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Making systems thinking work: lessons for the public sector

Chris Peck — Fri, 20 Nov 2020 02:12:32 +0000

A recent report developed in conjunction with Oxford Economics examines how public sector organisations can reshape their strategies to best serve citizens amid disruption.

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

The government has been forced to respond quickly to a multitude of challenges as the reality on the ground changes fast and almost all of us have felt the impact of its initiatives in one way or another.

The pandemic has been an extreme example of how quickly disruption demands action. But it won’t be the last time the public sector is forced to adapt. �鶹ԭ�� wanted to explore the impact of disruption and find out what underpins the most successful responses by public sector agencies and teams.

In our August 2020 report, developed in conjunction with Oxford Economics,��, we examine how public sector organisations can, and are, reshaping their strategies to best serve citizens amid disruption.

The research paper is based on a global study by Oxford Economics of 3,000 senior executives, including 300 from the public sector. Oxford Economics also conducted in-depth conversations with a handful of executives from the private sector about their progress toward applying systems thinking and lessons learned along the way.

Interconnection integral to effectiveness

The results of our research show that an interconnected approach to management – known as systems thinking – can increase effectiveness as organisations navigate uncertainty. Public sector agencies must focus on engaging employees, improving collaboration with internal and external stakeholders, and upgrading technology. Underpinning this are three priorities that are the key to success:

Simplify processes to reduce complexity
Prioritise experiences for employees and citizens
Boost secure data-sharing across government and private-sector partners

Systems thinking involves an organisation, including external partners and customers, using real-time insights from high-quality data to make decisions and solve problems.

However, although an interconnected approach to management can increase effectiveness, only a small subset (six per cent) of respondents in the research survey qualified as leaders in applying systems thinking.

Most also still have work to do when it comes to collaboration and data-sharing. That’s despite such initiatives being likely to make the effective sharing of limited resources easier, by improving decisions and efficiency, reducing fraud and abuse, and enhancing citizen and employee experiences.

The good news is that for those who have taken the lead on applying systems thinking in their organisations, their efforts pay off in several ways. Our research found that those who are leading in this area are more likely to have done the following, each of which make strategic action by public sector organisations more achievable:

Integrated communication and data-sharing processes across the organisation
Achieved greater transparency in their operations
Broken down organisational silos and invested in collaborative technologies.

The experience and transparency gap

For the citizens the public sector serves, trust is a huge component of their satisfaction with public agencies. Yet we found that while some public sector organisations have implemented measures to address transparency, fraud, and more – with those who are leaders in systems thinking most likely to have done so – 13 per cent of organisations admit to having taken no steps at all to improve transparency in their organisation.

In terms of employee experience, the research makes clear that the public sector places significantly more emphasis on this than their private sector peers. Over half say employee satisfaction has the greatest influence on organisational strategy. However, while many believe improving employee experience would advance their reputation and have created feedback systems as a result, a much smaller proportion have made decisions that would improve their employee experience in response.

Seen in light of another strategic challenge for the public sector – a shortage of skilled talent to meet strategic change initiatives, highlighted by 61 per cent of respondents – this is clearly an area worthy of increased attention. More than half (54 per cent) of public sector respondents say improving employee experience would advance their reputation as an industry leader.

While the public sector has in many cases made a herculean effort in response to the coronavirus pandemic, leaders in this sector are showing just how much more effective it could be.

In a sector often faced with legacy systems, inflexibility, and funding constraints, a cohesive, adaptable approach that focuses on improving collaboration with internal and external stakeholders, along with engaging employees and upgrading technology, can turbocharge the public sector’s impact.

Public sector agencies should work to increase transparency and improve trust, boost secure data sharing with public and private sector partners, and prioritise HR integration to better motivate employees. By doing so the public sector can deepen its impact amid disruption.

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Dealing with Disruption: �鶹ԭ�� Reference Architecture

Greg Owens — Thu, 22 Oct 2020 00:30:47 +0000

An �鶹ԭ�� reference architecture for Digital Nudges

The last article in our “Dealing with Disruption” 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’ll map our conceptual architecture to �鶹ԭ�� 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.

��鶹ԭ�� Reference Architecture

Mapping our conceptual architecture to �鶹ԭ�� products provides assurance that our conceptual architecture can be delivered in practice.

Figure 2: An example reference architecture for digital nudges.

Note that �鶹ԭ��’s 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 �鶹ԭ��.

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 �鶹ԭ��’s Public Sector customers and products, visit:

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Dealing with Disruption: Conceptual Architecture

Ryan van Leent — Sun, 11 Oct 2020 08:10:42 +0000

A conceptual architecture for Digital Nudges to assist in crisis communication around COVID-19

The first two articles in our “Dealing with Disruption” 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 �鶹ԭ�� 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’s 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:

“When I came back home I did see hundreds of cops everywhere, so it was really intimidating.”
“It’s been getting more and more intense, people are really panicking.”
“We weren’t told any information, they just shut us down, didn’t let us leave our houses.”
“I just feel like we’re being treated like criminals.”
“We do not need 500 officers guarding the nine towers. We need nurses, we need counsellors, we need interpreters.”

In what has been an unprecedented year, the hard lockdown of Melbourne’s 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 – not only in ensuring compliance – 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��

Figure 2: A conceptual architecture for digital nudges.

Nudging is a delicate process, with significant preparation required to avoid unintended consequences – 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’ 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 “fake news” as a measure of effectiveness.
Analyze and improve: Here we distinguish between measurement and analysis, specifically within the context of diagnostics – 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’s 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’t 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’t, 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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Responsive Government: Reflections on our Citizen Experience poll

Ryan van Leent — Wed, 24 Jun 2020 04:46:44 +0000

On 23 June, the��, hosted a Responsive Government webcast, featuring presentations by the��and the��.

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’s a transactional measurement – 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’s possible to adapt the questions to focus rather on citizen trust in government.

Another measurement worth considering is��, 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’t 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’s the optimal approach… 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 – it’s important that agencies respond in a variety of ways to close-the-loop with citizens.

We’ve observed that citizen satisfaction is increasingly being included in agency service commitments, and it’s 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’s 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��, 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’s 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’s 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’ responses to be very insightful and through-provoking, and we hope that sharing these reflections will further progress the conversation.

If you’d like to find out more about becoming a Responsive Government,��.

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Using data to meet citizens’ needs: why responsive Government is the future of the public sector

The �鶹ԭ�� Institute for Digital Government — Mon, 18 May 2020 01:26:08 +0000

To become more responsive, rebuild public trust, and deliver on their mission, governments need to connect data from their operational systems and from citizen and employee experiences.

Only by joining the dots between data that explains how people feel – their experiences, emotions, beliefs, sentiments: what we call experience, or ‘X-data’ – with data which shows what is happening – like budget data, service requests, tax receipts: what is described as operational, or ‘O-data’ – 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’ 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’ 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�� 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.�� 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.�� 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 ‘new normal’, governments will need to ensure that they are all the technology and tools required for responsive government, or risk losing the trust of citizens.

To read the full whitepaper about how to become a responsive government, visit the .

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