve product features, such as the recently added Insight Advisor with natural language questions.
• 10:04 Developing soft skills like challenging assumptions and reflecting on decision-making is crucial for data literacy and can be applied in various contexts beyond statistics.
  • Get over the phobia of data and realize that everything you do every day involves data.
  • Developing soft skills, such as challenging assumptions and reflecting on decision-making, is crucial for data literacy and can be applied in various contexts beyond statistics.