Are Big Data and Data Science a Threat to Higher Education?

It was encouraging to read an UPCEA blog post from August 2018 in which Mindmax CEO Lee Maxey critiqued our data and knowledge environment.  Mr. Maxey’s description of how we’re drowning in data was on point; the sheer mass of analytics, metrics and all things measuring is indeed a problem.  With Big Data, comes Data Science, which many view as the panacea for dealing with the volumes of information around us, and perhaps even organizational sense-making writ large.  Data Science is a bit of a nouveau business fad, and as with most tools borne from business, it is difficult to know how viable and enduring the ideas really are.  Online higher education is not immune from the possible ill-effects of Big Data and a potentially faddish affair with Data Science; the growth of learning analytics inside Learning Management Systems and elsewhere could create unfortunate side effects, or is at least worthy of continued careful scrutiny.

While data and its analysis can clearly be helpful, relying too much on Big Data and Data Science is not.  There is a difference between leaders interested in good analysis, versus leaders who require esoteric data analysis in order to make a decision.  If leaders struggle to make decisions based on experience, intuition, or counsel from others, they may become indecisive, or worse, may pressure analysts to manipulate their data to justify “hard” decisions to boards, trustees or other stakeholders.  Indeed, citing one’s data, however shaped and designed, can be a way of distancing oneself from the accountability and effects of their decisions.  Moreover, since exotic data analysis typically requires mathematical expertise and a deep familiarity with statistics, those with these technical skills tend to gather more power and control in their organizations, displacing more experienced line leaders.  Stilted indecisive leadership, and managerial or overly process-centric control are rarely good things. 

Perhaps the most glaring example of how analytics can be misused is Robert McNamara’s (mis)management of the Vietnam War.  Known as one of the “Whiz Kids” of Big Data’s inception at Harvard in the late 1930s, its application during WWII helped coordinate the bombing effects of US Army Air Force and British Royal Navy actions on Germany.  This effort was part of a larger “scientification” of management dating at least as far back as Taylor’s 1911, Principles of Scientific Management if not back to Durkheim’s 1895, Rules of Sociological Method or even Comte’s positivism.

McNamara later applied his techniques at Ford, where he was notorious for being an aloof and difficult leader, while still “turning around” the organization into greater profitability.  Later still, his notion of mathematical precision applied to human behavior become standard while Secretary of Defense under JFK and LBJ in the early to mid-1960s.  The US military still uses his Planning, Programming, Budgeting and Execution (PPBE) Process.  Note the daunting complexity, something that has long since required its own process (versus people) expertise, and has created an entire industry of technocratic sense-makers.  Even with the intended precision, the Department of Defense wastes billions of US Dollars per year.  McNamara said in 1962, three years before most historians place the beginning of the conflict, and a solid 13 years before the tragic end of the war, that, “Every quantitative measurement we have shows we’re winning this war.”

While the stakes for higher education and its relationship with Big Data and Data Science might be less than that of the Department of Defense, the negative implications of short-sighted data use are clear. 

Brian Wierman is the Chief Strategy Officer for Freedom Learning Group (FLG), a growing provider of courseware, content development, accessibility and assessment writing.  FLG’s mission is to provide career opportunities in education to underemployed Military Spouses and Veterans.  Find FLG at freedomlearninggroup.com.