Are you bad cop or good cop when using data for social housing decision making? Getting the most out your data.

by Chris Shaw

New York early 1990s – crime was on the rise and a solution was needed. One smart cop who had previously always worked on hunch when trying to solve a crime thought he would take a different tactic. He turned to data, data and more data.

He wanted to understand the big picture – in one go - of where crime was taking place, the type of crime, the types of areas, crime solving success rates and number of police in each area.

The police developed a system to bring together the data they received from many different sources and then analyse it to give a complete picture. They were able to look at the whole city and understand the crime hotspots. They soon realized they could save a lot more money patrolling the trouble areas and stopping crime from happening in the first place. Much more efficient than having to react to crime.

Social housing in the UK, a world away from Brooklyn or Queens, but lessons learnt from the use of data on the New York’s streets remains with us today. Through our work at Shaw we find that many housing organisations are collecting all the information they need… but it is spread across separate systems with no easy way of linking and making sense of it. The effect is that all these separate pieces of information are like a complex 1000 piece jigsaw puzzle where all the pieces are blank.

Like the old NYC cops before discovering data, we’ve found that organisations are often still relying on hunches, feelings or experience when it comes to decision making. The data is all there but there is no way of joining the dots.

Housing associations certainly don’t have a shortage of decent of data. Many just need support on using it effectively. The data is being collected, but then the reports produced don’t give the visual insight that is needed. In effect organisations need their own GIS “data” system with many layers of accessible information.

We’ve recently been working with a Fire and Rescue Service. They have a geographical map of their area and then they have detailed information integrated that gives them greater connected levels of insight. So, for example, they will have information around where they've had fires or repeat fires. And they will have a layer of information showing deprivation and the type of housing. And then, based upon this information, they will also have a layer of information that says where they have gone out to provide fire prevention training or install smoke alarms. The result is that they are linking activity to insight and then using that to improve the delivery of their service and educate the public about fire safety.

Of course, there are housing associations who have got all their data unified and are delivering the right information to the right departments. This means each department has their own digital dashboard with all their KPIs. These dashboards are being refreshed in real time based on activity that's happening across the organisation. By utilising advances in AI and machine learning they have been able to successfully combine real time data with human knowledge and experience. It allows effective insight to inform any actions and decisions.

The housing associations losing out are those that collect information on paper or don't have a good enough system to collect it in a structured way. The result is there is one team using one solution for collecting information and another team using a separate solution. It is incredibly inefficient and time consuming to take this approach. There are often half a dozen different people feeding in information to create a model. In one instance an association we were working with said it took on average 30 days to pull together a quarterly report - that is a big cost when the information can made available at the press of a button.

Our role at Shaw is to understand the information architecture and link it together in a way that gives some real insights. This then enables better automated processes giving greater insight for decision making. It will save time and money – and ultimately decision making on a hunch or half information are a thing of the past. No more bad cop, just good cop when dealing with data management.

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