Research Journal Entry #11

I got so much work done on both my papers this past week! My research proposal is about 98% finished at this point, thank goodness. I just need to proof-read it once or twice for errors and awkwardness, and I will be totally set with it. The literature review for 2.0 is about 90% done – the concluding paragraph is still unfinished, and I need to proof read it all to make sure the topic sentences go with the thesis and all, but otherwise I am happy with it. I’ve also made a lot of progress on my 2.0 final project – the blog is up and running, though a little lacking in updates because Ashaway is a sleepy little town and summer reading is only just kicking off. I wrote the majority of the implementation plan for it yesterday on my lunch break, and it’s going pretty smoothly. Oh, and I also aced my last Cataloging quiz, which is a small miracle because I had to guess at a lot of it. I am beyond thrilled that everything is running so on schedule and I was able to stick to my own deadlines – I’m a little proud of myself, too, I have to say.

I’m not out of the woods, yet, though. I still have to get through the rest of Cataloging, and while I have managed to build a decent margin of error from doing so well on the quizzes, that can easily be shattered by the fact that I do not understand MARC whatsoever, or almost anything else for that matter.

Anyway, I’m giving myself the weekend off cause it’s the holiday and I deserve it. I’ll still have to check in at some point to copy/paste or upload my annotations and these journal entries onto Sakai, but since they’re basically done, I’m not counting it. Otherwise, I plan to be at the beach for as much time as possible – basking in the gorgeous weather that the meteorologists have promised, dancing at one of the two foam parties going on at Paddy’s, and/or attending the Pimps & Hoes Ball…also at Paddy’s. I might finally get around to making that berry pie I’ve been dreaming about for a month, too.

Big Data, Big Brother

The first blog entry I read was called Big Data and the Benefits from the Bubble, from Andrew McAfee’s blog. He argues that Web 2.0 has led to Bubble 2.0, a technology and information bubble that has produced “big data” (which Dan Kusnetzky explains as “the tools, processes and procedures allowing an organization to create, manipulate, and manage very large data sets and storage facilities”). Big Data is currently being used in a variety of ways, such as tracking and storing weather patterns from the Nation Weather Service and personal information on Facebook, but Andrew McAfee focuses his argument primarily upon its use to track and determine trends for the sake of marketing and advertising. The problem, as many people see it, is that all bubbles burst eventually and leave investors and employees scrambling to salvage their money and careers – in this case, investors in large marketing and advertising companies and those experts who are currently making a great living off of using data to get the right advertisement to the right person’s online experience. Andrew McAfee, however, believes that when Bubble 2.0 does burst, the technology will still be very useful in its own right and also be highly applicable to analyzing even more significant issues and making greater advances, in the fields of science, medicine, politics, language, etc.

My only concern with his article is that I don’t believe that Big Data is only being used first and foremost for marketing. Marketing and advertising is inarguably of enormous importance to the world and in the U.S. especially, but but I can’t imagine that all the other extremely well-known (and even more lesser-known) social, moral, and health issues are being overlooked and underanalyzed by all of the technological advances made in recent years.

I also read a blog entry called How Social Media and Big Data Will Unleash What We Know Dion Hinchcliffe. In it, the author discusses the invaluable resources that savvy businessmen and marketers can tap into through the private information that individuals now so freely offer up to the public through social media, and the methods through which nearly innumerable amounts of fast-paced and ever-changing data is gathered and analyzed as Big Data. Hinchcliffe also acknowledges the difficulty in “separating the wheat from the chaff,” which is a common phrase now used in relation to information overload and is here applied to the fact that although all of the information shared through social media networks is legitimate, not all of it is especially useful for the purposes of determining trends (think of the innocuous “yeah lol me 2” comments that may stem from a more significant statement of loving a new recording artist or brand of clothes), and this is where Big Data tools and processes are arguably most helpful. Finally, Hinchcliffe delves into the most pressing issue facing marketers and social media data analysists today, which is the speed with which they can process the information to determine trends, and the goal of being able to then predict those other trends to follow before they even happen. By beating time, they can (at least in theory) harness the power of suggestion to the fullest and then (as Andrew McAfee has also opined) be most able to get the right information/advertisement to the right person, even before they necessarily know it’s right themselves.

We are actively participating in our own objectification and commodification.

And so my readings come full circle.