Assignment #5A & #5B

Running Head: WORK ANALYTICS REVIEW 1

WORK ANALYTICS REVIEW 2

Data Analytics Best Practices

Alex Jean

Arizona State University

CON 598

06/14/2021

Data Analytics Best Practices Review

Data analytics is identified as being a developed set associated with the data analysis types of tools and even metrics for the identified comprehensive workforce associated performance measurement and even enhancement. People analytics, sometimes referred to as HR analytics, encompasses workforce analytics software. People analytics is a wide word that relates to all elements of hiring and managing personnel, whereas workforce analytics is frequently more strictly focused on workforce-planning difficulties. It is used in the analysis of the recruitment as well as staffing, training and even development aspects, compensation and other benefits or standards. This is vital in the overall company success along with efficiency and effectiveness. Workforce analytics is vital in achievement of success in the evaluation as well as monitoring of different aspects of a project and in changing where necessary (Labbe, 2019). 

Pros

Workforce associated analytics is vital to an organization due to numerous aspects. Data analytics software may assist business executives in developing and improving recruitment processes, making hiring decisions, and retaining the best employees. It is essential in making the company to be smarter as well as highly strategic and even highly informed types of decision talents. With the identified data analytics, it is highly possible for the organizations to find the most appropriate work choices along with making smarter workforce types of decisions and in the increment of employee associated performance and even retention. It is also essential in facilitating the application of a sophisticated type of data science along with machine learning to assist companies in the most efficient and even effective management of the work associated practices (Ozcan & Linhart, 2017).

The data associated analytics are vital in giving companies the most appropriate options for the viewing along with the comprehension and even acting on the workforce data across the whole workforce associated lifecycle. Workforce analytics aids in data optimization by gathering, organizing, and translating data into useful human resource decision-making information. This is in relation to the interactive form of data visualization linked program, which often provides company executives and project managers with comprehensive information about their numerous responsibilities in the workforce plans. It also helps in offering the most appropriate platform for planning and in the creation along with the management and execution of the entire plan over a wide range of analyzed aspects (Vengatesan et al., 2020).

Trends in Workforce Analytics

There are some trends that are usually involved in the workforce associated analytics. First and foremost, there is the from one time to the actual time. Most of the data associated analytics efforts usually begin as a form of the consultancy associated project. Currently, it has become an actual time type of process which entails people being interviewed as well as data collected. However, from people-related analytics to recognized workforce-related analytics, another important factor is explored. More and more, the identifiable workforce is seen to be made up of more than just persons. Robots along with the chatbots are now getting into the identified Workforce (Fadler & Legner, 2020).

There is also increment in the level of transparency. The overview associated with the identified workforce analytics trends is one that cannot be considered to be complete without having a reference to the identified GDPR. GDPR is linked to a number of good developments, one of which is regarded to be a high level of openness. In terms of what types of data are regarded to be gathered, how they are used, and how algorithms are used to make the best decisions for individuals. However, it is vital to remember that there are some drawbacks to it. GDPR has been associated with the creation of high level of uncertainty regarding the kind along with the extent associated with data processing which is considered as being acceptable. Firms also have been associated with putting a wide range of measures to help in granting access to data. Employees can be associated with erasing their identified data and firms are needed to make processes which help them in doing so (Ranjeeth et al., 2019).

Lessons learnt

Since the specified capacity to locate current employees is thought to be a constraint to development, the specified selection criteria are generally decreased because most people are needed quickly. These individuals are not thought to be as productive as the previously recognized team. It is critical to improve the productivity of coworkers by utilizing the information and skills learned (Dinis et al., 2020). Talent is intimately tied to delivering corporate value. From Work Analytics, leaders can ensure that the right people are in the appropriate position to address the most pressing organizational issues. Work Analytics assists the firm in becoming more strategic; data aids in the resolution of current difficulties as well as the better planning of future operations.

References

Labbe, P. (2019). Hands-on business intelligence with Qlik Sense: Implement self-service data analytics with insights and guidance from Qlik Sense experts. https://books.google.co.ke/books?id=NnCLDwAAQBAJ&printsec=frontcover&dq=Work+Analytics&hl=en&sa=X&redir_esc=y#v=onepage&q=Work%20Analytics&f=false

Ozcan, Y. A., & Linhart, H. A. (2017). Analytics and decision support in health care operations management: History, diagnosis, and empirical foundations. https://books.google.co.ke/books?id=vDxGDgAAQBAJ&printsec=frontcover&dq=Work+Analytics&hl=en&sa=X&redir_esc=y#v=onepage&q=Work%20Analytics&f=false

Dinis, D., Teixeira, Â. P., & Barbosa-Póvoa, A. (2020). ForeSim-BI: A predictive analytics decision support tool for capacity planning. Decision Support Systems, 131, 113266. https://www.sciencedirect.com/science/article/pii/S016792362030021X

Fadler, M., & Legner, C. (2020). Building Business Intelligence & Analytics Capabilities-A Work System Perspective. https://aisel.aisnet.org/icis2020/governance_is/governance_is/14/

Kirsh, I., & Joy, M. (2020, June). Splitting the web analytics atom: from page metrics and KPIs to sub-page metrics and KPIs. In Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics (pp. 33-43). https://dl.acm.org/doi/abs/10.1145/3405962.3405984

Ranjeeth, S., Latchoumi, T. P., & Paul, P. V. (2020). A survey on predictive models of learning analytics. Procedia Computer Science, 167, 37-46. https://www.sciencedirect.com/science/article/pii/S1877050920306451