What do you do if your data analysis results are unclear?

When your data analysis results are unclear, revisit your question/problem/hypothesis. In my experience, unclear results often indicate unclearly formulated hypotheses or a misunderstood problem. While it is true that many issues, such as improperly handled outliers, multicollinearity, omitted variable bias, etc., will hurt the quality and clarity of results, too often, data analysis suffers from misalignment between the inputs in a model and the target question. An in-depth understanding of the problem/question the data analysis aims to address is essential to understanding its results.

First, revisit your data cleaning steps. Did you handle missing values? Are there any inconsistencies in formatting? In Python (pandas), I like using .isnull().sum() to check for missing entries and .value_counts() to identify unexpected data formats. Also, consider outliers. Try plotting your data with boxplots to see if there are extreme values that might skew your analysis. Remember, clean data is the foundation for clear insights!

If data analysis results are unclear: Review data: Check for errors or anomalies. Refine methods: Adjust techniques or algorithms used. Seek insights: Collaborate with peers or domain experts. Visualize data: Use graphs or charts to gain clarity. Conduct further analysis: Explore alternative approaches. Communicate uncertainty: Transparently share limitations or assumptions. Iterate: Repeat steps to refine understanding

If data analysis results are unclear, reassess data quality, refine analytical techniques, seek alternative methods, consult peers or experts, consider additional variables, conduct sensitivity analysis, explore outliers, and validate findings. Iteratively refine until insights emerge or acknowledge ambiguity with transparent reporting.

In addition to programming tools, consider employing statistical tests, cross-validation techniques, or sensitivity analysis to rigorously evaluate your methods and their outcomes. Statistical tests can help assess the significance of your findings, while cross-validation provides a robust measure of model performance across different subsets of your data. Sensitivity analysis allows you to assess the impact of varying assumptions or parameters on your results, helping to identify potential sources of uncertainty or bias.

Acknowledge any challenges, limitations, or uncertainties encountered during analysis. Take proactive steps to address them, whether through additional research, alternative approaches, or seeking assistance.

Furthermore, employing storytelling techniques, narratives, or illustrative examples can help contextualize your analysis findings within a broader narrative, making them more relatable and engaging for your audience. By ensuring that your context remains relevant, clear, and consistent, you can enhance the impact and accessibility of your data analysis, enabling stakeholders to make informed decisions based on your insights."

If data results seem unclear in a report, first check your method to ensure you’re transforming/mapping the data correctly. Confirm with system owners the unique identifiers you’re classifying are accurate and best practice. Next, when/if you’re sure there’s no data slipping through the cracks or misidentified during coding, get more context from the initial requestor (usually business). Ask them to clarify what they’re expecting to observe/gain from the end result and how it’s going to drive their decision making skills. Based on this discussion, you either find a way to better display the output for clearer results or come up with another solution that involves relevant data, as the current data elements aren’t insightful for team’s need.

Additionally, consider the sustainability of your chosen alternatives in terms of computational resources, time constraints, and maintenance efforts. Evaluating the scalability and sustainability of your alternatives ensures that your data analysis approach remains viable and effective over the long term, even as your data volume grows or your analysis requirements evolve."

Additionally, discuss potential sources of bias or confounding factors that may have influenced your results. By addressing these limitations and uncertainties, you can enhance the credibility and reliability of your conclusions and ensure that stakeholders have a realistic understanding of the findings' implications.

Document your insights, lessons learned, and best practices from each analysis project. Use documentation, comments in code, or project reviews to record your learning and share it with others in your team or community.

Stay current with advancements in data analysis tools and techniques. Invest time in continuous learning through online courses, workshops, or self-study to enhance your skills and knowledge.