A couple of years later, I am discovering the painful truth that many of these dataset are no longer available. The reason is most likely due to the company who shared the data pulling their data out. This unexpected twist is extremely harmful to both academia and industry: instructors and teachers who design and teach data mining courses build course plans, assignments, and projects based on the assumption that the data will be available. And now, we have to revise all our materials! What a waste of time, resources, and energy. Obviously, after the first time this happens, I think twice whether to use contest data for teaching. I will not try to convince you of the waste of faculty time, the loss to students, or even the loss to self-learners who are now all over the globe. I’ll wear my b-school professor hat and speak the ROI language: The companies lose. In the short term, all these students will not be competing in their contest. The bigger loss, however, is to the entire business sector in the longer term: I constantly receive urgent requests from businesses and large corporations for business analytics trained graduates. “We are in desperate need of data-savvy managers! Can you help us find good data scientists?”. Yet, some of these same companies are pulling their data out of the hands of instructors and students.
I am perplexed as to why a company would pull their data away after the data was publicly available for a long time. It’s not the fear of competitors, since the data were already publicly available. It’s probably not the contest platform CEOs – they’d love the traffic. Then why? I smell lawyers…
|Data access is no longer available. Although it was for 2 years.|
I’d like to send out an appeal to Heritage Provider Network and other companies that have shared invaluable data through a publicly-available contest platform. Please consider making the data publicly available forever. We will then be able to integrate it into data analytics courses, textbooks, research, and projects, thereby providing industry with not only insights, but also properly trained students who’ve learned using real data and real problems.
This time, industry can contribute in a major way to reducing the academia-industry gap. Even if only for their own good.