The IT shortage of big data skills is well documented. Consequently, students of IT in universities are pursuing big data careers in place of the network certifications that were so wildly popular only a few years ago. The problem is not every university teaches big data. When it does, the big data training can be highly abstract, theoretical, and difficult to apply to industry. Traditional academic big data and analytics training filters through the school of computer science, where engineers primarily trained in statistical analysis and mathematics engage in lofty projects such as determining whether there is life on other planets, or working out complex genomic structures.
In striking contrast to this are the big data efforts of most enterprises, which are grounded in questions such as: Is a 21-year-old housewife in North Texas more likely to purchase ground beef or chicken on her next trip to the grocery?; or, will weather disturbances likely disrupt trade routes in South Asia next year?; or, what’s the chance that a young adult in South Cleveland with a family history of diabetes is likely to be diagnosed with the disease?
A different approach to teaching big data
Syracuse University has an IT program that actively partners with Pecan Street, a research and development organization at the University of Texas-Austin that focuses on developing and testing advanced technology, business model and customer behavior surrounding advanced energy management systems. The Pecan Street project gives Syracuse University’s IT students direct access to home meter data that is collected over a network from home meter end points and then consolidated into a big data database on an IBM mainframe computer at Syracuse. From there, Syracuse IT students undertake big data and analytics projects. Students have the opportunity to work on real-life problems in a collaborative way with business. Businesses also have the opportunity to see these students’ early work; they often like what they see and end up hiring the students into full-time employment.
“In the classroom and the lab, the idea is to capture this home meter data and then to analyze it for usage patterns and best practices on home energy management and consumption,” said David Dischiave, Associate Professor of School of Information Studies,Program Director, Global Enterprise Technologies Systems and Information Science at Syracuse. The Pecan Street energy project at Syracuse paves the way for similar work that Dischiave hopes the university IT program will commence with National Grid, a local Massachusetts, Rhode Island, and New York utility, in 2015.
In both cases, students rub elbows with IT and business experts. They obtain first-hand experience in real-life projects that not only build their resumes, but that give them a big data experience that is more than an isolated Linux server in a lab that performs a hypothetical assignment.
Does it make a difference? In 2010, 38% of all Syracuse IT student internships with business partners resulted in permanent job offers for these students with their sponsoring companies.
The takeaways for universities teaching big data
You don’t always have to hit home runs with big data.
It’s great to solve major world problems that have eluded us for years, but it’s also important to attack the lower hanging fruit, such as informing cities which routes are most likely to require widening for traffic over the next 10 years, or assisting a manufacturer in identifying where its supply chain risks are greatest.
Applicability is the key.
The closer students can get to actual industry problem solving in their studies, the more attractive they will be for companies that are hoping they can offer fresh skills and insights.
University educators should be tuned in.
One reason many universities stick to the highly theoretical in their analytics programs is that they lack professors and instructors who have real-life experience in the business world. Universities should exert greater effort in hiring practitioners who can keep their curricula fresh and deliver high value to students who seek employment and to employers that seek specific skills.
You should use placement metrics.
A great way to determine if a university IT big data program is on target is to measure the percentage of graduates who obtain IT employment each year. Universities that orient themselves toward delivering value to business and to students by equipping students with real-life skills apply these metrics to their programs.