When creating architectures for government solutions, it is important to design for the purpose of building and reusing the knowledge rather than just building another legacy system.
Conventional economic tools and growth strategies in the majority of countries have resulted in growth fueled by the exploitation of both human and natural resources.
Policies focused on short-term economic gains – “grow now, clean up later” – using mainly gross domestic product as key performance indicators have led to rising hidden costs such as social inequality (needed in order to increase consumer potential), resource depletion, environmental degradation (e.g. environmental degradation costs China nearly 10 percent of its GDP, and climate change is estimated to represent 5 percent of world GDP annually), as well as a change in the global economic situation (e.g. rising prices of raw materials and energy).
Not addressing these issues during policymaking will have a negative impact on the long-term growth, competitiveness and economic stability of a country. Furthermore, this creates a vicious cycle in which short-term solutions are used as patches, further inflating the balloon that will inevitably burst. Governments worldwide are aware of these unmet needs and challenges and are seeking more sustainable growth strategies based on investments in human capital while conserving rather than exploiting natural capital. For developing countries, this also represents a way to escape the low and middle-income trap.
The potential of Information and Communication Technologies (ICT) as a powerful “change agent” and driver of change in government has been underestimated up to now. One reason is that governments, being the architects of e-government solutions, focused on computerizing existing business processes with the exclusive goal of achieving greater efficiency and cost effectiveness.
In the past decade, efforts have been made by some governments to extend conventional growth strategies and tools through the smarter use of ICT. The term used to describe this change is Digital Government or Digital Strategy. Digital Government is not only about a change in technology, but also a change in the processes, structures and organizations that deliver them by placing digital services and innovation at the very center of government operations and service delivery in order to help ensure sustainable growth.
In order for Digital Strategy to provide a long-term platform and to allow government systems and knowledge to evolve together with advancements in technology, the focus should be shifted from software engineering to knowledge engineering (the success of companies like Google, Facebook and others provide sufficient evidence), with the following goals:
• Quality of service: citizen-centric services that eliminate the need for users to understand the complexity of government
• Enable public participation though a government ICT platform
• Closing time gaps that favor short-term investment
• Rebalance the public-private sector role
• Monitor policy effects in real-time and apply adaptive strategies
• Understand the relationship between micro-level interactions and macro-level behaviors/trends, and vice versa, to anticipate negative cascading effects or improve recovery times.
Ontologies and Big Data
Government services and public participation ICT platforms can facilitate all relevant data collection in real time. On the other hand, ontologies can be used to model domain knowledge of all government agencies and establish relationships between the collected data.
By combining big data and ontologies to understand information relationships, it becomes possible to perform a statistical analysis and to test various Artificial Intelligence algorithms and policy scenarios. This can provide a decision support platform for policy makers to understand, measure and monitor the impact of their policies, as well as the policies of other governments to achieve greater economic resilience. Additionally, for citizens, the quality of services can be improved in terms of service discovery and semantic searches – searching until the goals of citizens are unambiguously defined.
Workflow and rule engines
Workflow and rule engines play an important role in business process automation through the centralization of knowledge, as well as bringing relevant information closer to where the activity is actually taking place. This is important in order to facilitate more dynamic and efficient decision-making. Rule engines are known to be capable of solving highly complex problems, providing an explanation of how the solution was reached and why each decision along the way was made.
Managing this kind of knowledge, and also linking it to documents, is not that easy if the logic is implemented directly at the code level (potential loss of knowledge). Furthermore, rule engines can be integrated with ontologies through customized protocols and using tagging laws and regulations with metadata, so machines can translate them into executable logic and/or extend existing knowledge.
Another aspect is the decoupling of presentation and information, through the use of web APIs and web API service descriptors. Traditional e-government systems have a tight coupling of presentation and information. In such systems the underlying information is hard to extract, reuse and adapt to changing internal and external needs. This leads to the duplication of efforts and the building of multiple systems to serve different audiences, where a single system would suffice. Another benefit of an information-centric approach is providing context to the information (e.g. the contact details of a person vary depending on whether the person is observed as an investor, patient, building owner, etc.), which is essential for any form of communication.
When creating architectures for government solutions, it is important to design for the purpose of building and reusing the knowledge rather than just building another legacy system. Collecting data from various sources (government registries, public services, public participation platform, etc.) can help develop models that combine technological, social and economic aspects and the systems that support them. On the other hand, better knowledge of these models will instigate a new generation of ICT systems. This has the potential to facilitate a mutual co-evolution of ICT and government.