Many industries were agile and fortunate enough to operate remotely, creating the digital environments necessary to work from home. But how will how we work? Our businesses evolve sustainably? And how will the data underlying the technologies that support our businesses provide us the necessary insights for differentiation?

These ideas will be explored with research analysts and local executives at the upcoming 麻豆原创 ANZ digital event, on 24th March at 9:30 am AEDT / 11.30 am NZ.

This digital forum offers three panel discussions running sequentially: The Future of Work, Purpose-led Sustainability, and Creating a Data-driven Culture.

As the nature of work has changed dramatically since the pandemic, businesses everywhere are still trying to understand what workforce strategies can keep their employees safe, connected, and empowered.

Delve into The Future of Work with panellists Nicola Bourke, General Manager IT Program Delivery, The Star Entertainment Group, Gaston Carrion, Managing Director of Strategy and Consulting for Asia Pacific Talent and Organisation Lead, Growth Markets, and Global Employee Experience Lead at Accenture; and Abbie Lundberg, Business Technology Analyst, Harvard Business Review Analytic Services.

The panellists will discuss how with the rise of hybrid workforce, we鈥檝e definitely seen a paradigm shift when it comes to how organisations maintain employee engagement and morale. How do you keep employees engaged and enthusiastic about what they do? Hybrid working arrangements means that it鈥檚 now possible for a lot of candidates to pick up positions in entirely different cities or even countries so although there is a wider pool of available and suitable talents it also means the environment is far more competitive.

How will organisations need to re-think their talent acquisition policies?

In the second session, understand how we can work together and create sustainable, scalable solutions for our shared global challenges with Purpose-led Sustainability. Join panellists: , Graham Winkelman, Head of Carbon Management, BHP and Todd Pruzan Senior Editor for Research and Special Projects at Harvard Business Review Group.

This panel discussion will look at how organisations can pivot profit towards purpose by supporting a purpose-led and sustainable agenda. It will look at how the panellists are fostering a culture of 鈥榳in-win鈥� sustainability and embracing the circular economy. They鈥檒l also delve into how can organisations of all sizes manage to scale and remain competitive with sustainable business practices at the centre of their strategy.

A vital tool in creating a better future for all of us is intelligent technology backed by data-driven insights. The power of data analytics can create lasting business change that enhances the experiences of customers, suppliers and employees.

To create ongoing success in this digital economy, tune into Creating a Data-driven Culture with: Julia Jack, CMO for Mercury; David Scullin, Chief Digital Officer for Zespri; Andrew Birmingham, Editor-in-chief and Associate Publisher at Which-50; and Alex Clemente, Founding Managing director of Harvard Business Review Analytic Services.

厂础笔鈥檚 will reflect on the future of work and how to sustainably grow your business, and the power of intelligence in delivering great customer experiences. and join us, 24th March at 9:30 am AEDT / 11.30 am NZ.

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Sail Insight mobile app was developed by 麻豆原创 in partnership with Sailing Yacht Research Foundation (SYRF). The app democratises tracking and analysing live sailing for all segments of the sailing community. What was traditional a prohibitive cost for many sailors and race organisers, this app is available free in 2020, allowing users to create a sailor profile, participate in regattas, test all features, conduct self-tracking, share results, and send invitations on social media. Next year, Sail Insight will shift to offer both free access and a subscription-based model managed by SYRF, unlocking additional features and benefits.

Sail Insight is powered the world鈥檚 largest and most robust sailing database 鈥� , which provides insights and transparency using cloud and in-memory technology that processes GPS and wind measurement data in real time.

麻豆原创 Sailing Analytics is the largest and most robust sailing database and platform in the world, offering the sailing community a full suite of features available remotely and in one place such as analytics, live leaderboard, self-tracking, simple race management (InApp), and social/sharing integration.

The app is designed to be intuitive, helpful, convenient, and user-friendly, providing sailors, coaches, clubs and fans with live data and analytics. The app offers live leaderboards, a handicap racing leaderboard with live predictive handicap scoring, and access to superior analytics for sailors globally.

This is critical resource when races are cancelled or postponed, as data can help keeping people connected and informed, assisting sailors and coaches in training and prediction, while giving clubs and race organisers access to live tracking to enhancing the viewing experience. 麻豆原创 and SYRF are dedicated to improving sailboat racing through technology for the good of the sport and its supporting community.

