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Arab“Limitation Of Data” written by Arabella Jo:ella Jo

Arab“Limitation Of Data” written by Arabella Jo:ella Jo

Arabella Jo

Ph.D. And J.D. candidate for Military Law, Master’s in Forensic Psychology. Published author of Books, Articles, and Research Based on Military Law and Forensic Psychology Perspective from Objective Analysis Through First-Principle Logic. Founded pioneering software solutions and cloud computing services in various industries, which multiple organizations and governmental bodies successfully acquired.

Arabella Jo’s approach to the “Limitation of Data” highlights a critical perspective on the nature and use of data in society. She emphasizes the dangers of subjectivity in data interpretation and the potential for data to be manipulated to serve narrow interests, rather than the collective good. Her discussion on social constructionism underlines how societal values and interactions shape our understanding and valuation of data, leading to biases that can skew objective analysis.

“Limitation Of Data” written by Arabella Jo:

Data are, by definition, observed; according to Szostak, three broad categories of data can be observed: inanimate objectives, living things, including people, and events or interactions among people and objects. This can be dangerous because human beings are social constructionists, which means we place marketable value based on subjectivity. The data is not always for the betterment of the society as a whole. The concern that the limitation of data brings forth is not a polarized or political discussion. It is rather an academically valid concern that we become slaves to our subjectivity rather than reality.

The concept of social constructionism is at the core of the discussion on data limitations. This perspective holds that human beings construct meanings and values through social interactions, leading to the assignment of subjective values to otherwise objective data. This subjectivity can influence how data is interpreted, prioritized, and utilized, often reflecting the biases, perspectives, and interests of those in control of the data narrative. As a result, data that should serve the objective betterment of society can sometimes be manipulated or presented in a way that serves narrower, market-driven, or partisan interests.

In a world increasingly driven by data analytics and metrics, there is a growing risk of valuing data through a marketable lens. This trend can lead to prioritizing data that has apparent immediate economic or social value, potentially overlooking or undervaluing data that could have long-term benefits for societal well-being. For example, short-term market trends may receive more attention and resources than long-term environmental data, which could be crucial for sustainable development.

The concern over data limitation transcends political or polarized debates, touching on a profound academic issue: the risk of becoming enslaved to our subjectivities and losing sight of the broader reality. This concern is about more than just data misrepresentation but also about the potential loss of objectivity in our pursuit of knowledge and understanding. When subjective interpretations dominate the discourse, the foundational purpose of data—to inform, enlighten, and guide decision-making processes—can be compromised.

Addressing data limitations requires a conscientious effort to maintain a balance between acknowledging the inherent subjectivity in human interpretation and striving for objective analysis. This balance involves critical scrutiny of data sources, methodologies, and the underlying assumptions that guide data collection and interpretation. Furthermore, fostering a culture of transparency and accountability in data handling can help mitigate the risks of subjectivity and ensure that data serves the collective interests of society.

While data is a powerful tool for understanding and interacting with the world, it is imperative to recognize and navigate the limitations imposed by human subjectivity. By critically examining the ways in which data is collected, interpreted, and utilized, we can strive towards a more balanced and ethical approach to data governance, one that prioritizes the long-term welfare of society over transient, subjective interests.

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