Data & Analytics · Professional Services
A decision-support layer over a staffing platform, turning candidate, requirement and recruiter activity into KPIs that mean the same thing whoever is looking at them.
Repository not shown as per company policy
0
fact and dimension tables modeling the recruitment lifecycle
0+
standardized KPIs tracked across the funnel
0
predictive models in production
0
single source of truth for recruitment reporting
01Challenge
Recruiters could source, submit and place candidates every day, but management could not see the funnel behind those actions: which requirements were aging, which clients were slow to respond, or why a requirement with plenty of submissions still had not closed. Three teams computed time-to-fill three different ways and all three defended their number.
02Approach
Model once and let the KPI layer drive every dashboard. Define each metric centrally, compute it in one place, and have every report consume it rather than recalculate it. Then use the same governed model that reports what happened to score which open requirements are likely to miss their target date.
03Outcome
The operational platform ran the recruitment lifecycle. This layer sat on top of it, turning candidate, requirement, recruiter and client activity into KPIs that meant the same thing whoever was looking at them.
Time-to-fill, fill rate and every conversion ratio are computed once, centrally, and reused everywhere rather than redefined per report.
One star schema of facts and dimensions feeds the executive, recruiter, client, funnel and aging dashboards alike.
The same layer that reports what happened also scores which open requirements are likely to miss their target date.
What changed
The challenge
Model once. Let KPIs drive every dashboard. Keep recruiters in charge of the outreach.
How it works
Operational data is extracted into an analytical warehouse, modeled as a star schema, validated for quality, measured through a central KPI layer and scored by machine-learning models, then delivered as role-scoped dashboards.
01 / 05
Inside the system
More than fifty KPIs, grouped by the thing they describe. Each is defined once and traceable back to its source tables.
Productivity index, response time to a new requirement, fill rate, workload balance, submission quality, specialization index, activity-to-outcome ratio, and performance against tenure cohort.
GrainPer recruiter, per period, so individual and team views reconcile.
Aging buckets, difficulty index, reopen rate, cancellation rate, skill scarcity, SLA adherence by priority, source mix and location-based fill rate.
GrainPer requirement, segmented by client, recruiter and skill.
Feedback turnaround time, delivery efficiency and relationship health measures per client.
GrainPer client, so slow feedback loops are visible as a cause rather than a symptom.
Built with
Raw activity in. A decision a manager can actually act on out.
More work
For an established independent book publisher, a decade of accumulated ETL, warehouse and dashboard tools replaced with one governed cloud platform, without breaking a single number the business depended on.
Reporting, prediction and document processing for a medical data platform, delivered with a cross-functional business intelligence, data engineering and machine learning team.
For an online retailer, an analytics platform that brings order, product and customer data into one governed view, so trading meetings start from agreed numbers.
If something here is close to what you need, tell us about your situation and we will walk you through how we would approach it.
Intelligence → Innovation → Automation → Growth