Post 12 - Big Discussions

In a discussion published by Cloudera, conversation is sparked around "the “three V’s” — volume (you have lots of data), variety (you have lots of different kinds of data) and velocity (it’s arriving at a furious rate). Any one of those conditions can create trouble for current data management infrastructure."  Data management infrastructure has a huge basis in "Big Data."As was made clear in Six Provocations for Big Data, the term "Big Data" refers not to data that takes a large amount of computer power to process, but to networked data. "Its value comes from the patterns that can be derived by making connections between pieces of data, about an individual, about individuals in relation to others, about groups of people, or simply about the structure of information itself." Just like innovations of Google's organization and discovery tools, the connectivity of Big Data has implications for almost every facet of life in which we process information, whether we are online or not.

Big Data, as produced, consumed, and analyzed by human individuals within networks, is in as neutral or objective as any human can be. "Just as Ford changed the way we made cars – and then transformed work itself – Big Data has emerged a system of knowledge that is already changing the objects of knowledge, while also having the power to inform how we understand human networks and community." To summarize Danah Boyd's words, as we change Big Data, it changes us.

We are constantly recycling data and information within complex networks of connectivity incorporating people, their environment, and products of both. In a post called The Thing About Networks, or Big Data Rhetoric, the author demonstrates that "data—especially social network data—has joined the embed, repurposing, and mashup cultures historically associated (in terms of the web, at least) with video and audio." The problem with this is the three v's; the phenomenon is too quick and too large for us to not run, stupid as humans are, headlong into issues involving how we use these networks and the ethics behind Big Data.

One of the larger issues is that we seem to forget that Big Data, as it relates so closely to humanity, is in no way without bias. "Raw data is both an oxymoron and a bad idea; to the contrary, data should be cooked with care. Geoffrey Bowker (2005, p. 183-184)" This does not mean it isn't valuable or useful, in fact much of the readings discussed the problems in the research being done on social media and Big Data.

In the Cloudera discussion they insisted that "the Big Data movement is also about technology and the scalability of how we can leverage industry standard hardware to spread out the hard problems and solve them much more quickly." The use of Big Data to map human patterns and networks allows us more accurate guesses at "big questions." Unlike those in the Cloudera discussion, however, I don't think they help us answer them. "Big" is a word used in these contexts to illustrate large scale, essentially of complexity. Just as there is not one way to tackle the use of Big Data, there will very rarely be a single answer to our big problems and big questions.