In this post we will provide some insights on the current development of an appropriate business model for the AEGIS platform.
In the early stages of the project, two business models have been identified as suitable for AEGIS: the marketplaceand the subscriptionones (or a mix of the two). However, it is worth noting that in a marketplace business model it is relevant to recruit vendors (in this case, users as data providers) and monetization is based on a commission per sale; whereas, subscription asks for a focus on customization and maintenance of the services (in this case, business intelligence solution), and monetization is based on the time of access and features used. It is worth noting that the two business models are often considered as different “types” or even “archetypes” of business model (Cabage and Zhang, n.d.; Afuah and Tucci, 2000; Rappa, 2001)and can be coupled in hybrid models as well as in other types such as a “service” business model. As a consequence, we have considered them as separated for analytics purposes.
Compared to current players in the data platforms competitive environment, such as, e.g. data.world (https://data.world/), the focus on “data-driven innovation” in Public Safety and Personal Security (PSPS) sectors is relevant to differentiate the AEGIS proposals. Furthermore, data-driven innovation should be accessible and not bounded by technical (advanced knowledge of data management, statistics, etc.) or technological issues (advanced knowledge of big data infrastructure components), especially in PSPS related businesses or organization, where also lay users or managers with no data scientists background are called to act in decision-making or service proposals/design. Considering these issues, a specific choice has been made to identify the AEGIS Minimum Viable Product (MVP), that is elaborated as follows: “Easier Transition to Big Data Analysis in the Public Safety and Personal Security domains for tech-wise as well as non-advanced users”.
Thus, the chosen focus facilitates non-advanced users while not preventing the use of the platform by advanced users. Actually, considering the evolution of the business model of the AEGIS platform as first a “Two-sided platform” to further develop it as a “Multi-sided platforms” (Eisenmann, Parker and Van Alstyne, 2006; Bharosa, Janssen, Klievink and Tan, 2013; Hagiu and Wright, 2015). Indeed, due to the service side of AEGIS, we can see an opportunity, for example, for advanced data scientists (acting as suppliers) to provide their datasets elaborations (e.g., views) for a fee to tech-wise non-advanced users (acting as customers) or vice-versa these latter providing their datasets for advanced elaboration to advanced data scientists, under a collaboration agreement enforced by the platform itself.
In any case, the AEGIS minimum viable product (MVP) should support network effects to increase the number of datasets offered as well as the number of users demanding for them. As to these issues the above-mentioned features should be oriented towards the building of accessible(also in terms of channels: e.g., via mobile) personal project spaces, which should enable the platform dynamics just exemplified.
Finally, Considering the value proposition, a general preliminary statement has been introduced (“Providing a curated, semantically enhanced, interlinked and multilingual data platform for public safety and personal security – to allow businesses and developers to provide better and personalized services to users”) has been further detailed considering the layers of strategic interest for AEGIS positioning:
- Delivering data access and AI services to private and public organization [market: Data Science and Machine Learning]
- Enabling business innovation and decision-making capacity for data-driven safety and security (Economic value through social value) [market: Big Data predictive analytics]
- Support for big data with scale out GPU compute and multitenant data governance model enhanced with semantics and (open) public data integration [market: Big Data Fabric]
In general, the AEGIS business model exploits the free and open source platform to allow partners having revenues from support, consultancy, and customization activities. It is worth noting that a similar business model has been adopted by benchmark companies at global level in the Data Science and Machine Learning market, such as, e.g., H2O.ai (https://www.h2o.ai/), see also (Idoine et al., 2018).
Blog post authors: Gianluigi Viscusi (research fellow at CDM-EPFL)
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- Bharosa, N., M. Janssen, B. Klievink and Y. Tan. (2013). “Developing Multi-sided Platforms for Public-Private Information Sharing: Design Observations from Two Case Studies.” Proceedings of the 14th Annual International Conference on Digital Government Research. New York, NY, USA: ACM.
- Cabage, N. and S. Zhang. (n.d.). “Business Model Archetypes: “The 7 Fundamental Business Model Personalities.”” Retrieved from: hhttp://smarterstartup.org/framework/business-model-archetypes/ – Accessed on November 8, 2018.
- Eisenmann, T. R., G. Parker and M. W. Van Alstyne. (2006). “Strategies for Two-Sided Markets.” Harvard Business Review, October, 1–12.
- Hagiu, A. and J. Wright. (2015). “Multi-sided platforms.” International Journal of Industrial Organization, 43, 162–174.
- Idoine, C. J., P. Krensky, E. Brethenoux, J. Hare, S. Sicular and S. Vashisth. (2018). Magic Quadrant for Data Science and Machine-Learning Platforms. Gartner.
- Rappa, M. (2001). “Business models on the web: Managing the digital enterprise.” Retrieved from digitalenterprise.org/models/models.html – Accessed on November 8, 2018.