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As a people professional, applying evidence-based practice is critical to making the best people decisions. It means collecting and analysing various forms of data from across the organisation to understand employee and organisational needs as evidenced by ‌ Boatman (2021). This includes metrics like engagement scores, turnover rates, learning and development data, compensation benchmarks, and job performance reviews. It also means staying current on HR trends and what is working at other companies through research. Analysing all of this evidence allows me to propose solutions and strategies that are most likely to improve things like culture, retention, and business results. It creates practices grounded in real organizational data rather than assumptions or anecdotes alone.

Examples of Evidence-based people practice decisions include:

Hiring decision- When recruiting for a sales role, as a people professional, I analyzed previous hiring patterns, top performer profiles, sales data, and market trends. The evidence showed I would been too focused solely on past sales experience versus strengths in communication and adaptability. I updated the job posting and scoring rubric accordingly. The new approach led to hires that fit the evolving needs and exceeded their sales targets in the first year.

Reward decision- Our compensation benchmarking revealed we were below market on bonuses for our customer service team despite their impact on retention. Customer satisfaction metrics also correlated higher bonuses with lower attrition. As people professionals, we redesigned bonuses to incentivise exceptional service rather than headcount. Service quality improved as reflected in fewer customer complaints. Attrition dropped by 15% year-over-year.

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Informed Decision Making- Data provides objective insights into various aspects of the organisation, including employee engagement, productivity, and efficiency. By analysing this data, decision-makers can identify patterns, trends, and areas for improvement. This informed decision-making process helps in devising strategies to enhance organisational culture and performance effectively (Calzon, 2022).

Targeted Interventions- Data allows organisations to pinpoint specific areas that require attention. Whether its addressing employee satisfaction issues, refining processes, or optimising resource allocation, data-driven insights enable targeted interventions (Frankenfield, 2023). This targeted approach ensures that efforts and resources are allocated efficiently, leading to more significant improvements in organisational culture and performance.

Measurable Progress- Data serves as a yardstick for measuring progress towards organisational goals. By setting clear metrics and benchmarks, organisations can track their performance over time as evidenced by Rizwan (2024). This not only facilitates continuous improvement but also enables the identification of successful strategies and areas needing further refinement. Ultimately, data-driven monitoring and evaluation foster a culture of accountability and transparency within the organisation, driving sustained improvements in culture and performance.

Importance of Accurate Data

Accurate data is vital for problem identification as it underpins sound decision-making processes, facilitating effective resolution of issues as evidenced by LinkedIn (2023). Inaccurate data can lead to incorrect assessments, misguided solutions, and wasted resources. Reliable data ensures that organisations address the root causes of problems, rather than treating symptoms. It enhances the credibility of findings, fosters trust among stakeholders, and increases the likelihood of successful interventions. Ultimately, accuracy in data drives informed actions, enabling organisations to resolve issues efficiently and achieve their goals effectively.

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Qualitative data refers to non-numerical data that is collected through open-ended questions, interviews, and observations (Bhat, 2019). It provides insights into people’s experiences, perceptions, opinions, and feelings in a narrative form rather than a numerical form. Qualitative data aims to understand and explain phenomena from participants’ perspectives through themes, meanings, and descriptions rather than statistical analysis or counts.

Two examples of qualitative data are observation notes and exit interview notes as outlined by Eval Community (2023). Observation notes typically record descriptive information about people’s behaviors, actions, activities and interactions in a natural setting through detailed field notes. They provide context and insight. Exit interview notes capture participants’ views and opinions gathered through open-ended questions at the end of a program or event. Both observation notes and exit interview notes are narrative, non-numerical data that help understand experiences and perspectives.

Quantitative data

Quantitative data comprises numerical information obtained from closed-ended questions and experimental methods, tests, and surveys that can be analysed statistically as evidenced by TolaData (2021). It comes in numerical form that can be counted such as numbers, frequencies, percentages and statistical measurements. Quantitative data aims to quantify and generalise results from a sample population through statistical analysis and tests.

Examples of quantitative data are:

Number of employees – This would provide a specific numerical figure to quantify how many people are currently employed within an organisation.

Absence data – Statistics collected on staff absence rates through tracking numbers of sick days taken (CIPD, 2023). This could include numerical figures on absence frequency, duration, reasons and trends over time that can analysed statistically.

Significant in decision making

Qualitative data like observation notes and interviews provides rich insights into the human experience to help design effective programs and policies. Quantitative data such as employee counts and absence rates allows evaluation of initiatives, comparisons over time, and objective workforce planning. Together this forms a comprehensive understanding for evidence-based decision-making – qualitative data gives context while quantitative data measures progress (LinkedIn, 2024). This supports people professionals to make informed choices centered on understanding and improving experiences.

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