Which Project Management App Is Free? UK Guide
16 March 2026•5 min
Data is already flowing across your projects — daily diaries, labour returns, plant logs, RFIs, inspections, progress photos, programme updates and commercial reports. The question isn’t whether you have data; it’s whether you’re analysing it in a way that helps you build safer, faster and more profitably.
If you’ve been searching “what data analytics construction”, you’re usually looking for something practical: What types of analysis exist, and how do I apply them on a live UK site without hiring a team of data scientists?
Below are the four types of data analysis (often called the four types of analytics) and how they map to day-to-day construction decisions — with examples and ways to run them using SiteSamurai.
1) Descriptive analysis: What happened?
Descriptive analysis summarises historical data to show what has already happened. In construction, this is your baseline: progress, productivity, defects, incidents, labour hours, plant utilisation, delays and variations over a defined period.
What it looks like on site
A Site Manager wants to know:
- How many snag items were raised this week vs last week?
- How many operative hours were booked to groundworks?
- What percentage of planned tasks were completed (PPC) on the weekly plan?
- How many RFIs are outstanding and for how long?
Real site example (UK fit-out)
On a CAT A office fit-out, the team notices the programme slipping. Descriptive analysis highlights that:
- Drylining completion averaged 62% of planned output across the last two weeks.
- The number of ceiling inspections failed rose from 3 to 11 in one week.
- RFIs related to M&E coordination have been open for an average of 9 days.
That’s not yet explaining why — but it’s making the problem visible, quickly.
How to do it in SiteSamurai (practical workflow)
- Standardise daily capture: ensure supervisors log daily progress, labour and issues consistently.
- Use dashboards and filters: view progress by work package, floor/plot, subcontractor or date range.
- Track trends: compare week-on-week output, defect counts, inspection pass rates and RFI ageing.
Best practice tip: Descriptive analysis only works if your inputs are consistent. SiteSamurai templates for daily reports, inspections and snagging help keep the data clean enough to trust.
2) Diagnostic analysis: Why did it happen?
Diagnostic analysis investigates causes. It goes beyond “we slipped” to “we slipped because…”. In construction, this typically means linking outcomes to constraints: design information, access, materials, labour, plant, permits, weather, or sequencing.
What it looks like on site
A Project Manager asks:
- Why did productivity drop on the blockwork gang?
- Why are we seeing repeated waterproofing failures?
- Why are deliveries causing lost time at the gate?
Real site example (groundworks and RC frame)
On a multi-storey RC frame, concrete pours start missing planned dates. Diagnostic analysis shows:
- 52% of delays are logged as “permit to pour / inspection not ready”.
- The same two locations repeatedly fail pre-pour checks due to incomplete rebar tagging and missing cube test paperwork.
- Delays cluster on Mondays after weekend works, pointing to handover and readiness issues.
The team can now act: tighten pre-pour readiness checks, adjust weekend-to-weekday handover, and enforce documentation completion.
How to do it in SiteSamurai
- Tag issues consistently: when logging an NCR, snag, inspection fail or delay, select a cause category.
- Drill down: filter by subcontractor, plot/floor, work package, or inspector to spot patterns.
- Link evidence: attach photos, drawings, method statements, and comments to build a defensible audit trail.
Best practice tip: Diagnostic analysis is where SiteSamurai’s structured forms pay off — free-text notes are useful, but categorised fields make trends measurable.
3) Predictive analysis: What is likely to happen?
Predictive analysis uses historical patterns and current signals to forecast future outcomes. In construction, that means predicting risks like delays, defects, cost overruns, or resource bottlenecks before they land.
<ul class="my-4 space-y-2">Predictive doesn’t have to mean complex AI. Often it’s practical forecasting based on leading indicators:<li class="ml-4 list-disc list-inside">RFI ageing predicts design-driven delays</li><li class="ml-4 list-disc list-inside">Inspection fail rates predict rework and lost time</li><li class="ml-4 list-disc list-inside">Labour vs output trends predict productivity issues</li><li class="ml-4 list-disc list-inside">Material lead times predict upcoming constraints</li></ul>Real site example (housing development)
On a 120-unit housing scheme, the team tracks:
- Average time to close snags per plot
- Number of first-fix inspection fails
- Subcontractor attendance vs planned labour
Predictive analysis flags that plots with more than 8 first-fix fails in a week are twice as likely to miss the target handover date. The team responds by adding a targeted “quality push” (supervision and toolbox talks) on those plots before second-fix starts.
