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Solution
Table of Contents
1.1 Applications of Evidenced -Based Practice to organisations. 2
1.2 Importance of Data to Organisation. 3
Why ensuring data accuracy is necessary. 3
1.3 Types of Data Measurements. 4
1.6 Application of Agreed Policies and Procedures. 5
2.1 How People Professional Creates Value. 6
2.2 Customer-Focused and Standards-Driven. 6
SECTION 1
1.1 Applications of Evidenced -Based Practice to organisations
Evidence-based practice refers to using the best available research and data to guide decisions and ensure the most effective policies, programs and services are developed and implemented as defined by CIPD (2024). At its core, evidence-based practice aims to integrate high-quality research findings with practitioner expertise and other sources of evidence to deliver optimal outcomes.
One of its key strengths is basing choices and actions on objective research rather than assumptions, anecdotes or tradition alone. This helps reduce bias and subjectivity. However, a potential weakness is that not all decisions lend themselves to clear or definitive research evidence. Interpreting data also requires skill to account for situational factors.
Within an organisation, evidence-based practice could guide people function policies and processes. Firstly, regular reviews of metrics related to recruitment, retention, engagement and productivity could identify trends and common pain points (Aarons ET AL. 2019). Surveys, exit interviews and focus groups would provide additional insights from employees. Together, this data could highlight where current approaches are working well and reveal opportunities for change supported by hard facts rather than untested hunches.
Secondly, benchmarking key people metrics against other high-performing companies in periodic studies could expose alternative best practices worth trialling. For instance, if research shows flexible working consistently improves engagement at comparable organisations, a pilot flexible working program might be introduced (Mockett, 2012). Outcomes would then be closely tracked and analyzed to see if it reliably boosts retention, collaboration and productivity as predicted based on existing evidence.
Employing evidence-based practice in this way would help the people function develop strategies, policies and processes grounded in reality rather than assumptions. It also enables continuous learning and improvement by tracking impacts of interventions against initial research-backed hypotheses.
1.2 Importance of Data to Organisation
Houghton and Green (2018) define Data as facts and statistics collected together for reference or analysis. In organisations, data is invaluable for making informed decisions and achieving strategic goals. With the rapid growth of technology, massive amounts of operational and customer data are now generated daily within companies.
When captured and analysed effectively, data provides objective insights that can guide management. Performance metrics reveal what is working well and opportunities for enhancement ( Personio, 2019). Customer profiles and purchasing patterns highlight preferences to adapt products or services. Employee records show productivity, turnover and training needs. Market research feeds new ideas and aids competitive analysis.
By transforming raw numbers into useful information, data equips leadership with the ability to predict trends, understand user behavior, and measure outcomes of initiatives more precisely. It offers a factual basis to assess assumptions and strategies. For functions like sales, marketing, HR and finance, data is central to planning effectively (RIB, 2024). With access to regularly updated insights from data, decisions can be optimized to deliver maximum value for both the business and customers over the long term. Embracing a culture of using quality data improves overall agility, transparency and success of modern organisations.
Why ensuring data accuracy is necessary
For organisations to properly identify and address problems or underperformance, the data used must be accurate and reliable. Inaccurate or incomplete data runs the risk of misdiagnosing issues or masking real problems that need attention as evidenced by Technologies (2024). Determining the root causes and solutions from faulty data will lead decision-making astray. With unreliable information, managers lose the ability to pinpoint strategic or operational challenges based on facts. This undermines credibility in the decision-making process and wastes resources addressing incorrect or irrelevant matters.
Ensuring data quality requires validated collection methods, input verification, and testing for anomalies or inconsistencies. Gaps or illogical values must be identified and corrected. Multiple data points need cross-referencing to surface discrepancies. Data definitions and handling procedures should uphold standardised protocols. Those analyzing data also require training to recognise limitations, question assumptions, and judiciously interpret findings (Data to policy, 2024). Only with accurate inputs can data and analytics truly fulfill their purpose of providing clear visibility into what issues plague business performance.
1.3 Types of Data Measurements
Different types of data measurements used by people professionals include in decision making:
When making decisions, people professionals often rely on nominal data to segment the workforce. Nominal data places employees into categories like department, location or job role without quantitative relationships. While easy to collect and analyse counts of, the lack of rank or amount obscures comparisons. However, nominal data benefits strategic workforce planning by revealing basic headcount needs across different categories.
Ordinal data is frequently used to measure employee perceptions through surveys. Gauging sentiment on scales like “agree-disagree” or “satisfied-dissatisfied” provides ranked feedback but not precise distances (UNSW, 2023). This allows some Before-After comparisons when testing initiatives yet true impacts may be unclear. The benefit is gathering ranked opinions unsuitable for higher metric types.
Interval data has applications in tracking metrics with consistent scoring ranges yet abstract scales. Performance management typically relies on interval ratings that are meaningfully higher or lower yet scores alone do not indicate absolute amounts. While averages and trends are calculable, values remain relatively defined (UNSW, 2023). The plus is permitting benchmarking against defined rubrics despite imprecise magnitudes.
Ratio data yields the most robust insights through quantitative analyses. Turnover rates, productivity, costs-per-hire are all ratio metrics with true zero points, allowing full statistical use. Precise comparisons reveal what really drives outcomes. However, some people data like engagement may be imprecisely measured on ordinal or interval scales instead due to conceptual limitations.
Choosing the right measurement type guides sound decision-making. Recognising each type’s constraint supports objective interpretation while their respective benefits unlock informed choices through contextualized people analytics. In each case, understanding what type of data is being measured guides appropriate analysis and ensures interpretation limitations are respected. This brings analytic objectivity for evidence-based people decisions.
1.6 Application of Agreed Policies and Procedures
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