As infrastructure becomes smarter, with sensors, automation and context-aware devices playing an increasingly prevalent role in our day-to-day lives, working out how these technical systems interact with people in the real world is critical.
What is “smart infrastructure”?
In nature, the ecosystem provides feedback about its health to its inhabitants constantly. In the best cases these signals enable inhabitants to adjust their behaviour to reduce environmental stress.
Perhaps ironically, digital technologies are enabling this same sort of feedback to be collected and presented to enable the more efficient and cost effective management of key infrastructure.
Low-cost sensors, smart meters, wifi, 3G/4G networks, and low cost microprocessors (including open source hardware such as Arduino) are enabling tracking of all sorts of relevant environmental factors—from energy and water, temperature, air quality, and more. Estimates are that such devices are already numbering in the billions, and continuing to grow.
This data can then be used for all manner of purposes—from identifying poorly performing resources, guiding investment decisions, achieving efficiency, tracking the effectiveness of sustainability initiatives, or enabling a sharing city strategy.
Governments at all levels, but particularly councils and city managers, have an obvious interest in these sorts of technologies. As do facilities managers who are seeking ways to not only achieve more efficient operations, but who are also responding to strong market demand for more efficient and environmentally-friendly premises.
Much literature and focus of activity in the commercial and public sectors has been on the underlying technology—rolling out these sensors and mechanisms for collecting the data. However, these are “socio-technical” systems—and as such human factors are often critical in the translation of technological innovation into successful outcomes.
These factors are often missed as new technology is rolled out, resulting in projects that don’t deliver on their potential. You’ve probably experienced or seen examples yourself where great technical solutions have not been adopted due to a lack of consideration of how these solutions fit within the larger personal and social context.
Have you ever:
- Looked at an information dashboard that’s meant to help us make a decision and thought “I don’t even know where to start?”, or where a critical bit of information is missing?
- Seen a “best practice” technology system implemented in your workplace, only to be thwarted at the final yard because someone didn’t undertake a critical manual action?
- Had a poor experience with an organisation due to a disconnect between an organisations’ digital and real-life services?
A solid user experience—applying interaction and information design principles—is becoming more and more critical as expectations are raised by popular digitally-enabled services. But more often than not there are other factors that need to be considered for a successful outcome in the sustainability space—behavioural economics, network effects, social and organisational change models, for example.
How we can help
We can apply our capabilities to help you:
- identify opportunities for where smart infrastructure and digitally-supported business practices can achieve efficiencies and sustainability outcomes
- develop programs, products and services that achieve their intended objectives through the application of human-centred and participatory design practices
- consider how behavioural change and social marketing principles can boost the impact of your programs, products and services
- understand the motivations, perspectives, frustrations/pain points, and potential barriers to adoption of new technologies within your target groups
- design more usable interfaces to devices, dashboards and other systems that better meet the needs of their intended users
- develop behavioural change strategies supporting the introduction of new technologies in your organisation or the broader community
- employ robust evaluation processes to detect negative rebound effects that may emerge