Intellectually Disability
People analytics can be defined as deep memory-based and determined of studying people. People analytics study people on the basis of operation, purpose, challenges and moments at work (DiClaudio, 2019). All these observations elevate the system and achieves sustainable business success. People analytics is also known as HR analytics or talent analytics. With the other data interpretation techniques and statistics applications, gathering and assessing people analytics leads to better decision making. But there are some challenges related to people analytics faced by every HR and the company. This report will focus on two such challenges faced while performing data analytics and will also discuss about its solution and will also discuss about its practitioners need to overcome these challenges (Gelbard, 2018).
Over the past decades, employees want to experience meaningful, digital and deeply personalized consumer-grade experience at workplace. This simply means operating a product with little or no training. In other words, if someone is using an application, the individual opens the box, download the app and by pressing one button, it starts working. This is the reason why human resource professionals recognizes employee experience by themselves (Plaskoff, 2017). Without people analytics, one cannot measure employee experience personally and no improvement will take place (Khan & Millner, 2020). But there are some challenges which is faced by human resource professionals while performing people analytics. The two challenges that will be discussed in this report are bringing together all the data collected from different places and taking ‘human’ out of human resource.
Today, there are data-driven organizations everywhere. An organization receives data and information of every event, incident and interaction that take place in an organization on daily basis. This much amount of data leaves analysts with thousands of interlocking data set. With so much data available, it becomes very difficult to dig down and access the important data, which could be challenging (Belizón, 2022). There are many outdated data that can make a negative impact on decision-making and it also becomes impossible to gain real-time insights on what is happening. For solving this problem, there is a need for a data system that automatically collects data and organizes information. In addition, overburdened employees may not fully analyze all the data, which can later cause trouble to the organization. Collection of data from multiple disjoint sources can be very confusing as it always leads to incomplete and inaccurate analysis. On the other hand, combing data manually can be time-consuming and it can also limit understanding to what can be viewed easily. So, while collecting data, there should be a clear and centralized system so that employees can have all types of information at one location (Lengnick-Hall, 2018). This will free up time spent in accessing multiple sources for data as well as it will allow easy cross-comparison and ensures that the collected data is completed. This is the only way to overcome the challenge faced by bringing together all the data at one location from different source. Picking up data from different sources can be headache for human resources as well. However, some human resource sync one application to another to transfer data from different sources but this can be time consuming, error can occur, and can result in incomplete human resource data. There are many applications and systems which don’t communicate with each-other and there are chances that they don’t transfer data back and forth with each-other. This will lead to not getting the full notion of what is going on in the organization (Rex, 2020). In addition, this will heckle the human resource in making the most meaning-full decision in a very graceful and timely manner. This will obviously bother the manager. Managers and human resource can avoid this problem by collecting data from one or two sources only. Human resource and manager can take the use of HR tools and database and can insure one-click synchronization. For visualization and analytics, they can even take the help of ready-to-use apps and templates.
There is no doubt that people are now living in a time, where almost everything is possible to achieve, be it any big milestone. Getting a clear image of the vast galaxies around the universe with the help James Webb telescope is a huge leap of faith for mankind. With the help of new technologies and artificial intelligence, the lives of people are getting much easier with every passing day. From the smallest task of packaging a food item to building an entire car, AI has made the lives of people much better or it will be safe to say much easier than ever before (Nica, 219). But as there are two sides to the coin. No matter how many benefits artificial intelligence is providing to mankind, there are multiple things it is taking from humans, as slowly artificial intelligence is getting into the daily lives of the people. If to describe more briefly, one of the examples that can be considered to explain this situation is the human resource management. There are many people around the world who are jobless for a certain period of time. And the major possibility of being job-less can be the high dependency of people on artificial intelligence. In addition due to the high dependency of humans on the artificial intelligence, it cannot be denied that people will eventually become less productive and will soon start having the lack of creativity. The motive of every head of a company or any organization is clear, the employees who are willing to come aboard should be having enough experience and knowledge about the work of the company. Only if the person is perfect for the job, he/she will be selected by HR’s. But as HR are humans and might unintentionally ignore some drawbacks of a person they are interviewing. It might not be good option for the organization in the long run. To eliminate these possibilities, there are some decisions that are being taken in some renowned organizations which include taking the ‘human’ out of human resource in the name of people analytics. As the name suggests, the meaning is obvious, to replace the human resource with artificial intelligence. Organizations are using artificial intelligence technology to select employees in order to make sure that the employees who will be selected will be having the required skills to become an asset to the organization. Besides this, artificial intelligence will also do wonders for the other problems of the organization, where it will take care of the needs and requirements of the organization, hence it will lead to an increase in the quantity and quality of the productivity of work. This particular instance is getting fit into the list of limitations in people analytics with its characteristics. It might be an important and necessary step from the organization’s perspective but is not from the human resource-related entities. This particular limitation is something that will not be a good sign for the human resource sector by looking into its characteristics.
