When it comes to artificial intelligence and data analysis, we are constantly bombarded with promises that emphasize speed of analysis (the famous "5X"), ease of use, and simplicity in achieving surprising results. But how much of this is reality and how much is just marketing?
Our experience in the field has taught us that although AI offers undeniable advantages, analytics does not begin at the moment AI processes the data: there are meticulous preparatory steps that directly affect the quality of the results. And if these are not taken care of, the speed of AI becomes an illusion, as inaccurate results lead to starting over.
Therefore, before starting an AI analysis, it is essential to follow some key steps.
To take full advantage of the potential of AI in data analysis, we have identified a few key steps that require time and attention but ensure higher quality results:
🗂️ Proper formatting of sources
AI models are not universally compatible with all file formats. Each system has its own preferences and limitations. It is essential to convert your data to formats recognized by the specific model you intend to use. This may mean, for example, turning spreadsheets into CSV, PDF documents into plain text, or vice versa…
A recommended approach is to create a repository of predefined templates for the most commonly used formats, saving time in future analyses.
📂 Strategic file nomenclature
File naming is not purely an organizational issue; it directly affects the AI's ability to contextualize the data. Renaming files to include the target audience (e.g., “Data_Millennials_Q1_2025.csv” instead of “Focus group_feb25.csv”) provides the AI with crucial information before it even begins the analysis.
By implementing a consistent naming convention, it amplifies the benefits of this practice.
🎯 Clear attributes, with attention to data privacy
Accurate identification of data origin is critical for accurate interpretations. Indicating to whom responses “belong” by associating user names (without sensitive personal information) and specific variables allows AI to correctly segment the analysis and identify meaningful patterns among different demographic groups.
This practice must be balanced with strict privacy standards, eliminating any data that could lead to the personal identification of individuals.
📝 Accurate and complete verbalizations
The quality of the prompts and instructions given to the AI directly determines the quality of the output. Ensuring that verbalizations are accurate, unambiguous, and complete is a crucial step that requires expertise and attention.
A record of effective prompts can become a valuable resource by creating a library of optimized instructions for different types of analysis.
When implemented correctly, these steps transform AI analysis, making it:
More faithful to sources: Properly preparing data dramatically reduces the risk of “hallucinations” (conclusions not supported by the data) and ensures that analyses accurately reflect the reality represented in the datasets
Deeper: Well-educated AI can explore dimensions of the data that might escape human analysis, identifying non-obvious correlations and generating more sophisticated insights!
More cross-sectional: the proper segmentation and identification of sub-targets allows AI to highlight significant differences between different groups, offering more nuanced and comprehensive insights
More complex: the ability to simultaneously analyze data from multiple research stages or different sources is one of the most significant advantages of AI
Watch the video below and learn how AI is transforming the world of qualitative research! 🚀
