Next Generation Business Intelligence and Analytics

Vo, Quoc Duy; Thomas, Jaya; Cho, Shinyoung; De, Pradipta; Choi, Bong Jun

doi:10.1145/3278252.3278292

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Their research also recommends utilizing cloud-based solutions to keep the processing capabilities elastic to reduce additional investments. Vo, Thomas, Cho, De, and Choi (2018) recommends three new features for the next generation BI, Operational BI (Near Real-time), Situational BI (Real-time), and Self Service BI ( reduced dependency of IT Staff) [5]. Santos et al (2014), in their research, recommended a grid environment-based scheduling solution for small to medium ETL processing needs [3].…”

Section: Related Workmentioning

confidence: 99%

Untitled

2020

IJDMS

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The effectiveness of a Business Intelligence System is hugely dependent on these three fundamental components, 1) Data Acquisition (ETL), 2) Data Storage (Data Warehouse), and 3) Data Analytics (OLAP). The predominant challenges with these fundamental components are Data Volume, Data Variety, Data Integration, Complex Analytics, Constant Business changes, Lack of skill sets, Compliance, Security, Data Quality, and Computing requirements. There is no comprehensive documentation that talks about guidelines for ETL, Data Warehouse and OLAP to include the recent trends such as Data Latency (to provide real-time data), BI flexibility (to accommodate changes with the explosion of data) and Self-Service BI. This research paper attempts to fill this gap by analyzing existing scholarly articles in the last three to five years to compile guidelines for effective design, implementation, and assessment of DW, ETL, and OLAP in BI.

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Section: Related Workmentioning

confidence: 99%