scholarly journals Service-oriented Cost Allocation for Business Intelligence and Analytics: Who pays for BI&A?

2017 ◽  
Author(s):  
Raphael Grytz ◽  
Artus Krohn-Grimberghe
Keyword(s):  
Business Intelligence ◽  
Cost Allocation ◽  
Service Oriented

Service-Oriented Cost Allocation for Business Intelligence and Analytics

2017 ◽  
Vol 7 (2) ◽  
pp. 40-57 ◽  
Author(s):  
Raphael Grytz ◽  
Artus Krohn-Grimberghe
Keyword(s):  
Business Intelligence ◽  
Cost Estimation ◽  
Cost Allocation ◽  
Cost Evaluation ◽  
Allocation Model ◽  
Pricing Scheme ◽  
Service Portfolio ◽  
Service Oriented ◽  
The Cost ◽  
Cost Awareness

Quantifying and designing the cost pool generated by Business Intelligence and Analytics (BI&A) would improve cost transparency and invoicing processes, allowing a fairer, more exact allocation of costs to service consumers. Yet there is still no method for determining BI&A costs to provide a base for allocation purposes. While literature describes several methods for BI&A cost estimation on an ROI or resource-consumption level, none of these methods considers an overall approach for BI&A. To tackle this problem, the authors propose a service- oriented cost allocation model which calculates BI&A applications based on defined services, enabling a cost transfer to service consumers. This new approach specifies steps towards deriving a usable pricing scheme for an entire BI&A service portfolio – both for allocation purposes as well as improving cost evaluation of BI&A projects. Moreover, it increases customer understanding and cost awareness. Based on this approach, the authors introduce a BI&A value creation cycle which helps customers to use BI&A services cost-effectively.

scholarly journals A analytics study Mapping Study of Collaborative business intelligence and self-service

2021 ◽  
Vol 9 (12) ◽  
pp. 1032-1037
Author(s):  
Tanmayee Parbat
Keyword(s):  
Business Intelligence ◽  
Design Science ◽  
Science Research ◽  
Research Approach ◽  
Knowledge Database ◽  
Architecture Model ◽  
Service Oriented ◽  
Decision Making Processes ◽  
Self Service ◽  
Self Learning

Abstract: Self-service Business Intelligence (SSBI) is an emerging topic for many companies. Casual users should be enabled to independently build their own analyses and reports. This accelerates and simplifies the decision-making processes. Although recent studies began to discuss parts of a self-service environment, none of these present a comprehensive architecture. Following a design science research approach, this study proposes a new self-service oriented BI architecture in order to address this gap. Starting from an in-depth literature review, an initial model was developed and improved by qualitative data analysis from interviews with 18 BI and IT specialists form companies across different industries. The proposed architecture model demonstrates the interaction between introduced self-service elements with each other and with traditional BI components. For example, we look at the integration of collaboration rooms and a self-learning knowledge database that aims to be a source for a report recommender. Keywords: Business Intelligence, Big Data, Architecture, Self-Service, Analytics

Integrated Supply Chain Intelligence through Collaborative Planning, Analytics and Monitoring

2011 ◽  
pp. 43-92 ◽  
Author(s):  
Nenad Stefanovic ◽  
Dusan Stefanovic ◽  
Bozidar Radenkovic
Keyword(s):  
Supply Chain ◽  
Performance Management ◽  
Business Intelligence ◽  
Service Oriented Architecture ◽  
Collaborative Planning ◽  
Research Trends ◽  
Future Research ◽  
Web Based ◽  
Service Oriented ◽  
Integrated Supply Chain

As supply chains are growing increasingly complex, from linear arrangements to interconnected, multi-echelon, collaborative networks of companies, there is much more information that needs to be stored and analyzed than there was just a few years ago. Today, there are variety of business initiatives and technologies such as joint planning and execution, business intelligence, performance management, data mining and alerting that can be used for more efficient supply chain management. However, organizations still lack methods, processes and tools to successfully design and implement these systems. In this chapter, the authors present the integrated supply chain intelligence (SCI) system that enables collaborative planning and decision making through web-based analytics and process monitoring. The system is process based and utilizes business intelligence and Internet technologies. Multi-layered and service-oriented architecture enables composition of the new breed of SCI applications. They describe main elements and capabilities of the system, its advantages over existing systems and also discuss future research trends and opportunities.

