Hardened platform to deliver local cloud services designed for听government and regulated industries

Canberra, Australia听鈥斕�听听(NYSE: 麻豆原创) today announced听麻豆原创 Critical Data Cloud, a fully managed service capable of powering the operations of government and regulated industries across both Australia and New Zealand

This is designed to help protect the core business applications of governments and highly regulated industries including financial services, healthcare and utilities. Planned to be operational in the second half of 2021, the platform is intended to support the Australian Government鈥檚听Official: Sensitive听and听Protected听information, enabling customers to make faster, more informed decisions and with greater peace of mind.

麻豆原创 Critical Data Cloud is a significant investment recognising the increased focus on improving whole-of-economy cybersecurity. The hardened platform provides customers the full functionality of 麻豆原创鈥檚 multi-tenanted cloud applications. 听It is initially available for human resources (麻豆原创 SuccessFactors) and 麻豆原创鈥檚 full suite of finance, analytics and machine learning applications (麻豆原创 Business Technology Platform, 麻豆原创 Analytics Cloud and 麻豆原创 S/4HANA).

In addition to providing customers with the constantly evolving benefits of contemporary business cloud applications, 麻豆原创 Critical Data Cloud enables customers to extend functionality within the same certified framework. Importantly it also supports secure integration to other systems, for example public cloud, bespoke applications.

麻豆原创 Critical Data Cloud services and applications are continuously reviewed and hardened to ensure they stay current and are projected to meet evolving government security policy requirements. Customers are offered transparent assurance that data remains supported by appropriately cleared personnel.

鈥淭he legislative environment and cyber considerations in both Australia and New Zealand require organisations of different sizes to think hard about moving to cloud,鈥� Damien Bueno, President and Managing Director, 麻豆原创 Australia and New Zealand said.

鈥淚n support of those pursuing a cloud agenda, 麻豆原创 is providing a service that puts all its software and service assets in an environment that exhibits the cloud and security characteristics needed to meet the legislative and security requirements of government.

鈥淎s a global enterprise provider and supporter to some of the world鈥檚 largest organisations, we鈥檝e looked at how to drive efficiencies and provide customers with the capacity to deliver the sovereignty, security and confidence they need from a trusted platform. This follows the launch of听, providing customers with everything they need to transform their organisation in a way that works best for them.鈥�

Richard Bergman, Lead Partner for EY鈥檚 Oceania Cybersecurity, privacy and trusted technology practice, said, 鈥淧lanned changes to the Security of Critical Infrastructures Act and the increasing prevalence of foreign interference targeting the Australian public and private sector are driving an increasing need for sovereign cloud and security solutions and services.鈥�

鈥溌槎乖粹�檚 approach leveraging hardened patterns and templates is a great way of assisting Australian organisations to combat the increasing threat landscape and changing regulatory requirements,鈥� continued Mr Bergman.

Leveraging its work with听听(NS2), 麻豆原创 Critical Data Cloud is tailored for Australian and New Zealand legislative requirements. It empowers public and private customers to rapidly and safely digitise customer, citizen and employee services, while remaining current with legislation and policy. This reduces the cost and complexity of assessing and certifying multiple systems and platforms.

Visit the听麻豆原创 News Center. Follow 麻豆原创 on Twitter at听.

To find out more about the 麻豆原创 Critical Data Cloud, visit the听.

About 麻豆原创

麻豆原创鈥檚 strategy is to help every business run as an intelligent enterprise. As a market leader in enterprise application software, we help companies of all sizes and in all industries run at their best: 77% of the world鈥檚 transaction revenue touches an 麻豆原创庐 system. Our machine learning, Internet of Things (IoT), and advanced analytics technologies help turn customers鈥� businesses into intelligent enterprises. 麻豆原创 helps give people and organizations deep business insight and fosters collaboration that helps them stay ahead of their competition. We simplify technology for companies so they can consume our software the way they want 鈥� without disruption. Our end-to-end suite of applications and services enables business and public customers across 25 industries globally to operate profitably, adapt continuously, and make a difference. With a global network of customers, partners, employees, and thought leaders, 麻豆原创 helps the world run better and improve people鈥檚 lives. For more information, visit听

About 麻豆原创 NS2

At 麻豆原创 NS2^庐, we support the mission of national security by providing innovative computing, analytics, and cloud solutions. From custom development to secure cloud, and virtually everything in between, 麻豆原创 NS2 powers the secure intelligent enterprise. Learn more at听.

