Big Data, Big Qualitative Data

As someone that occasionally plays in quantitative data world, I know that many quant researchers, including too many of my MPA teachers, place an infallible value on quantitative data over qualitative data. One reason quantitative data is so valued is its simplicity, very little context is needed to make grand analyses, and the bigger the n the better because that just increases your validity, and, after all, it’s not like you have to actually read all of those responses, just push a button and run your regression.

Now having the debate over quant and qual data is not the point of this post, but rather to ponder what to do with big qualitative data? There is no regression to write that will make analysis or recording easy, instead I have to actually read and make sense of all the individual responses, and if I was being methodical than code all of those responses. But when you don’t have the time to code, what is the best use of all that “extra” data? From my sixteen stakeholder interviews, I have scoured through and essentially categorized or coded for a few key topics that have generated and support my recommendations. But now I have pages and pages of transcribed notes that likely have all sorts of nuggets of wisdom for the organization that extend far beyond my project scope. Without just handing over my notes and breaking confidentiality (even if de-identified), I’m a bit perplexed what to do with all this information. I could write a 20, 30, 40-page report with all of the information collected, but that is the last thing this organization needs; in fact, they brought me on specifically to simplify and prioritize someone else’s big, long report. I wish this was a one-time problem, but it is something that I have faced time and again with client processes that involve semi-confidential stakeholder interviews. I still don’t have an answer, but maybe next time I go into a similar process I’ll spend more time with the client reality testing what different outcomes are appropriate for all the “rest of the data.”


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