Jenkins graduate students learn to apply big data analytics to business decision-making

Students typically focus on finding the right answer to questions posed by their instructors.

In a new course based on data-driven analytics, Jenkins graduate students at the NC State University Poole College of Management are challenged to come up with the right questions that are essential in making big data analytics into an effective tool for companies' strategic decision-making processes.

The course – data-driven decision making – was developed and taught for the first time in spring 2013 by Dr. Michael Kowolenko, teaching professor at Poole College and an industrial fellow and principal research scholar with the Center for Innovation Management Studies (CIMS), based in the college. 

Big data: It requires asking the right questions

Despite the high-powered computing and cutting-edge software involved in big data analytics, Kowolenko is not teaching rocket science, he states in a CIMS newsletter article on the topic.

“The overall goal of the class is to teach (students) to do critical thinking,” he said. “Big data is a tool, not a technology to be worshiped. The computer never gets bored, and it doesn’t make mistakes. But it doesn’t know how to ask questions. It’s about asking the right questions,” he said.

In developing the new course, Kowolenko drew on his decades of experience as a pharmaceutical industry executive as well as his more recent work at CIMS with companies interested in finding answers to their problems – and opportunities – in cloud computing.

Working on teams and using the auto industry as a test case for their searches, the Jenkins graduate students learned how to create models and dictionaries to plug into the natural language processing software, which was developed by IBM and has been beta tested at Poole College. The information the computers delivered would help automotive and other companies make better decisions about new products, new business models, customer sentiment and competitive positioning.

Bringing value through data-drive decision making

“Companies that reward good data will succeed more than those that reinforce ‘feelings,’” Kowolenko said. Students who understand how to feed the right questions into the computer can bring value to their employers.

The importance of this kind of decision making is reflected in part by the attention it was been receiving in the news media, where a search on the term ‘big data’ yields scores of pages of links. One of those points to the “World’s Top 10 Most Innovative Companies in Big Data” published by Fast Company. And one of the companies on that list is also on the magazine’s roundup of the “Top Ten Most Innovative Companies in 2013.”

Prior to launch of the new Jenkins MBA course, CIMS had paired individual students and student teams with its partner companies to work on big data-related projects, including particularly students in the college's Master of Global Innovation Management program.

One of those students was Tim Michaelis, who was in the program in 2011-12. MGIM students also complete a summer internship, and Michaelis continued working with CIMS partner companies initially as a summer intern with Air Products and Chemicals Inc., and then as a research associate with Kowolenko and CIMS.

Applied learning: finding potential customers from terabytes of data

The company’s goal was to determine the value of using big data and Unstructured Text Analytics (UTA) for targeted market research applications.

Michaelis focused on demonstrating the feasibility of using the CIMS Big Data Analytics Platform (BDAP™), developed with IBM, to collect terabytes of data from any public web source, apply Natural Language Processing (NLP) models as data filters, and present relevant information to the research staff and marketing managers at Air Products and Chemicals Inc.

The first step for Michaelis and his fellow student team members was to generate a business question specific to Air Products’ core strategies: “How do we effectively and efficiently identify potential customers when our products and services are highly transferable among many markets?”

“Big data and UTA can address our question by providing information that identifies new customers or potentially under-served markets more accurately,” Michaelis wrote in the project’s summary report. “Better information allows Air Products to allocate resources efficiently, reduce uncertainty and construct a more complete market map. The information also allows the marketing managers to assess market demand more easily.”

The team’s second step was to identify the relevant information sources. “For example, we conducted a large-scale web crawl using more than 300 URLs to create a multi-million-document corpus of text representing the metals processing market. Each website was crawled to a depth of 16 layers – URL links – thereby expanding the database exponentially,” he states in the report.

Subject matter experts are key in selecting search terms

Michaelis then worked one-on-one with Air Products’ function managers and its senior technologist to develop semantic word lists that framed context around each market segment.

A key take-away from the project reinforces Kowolenko’s statement about big data analytics: it is not about the technology; it’s about the question.

“Air Products was able to identify potential new customers and underserved markets for industrial gases because it could frame a big data question about what drives its business growth,” Michaelis stated.

Additional key take-aways from the project:

• Institutionalizing big data capabilities is no simple task. Combining the right resources takes time and cooperation among many individuals. 
• Adopting a disruptive innovation is difficult. One frustrated manager who cannot easily identify or extract value from big data can make or break the adoption process. In-person action learning workshops can help remove the abstractness of data analytics.
• UTA is not like a Google search. Google tags the key words written in the search bar and their spatial relationships in text: how far each word is from another in a web page. The CIMS Big Data Platform with UTA allows combining multiple semantic keyword lists in order to return only those context-specific results that can be located within a document, paragraph or a single sentence.
• A critical part of introducing big data and UTA into a business unit is to have subject matter experts work directly with the technology. These experts must be a part of the process of developing semantic word lists and using data to influence decisions.
• Logically, the more experienced managers will be able to better articulate the current trends associated with a market and can more accurately define what is and is not good information. Introducing big data and UTA into an organization will not be successful without data-savvy managers, individuals who can both identify the core business question and use big data technologies to drive their decisions with data.

That last point is a key reason that Kowolenko developed the Jenkins MBA course in data-driven analytics. “Companies that reward good data will succeed more than those that reinforce ‘feelings’. Students who master this process will have a skill set unique to them,” he said, adding, “People who know how to apply big data analytics to decision making will be in demand.”

For more information about the Air Product project and other CIMS-sponsored work with big data analytics, visit cims.ncsu.edu and type ‘big data’ into the search bar. For more information about the Jenkins MBA data-driven decision making course (MBA 590), visit the MBA course information page.

Michaelis is continuing his studies at NC State, now as a student in a doctoral program in psychology at NC State’s College of Humanities and Social Sciences. He is focusing his studies on decision making, organizational behavior, innovation and technology."  

Kowolenko will be teaching the data-driven decision making course in fall 2013.

About the Photo 

Tim Michaelis and Brian Boothe, representative of CIMS’ partner company Pentair Aquatic Eco Systems, are in discussion following a CIMS presentation on its work with big data and data-driven decision making. Over 100 industry and academic guests attended the presentation, held at NC State’s Hunt Library auditorium as part of CIMS’ spring 2013 meeting.