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Discussion and Implications

The two opening government data cases presented here show how the various stakeholders and their patterns of interaction will change over time and will require new practices, new governance arrangements, new policies, and different ways to measure the value generated. Based on our analysis of the two cases, we present the following considerations for agencies in pursuit of opening government data.

Release government data that are relevant to both agency performance and the public interest.In both cases, demand for the data already existed and the act of “opening that data” just further improved services for both government and the public. However, most government agencies are facing an expanding list of ‘data’ to populate in open data catalogues. Therefore, trying to balance resources, time, and effort and choosing which data to release is not an easy task. Anecdotal evidence suggests that the majority of available open data initiatives do not enjoy the quick success or public value created in the case examples. The number of available data sets far exceeds the number of success stories. As the dynamic model indicated, looking for ‘quick wins’ in the fi rst few years is easiest, relying on data that is easily opened and has an established stakeholder base. In the long run, the quick wins will diminish, reducing the number of open data sets folks are looking to use, which may result in a loss of interest and less stories of use, and the virtuous cycle slows. Releasing government data sets that are relevant to both agency performance and the public interest are always a good investment.

Invest in strategies to estimate how different stakeholders will use the data. The wide range of potential uses underscores the fundamentally versatile and valuable nature of open data and explains why it is an attractive strategy. But it will be just as important to understand citizen demand as it is to understand intergovernmental demand, as it is to understand developer or third-party entrepreneur demand for the data. In the road construction data case, business owners reported that some stakeholders have started to ask for additional types of data. For example, public expectation that this data resource can provide them with more real time updates on traffi c disruptions, or changes in project status, would require changes in the type and timeliness of the data currently collected. As business owners, the agency will need to make the decision whether they can and should invest in new business or data collection processes in the future

Devise data management practices that improve context in order to ‘future-proof’ data resources There are no sure fi re ways to eliminate confl icts of meaningwhen it comes to government data. What makes data fit for use is context dependent. The intended use determines the specific data attributes needed by users. For example, the context of a motorist looking at road construction data to plan her morning’s commute is far different from that of an electrical utility engineer seeking ways to use the data to understand the impact of road construction on an infrastructure upgrade project. However, the dynamics of these cases suggest some possible strategies for contextualizing and better ‘future-proofing’ the data resources. These two examples imply quite distinct requirements for various stakeholders regarding data quality, timeliness of data needs, useful formats, and metadata that make it more or less useful for the variety of stakeholders interested in the data. By providing users the opportunity and a mechanism to communicate data errors and enhancements back to the source, the overall integrity and quality of government data can improve while increasing benefit to all future users

Think about sustainability.Think about sustainability. Data that is not ‘demanded’ by a stakeholder group may experience little or no value creation. Without extensive prior research, it is unlikely that most agencies will fi nd it easy to accurately predict demand for a new or enhanced data resource. However, it is not harmful to think of opening data as a virtuous cycle, where opening data leads to use and more use. But, as our dynamic model indicated, there are constraints that can affect the positive aspects of opening government data. Downstream assessment of the impacts of open data initiatives should also be part of the longer term picture. At some point, baseline usage data and attention to performance metrics early in the process can Center for Technology in Government The Dynamics of Opening Government Data 29 have substantial longer term benefi ts for existing and new initiatives. In addition, attention to immediate and downstream governance issues is also critical. If the existing governance arrangements for an initiative’s data ownership and use policies are not clear or well-structured, attention to those issues should be part of the overall effort.


The holistic approach described in this white paper can help planners and decision makers understand proposed and existing open data initiatives. An information polity perspective provides a way to identify the various stakeholders and their patterns of interaction that influence or control the generation, fl ows, and uses of enhanced information resources in open data initiatives. The dynamic modeling techniques used highlight the ways different constraints can impact the system as a whole and affect value creation. These tools support planners’ ability to generate informed hypotheses about changing patterns of interaction among existing and potential new stakeholders. In this way, governments can better evaluate the costs, risks, and benefi ts of a wide variety of open data initiatives. The goal is to become better at building the capability between government and other stakeholders to address the ways that open data initiatives change power relationships, expectations, and performance.

Although tested and refi ned by a combination of expert feedback and two opening government data cases, our approach is still a work in progress. The next steps in our research and examination of practice will be to use our initial results to guide new investigations. The possible variety of open data initiatives is huge. We believe that our approach can be useful across a much wider range of initiatives, but that belief requires testing. Additional research and review of new developments in practice can further our understanding of information polities. It is also potentially valuable to test the use of these analytical and modeling methods with other open data and related government transformational efforts. In addition we plan to use this work as a basis for developing practical tools to support efforts to open government data.