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)

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