Digital Innovation Management and Data-Driven Business Models
Digital Innovation Management
In the CAS Digital Industry at FHNW and at HSLU, I had the opportunity to talk about the digitalization of the innovation process and showcase practical examples.
Idea Management
At the implementation level, the digital management of idea management is of great interest, especially because of:
- long-term perspectives
- Idea exchange between organizational units
- The global collaboration model
- The implementation with social media and mobile infrastructure
Specifically, it was shown using the example of Viima that high ergonomics and process adaptation lead to success (examples shown: digital health, medtech devices, SaaS products).
Assessment and Benchmark (including ISO 56002 “Readiness Check”)
At the strategy level, the topic of metrics and benchmarking was explored in depth with participants from SBB, Die Post, ABB, and others. In detail:
- Innovation strategyculture
- Team and personalities (based on the 10 faces of innovation)
- Types of innovation
- Aspects and capabilities
Finally, the new ISO 56000 standard was briefly discussed. Based on the above-mentioned metrics, a “readiness check” for the ISO 56002 guidance could be easily generated with InnoSurvey.
Data-Driven Business Models
In today’s rapidly evolving digital landscape, data-driven business models are becoming increasingly essential for maintaining a competitive edge. These models leverage data to inform decision-making, optimize processes, and create new value propositions. Key aspects of data-driven business models include:
- Data Integration: Seamlessly integrating data from various sources to provide a comprehensive view of the business environment.
- Real-Time Analytics: Utilizing real-time data analytics to make informed decisions quickly and efficiently.
- Personalization: Tailoring products and services to individual customer preferences based on data insights.
- Predictive Analytics: Using historical data to predict future trends and behaviors, enabling proactive strategies.
- Automation: Implementing Al and machine learning to automate routine tasks, freeing up human resources for more strategic activities.
For example, companies like Dropbox and Netflix use data-driven models to recommend products and content to their users, significantly enhancing customer satisfaction and loyalty. By embedding data into every decision, interaction, and process, businesses can achieve higher efficiency, better customer experiences, and innovative solutions.