麻豆原创 has been a trusted leader and pioneer in the worldwide sailing community since 2011. With over 99.995% availability, 麻豆原创 provides a unique platform to showcase innovations such the world鈥檚 biggest sailing database, covering 17,787 tracked races, 441 events, approximately 78,000,000 GPS-fixes tracked with Sail Insight, and 405,000,000 GPS-fixes in total.

accounts for 1.4 million unique visitors annually and has garnered 2.4 million impressions on social media (from own-generated assets on owned channels). With 70 million active sailors and 250 million fans worldwide according to the World Sailing Federation, sailing is the best sport to showcase sustainability, innovation, and digital disruption on a global stage.

The technical core of Sail Insight, 麻豆原创 Sailing Analytics provides a live sailing data analytics engine as well as the data mining engine to analyse across different timespans and perspectives. 麻豆原创 Sailing Analytics generates the live leaderboards, analytics, and post-race reports that will be displayed in the app. This means sailors, coaches, and clubs around the world can set up and manage their own races, straight from an app, while also enabling sailing fans to track data and live scoring as they follow events and regattas from wherever in the world they log into Sail Insight.

To ensure the app meets the needs of the entire sailing community, a broad range of stakeholders were involved in its development, including the World Sailing Association, Australian Sailing Team, US Sailing, top sailors, professional race officers, six national sailing leagues and the SAILING Champions League Series, 麻豆原创 programmers and consultants, SailTracks (professional sailing media production and distribution company), key media and app agencies from 麻豆原创麓s network, and our key partners 鈥� the SYRF.

is a non-profit organisation dedicated to growing the sport of sailboat racing using technology. The main focus currently is contributing funds, experience and connections to building data platforms, such as Sail Insight and 麻豆原创 Sailing Analytics for the sport. Since 2006, SYRF has funded numerous projects including the 鈥淲ide-Light鈥� CFD project, Downwind aero forces and moments project, Manual PCS calculator and scoring system for offshore multihulls, a foil project in conjunction with ORC, and a SYRF鈥檚 Offshore Scoring System.

麻豆原创 Sailing is committed to accelerating growth for the worldwide sailing community through a combined Grassroots and Pinnacle approach. Grassroots via foundation SYRF and a self-service intelligent platform. And partnering with Pinnacle events like the Sailing World Cup and Sailing World Championships. Future goals include providing 麻豆原创 Sailing Analytics for the next Olympic Games Sailing Regatta and releasing a self-sustaining 麻豆原创 Sailing platform by 2022.

鈥淪ince 2011, 麻豆原创 has been a trusted partner and innovation leader across the sport of sailing, creating solutions that improve performance for sailors, teams and race organizers while also growing fan interest and participation,鈥� 麻豆原创 Chief Technology Officer and Executive Board Member, Juergen Mueller said. 鈥淭he new app leverages the power of our sailing analytics database to create a mobile app that allows members of the sailing community to access data and information to help improve performance and enhance their race and event experience.鈥�

SYRF Technical Director Larry Rosenfeld said 鈥淲e are thrilled to be working with 麻豆原创 to help grow sailing worldwide. The technology and capabilities that 麻豆原创 bring are tremendous, and together with our board鈥檚 lifetime of work in the sport, we believe we can make progress even faster together by introducing and getting feedback from the community now.鈥�

As sailing is not only thrilling to watch and participate in 鈥� the sport has a key role in supporting the UN鈥檚 Sustainable Development Goals and intelligent technologies contribute to making more environmentally conscious decisions and partnerships. To learn more about these innovative and free resources for sailing fans, visit the

This article originally published on

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The first two articles in our 鈥淒ealing with Disruption鈥� series looked at how digital technologies might enable governments around the world to nudge citizens towards cooperation and coordinated action in containing COVID-19, and to address issues of hand washing, face touching, self-isolation, collective action, and crisis communication. In this article, the 麻豆原创 Institute for Digital Government (SIDG) will present a conceptual architecture for Digital Nudges and demonstrate how it could be applied to improve crisis communications relating to a second-wave outbreak of the Coronavirus.

