scholarly journals Business Intelligence and Data Analytics as a Driver of Dynamic Capability Strategic Approach

2018 ◽  
Author(s):  
Maoloud Dabab ◽  
Charles Weber
Keyword(s):  
Business Intelligence ◽  
Data Analytics ◽  
Dynamic Capability ◽  
Strategic Approach

Big Data Predictive and Prescriptive Analytics

2014 ◽  
pp. 370-391 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Action Plan ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Future Planning ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

Big Data Predictive and Prescriptive Analytics

Big Data ◽  
2016 ◽  
pp. 30-55 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Action Plan ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Future Planning ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

Extending and Applying Business Intelligence and Customer Strategies for Green ICT

2011 ◽  
pp. 1198-1212
Author(s):  
Bhuvan Unhelkar ◽  
Amit Tiwary
Keyword(s):  
Customer Service ◽  
Carbon Footprint ◽  
Business Intelligence ◽  
Environmental Performance ◽  
Business Performance ◽  
Multiple Contacts ◽  
Extra Energy ◽  
Strategic Approach ◽  
Customer Services ◽  
First Contact

This chapter extends and applies the concepts of Business Intelligence (BI) within business to help improve its environmental performance. When BI is used to improve customer service and optimize business performance, the result can also be used to reduce the carbon footprint of the organization. Various ways to improve customer service as well as cross-selling and up-selling to customers are discussed in the context of the carbon footprint – and with suggestions to improve that footprint. This is a strategic approach to the use of BI in environmental performance – resulting in what is called Environmental Intelligence. The suggestion is to use Business intelligence to improve the overall resources usage (by reducing energy and paper usage) of the organizations without compromising on customer services. For example if the customers are serviced on first contact, the follow on activities involving multiple contacts with customers and marketing paper material could be reduced. This will provide the organizations with better customer satisfaction and also reduce the extra energy usage in developing heavy duty BI infrastructure and paper used for the marketing purpose to woo back the customers.

The Nature of Intelligent Analytics

2021 ◽  
pp. 1-21
Author(s):  
Zhaohao Sun ◽  
Andrew Stranieri
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Research And Development ◽  
Business Intelligence ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Novel Approach ◽  
Advanced Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

Text-Driven Reasoning and Multi-Structured Data Analytics for Business Intelligence

2016 ◽  
pp. 1-32 ◽  
Author(s):  
Lipika Dey ◽  
Ishan Verma
Keyword(s):  
Business Intelligence ◽  
Data Analytics ◽  
Statistical Significance ◽  
Heterogeneous Data ◽  
Structured Data ◽  
Text Data ◽  
Business Operations ◽  
Unstructured Text ◽  
Business Data ◽  
Heterogeneous Resources

Business Intelligence (BI) refers to an organization's capability to gather and analyze data about business operations and transactions in order to evaluate its performance. The abundance of information both within the enterprise and outside of it has necessitated a change in traditional Business Intelligence practices. There is a need to exploit heterogeneous resources. Text data like news, analyst reports, etc. helps in better interpretation of business data. In this chapter, the authors present a futuristic BI framework that facilitates acquisition, indexing, and analysis of heterogeneous data for extracting business intelligence. It enables integration of unstructured text data and structured business data seamlessly to generate insights. The authors propose methods that can help in extraction of events or significant happenings from both unstructured and structured data, correlate the events, and thereafter reason to generate insights. The insights extracted could be validated as cause-effect pairs based on the statistical significance of co-occurrence of events.

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