A Service-Oriented Foundation for Big Data

2020 ◽  
Vol 10 (1) ◽  
pp. 1-17
Author(s):  
Zhaohao Sun
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Technological Characteristics ◽  
Service Oriented ◽  
Technological Level ◽  
Multi Level ◽  
Big Service

This paper provides a service-oriented foundation for big data. The foundation has two parts. Part 1 reveals 10 big characteristics of big data. Part 2 presents a service-oriented framework for big data. The framework has fundamental, technological, and socio-economic levels. The fundamental level has four big fundamental characteristics of big data: big volume, big velocity, big variety, and big veracity. The technological level consists of three big technological characteristics of big data: Big intelligence, big analytics, big infrastructure. The socioeconomic level has three big socioeconomic characteristics of big data: big service, big value, and big market. The article looks at each level of the proposed framework from a service-oriented perspective. The multi-level framework will help organizations and researchers understand how the 10 big characteristics relate to big opportunities, big challenges, and big impacts arising from big data. The proposed approach in this paper might facilitate the research and development of big data, big data analytics, business intelligence, and business analytics.

Envisioning the Paradigm of Service Oriented Hydrology Intelligence (SOHI)

2010 ◽  
pp. 172-198
Author(s):  
Pethuru Raj Chelliah
Keyword(s):  
Real Time ◽  
Business Intelligence ◽  
Information Technologies ◽  
Service Oriented Architecture ◽  
Knowledge Engineering ◽  
Reference Architecture ◽  
Data Intensive ◽  
Related Data ◽  
On Demand ◽  
Service Oriented

Hydrology is an increasingly data-intensive discipline and the key contribution of existing and emerging information technologies for the hydrology ecosystem is to smartly transform the water-specific data to information and to knowledge that can be easily picked up and used by various stakeholders and automated decision engines in order to forecast and forewarn the things to unfold. Attaining actionable and realistic insights in real-time dynamically out of both flowing as well as persisting data mountain is the primary goal for the aquatic industry. There are several promising technologies, processes, and products for facilitating this grand yet challenging objective. Business intelligence (BI) is the mainstream IT discipline representing a staggering variety of data transformation and synchronization, information extraction and knowledge engineering techniques. Another paradigm shift is the overwhelming adoption of service oriented architecture (SOA), which is a simplifying mechanism for effectively designing complex and mission-critical enterprise systems. Incidentally there is a cool convergence between the BI and SOA concepts. This is the stimulating foundation for the influential emergence of service oriented business intelligence (SOBI) paradigm, which is aptly recognized as the next-generation BI method. These improvisations deriving out of technological convergence and cluster calmly pervade to the ever-shining water industry too. That is, the bubbling synergy between service orientation and aquatic intelligence empowers the aquatic ecosystem significantly in extracting actionable insights from distributed and diverse data sources in real time through a host of robust and resilient infrastructures and practices. The realisable inputs and information being drawn from water-related data heap contribute enormously in achieving more with less and to guarantee enhanced safety and security for total human society. Especially as the green movement is taking shape across the globe, there is a definite push from different quarters on water and ecology professionals to contribute their mite immensely and immediately in permanently arresting the ecological degradation. In this chapter, we have set the context by incorporating some case studies that detail how SOA has been a tangible enabler of hydroinformatics. Further down, we have proceeded by explaining how SOA-sponsored integration concepts contribute towards integrating different data for creating unified and synchronized views and to put the solid and stimulating base for quickly deriving incisive and decisive insights in the form of hidden patterns, predictions, trends, associations, tips, etc. from the integrated and composite data. This enables real-time planning of appropriate countermeasures, tactics as well as strategies to put the derived in faster activation and actuation modes. Finally the idea is to close this chapter with an overview of how SOA celebrates in establishing adaptive, on-demand and versatile SOHI platforms. SOA is insisted as the chief technique for developing and deploying agile, adaptive, and on-demand hydrology intelligence platforms as a collection of interoperable, reusable, composable, and granular hydrology and technical services. The final section illustrates the reference architecture for the proposed SOHI platform.

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