3CO02 Principles of Analytics explores how people professionals make both simple and complex decisions in their roles. It emphasises the importance of drawing on a wide range of evidence and analytics to improve working practices and support effective, context-specific decision-making that creates value.
You will examine how evidence-based practice shapes measures and outcomes, and how it is applied within your organisation.
You will also explain why data matters, the different types of data and measurement methods, and how these are used to inform decisions. This includes interpreting basic financial information through critical thinking and common calculations.
In addition, you will review what “creating value” means and describe how your organisation delivers value for customers and other key stakeholders.
Finally, you will explore how analytics and technology can enhance working practices, alongside the risks and limitations associated with their use.
Table of Contents
Task One – Written Questions
AC 1.1 Explain what evidence-based practice is and provide two examples of how Company X could apply it.
Defining Evidence-Based Practice
Evidence-based practice (EBP) is a systematic approach to organisational decision-making that prioritises the critical appraisal and integration of the best available evidence drawn from multiple sources. Rather than relying on intuition, tradition, or anecdotal reasoning, EBP requires practitioners to evaluate data rigorously before implementing strategies or interventions (CIPD, 2024). The approach has its roots in the medical profession and has gained significant traction within people management as organisations increasingly recognise that subjective judgement alone produces inconsistent and sometimes counterproductive outcomes.
The CIPD identifies four principal sources of evidence that underpin this approach: scientific literature and peer-reviewed research, organisational data and analytics, practitioner expertise and professional judgement, and stakeholder perspectives including those of employees, managers, and customers (Barends and Rousseau, 2018). The quality of decisions improves when multiple evidence sources are triangulated, as this reduces the risk of bias inherent in any single source.
For people professionals, adopting EBP means questioning assumptions, seeking relevant data before recommending changes, and continuously evaluating the outcomes of implemented interventions against measurable criteria. This demands both analytical competence and intellectual curiosity, alongside a willingness to challenge established organisational norms where evidence suggests alternative approaches would prove more effective.
Example 1: Addressing Overtime Patterns at Blue Mountain Patisserie
Company X could apply evidence-based practice when investigating the overtime patterns observed at its client, Blue Mountain Patisserie. Rather than simply mandating overtime reductions or accepting excessive hours as inevitable, Company X’s people professionals could systematically gather and analyse multiple evidence sources to understand the underlying causes and develop targeted solutions.
Organisational data from payroll records and timesheets would reveal which employees consistently work excessive overtime and during which periods demand peaks occur. Scientific research on the relationship between prolonged working hours and productivity decline, error rates, and employee wellbeing would provide context for understanding the consequences of current patterns (Pencavel, 2015). Stakeholder consultation with Blue Mountain Patisserie’s line managers and employees would surface practical insights regarding workflow bottlenecks, staffing adequacy, and scheduling inefficiencies that quantitative data alone cannot capture.
By triangulating these evidence sources, Company X could present Blue Mountain Patisserie with recommendations grounded in both data and established research rather than speculative assumptions about what might resolve the overtime challenge.
Example 2: Redesigning the Employee Induction Process
A second application involves Company X reviewing its own employee induction procedures using evidence-based principles. If new starter retention data indicates that a disproportionate number of employees depart within their first six months, this organisational evidence signals that the current induction process may be failing to establish adequate engagement and integration.
Company X could supplement this internal data with published research on effective onboarding practices, which consistently demonstrates that structured induction programmes incorporating social integration, role clarity, and early performance feedback significantly improve retention outcomes (Bauer, 2010). Gathering qualitative evidence through structured conversations with recent joiners about their induction experience would identify specific gaps between current practice and best-practice standards.
Professional judgement would then guide the prioritisation and sequencing of improvements, ensuring that changes are practical, resource-appropriate, and aligned with Company X’s organisational culture and operational constraints. The subsequent monitoring of retention metrics following implementation would complete the evidence-based cycle by evaluating intervention effectiveness.
