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Using People Analytics for a Sustainable Remote Workforce
This document discusses using people analytics for a sustainable remote workforce. It outlines how people analytics can help frontline managers with decision making, monitoring operations, productivity, safety and recruitment using metrics from applicant tracking systems. It also discusses the challenges faced by Chief Data Science Officers in areas like data wrangling, mature AI operations and building skilled teams. Emerging technologies around hyperautomation, internet of behaviors and total experience will facilitate real-time analytics and seamless workflows across HR and collaboration tools to drive business outcomes.
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Using People Analytics for a Sustainable Remote Workforce
- 1. Using People Analyticsfor a Sustainable Remote Workforce Mar 17, 2021 Shrikant Pattathil President Harbinger Systems
- 2. ─ Understand marketshifts in People Analytics and the Future of Work ─ Learn about the challenges faced by Chief Data Science Officers(CDSO) ─ Bridge the data chasm between HRTech and WorkTech applications ─ Leverage emerging technology and design trends to deliver better analytics By the End of this Session, You Will be Able To…
- 3. Market Shifts in PeopleAnalytics and the Future of Work
- 4. • Create aCulture and Strategy around data • Focus on outcomes as opposed to record keeping Features of a Data Driven Org.
- 5. Data Analytics forFront Line Managers • Better HR decision making • Using data to monitor operations Productivity Employee Safety, Employee Wellbeing Recruitment
- 6. Workplace Analytics MaturityModel Level 1 – Operational Reporting Level 2 – Advanced Reporting Level 3 – Advanced Analytics Level 4 - Predictive Analytics ATS Talent Learning Payroll HRIS Emp. Engagement … n
- 7. Example – Metricsin Applicant Tracking Time to fill Time to hire Source of hire First-year attrition Quality of hire Cost per hire Application completion rate Vacancy rate Fill rate Applicants per hire Qualified candidates per hire Time in workflow step Pass- through/Convers ion rate Reach for hire Yield ratio Source quality Offer acceptance rate Hired to goal Candidate Net Promoter Score - Most of metrics (12 out of 19) are primarily dependent on data generated by ATS - Some dependency on Job Boards, HRIS(TM) and Payroll
- 8. Challenges faced by ChiefData Science Officers
- 9. Relevant CDSO HotButtons Data Wrangling (Scalable ETL) Mature AI Ops (Lab to Production) Skilled Team (Diverse Skills – AI/ML, Design, Cloud, Mobile) CHRO Expectations From Data • Predicting workforce availability • Guiding whether to hire new or reskill existing workforce • Effectively moving to a remote work model that is productive • Need for faster AI-driven processing of diverse data type (like raw data, images, videos) And more…
- 10. Problems and Solutions DataWrangling Build an ETL or ELT(s) strategy – work with structured and unstructured data Mature AI Ops Update and scale AI components and ML models from prototypes to production cloud apps Skilled Team Form a team of data scientists, data engineers, cloud engineers and UI designers
- 11. The Data Chasm- HRTech and WorkTech
- 12. WorkTech- Span ofData Sources is Multiplying Productivity Collaboration • Faster action on HR tasks • Nudge Learning • Distributed workforce management HRTech • Measuring Learning and Training Effectiveness • Correlating Productivity with Engagement Workday & Salesforce Partnership for better productivity, and back to work solution Microsoft & Workday Partnership for Teams and Azure integration Harbinger’s WorkTech View
- 13. Focus on BusinessOutcomes HRIS Systems Drive Outcomes (guided decisions) Integrations Data Warehouse Data Lakes Increase Efficiency (speed) Increase Effectiveness (cost realizations) Recruitment Systems Productivity Systems Collaboration Systems …
- 14.
- 15. Tech Trends ImpactingFuture of Work Hyperautomation Internet of Behaviors Total Experience
- 16. • People Analyticswill be a key requirement for organizations focused on data-driven decision making • People Analytics should extend beyond HR platforms to Collaboration and Productivity tools • CDSO Pain points (scalable ETL, lab to production, skilled team) • Emerging technologies like Hyperautomation, Total Experience and Internet of Behaviors will facilitate real-time data analytics, seamless workflows and rich user experience Takeaways
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