What are the key differences in data analysis for qualitative vs quantitative?

Explore your landscape by doing your research, unleashing your curiosity, and collaborating with stakeholders. Read, interview experts, use GenAI, and investigate the situation with questions that start with "What are we trying to understand?" Be curious and keep digging. Use your imagination by asking questions that start with "What if..." Build a thesis. As you do your research, start trusting your gut about what factors might be at play. You'll test these with data. Finally, ask stakeholders, "So what?" Explore why your findings might matter. Finally, ask one more question: "What's missing?" Explore different landscapes. The deeper your exploration of the landscape, the better.

Don't start with data. Don't trust your data. Don't assume you've got it all. Build a semantic data layer first. Remember: garbage (data) in, garbage (insights) out. A semantic layer starts with a business-level description of the data you need. It should be free of technical jargon and grokable by business stakeholders. Once you have a semantic understanding of the data you need, start building your technical sources. But don't just start with the data you have on hand--be curious about what's missing. Get your data semantics right and quality insights can follow.

The key differences in data analysis for qualitative versus quantitative data lie in the methods of interpretation and the nature of the data itself. Qualitative data, often textual or descriptive, requires a more subjective and nuanced approach to analysis. It involves identifying themes, patterns, and meanings within the data through techniques like coding, thematic analysis, and narrative interpretation. On the other hand, quantitative data, typically numerical and measurable, lends itself to more objective and statistical analysis methods. This involves summarizing data, testing hypotheses, and identifying relationships using statistical techniques such as regression analysis, correlation, and hypothesis testing.

Spend two-thirds of your time formulating your questions, not answering them. Too often, analysts leap to math too fast. And don't trust your data. Build a clean semantic data layer. Remember: garbage (data) in; garbage (insights) out. Einstein said, "The formulation of a problem is more essential than its solution. To raise new questions and possibilities, to regard old problems from a new angle, requires creative imagination and marks a real advance in science." Good questions and good data are just as important, if not more important, than your solution. Put in the hard prep work to get it right.