Post-Cleaning Reflections: Questions to Ask

After you've gone through the meticulous process of cleaning your data, it's time for some reflection. Think of this as a post-game analysis where you evaluate your performance and strategize for the next round. Here are some pivotal questions to consider:

1. Data Sensibility Check

Ask yourself, does the cleaned data make logical sense? This is similar to proofreading a document. Ensure that the data aligns with the known facts and constraints related to the subject matter.

2. Field-Specific Rules

Does the data adhere to the rules or standards specific to its field? For example, if you're working with financial data, check if the fiscal parameters like revenue, costs, and margins fall within industry norms.

3. Hypothesis Evaluation

Does the cleaned data support, refute, or bring new insights to your initial hypothesis? This is the moment of truth where you see if your hypothesis stands or needs revising.

4. Pattern Discovery

Can you identify any trends or patterns that could inform your next hypothesis or research question? Like connecting the dots in a puzzle, look for relationships that can guide your next steps.

5. Data Quality Assessment

Are there any lingering issues that could be attributed to poor data quality? Be honest in this evaluation. If you identify problems, you may need to revisit some steps in your data cleaning process.

The post-cleaning phase is an opportunity for introspection and analysis. By carefully considering these questions, you not only validate the quality of your cleaned data but also set the stage for meaningful analysis and future research.