Data is the foundation of today’s technological age. With the exponential rise in data, the importance of using big data analytics across industries is also growing rapidly. As the world continues to turn to analytics to make business decisions, the need for institutions which offer programs focused on state-of-the-art information technologies and analytical techniques has become even more important.
Keeping the same in mind, we had an exclusive conversation with Karthik Kannan, the Thomas Howatt Chaired Professor in Management at Purdue’s Krannert School of Management and the Director for BIAC (Business Information and Analytics Center) to understand how Purdue University is using analytical business approach to learning real-world problems through experiential learning and developing industry leaders of tomorrow.
The industry is seeing a rising importance of Big Data Analytics and AI. How do you see these emerging technologies impact the business sector?
Wall Street Journal – when they changed the title of their magazine from “Marketplace” to “Business and Tech.” – noted that every company is a digital company. So, in that regard, companies are all dealing with information and analytics. Companies are quite varied in terms of their analytics capabilities. Some are attempting to wet their feet. Others have developed extensive internal capabilities. They are attempting to solve pretty sophisticated analytics problems.
At the first glance, people tend to view analytics from a narrow perspective. Often, it is used by companies for predictive purposes. For example, predictive maintenance is a big opportunity for industrial companies such as engine manufacturers, etc. Some have taken the next step of thinking about not just predictions, but prescriptive analytics. For example, one can develop an app that can predict the credit rating impact if a specific individual bought a piece of furniture or an expensive TV. However, it is possible for a company to develop a more prescriptive app that advises on the kind of products that the same user can purchase so as to improve the credit score. The former is an example of predictive analytics whereas the latter is an example of prescriptive analytics.
Yet others are thinking about completely disrupting the industry through the digital business model. An example of this is PayPal’s Venmo. This digital payment system has become a significant threat to traditional banks that they have responded with their own digital payment system called Zelle. BTW, PayPal’s CIO is a Krannert alum.
How is Purdue University, Krannert School of Management’s Analytics and Data Science Program contributing to the growth and transformation of analytics and big data education?
There is a serious push regarding Data Science and analytics throughout the university. Recently, the university launched the Integrated Data Science Initiative. Our program in Krannert – which is business analytics and information management (BAIM) – is the only graduate-level data science program currently. We have three themes of emphasis: technologies, techniques, and relevance.
If one does analytics, a large portion of the time is spent on collecting and cleaning data. This requires students to be familiar with programming to modify the data (such as python) and dealing with data systems (such as SQL, big data). That is the rationale behind why we emphasize technologies as a theme.
Techniques correspond to methods of doing data analytics. Whether you run regression, or clustering techniques or the appropriate method for analysis is the emphasis here.
Relevance is a distinguishing feature of the program. We often hear concerns that people with analytics background find it hard to communicate properly. For example, when you are presenting the insights from your analysis, it may not be necessary to walk through every detail of the analysis you ran when talking to senior managers. So, understanding the audience and, similarly, understanding the context is important. We believe that focusing on the three points of technologies, techniques and relevance are what is going to distinguish our program.
What is the edge Purdue University, Krannert School of Management’s Analytics and Data Science Program has over other institutes in the industry?
1. We have several Krannert and Purdue alums who are actively connected to the school and university and that are in senior level positions in the respective companies. This gives us access to real-world problems. As an example, Krannert’s Business Information and Analytics Center (BIAC) ran one of the first data dives in a college campus in 2016 with the help from folks in Walmart. More generally, we provide access for students to run projects.
2. We pay close attention to feedback from the companies. When we ran the Walmart data dive, we quickly realized the difficulty some students were facing with large datasets. In response, we have put together a course on computational issues in analytics.
3. While we teach the standard Python, R, and other tools-oriented courses, there is a significant focus on unique thinking-oriented courses also. We have a course on designing for human instincts, for example.
Kindly brief us about your role at Purdue University, Krannert School of Management’s Analytics and Data Science Program and your journey in this highly promising sector.
I was the founding academic co-director of the business analytics program. I stepped away from it this year to take up the role of director for Business Information and Analytics Center (BIAC).
What would be your advice to aspiring big data and analytics candidates?
I usually find that candidates are much more interested in learning tools. Please note that the tools change over a period of time. From a programming language standpoint, it used to be C++, then Java, and now we are seeing interests in Python. On the horizon, we are seeing Julia. Focusing excessively on tools does not necessarily develop your thinking. Moreover, if you are focused on doing simple analysis using tools, they are highly likely to be automated anyway. So, it is important to broaden your thinking.
What are some of the challenges faced by the industry today?
As mentioned earlier, companies are quite different in terms of how prepared they are with analytics.
• Some companies are trying to get their feet wet.
• Other companies have an understanding of the problem but are struggling to find talent.
• Several top-name companies already have a large number of PhDs but need additional depth for context-specific problems.
Our Business Information and Analytics Center (BIAC) can help all of these companies. The center can act as a safe haven to explore some projects that may be useful for the organization as well as giving our students a chance to work on real-world problems. It can provide access for companies to find top talent, and in fact, some of our students have been recruited by companies for which they performed projects. And we are engaged with context-specific research problems involving company researchers, faculty and Ph.D. students.
Please share some major achievements of Purdue University, Krannert School of Management’s Analytics and Data Science Program under your leadership.
We have been ranked well in the business analytics programs across various publications. Our master’s degree program in business analytics and information management was ranked #8 in the United States by ValueColleges.com and #9 globally in the QS World University Rankings. That program has quadrupled in enrolment in just two years. Krannert has also consistently placed in the top 20 in management information systems in the U.S. News & World Report rankings. We have significantly ramped up the faculty numbers to offer various kinds of programs. The number of faculty offering BA and IS courses is perhaps one of the largest in the country.
Can you throw light on the latest employment trends in big data and analytics industry?
There will be more interest in recruiting people who have a broader knowledge of all the three dimensions: technologies, techniques, and relevance to the specific context. I think people’s ability to communicate is going to be important. Exposure to things beyond simple analytics will become extremely critical. For example, it may be exposure to robotics, which requires an understanding of cyber-physical systems.