Data-Driven Innovation: Algorithms, Platforms and Ecosystems
The low-hanging fruit of data-driven innovation may be clear, but the full scope of potential benefits is much more difficult to grasp, resulting in opportunities that may be lost. (OECD 2015)
We define socio-technical resources as organizational means that allow for innovative computational and/or manual processing of newfound data sources emanating from the utilization of digital technologies within and across organizations. These resources promise to radically change and spur how organizations orchestrate and deliver services by exploiting data sources and data volumes unavailable in the past. Today, however, few organizations are successful in their efforts to achieve data-driven innovation. That is, newfound data sources remain largely underexploited because data barriers, data overloads, and analysis bottlenecks effectively hamper such innovation. Despite these difficulties, organizations make considerable investments to harvest and analyse data from multiple sources. Indeed, several of our business partners recognize data analytics as a key capability for innovation and have already designed their processes to cater for such capability.
The purpose of these resources is to help the business partners to create competitive advantage by assembling resources that work together to create organizational capabilities. The research project seeks to answer the following overarching core question: How can socio-technical resources enhance data-driven innovation in organizations? The research project includes three sub-projects:
- Software Algorithms for Data Analysis
- Digital Platforms for Service Innovation
- Ecosystem Strategies to Navigate Data Barriers