Do the Data and Metrics-Driven Culture and Journal Ranking Adversely Impact Local Journals?

Data-driven culture can be equated with evidence-based decision-making and involves activities that are governed by data and statistics rather than intuition and personal experience. A metrics-driven culture refers to activities that are driven by deadlines and predetermined objectives. In addition to providing a prescribed and structured approach to working, data- and metrics-driven cultures also help identify shortcomings. However, overreliance on a structured approach destroys originality and diminishes passion for the task. Furthermore, a metrics-driven culture based on parameters and statistics would create a “number game culture” without ensuring originality. In a scholarly journal, the two terms have different implications. The purpose of journalism is to display knowledge discovery. Knowledge is the business entity of a journal. Eastern scholars are overly responsive to metrics- and data-driven cultures intended to serve Western interests. Scholars’ statistics may rise at the expense of the East’s collective gains. Ideally, knowledge should be able to remove the detrimental effects of dependency and discrimination theories. For local knowledge to contribute toward reducing the effects of dependency and discrimination theories exacerbated by Eastern elites, it is important to be competitive in the global market. It must, however, not simply be a naive customer of the international knowledge hub, but ensure its proportionate share of it.

INTRODUCTION

We often get confused in understanding the terms of data-driven and metrics-driven culture. Despite both connotations maintaining a reciprocally symbiotic relationship, they also remain within the boundary of certain distinctions. The writing that follows, will explain the peculiarities between the two terms and their implication in a scholarly journal.

Genesis and discourse: The term data-driven culture often refers to the progress of an activity that is obligated by data and statistics, without being overly relied on intuition and personal experience. As far as process management is concerned, data-driven culture can also be equated with evidence-based decision-making. On the other hand, metrics-driven culture is referred to the activities that are driven towards a deadline and the objectives that are pre-set by the standard parameters and domains.

On one hand, the performance of a parameter of a domain is measured by statistics. The collective performance of the parameters or domains reflects the overall performance of a sector or a unit. Hence, data science is also the salient point that testifies the growth. It is common for data-driven cultures to analyze facts and figures in detail and in depth from a variety of perspectives. This type of analysis is conducted in a structured manner. It may eventually be possible to develop some parameters and domains through such a structured process.

Parameters/domains would also be the final product of data analysis if an unstructured process were involved. Under such circumstances, both terms (data-driven and metrics-driven cultures) serve for the same course of action while they may adopt a slightly different method. This further generates an unsettled debate-whether action/task produces the data/statistics and these statistics subsequently speak for parameters and domains, or data and statistics may talk in favor of an action/task.

Modern society has become more accustomed to achieving statistics without adhering to the novelty and genesis of an action/task. An action or task’s exquisiteness might have been compromised by this approach. Before analyzing their implications for scholarly journals, we examine the positive and negative impacts of data-driven and metrics-driven cultures.

Impact of data and statistical science: Data and statistical sciences are developed by scholars to measure and evaluate the actions undertaken. All actions have their purpose and role. While data and statistical sciences may testify to the quantitative achievement of such purpose and role, the qualitative outcome of an action may demand an evaluation-beyond data science. However, we will discuss the merits and demerits of a data- and metrics-driven culture in the following writing.

Firstly, data- and metrics-driven cultures provide a prescribed and structured path for working, defining clear milestones to achieve. Using such guidance, it may be easier to follow a path without being greatly de-tracked. In addition to setting clear aims and objectives, following a track allows accomplishments to be measured without introducing any complexity. Secondly, this approach may assist in identifying shortcomings. Once these gaps have been identified, the next step can be taken to address them. Lastly, it may be helpful to make a time series and comparative study based on history.

A data- and metrics-driven culture also poses graver concerns. The first thing that allows an action to stand out from the rest is its originality. The originality of action should be considered when defining it. Passion for a task always results in originality. The over-reliance on a structured process backed up by statistical science may destroy such a concept. In addition, a metrics-driven culture supported by parameters and statistics would create a “number game culture” without ensuring originality. In the end, today’s statistics may dominate yesterday’s facts and figures, while tomorrows may triumph over todays. In exchange for a competitive society, however, this “game of statistical defeat” may compromise sustainability.

Impact of data and metrics-driven culture on scholarly journal: Journalism serves as an outlet to display knowledge discovery. In the past, knowledge was viewed as a holistic and humanistic product. Challenging this view, knowledge has internationally become a commercial product. Consequently, a journal a business entity-serves the commercial interest of knowledge. The knowledge discovery has experienced various paradigm transformations. The was once the hub of knowledge discovery. Since the 1100s, the West started dominating knowledge discovery. The collective attitude of Western Nations supported them to dominate. The knowledge discovered in the West is also labelled as international knowledge. On the other hand, the Eastern Nations remain in an isolation are reluctant to produce knowledge collectively.

Most Eastern scholars aim to contribute to international knowledge cultivated in the West. To do so, Eastern scholars are overly responsive toward metrics- and data-driven culture-designed for Western interest. This may boost the scholar’s statistics at the cost of the collective gains of the East. Moreover, the East has become the borrower of Western knowledge. The East spends its natural resources or labour-incentives goods to buy the knowledge incentives goods from the West, thereby generating dependency theory. The Eastern elites often consume international knowledge by aggravating discrimination theory. For example, science, technology and innovation have become international knowledge that benefits the West greatly. Another hand, the elites of the East-being the consumers of international knowledge deprive their disadvantaged counterparts.

CONCLUSION

Knowledge should ideally be able to remove the adverse effect of dependency and discrimination theories. It is important for local knowledge to be competitive in the global market for such contributions. The East, however, must ensure its proportionate share in the international knowledge hub without simply being a naive customer. However, with an over-dependency on internationally motivated data- and metrics-driven culture, the growth of local knowledge and journal would be dented. They would also be failed to contribute to knowledge discovery either locally or internationally. Eastern scholars and journals should work to bestow a meaningful contribution to knowledge development which in turn helps them to ensure their sharing in an international knowledge hub. Such contributions would finally develop statistics. These statistics would eventually contribute to the desired metrics.