Conversations around the planning table for new programs often hinge around the word “demand.” Employer demand usually gets the first mention, and hopefully student demand gets a nod as well. Yet instead of plugging an institution into the heartbeat of their local economy, sometimes “high-demand” programs actually set the institution up for failure.
Consider data analytics. Programs in data analytics are gaining attraction to both employers and prospective students, with seemingly no end in sight. So, to answer this demand, many colleges have begun pooling resources and forming teams to incorporate data analytics programs into their institutional offerings.
Yet, the development and maintenance of a data analytics degree program can provide more logistical challenges than it’s worth, for two main reasons: an ill-prepared student body and difficulty in meeting faculty requirements.
Students are Underprepared for These Programs
Preparation for some degrees, particularly those in the STEM fields, really starts in grade school—long before these programs have been marked with the “high demand” signifier.
While students may understand that a degree in data analytics would give them a competitive advantage in the workforce, many students simply do not have the background in subjects like calculus and statistics to adequately prepare them for success in these programs.
Though larger R1 schools may have an adequate pool of prepared students to draw from when filling such programs, smaller private schools will often either have trouble filling these programs or have trouble graduating students from them.
Neither of these scenarios justifies the cost it takes to develop and maintain a full data analytics degree program.
Faculty Positions are Hard to Fill
Frankly, faculty salaries in subjects like data analytics tend to be quite high. And again, while larger schools might have a large pool of faculty members to draw from to fill the requirements of such programs as data analytics, the salary requirements of qualified faculty might be too expensive for small, private nonprofit institutions to hire.
If a professor in a data analytics program is making more than some C-level executives at the institution, the situation becomes untenable from both a logistical and political standpoint.
So what to do? If the logistical constraints of adding degree programs like data analytics may be unmanageable, there are a few mitigating options you can explore.
(Next page: 2 ways to implement high demand programs aspects in a reasonable way)
Make the Subject a Concentration of an Existing Program
Considering that you have the faculty to support it, subjects like data analytics can become concentrations of larger programs. This reduces the resources necessary to incorporate the subject and plugs the subject into larger contextual programs that may actually provide a greater benefit for students.
If you can’t add a full degree program in data analytics, consider adding a data analytics concentration to existing business or human resources (HR) degree programs.
Integrate Aspects of the Program into Curriculum of Existing Programs
In the event that students want to achieve some acumen in a particular subject without completely submerging themselves, consider building coursework in the subject into the curriculum of existing programs.
To revisit the HR example, HR now features a certain amount of data analytics into the regular practice of the profession. While the profession itself doesn’t require everyone to be analytics experts, proficiency in this subject sets practitioners up for both a competitive advantage on the job market and professional success in the field. The same can be said for business degrees, nursing degrees, and even some social science degrees.
New program planning absolutely can, and should be, weighted toward the realities of both market and student demand. But when trying to operate within the business confines of today’s higher ed market, institutions have a missional and operational obligation to acknowledge the logistical realities for any new program, then search out resourceful solutions.
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