In his talk about Big Data, Aaron Hanlon most interesting points centered around using Google Trends and another Google function that analyzed how many times electronic books mentioned certain words. Hanlon noted that the word “data” has been used much more often in books published in recent years while words like “truth” and “fact” have been used less often on a downward trend. Now, these are only words in books that have been electronically transferred to Google Books, but it brings up an interesting point. He discussed how truths were known on a theoretical basis in the early literary years (1600’s and before). Truths and facts were primarily used interchangeably with the word “evidence”, but in recent years it seems that “data” is becoming the new word to interchangeably use with evidence. To paraphrase Hanlon, “When data become the main form of evidence, that’s revolutionary”. However it may be problematic with using data as the main phrase associated with evidence. Depending on the subject matter, almost all data taken can be taken with a certain bias to create and back up an argument. For example, most surveys have an inherent bias depending on whether it’s an online survey or whether it’s taken in person, who answers the survey and where the survey is taken amongst other biases. While all data is not based on human response, where data is taken can be biased to “prove” an argument.
I cannot help but think about the 2016 Elections when I think about this talk about Big Data and how data is used to argue so many issues that presidential, senatorial, and congressional candidates stand for and against. Not only on arguing issues, but the reliance on “Big Data” can be and was disillusioning for predicting the president-elect in 2016. Donald Trump is going to be the President of the United States and almost no political analysts or pollsters saw it coming. Hillary Clinton was expected to win (some said by a landslide) in almost every “legitimate” poll released and many millions of Americans were disillusioned when the result of the election went the opposite way. While it is hard to think of a different method of trying to figure out who will win elections, the over reliance on data and its inherent bias can be extremely misleading in some cases.
During this election season I have noticed that on social media, most prominently Facebook, users bicker back and forth about politics using data to argue points about race, violence, the environment, and many other prominent issues. There is an overabundance of information sources in this day and age online, which allows people to pick and choose which sources to follow on their Facebook “feeds”, instilling certain ideas and values depending on what side of the political spectrum the user falls on. Most sources I see shared about politics come from biased sources whether that be on the right or left. While I definitely fall on the left side of the political spectrum, it can be annoying and concerning to see fellow “liberals” share posts about some of these issues that are blatantly wrong with biased data to further their argument. However, it can be more frustrating to me when people on the other side of the “aisle” share very biased sources talking about things like “black-on-black violence in inner cities” to argue that police officers are not abusing their power in certain parts of the country. Either way, data needs to be looked at carefully when making arguments.