I am delighted to promote and support another report on data challenges in social housing.
This one is written by Dr Simon Williams of Service Insights Ltd and a friend of Golden Marzipan.
The idea for this research study arose from our work delivering customer satisfaction surveys in the social housing sector. Housing providers often experience complications accessing what could be considered basic information, that is, combining tenant profile and property information into a single spreadsheet.
I say another report because so far, I have collected half a dozen similar reports in the last year, including:
- Better Social Housing Report
- Regulator for Social Housing reports and consultations on Tenancy Satisfaction Measures, Consumer Regulation, and Sector Risk Profile
- Housing Ombudsman’s Spotlight Report on Knowledge and Information Systems.
The latter is also eminently readable and highly referenced in Dr Williams’s study.
You can download a full copy of the report at Research: Data Challenges in Social Housing – BCN
The report is highly readable, and I found the section on the impact on housing association staff the most interesting. This section illustrates how inaccurate data leads to poor decision-making, inaccurate reporting, operational inefficiencies, missed opportunities, and negative customer experiences. Employees express frustration with disparate data sources, manual manipulation, and a tendency to accept data challenges as normal.
But all is not lost. Artificial Intelligence can improve productivity, and continued investment in information systems and cloud computing can improve things.
There are five recommendations covering silos, data quality, culture, and systems. The fifth and most important
Consider a sector-wide data strategy: As part of this research, an employee observed that, “Demands on social housing are increasing, data is becoming more important… maybe it’s time we need a sector-wide data strategy?”.
This may form the basis of useful debate. Because all social landlords will have similarities through the management of their homes and tenants, in principle, these similarities could be replicated at a much larger scale – through standardised practices technologically, and though standardised data terminology.
If this or similar approaches were undertaken to bring housing management data closer together at scale, it may lay even greater foundations and opportunities for maximising new knowledge gained through artificial intelligence to the benefit of the sector as a whole.
There are considerable overlaps with our thinking on the National Database for Social Housing Properties.
The Case for A National Database of Social Housing Property | Golden Marzipan
So, who is going to lead us on this quest from the Holy Grail?