Hence, it becomes necessary to find solutions for this limitation, and to do so, there are certain solutions that can be taken into account to solve this problem. The most impactful and effective way to solve this problem is the collaboration between human resource management and artificial intelligence. Every organization’s main objective is to increase productivity and keep the quality at a balanced state. When an organization take the decision to encourage the collaboration of AI and human resources it will bring an ample number of benefits to the organization (Creese, 2020). The first benefit that will be visible is innovative changes in the organization. The mutual strategies of the human resource and AI will bring many positive changes in the organization that will increase the quality of the organization and ultimately will be seen as more than just an organization. Besides this, AI will surely conduct its traditional responsibility of reducing and in some cases eliminating the workload of humans. This will allow the human resource to continue their work and focus on more of the creative and fundamental aspects of the work or issues. In addition, humans can also take the help of the AI to detect the solutions to complex difficulties that are arising in the organization, which will be much easier for the AI reason being their vast experience and knowledge. By looking into these reasons, it will be safe to say that the collaboration between artificial intelligence and human resource will definitely take the organization towards its best form and will help it at every stage of difficulties.
This report attempts to conclude that challenges in people analytics can be overcome by some studies and concepts. This report focuses on two major challenges faced in people analytics and how human resource and managers can work to come over it. The first challenge discussed in the above report was collecting data from different sources in which initially the negative impacts were discussed. Following this, the solutions for this particular limitation were also mentioned with the help of supporting evidences (Tursunbayeva, 2021). Similarly, in the second limitation which was taking the ‘human’ out of human resource, its negative impacts were briefly mentioned and at the end of that discussion, how to overcome those limitations or negative impacts were briefly discussed. At the end, it can be concluded that even after all these negative impacts, these two limitations portrayed. They have their solutions as well. And by taking care of those solutions, people analytics can be used effectively and efficiently.
References
Belizón, M.J.a.K.S., 2022. Human resources analytics: A legitimacy process. Human Resource Management Journal, 32(2), pp.603-30.
Creese, S., 2020. The threat from AI. United Kingdom: Routledge.
DiClaudio, M., 2019. People analytics and the rise of HR: how data, analytics and emerging technology can transform human resources (HR) into a profit center. Emerald Insight, 18(2), pp.42-46.
Gelbard, R..R.R..C.A..B.R.M.a.T.R., 2018. Sentiment analysis in organizational work: Towards an ontology of people analytics. Expert Systems, 35(5).
Khan, & Millner, , 2020. Introduction to People Analytics: A Practical Guide to Data-driven HR. United Kingdom: Kogan Page Publishers.
Lengnick-Hall, M.L..N.A.R.a.S.C.B., 2018. Human Resource Management in the Digital Age: Big Data, HR Analytics and Artificial Intelligence. In Management and technological challenges in the digital age, pp.1-30.
Nica, E..M.R.a.K.E., 219. Artificial Intelligence-supported Workplace Decisions: Big Data Algorithmic Analytics, Sensory and Tracking Technologies, and Metabolism Monitors. Psychosociological Issues in Human Resource Management, 7(2), pp.31-37.
Plaskoff, J., 2017. Employee experience: the new human resource management approach. Strategic HR Review, 16(3), pp.136-41.
Rex, T..B.S..N.K.a.B.P., 2020. Opportunities and Barriers in the Practice of Human Resource Analytics. Emerald Insight, pp.53-72.
Tursunbayeva, A..P.C..D.L.S.a.A.G., 2021. The ethics of people analytics: risks, opportunities and recommendations. Emerald Insight, 51(3).
