What are the most common data analysis pitfalls for Operational Planning?

1 Pitfall 1: Not defining your problem clearly

One of the first steps in any data analysis project is to define your problem clearly and precisely. What are you trying to achieve, what are the key questions you want to answer, and what are the assumptions and constraints you have? Without a clear problem definition, you risk wasting time and resources on irrelevant or misleading data, or missing important aspects of your situation. To avoid this pitfall, you should use a structured approach to define your problem, such as the SMART framework (Specific, Measurable, Achievable, Relevant, and Time-bound) or the Five Whys technique.

2 Pitfall 2: Not validating your data sources and quality

Another common pitfall in data analysis is not validating your data sources and quality. Data is not always accurate, reliable, or consistent, and it can be affected by errors, biases, or gaps. If you use data that is not valid, you risk drawing false or inaccurate conclusions from your analysis, or making decisions based on faulty information. To avoid this pitfall, you should always check your data sources and quality before you start your analysis. Some of the questions you should ask are: Where does the data come from, how was it collected and processed, how often is it updated, and how does it compare to other sources?

3 Pitfall 3: Not choosing the right analytical methods and tools

Another common pitfall in data analysis is not choosing the right analytical methods and tools for your problem and data. Different problems and data require different types of analysis, such as descriptive, diagnostic, predictive, or prescriptive. Moreover, different types of analysis require different tools, such as spreadsheets, databases, statistical software, or visualization tools. If you choose the wrong methods or tools, you risk missing important insights, making wrong assumptions, or overlooking key factors. To avoid this pitfall, you should always match your methods and tools to your problem and data, and use appropriate techniques, such as hypothesis testing, correlation analysis, or scenario analysis.

4 Pitfall 4: Not interpreting your results correctly

Another common pitfall in data analysis is not interpreting your results correctly. Data analysis is not just about crunching numbers and producing charts, but also about making sense of what the data tells you and how it relates to your problem and goals. However, interpreting data can be challenging, as it involves critical thinking, logical reasoning, and contextual knowledge. If you interpret your results incorrectly, you risk making wrong inferences, ignoring alternative explanations, or jumping to conclusions. To avoid this pitfall, you should always validate your results with other sources, test your assumptions, and seek feedback from others.

5 Pitfall 5: Not communicating your findings effectively

The final common pitfall in data analysis is not communicating your findings effectively. Data analysis is not only a technical skill, but also a communication skill, as you need to share your findings with others, such as stakeholders, managers, or customers. However, communicating data can be difficult, as it involves choosing the right format, language, and style for your audience and purpose. If you communicate your findings poorly, you risk confusing, boring, or misleading your audience, or losing their interest or trust. To avoid this pitfall, you should always tailor your communication to your audience, use clear and concise language, and use visual aids, such as graphs, tables, or dashboards.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?