If you’ve been amusing the family by asking Alexa to tell a joke, or to reveal where Santa is at the moment, then you’ll know already how ubiquitous headless user interfaces are becoming.
Taken for granted by the young, but something which still fills many of us with awe, artificial intelligence has moved off our screens to become part of our everyday lives and is being embraced by software development teams working to support public and community services, such as housing, education, social care and benefits.
These teams are looking at areas such as predictive analytics and the Internet of Things to see how the collation and interpretation of data can help with the burden and cost of managing homes and services, whilst improving people’s quality of life.
The Internet of Things (known as IoT) has the potential to unlock the world of big data for housing organisations and local authorities with the use of smart home-connected sensor technology. For example, by using connected devices to capture data on temperature, carbon monoxide, humidity and potentially on energy efficiency, landlords could pre-empt issues before they become an expensive liability, helping them to sustain their property portfolio and protect their investment.
It’s certainly not limited to cost benefits – there are opportunities to really transform how councils and social landlords deliver their services. Tenants could be offered easier access to contact their provider through headless user interfaces such as Alexa or Google Home or touchscreens that connect them remotely with their housing officer. People applying for benefits, transport and for school places could also use headless user interfaces to complete their application forms – ideal for supporting those with visual impairments, literacy challenges or those for whom English is a foreign language.
The proactive monitoring of properties using connected sensor systems could also help safeguard vulnerable people, perhaps providing an early warning system to highlight tenants who may be struggling, perhaps with legionella, damp or fuel poverty, before these become major health issues.
And at a more macro level, when IoT data is used on a larger scale, as we learn about how people are living and the performance of homes, the more we can understand about how efficiently a home is being heated, make more informed building decisions, and perhaps even learn about links between heating and the health of residents.
With predictive analytics, or machine learning, a computer can look at numerous considerations and factors about a family, alongside other data which reflects similar characteristics, to find the relationship between the data, trends and probability. This can uncover anything from understanding how elderly people can be supported to live independently for longer, to knowing when a tenant needs extra support to cover their rent payment that month.
No-one would argue against the fact that a person’s home is a key factor in their general sense of well-being. And yet sometimes tenants inadvertently fall into arrears, and eventually into homelessness, because they’re struggling to cope with the responsibility of managing their finances. Normally housing providers would only know the tenant was in difficulty after a rent payment had been missed, with the crucial setback that this represents in terms of the tenant having already braced themselves to miss a payment.
Predictive analytics can help change that by enabling teams to see early if a tenant is facing an increase in financial stress. A link with the scoring system used by credit reference agencies helps social landlords predict a tenant’s propensity to pay, taking into account recent loans and missed credit payments. Knowing this in advance informs the best course of action to mitigate the risk of arrears whilst supporting the tenant through the difficult period, perhaps by suggesting a payment plan, or, if things are more serious, referring them to a debt counselling service.
Being able to provide this early help before people reach crisis point not only ensures a steady flow of rental income but also encourages citizens to remain in control of their finances, contributing to a better quality of life overall.
It’s also worth considering an emerging trend – that of data science. It’s increasingly recognised that, to derive meaningful outcomes from the rich sources of information being collated every day, you need to be able to interpret it. A data scientist will interrogate the data in a way where the relationships and patterns become evident, to transform what starts off as many separate records into valuable conclusions and evidence on which to base decisions.
From using machine learning to improve children’s life chances and anticipate tough times ahead for tenants, to installing smart sensors which analyse household data to identify where fuel poverty or overheating is an issue, the technology already exists to help. And there are huge benefits to be realised – whether it’s housing providers protecting their customers and stock, or children and families services identifying and supporting the most vulnerable - predictive analytics and the Internet of Things can provide public and community organisations with high quality insights into their communities. With more informed decision-making based on these insights, they can continue to improve services, and consequently people’s lives.