See attrition coming before the resignation letter.
People analytics for attrition, capacity, skills and productivity, built on governed HR data.
One call with a senior engineer. A straight answer on what it would take.
Where you are. Where you’ll be.
You need this if
- Resignations surprise managers
- Headcount planning is done in spreadsheets
- You can't see which skills you have
What changes for your business
- Early warning on attrition risk
- Better workforce and capacity planning
- Skills gaps visible before they hurt delivery
What we hand over
- People data model with privacy controls
- Attrition and engagement analytics
- Capacity and skills planning dashboards
- Workforce planning models
What it is
Workforce analytics applies data analysis to people decisions: who is likely to leave, where capacity falls short, which skills exist and which are missing, and what drives productivity. Because it uses personal data, privacy safeguards and aggregation are part of the design, not an afterthought.
Workforce decisions are often made on gut feel. We bring HR, finance and operational data together with privacy built in, and build analytics for attrition risk, capacity planning, skills coverage and productivity, so leaders can plan headcount and investment with evidence.
- Why now
- 39% of workers' core skills are expected to change by 2030. World Economic Forum, Future of Jobs Report 2025 (opens in a new tab)
- Last reviewed
How it runs
- 01
Diagnose
Typically 2–4 weeksWe map the problem, your data and your systems, and agree the one number that defines success.
- 02
Prove
Typically 4–8 weeksA working pilot on your real data, measured against that number. Not a slide demo.
- 03
Ship
Scoped to the outcomeProduction build with security, monitoring, cost controls and documentation included, not upsold.
- 04
Run
Ongoing, optionalWe operate what we built against clear service levels, or train your team to. Your call. No lock-in.
Questions you’ll ask
- Can analytics predict resignations?
- It can flag teams and roles at higher risk, based on patterns such as tenure, workload and pay position, so managers act earlier. It's used for team-level planning and support, not to judge individuals.
- How do you protect employee privacy?
- With data minimization, aggregation thresholds so small groups can't be identified, role-based access and alignment with GDPR and local employment rules. Works councils and employee representatives are involved where required.
- What data do you need?
- Typically core HR data, organizational structure, compensation bands, time and absence data and relevant operational metrics. We start with what's available and add sources only where a decision needs them.
Sound familiar? Let’s fix it.
One call with a senior engineer. You’ll leave with a straight answer on what it would take.
Let's Build Together