Prem Vishnoi(cloudvala)
2 min readJan 29, 2024

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Amazon style 10 min Intro.

Here’s a structured approach you can take during the 10-minute read-through:

1. Initial Reading (First 3–4 minutes)
Objective: Get a general understanding of the document’s content.
Focus: Skim through the document, noting the main topics, the purpose of the document, and the conclusions or recommendations it presents.
2. Detailed Review (Next 3–4 minutes)
Objective: Understand the details and nuances of the content.
Focus:
Pay attention to data, graphs, or any evidence supporting the claims or conclusions.
Note any references to external sources or data that may require further validation.
Identify any assumptions made in the document and consider their validity.
3. Critical Analysis (Next 2–3 minutes)
Objective: Critically evaluate the content and prepare for the discussion.
Focus:
Questions: List any questions that arise. Are there points that are unclear or require further explanation?
Comments: Note any comments you have about specific sections. This could be about something you found particularly compelling, or it could be about perceived gaps or weaknesses in the argumentation or data.
Additions: Consider what might be missing from the document. Is there additional data or a different perspective that should be considered?
4. Prioritization (Final minute)
Objective: Prioritize your points to ensure the most important items are discussed.
Focus:
Decide which questions or comments are most critical to address during the meeting.
Consider the flow of the meeting and how your points might fit into the larger discussion.
During the meeting, use your notes to engage constructively, asking questions, providing insights, or offering additional information where relevant. Remember, the goal of this style of meeting is to ensure a deep and shared understanding of the material, leading to more productive discussions and better decision-making.

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Prem Vishnoi(cloudvala)
Prem Vishnoi(cloudvala)

Written by Prem Vishnoi(cloudvala)

Head of Data and ML experienced in designing, implementing, and managing large-scale data infrastructure. Skilled in ETL, data modeling, and cloud computing

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