The objective of the framework is to develop a best practice guide for use in promoting value generation and sharing of ideas within the big data and AI innovation ecosystem.
The goals are to:
- Foster collaboration and promote sharing of best practices and know-how among CoEs and national initiatives
- Provide expert guidance and (non-financial) support to member states looking to establish a new national CoE for big data and AI.
Within the framework as illustrated in Fig. 1, there is a process flow in the form of a value chain starting from the environment (which supplies input) through the core BDAI CoE capabilities (which process the input) to the output represented by the impact of the output received by the society under various categories: economic, scientific and societal. There is a backward flow (feedback) from the impact of a CoE back to the CoE and to the environment in which the CoE operates. For example, a CoE may hire personnel it trained as postgraduates or receive income from services rendered to a partner, which can return value to the CoE. Similarly, the impact created can influence the environment in which it operates, particularly regarding policymaking and funding decisions. The quality of output from a CoE is often the most significant determinant of funding decisions by funding agencies.
Excerpt from: Curry E., Osagie E., Pavlopoulou N., Salwala D., Ojo A. (2021) A Best Practice Framework for Centres of Excellence in Big Data and Artificial Intelligence. In: Curry E., Metzger A., Zillner S., Pazzaglia JC., García Robles A. (eds) The Elements of Big Data Value. Springer, Cham. https://doi.org/10.1007/978-3-030-68176-0_8