Stephan Kudyba is an associate professor at NJIT's Martin Tuchman School of Management. He analyzes how different companies innovatively mine data to become more efficient. In particular, he explores data mining and management information systems in B2B marketing, digital transformation, data products, health care, fintech and supply chain management.
Areas of Expertise (10)
Strategic Information Systems
Rensselaer Polytechnic Institute: Ph.D., Economics
Lehigh University: M.B.A
Siena College: B.S., Economics / Computer Science
Will Generative AI Disrupt your Company and your Need for Workers?The European Business Review
As Stephan Kudyba explains, generative AI affects companies in different ways….it all depends on the data that they rely on.
What Companies Need to Know Before Investing in AIHarvard Business Review
Different forms of AI can improve performance through prediction, automation of routines and identification of images, keywords and patterns in voice and text. However, organizations often struggle with knowing where investments in AI will really pay off.
Build a Better Dashboard for Your Agile ProjectHarvard Business Review
Good, reliable data is often the key to making an agile project successful. But project managers often struggle to get the data they need — or to find it in a sea of data they don’t.
Machine Learning Can Help B2B Firms Learn More About Their CustomersHarvard Business Review
Stephan Kudyba and Thomas H. Davenport
Web content that provides robust, detailed descriptions of companies has valuable descriptive information. However, these digital resources yield little value unless individual customers are identified and their detailed backgrounds and interests are analyzed. That’s where AI techniques can help.
Enhance Lead Qualification in SaaSInformationWeek
With SaaS technologies, organizations can better qualify their leads through data touch points and data science.
Designing and Developing Analytics-Based Data ProductsMIT Sloan Management Review
Companies are creating products that combine data with analytical capabilities. Creating an effective development process for these data products requires following well-established steps — and adding a few new ones.