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The need to bring the benefits of the digital revolution to public administrations is reflected in the large number of initiatives that are currently trying to tackle this challenge worldwide.
The main goal of the digitalisation of the public sector, however, is not about automating existing processes. It is about fostering an ambitious redesign of public administrations, which would, in turn, promote inclusion and eliminate inefficiencies.
This digital transformation, nonetheless, needs to be scaled properly to address the multi-layered actors in the public decision-making process. Digital solutions limited to the top layers of the process – mainly central public administration and governmental agencies – will prevent an efficient trickle-down effect on local governments with detrimental effects on development.
Interoperability, standards and ecosystem development at the forefront of future innovation
On the contrary, an excessive focus on bottom-up approaches would not translate into a proper link between regional administrators and central policymakers. This would also lead to the persistence of systemic asymmetries between geographical areas at different stages of the development process. A balance between these extreme options should be found in a virtuous mix of top-down digitalisation strategies – that set the basis for state-level interoperability and the definition of technological standards and best practices – and local, participatory initiatives.
Moreover, a multi-layered approach is useful to maintain high degrees of efficiency in the management of public data. Too often, data collected and used by public administrations are poorly managed, which creates suboptimal results. This applies both at the upstream phase, in which the same data are collected more than one time with no harmonisation among public databases, and the downstream phase, in which public bodies and private stakeholders cannot access useful data.
The already mentioned interoperability feature helps to overcome this problem, but it is not enough: to reach an acceptable level of efficiency in the management of public data during the digital transformation process, it is fundamental to specify state-level standards that clearly define privacy and usability issues. Apart from the benefit for citizens, this leads to a more transparent relationship between governments and technology providers.
This is the backdrop for decision-support systems (DSS) aiming at improving policy-making. The core part of WiseTown’s dashboards, DSS are composed of three main parts: the knowledge base, the model management system (MMS) and the user interface.
In the public domain, and especially in the smart city sector, the knowledge base refers to the whole amount of external data collected from different sources (e.g. sensors, satellites, social media or open data) and the large number of procedural information internal to public administration. The former constitutes the backbone of the dominant smart city framework.
However, there are three relevant dimensions to consider when building a smart city knowledge base.
Firstly, in many circumstances, data collection ultimately ended up in diffused sentiments of backlash, which is detrimental to the goal of building trust between citizens and public administrators.
Secondly, data collected from passive technological instruments provides a limited picture of urban life. Hence, it is fundamental to integrate this kind of information with human inputs. Civic tech solutions in this field favour the exchange of information and feedback between citizens and local authorities, with positive effects on policy making.
Finally, a fundamental step involves building efficient data governance models that put together the apparently divergent goals of maximising privacy and security and making data accessible for local development. This is particularly relevant for raw non-personal data, which can be managed in an easier manner within the Data Commons framework.
If the knowledge base provides the fundamental input for a public DSS to work properly, model management systems define their overall effectiveness. Model management systems in the smart city context should be field-specific. In other words, each area of urban management (e.g. healthcare, green areas, economic development, security or e-government) should have a dedicated model tailored to its specific needs. With this structure, the AI component of the system would be trained in less time and show better accuracy.
User interface: a fundamental feature for successful digital solutions
Moreover, policymakers across each sector – and at each level – of public administrations would have a tailored instrument to use, depending on their needs. In turn, this leads to the third feature of the DSS: the user interface. Too often, this is an undervalued element of digital solutions for the public administrations. On the contrary, an advanced UX design is fundamental to make digital solutions easily accessible for users at different levels.
The elements that stand out in this brief description of the impact of DSS on public decision-making are human-centricity, data-driven algorithms and public data. The balance between these elements leads to sustainable innovation in the public sector. To that extent, in order to guarantee interoperability and foster the development of local technological actors, open source architectures and modules play a pivotal role.
The use of open source technologies such as FIWARE’s Context Brokers and generic enablers allow public administrations to reduce technological risk, since they can constantly improve and personalise digital solutions through an active, thriving community of developers.
For more details on urban data and how WiseTown can help public administrations to explore all the available opportunities that new technologies offer for cities and communities, visit wise.town/civic-tech.
by Francesca Nafissi, Urban Planning and Account Manager at WiseTown.