Elementary and secondary school principals must solve a challenging optimization problem. Faced with a deluge of applicants for teaching positions, demanding teaching environments, and very little time to spend on the applicant review process, school principals need a search algorithm with ranking analytics to help them find the right candidates. This is a classic data science problem.

Elsewhere, I have described the ideal data scientist, a balanced mix of math and statistics sophistication, programming chops, and business savvy: A rare combination, indeed. To solve the teacher applicant ranking problem, does every school in the country need to hire one of these “unicorn” data scientists to create a system to automatically identify the best teacher candidates?

I propose that the answer is “No!” and Big Data startup TeacherMatch agrees with me. It is not a good use of resources for every school to hire a data scientist to help analyze teacher applications with advanced analytics, natural language processing and machine learning, yet the need to make teacher candidate selection more effective and efficient is huge. The solution is to leverage the work of an expert who has already done that analysis.

TeacherMatch is such an expert. Based on a huge amount of historical data, TeacherMatch has developed a score for ranking teacher applicants, the EPI score, based upon a prediction of how likely a candidate is to be successful in the environment that the principal is looking to fill. Suddenly, it is nearly instantaneous to identify the top handful of candidates out of a list of potentially hundreds of applicants.

I met Don Fraynd, CEO of TeacherMatch, last year when he joined a [data science] (http://www.cio.com/article/2931082/data-analytics/discussing-data-science.html). panel that I hosted I was impressed with his deep understanding of the challenges of hiring good teachers and with his practical approach to analytics. He has created a big data analytics solution that is sensible to incorporate into any organization that needs to hire teachers. See for yourself in this very interesting video about TeacherMatch.

Looking more broadly at the needs of all industry for more powerful analytics, the shortage of data scientists available to hire is a challenge. TeacherMatch’s model represents a real solution. In fact, I suspect that analytics-as-a-service will help drive a new era of advanced data analytics because it allows business users of analytics to leverage the output of a small number of data scientists who solve common problems across different organizations. In this regard, TeacherMatch represents the future of analytics.

Through the example of TeacherMatch, it appears that our principals and teachers are taking us to school on analytics.