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Combining big data and lean startup methods for business model evolution

Steven H. Seggie (), Emre Soyer () and Koen H. Pauwels ()
Additional contact information
Steven H. Seggie: ESSEC Business School
Emre Soyer: Ozyegin University
Koen H. Pauwels: Northeastern University

AMS Review, 2017, vol. 7, issue 3, No 6, 154-169

Abstract: Abstract The continued survival of firms depends on successful innovation. Yet, legacy firms are struggling to adapt their business models to successfully innovate in the face of greater competition from both local and global startups. The authors propose that firms should build on the lean startup methodology to help adapt their business models while at the same time leveraging the resource advantages that they have as legacy corporations. This paper provides an integrated process for corporate innovation learning through combining the lean startup methodology with big data. By themselves, the volume, variety and velocity of big data may trigger confirmation bias, communication problems and illusions of control. However, the lean startup methodology has the potential to alleviate these complications. Specifically, firms should evolve their business models through fast verification of managerial hypotheses, innovation accounting and the build-measure-learn-loop cycle. Such advice is especially valid for environments with high levels of technological and demand uncertainty.

Keywords: Business model; Innovation; Big data; Lean startup; Confirmation bias; Innovation accounting; Build-measure-learn-loop (search for similar items in EconPapers)
Date: 2017
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