Towards the end of the noughties, I got a job in housing, working as a frontline office for a housing association operating in South West London.
When a request for a repair came in, we logged the request on our computer, and hoped that the repair would be made. When I used to look at the customer satisfaction figures, however, we never seemed to be able to improve our repairs performance.
This won’t be that surprising to many people who, like me, have worked in social housing.
But I was intrigued by the data. The system we used to input the repairs requests was the same one that the contractor used. The issue was that the data it generated didn’t tell us anything other than the repairs we had booked. You couldn’t undertake any analysis.
This led me to asking my line manager about my doing some quantitative research into the statistics, to see whether there was a story behind the numbers.
There was. But it wasn’t immediately obvious.
Seven years later, after spending time studying and gaining a degree in Housing Studies and a Masters in Social Policy Research, I wanted to return and work in the sector. My worry was that I had missed out on numerous developments that I’d read about, but hadn’t had the opportunity to observe in person.
So I booked four days of shadowing at a medium-sized housing association over two weeks, working in their lettings department and retirement housing. I was most interested in changes to their processes, and, following discussions with management, we agreed to focus in particular on their use of data.
During the first week, I was privileged to observe the lettings officer, both within their office environment and outside the office. The first day I was in the office, before spending the second day observing the viewing of a property by a customer who had been nominated by the local council. I also interviewed a couple of staff members to help me understand the information I was after.
In the second week, I visited a retirement housing scheme and two extra care schemes. Following these visits, some changes became apparent to me.
First, the housing association had changed their working patterns so that hot desking, working from home and flexi hours were now the norm.
Secondly, the majority of the housing association’s services were now online and residents were strongly encouraged to book repairs online, although a phone service was available if required. This ensured that elderly and vulnerable residents with little or no experience of using online services had access to services as others.
When it came to data, however, little had changed.
In running the day to day activities within the organisation, several systems were employed such as CRM, Keystone, Mears, and internal spreadsheets to monitor lettings. Data was collected at different points such as when issuing a notice of the end of tenancy, the end of tenancy, the void inspection and when a nomination is received from the council. Despite several points of data collection, the data only related to the property and not the resident. Any data relating to the resident is initially stored in a CRM system, and an older system known as QLX.
All these systems were doing their work individually and in isolation of each other. Consequently, a fundamental issue still existed. The systems did not have the ability to communicate with each other. As a housing provider, you’re still constrained in not being able to undertake any analysis.
Today, housing remains a key issue, because it affects everyone. In assuming the role of one of the main housing providers, housing associations have been forced to behave like businesses. A system that ensures housing associations, contractors and customers are all singing from the same song book would be a dream come true. One that ensures that the same data standards are being employed internally and externally.
As someone who used to be a frontline member of staff, I have a great appreciation for the need for data standardisation. It seems to me that data standards could be a cure for this issue in the housing sector... Imagine a world where contractors can understand what housing associations are saying, and housing associations can understand customers. This can save administrative time, result in service improvement and consistency of service.
Our data should be able to tells us so many stories about business, customers and contractors. Some organisations are notorious for not using the data to improve service or tailor services to the needs of our residents. Are we listening to our data? If so, what’s our data telling us? Are you able to analyse the data to make it work for your business?
The housing sector needs data standards. Data standards are an integral part of our digital future.