Transparent Peer Review By Scholar9
Harnessing Data Analytics for Strategic Decision-Making: Techniques, Tools, and Real-World Applications Across Industries
Abstract
Data analytics has emerged as a powerful tool for improving strategic decision-making in businesses across industries. This research delves into how data analytics, when effectively harnessed, can transform decision-making processes by offering valuable insights and facilitating data-driven strategies. The study investigates various techniques and tools used in data analytics, including machine learning algorithms, predictive analytics, and data visualization, and explores their applications in different sectors such as healthcare, finance, retail, and manufacturing. By examining case studies from leading companies, the paper highlights how data analytics supports strategic decisions, enhances operational efficiency, and drives innovation. Through a mixed-methods approach that combines qualitative research from industry experts and quantitative analysis of organizational performance, the paper reveals that organizations that leverage data analytics for decision-making experience enhanced competitive advantage, improved customer satisfaction, and optimized resource allocation. The study also identifies key challenges in implementing data analytics, such as data quality issues, lack of skilled professionals, and integration with existing IT systems. In conclusion, the paper provides recommendations for organizations to effectively utilize data analytics in their strategic decision-making processes, suggesting future research directions in emerging areas such as artificial intelligence and big data analytics.
Rajesh Kumar kanji Reviewer
27 Mar 2025 10:12 AM
Approved
Relevance and Originality
The research effectively highlights the growing importance of data analytics in strategic decision-making, a critical area for modern businesses. By covering multiple industries and exploring various analytical techniques, the study provides a broad perspective. However, the originality could be enhanced by focusing on emerging trends such as real-time analytics or edge computing, which are shaping the next generation of data-driven strategies.
Methodology
The mixed-methods approach, combining qualitative insights from industry experts and quantitative performance analysis, strengthens the study’s credibility. However, providing more details on data collection methods, sample size, and criteria for selecting case studies would enhance transparency. A comparative analysis of organizations that do and do not use data analytics could further validate the findings.
Validity & Reliability
The study successfully demonstrates the benefits of data analytics, but the extent of its generalizability depends on the diversity of case studies and data sources. Addressing potential biases in qualitative research and ensuring a robust statistical framework for quantitative analysis would improve reliability. A discussion on how different industries vary in their adoption and impact of data analytics would add depth.
Clarity and Structure
The research is well-structured, guiding readers from foundational concepts to real-world applications and challenges. The logical flow makes complex analytics topics accessible, but certain sections, such as challenges in implementation, could be streamlined for conciseness. Clearly distinguishing between the benefits and limitations of each analytical technique would improve readability.
Result Analysis
The findings provide strong evidence of data analytics’ role in improving strategic decisions and operational efficiency. However, incorporating specific performance metrics, success benchmarks, or industry comparisons would enhance the strength of the analysis. Discussing mitigation strategies for challenges such as data quality and integration issues would provide more actionable insights.
IJ Publication Publisher
Thank you Sir for your insightful feedback. We will enhance originality by incorporating real-time analytics and edge computing. Methodology details, comparative analysis, and statistical robustness will be refined for clarity. Expanding datasets, addressing biases, and incorporating industry comparisons will further strengthen the findings. Your valuable suggestions are much appreciated.
Thank you.
Rajesh Kumar kanji Reviewer