In our latest project, the goal was to plan out how to clean a dataset and, time permitting, start the cleaning process. This experience brought to light an invaluable lesson on planning and clarity in managing data projects—a lesson reminiscent of the benefits of a to-do list, as discussed in an earlier blog post on to-do lists.
What Went Right
The real game-changer was the detailed plan I laid out from the start. This wasn't just a to-do list; it was a step-by-step strategy that streamlined the entire cleaning process. The plan made it possible to clean the dataset efficiently, freeing up time for further improvements and future-proofing steps. It turns out, a well-thought-out plan (much like a good to-do list) can be a major time-saver.
Areas for Improvement
There were, however, lessons learned about ensuring clarity and improving decision-making:
- Clarify the Brief: Understanding exactly what’s needed is crucial. Early questions can prevent wasted time on unneeded features.
- Pause for Big Decisions: It's wise to stop and think before major steps. This ensures that efforts are aligned with the project's goals.
- Document the Process: Notes on the dataset or an accompanying document detailing the cleaning steps can be invaluable for anyone picking up the project later.
Technical Takeaway
One technical nugget I picked up was the effectiveness of using a left join when adding data to the main table. This method keeps all entries from the main table, even if there's no matching data in the joined table, which is often the desired outcome in data cleaning.
Conclusion
This project underscored the importance of a clear plan, the need for unambiguous communication, and making informed technical choices in data management. These reflections serve as a roadmap for more efficient and effective future projects.