What do you do if data analysis uncovers surprising insights or trends?

1 Verify your data

The first thing you should do is to check the quality and accuracy of your data. Are there any errors, outliers, missing values, or inconsistencies that could affect your analysis? Did you use the appropriate data sources, methods, and tools to collect, clean, and process your data? Did you follow the best practices and standards of data management and documentation? Verifying your data can help you avoid false or misleading conclusions and ensure that your analysis is reliable and reproducible.

2 Validate your results

The next step is to validate your results by using different techniques, perspectives, and criteria. For example, you can use statistical tests, cross-validation, or sensitivity analysis to measure the significance, robustness, and generalizability of your findings. You can also compare your results with previous studies, benchmarks, or domain knowledge to see if they are consistent or contradictory. You can also seek feedback from your peers, mentors, or experts to get their opinions and suggestions. Validating your results can help you confirm or refute your surprising insights or trends and understand their implications and limitations.

3 Visualize your findings

Another way to handle surprising insights or trends is to visualize them using charts, graphs, maps, or dashboards. Visualizing your findings can help you explore, communicate, and present them more effectively and intuitively. You can use different types of visualizations to highlight different aspects of your data, such as patterns, relationships, distributions, or comparisons. You can also use interactive or dynamic visualizations to allow your audience to manipulate, filter, or drill down your data. Visualizing your findings can help you gain more insights and generate more questions.

4 Verify your assumptions

One of the reasons why you may encounter surprising insights or trends is that you have some assumptions or biases that influence your analysis. For example, you may have some preconceived notions, expectations, or hypotheses about your data, your variables, your models, or your outcomes. You may also have some preferences, opinions, or values that affect your interpretation or presentation of your findings. To verify your assumptions, you need to be aware, transparent, and critical of them. You need to test, challenge, and revise them based on your data and results. Verifying your assumptions can help you avoid errors, confusion, or misrepresentation.

5 Value your learning

The final tip is to value your learning from surprising insights or trends. Rather than dismissing or ignoring them, you should embrace and appreciate them as opportunities to learn something new, to improve your skills, to expand your knowledge, or to innovate your solutions. You should also share and discuss them with your colleagues, clients, or stakeholders to spark curiosity, interest, or collaboration. Valuing your learning can help you grow as a data analyst and contribute to your field or industry.