Data-driven Roadmapping Turning Challenges into Opportunities

A number of organizations are struggling with roadmap implementation. While some companies implement it successfully, many cannot effectively apply the roadmap to their strategic operations. Keeping an up-to-date roadmap to reflect changes in the business environment is considered a major challenge in the field. The main focus of this explorative study extends the understanding of roadmap implementation and addresses the opportunities and challenges for future research by illustrating four case studies from both the private and public sectors. Semi-structured interviews with top management were conducted to obtain common critical components. The findings from this study highlight and confirm the major challenges involved in keeping the roadmapping process alive, as represented in previous studies. The results of case studies reflect the challenges and opportunities with integrating big data and transforming existing processes into data-driven roadmapping. This paper proposes a conceptual design for a system to help keep the roadmap alive—accurately reflecting current economic and business conditions, based on insights constantly obtained from various streams of information sources. Comprehensive analyses of existing data could help to detect the ongoing changes and indicate economic, social, and technological tendencies. Supervised learning, unsupervised learning, time series, and text mining are suggested techniques for providing useful insight and substantial information from the multitude of data. This approach can be integrated into the decision support system, based on an algorithmic, semi-automatic evaluation of roadmap status.