- Contact Us: 07001237555; contactcenter@cipmnigeria.org
Menu
| SESSION 01 | From Data to Decisions: The Analytics Mindset |
| This foundational session establishes the analytical mindset, vocabulary, and framework that will underpin all subsequent learning. Participants examine why HR analytics matters, where most HR functions sit on the analytics maturity curve, and how to cultivate the disciplined, question-first thinking that distinguishes genuine analysis from data reporting. |
| Topics and Practical Activities |
| – The business case for people analytics: how data-driven HR functions outperform their peers on retention, quality of hire, and organisational agility |
| – The HR analytics maturity model: descriptive, diagnostic, predictive, and prescriptive analytics (definitions, examples, and organisational benchmarks) |
| – The analytics mindset: moving from ‘what does the data say?’ to ‘what question are we trying to answer?’ |
| – Framing analytical problems: translating vague business challenges into precise, answerable analytical questions |
| – The HR analytics workflow: a disciplined end-to-end process from question definition to action |
| – Common failure modes in HR analytics: reporting bias, vanity metrics, and the perils of data without context |
| – Mapping your analytics landscape: identifying the data assets, tools, and capability gaps in your current HR function |
| SESSION 02 | People Data: Sources, Quality, and Governance |
| Before analysis can begin, practitioners must understand the data they are working with — where it comes from, how reliable it is, what it actually measures, and what ethical and legal obligations govern its use. This session provides a comprehensive map of the HR data ecosystem and equips participants to evaluate, prepare, and govern people data with rigour and responsibility. |
| Topics and Practical Activities |
| – Mapping the HR data ecosystem: HRIS, ATS, LMS, engagement survey platforms, payroll systems, exit interviews, 360 tools, and external labour market data |
| – Data quality dimensions: completeness, consistency, accuracy, timeliness, and fitness-for-purpose — and how to assess them before analysis |
| – Common HR data problems: missing fields, definitional inconsistency, system fragmentation, and self-reporting bias |
| – Data cleaning and preparation fundamentals: structuring HR datasets for analysis using accessible tools (Excel, Google Sheets, or People Analytics platforms) |
| – GDPR, data privacy, and employee rights in the context of people analytics: legal obligations and practical compliance strategies |
| – Data ethics in HR: the difference between what you can measure and what you should measure |
| – Building a data governance framework for the HR function: ownership, stewardship, access controls, and retention policies |
| SESSION 03 | Descriptive and Diagnostic Analytics in Practice |
| This is where the analysis begins. Participants move from theory into practice, working with HR datasets to calculate, interpret, and interrogate the metrics that matter most across the core HR domains. The session focuses on building fluency with descriptive and diagnostic analytic; the foundation of all higher-order analytical work. |
| Topics and Practical Activities |
| – Core HR metrics by domain: talent acquisition (time-to-fill, quality of hire, offer acceptance rate), retention (voluntary attrition, regrettable loss, survival curves), engagement, performance, L&D effectiveness, and diversity |
| – Cohort analysis: tracking how different employee groups behave over time using onboarding success, attrition by tenure band, promotion velocity by demographic |
| – Trend analysis and period-over-period comparison: separating signal from noise in HR time-series data |
| – Benchmarking: using internal comparisons and external market data to contextualise HR performance |
| – Root cause diagnostics: applying the ‘5 Whys’, fishbone analysis, and segmentation techniques to people data to move from symptom to cause |
| – Attrition deep-dive: a worked case study in diagnosing the real drivers of unwanted turnover using a layered analytical approach |
| – Breakout analysis exercise: participants work in small groups to diagnose a workforce challenge using a provided HR dataset, presenting findings to the group |
| SESSION 04 | Data Visualisation and Insight Communication |
| Analysis that cannot be communicated is analysis that cannot drive change. This session equips participants with the design thinking, visual literacy, and storytelling frameworks needed to present HR data in ways that engage senior leaders, compel decisions, and build the credibility of the HR function. The focus is relentlessly practical: participants will build and critique real HR visualisations during the session. |
| Topics and Practical Activities |
| – Choosing the right chart: bar, line, scatter, waterfall, heat map, and treemap when to use each and why the wrong chart undermines your message |
| – Data visualisation design principles: signal-to-noise ratio, colour theory for data, annotation, and the elimination of chart junk |
| – Building HR dashboards: structuring a dashboard for different audiences, operational managers, HRBPs, CHROs, and Boards |
| – Common HR dashboard mistakes and how to correct them: a live critique exercise using real examples |
| – The data storytelling framework: opening with the so-what, building with evidence, and closing with a clear call to action |
| – Structuring an executive HR analytics brief: format, length, language, and the use of data to anchor strategic recommendations |
| SESSION 05 | Predictive Analytics and Workforce Forecasting |
| Predictive analytics represents the frontier of HR’s strategic contribution — the ability to anticipate workforce challenges before they become crises and to offer the business forward-looking intelligence rather than rear-view reporting. This session demystifies predictive analytics for HR practitioners, building conceptual understanding and practical confidence without requiring statistical or coding expertise. |
| Topics and Practical Activities |
| – The shift from descriptive to predictive: why understanding the past is not enough and how predictive models add strategic value |
| – Predictive analytics fundamentals for HR practitioners: regression, classification, clustering, and machine learning concepts explained in plain language |
| – Attrition prediction modelling: identifying the variables that most powerfully predict voluntary turnover — and using that knowledge to intervene earlier |
| – Flight risk scoring: designing and operationalising an early warning system for at-risk talent |
| – Workforce demand forecasting: using internal headcount data, business growth projections, and attrition modelling to forecast future talent needs |
| – Predicting performance and potential: using pre-hire and post-hire data to refine talent identification and early career investment decisions |
| – Critical evaluation of predictive models: understanding confidence intervals, model limitations, and how to communicate predictions responsibly to business leaders |
| SESSION 06 | Building an Analytics-Driven HR Function |
| The final session brings the masterclass learning together into an integrated operating model for the analytics-driven HR function. Participants design a personalised analytics roadmap, explore how to build a data culture within HR and the wider organisation, and develop the stakeholder engagement and change management strategies needed to make analytics stick. The session concludes with each participant completing a capstone Analytics Action Plan. |
| Topics and Practical Activities |
| – Designing an HR metrics framework: distinguishing activity metrics, efficiency metrics, effectiveness metrics, and business impact metrics at each organisational level |
| – Building the HR analytics operating model: roles and responsibilities, technology stack, data architecture, governance, and reporting rhythms |
| – Analytics maturity progression: a phased roadmap from reactive reporting to predictive intelligence over a 12-to-24-month horizon |
| – Building analytical capability within the HR team: data literacy development, upskilling pathways, and the case for a dedicated people analytics resource |
| – Selling analytics to sceptics: strategies for securing executive sponsorship, business unit buy-in, and the budget to invest in analytics infrastructure |
| – Responsible analytics leadership: embedding ethical review, bias auditing, and employee transparency into the HR analytics operating model as permanent practices |
| – The future of people analytics: workforce intelligence platforms, real-time analytics, AI-augmented insight generation, and the evolving role of the HR analyst |
1 CIPM Avenue, Off Obafemi Awolowo Way, Opp. Lagos State Secretariat, Alausa, Ikeja, Lagos
07001237555
