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Revisiting our Model

In document FROM DUST TO DATA (Sider 87-91)

Public Value vs. Economic Value

In chapter 2, we discussed and defined what ‘value’ contains when considering a data-driven approach to value creation in the museum context. This was summarized as the dimensions of public and economic value. Big Data technologies are primarily seen as means to generate economic value for an organization, for example by being able to market products and services in a more targeted and hence effective way to customers. This, as illustrated in the analysis, also applies to a museum context, where museums can gather more detailed data on their visitors in order to present them

with more personalized experiences. However, the competitive situation for public museums differs from most private organizations. While private companies can use their data, analytical capabilities and data insights as a competitive advantage, public museums face a much more challenging situation in regards of establishing a competitive advantage based on their data insights. Private organizations can ensure their competitive advantage by establishing a controlled access to their data (Günther et al., 2017). However, museums are required to choose an open mode of access due to their public nature and the public value they are expected to deliver. As a consequence, the need to generate public value can have limiting effects on museums’ ability to create economic value with a data-driven approach. This can also be illustrated with the impact of social risks that lie within the use of Big Data. While private organizations also face social risks, they can choose to neglect them or react on them reactively, as numbers of cases illustrates. Facebook, for example, addressed the social problems that were a result of their negligent use of personal user data after these practices were publicly revealed (BBC, 2018; Forbes Agency Council, 2018). Public museums, however, have to act more proactively in order to deliver on the public value of being a trusted and legit institution in society. Jeopardizing this position through the careless use of Big Data would not only affect the museums themselves, but would also negatively reflect on the government which funds and regulates the museums. In general, the relevance of the public (social) value dimension in terms of data-driven value creation hence appears to be more prominent for public museums than for private organizations.

The Strength of Interconnectivity

When we proposed our model in chapter 3, the data-driven approach formed the center of the model, which corresponded to Nograšek and Vintar (2014, 2015) model of organizational transformation, where Technology is placed in the middle. Even though a data-driven approach is not a technology as such, it is facilitated through different technologies as well as built on the foundation of digitization. However, in Nograšek and Vintar (2014, 2015) model, technology takes the central role because the authors understand it to be the driving, deterministic force for the organizational transformation in the different dimensions (structure, culture, processes, people). Already in our initial theoretical reflections, we implied that this deterministic force would not apply to a data-driven approach, as we understand Big Data from a socio-technological perspective, i.e. that society is influenced by Big Data and Big Data technologies and practices in turn are shaped by society. In an organizational context, we therefore expected that a data-driven approach could cause changes in all of the different dimensions while the impact of it would highly depend on the other dimensions.

However, what we did not fully account for from the beginning, and what we discovered throughout our analysis, was that the initial change does not necessarily start with the data-driven approach.

Changes in the other dimensions can just as well facilitate a data-driven approach. In other words, none of the dimensions can be prioritized over one another as the change can start in any dimension or even happen simultaneously in several dimensions. This, of course, also includes the supra-organizational dimension. While Big Data is a broad and global phenomenon, the translation of that phenomenon into an organizational context is the data-driven approach. In light of this we have revised our model as illustrated below (cf. Figure 4).

The high degree of interdependence does not only apply to the different dimensions, it is also reflected in the elements that we assigned to these dimensions. Throughout the analysis, it became clear that these interdependencies seem stronger than initially recognized in the theoretical conceptualizations. While we clearly assigned the elements to the different dimensions based on theory, applying our classification to the National Museum made it clear that such an assignment is less precise in practice. While some elements can be assigned to one dimension, others could be assigned to several dimensions. We, for example, classified leadership as an element of the people dimension because the prevalent leadership-style is shaped by the managers in leading positions.

However, one could also argue that it is a cultural element, as leadership also forms the organizational culture.

The elements cannot only to some extent be assigned to different or multiple dimensions, they can also be extended for different purposes. The dimensions are not limited to the elements proposed by us as other elements might emerge in practice. This became evident from our analysis where we treated sourcing as a separate element of processes based on its prominence in literature.

However, in practice, sourcing did not appear to be more relevant than other processes. In fact, our analysis indicated that processes in research and marketing were more prominent and hence could have been treated as independent elements. In light our empirical analysis and in line with the socio-technological perspective, we can conclude that our proposed model provides a conceptual understanding of how a data-driven approach is likely to form and take form in a public museum.

However, it is not exhaustive in its applicability due to the fact that technology is shaped by the different contexts.

The Iceberg Metaphor

Our focus on the visitor orientation and the business-side of things appeared naturally as a result of the current changes in the museum field, which are, as explained before, characterized by increasing financial and competitive pressure, which ultimately results in the question of how to draw more visitors in. However, all visitor-related activities mostly focus on one of the Danish public museums’

tasks - the task of dissemination, leaving the other four tasks collection, registration, preservation and research, largely unaddressed. Furthermore, the visitor experience and other visitor-related activities are primarily visible to the public, but they only constitute the ‘tip of the iceberg’. The activities that are related to the other four tasks are in contrast not directly visible from an external point of view - using the same analogy - they form the rest of the iceberg that is hidden underneath the surface of the water. By focusing on the ‘tip of the iceberg’-activities and relate them to the use of Big Data, potentials in using a data-driven approach and Big Data related technologies for the other four tasks lie largely undiscovered. So far, Big Data has often been discussed in literature in terms of deriving customer insights and from this point of departure, focusing on visitor-related topics when applying Big Data to the context of public institution seems natural. However, in the course of our analysis, it became apparent that the Big Data perspective could also be used to study the activities related to other tasks, as we for example also draw on the implications of a data-driven approach on research. Studying how to use a data-driven approach to fulfill the other tasks more efficiently, effectively or innovatively, could further enhance value creation for public museum.

Figur 4: Revised model of Big Data value creation

In document FROM DUST TO DATA (Sider 87-91)