Which of the following is NOT a recommended principle for organizations to start small data initiatives?

Starting small data initiatives involves strategies that promote collaboration, enhance understanding, and ensure quality outcomes. Emphasizing quality human input over quantity of machine output is essential. This principle recognizes that while machines and large datasets can generate volume, the quality and contextual understanding that human input brings is invaluable. Relying solely on machine-generated data can lead to insights that lack relevance or applicability to real-world scenarios.

Similarly, balancing machine learning with human domain expertise reinforces the importance of having knowledgeable team members who can interpret data and provide insights that machines alone may miss. Acknowledging social dynamics within small data teams encourages effective collaboration and communication, which are crucial for innovative problem-solving and successful project execution.

By limiting input from team members, an organization undermines the collaborative spirit and diverse perspectives that are vital for data-driven decision-making. Ensuring all relevant voices contribute to the initiative fosters a broader understanding of the data's implications and leads to richer insights and solutions. Thus, this principle stands out as one that does not align with the best practices for initiating small data projects.