Big data, machine learning, cognitive computing and cybersecurity were among the subjects discussed at the Toronto conference. There were 2 sessions about smart cities technology. The majority of presentations and panel discussions were private sector focused. Nevertheless, there are some important insights for public sector organizations:
1) Urbanization is driving innovation because of infrastructure challenges and the promise of technology solution. The City of Toronto, for example, now has a Chief Innovation Officer, a Chief Resilience Officer, and a Chief Transformation Officer.
2) The impact of artificial intelligence to government is not yet understood. AI has shown potential in smart cities technology. But, the impact of widespread AI adoption in business to employment, education, tax revenue and social service expenditures is not yet understood.
3) Legacy government mechanisms for budgeting and procurement stand in the way of smart government innovation. Procurement methods favour proven older technology and make it difficult to engage small firms with new innovative solutions.
4) Decision Support in the big data era is complex. Governments are faced with integrating silos of legacy software systems, social media and IoT media streams. Visualization and machine learning could help to provide decision-makers with actionable information.
5) Privacy and cybersecurity are critical concerns for smart government. The more data collected to make smart decisions, the more attractive the government is to hacking.
6) Opendata is an ingredient for smart government. Businesses and non-profits can harness open data for innovation. Yet, there does not seem to be measurable impact as yet.
1) Urbanization is driving innovation
Urbanization drivers for smart government include water, waste, transportation, and energy infrastructure needs. Climate change is affecting the environment resilience of cities.
2) The impact of artificial intelligence to government is not yet understood