From Reactive to Predictive

Most maintenance teams live in a reactive world.

A defect appears, someone logs it, and a crew is sent to fix it. It’s an endless cycle of urgent work orders, phone calls from the council, pressure to respond quickly, and the constant juggle of resource and budget.

For many contractors, this isn’t a failure of process; it’s simply the nature of maintenance. Ageing infrastructure, unpredictable conditions and tight SLAs mean teams are always responding to what’s right in front of them. But it also means budgets are stretched, crews are firefighting, and councils struggle to justify long-term investment because they don’t have a clear picture of what’s happening across the network.

The problem with reactive QA

Traditional QA in maintenance is inconsistent and fragmented. Photos are saved to phones or buried in emails. Evidence is uploaded days later, or not at all. Sign-offs happen, but they’re not structured or standardised enough to be useful beyond proving basic compliance.

• Evidence is collected after work is already complete
• Issues are only discovered when they become failures
• Councils can’t see patterns, they can only see what’s already gone wrong
• Contractors can’t forecast, because the data is limited to the latest defect

It’s compliance, not insight. And because it sits on the back end of the process, often in hard copies or uploaded in batches, it has no ability to shape future decisions.

• where defects tend to appear
• which assets degrade fastest
• seasonal or environmental trends
• which crews or methods produce better outcomes
• locations that repeatedly require rework
• areas where SLAs are at risk

This isn’t theoretical. Contractors who capture this data well can see early indicators of failure long before they appear in the defect backlog.

Predictive QA lets you intervene before the asset fails.

1. Structured QA at the point of work

Every lot captures the same information the same way, every time: photos, measurements, sign-offs, locations, crew details, materials, environmental notes. The data is standardised from day one, meaning it’s ready for analysis, not just for compliance.

2. Real-time dashboards to identify patterns early

CONQA’s Lot Dashboard gives contractors and councils a running picture of what’s happening across the network. As soon as QA is submitted, it appears in live dashboards. This means:

• repeat issues surface immediately
• hotspots can be identified before they become budget blowouts
• foremen and supervisors don’t have to wait for end-of-month reporting
• operations managers can reprioritise crews based on emerging trends

3. Integration with asset management systems

Through APIs, QA data can flow into asset management platforms like RAMM, GIS tools, or council AMS systems.
This creates a single picture of asset health: work completed, underlying conditions, locations of recurring issues, and performance over time.

When QA data enriches asset management data, you get more than a record of completed tasks, you get insight into the long-term behaviour of assets. That’s the heart of QA data asset management.

4. Better visibility strengthens relationships

Increasing demand for evidence-backed reporting means contractors need clarity, not complexity.

Predictive QA strengthens that trust by giving clients visibility into not just what was done, but what’s coming.