🌍 Case Study: People Analytics Transformation – Energex Nordic AS (Vision 2035)
Theme: Human Intelligence for the Future of Energy
🏢 Company Overview
Aspect | Details |
Company Name | Energex Nordic AS |
Headquarters | Stavanger, Norway |
Emerging Market Operations | Bhubaneswar, Odisha, India |
Sector | Energy – Renewable Generation, Smart Grid, and Hydrogen Solutions |
Founded | 2015 |
Vision 2035 | “Humanizing Energy through Intelligent People Systems.” |
Transformation Program | Project HUMINEX 2035 (Human Intelligence Experience) |
Core Platform | SAP SuccessFactors + SAP Analytics Cloud + SAP BTP AI Services |
🌱 Background
Energex Nordic AS, a leading clean energy and smart grid company, operates across Scandinavia, Central Europe, and South Asia.
As part of its Vision 2035, Energex seeks to become a “Human-Centric Digital Enterprise”, where every decision — from workforce planning to leadership succession — is data-informed, ethically guided, and globally integrated.
However, leadership recognized that while financial analytics were mature, People Analytics was underdeveloped, scattered across spreadsheets, and lacked predictive power.
The HR Transformation Office launched a dedicated initiative to build a Global People Analytics Hub, connecting Norway’s sustainability mindset with India’s digital innovation talent.
🔭 People Analytics Vision 2035
“Data tells us what is happening. People Analytics helps us understand why — and what to do next.”
- Ingrid Solheim, Chief People Officer, Energex Nordic AS
By 2035, Energex Nordic aims to:
- Build an AI-powered HR Intelligence Platform that predicts workforce trends
- Integrate sustainability (ESG) and human performance metrics
- Foster a borderless talent ecosystem linking Nordic innovation with Indian delivery centers
- Enable data literacy across all HR and business leaders
🧩 Strategic Objectives
Focus Area | Description | Target Outcome by 2035 |
Unified Data Foundation | Integrate HR, Payroll, Learning, and ESG data | 100% workforce visibility |
Predictive Analytics | Predict attrition, mobility, and skill trends | 85% prediction accuracy |
Workforce Planning | Simulate future skill supply vs. demand | Workforce balance across 5 regions |
Sustainability & DEI Analytics | Embed ESG metrics into People dashboards | ESG-linked KPIs in 100% reviews |
Data Culture & Literacy | Upskill managers in data interpretation | 80% HR and business users data-trained |
⚙️ Agile Implementation (People Analytics Hub)
(Each sprint = 2 weeks; 6 sprints for pilot → global rollout)
Epic / Theme | User Story | As a (Role) | I want to... | So that I can... (Business Value) | Acceptance Criteria | Sprint |
Data Integration | HR Data Model Setup | HRIS Analyst | integrate EC + Payroll + ESG + Learning data | create a unified workforce data foundation | Model validated; APIs connected to SAP BTP | Sprint 1 |
Data Governance | Define KPIs & Data Quality Rules | HR Data Steward | create HR data standards | ensure integrity and consistency across regions | Data dictionary published; audit compliance achieved | Sprint 1 |
Descriptive Analytics | Diversity & Headcount Dashboard | HRBP | visualize workforce mix and diversity | support DEI strategy for Nordic–India teams | Dashboard live in SAC; updated daily | Sprint 2 |
Predictive Analytics | Attrition Risk Model | Data Scientist | train ML model using BTP AI Core | predict likely attrition 3 months in advance | Model accuracy ≥85%; CHRO dashboard live | Sprint 3 |
Skills & Learning Analytics | Skill Gap Heatmap | L&D Head | identify skill readiness for hydrogen & AI projects | align training investments | Heatmap available by BU & skill cluster | Sprint 3 |
Workforce Planning Simulator | “What-if” Scenarios | HR Analytics Lead | forecast manpower needs | plan for renewable & hydrogen expansions | Simulator runs 3 scenarios successfully | Sprint 4 |
Executive Dashboards | Global People KPI Suite | CHRO | view talent, engagement, diversity & sustainability KPIs | make informed board-level decisions | Dashboard published in SAC; mobile-ready | Sprint 5 |
Self-Service Analytics | Line Manager Portal | Business Manager | access my team’s analytics on-demand | make faster data-driven decisions | Role-based access live; usage >80% | Sprint 5 |
Ethical AI & Transparency | Explainable AI Layer | Compliance Officer | audit decisions of predictive models | ensure ethical & unbiased analytics | Explainability report integrated in SAC | Sprint 6 |
Capability Building | Data Literacy Training | L&D Team | train HR & business users | sustain adoption and critical thinking | 100 participants trained; NPS ≥4.5/5 | Sprint 6 |
🧭 Architecture Overview (Simplified)
Layers:
- Data Source: SAP SuccessFactors (EC, Learning, Compensation), Payroll, ESG tools
- Data Integration: SAP Integration Suite + SAP BTP Data Services
