Mobiliario urbano y embarcadero deportivo.

In the second chapter of this thesis two hypotheses were formulated as provisional answers to the research question. By analysing the three cases in chapter four and making a comparison between the findings these hypotheses were confirmed or rejected.

The discussion showed that the first hypothesis which states that data-driven working

implicates the introduction of technological tools, data-related computer tasks, and the task to ground and interpret the outcomes of data-analysis, which requires of frontline professionals to have technological and analytical competences can be partially supported.

First of all it is founded that data-driven working implicates the introduction of technological tools. In the case of the Orphanbike- and Safety-project this implicated that an element or system was added to the technological device already in use. In the case of the Neighbourhood-project the technological tool was totally new for the frontline professionals. Thus it is found in this study that data-driven working implies the introduction of (additional) technological tools.

Second, organising and analysing data are for the greater part computer-related tasks. However, despite analysis done by the frontline managers, these tasks are mostly conducted automatically or by others than the frontliners. Furthermore, the take-over some of the tasks of frontliners by computers is a reasonable possibility. This implies that frontline professionals need to work more with computers to execute their job. However, it is only in the case of the Neighbourhood-project that frontliners are obliged to work more with computers than before.

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Third, dependent on the project, grounding and interpreting data seem to be a fairly important task in data-driven working. The data-analysis in the Orphanbike-project are rather straightforward and therefore do not require thorough interpretation efforts. In contrast, the grounding and interpretation of data in the other two projects is an important part of data-driven working. This is not done in isolation, but by active collaboration seeking in order to rightfully judge the data and create useful knowledge. Underlined by experts, the results indicate, that despite exceptions, the grounding and interpretation of data is an important task in data-driven working.

Concerning technological and analytical competences it is found that both indeed are (partly) required in order to work data-driven. The results show that technological skills are demanded in order to work data-driven. However these competences are not new, or at least not difficult to learn. Analytical competences are found to be important for managers, but do not necessarily require extra training. Of most other frontline professionals no additional analytical skills are required. And if required, frontliners can at least fall back on their managers.

The second hypothesis stated that data-driven working leads to the generation of

information and knowledge which inform and direct the decisions of frontline professionals, resulting in a diminishment of their discretionary space and is predominantly rejected.

Concerning the generation of information and knowledge it is found that data-driven working indeed contributes to this. This finding is quite obvious due to the aim of big-data analyses in general, and the reasons to start with the data-driven projects in the researched municipality in particular. However, it is showed that this information-generation does not replace others sources of information such as experience and intuition. These sources are even necessary to make sense of the outcomes of data-analyses.

A remarkable finding is the importance of collective interpretation in order to create knowledge from the data-analyses. The frontline professionals who are in the position to analyse data-outcomes (such as area managers) actively seek collaboration with the frontliners in their team as well as with other partners to collectively seek the actual meaning of the data- outcomes. At the one hand it can be argued that the discretionary space of the individual frontliner decreases. At the other hand it can be stated that knowledge-creation at the lower levels of implementation provides the frontliners with more ground for decision making and thereby makes them more powerful. However, it is also found that the innovative data-analyses are not able to rationalize complex phenomena. Therefore data-driven working is considered to be an additional source of information without the ability to provide answers and solutions. Nevertheless the findings indicate that data-driven working is in some cases, and has the ability

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in other cases to become, (partly) directive. Since it also concerns the replacement of routines, and since it are future-expectations we cannot confirm that data-driven working will decrease the discretionary space of frontline professional. In addition, in the end it is still politics who decide which limits the potential power of data-driven working to decrease the discretionary space of frontline professionals.

In conclusion it can be stated that data-driven working indeed leads to the generation of information and knowledge, but not in itself. (Collective) interpretation is required in order to turn data-analyses into useful knowledge. Then, it has the ability to inform and even slightly direct the decisions of frontliners. However, other sources of knowledge are not replaced, even required for correct interpretation, so its power is limited. In addition, even when data-driven working decreases the discretionary space of (the individual) frontline professionals, it only does so as far as politics allows.