Can cultural filters screw up big data results?

Posted by Gina Rosenthal in big data, cultural filters | Leave a comment

I have a few friends – from north of the Mason Dixon line – who will challenge things I say when it seems like I’m contradicting myself. I think what I say makes sense. I think when other Southerners hear me they understand.

Here’s a universal, classic example. Where I’m from if someone says “Hey I’m fixing to go to the store, you want me to bring you back a coke?”, a perfectly acceptable answer is “Yes, please. I’d like a Dr. Pepper”. Where I’m from coke = soda. If I answered “yes please” I would get a coke. Cuz I wasn’t specific.

Yesterday I was talking about how much I hate Vegas and the conference scene. And I mentioned that I hate getting all dressed up. Of course at the time, I was all dressed up to go to SXSW. Makeup and all (ug).

I do hate getting all dressed up, and you can ask any of my girlfriends about how much I like makeup (not at all). I’d prefer to wear the Gulf Coast/Austin uniform everyday: jeans/short skirt, t-shirt, flip-flops, and ponytail/ballcap. But I can’t do that, if I do I’m not taken seriously. I have to put on the “uniform” of the event that I’m attending.

I can’t help it that I clean up pretty good. 😉

So it seems like I’m almost lying, but I’m actually being really honest. Since I’m from a story-telling culture, I think southerners get it – I’ve never had someone from the south act surprised when things I say seemingly contradict each other.

So this made me think about “big data” – and this is really an age old problem. Big data queries are still created by humans. Humans have cultural filters, we don’t even recognize that we have them at times. As much as we intentionally try not to use those filters, queries are impacted by them. How we search for and construct the storage of information depends on the lens we are using at the time.

It is all about the data, but it’s also about how we view and interpret the data. This is one of the reasons it’s so important to have a diverse group of people managing, storing, and protecting the world’s data. Maybe even more important to have a diverse group of people constructing the big data queries that may impact entire societies.

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