7 Data Analysis Techniques: A Practical Guide

AI-native analytics platforms like Hex have revolutionized data analysis, enabling users to conduct sophisticated analyses using plain language to generate SQL, visualizations, and initial interpretations. However, the effectiveness of these platforms depends significantly on how questions are framed, as choosing the appropriate analysis technique is crucial for obtaining accurate insights. The text outlines seven key data analysis techniques—regression, Monte Carlo simulation, factor analysis, cohort analysis, cluster analysis, time series analysis, and sentiment analysis—each suited to specific types of inquiries and data characteristics. Understanding the differences between quantitative and qualitative data is essential for selecting the right method, as these techniques are designed to address different questions, such as predicting outcomes, identifying patterns, or understanding behavioral changes over time. The guide emphasizes that while AI can facilitate the process, human judgment is essential in refining the questions and interpreting results, ensuring that analyses translate into actionable business insights. By integrating SQL, Python, and no-code features in one workspace, platforms like Hex streamline the analysis process, reducing the need for tool-switching and enhancing efficiency in deriving meaningful results.