Databricks

Databricks Predictive Analytics for a US Staffing Provider | Adroitent Home/ Customer Stories/ Databricks 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. Adroitent Case FileStaffing & WorkforceDatabricks · Delta Lake · MLflow3 min read 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 DatabricksDelta Live TablesUnity CatalogMLflowMedallion 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 Databricks PlatformDelta Live TablesDelta LakeUnity CatalogMLflowMedallion Architecture This engagement was delivered as part of Adroitent’s Databricks & AI Analytics practice. Good to knowFrequently asked questions What is a Databricks Lakehouse? A Lakehouse combines the low-cost, flexible storage of a data lake with the reliability, governance and performance of a data warehouse. On Databricks it lets BI, streaming and machine-learning workloads run on one governed copy of the data. What is the medallion architecture (Bronze, Silver, Gold)? It’s a layered pattern for refining data. Bronze holds raw ingested data; Silver holds cleaned, standardized and enriched datasets; Gold holds curated, business-ready tables and ML features — so quality improves at each stage and lineage stays clear. What is Unity Catalog used for? Unity Catalog provides centralized governance across a Databricks estate: role-based access control, auditing and data lineage. It’s how organizations keep sensitive data such as PII secure and compliant while still making data broadly usable. What is MLflow? MLflow is an open-source platform for managing the machine-learning lifecycle — tracking experiments, packaging models, and managing deployment and versioning. It’s what turns a promising model into something reliably running in production. How does predictive analytics improve workforce planning? By forecasting demand, candidate availability, placement success and attrition, teams can act ahead of need — pre-building talent pipelines, reducing time-to-fill, and planning revenue and capacity on evidence rather than intuition. Turn your data into foresight. Databricks Lakehouse, governance and production ML — end to end. Start a conversation Explore Databricks Keep reading Related customer stories Databricks Secure healthcare data access via a Databricks Lakehouse Read story GCC solutions Scalable engagement model empowered a global consulting firm Read story Software engineering Driving digital excellence for a North American automotive leader Read story More from Adroitent: all customer stories · insights & blog · adroitent.ai

Secure Healthcare Data Access via Databricks Lakehouse | Adroitent Home/ Customer Stories/ Databricks Customer Story · Databricks Governed by design: a HIPAA-aligned Lakehouse How Adroitent unified EHR, claims, lab and radiology data on a Databricks Lakehouse for a large healthcare provider network — delivering AI-ready analytics with HIPAA-aligned governance over sensitive PHI. Adroitent Case FileHealthcareDatabricks · Unity Catalog · HIPAA3 min read The inflection pointSilos, fragile pipelines, sensitive data The customer is a large healthcare provider network operating across multiple facilities, aiming to modernize its data and analytics environment to improve clinical, operational and financial insights — while maintaining strong controls over sensitive healthcare information. Data silos across EHR, claims, laboratory, radiology and other clinical and operational systems, limiting unified data access and insights Slow analytics and delayed reporting caused by legacy and fragile ETL pipelines, impacting timely decision-making Limited data governance and inconsistent access controls, creating challenges in securely managing sensitive PHI datasets Challenges enabling ML and AI use cases — readmission risk prediction, capacity forecasting, revenue leakage detection — due to unreliable and duplicated data sources In healthcare, insight is worthless without governance. The interventionA Lakehouse on the medallion pattern Adroitent implemented a Databricks Lakehouse architecture using the Medallion pattern (Bronze, Silver and Gold) to create a unified, governed and AI-ready data platform. Bronze — data ingestion Ingestion from EHR records, claims data, HL7/FHIR messages and lab data Raw data landed into Delta Live Tables to support data reliability Silver — transformation Standardization, deduplication, patient/provider entity resolution, ICD/CPT code normalization and data quality rules Pipeline orchestration via Databricks-native workflows with structured monitoring Gold — curated data Curated data for quality measures, revenue cycle dashboards, clinical ops and population health BI enablement and model-ready feature tables for ML orchestration Data reliability & performance Standardized on Delta Live Tables for ACID reliability, schema enforcement and historical versioning Improved trust and auditability across the platform The game changer Unity Catalog and HIPAA-aligned controls Centralized access control, auditing and lineage across workspaces and data assets Role-based and attribute-based access for PHI vs non-PHI datasets, with row/column-level controls where required Audit logging and lineage visibility for compliance and investigations HIPAA-aligned configuration approach based on Databricks HIPAA guidance Unity CatalogDelta Live TablesMLflowHL7 / FHIRHIPAA By the numbers Secure, compliant PHI access Faster data onboarding Accelerated reporting AI-ready data products By the numbers Secure, compliant PHI access Faster data onboarding Accelerated reporting AI-ready data products The payoffSecure, trusted, faster Secure, compliant access to sensitive data Effective governance through Unity Catalog delivered secure, compliant access to sensitive healthcare data, with audit-ready controls over PHI. Faster patient data onboarding Reusable data ingestion patterns accelerated the onboarding of new patient data sources. Improved patient reporting Streaming and real-time analytics improved the timeliness and quality of patient reports. Accelerated reporting & decisions Comprehensive dashboards accelerated reporting, enabling faster and more informed decision-making. Improved trust in analytics Standardized, curated datasets and governed data access raised confidence in the numbers. Accelerated ML initiatives Feature-ready data products accelerated the customer’s machine-learning initiatives. The stack behind itTools & technology Databricks PlatformDelta Live TablesUnity CatalogMLflowHL7 / FHIRMedallion Architecture This engagement was delivered as part of Adroitent’s Databricks & AI Analytics practice. Good to knowFrequently asked questions What is a healthcare data lakehouse? A healthcare lakehouse unifies clinical, claims, lab and operational data in one governed platform that supports both BI reporting and machine learning — replacing siloed warehouses and fragile pipelines with a single reliable source. How is HIPAA compliance handled in a cloud data platform? Through layered controls: encryption, network isolation, strict role-based and attribute-based access to PHI, comprehensive audit logging and lineage, and configuration aligned to the cloud provider’s HIPAA guidance — plus contractual safeguards such as a BAA. What are HL7 and FHIR? HL7 and FHIR are interoperability standards for exchanging health information between systems. FHIR is the modern, API-based standard, making it far easier to move clinical data reliably between EHRs, applications and analytics platforms. What are Delta Live Tables? Delta Live Tables is a Databricks framework for building reliable data pipelines declaratively. It brings ACID transactions, schema enforcement, data-quality expectations and historical versioning — so pipelines are auditable and trustworthy rather than fragile. What is PHI and why does governing it matter? PHI is Protected Health Information — any health data that can identify an individual. Governing it is a legal obligation under HIPAA and a matter of patient trust: access must be controlled, limited to those who need it, and fully auditable. Unify health data without compromising it. Governed Databricks Lakehouse platforms for regulated industries. Start a conversation Explore Databricks Keep reading Related customer stories Databricks Databricks predictive analytics for a US staffing provider Read story GCC solutions Scalable engagement model empowered a global consulting firm Read story Software engineering Driving digital excellence for a North American automotive leader Read story More from Adroitent: all customer stories · insights & blog · adroitent.ai