Research

Collaborative Predictive Analytics: An Open Innovation Framework for Data Ecosystems.

My research interests in Data Ecosystems and how Data Labs can interact to foster open innovation directly address significant challenges that the world is currently facing. Data-driven ecosystems have become critical in enhancing predictive accuracy, enabling more informed decisions in both social and financial sectors (Scholten & van der Heijden, 2017). By facilitating the sharing of insights, models, and data between labs, these ecosystems provide a platform for collaborative breakthroughs essential in addressing complex, cross-sector challenges (DalleMule & Davenport, 2017).

The concept of Data Ecosystems has been extensively studied in recent years, particularly in fields such as big data value creation and artificial intelligence. For instance, the development of European Big Data Value Ecosystems highlights how interconnected systems can drive economic and societal growth by fostering innovation through collaboration (Curry & Sheth, 2018). These ecosystems encourage cooperation between the public and private sectors, creating a dynamic environment where stakeholders can share data and resources to achieve mutual benefits (Blasiak et al., 2019). This collaborative approach is not only improving market forecasting but is also being applied to areas like public policy and sustainability to tackle societal issues more effectively.

Moreover, the Data-Driven Innovation (DDI) framework emphasizes that data-driven ecosystems can solve supply and demand issues by aligning technological capabilities with business and societal needs (Mikalef et al., 2019). This aligns perfectly with my research focus, which is to build interconnected Data Labs that refine predictive models for both financial markets and social decision-making.

On the social side, data ecosystems have proven invaluable in addressing urban challenges. Research on data-driven smart cities illustrates how datafication—the transformation of urban environments into quantified, analyzable entities—enables cities to optimize their operations and enhance sustainability, efficiency, and quality of life (Albino, Berardi, & Dangelico, 2015). By creating a collaborative environment where data flows between sectors like healthcare, urban planning, and environmental policy, my research can help solve pressing societal challenges by generating shared insights that drive smarter, more informed decisions (Kitchin, 2014).

My research is not just theoretical—it addresses a critical need for innovation across industries and sectors, especially as organizations increasingly recognize that isolated data cannot solve complex global problems. The push toward collaborative data-driven environments makes my research highly relevant, and its real-world applications in improving financial stability and solving societal challenges provide a strong basis for continued academic exploration (Wamba et al., 2015).

In the academic world, the concept of Data Ecosystems and Open Innovation has gained significant traction, but there are key challenges and gaps that my PhD research aims to address. Existing Data Ecosystems often struggle with fragmentation and siloing, where data labs or organizations operate independently without sufficient interoperability, real-time data sharing, or collaboration across sectors (Vendrell-Herrero et al., 2018).

For example, a recent study on Open Data Ecosystems (ODE) highlights that while there are examples of cross-sector collaborations, such as in public transport data in Sweden, the potential of commercial and public data sharing is still underexplored (Janssen et al., 2017). This fragmentation restricts the potential to harness data for broader societal and financial gains, especially where cross-sector integration could lead to better outcomes, like using healthcare data to inform financial decisions during a pandemic or urban planning data to predict economic impacts of city infrastructure developments​ (Raman & DeSanto, 2017).

The Open Innovation framework emphasizes the need for cooperation across sectors and industries, which is where my research steps in to solve a real gap in how these ecosystems function (Chesbrough, 2006). While much research has been conducted on value chains and innovation networks, there is still a lack of comprehensive frameworks that address the practical challenges of cross-sector data sharing, such as privacy, data standardization, and real-time data integration (Giest, 2017).

Data collaboratives have been shown to help tackle societal challenges by enabling the flow of data across sectors, but these efforts are often limited by the interests of commercial entities, who are reluctant to share data due to competitiveness and privacy concerns (Verhulst et al., 2020). My research will help to build a framework that not only ensures interoperability and real-time access, but also incorporates privacy-preserving technologies such as data anonymization and secure multi-party computation to ensure secure data sharing across both public and private sectors​ (Hardjono et al., 2019).

By addressing these gaps, my research can provide a critical contribution to the growing field of Open Innovation and Data Ecosystems, creating a model that integrates financial and social sectors to improve both market forecasting and societal decision-making. This framework will help solve one of the main challenges facing current data ecosystems: how to foster trust, collaboration, and innovation across industries while maintaining data security and integrity.

References

Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart cities: Definitions, dimensions, performance, and initiatives. Journal of Urban Technology, 22(1), 3-21. https://doi.org/10.1080/10630732.2014.942092

Blasiak, R., Spijkers, J., Tokunaga, K., Pittman, J., Yagi, N., & Österblom, H. (2019). Climate change and marine fisheries: Least developed countries top global index of vulnerability. PLoS ONE, 12(6), e0179632. https://doi.org/10.1371/journal.pone.0179632

Chesbrough, H. W. (2006). Open innovation: The new imperative for creating and profiting from technology. Harvard Business Press.

Curry, E., & Sheth, A. (2018). Next-generation smart environments: From system-of-systems to data ecosystems. IEEE Intelligent Systems, 33(6), 69-76. https://doi.org/10.1109/MIS.2018.2877757

DalleMule, L., & Davenport, T. H. (2017). What’s your data strategy? Harvard Business Review, 95(3), 112-121. https://hbr.org/2017/05/whats-your-data-strategy

Giest, S. (2017). Big data for policymaking: Fad or fast track? Policy Sciences, 50(3), 367-382. https://doi.org/10.1007/s11077-017-9281-8

Hardjono, T., Shrier, D., & Pentland, A. (2019). Trusted data: A new framework for identity and data sharing. MIT Press. https://doi.org/10.7551/mitpress/11170.001.0001

Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2017). Benefits, adoption barriers and myths of open data and open government. Information Systems Management, 29(4), 258-268. https://doi.org/10.1080/10580530.2017.1254435

Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1-14. https://doi.org/10.1007/s10708-013-9516-8

Mikalef, P., Pappas, I. O., Giannakos, M., & Krogstie, J. (2019). Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and e-Business Management, 17(3), 547-578. https://doi.org/10.1007/s10257-019-00437-7

Raman, P., & DeSanto, S. (2017). Open data and the rise of data collaboratives: Tackling societal problems through data sharing. Stanford Social Innovation Review, 15(3), 30-35. https://doi.org/10.48558/ZZFG-XZ71

Scholten, H., & van der Heijden, A. (2017). Big data and the future of urban planning. Cities, 61, 39-50. https://doi.org/10.1016/j.cities.2016.06.012

Vendrell-Herrero, F., Bustinza, O. F., Parry, G. C., & Georgantzis, N. (2018). Servitization, digitization and supply chain interdependency. Industrial Marketing Management, 60, 69-81. https://doi.org/10.1016/j.indmarman.2016.06.013

Verhulst, S. G., Young, A., & Winowatan, M. (2020). The potential and limits of data collaboratives for sustainable development. International Journal of Public Administration in the Digital Age (IJPADA), 7(1), 1-21. https://doi.org/10.4018/IJPADA.2020010101

Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2015). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365. https://doi.org/10.1016/j.jbusres.2016.08.006