مدیریت منابع در طرح های کلان داده
Abstract
1- Introduction
2- Theoretical foundations
3- Empirical evidence
4- Outcomes from the big data initiative
5- Lessons learned
6- Discussion
7- Implications for practice
8- Future business research
9- Conclusions
References
Abstract
Effective management of organizational resources in big data initiatives is of growing importance. Although academic and popular literatures contain many examples of big data initiatives, very few are repeated in the same organization. This suggests either big data delivers benefits once only per organization or senior managers are reluctant to commit resources to big data on a sustained basis. This paper makes three contributions to the Special Issue's theme of enhancing organizational resource management. One is to establish an archetype business process for big data initiatives. The second contribution directs attention to creating a dynamic capability with big data initiatives. The third identifies drawbacks of resource based theory (RBT) and it's underpinning assumptions in the context of big data. The paper discusses lessons learnt and draws out implications for practice and business research. The paper's intellectual and practical contributions are based on an in-depth case study of the European ICT Poles of Excellence (EIPE) big data initiative and evidence from the extant literature.
Introduction
This paper develops an archetype business process for big data initiatives and the roles required for effective big data resource management. The literature assumes processes for big data initiatives exist and that resources are managed accordingly. This assumption appears baseless as the literature lacks coherent processes for big data initiatives within which to manage resources. This concern is compounded by vast amounts of resources businesses (public, private and third sector) put into big data. The analysis identifies limitations in resource based theory in the context of big data initiatives. This paper has three objectives; the first objective is to set out an archetypical business process for big data initiatives. The literature has several reported examples of big data successes, see for example Davenport (2013) yet, very few examples are of repeated success by the same organization. Big data appears to be a ‘one off’ incident in most organizations. This paper argues that for big data to be truly strategic, senior leaders need a process they can implement to ensure benefits are delivered from investments made in organizational resources for big data. Previous scholarly studies show little or no attention is given to proposing a coherent and sustainable process for implementing big data initiatives. The tradition of developing archetypes is established in the management literature (Greenwood & Hinings, 1993). More contemporary examples include studies of buyer and supplier archetypes (Kim & Choi, 2015). The second objective is to examine roles in big data initiatives. The concern is that lack of clarity in various roles necessary for big data initiatives hampers organizations from using resources strategically. Conventional approaches to strategy suggest mission critical roles are carried out by organizations or their close partners. This paper argues that in big data programs many roles are outside organizations and the nature of relationships are more transitory than previous partner relationships formed through alliances, joint ventures or outsourcing agreements.