EY’s global research shows that only 17% of senior government executives said their organisation was data centric. If we bring that into a Aotearoa New Zealand context, it underscores the challenges of being data-centric across a range of organisations that operate within their own complex system contexts.
How can agencies start baking system value into BAU?
Governments solve problems by martialling financial, human, technology and physical resources. When it comes to complex issues, agencies need to inform this approach with a system view, not an agency view. For this to work, it needs to be clear where agencies should act as a system and where an individual response is more appropriate. This is a key choice for policy makers, where to harness the combined skills of different agencies.
In practical terms, to cement system value in the way agencies think, public sector leaders could ask themselves:
1. What proportion of effort is spent on system issues?
As noted above, most requirements of citizens can be met by single agencies undertaking their core business. However, for those individuals and whānau who have more complex needs, the effort required to improve social outcomes is significantly larger, but so are the rewards, both for the people concerned in terms of their potential outcomes, and for the agencies. It is worth considering how much effort is spent on people with these more complex needs. Using system data to allow a more holistic view of benefits from the citizens’ points of view is likely to create a case for increasing the proportionate effort focussed on these individuals and whānau.
2. Who else shares our goals? What data do they have that will help us improve citizen outcomes?
When government organises activity around citizen needs through shared responsibility goals, data and systems, the potential rewards for citizens are massive.
In Barking and Dagenham, one of the UK capital’s poorest boroughs, data sharing proved to be the hurdle to improving citizen outcomes. The council delivering social services wanted to target its interventions where they would have the greatest impact. But the information needed to make this happen was stored on different case management systems in different agencies, making it almost impossible for council staff to gain a holistic view of households and individuals. Lacking the bigger picture, staff tended to react to immediate needs. To solve this problem, the council developed One View, a data management, analytics and predictive modelling platform that brings together disconnected adult and children’s service datasets from multiple agencies. The insights available through One View include early indicators of homelessness. Acting early to intervene in these cases has helped the council to reduce the use of temporary accommodation. Staff can also now take a more strategic approach, acting before a crisis to prevent small problems from escalating, saving money and delivering better citizen outcomes.
3. What use cases for system collaboration beyond the traditional are being actioned elsewhere in the world?
The field of data analytics and technology has rapidly evolved over the past five years. Data is now being used in new and innovative ways to solve problems and create value. For several years, governments have been using data from multiple sources to:
- Predict disease outbreaks and allocate resources such as vaccines and medical supplies to areas that are most at risk.
- Adjust traffic lights in real-time to optimise traffic flow.
- Provide personalised recommendations for social services based on individual circumstances and needs.
- Predict when infrastructure will need maintenance and repairs.
The next stage of these use cases is to apply this capability across multiple domains to solve more complex problems.
In Australia’s Northern Territory, the government has been looking at system data to identify more effective interventions in Indigenous communities. A key finding was that local delivery of dialysis in community, whilst having a higher unit cost, led to better individual and community outcomes. Furthermore, treatment in centralised locations required other government costs to be incurred, such as housing, which overall led to community delivery being better value from a whole government perspective.
In the US, Cityblock provides personalised medical and social care to low-income neighbourhoods through technology and community-based health navigators. Their digital platform connects multidisciplinary support teams, streamlining data and services, including virtual care. Notably, they've achieved a 20% reduction in inpatient hospital stays, a 15% reduction in ED visits and have an ambitious goal to reach 10 million patients by 2030.
4. How can we use system data to better understand unmet need?
System data also comes into its own when identifying unmet needs. This is especially important for vulnerable citizens who are least likely to report abuse, voice their concerns or understand that help is available.
Where mental health problems go untreated, or domestic violence is unreported, these issues are not recorded in the system. System data can alert case workers to issues that suggest children or families may need additional support before issues are reported, and hopefully long before lives are at risk.
For example, in Victoria, EY Citizen Intelligence Solution is being used to underpin the ChildLink Program. ChildLink provides a single view of a child and alerts authorised personnel if there is an issue relating to the child. Case workers know to drop in more regularly. Teachers revise their homework expectations. The system mobilises to support the child. Further, this information is being used to identify opportunities for earlier intervention to try and avoid the issues arising in the first place.
5. How aligned is our thinking and frameworks with system value?
In order to deliver value across the system, all involved agencies need to be aligned on the circumstances under which multiple-agency responses are required, how data will be collected to support this and what the rules of engagement are. Where system value is most important, shared KPIs will need to be developed to encourage collaboration and data sharing. Each agency should ask whether these fundamental building blocks are in place to drive collaboration where it is most needed.
