Data is a strategic asset in today’s digital housing landscape but in our experience, many housing providers still face significant challenges managing it. Data related to housing, repairs, assets and finance is often held across different systems with limited integration. This leads to inconsistencies, incomplete records and a general lack of trust in the information used to make critical business decisions.
How can housing providers ensure that their asset-related data is consistent, complete and dependable?
The challenges
Despite their heavy reliance on data for everything from compliance to investment planning, we’ve found that housing providers frequently encounter the following problems:
- Inconsistent address structures and formatting – Address data can vary significantly between systems due to differences in naming conventions or input formats. This undermines any efforts to link datasets or conduct accurate reporting across departments. One example of this could be incomplete, incorrect or missing postcode data.
- Poor integration between systems – For example, a completed repair might be recorded in the repairs system but not reflected in the asset management system. This disconnection makes it difficult to maintain an accurate view of property condition and plan future investments effectively. It can also lead to wasted time and effort and increased costs if a contractor is sent to carry out a repair that has already been dealt with, not to mention frustrated tenants.
- Lack of confidence in data quality – Housing teams are often hesitant to rely on internal reports due to concerns about data completeness and accuracy. This distrust can delay decision-making and create a culture of work-around processes.
Recommendations for building better data structures
To overcome these challenges, we would recommend that organisations take a proactive, cross-departmental approach to managing their data. The following strategies are recommended:
1. Identify and understand gaps in current datasets
Start with a comprehensive data audit. Map out where your data lives, who owns it, how it flows and where gaps or inconsistencies occur. This is the foundation for targeted clean-ups and improvements.
2. Enable real-time and collaborative data updates
Asset data should be updated regularly, using input from across the organisation, including tenants, contractors, housing officers and surveyors. Implement tools and workflows that allow the timely, structured capture of data at every touchpoint.
3. Standardise data as early and upstream as possible
Standardisation should happen at the first point of entry, whether that’s a contractor submitting a job-completion form, a tenant reporting a repair or a housing officer updating a property record. Defining and enforcing common formats for key fields such as address, asset type and maintenance category prevents downstream errors and supports seamless integration.
4. Define and track a data quality index
Create a data quality index (DQI) that scores your datasets on completeness, accuracy, consistency and timeliness. This measurable approach will help you to prioritise where to invest effort and allow you to track your progress.
5. Review and redesign business processes
Bad data often comes from outdated or poorly-designed processes. Review how your data is currently entered, validated and transferred between teams. Replace manual hand-offs with digital solutions where possible and embed quality controls into your workflows.
Strategic outcomes
We’ve seen that organisations investing in improving their data structures gain significantly, particularly in these areas:
- Better decision-making – With consistent and up-to-date data, leadership teams can make faster and smarter investment and operational decisions.
- Improved operational efficiency – Reducing the time spent on data correction or reconciliation frees up staff for higher-value tasks.
- Higher tenant satisfaction – Accurate asset and repair records enable more timely responses and better service.
- Regulatory confidence – Clean, transparent data makes reporting regulatory compliance more reliable and less labour-intensive.
Conclusion
Improving your data structures isn’t just a technical task, it’s a strategic imperative. By tackling inconsistency at the root, breaking down silos and embedding data quality into daily processes, housing providers can unlock the full value of their asset data. The result: better outcomes for the organisation, for frontline teams and ultimately for the tenants they serve.
Martyn Whyte is head of data and client services at PIMSS Data Systems.
