Este artigo do Roger Pang exemplifica essa mistura explosiva.
Abaixo os três principais problemas elencados no artigo:
- Big Data are often “Wrong” Data. The students used the sensors measure something, but it didn’t give them everything they needed. Part of this is that the sensors were cheap, and budget was likely a big constraint here. But Big Data are often big because they are cheap. But of course, they still couldn’t tell that the elevator was broken.
- A failure of interrogation. With all the data the students collected with their multitude of sensors, they were unable to answer the question “What else could explain what I’m observing?”
- A strong desire to tell a story. Upon looking at the data, they seemed to “make sense” or to at least match a preconceived notion of that they should look like. This is related to #2 above, which is that you have to challenge what you see. It’s very easy and tempting to let the data tell an interesting story rather than the right story.