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Embracing technology: the public sector of the future

To better serve its citizens, the public sector faces an existential need to become more agile, more mobile and more efficient. Some of the most hotly anticipated solutions include those enabled by artificial intelligence (AI). Ranging from predictive analytics to machine learning to intelligent robotic process automation, AI is one of the surest paths for extracting insights and value from growing volumes of data.

This has fuelled aspirations for everything from advanced smart cities to new approaches in population health management 鈥� often these solutions involve predictive analysis that could help agencies make better decisions, respond faster during crises and even pre-empt problems altogether. Some agencies are making use of AI applications already, like听, which used machine learning to predict tax non-compliance and netted the state an extra $27 million in revenue.

Government also has a unique role to play when it comes to AI 鈥� since all Australians are impacted in some form or other by government鈥痵ervices, governments must take the lead in their use of AI, whether through operations or service delivery.

Yet broader adoption remains low. A 2018 investigation by the 麻豆原创 Institute for Digital Government (The SIDG) found that, while 80 per cent of public sector organisations were working toward data transformation, less than 15 per cent had progressed beyond the prototype stage.

The SIDG teamed up with University of Queensland researchers to assess where the sector is at in 2020. The resulting white paper,听, identifies the biggest AI challenges in the public sector 鈥� and how leaders can overcome them to finally harness the true potential of AI solutions.

The resource challenge: building AI capability and securing human talent

AI relies on large datasets, high-quality data, the right platforms and 鈥� importantly 鈥� data science talent.

This is resource-intensive 鈥� an acute challenge in the public sector where data is often purposefully siloed, and fractured across complex, ageing legacy systems. These overlapping issues create a sort of chicken-egg dilemma, where leaders may struggle to secure funding and executive buy-in without proven value 鈥� but proving value depends on funding and executive buy-in.

The research did uncover examples of success, though. One agency was able to overcome data-sharing barriers by outsourcing its AI model development, which was then trained with citizens鈥� payment data instead of sensitive personal data. Another agency chose a commercial-off-the-shelf AI development platform to decrease maintenance burdens.

Misunderstandings about AI and inflated hopes also demand project-level governance to manage expectations and encourage ongoing commitment from executives.

The process challenge: pre-empting machine fallacies by keeping humans in the loop

Despite myths of robot overlords and job losses, algorithms only outperform humans in their ability to process huge datasets. They still lack the context-specific reasoning capabilities that we have, which means AI solutions can鈥檛 simply be plugged into existing workflows. Agencies will need to rethink processes to combine the strengths of machines and people.

This is complicated because of the barriers that often separate data scientists and subject matter experts, demanding redesign for entire workflows. The researchers found that agencies who were able to reconcile these issues were those who embedded data scientists in everyday operations and encouraged collaboration with subject matter experts.

Successful approaches include co-location and collaborative workshops but, interestingly, interview data also highlighted the importance of attracting data scientists with strong soft skills and good communication.

Organisations were keenly aware of the need for human oversight and the risks of deferring to automation. Many were already redesigning workflows to ensure AI was doing the heavy lifting and data-crunching, with human workers acting as the controllers of the AI and making final decisions.

The explainability challenge: minimising bias and enabling transparency

Advanced AI models have an 鈥渆xplainability problem鈥� 鈥� that is, the complexity of their logic and the sheer volume of data can make decision-making inscrutable to us.

This is a massive hurdle in the public sector, where public trust often depends on transparent rationale and straightforward accountability. It鈥檚 an even bigger challenge once we consider that algorithms have already demonstrated a serious risk of bias and error.

The researchers found that some agencies have been establishing strict oversight and procedural systems with these specific risks in mind. For instance, one agency excluded demographical data in favour of behavioural data to minimise bias in the model鈥檚 predictions.

Another created a more extensive end-user interface that visualised a customer journey and highlighted risky payment behaviours. This provided visibility into the factors affecting the overall risk estimate.

The culture challenge: reducing distrust among employees and citizens

Despite research indicating AI adoption rarely comes from a desire to reduce headcounts, job security fears abound. Additionally, the researchers found some human workers continuing to distrust AI鈥檚 decisions.

One solution is educating employees about the potential of AI-enabled tools 鈥� this can be an easier sell once employees witness the elimination of low-value tasks and admin burdens, freeing them to focus on more strategic and interesting work.

The public sector faces public resistance, too. Some agencies have the added challenge of a power imbalance, as citizens who rely on their services may not be able to switch providers like they would in the private sector.

