Leveraging Predictive Power with the Workforce Analytics Module Lillian Thomas, Analytics Manager Luis Unda, Technical Lead National Institutes of Health September 18 th, 2015

Agenda  NIH Overview  Predictive Analytics – An Introduction  What is SMARTHR?  Showcase of the Workforce Analytics Module

About NIH 3 The National Institutes of Health (NIH), a part of the U.S. Department of Health and Human Services, is the nation’s medical research agency—making important discoveries that improve health and save lives. NIH Mission: to seek fundamental knowledge about the nature and behavior of living systems and the application of that knowledge to enhance health, lengthen life, and reduce illness and disability. Organizational Structure: 27 different components called Institutes and Centers. Each has its own specific research agenda. The Office of the Director is the central office at NIH for its 27 Institutes and Centers, and includes a centralized Office of Human Resources (OHR).Institutes and Centers Size: ~20,000 full-time, federal employees + ~22,000 contractors/ fellow staff 300,000 research personnel at over 2,500 universities and research institutions

About Our Analytics Group Workforce Analytics Branch: Provides the workforce data, analysis and related products and services that enable the organization to make better business decisions around its human capital resources.  Business Intelligence and Advanced Analytics  Business Process Re-engineering  Data Management and Governance  Survey Design and Analysis  Project Management and Consultation National Institutes of Health (NIH) Office of the Director (OD)Office of Management (OM) Office of Human Resources (OHR) HR Systems, Analytics & Information Division (HR SAID) Workforce Analytics Branch (WAB)

What is Predictive Analytics?  Predictive analytics utilizes various statistical techniques to predict probabilities and trends based on current and historical facts. Strategic Decision Making Key influencers Expected patterns Situational impacts Ideal formula(s) Historical trends

Why Use Predictive Analytics for HR?  Strategic Workforce Planning  Forecast staffing mix  Identify future gaps, needs, and opportunities  Succession Planning  Identify retirements and predict turnover  Determine hiring, training, mission critical occupations, and pipeline needs  Maximize Retention, Engagement and Productivity  Top influencers on decisions to stay/leave  What factors lead to increased engagement

What is SMARTHR?  In-house developed tool; released in June 2012 by NIH-OHR  Automates redundant and specialized reporting tasks  Bridges reporting gaps across multiple HR and non-HR systems  Allows for custom-designed logic to incorporate data models and data visualizations  Promotes on-demand customer self-service  Granular security, based on business role and organizational scope  Actionable information for Business, Power, and Leadership users Self-Monitoring Analytics Reporting Tool for Human Resources

SMARTHR Configuration SMARTHR Power User Managers/Leadership Business User SharePoint / ASP.NET SQL Reporting Services Office 365* - Excel Power Query, View, Pivot - Excel SQL Server Data-Mining Add-In Office 365* - Power BI Data-Warehouse Oracle Business Objects Other OLTPs Flat-Files / XML SQL Server Database Engine Analysis Services Integration Services Azure Machine Learning * Actions & Pay Demographics Time and Attendance Training Surveys Other Supplemental Diagnostic Capabilities * CY16 Implementation Detailed Reports High Level Dashboards eOPF Misc. Programs Workflow / USA Staffing * Other Sources Flat-Files SharePoint Insight Foresight Hindsight

Benefits of SMARTHR  Supports Strategic Workforce Planning  Data driven decisions  Automation and streamline reporting  Allows efforts to be concentrated on strategic analysis  Facilitates descriptive, diagnostics, predictive, and prescriptive analytical capabilities  Minimizing human error and data manipulation  No user costs for NIH users  Securing information (SSO)  Supports mobile workforce

Workforce Analytics (WFA) Module Background 10 PURPOSE: Help NIH staff identify workforce trends and projections, to satisfy data requests and facilitate strategic planning. Filterable by Demographics Historical Trends Predictive Statistical Models Integration of Opinion Data Data in Context (Comparisons) Features:

WFA Business Need & Requirements Workforce Analytics Module Survey results/ Needs analyses Customer input - SMARTHR project requests - Stakeholder group requirements Historical HR data requests requirements Industry trends Agency initiatives

WFA Module Benefits  Proactive approach through predictive analytics  Streamlines and standardizes recurring report needs  Integrate quantitative and qualitative data  Additional insight into current data trends and workforce issues.  Fill gaps in strategic human capital planning  Summary level NIH data  Self-service  Dynamic by filter criteria 12 Facilitates Strategic Planning!

WFA Structure Overview 13 Workforce Demographics Onboard Count & Trends Workforce Proportions Supervisory Status Turnover Trends Separation & Accession Rates Employee Satisfaction (Exit Survey & EVS) NIH OHR Employee Survey Results (OHR only) Retirement Models Actual Retirements Retirement Eligibility Adjusted Eligibility Model Demographics Filters

Predictive Power with WFA 14 Are gaps in certain positions, levels, organizations expected? Where will deficits occur, based on the optimal staffing mix for the future? Workforce Demographics – how will the staffing mix change in the future? What recruitment and succession management needs look like? What factors contribute to turnover patterns and how might those change in the future? Turnover Trends – what will staff churn look like in the next three years? How can leadership plan for knowledge transfer and backfill of critical positions? In what ways may the organization change, based on the generational shift of the workforce? Retirement Models – when will critical staff leave the organization?

WFA Demo 15 WFA DEMO DEMO

Future of Workforce Analytics Module 16 Heat Maps Action Planning Targeted Groups Prescriptive Analytics Best Practice Sharing Community Templates Interactive Planning Cross Collaboration/ Community of Practice Data Fields (Compensation, Training, Performance) Social Media Additional Opinion Data Expanded Data Connections Conditional, Scenario-based Modeling Organization-specific Models for More Predictive Power Analysis on Opinion Data (Factor Analysis, Prediction) Dynamic Modeling Combine with Organization-specific Data Enhanced Graphics and Visualizations On-demand Models and Dashboards Power User SMARTHR Workforce Analytics Module SMARTHR Workforce Analytics Module

Future of WFA – Heat Map Example 17 Heat Map  Prescriptive Analytics (Target areas to obtain & maintain optimal workforce) Predictive Models (Future Scenarios, Gap Assessment) Workforce Data (Turnover Trends, Retirement Eligibility) Survey Information (Exit, EVS, Pulse) Heat maps will highlight components of the mission- critical workforce that are at the most risk for turnover based upon survey feedback, historical trends, workforce demographics, and projections.

Contacts Lillian Thomas Analytics Manager HR Systems Analytics & Information Division (SAID) Office of Human Resources (OHR) Office of the Director (OD) National Institutes of Health (NIH) Phone: Luis D. Unda Information Technology Specialist, Technical Lead HR Systems, Analytics & Information Division Office of Human Resources National Institutes of Health Phone:

Questions 19 Thank you!