If you want to see the future of business analytics, look no further than the university classroom.
While educational institutions are at the forefront of development and research, academia has lagged in an applied field like analytics because members of the private sector were the early adopters of data analysis technologies. Universities are catching up quickly, however. More than 250 universities offer Master of Science in Business Analytics (MSBA) or related degrees with more coming onboard every year. Students can specialize in operations research, project management, database analysis, and predictive analytics.
Analytics builds off long established fields like statistics, economics, computer science/information systems, and management—all core competencies in the classroom. The challenge has been how to bring these disciplines together and prepare today's students for tomorrow's jobs.
To help make sense of the trends in business analytics and its development in the university, we invited Kyle Hofer-Mora, Director of Business Analytics Graduate Programs at the California Polytechnic State University Orfalea College of Business, to the Oracle Advantage Podcast. Hofer-Mora joined Cal Poly in 2016 as the university geared up to launch its MSBA program.
Hofer-Mora oversees two graduate-level business analytics programs: Professional Certificate part-time, and MSBA. The certificate program, which launched in 2015, was a precursor to a full master's program. The MSBA covers advanced statistical modeling, data mining, machine learning, data visualization, and storytelling. Like other university programs, corporations like Oracle offer their software, advisory committees, and sponsorship support. Of the companies that have sponsored projects at Cal Poly over the past three years, 75 percent of them have hired at least one student from the program.
"A great thing about the analytics skillset is the applications of analytics across every industry, which allows students with these specialized skills to find an industry they are passionate about, or a functional domain in which they have an interest such as marketing analytics, or HR/people analytics," Hofer-Mora says.
The master's candidates are assigned teams and there are multiple teams per project. This allows for a healthy competition and gives the partner companies multiple views into the same data and strategic questions. Some of the Cal Poly projects have included data analysis with the goal of optimization of cell tower maintenance; marketing mix modeling for an ecommerce fashion retailer; optimization of test ordering patterns in emergency rooms to reduce wait time, radiation exposure, and cost; price sensitivity for data center subscription contracts; and “predictive propensity to buy” models looking at what other products a customer might buy based on previous buying patterns.
More Machine Learning
Hofer-Mora says the most exciting projects are often centered on the machine learning models the students create and utilize. These range from regression algorithms to classification or clustering algorithms. Other techniques that come up quite frequently in these projects are dimensionality reduction algorithms such as principal component analysis.
"I would say that most of our students don't employ deep learning methods—though we give them some conceptual exposure to these," Hofer-Mora says. "In a one-year program, there is only so much ground we can cover effectively. And we do emphasize the application, interpretation, and strategic implications of these models because of our business-orientation, more than you might get in a Master of Data Science, or Master of Computer Science where you might see more of the deep learning, natural language processing, and artificial intelligence.”
In addition to offering master's degrees, Cal Poly hosts an Analytics Forum annually which brings together the business community, alumni, current students, and prospective students.
"It has been rewarding to see graduates from our first cohort already be promoted and advancing in their careers," Hofer-Mora says. "We also actively engage with them to understand what is essential in preparation for the program. There are well-oiled undergraduate recruiting machines, and traditional MBA pipelines, but Master of Science programs in a variety of fields tend to be an afterthought for corporate recruiting teams. Because the skillset is so lucrative, and the economy has been hot, there isn't an issue with our graduates finding jobs, but it comes with a lot more effort simply because I think the newness of these programs has meant they aren't on a lot of companies' radar.”
As for the future of analytics, Hofer-Mora says the highly technical efforts that will push the boundaries of what's possible and what's ethical will continue, so that is an undoubted part of the future. If only business leaders could change their current analytics practices.
"It is surprising how many big decisions still get made from a rudimentary spreadsheet," Hofer-Mora quipped.
"I actually think the opportunity to translate complex data analysis into a digestible and experiential journey during the decision-making process will be a hot frontier," he adds. "We have a corporate world facing unprecedented levels of retirements in the coming years, and less experienced leaders will need to be even more data reliant in their strategic decision making. Who is going to do the hard work of robust data mining, learning, and analysis, and concisely package it in a way that can be quickly digested and put into action? It's not just analysis, it's not even the insights of the analysis, it's the action coming out of those insights that matters, and it's a bridge that isn't fully developed in many organizations."
Check out the entire conversation on the Oracle Analytics Podcast (click the icon below to listen):
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