While wider societal perceptions may evolve in a way that reduces distrust, there鈥檚 no simple solution to these challenges. Trust will depend on proven value and the effective management of unintended consequences 鈥� which will in turn depend on many of the solutions mentioned above.

The public sector faces unique challenges with AI solutions but also stands to gain some of the biggest rewards. And, promisingly, some agencies are already demonstrating how to address these issues.

Using an even deeper look into the public sector鈥檚 relationship with AI,听听provides a practical framework for developing the foundations necessary for effective AI development in government.

However, it鈥檚 an area that requires deeper exploration, which is why The SIDG will continue partnering with the University of Queensland to understand ongoing challenges.

To read more about 麻豆原创 Australia’s public sector offer, 听

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From COVID-19 testing to business stimulus programs, quarantine measures to training schemes, many citizens who may rarely be aware of the government鈥檚 role in our lives have become much more so over the months since the coronavirus pandemic hit Australia.

on the 麻豆原创 Public Sector Homepage.

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The last article in our 鈥淒ealing with Disruption鈥� series presented a conceptual architecture for Digital Nudges and demonstrated how it could be applied to improve crisis communications relating to a second-wave outbreak of the Coronavirus. In this companion piece, we seek to demonstrate that governments have ready access to the business applications and technologies required to deliver digital nudges today.

To achieve this, we鈥檒l map our conceptual architecture to 麻豆原创 products that are generally available and are already in use by governments around the world.

Conceptual Architecture

For reference, our conceptual architecture for digital nudges is depicted below.

Figure 1: A conceptual architecture for digital nudges.

听麻豆原创 Reference Architecture

Mapping our conceptual architecture to 麻豆原创 products provides assurance that our conceptual architecture can be delivered in practice.

Figure 2: An example reference architecture for digital nudges.

Note that 麻豆原创鈥檚 will evolve over time, so this bill of materials should be considered representative rather than prescriptive.

• Predictive Analytics:
  • : enables organizations to analyze the behavior of customers and to generate risk scores and insights.
• Contextualization:
  • : enables organizations to use consent-based marketing and advanced data analytics to engage customers with pinpoint accuracy.
• Experience Management:
  • : enables organizations to gather experience data and combine it with operational data to close experience gaps.

• Analytics:
  • : enables organizations to provide a single source of truth to decision makers about the most important business metrics in real time.
    : enables organizations to combine BI, planning, predictive, and augmented analytics capabilities into one simple cloud environment.
• Intelligent Technologies:
  • : enables organizations to process distributed data and provide users with intelligent, relevant, and contextual insights with integration across the IT landscape.
    : enables organizations to define functions that can be called from within SQLScript procedures to perform analytic algorithms.
• Data Management:
  • : enables organizations to deliver a data warehouse in the cloud to unite multiple data sources in one solution.
    : enables organizations to accelerate data-driven, real-time decision-making and actions via a high-performance in-memory database.
• Application Development & Integration:
  • : enables organizations to model, implement, integrate, and monitor custom process applications and integration scenarios.
  • : enables organizations to accelerate integration, simplify development of application extensions, and expand business value with an open ecosystem.

In presenting this reference architecture, our intent has been to provide a worked example to demonstrate that governments have ready access to the business applications and technologies required to deliver digital nudges today, using business and technology components from 麻豆原创.

While other vendors might be able to offer some components of a digital nudge platform, we believe there is a benefit in sourcing the end-to-end solution from a single vendor.

To read more Public Sector content or find out more about 麻豆原创’s Public Sector customers and products, visit:

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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听

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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The online event attracted over 60 delegates from the Australian and New Zealand public services, representing all levels of government.

Measuring citizen engagement

Included in the agenda was an online poll, focussing on how agencies measure the citizen experience and how they respond to citizen feedback. While the sample size is small and not necessarily representative of citizen engagement across the public sector, the responses were intriguing and prompted valuable discussion.

As shown, Customer Satisfaction (CSAT) is the most popular approach for measuring the citizen experience among our respondents.

A characteristic of this approach is that it鈥檚 a transactional measurement 鈥� CSAT reflects satisfaction with a specific interaction or service.

By comparison, relational measurements like Net Promoter Score (NPS) are better approaches for longitudinal analysis. Admittedly, it can be difficult to apply standard NPS questions about customer loyalty within a public sector context, but it鈥檚 possible to adapt the questions to focus rather on citizen trust in government.

