Case Study of the Use of Big Data in Business Decision Making
Bibliographic record
Abstract
This study explores the impact of Big Data Analytics (BDA) on business decision-making and business performance at PT XYZ, a leading e-commerce company in Indonesia. As businesses increasingly rely on data-driven strategies to remain competitive, Big Data has emerged as a critical tool for enhancing decision-making processes and optimizing business outcomes. The primary objectives of this study were to analyze how PT XYZ utilizes Big Data to inform strategic and operational decisions, to assess the benefits of this implementation, and to identify the challenges faced during the process. Through qualitative research methods, including interviews, document analysis, and observations, this study investigates the ways in which Big Data analytics tools such as predictive modeling, machine learning algorithms, and customer segmentation are integrated into the company’s decision-making framework. The findings suggest that the use of Big Data has led to significant improvements in revenue growth, customer satisfaction, and operational efficiency at PT XYZ. Specifically, the company saw a 12% increase in revenue, a 15% improvement in customer satisfaction, and an 8% reduction in operational costs following the adoption of Big Data-driven decision-making processes. Despite these positive outcomes, the implementation of Big Data faced several challenges, including data integration issues, skill gaps among employees, and high initial costs associated with adopting new technologies. The study concludes that while Big Data Analytics can greatly enhance business performance, companies must address these challenges to fully realize its potential. The research provides valuable insights for other businesses considering the integration of Big Data into their decision-making processes. It also contributes to the growing body of literature on Big Data's role in modern business management and its ability to drive strategic and operational improvements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".