Using digital nudges to support government responses to coronavirus

To demonstrate how our conceptual architecture might be applied, we will consider the scenario of a second-wave outbreak of the Coronavirus, such as was .

Figure 1: The first- and second-wave outbreaks of COVID-19 in Australia.

was identified on 25 January 2020. The number of new cases rapidly increased and peaked nine weeks later, with reported on 28 March. The Australian government responded very successfully with a for flattening the curve, and by mid-April there were a relatively low number of new cases being reported daily. Although the virus had not been eliminated, it appeared to have been suppressed sufficiently for lockdown restrictions to be eased across Australia. Unfortunately, were identified in Melbourne on 20 June, foreshadowing a second-wave and prompting a reinforcement of restrictions to contain the outbreak. Even so, Australia鈥檚 second-wave proved more difficult to contain than the first, peaking at reported on 5 August.

Due to the localized nature of the second-wave outbreak, stay-at-home restrictions were reintroduced only in metropolitan . Most notably, in North Melbourne and Flemington were immediately locked-down, with residents of 33 Alfred Street subsequently required to isolate for two weeks. While it was generally agreed that this was a necessary measure, the immediacy of the action combined with various communication challenges resulted in widespread confusion and concern among the 3,000 public housing tenants. captured the sentiment at the time:

• 鈥淲hen I came back home I did see hundreds of cops everywhere, so it was really intimidating.鈥�
• 鈥淚t鈥檚 been getting more and more intense, people are really panicking.鈥�
• 鈥淲e weren鈥檛 told any information, they just shut us down, didn鈥檛 let us leave our houses.鈥�
• 鈥淚 just feel like we鈥檙e being treated like criminals.鈥�
• 鈥淲e do not need 500 officers guarding the nine towers. We need nurses, we need counsellors, we need interpreters.鈥�

In what has been an unprecedented year, the hard lockdown of Melbourne鈥檚 public housing towers was an unprecedented action by the Australian government, law enforcement and public health services. To that point, Australian citizens had not experienced a lockdown under guard, except in cases of returned citizens undertaking hotel quarantine.

In special cases such as this, efficient and effective crisis communication is key 鈥� not only in ensuring compliance 鈥� but in promoting cooperation through credibility, empathy and respect. Behavioral Science can assist by influencing individual decisions towards the most positive outcome, and digital technologies can be used to scale and personalize traditional nudges to improve outcomes for mass cohorts.

Conceptual Architecture for digital nudges聽

Figure 2: A conceptual architecture for digital nudges.

Nudging is a delicate process, with significant preparation required to avoid unintended consequences 鈥� especially when the stakes are as high as they are in the case of COVID-19. These stakes are raised even higher when the nudges are to be delivered by governments, at scale, using digital technologies. The is to optimize utility and mitigate risk using an iterative process of randomized controlled trials with rapid cycle evaluation. Whether the nudge is to be delivered as part of a trial, or to the population at large, an iteration of the nudging process typically spans:

• Design and contextualize: The nudge is designed to achieve the outcome of interest, based on an exploration of the available data. A key consideration is the situational and social context of the environment in which the nudge is to be deployed. In the case of crisis communications, nudges need to for citizens鈥� circumstances.
• Simulate and deploy: Randomized controlled trials can be used to simulate the likely response to a given nudge. A variation of this approach would involve using , to enable simulations to be run faster and safer than with human subjects. In the case of crisis communications, these simulations could be aligned to the accepted thresholds of a national or local containment strategy.
• Monitor and measure: Having deployed the nudge, social listening and devices can be employed to monitor the actual response. Although it may be difficult to measure the effectiveness of nudges as a behavioral modifier, a control group who does not receive the nudge may be used. In the case of crisis communications, we might also consider performance against 鈥渇ake news鈥� as a measure of effectiveness.
• Analyze and improve: Here we distinguish between measurement and analysis, specifically within the context of diagnostics 鈥� analyzing why a particular action has been taken or a particular outcome achieved. Based on this analysis, improvements can be made to the design of the nudge, and thus the iteration continues. In the case of crisis communications, certain visualizations (e.g. ) might be published to encourage community cooperation and coordinated action.

