Big data has become a big deal, with more than 2 billion gigabytes created each day. Huge sets of information can help universities pinpoint trends that can aid them in decisions on admissions and marketing — or can they?
There are several ways that universities, and analysts in general, can misinterpret data. American Sentinel University’s Information Technology blog points out that big data shows trends in the past and may not accurately predict the future. In “Big Data: Important and Tricky,” we are offered these caveats, among others:
- Know what’s in the driver’s seat. In statistics, there is a difference between causality and simultaneity. In a farcical case, a little old man walks to the beach every morning for a year, rain or shine, right before dawn and stays until after the sun is up. The two events are simultaneous, but not causal. The man awakes and leaves his house before dawn so the sun’s appearance doesn’t seem to be the direct cause of his routine. Clearly the sun doesn’t wait for the man’s walk to the beach. Confusion between simultaneity and causality is rarely so clear-cut, at least to the person trying to make a connection between two sets of data.
- People make mistakes. The biggest source of error is human in nature. Human beings can look at the wrong data, incorrectly perform calculations, or misuse data to support conclusions they had already drawn. When they do, chances are good that some will try to blame the data or the tools for not delivering what the users expected.
Audrey Watters has her own concerns about big data, specifically about data mining, which she shares with readers of her blog, Hack Education:
Despite all the potential and all the buzz about (big) data, data-mining remains something with a fairly negative connotation. Advertisers. Political campaigns. Big government. All sifting through your personal data, trying to uncover the things that nobody knows about, trying to get you to buy or sell or vote.
She also considers the ethical predicament of certain data gatherers (think Facebook and Google) who collect information many would consider anonymous or meant to be private. Read Watters’ post, “On Educational Data Mining,” on Hack Education.
Big data can be an ethical minefield and tricky to use effectively. But the accumulation of data — from Google Analytics and social networking to web logs and financial records — presents an opportunity for universities if collected and handled with care.
American Sentinel University and Hack Education both made The Dean’s List, EdTech: Focus on Higher Education’s list of 50 must-read higher education technology blogs.