Okay, fellow data wranglers, I need to vent a little and also see if anyone has cracked this particular nut. 😅
Like many of you, I live in our A/B testing and analytics dashboards, but governance data from OneTrust is becoming increasingly crucial for our conversion optimization work. Understanding consent rates by region directly impacts how we personalize landing pages and email campaigns. So, I was thrilled to discover you can export assessment response data directly to CSV. No more manual screenshots or building every single view in their UI!
But my enthusiasm quickly turned into a spreadsheet headache. The export schema is... an adventure. Here’s what I mean:
* **Extreme Flat Structure:** Every single question and sub-question becomes its own column. A moderately complex assessment can generate a CSV with **300+ columns**. It's overwhelming.
* **Cryptic Column Headers:** Instead of the actual question text, you get internal IDs (e.g., `section_a.question_3.subpart_f`). You have to constantly cross-reference with the assessment template to decode what you're looking at.
* **Data Format Inconsistency:** Some multi-select answers are pipe-delimited (`|`) within a cell, others might be semicolons. Date formats sometimes include timestamps, sometimes they don't. It makes building automated reports in our CRM or analytics pipeline a manual cleanup job every time.
* **No Relational Logic:** If you're exporting a list of assessments, the respondent info (like department, date) is repeated on *every single row* for a given record, instead of being a nice, separate table you could join.
I wanted to quickly analyze trends in "Data Processing Impact Assessments" over time to correlate with user research phases, but I spent more time normalizing the data than analyzing it.
Has anyone built a reliable parser or transformation script for these exports? Maybe using Python's pandas? Or found a hidden setting to get a more sensible, relational data dump? I'd love to hear about your workflows or if you've just accepted the chaos.
Happy evaluating
Oh, the flat structure is the worst part! It's the opposite of how a human would ever want to look at this data.
For the cryptic headers, our team built a small reference table mapping the internal IDs to the actual question text. It's a one-time pain to set up, but it saves so much frustration later. We just keep it in a shared doc and paste it in when we're cleaning a new export.
Hope that helps a bit. The pipe-delimited values are another fun surprise waiting for you, by the way. 🙃
Docs save time