Digital nudges: Core capabilities

As described in our first article, predictive analytics, contextualization, and experience management are the core capabilities required to deliver digital nudges. Breaking down these capabilities will enable us to illustrate how they can support policymakers and service agencies, working with behavioral scientists and technology partners, to improve the effectiveness of traditional nudges.

• Predictive Analytics:
  • Behavioral Insights: The ability to detect patterns in citizen behavior, based on transactional and experiential data. For example, based on their prior responses to government requests, we can expect Citizen X to comply with stay-at-home orders.
  • Journey Visualization: The ability to visualize the citizen鈥檚 journey over time, including major life events, changes in circumstance, and their interactions with government. For example, based on the healthcare, social services and financial supports they have recently accessed, Citizen X is likely a vulnerable person who will need additional supports.
  • Simulation: The ability to simulate the likely responses to a digital nudge, including the ability to compare alternative approaches. For example, Nudge A will increase compliance with stay-at-home orders by 5%, with 80% confidence.
  • Next Best Action: The ability to recommend the optimal course of action, based on (autonomous) machine learning. For example, Nudge A will be most effective for Citizen X, while Nudge B will be most effective for Citizen Y.

• Contextualization:
  • Profiling: The ability to assemble a digital profile of a citizen, by combining data from multiple sources (as permitted by government regulations). For example, we know that Citizen X is at high risk, since they are over 80 years of age and live in high-density public housing.
  • Segmentation: The ability to create target groups, comprising citizens with similar profiles and needs. For example, Segment A comprises citizens of working age, who are likely concerned about the impact of stay-at-home orders on jobs.
  • Campaigns: The ability to proactively outreach to target groups with nudges tailored to their circumstances. For example, Nudge A will be delivered to citizens of working age, while Nudge B will be delivered to citizens over the age of 65.
  • Preferences: The ability to communicate with citizens via their preferred channel, and at their preferred time and place. For example, Citizen X usually responds promptly to SMS sent around lunchtime.

• Experience Management:
  • Social Listening: The ability to monitor social media to track changes in citizen sentiment over time. For example, citizens under lockdown are complaining that police presence is making them feel like criminals.
  • Surveys: The ability to solicit direct feedback from citizens. For example, Citizen X responded that they couldn鈥檛 understand the specifics of the stay-at-home order because English is their second language and no translation service was provided.
  • Measurement: The ability to measure the response to a digital nudge, based on transactional and experiential data. For example, Nudge A increased compliance with stay-at-home orders by 3%, compared with the control group who did not receive the nudge.
  • Diagnostic Analytics: The ability to uncover why certain nudges are, or aren鈥檛, working. For example, Nudge A was widely criticized as being disrespectful, resulting in a lower level of compliance than anticipated.

The underlying business platform supports the design, development, and management of our digital nudges.

• Analytics: The ability to analyze transactional and experiential data. Desirable features include the ability to:
  • surface actionable insights based on predictions;
  • dynamically drill-down into records of interest;
  • visualize citizen journeys over time; and
  • update data and visualizations in real-time.

• Intelligent Technologies: The ability to build, execute and manage machine learning applications. Desirable features include the ability to:
  • process big data holdings to build advanced machine learning models;
  • support profiling and segmentation of data in line with contextualization capabilities;
  • generate predictions and next best action recommendations; and
  • make improvements based on (autonomous) machine learning.

• Data Management: The ability to access and work with big data, in real-time. Desirable features include the ability to:
  • consolidate data from multiple sources;
  • work with transactional data in real-time, without impacting operational systems;
  • work with analytical data in-place, without the need for replication; and
  • ensure the security and privacy of citizen data.

• Application Development & Integration: The ability to develop and integrate business applications. Desirable features include the ability to:
  • accelerate the design and development of advanced machine learning applications;
  • run simulations in support of what-if analysis;
  • support an open ecosystem of development partners; and
  • integrate with external systems (e.g. geographic information systems).

In presenting this conceptual architecture, our intent has been to provide a framework that governments can use to deliver digital nudges. We believe this framework to be general-purpose, while acknowledging that certain scenarios will require additional capabilities. Our chosen use case of crisis communications serves as an illustrative example. Please note that, since this conceptual architecture is vendor-agnostic, the described capabilities could be sourced from any technology provider.

To read more about how digital technology can be used to improve public sector services, visit .

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