Another measurement worth considering is听, which reflects the ease (or difficulty) of doing business with the organisation. In the commercial world, CES is an excellent predictor of customer churn, and while this typically isn鈥檛 an issue for government, agencies are motivated to make their online services accessible and easy to use.

Since this was a multiple-choice question, it was possible for the survey participants to select more than one response, and possibly that鈥檚 the optimal approach鈥� A sensible combination of these measurement tools can provide excellent insight into citizen satisfaction with service delivery, and the impact that experience has on citizen trust in government.

Using feedback

Encouragingly, all our respondents ask the citizen about their service delivery experiences.

Yet the responses to this question seem to align with the transactional measurement approach of CSAT.

Adopting a more relational approach, by embedding feedback throughout the process, can enable agencies to take proactive action and mitigate risks before they turn into problems.

We could argue the merits of all these responses 鈥� it鈥檚 important that agencies respond in a variety of ways to close-the-loop with citizens.

We鈥檝e observed that citizen satisfaction is increasingly being included in agency service commitments, and it鈥檚 encouraging to see that this feedback is also being actively used to inform service design.

Untapped opportunity

There appears to be an untapped opportunity for data-driven policy development among our respondents, to truly close-the-loop on citizen feedback.

It鈥檚 interesting that more than half of respondents cited issues with motivating and engaging a representative sample of citizens as their biggest challenge in measuring citizen experience.

SIDG research into听, suggests that a bi-directional view could help to increase participation in government surveys.

Two-way conversation

The rationale being that, if the citizen can see how the data the government is collecting will be used to serve them better, they will be more willing to engage and contribute.

Improving the efficiency and effectiveness of service delivery has always been a motivating factor for collecting citizen feedback, so the leading response here is not all that surprising.

It鈥檚 encouraging to see a relatively high percentage of our respondents wanting to focus on keeping citizens informed throughout the service delivery process.

Public sector best practice

Experience from leading government agencies suggests that providing transparency and traceability into government processes can improve the citizen鈥檚 perception of the timeliness of service delivery.

This might be because the citizen can see their case progressing through the system in real-time, giving them confidence that their feedback has been heard and is being actioned.

The SIDG would like to thank all respondents to our online poll, as well as our partners from the PSN and QUT. We found the participants鈥� responses to be very insightful and through-provoking, and we hope that sharing these reflections will further progress the conversation.

If you鈥檇 like to find out more about becoming a Responsive Government,听.

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Only by joining the dots between data that explains how people feel 鈥� their experiences, emotions, beliefs, sentiments: what we call experience, or 鈥榅-data鈥� 鈥� with data which shows what is happening 鈥� like budget data, service requests, tax receipts: what is described as operational, or 鈥極-data鈥� 鈥� can governments truly deliver exceptional services to citizens.

Changing expectations

Expectations around experience have shifted significantly in recent years, as people have been exposed to innovative, intuitive and personalised services for everything from e-commerce to transport. Increasingly, they expect the same experiences with government.

However, research suggests government is ranked near the lowest of all sectors in the economy for customer service. Coupled with data and privacy concerns, this had led to a deterioration of trust in government.

The good news is that improving citizens鈥� experiences with government has been shown to increase trust in the public sector. More importantly, intelligent, data-led government leads to better services and outcomes. Shifting to a system of innovation that can turn insights into action is the future of the public sector and will help governments around the world deliver their objectives and mission.

Improving experience to build trust

To arrive at responsive government, services need to be delivered efficiently and effectively so that citizens鈥� expectations are met. Every interaction between a citizen and their government is important. But governments should prioritise where and how they start their journey to become a responsive, experience-driven organisation.

Experience drivers in the public sector

Measuring success

Experience strongly influences efficiency and effectiveness in government services and is a leading indicator that can be used to improve service delivery. This allows the public sector to improve relationships with citizens while also delivering the outcomes citizens and government care about.

In a responsive government, experience becomes a key performance indicator for every agency and department. Already, many public sector organisations will seek feedback and try to understand how people feel in response to their experiences.

Yet too often, this is done in an ad-hoc or irregular manner. By building the measurement of experience into every interaction, governments can build a continuous feedback loop to understand the impact of any changes and inform future decisions.

Using experience to measure听 performance

Getting started with X and O

Becoming a responsive government is not easy. Starting small, by identifying one use case based on the most pressing public policy problems can build confidence and help show results quickly. Below, we outline six use-cases that can assist in identifying your initial projects.

