Customer Story · Databricks
Forecasting the workforce: Databricks predictive analytics
How Adroitent unified fragmented recruitment data on the Databricks Lakehouse for a leading US staffing provider — operationalizing ML across the staffing lifecycle to forecast demand, placements and attrition.
The inflection pointData everywhere, foresight nowhere
The customer is a leading US-based staffing and workforce solutions provider supporting multiple industries at scale, with a network of over 10,000 contractors across the healthcare, IT and financial services sectors. Fragmented systems left them reacting rather than anticipating.
- Fragmented data across ATS, CRM, VMS, payroll and other systems
- Manual forecasting of demand and candidate availability
- Inability to accurately predict placement success, employee attrition and revenue trends
- Limited reporting without predictive or forward-looking insights
- Slow Time-to-Fill (TTF) rates for niche roles
Reporting tells you what happened. Prediction tells you what's next.
The interventionA Lakehouse for the staffing lifecycle
Adroitent leveraged the Databricks platform to unify data, apply advanced analytics and operationalize machine learning models across the staffing lifecycle — empowering the customer with streaming and real-time analytics.
Unified data platform
- Consolidated structured and semi-structured data from ATS, CRM, VMS and payroll
- Brought into Databricks using Delta Lake
Medallion architecture
- Bronze: raw ingestion of candidate, job, placement and sales data
- Silver: cleaned, standardized and enriched datasets. Gold: curated, ML-ready feature tables
Governance & security
- Unity Catalog deployed so sensitive PII stayed secure and compliant with US data privacy regulations
- Centralized governance with secure role-based access and audit-ready controls
ML & predictive analytics
- Demand forecasting for open roles and future requisitions; placement success prediction
- Attrition and redeployment risk scoring; revenue and pipeline forecasting
The game changer
Models in production, insight in the dashboard
- ML model development and validation using Databricks MLflow
- Predictive insights delivered through dashboards for recruiters, sales leaders and C-level executives
- Accelerated data engineering through reusable frameworks and components
- Analytics-optimized Lakehouse architecture with ongoing managed services
By the numbers
Improved forecasting accuracy
Faster decision-making
Streamlined workforce planning
Better placement outcomes
By the numbers
Improved forecasting accuracy
Faster decision-making
Streamlined workforce planning
Better placement outcomes
The payoffPredict, plan, place
- Improved forecasting accuracy
- Predictive models delivered materially better accuracy for demand and placement forecasting across the staffing lifecycle.
- Faster decision-making
- Actionable data insights, surfaced through dashboards, enabled recruiters and leaders to decide faster.
- Streamlined workforce planning
- Manual intervention was reduced, with planning driven by curated, ML-ready data rather than spreadsheets.
- Better placement outcomes
- Predictive demand insights improved candidate placement decisions and reduced time-to-fill on niche roles.
- Enhanced operational efficiency
- Efficiency improved organization-wide, supporting stronger global business growth.
The stack behind itTools & technology
This engagement was delivered as part of Adroitent's Databricks & AI Analytics practice.
Good to knowFrequently asked questions
What is a Databricks Lakehouse?
What is the medallion architecture (Bronze, Silver, Gold)?
What is Unity Catalog used for?
What is MLflow?
How does predictive analytics improve workforce planning?
Turn your data into foresight.
Databricks Lakehouse, governance and production ML — end to end.