Intersections of Data Analysis

A Literature Review

What?

The application of data analysis across diverse fields, including health, business, e-commerce, and software development, has transformed decision-making and operational efficiency. By harnessing advanced data-driven techniques, organizations can optimize processes, predict trends, and mitigate risks effectively. This paper presents a comprehensive review of existing literature in four critical areas: the prevalence of physical frailty following COVID-19 hospitalization, the role of Twitter in business marketing, optimization of cross-border e-commerce through big data, and risk assessment in agile software development.

Why?

As industries face increasing complexity and data volume, the ability to extract actionable insights from data has become a key differentiator. This work identifies how domain-specific challenges—ranging from post-COVID health assessments to agile project risks—can be addressed using statistical modeling, simulation, and big data techniques.

How?

Conducted a thematic literature review across four industry sectors, maintaining a structured logbook to capture key insights and support traceable synthesis from academic and industry sources.
Proposed research directions, including federated data integration, AI-driven summarization, and privacy-preserving analytics for sensitive sectors like healthcare and finance.

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