How to do it in SiteSamurai
- Monitor leading indicators: set up views for RFI ageing, inspection pass rates, snag closure times, and productivity.
- Use thresholds: agree trigger points (e.g., “If RFI ageing > 7 days, escalate”; “If pass rate < 85%, review supervision”).
- Forecast workload: use outstanding snags/inspections to forecast the labour needed next week.
Best practice tip: Predictive analysis becomes powerful when you review it routinely — for example in weekly progress meetings — and assign actions, owners and due dates in the same system.
4) Prescriptive analysis: What should we do about it?
Prescriptive analysis recommends actions. It combines what happened, why it happened, and what’s likely to happen to decide the best next step.
<ul class="my-4 space-y-2">In construction terms, prescriptive analysis helps you choose between options:<li class="ml-4 list-disc list-inside">Re-sequence works or add labour?</li><li class="ml-4 list-disc list-inside">Bring in a different subcontractor or increase supervision?</li><li class="ml-4 list-disc list-inside">Change inspection hold points?</li><li class="ml-4 list-disc list-inside">Expedite materials or redesign a detail?</li></ul>Real site example (logistics and access constraints)
On a city-centre project with tight logistics, descriptive analysis shows frequent lost time at the gate; diagnostic analysis points to unbooked deliveries and incomplete permits; predictive analysis indicates Friday afternoons will be worst due to stacking trades.
<ul class="my-4 space-y-2">Prescriptive action plan:<li class="ml-4 list-disc list-inside">Introduce mandatory delivery booking slots</li><li class="ml-4 list-disc list-inside">Require permit approval before a delivery slot is confirmed</li><li class="ml-4 list-disc list-inside">Stagger high-risk trades on Fridays</li><li class="ml-4 list-disc list-inside">Add a banksman during peak periods</li></ul>The result is fewer aborted deliveries, less plant idle time, and improved programme certainty.
How to do it in SiteSamurai
- Turn insights into tasks: create actions directly from issues/inspections with responsible persons and deadlines.
- Standardise playbooks: store agreed corrective actions (e.g., for recurring waterproofing defects) as templates.
- Close the loop: track whether actions reduced the metric (pass rate improves, snag closure time drops, delays reduce).
Best practice tip: Prescriptive analysis fails when actions aren’t tracked. SiteSamurai helps by keeping actions, evidence, and outcomes together — useful for internal reporting and client assurance.
How the 4 types work together (a simple construction cycle)
<ol class="my-4 space-y-2">On a well-run project, the four types of data analysis form a loop:<li class="ml-4 list-decimal list-inside">Descriptive: “We had 11 ceiling inspection failures this week.”</li><li class="ml-4 list-decimal list-inside">Diagnostic: “Most failures relate to fire stopping around service penetrations on Levels 3–4.”</li><li class="ml-4 list-decimal list-inside">Predictive: “If this continues, we’ll lose two weeks to rework and delay commissioning.”</li><li class="ml-4 list-decimal list-inside">Prescriptive: “Run a focused quality intervention: toolbox talk, mock-up approval, increased supervision, and a hold point before closing ceilings.”</li></ol>That is what data analytics in construction should look like: not a dashboard for the sake of it, but a practical system for making better decisions.
Getting started: a quick checklist for construction teams
<ul class="my-4 space-y-2">If you want to apply these four types without overcomplicating things, start here:<li class="ml-4 list-disc list-inside">Pick 5–8 core metrics (progress vs plan, PPC, inspection pass rate, snag closure time, RFI ageing, labour hours vs output).</li><li class="ml-4 list-disc list-inside">Standardise capture using SiteSamurai templates (daily reports, inspections, snagging, RFIs).</li><li class="ml-4 list-disc list-inside">Review weekly: one page of trends, one page of causes, one page of forward risks.</li><li class="ml-4 list-disc list-inside">Assign actions in-system and track closure.</li><li class="ml-4 list-disc list-inside">Keep evidence attached (photos, comments, documents) for commercial and client confidence.</li></ul>Final thoughts
The four types of data analysis — descriptive, diagnostic, predictive and prescriptive — are not academic categories. They’re a practical way to move from “we think” to “we know” on live projects.
With SiteSamurai, UK construction teams can capture site data consistently, analyse it quickly, and turn it into actions that reduce rework, improve programme performance, and strengthen compliance.
If you want, share your project type (housing, fit-out, civils, RC frame) and I’ll suggest the most useful starter metrics and a simple SiteSamurai setup to support them.