Digital Innovation: Experimentation Strategies
Develop expertise in strategic testing methodologies to drive breakthrough digital innovations, leveraging insights for transformative business growth.
Develop expertise in strategic testing methodologies to drive breakthrough digital innovations, leveraging insights for transformative business growth.
This advanced course teaches how to leverage data-driven experimentation to drive digital innovation across technical, organizational, and strategic levels of business. Students will learn to develop iterative business experiments using agile methods, focusing on both incremental improvements and disruptive innovations. The course covers experimentation across three layers: technical infrastructure, organizational knowledge management, and strategic digital transformation.
Instructors:
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What you'll learn
Implement a business experimentation cycle to achieve desired outcomes
Develop and manage small-loop experiments for continuous improvement
Identify opportunities and manage risks for big-loop experiments driving disruptive change
Use agile methods and modular design to create iterative experiments
Manage knowledge interfaces among diverse experts in experimental processes
Leverage technical and organizational infrastructures for large-scale experimentation
Skills you'll gain
This course includes:
Scheduled video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course provides a comprehensive framework for driving digital innovation through experimentation. It covers the business experimentation cycle, focusing on both small-loop experiments for continuous improvement and big-loop experiments for disruptive change. Key topics include using operational processes and modular design for iterative experiments, managing knowledge interfaces among experts, and scaling experimentation capabilities across an organization. The course emphasizes the integration of technical infrastructure, organizational processes, and strategic decision-making to create a culture of continuous innovation. Students will learn to leverage data to build knowledge and apply this knowledge to improve business outcomes and create strategic advantages in the digital marketplace.
Fee Structure
Instructors
Leading Expert in Knowledge Management and Business Innovation at Boston University
Paul R. Carlile serves as an Associate Professor of Management and Information Systems and Senior Associate Dean for Innovation at Boston University's Questrom School of Business, following his previous faculty position at MIT Sloan School of Management. His groundbreaking research has established him as one of the world's foremost experts in addressing knowledge boundaries between different expertise domains, enhancing collaboration and innovative outcomes. His academic credentials include a BA in Philosophy and Masters in Organizational Behavior from Brigham Young University, and a PhD in Organizational Behavior from the University of Michigan. His most influential works include "A Pragmatic View of Knowledge and Boundaries" and "Transferring, Translating, and Transforming," which have received over 5,000 and 4,400 citations respectively. As Senior Associate Dean, he has led significant innovations in business education, including the development of an integrated Master of Science in Management Studies program that was recognized as the Most Innovative Business School Idea of 2015. His entrepreneurial background includes helping launch two technology companies prior to his academic career, and his current work focuses on enhancing student learning through curricular innovation and new models of program delivery.
Leading Expert in Information Systems and Digital Innovation at Boston University
Benjamin Lubin serves as a Clinical Associate Professor in Information Systems and Faculty Director of the MS in Digital Technology Program at Boston University's Questrom School of Business. His academic journey includes both a Bachelor's degree and Ph.D. in Computer Science from Harvard University, followed by six years of industry experience at BBN Technologies, where he worked on advanced multi-agent modeling and logistics systems. His research expertise spans three primary areas: mechanism design with a focus on combinatorial auctions and exchanges, the application of spectral graph theory in social network analysis, and the integration of machine learning with mechanism design. His work has garnered significant recognition, including the Siebel Fellowship and a Yahoo Key Technical Challenge award, with his publications receiving over 850 citations. His research has been published in prestigious journals including Management Science, Information Systems Research, and the Journal of Artificial Intelligence Research, with notable contributions to computational resource allocation, spectrum allocation, and healthcare delivery systems analysis.
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