Stephanie Fuentes on Harnessing Big Data in Higher Education: Evaluators as Data Scientists

I’m Stephanie Fuentes, an institutional researcher for a small, for-profit college and I’m totally fascinated by the hype around data scientists and predictive analytics. Tom Davenport and D.J. Patil call it the sexiest job of the 21st century (according to an often-cited Harvard Business Review article). Who knew evaluators were in such demand?

As evaluators we don’t often get a lot of press about the technical and deep nature of our work to investigate questions of interest that yield results that get used. We understand the complexities and context that drive data.

What can you do as an evaluator to better position yourself in the big data movement?

Lessons Learned: Know what big data can and can’t do. Just because you know “what” doesn’t mean you know “why”. It takes the “why” to move the needle on many metrics important to organizations. Evaluators are experts at finding and leveraging the why.

Partner with other experts. Data scientists are often described as unicorns. Why is that? Because it’s extremely difficult to develop skills in both evaluation and in programming simultaneously. Following on the prior point, just because you have data doesn’t mean it’s useful. Evaluators bring balance. Find technical partners in IT, programming, and database administration to help you bring data and meaning together. The real breakthroughs happen in cross-disciplinary relationships among experts.

Expect evolution. The Big Data movement has only been possible in the past few years because of technological advances in data collection and storage. There’s more data out there than we have the time to analyze. Think about how easy it is to collect, and how hard it is to develop a focused question to get an answer from that vast sea of data. Someone has to think through how to use that data meaningfully. The ability of individuals to ask intelligent questions that generate usable results is just being realized.

There are new communities of data scientists being hosted by both companies (like IBM) and organic groups (LinkedIn). If you don’t already know what competencies evaluators should be able to demonstrate, pick up a copy of Evaluator Competencies (a must-have for evaluators’ performance reviews).

Hot Tip: To keep tabs on how the Big Data movement is evolving, monitor the HBR Blog Network postings. The most current thinking on this movement is often featured here.

Above all, keep asking questions. Big Data has not replaced the value of being able to think.

Rad Resource: Check out this handout in the AEA Public eLibrary from my recent AEA Coffee Break Webinar.

Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to . aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.



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