When emergencies and natural disasters occur, such as the COVID pandemic or the 2019-2020 bushfires, people need help urgently.

I previously led the Emergency Services Portfolio at Service NSW, which was responsible for standing up and distributing more than $1 billion of emergency and recovery grant payments in NSW.

Through this role, it became clear how important it is to get money to afflicted people quickly. Government recovery and emergency grant programs typically take weeks to set up and distribute and are an administrative-intensive process on both sides.

Citizens already struggling with basic daily existence and possibly without reliable power or communications need to fill in an application form, find supporting documentation, submit it for approval and then wait for manual assessment. This process usually takes several weeks. Many people may not even apply for funds because they are too busy trying to navigate the disaster.

This, in turn, hampers the recovery of the area. It’s not uncommon for recovery to take years, and it has a huge mental and physical strain on people.

Having lived through bushfires personally, we still have fire-weakened trees that keep falling on our property, continually damaging fencing that was rebuilt after the fires, or even breaking the back of cattle that might be unfortunate to have stood under a falling branch.

By taking a more proactive approach, governments could shorten the grant-relief process to days or even hours by reaching out to offer assistance rather than people having to apply.

Governments hold a vast amount of data on citizens and residents. Collectively, the government knows where someone resides or runs their business, if their address was impacted, the level of impact, what payroll tax threshold their business is on and what income they earn. In an emergency, they should be able to immediately identify people in a disaster zone and get funds urgently in the hour of need.

The problem is that data is extremely fragmented and siloed between federal agencies, state departments and local councils. If someone reaches out through the grants process, how do you automate the connection to other services for them? Can you review the debt they owe to the government and manage that for them, can you prioritise housing needs for them, can you guide them through any of the rebates and vouchers that may be available to them?

Some profiles see everything matching and you can have a high level of confidence that someone’s situation has met the eligibility criteria. But many records aren’t that consistent. Data is frequently incomplete or inaccurate. Someone’s address change might have been updated on their driving licence but not in their Medicare records. How can you be certain that ten differing records in different databases are the same person? Is that person still living in the affected area?

A more unified approach, possibly through data lakes and master data management solutions, could facilitate better matching and distribution of aid. If data from different government databases could be aligned, such as council rate information with emergency impact data, it should be possible to identify eligible citizens with a high level of confidence and directly provide them with the necessary support. This method would not only enhance efficiency but also ensure aid reaches those who need it most.

Centralising government data

Currently, each state is responsible for handling its emergencies, with some federal support. States need to start matching data and getting a “single view of the customer” across government. This has two layers. First, it’s important to have all the data in one secure place, managed and controlled.  Some of today’s platforms allow you to collaborate and move data without copying it. Secondly is the single customer record: the golden record. This is where you take all of the collated data and link it together.

But citizens need to give explicit consent for their data to be shared. The key to this is transparency and clearly communicating the benefits, including the purpose, what data will be collected and how it will be shared and used. Our research indicates that citizens are willing to share data when they need support.

Designing for fraud, security and privacy is also fundamental. Embedding this into the solution from the outset is vital to ensure that people’s identity is protected and that impacted people receive support quickly.

Generative and predictive artificial intelligence (AI) can play an important role in matching data. Government may not have the people or the budgets to collate and sync all of the data into a cohesive view. This is where AI and machine learning can help to automate the process.  It’s important to create the data frameworks, catalogues and infrastructure to enable this. For the framework, we must collate the minimum amount of data needed to provide a citizen experience.

When a declaration of an emergency and disaster is in force, Part VIA of the Privacy Act enhances and enables the collection, use and disclosure of personal information between Australian government agencies and state and territory authorities, private sector organisations, non-government organisations and others. Governments should leverage this to provide proactive assistance to people and communities and improve the experience, cutting weeks of waiting to just days or hours.

The benefits of a single view

We’ve already seen how effective the results can be when data is used effectively. In a recent bushfire emergency, the Office of Local Government wanted to pay council rates for those affected. Usually, this would involve setting up an online form, asking applicants for evidence — such as last year’s rates notices — and then going through an assessment process.

Instead, we were able to take council rate data and match it with impact data. Each of the relevant councils was paid their council rates, and citizens were then sent notices that their rates were already paid. The councils had their money and the citizens were relieved of payment that year, without having to apply or do anything. This was an important way to show understanding that they had been through a very difficult time and had one less thing to worry about.

Advanced data strategies and technology are key to achieving a single view of the citizen, which would allow for a more targeted and efficient distribution of grants. The focus should be on accurate data matching and management, with AI and machine learning playing a carefully controlled and governed role.

This process won’t necessarily cost a lot. It is very doable with the latest technology, security and privacy designs, and working with the right people and organisations that strive for better citizen experiences. It’s also vital for improving efficiency, serving citizens better and more quickly and reducing fraud. All of this further enhances trust and confidence in government.

By Sanja Galic, senior client partner at Publicis Sapient

This article was first published by The Mandarin