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Retrospective - Milestone 2: Data Cleaning

Date: 29 January - 2 February, 2024

Stop Doing

  1. Avoiding Transparent Communication: cease withholding concerns or uncertainties about specific tasks. Openly communicate challenges to foster better collaboration.
  2. Neglecting Individual Progress Updates: Stop overlooking individual progress updates. Regularly check in on the team's progress to ensure everyone is on track.
  3. Duplicating Data Files: read from the original data file instead of spliting it everytime and uploading a new data file.
  4. Merging without reviewing

Continue Doing

  1. Thorough Data Cleaning Practices: continue the commitment to meticulous data cleaning, ensuring the reliability and accuracy of the dataset.
  2. Collaborative Decision-Making: maintain the practice of involving team members in decision-making, encouraging diverse perspectives for more informed choices.
  3. Iterative Approach: Continue the iterative approach to data cleaning, allowing for adjustments based on continuous feedback and insights.

Start Doing

  1. Implementation of Automated Validation: Begin exploring automated tools for data validation to streamline and expedite the verification process.
  2. Regular Check-ins with Domain Experts: Initiate regular check-ins with domain experts to ensure ongoing alignment with the evolving project goals.

Lessons Learned

  1. Importance of Proactive Communication: Recognized the critical role of proactive communication in preventing misunderstandings and enhancing team cohesion.
  2. Iterative Data Cleaning: Learned from the iterative data cleaning process, emphasizing the significance of refining approaches based on evolving requirements.
  3. Balancing Complexity and Simplicity: Acknowledged the need to strike a balance between addressing project complexities and maintaining simplicity for better understanding.

Strategy vs. Execution

What Went as Expected:

  • Data Relevance: The strategy to focus on relevant data sources aligned with expectations, providing a solid foundation for the subsequent phases.
  • Team Collaboration: Collaborative decision-making and regular check-ins contributed positively to team dynamics.

What Didn't Work Out:

  • Unexpected Data Challenges: encountered unexpected challenges during the data cleaning phase, leading to a reassessment of the initial strategy.
  • Timeline Adjustments: delays in certain tasks required adjustments to the overall timeline.

Adjustments to the Strategy

  1. Enhanced Data Validation Steps: additional steps were incorporated to ensure a more thorough and reliable data validation process.
  2. Iterative Model Refinement: recognizing the need for ongoing model refinement, adjustments were made to accommodate evolving project requirements.

Conclusion

This retrospective highlights our commitment to continuous improvement and adaptability. By addressing challenges proactively and refining our strategies, we aim to enhance the effectiveness of our data cleaning process for the migration patterns project.