Group 1 - The article discusses the challenges of data management in the manufacturing industry, emphasizing that despite the abundance of data, useful information is increasingly scarce [1] - It highlights that data cleaning consumes a significant amount of time, with data analysts spending up to 70% of their time on this task [2][8] - The article identifies several reasons for the inefficiency in data management, including disorganized data sources, frequent errors, data silos, and lack of contextual understanding [3][4][6][7] Group 2 - It addresses the misconception in manufacturing digitalization where companies collect excessive data without understanding its relevance, leading to wasted resources [9] - The article suggests that valuable data should support decision-making, explain underlying causes, and be concise and relevant [10][11][12] - It critiques the common practice of data visualization, noting that overly complex charts can obscure important insights rather than clarify them [13][14] Group 3 - The article emphasizes the need for collaboration between data scientists and manufacturing personnel to ensure data analysis reflects real-world conditions [19][20] - It points out that understanding the context of data collection and the variables affecting outcomes is crucial for accurate analysis [22][23][24] - The article concludes that data must be actionable, connecting directly to problem-solving processes and communicated in an understandable manner [25][28][30]