Happy new year everyone! Here are my interesting reads from the holidays up to today.
Developing a unified definition of digital transformation – studies that aim to define a certain concept are always fascinating. In this study, they review the digital transformation literature and complement it with a survey of practitioners and academics to get a unified definition of DT:
A fundamental change process, enabled by the innovative use of digital technologies accompanied by the strategic leverage of key resources and capabilities, aiming to radically improve an entity* and redefine its value proposition for its stakeholders.
Organizing for innovation: a contingency view on innovative team configuration – study on how a team’s expertise should be tailored according to the domain they are trying to innovate in. The researchers explore 4 domains varying by modularity and breadth of application – MRI, RFID, stem cells and nanotubes. Modular domains like MRI and RFID do not need a lot of overlap in knowledge across their inventors. Domains with broad applications benefit from teams with wide knowledge breadth as well.
The front end in radical process innovation projects: Sources of knowledge problems and coping mechanisms – It’s worth the read even just for the review on the difference between complexity, uncertainty, equivocality and ambiguity. These are terms that are thrown around interchangeably and this article builds on previous works to explain them more clearly:
- Uncertainty – refers to the lack of sufficient information needed to get to the desired outcome. Put simply, not knowing the answers.
- Complexity – related to the number of parts in a system and the difficulties in predicting the interactions among them. In other words, not knowing how to find the answers to the question.
- Ambiguity – the inability to interpret. In other words, not knowing what questions to ask in the first place.
- Equivocality – potential for multiple interpretations, even when information is available. Put simply, having multiple answers.
Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet – despite moving away from pharma research, I still enjoy reading news about the industry. The summary of this article is the classic line garbage in, garbage out. The conclusion perfectly illustrates this: “with our current ways of generating and utilizing data, we are unlikely to achieve the significantly better decisions that are required to make drug discovery more successful… we need to understand what to measure, and how to measure it… Only once these data are available for AI approaches can the field be expected to make real progress.”