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Empirical Examination- Big Data Analytics

Empirical Examination- Big Data Analytics


During the modern era of data-driven decisions, companies are faced with vast amounts of raw data with different varieties, scales, formats, and processing speeds. Dealing with such multilayered details is quite challenging, hence requiring more advanced data analysis techniques, structures, and software. Therefore, big data analytics help reveal hidden correlations, patterns, and industry dynamics, among other crucial knowledge that conventional methods cannot obtain. However, there is a rising concern on the potency and extensibility of the analytic techniques creating the need for factual examination. This paper explores the scalability and efficacy of mechanisms used by prominent data analysts to create the need for empirical analysis.


Natural Language Processing (NLP) is an advanced tool that facilitates analyzing, interpreting, and generating texts in big data. Some of its common styles of operation include word sense disambiguation, Part of part-of-speech tagging (POS), and lexical acquisition. Its effectiveness in text mining is fundamentally supported by topic modelling, text classification, summarization, opinion mining, and question answering. However, detailed analysis indicates that uncertainties significantly hinder the effectiveness and scalability of NLP. For instance, a keyword search might bring out related documents, but their relevance might be comparatively low. The uncertainty stretches to automatic POS taggers, especially in the context of ambiguous words. Hariri et al. (2019) propose that the real-time handling of voluminous textual data can be leveraged with the aid of fuzzy and probabilistic set modelling. A further empirical examination of NLP is recommended to ascertain its coherence.

Machine Learning (ML) is yet another pivotal big data analytics centrally based on Artificial Intelligence (AI). It plays a crucial role in creating knowledge discovery and prediction models that would otherwise be impossible with traditional computational methods. Examples of commonly used ML approaches for big data analytics are deep learning, distributed learning, transfer learning, and active learning. However, the efficient use of this analytic criterion is obscured by the fact that it is a new and complex venture. As such, it has inconsistencies in eliminating data bias and inducing advanced mathematical calculations. The articulation of fuzzy support vector machines is highly recommended in ML dilemmas.

The use of Classification Tree Analysis (CTA) in big data analytics has gained momentum over the years. It is a special algorithm applied in the classification of remotely ancillary and remotely sensed data. It works by representing decisions with leaves and attributes with branches. However, it deserves further empirical analysis to address most of the current issues. For instance, its calculations are extremely complex, and minor alternations in data result in extensive instabilities in the decision tree (Grover & Kar, 2017). Nevertheless, it seems to be ineffective in handling regression and continuous values. Therefore, it deserves to be subjected to further scientific examination and improvement.

Associative Rule Learning (ARL) is incredibly useful in discovering correlations between elements in massive data sets. Supermarkets highly welcomed its presence to unearth the interdependence of clients’ buying habits. It is further used in monitoring system logs and analysis of biological data. However, it raises multiple concerns that consequently impend its effectiveness in E-learning. The derived rules are voluminous, most of which have low comprehensibility and are non-interesting (Rajaraman, 2016). Nevertheless, the method uses excessive parameters that potentially attenuate its efficacy.

A/B testing is innovative randomized control analytics that compares two versions of a variable under a controlled environment. It is widely used by organizations, especially in benchmarking data, as a strategic way of improving internal and external operations. It is a perfect suit for big data analytics due to its exclusive features. Its relevance has declined over the years as its system setup requires extensive time. Besides, it incurs sizeable resources rendering it inefficient in the long run. In addition, sometimes, it becomes strenuous to determine the type of variables to be included in the tests (Grover & Kar, 2017). Some of these drawbacks make its use a debatable topic that requires further research.

The application of regression analysis in big data analytics has a long history as it has been widely used to find the relationship between customer satisfaction and loyalty. It is primarily used in the development of a knowledge base from a set of existing data. Its relevance is more pronounced when dealing with linear-related variables. The method’s predictive nature works best with independent and independent predictors. Its widespread use is confronted by the inapplicability of a qualitative phenomenon. Its complicated and lengthy nature is also not friendly to most analysts. Therefore, it is open to further advancements and simplifications.

Having looked at the advanced data analysis techniques and their scalability and effectiveness, it is imperative to consider the potential empirical rectification strategies. First, parallelization appears to be a best-fit solution as it spreads work units across multiple processors. It is a supercomputing approach and ensures that problems are solved simultaneously. This method is contrary to the time-intensive serial computing that handles a single issue at a time.

Feature selection is yet another process that can be applied to distinguish non-redundant, consistent, and relevant elements in a model. It is very applicable when the variety and size of the dataset are projected to grow exponentially. Feature selection promotes precise data coverage as intended by the analyst. It can help resolve scalability issues due to the filter-based measures. The primary benefits are improving accuracy, reducing overfitting, and minimizing training time

The integration of incremental learning can further enhance the big data analytics from various dimensions. It is much more appropriate for the ML and works by extending the current knowledge model through input data. The method ensures that the existing knowledge is successfully maintained during the acquisition of new data. Its ability to embrace changes without compromising integrity renders it a better corrective measure for effectiveness issues. Incremental learning is resource-conservative and thus preferred in many instances.

The divide and conquer method is the final potential strategy appropriate for big data analytics in the light of empirical examination. This algorithm is a precise fit for scalability challenges since it is effective in processing extensive data. It works by recursively dividing a problem into finer sub-problems until reasonable and solvable sets are attained. The particular solutions are then integrated to collectively contribute to the overall solution (Rajaraman, 2016). Therefore, the mechanism is effective in solving complicated issues in data analytics.


There exist numerous big data analytic techniques, some of which are complex and daunting. Their efficacy and scalability vary to a great extent implying their application and relevance differ significantly. Some of their demonstrated weaknesses make it necessary to apply empirical evaluation as a critical way of enhancing productivity. The proposed corrective measures can be exploited for a better future in big data analysis. A comprehensive empirical analysis is an effective way of increasing the validity and reliability of the analytic methods.


Grover, P., & Kar, A. K. (2017). Big data analytics: A review of theoretical contributions and

tools used in literature. Global Journal of Flexible Systems Management18(3), 203-229.

Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics:

survey, opportunities, and challenges. Journal of Big Data6(1), 1-16.

Rajaraman, V. (2016). Big data analytics. Resonance21(8), 695-716.


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“The scalability and efficacy of existing analytics techniques being applied to big data must be empirically examined.”

Your paper should meet these requirements:

Empirical Examination- Big Data Analytics

Empirical Examination- Big Data Analytics

Be approximately FOUR to SIX pages in length, not including the required cover page and reference page.
Follow APA 7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.
Support your answers with the readings from the course and at least TWO SCHOLARLY JOURNAL ARTICLES to support your positions, claims, and observations, in addition to your textbook.
Be clearly and well-written, concise, and logical, using excellent grammar and style techniques.

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