CIPD 3CO02 Assignment Example | Principles of Analytics

CIPD 3CO02 Assignment Example | Principles of Analytics

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CIPD 3CO02 Principles of Analytics

Explain what Evidenced-based practice is and how it might be applied within an organisation. (AC 1.1)

principles of analytics

Evidence-based practise refers to the different ways an organisation may make choices supported by evidence and professional practise ((Gill 2018). The organisation’s use of evidence-based practise means depending on the following sources of evidence: (CIPD 2021).

       Data gathered by an organisation may utilise data acquired from diverse stakeholders (CIPD 2021).The data acquired could serve as a basis for assessing and assisting learning needs. For instance, a corporation could acquire data from customers evaluating their level of customer service to determine the employees’ skill and potential. 

 

       Scientific research/literature: this category comprises all data and research results published in academic journals and serve as key evidence. For instance, if a corporation needs to address the problem of low employee engagement or turnover, various journal publications on the topic may provide advice on the suitable technique.

 

       Expert opinion: the organisation may rely on data from internal or external professionals. These experts include managers, business leaders, and consultants who have access to vast volumes of data and can use it to make smart decisions (CIPD 2021a).  

Explain the importance of using data in organisations and why there is the need to ensure that data is accurate when determining problems and issues (AC 1.2).

3CO02 Assignment Example

 

 

 

The data that should be used in the organisation should be timely, accurate, and ethical.

Timely Data: As the term indicates, timely data relates to how current the information is. Important data quality characteristics include the timeliness of information, since out-of-date data may lead to individuals making poor judgments and subsequently financial and reputational harm ( Bhagwan, Grobbelaar & Bam, 2018).

 

Data Accuracy: Data Accuracy is crucial because wrong data leads to inaccurate forecasts. If the expected consequences are inaccurate, time, money, and resources are squandered. Accurate data improves decision-making confidence, boosts productivity, efficiency, and marketing, and reduces expenses (Bhagwan, Grobbelaar & Bam, 2018).

Ethical Data: The key ethical and moral dilemma surrounding the sharing of data is whether your activities respect the privacy of an individual or group. When managing data, an analyst’s major concern should be how the data is being used in the organisation, since the improper use of data might harm whole communities or demographics (Bhagwan, Grobbelaar & Bam, 2018).

Explain the different types of quantitative and qualitative data measurements that people professionals use (AC 1.3).

In measuring data for informed decision making in a medium-size Enterprise, factual data provides a better insight into the current problems and issues (Vidgen, Shaw & Grant, 2017)

Taking the data from the opinion and perspective of different stakeholders through qualitative data gives a better depth of the quantitative process. This indicates the benefit of applying a mixed process of measuring data.

3CO02 assignment example

Integrating the fragmented pieces of data and knowledge and understanding could be achieved. This makes the propensity of primary data collection methods through surveys, interviews, observations, feedback and statistical data more important. In the workplace or HR analytics, the people analytics process is enabled by technology to apply statistical methods to interpret people data in the HR process by keeping human capital, HR systems, and organizational performance in mind.

 

*Examples of information are used by professionals:

– Employees contacts.

– Investigation record.

– Time and attendance.

 

Explain the different types of quantitative and qualitative data measurements that people professionals use (AC 1.3).

3CO02 assignment Example

Qualitative data is more descriptive than numeric. Unlike quantitative data, it is less concrete and can be easily measured. 

Surveys are the ways that help to gather information from the stakeholders through either qualitative or quantitative data (Tripathi et al. 2018). Both qualitative and quantitative data can be used in researches. 

While understanding that mixed methods encompass collecting and analysing quantitative and qualitative studies, they are helpful as they enable to complement the data from both methods. The weakness of the quantitative is complemented by that of the qualitative and vice versa (Arunachalam, Kumar &Kawalek, 2018). They can be applied when an organisation wants to collect baseline data in quantitative to form basis for qualitative data collection and analysis and hence better decision making.

 

Other 3CO02 Principles of Analysis Topics

Explain how the application of agreed policies and procedures inform decisions (AC 1.6).

Explain how people professionals create value for people, organisations and wider stakeholders. (AC 2.1).

Summarise the ways in which you can be customer-focused, and standards-driven in your own context (AC 2.2).

For department’s C and D, present each age range as a percentage of the total in each department (AC 1.4).

Present your findings using two different diagrammatic forms so it can be easily understood by end users and from analysis of the findings, comment on any issues that might be revealed in the data and recommend potential solutions (AC 1.5).

 

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References 

 Arunachalam, D., Kumar, N., &Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416-436. Retreived on 23 May 2022 from:https://eprints.whiterose.ac.uk/143071/1/Final%20accepted%20Manuscript%20-%20TRE.pdf

 Bhagwan, V., Grobbelaar, S. S., & Bam, W. G. (2018). A systematic review of the due diligence stage of mergers and acquisitions: Towards a conceptual framework. South African Journal of industrial engineering, 29(3), 217-234. Retrieved on 23 May 2022 from:http://www.scielo.org.za/pdf/sajie/v29n3/18.pdf

 Boswell, C., & Cannon, S. (2022). Introduction to nursing research: Incorporating evidence-based practice. Jones & Bartlett Learning. Retrieved on 23 May 2022 from:https://radioportal.ru/sites/default/files/pdf-introduction-to-nursing-research-incorporating-evidence-based-pr-carol-boswell-sharon-cannon-pdf-download-free-book-2f719e2.pdf

 Cairney, P., & Oliver, K. (2017). Evidence-based policymaking is not like evidence-based medicine, so how far should you go to bridge the divide between evidence and policy?. Health research policy and systems, 15(1), 1-11. Retrieved on 23 May 2022 from:https://health-policy-systems.biomedcentral.com/articles/10.1186/s12961-017-0192-x

