Today, every organization or company’s focus is to vacuum up data in a bid to make better and well-informed decisions inclined to product development, profitability, advertising, recruitment of employees, and productivity. It is not a surprise that the analysis of data has to a large extent outperformed human intuition in various circumstances in organizations in the world today. Irrefutably, data analysis has been considered to be one of the key components in business organizations. In fact, some organizational leaders have been obliged to outsource data analysis whereas others have been forced to uphold and stick with in-house data analysis.
The primary role of data analysis in business organizations is to facilitate the conversion of raw data collected from various sources through the use of different tools into data deemed meaningful and helpful in various business functions and operations. Once raw data is analyzed, the meaningful and helpful data is then presented to organizational managers, and this objective is achieved through reporting tools (Ediger, 2003). Without a doubt, analyzed data play an integral role in the decision-making processes of organizational leaders. Effective and good analysis of data ultimately influences good decision-making by organizational leaders and this in the end, results in successful business practices and operations.
Real-world business situation
A real-world business situation that would benefit or be addressed by the collection and analysis of sets of data is poor human resource management. Business organizations cannot be functional, operational, or successful unless human resources are in place. Human resources are the individuals making up the workforce of any organization, an economy, or business sector, and their primary objective is to promote effectiveness, efficiency, productivity, profitability, and growth. If not controlled and managed effectively, it is highly likely the long-term organizational objectives cannot or may not be achieved.
From this perspective, the concept of human resource management as embraced by most organizational leaders is drawn. It refers to the maximization of the performance of the employees or workforce with the objective of achieving the strategic objectives of the employer. It is unfortunate that poor human resource management has crept into modern-day business organizations and institutions; and the only way out of the negative situation is to capitalize on the collection and analysis of data (human resource data). If human resource data is collected, analyzed, and presented to organizational leaders, the decisions made towards human resource management will be effective, and thus, poor human resource management will be addressed and gradually done away with in business organizations (Ediger, 2003).
Question relevant to poor human resource management
Data collection and analysis of data must focus on answering a particular question. While trying to address challenges and risks, organizations develop questions that they strive to answer by collecting and analyzing data revolving around them. In this case, the real-world business situation to be addressed is poor human resource management, and possible question to be answered is:
Is there a perceived difference in the effectiveness and efficiency of new employees and old employees?
Based on the above question, data revolving around the recruitment of new employees and retention of old employees may be collected and analyzed. As a result, the decisions made by organizational leaders in the recruitment of new employees and retention of old employees will be influenced (Ediger, 2003). For example, from the data analysis, organizational leaders might find out that old employees are more effective and efficient than new employees or vice versa, and this might help them make informed decisions, and thus, poor human resource management may be addressed in various business organizations.
Data to be collected that is relevant to poor human resource management
In this case, the data to be collected is the ratings of the effectiveness and efficiency of new employees versus old employees, where n= 30. This means that the sample size of the population where the data is to be collected is 30, and this will enable the provision of statistically reliable findings.
Data gathering methodology
The collection and gathering of data on the ratings of the effectiveness and efficiency of new employees versus old employees will focus on 30 managers sampled from different business organizations. The sampled managers would provide accurate data as they have information on who between new and old employees are more effective and efficient. In collecting this kind of data, the most appropriate methodology will be in-depth interviews, where the participants will be issued with questionnaires containing the question: Is there a perceived difference in the effectiveness and efficiency of new employees and old employees? The question is open-ended, hence will give the participants and opportunity of giving variant opinions on the research question regarding the issue of poor human resource management. It is anticipated that participants will be interviewed individually and not as a group as this will enable the collection of confidential and accurate data.
Appropriate data analysis technique
Once data is collected, it is then analyzed using various techniques, and the techniques used depend on the type of data collected. In this case, the data collected is the ratings of effectiveness and efficiency of new and old employees (n=30). This implies that there is a comparison of the data collected from the sample population. An appropriate data analysis technique, in this case, would be the paired sample t-test. In data analysis, t-test analysis technique is used in the comparison of values from different samples. In this case, different values are likely to be obtained on the effectiveness of new employees and that of old employees. It is argued that the collection of two samples from the same population, in this case, 30 managers, is unlikely to produce values that are identical. That is to say, a given value of the sampled population would support the effectiveness and efficiency of old employees whereas another value of the population would support the effectiveness and efficiency of new employees. The main problem or challenge, in this case, would be the differentiation of eh tow situations through the use of data from the two samples. However, the paired sample t-test analysis technique remains appropriate for the analysis of data collected in this case.
Why the sample t-test analysis technique is appropriate
The appropriateness of the t-test analysis technique in analyzing the data collected on the difference in the effectiveness and efficiency of new employees and old employees is owed to the fact that the technique is used in cases where the participants rate two brands differently. In this case, the data collected would indicate a difference in the effectiveness and efficiency of old and new employees, and thus, the sample t-test analysis technique would be the most appropriate.
Conclusion
In a nutshell, in the situation of poor human resource management in a business organization, a collection and analysis of data comparing the effectiveness of employees or workforces would help an organizational leader make an informed decision. With the accurate data, the leader would have an appropriate ground and basis of either recruiting new employees or retaining the old employees, and as a result, poor resource management in business organizations will have been addressed. Poor resource management involves the poor coordination of employees in an organization. However, once data is collected and analyzed in the relationship among employees, it is easier for organizational leaders to coordinate them.
- Ediger, M. (2003). Data driven decision making. College Student Journal, 37(1), 9.