1.听 Cause: Understand operational (O) data by finding explanations in experience (X) data. Operational data shows that many citizens fail to pay their taxes on time after acquiring new properties. The tax agency finds from experience data that some citizens are unaware that they’ve passed the threshold for property tax.

2. Driver: Find something happening in X-data, and look for operational conditions that are causing the situation. Experience data uncovers that citizens are periodically dissatisfied with the process for renewing their driver’s license.听 The agency finds from operational data that it tends to happen during school holidays, when staffing is low at certain branches.

3. Prediction: Build segmentation models based on a combination of X-data and O-data. An agency uses a combination of operational data (job categories, tenure, past attrition rates, etc.) and experience data (task assignments, caseloads, employee engagement scores, etc.) to predict future staff turnover.

4. Personalisation: Adjust how your treat people based on a combination of X and O data. A tax agency proactively offers payment holidays to debtors (operational data) who are impacted by a natural disaster (experience data).

5. Alerting: Send relevant alerts and information to people based on X and O-data. An agency sets up an alert with case file information (operational data) to be notified whenever a citizen reports feeling depressed (experience data), so they can offer counselling services.

6. Value measurement: Evaluate the value of improving experiences by examining the impact those changes have on business metrics. A social service agency calculates the impact of switching to digital channels by assessing the cost and time savings (operational data), against feedback about the online experience (experience data).

Governments can no longer afford to be static. They must continually update and improve their services and programs based on feedback from citizens and employees. The examples above show that for many organisations, only small changes are required to start building responsive processes and behaviours.

As technology continues to evolve, and as we continue to find ourselves operating under a 鈥榥ew normal鈥�, governments will need to ensure that they are all the technology and tools required for responsive government, or risk losing the trust of citizens.

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Right now, in our communities, we are witnessing the consequences of听limited rationality,听social preferences听and听lack of self-control. In their seminal work 鈥淣udge: Improving Decisions on Health, Wealth, and Happiness,鈥� Professors Richard Thaler () and Cass Sunstein postulated that these human traits systematically affect individual decisions and market outcomes. It鈥檚 instructive to explore how these factors might be influencing individual decisions, for example, to stockpile toilet paper:

• Limited rationality: People focus on the narrow impact of individual decisions rather than the overall effect. For example, I鈥檒l buy some extra toilet paper now because I鈥檝e heard that it might be in short supply later. I make this individual decision without realising that I鈥檓 inadvertently contributing to the overall effect of supplies running short, which will ultimately impact me 鈥� along with everyone else 鈥� in the long run.

• Social preferences: People have a social preference for equitable outcomes. For example, I鈥檒l be less accepting of my local supermarket increasing the price of toilet paper in response to a growth in demand than in response to a rise in their cost of supply. Even if the price rise is the same in both cases, my willingness to pay a premium is influenced by my perception of fairness.

• Lack of self-control: People tend to give in to short-term temptation rather than stick to a long-term plan. For example, even though I have more than enough toilet paper at home, I鈥檒l still buy more if I find it somewhere on sale. I know that I don鈥檛 have anywhere to store additional rolls of toilet paper, but when presented with the opportunity to purchase such a sought-after item at a discounted price, I won鈥檛 be able to resist.

As has been demonstrated across the globe, government assurances, pleas, and directives have failed to prevent emotional shoppers from emptying shelves in anticipation of future shortages. Now similar assurances, pleas, and directives are being made in relation to the much more serious issues of self-isolation, social distancing, and personal hygiene. Will citizens heed government rules and regulations now when they haven鈥檛 in the past? Certainly, the Chinese government听听in curbing the spread of COVID-19, but most Democratic governments don鈥檛 have the same controls available to them as in Communist China. What then is to be done?

In our aforementioned research, the SIDG and the ANU described how听digital nudging might be used by governments to drive behavioural change for social good. Empirical evidence told us that certain human actions result in better social outcomes, and digital technology is enabling us to reliably predict those outcomes based on observed behaviours. This caused us to ask: how might we leverage default human nature to positively influence social outcomes, and could we apply technology to influence individual decisions at scale?

Where Thaler and Sunstein (2008) defined a听nudge as: 鈥淎ny aspect of the choice architecture that alters people鈥檚 behaviour, in a predictable way, without forbidding any options, or significantly changing their economic consequences.鈥� We defined a听digital nudge听as: 鈥淚ndividually targeted processes, facilitated by information technology, to achieve social policy outcomes鈥� (Gregor & Lee-Archer, 2016).

