Unit 4: Data and future of HR analytics
HR Analytics notes · PTU syllabus (MBA 966-26)
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Unit summary
People data is sensitive and growing fast, so quality, ethics and new technology shape the future of HR analytics. This unit covers HR data quality and collection, big data for HR, data transformation and visualisation, ethical issues in HR analytics, AI in HR, and driving insights from HR analytics.
After this unit you can
- Ensure HR data quality and collection
- Use big data, transformation and visualisation in HR
- Address ethical issues in HR analytics
- Explain AI in HR and turn analytics into insights
PTU syllabus topics
- HR data quality and collection
- big data for HR
- data transformation and visualization
- ethical issues in HR analytics
- AI and HR
- driving insights from HR analytics
Consent and transparency
Privacy and data security
Avoiding bias in algorithms
Human review of AI decisions
Use data to help, not punish
Topic 1
HR data quality and collection
- Sources: HRIS, payroll, ATS (applicant tracking), LMS (learning), performance systems, surveys, collaboration tools, exit interviews.
- Quality issues: missing and outdated records, inconsistent job titles, duplicate entries, manual errors, different definitions across units.
- Remedies: data standards and definitions, master data management, validation rules, regular audits, data owners.
Topic 2
Big data for HR
- New data types: collaboration and email metadata (organisational network analysis), badge and sensor data, text from surveys and reviews (Glassdoor), learning platform clickstreams, skills data from profiles.
- Uses: identifying influencers and silos, predicting attrition, skills gap analysis, workplace design, well-being.
Topic 3
Data transformation and visualisation
- Transformation: cleaning, joining data from different systems by employee ID, deriving variables (tenure, time since promotion, pay ratio), anonymising and aggregating.
- Visualisation: headcount pyramids, attrition heat maps by department, funnel charts for recruitment, scatter plots of performance vs pay, network diagrams; tools — Excel, Power BI, Tableau, HRIS dashboards.
Topic 4
Ethical issues in HR analytics
- Issues: privacy and consent, surveillance and monitoring, algorithmic bias in hiring and promotion, transparency of decisions, data security, purpose creep (using data for unintended purposes), impact on trust.
- Law and principles: DPDP Act 2023 (consent, purpose limitation, data minimisation, rights of data principals), anonymisation, aggregate reporting thresholds, human review of automated decisions, ethics committees.
Example
An AI resume-screening tool trained on past hires that were mostly men may downgrade women's resumes — models must be tested for bias before use.
Topic 5
AI and HR
- Applications: resume screening and candidate matching, chatbots for HR queries, interview scheduling, skills inference and career pathing, attrition prediction, personalised learning, sentiment analysis, generative AI for job descriptions and policies.
- Cautions: bias, explainability, candidate experience, legal compliance, human oversight.
Topic 6
Driving insights from HR analytics
- 1
Ask a business-relevant question
- 2
Analyse with suitable methods
- 3
Interpret findings in context
- 4
Tell the story to decision makers
- 5
Recommend specific actions
- 6
Implement and measure impact
- Success factors: executive sponsorship, partnership with business leaders, analytical talent in HR, focus on a few high-impact questions, communication.
Key terms
- ATS
- Applicant tracking system
- Organisational network analysis
- Mapping collaboration patterns among employees
- Purpose creep
- Using data beyond its original purpose
- Algorithmic bias
- Systematic unfairness in automated decisions
- Data principal
- Individual to whom personal data relates (DPDP Act)
Quick revision
- HR data sources; quality issues and remedies.
- Big data in HR; ONA.
- Transformation steps; visualisation types and tools.
- Ethics: privacy, surveillance, bias, transparency; DPDP Act safeguards.
- AI in HR uses and cautions; data-to-action process.
Important exam questions
Practice questions written to the PTU exam pattern for this unit's syllabus: short answers (Section A style) and long answers (Sections B and C style).
Short-answer questions
- Q1.Name four sources of HR data.
- Q2.State two HR data quality problems.
- Q3.What is organisational network analysis?
- Q4.State three ethical issues in HR analytics.
- Q5.Give two applications of AI in HR.
- Q6.What is purpose creep?
Long-answer questions
- Q1.Explain HR data quality and collection.
- Q2.Discuss big data, data transformation and visualisation in HR analytics.
- Q3.Discuss ethical issues in HR analytics and how to address them.
- Q4.Explain the role of AI in HR and how insights are driven from HR analytics.
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