This is part 2 of a 5-part series on university/corporate partnerships in analytics and data science.
In our first article, we identified three broad objectives that companies – particularly those on the lower end of the analytics maturity continuum – have for partnering with universities in the context of analytics and data science. Here, we explore these objectives in more detail.
Undergraduate analytics degrees are typically geared towards entry level talent and positions, but they are also heavily reliant on general education requirements, providing less room for deeper study. Analytics hiring managers should look for honors projects or special entrepreneurial opportunities to partner with undergraduate students. These students, as a rule, have limited “domain knowledge” (and they almost always overestimate what they know). However, what you lose in technical ability you may gain in creativity of thought – with limited domain backgrounds, undergraduate students have the rare gift of truly looking at a data-centric problem with a neutral unbiased lens. Most universities have a number of options for engaging analytics undergraduates through various departments but almost always with some form of outside sponsor, partner, or mentor. The outcomes are typically innovation competitions or one-off solution challenges like “hackathons” and “data jams”. And, as a rule, undergrads are fairly cheap.
Masters students are typically pursuing either a thesis-based masters or a project-based professional masters. If the degree is thesis-based, it may be a precursor to a Ph.D. or other doctorate and more heavily research focused. If project-based, it may require (and be looking for) real world practicums which industry is best poised to provide. These sponsored practicums also provide students with an opportunity to develop domain knowledge as well as facility with the data, making them potentially attractive candidates for positions after graduation. Note that for most universities, collaboration at the Masters level is substantively more common than collaboration for talent pipelines at the undergraduate or doctorate level.
Doctoral students can be a particularly great talent resource for a company – particularly if the work is aligned with their research stream. For doctoral students, publications are the “coin of the realm”. These students are skills-heavy and less time constrained, as their programs often take years to complete. If you are thinking that all Ph.D. programs are woefully theoretical, think again. Ph.D.s in analytics and data science are just as likely to enter the private sector after graduation as they are academia (most will make substantively more than their faculty after graduation). When you hire them, please remind them of their alumni donations.
There are two points for analytics managers to consider here. One relates to dollars (doesn’t everything). At universities, there is an important consideration called the “indirect rate”, or essentially the percentage of any research funding that goes to support overhead (we call them “administrators”), as well as cover any plant/equipment use. Look for indirect rates in the 30% range – meaning that if you provide a university with a $100,000 research grant, the office of research will take $30,000 off the top.
The other consideration is related faculty. Faculty typically need to publish. Ask if your organization is ok with publication of findings and/or under what conditions this would be allowed. Publication is also a great way to engage junior faculty. Depending on the type of position they have (research only, tenure track, teaching only or some combination) they will see the value in research collaboration given the data you can bring to the table. Also, they are often the ones who are on the cutting edge of methods, having just finished their doctoral or post-doctoral study. Without publication, however, these same faculty may be dis-incentivized by their department chairs towards working with your organization. At both our universities we have created data labs which are essentially R&D spaces where faculty, students, and companies explore “the art of the possible” in analytics. The IP is shared, and the learning is robust.
Over the next five months, we will continue this series for analytics managers considering partnerships with universities, with specific examples and cautionary tales.
About the Authors
Dr. Jennifer Lewis Priestley is the Executive Director of the Analytics and Data Science Institute at Kennesaw State University. Dr. Robert McGrath is the Chair of Health Management and Policy at the University of New Hampshire.