Figure 1: At the intersection of agile policy, information technology and behavioural听science is the digital nudge.

Moreover, we proposed that听predictive analytics听and contextualisation听capabilities can improve the effectiveness of traditional nudging by enabling the shift from reactive to proactive interventions and by making nudges more targeted to individual circumstances.

• Predictive analytics is a specific field of data mining in which large stores of data are analysed to detect patterns and to predict future outcomes and trends. While predictive algorithms have been used for many years, they have typically been restricted to operating on pre-existing data. Real-time computing platforms have changed this by allowing data to be analysed as it鈥檚 created. This means that analytical discoveries can be applied to adjust government action dynamically, thereby influencing trends as they emerge.

• Contextualisation听is the next evolution of personalisation: blending together information about past interactions and anticipated behaviours with present motivations and intent. Where personalisation attempts to anticipate future behaviours based on past activities, it lacks the in-the-moment context of the citizen鈥檚 current circumstance. This is important because it鈥檚 precisely that current context that鈥檚 most relevant and useful for predicting future behaviour.

Figure 2:听Our framework for the design and application of digital nudges.

Of course, our thinking has evolved since 2016, and so we would now add听experience management听into the mix.

• Experience management听brings together operational data (O-data) about听what听is happening, with experience data (X-data) that tells us听why听it鈥檚 happening. This fusing of X+O data can enable governments to better understand citizen sentiments and motivations, and thereby take effective action. Importantly, since sentiments and motivations are constantly changing, governments need to embed feedback and analysis throughout their business processes and at every point of citizen interaction.

With this in mind, let鈥檚 return to our example of stockpiling toilet paper and see how governments might apply digital nudging to curb this behaviour鈥�

An online听听suggests that to last 14 days in isolation, each person requires only four rolls of toilet paper. So, the average American household (2.6 people) should be able to get by with just a single pack (10 rolls). Most likely, very few consumers did this calculation prior to purchasing, so a simple SMS informing citizens about how much toilet paper they actually need could be quite effective. It might even be possible to target the digital nudge by advising the required number of rolls for a given household.

Another approach would be to leverage the behavioural science influencer of .听听of over 6,000 Australians indicated that only 9% had purchased more than 20 rolls of toilet paper due to COVID-19. This sort of statistic could be promoted via digital channels, especially in geographic areas where a small percentage of people have been observed to be buying in bulk. To further improve effectiveness, the poll could be extended to understand what鈥檚 motivating consumer purchasing decisions (e.g.,听Why听did you decide to purchase X rolls of toilet paper?).

Figure 3:听A conceptual architecture for digital nudges.

These same capabilities could be applied by governments to nudge citizens towards cooperation with rules and regulations relating to self-isolation, social distancing, and personal hygiene. The Behavioural Insights Team鈥檚 provides nine of the most robust (non-coercive) influences on human behaviour, including:

• Messenger:听We are heavily influenced by who communicates information.听 suggests that 鈥淪cientists and physicians are the most trusted authorities [on COVID-19], along with officials from the World Health Organisation and the U.S. Centre for Disease Control.鈥�

• Norms:听We are strongly influenced by what others do. Governments, researchers, public health authorities, and the general public are听听successful responses to COVID-19 and to avoid repeating the missteps of others.

• Affect:听Our emotional associations can powerfully shape our actions. The CDC has dedicated听听to managing anxiety and stress related to COVID-19.

Finally, it鈥檚 important to be mindful of the iterative nature of our digital nudge framework. Under normal circumstances, nudges are tested with focus groups in听. While there鈥檚 a need to change certain behaviours relating to COVID-19 immediately, the potential for unintended consequences is heightened as a result of panic, so it鈥檚 important not to skip this important step. 听approaches can assist in expediting the test-and-improve cycle, both prior to disseminating the initial nudge and to inform adaptation of the nudge as circumstances change.

While digital nudging is not a silver bullet for containing COVID-19, it is part of the overall toolkit available to governments today. As we鈥檝e shown by way of examples, digital technologies can be used to both scale and personalise traditional nudges to improve outcomes for mass cohorts. Specifically, the combination of predictive analytics, experience management, and contextualisation capabilities can enable governments to predict social outcomes, understand what鈥檚 motivating those outcomes, and take effective action to avoid today鈥檚 emerging trends from becoming tomorrow鈥檚 next crisis.

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