Modelling in Support of Decision Making in Business Intelligence

Modelling in Support of Decision Making in Business Intelligence

Roumiana Ilieva (Technical University of Sofia, Bulgaria), Malinka Ivanova (Technical University of Sofia, Bulgaria), Tzvetilina Peycheva (IBS, Bulgaria) and Yoto Nikolov (Technical University of Sofia, Bulgaria)
DOI: 10.4018/978-1-7998-5781-5.ch006
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Abstract

Modelling in support of decision making in business intelligence (BI) starts with exploring the BI systems, driven by artificial intelligence (AI). The purpose why AI will be the core of next-gen analytics and why BI will be empowered by it are determined. The role of AI and machine learning (ML) in business processes automation is analyzed. The benefits from AI integration in BI platforms are summarized. Then analysis goes through predictive modeling in the domain of e-commerce. The use of ML for predictive modeling is overviewed. Construction of predictive and clustering models is proposed. After that the importance of self-services in BI platforms is outlined. In this context the self-service BI is defined and what are the key steps to create successful self-service BI model are sketched. The effects of potential threads which are the results of the big data in the business world are examined and some suggestions for the future have been made. Lastly, game-changer trends in BI and future research directions are traced.
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Background

Business Intelligence had become a thought leadership function through, among other factors, procuring highly skilled employees to research their respective industries, technologies and geographies and to inform executive decision makers. Many team members were working closely with their stakeholders, gaining a great deal of insights and efficiency in their function.

In order to better communicate their ideas and capabilities to their stakeholders, as well as continuously learn, BI decided to implement a knowledge management system that would make processes, methodologies, best practices and information sources easily accessible to all members of the organization. BI sought to create a unified standard data source and information management upon which a knowledge management system could be built. According to Rud (2009), Dedić and Stanier (2016) the following necessities were essential:

  • Needs for information:

    • o

      Team members needed quick and easy access to data as well as knowing where to get information.

    • o

      Stakeholders also needed easy access to data – so they did not have to continuously email team members to forward along the data – as well as transparency as to the source and the structure of the data.

  • Needs for knowledge:

    • o

      Team members benefited from quick access to best practices, explanations and examples of methodologies, as well as finished projects and insights already gleaned.

    • o

      Stakeholders benefited from an one-stop overview of BI capabilities, as well as easy access to market facts and knowledge about customers.

Key Terms in this Chapter

Business Intelligence: It includes technologies, techniques and methods for data analysis and knowledge presentation for facilitation the business decision making process.

Robotic Process Automation (RPA): It is a form of business process automation technology based on metaphorical software robots (bots) or on artificial intelligence (AI)/digital workers.

Decision Making: It is a process for alternatives recognition based on custom preferences and specific goals with aim an appropriate solution to be chosen.

Self-Service: It is possibility of business users to create their reports and to analyze within an approved and predefined data model in analytics platform without involving the organization’s BI and IT teams.

Adaptive Case Management (ACM): It is an approach to case management that helps knowledge workers who need the most flexibility when handling their cases.

Artificial Intelligence: It is a scientific area related to development and application of methods and algorithms for knowledge learning, perception, representation, reasoning, planning, etc. of a system that characterizes with flexible adaptation for achieving specific goals and solving problems with different level of complexity.

Predictive Modelling: It is a process related to models creation concerning data usage and processing with aim business items, events and processes to be predicted.

Big Data: It is a research field studying methods and algorithms for too large and complex data sets extraction, processing, and analysis.

E-Commerce: It is related to buying and selling goods and services via Internet where business transactions between businesses, business and customers, customers and customers, customers and businesses occur.

Business Analytics: It deals with technologies, techniques and methods for investigation, exploration and analysis of past business events and processes.

Machine Learning: It is research field part of artificial intelligence that provides a system opportunity to automatically learn from data with aim predictive, classification and clustering tasks to be solved.

Business Process Management (BPM): It is a discipline in operations management in which people use various methods to discover, model, analyze, measure, improve, optimize, and automate business processes.

Star Schema Modeling: It is a process for entity-relationship diagram development based on data processing about facts and dimensions.

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