 Elsaid, A., Salem, R., & Abdul-Kader, H. (2017). Research Article A Dynamic Stakeholder Classification and Prioritization Based on Hybrid Rough-fuzzy Method. Retrieved on 23 May 2022 from:https://www.researchgate.net/profile/Rashed-Salem/publication/315109747_A_Dynamic_Stakeholder_Classification_and_Prioritization_Based_on_Hybrid_Rough-fuzzy_Method/links/599cbc23aca272dff12bd63b/A-Dynamic-Stakeholder-Classification-and-Prioritization-Based-on-Hybrid-Rough-fuzzy-Method.pdf

 Gill, C. (2018). Don’t know, don’t care: An exploration of evidence-based knowledge and practice in human resource management. Human Resource Management Review, 28(2), 103-115. Retrieved on 23 May 2022 from:https://www.westminster.ac.uk/sites/default/public-files/general-documents/HRMR_Gill.pdf

 Hale, R., Norgate, C., & Traeger, J. (2018). From nurturing the H in HR to developing the D in OD–systemic benefits where action learning and organizational development combine. Action Learning: Research and Practice, 15(2), 154-167. Retrieved on 23 May 2022 from:https://www.researchgate.net/profile/James-Traeger/publication/324902751_From_nurturing_the_H_in_HR_to_developing_the_D_in_OD_-_systemic_benefits_where_action_learning_and_organisational_development_combine/links/5b452800aca272dc385f4eb1/From-nurturing-the-H-in-HR-to-developing-the-D-in-OD-systemic-benefits-where-action-learning-and-organisational-development-combine.pdf

 Lim, C., Kim, K. H., Kim, M. J., Heo, J. Y., Kim, K. J., & Maglio, P. P. (2018). A nine-factor framework for data-based value creation in information-intensive services from data to value. International Journal of Information Management, 39, 121-135. Retrieved on 23 May 2022 from:https://www.sciencedirect.com/science/article/pii/S0268401217300816

 Mahmud, A., Ding, D., & Hasan, M. M. (2021). Corporate social responsibility: Business responses to Coronavirus (COVID-19) pandemic. SAGE Open, 11(1), 2158244020988710. Accessed on 23 May from:https://journals.sagepub.com/doi/pdf/10.1177/2158244020988710

 Maillo, J., Ramírez, S., Triguero, I., & Herrera, F. (2017). CNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data. Knowledge-Based Systems, 117, 3-15. Retrieved on 23 May 2022 from:https://nottingham-repository.worktribe.com/index.php/preview/795302/acceptedVersion.pdf

 Markauskaite, L., & Goodyear, P. (2017). Epistemic fluency and professional education. Dordrecht: Springer. Retrieved on 23 May 2022 from:https://www.researchgate.net/profile/Lina-Markauskaite/publication/281509968_Epistemic_fluency_and_professional_education_innovation_knowledgeable_action_and_actionable_knowledge/links/61d3f763b8305f7c4b1ef4f9/Epistemic-fluency-and-professional-education-innovation-knowledgeable-action-and-actionable-knowledge.pdf

 Naumov, N. (2019). The impact of robots, artificial intelligence, and service automation on service quality and service experience in hospitality. In Robots, artificial intelligence, and service automation in travel, tourism and hospitality. Emerald publishing limited. Retrieved on 23 May 2022 from:http://nectar.northampton.ac.uk/13346/1/Naumov_Nikola_EP_2019_The_impact_of_robots_artificial_intelligence_and_service_automation_on_service_quality_in_hospitality.pdf

 Song, Q., Wang, Y., Chen, Y., Benitez, J., & Hu, J. (2019). Impact of the usage of social media in the workplace on team and employee performance. Information & Management, 56(8), 103160.

 Tripathi, N., Klotins, E., Prikladnicki, R., Oivo, M., Pompermaier, L. B., Kudakacheril, A. S., … &Gorschek, T. (2018). Anatomy of requirements engineering in software startups using multi-vocal literature and case survey. Journal of Systems and Software, 146, 130-151. Retrieved on 23 May 2022 from:http://jultika.oulu.fi/files/nbnfi-fe201901162270.pdf

 Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626-639. Retrieved on 23 May 2022 from:https://eprints.bbk.ac.uk/id/eprint/19847/1/EJOR%20analytics%20value%20creation%20resub2%20Feb%202017%20FINAL.pdf

 Williams, S. C., Shaw, N., & Allen, D. (2017). A customer feedback model for service performance improvement: Preliminary case study results. Retrieved on 23 May 2022 from:https://eprints.whiterose.ac.uk/118777/

 Hoboubi, N., Choobineh, A., Ghanavati, F. K., Keshavarzi, S., & Hosseini, A. A. (2017). The impact of job stress and job satisfaction on workforce productivity in the Iranian petrochemical industry. Safety and health at work, 8(1), 67-71. Retrieved on 23 May 2022 from:https://www.sciencedirect.com/science/article/pii/S2093791116300841

 Osborne, S., & Hammoud, M. S. (2017). Effective employee engagement in the workplace. International Journal of Applied Management and Technology, 16(1), 4. Retrieved on 23 May 2022 from:https://scholarworks.waldenu.edu/cgi/viewcontent.cgi?article=1239&context=ijamt

 Van der Walt, F. (2018). Workplace spirituality, work engagement and thriving at work. SA Journal of Industrial Psychology, 44(1), 1-10. Retrieved on 23 May 2022 from:http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S2071-07632018000100004

 

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