- Analytics & AI Layer: SAP Analytics Cloud + BTP AI Core + Predictive Planning
- Experience Layer: CHRO Dashboard, Line Manager Portal, Mobile App
- Governance Layer: Data Catalog, GDPR + EU Ethics Framework
📊 Key Metrics & Dashboards
Dashboard Name | Purpose | Key Metrics |
Global Workforce Overview | Operational visibility | Headcount, turnover, tenure, gender balance |
Predictive Attrition | Retention insights | Risk score, top 5 churn drivers |
Skills & Learning ROI | Development analytics | Skill readiness %, Learning ROI |
ESG & People Sustainability | Link HR to ESG | Employee emissions index, diversity impact |
Leadership Pipeline | Future readiness | Bench strength %, internal mobility |
🌏 Collaboration Model
Region | Role | Focus Area |
Norway HQ (Stavanger) | HR Strategy, Governance, Ethics | Define vision, ethics, and sustainability KPIs |
India (Odisha Delivery Hub) | Data Science, HRIS Operations | Build and run analytics models and dashboards |
Germany R&D Center | AI Algorithms, Predictive Models | Develop forecasting and optimization tools |
Global HR COE | Change Management & Adoption | Data literacy, continuous improvement |
💡 Outcomes (by 2030)
- 25% attrition reduction in renewable engineering talent
- 35% faster decision-making for project staffing
- 40% improvement in diversity hiring in emerging markets
- Predictive model accuracy ≥85% for workforce risk scenarios
- Workforce carbon footprint reporting integrated into ESG dashboard
🧱 Business Impact Summary
Dimension | Impact |
Efficiency | Unified global HR data; eliminated 22 legacy reports |
Predictive Power | Early attrition alerts; skill shortage forecasts |
Culture | Shift from reactive HR to insight-driven HR |
Governance | GDPR-compliant, ethical AI models validated |
Sustainability | People metrics linked to SDG 8 (Decent Work) and SDG 13 (Climate Action) |
🎓 For MBA Presentations
Case Title:
“Energex Nordic AS: Architecting People Analytics for a Human-Centric Energy Future.”
Suggested Student Deliverables:
- 10-minute presentation + 2-minute Q&A
- Cover: problem framing, analytics roadmap, ethical implications, and business value
- Tools reference: SAP SuccessFactors, SAP Analytics Cloud, SAP BTP
🎓 Top 5 Deep Thinking Questions for MBA Students (Digital Transformation & People Analytics Theme)
# | Question | Core Thinking Dimension | Expected Presentation Focus |
1 | 💡 In a world where AI predicts every career move, how do humans still find purpose at work? | Purpose, Motivation, Ethics | Explore the psychology of purpose vs. automation; propose frameworks for meaning-making in AI-driven workplaces. |
2 | 🌍 If sustainability is everyone’s job, how can HR truly measure the “carbon footprint” of people decisions? | ESG, HR Metrics, Systems Thinking | Students analyze HR’s hidden impact on sustainability (e.g., travel, turnover, digital carbon use) and design a People-ESG dashboard. |
3 | 🧠 What happens when predictive analytics starts deciding who gets promoted — and who doesn’t? | Ethics, Data Governance, Organizational Justice | Debate the balance between AI objectivity and human judgment; propose ethical frameworks for AI in performance management. |
4 | ⚡ Can a public-sector energy company become a “people-first digital enterprise” without losing its social mission? | Change Leadership, Public Value, Strategy | Students explore the paradox of digital efficiency vs. social inclusivity; propose transformation models for state-owned enterprises. |
5 | 🪞 If “skills are the new currency,” what happens to loyalty and culture when people constantly reskill and move on? | Talent Mobility, Culture, Economics of Skills | Analyze the cultural consequences of gigification; propose HR strategies to sustain belonging in fluid organizations. |
🧩 Guidelines for Student Presentations
Duration: 10–12 minutes per team
Structure:
- Problem Understanding (2 min) – Frame the question and why it matters now
- Deep Analysis (4 min) – Discuss frameworks, models, or theories (e.g., Maslow, Herzberg, McKinsey 7S, or ESG models)
- Case or Example (3 min) – Cite real organizations or data
- Your Recommendation (2–3 min) – Present your original insight or model
Evaluation Criteria:
Dimension | Weight |
Depth of Thought | 30% |
Originality of Insight | 25% |
Application to Real-World Context | 20% |
Clarity & Storytelling | 15% |
Visual Creativity | 10% |
🎯 Suggested Titles for Student Teams
To spark creativity, each team can give their presentation a thematic title:
- “The Algorithm vs. The Soul”
- “Carbon HR: The Hidden Footprint”
- “Ethical AI: The Invisible Boss”
- “From Coal to Code: Humanizing Energy”
- “The Loyalty Paradox in the Skills Economy”
Tagline:
“From Data to Decisions to Purpose — Energex Nordic is redefining how the world powers its people.”
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