Citation:

Santosh Kumar Singu, 2022. "Impact of Data Warehousing on Business Intelligence and Analytics", ESP Journal of Engineering & Technology Advancements 2(2): 101-113.

Abstract:

Business intelligence and analytics have known the importance of data warehouses as key substructures for capturing, storing, organizing, and facilitating the analysis of massive volumes of information. The combination of data warehousing with BI tools offers a systematic approach to the collection of information, the general improvement of decision-making processes, the accuracy of the data, and the efficiency of performance results. In this abstract, one is taken through an evaluation of how data warehousing aids in the enhancement of BI systems. In particular, it focuses on the following topics: how data warehousing works and how it converts raw data into useful information, architectures that are used to support this process, and new technologies such as BI analytics that changed the business. Critical success factors and risks involved with decision-making when considering data warehousing for BI and analytics are also discussed, along with advantages such as data governance, scalability, and cost implications of data warehousing for BI and analytics. Also, this study evaluates the effect of implementing new data warehousing technology, such as cloud data solution architectures and their effects on business operations, competitiveness, and data-driven management strategies. It also cautions on some of the risks that businesses might face, including high upfront costs and technical challenges. As data environments have evolved over the last decades, organizations have turned more and more to data warehousing as the base for sophisticated BI procedures, such as predictive analysis, machine learning, and real-time decision-making. The last part of the abstract is dedicated to the future trends and challenges of data warehousing and business intelligence, including Data Lake of information, the usage of artificial intelligence in BI automation, and the constantly increasing role of data governance and security while working on large-scale business intelligence projects.

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[21] Santosh Kumar Singu, 2021. "Designing Scalable Data Engineering Pipelines Using Azure and Databricks", ESP Journal of Engineering & Technology Advancements, 1(2): 176-187.

[22] Santosh Kumar Singu, 2021. "Real-Time Data Integration: Tools, Techniques, and Best Practices", ESP Journal of Engineering & Technology Advancements 1(1): 158-172.

[23] Santosh Kumar Singu, 2022. "ETL Process Automation: Tools and Techniques", ESP Journal of Engineering & Technology Advancements, 2(1): 74-85.

Keywords:

Data Warehousing, Business Intelligence (BI), Analytics, Data Governance, Predictive Analytics, Data Lakes, Data Security, Scalability.