Tuesday, January 22, 2013

Business analytics student projects a valuable ground for industry-academia ties

Since October 2012, I have taught multiple courses on data mining and on forecasting. Teams of students worked on projects spanning various industries, from retail to eCommerce to telecom. Each project presents a business problem or opportunity that is translated into a data mining or forecasting problem. Using real data, the team then executes the analytics solution, evaluates it and presents recommendations. A select set of project reports and presentations is available on my website (search for 2012 Nov and 2012 Dec projects).

For projects this year, we used three datasets from regional sources (thanks to our industry partners Hansa Cequity and TheBargain.in). One is a huge dataset from an Indian retail chain of hyper markets. Another is data on electronic gadgets on online shopping sites in India. A third is a large survey on mobile usage conducted in India. These datasets were also used in several data mining contests that we set up during the course through CrowdANALYTIX.com and through Kaggle.com. The contests were open to the public and indeed submissions were given from around the world.

Business analytics courses are an excellent ground for industry-academia partnerships. Unlike one-way interactions such as guest lectures from industry or internships or site visits of students, a business analytics project that is conducted by student teams (with faculty guidance) creates value for both the industry partner who shares the data as well as the students. Students who have gained the basic understanding of data analytics can be creative about new uses that companies have not considered (this can be achieved through "ideation contests"). Companies can also use this ground for piloting or testing out the use or their data for addressing goals of interest with little investment. Students get first-hand experience with regional data and problems, and can showcase their project as they interview for positions that require such expertise.

So what is the catch? Building a strong relationship requires good, open-minded industry partners and a faculty member who can lead such efforts. It is a new role for most faculty teaching traditional statistics or data mining courses. Managing data confidentiality, creating data mining contests, initiating and maintaining open communication channels with all stakeholders is nontrivial. But well worth the effort.


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