Two cases of data-driven management within the highly digitalized Danish universal welfare state are presented, analyzed and discussed. The first case shows how data-driven management is deployed in a hospital in Region Hovedstaden (Capital Region of Denmark). The focus is on how data-driven management supports organizational performance. Danish hospitals and the Danish healthcare system in general both have a long-standing tradition of data-driven treatments that support data-driven management in Danish hospitals. The second case investigates under what conditions it is possible to establish data-driven management in an environment without any tradition of data-driven decision-making processes. The focus is on how data-driven management can help to improve the provision of public welfare services to long-term unemployed citizens to help them to get jobs. The services are provided by social workers within Jobcenter Vordingborg located in Vordingborg Municipality. On the basis of the two cases of data-driven management these four questions are answered: (1) Where is data-driven management going? (2) Is the development desirable? (3) What, if anything, should we do about data-driven management? (4) Who gains and who loses because of data-driven management, and by which mechanisms of power? One of the key conclusions is: the creation of a delicate balance between top-down and bottom-up management is a must for data-driven management to work in practice.
John Storm Pedersen
John Storm Pedersen
The digital society, and consequently the digital welfare state, has been developing and is expected to develop further in the coming years. Within the digital welfare state, a new model of the provision of public welfare services to citizens as end-users on the basis of dataism is emerging. Dataism is defined as a belief in the logic of data – that data, not humans, can determine which public welfare services are best for citizens. The emerging model positions data analysts as a key profession, they hold the key to the model: digital algorithms. In addition, the emerging model has the potential to resolve the blind (weak) spots of the public welfare service semi-professionals’ ideal four-step model of the provision of welfare services to citizens. At present, the semi-professionals’ model dominates. The model reflects relationshipism. Relationshipism is defined as a belief in the logic of trust-based relationships between public welfare service semi-professionals and citizens – that trust-based relationships, not data, can determine which services are best for citizens. Consequently, trust-based dialogues between public welfare service semi-professionals and citizens in combination with co-production and co-delivery of public welfare services are the core of the public welfare service semi-professionals’ ideal model. Because the emerging model has the potential to resolve the blind (weak) spots of the public welfare service semi-professionals’ ideal model, it also has the potential to outcompete the semi-professionals’ model.
John Storm Pedersen and Adrian Wilkinson
Digital society appears to have established a position in the zeitgeist of modern society. We can see the origins of digital society as having roots in digitizing, algorithms, data-informed decision-making processes and data-driven management. These trends are likely to continue, so big data in that sense is not likely to go away. However, big data (or dataism), is seen as something more than a description of several connected threads and is often melded into prediction and prescription. Here, big data is the answer to many problems, and the claim and promise of the digital society is to improve firms’ competitiveness and public organizations’ provision of welfare services to the citizens as end-users. Of course, the spread of big data as a unified package owes much to consultants and the pressures on organizations. This book we contributes to these debates with research-based chapters from authors from many disciplines, and from across the globe, to provide an evidence base beyond the prescriptions of the guru and consultant tracts. The authors are experts in their fields and were not chosen because they have a specific stance on big data, but because they provide academic and critical perspectives.