Theory of Scientific Management
Scientific management theory is an aspect of management formulated by Frederic Winslow Taylor (1896-1915). It was based on four cornerstones: (a) The creation of a science of management to facilitate the evaluation of the optimal method of carrying out specified jobs. (b) Identification of employees who have the best skill set for a specific task. (c) The conscious training and empowerment of human resources. (d) A closer relationship between management executives and laborers ( Paramboor, Musah & al-Hudawi 2018). Big data analytics is the use of digitally stored information regarding customers, markets, and production behavior to plan the future course of action of an organization. Today, it has become a big part of how companies such as those dealing in online sales and services develop their strategy. Social media has played a vital role in enabling firms to create big data repositories (Ularu, Puican, Apostu & Velicanu 2012). This paper will analyze whether this recent surge in the use of big data is a new facet of the before mentioned theory of scientific management.
Elragal & Klischewski (2017) argue that big data analytics develops forecasts of performance without being theoretically informed about the particular topic of interest, be it sales or marketing, among others. It employs statistical methods to analyze large datasets and come up with salient features of the phenomena being studied. This way, predictions can be made based on past behavior. Data-driven firms include more recent giant corporations such as Amazon. On the other hand, the theory of scientific management uses more scientific methods to increase productivity. It is more suited to traditional firms dealing in assembly and physical production (Paramboor et al., 2018). Analysis can be done, such as quality tests, to determine which line or workers are suited to a specific task.
I argue that big data analytics are very mildly related to Taylor’s theory of scientific management. It is difficult to ascertain the need for large volumes of data in the philosophy of scientific management. Most likely, a small amount of recorded customer feedback is capable of informing an executive on the best course of action to take regarding how to organize better their factors of production to achieve better profits. Moreover, big data analytics focuses on predictive research, whereas the theory of scientific management is reliant on exploratory study (Elragal & Klischewski 2017). There are several aspects of big data analytics that are conspicuously missing from the theory of scientific management: the urgency of decision making from obtained data is far higher, the data must undergo complicated processing steps to increase its usefulness, the extensive use of IT procedures and the methods of obtaining user-specific data, which arguably sometimes borders on the unethical in its infringement on internet users privacy (Ularu et al., 2012).
Public managers must be well equipped to take their firms and juniors to the next level, especially in the face of big data analytics being the modus operandi used to predict future behavior. Kellis & Ran (2013) advocate for a leadership style with three core principles;
transformational, authentic, and distributed leadership. The new normal for firms today is operating in environments of regular crises. Moreover, there is an increased emphasis on improving the financial performance of the firm. It may be argued that big data analytics have facilitated managers in this regard (Ularu et al. 2012). However, the traditional notion of excellent financial performance has also undergone a transformation that has made it harder than ever to satisfy shareholders or the board. Partly because a lot of datasets that are part of the overall framework of big data are scrutinized at any given time.
The principal precept of the new public management hypothesis centers on the credibility and authenticity of leaders. Being the most focal quality for managers, authenticity, comprises a fundamental segment of administration in civil circles, forming a link between adequate supervision without which compelling authority is improbable, and, the transparency that is fundamental for a democratic style of leadership. Leaders must maintain their discretion without undue influence and only pay attention to the direction they are pointed in by management approaches such as the scientific theory or big data analysis conclusions. Transformational leadership is characterized by being a leader who is willing to embrace change and influence employees in the same direction that may be previously unexplored. It may be expanded to a laissez-faire approach whereby lower-level managers are encouraged to pursue the course of action they trust the most depending on the data that back it. Lastly, the third tent is distributed leadership. It is more in line with the scientific theory’s second principle. The overall manager should delegate roles to those who are best suited to them with confidence that they will perform exceptionally.
In conclusion, the theory of big data analysis is not a revival of the scientific method of management. There are too many discrepancies between the two and the fact that one conducts predictive research while the other uses exploratory analysis. Public managers must be credible, change-driven, and willing to divide tasks to other lower-level colleagues to create a wholesome progressive organization.
References
Elragal, A., & Klischewski, R. (2017). Theory-driven or process-driven prediction? Epistemological challenges of big data analytics. Journal of Big Data, 4(1), 1-20.
Kellis, D. S., & Ran, B. (2013). Modern leadership principles for public administration: Time to move forward. Journal of Public Affairs, 13(1), 130-141.
Paramboor, J., Musah, M., & al-Hudawi, S. (2018). Scientific Management Theory: a Critical Review from Islamic Theories of Administration. INTERNATIONAL JOURNAL OF ECONOMIC, BUSINESS AND APPLICATIONS, 1(1), 8-16
Ularu, E. G., Puican, F. C., Apostu, A., & Velicanu, M. (2012). Perspectives on big data and big data analytics. Database Systems Journal, 3(4), 3-14.