Literature Review Analysis : Comparison Of Different Business Intelligence Tools

Question

Task: This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to improve student presentation skills and to give students experience in researching a topic and writing a report relevant to the Unit of Study subject matter.

Instructions Perform a literature review on a known topic in business analytics. It can be any topic on tools, methodologies or applications.

Some examples include, but not limited to:
1. Use of predictive analysis in healthcare industry
2. Comparison of BI tools
3. Techniques of predictive analysis
4. Methods of representing multi-dimensional data in visualisations
5. Analytics techniques to improve logistics management
6. Security of data and privacy concerns in analytics

Please note that this is an individual project. Discuss with your lecturer before week 7 to decide on a topic. The topic needs to be chosen before week 7.

Based on your review you need to submit a report in IEEE format; see the word file in the moodle. Submit your report in a word or pdf format. Your report should be limited to 1200-1500 words.

Answer

I. Introduction
The aim of the paper is to perform the literature review analysis over the comparison of different BI (Business Intelligence) Tools for the business. The business intelligence was into existence even during the 1950s, from when it started growing to be a technology known as decision support. There are various companies that use decision support as it provides assistance in gain a competitive advantage. With the advent of rapid growing technological innovations, the market of Business Intelligence is facing rigorous growth along with the customers' needs and requirements. Adopting the system of Business Intelligence has made an important place in the market for which provides assistance in the promotion of diffusion of knowledge and has become the cornerstone of the decision-making processes in businesses. Further, the capabilities of technologies would also be discussed. The technological capabilities consist of user access, quality of data flexibility, support to the management of risk, and various other technological capabilities.

II. Research Methodology
In the project, secondary data is being used from various previous kinds of literature that relate to business Intelligence with a large number of articles and collection of data [1]. The main focus of Business Intelligence is on the collection, organization, and interpretation of data to their respective departments to ensure a proper making of decisions in the system, with the reduction in the uncertainty to achieve a respective goal [1]. In the paper, the Thematic Though School and the Business Intelligence aspect are being linked together to ensure a proper decision making

III. Review Of Literature
A. Business Intelligence
The main challenge with the number of definitions is that they keep changing from time to time, due to the alterations in the concepts. In the initial days, business intelligence was engaged with a software. Business Intelligence is featured as the set of frameworks that come together, alter, and show information that is properly organized from different sources reducing the time constraints [2]. This helps in the enhancement of the efficiency of the business and enables the proper decision making of the business. Business Intelligence refers to the framework that enables the transformation of the information to data and later into learning. It enhances the process of decision making of the company [2]. Business Intelligence provides assistance to the administrators to break the information down into different sources in a better leader in both strategic level for utilization for functional planning, farewell of frameworks of conventional data [3].

B. Data, knowledge and information
In the context of Business Intelligence, we often get confused using the terms data, knowledge, and information. There are times when we use them interchangeably [3]. This is because the three terms need to be studied upon the thin line difference between the three.

Data: data is defined as the codification on a structured basis, of the primary entities and the transactions that involve many primary entities. Data is divided into three kinds, which are structured data, unstructured data, and semi-structured data [4].

Information: Information can be defined as the outcome of the processing and extraction of data. It is meaningful for the recipient in a particular field. Knowledge [5].

Knowledge: The formation of knowledge was from information which helps in the decision-making process and development of corresponding actions. It can be said that knowledge is a part of the information, which leads to the enhancement of makers of a decision in the tackling and finding potential solutions to a specific problem [5].

Business Intelligence Architectures
The system of Business Intelligence is constructed by the help of a pyramid, the components of which are discussed below [6]:
Data Sources: the sources of data mostly include data that belong to the operation system, but also may consist of the data which is not structured like email, data that are received from the external providers [7].
Data Mart/Data Warehouse: The Data Mart/Data Warehouse help in the consolidation of various types of data to a central location by the usage of a process that is called extract, load and transform and standardization of those results along with the system that can be enquired upon [7].
Data exploration: Data exploration refers to a passive analysis of the Business Intelligence that consists of doubts and the systems of reporting and the method relating to statistics [7].

Data mining: Data Mining refers to the active Business Intelligence methods for information and extraction of knowledge from data [7].
Optimization: Optimization of data refers to the determination of the potent solution from a set of actions that are alternative. The actions are extensive and many a time infinite [7].
Decisions: When there is availability of methodologies of Business Intelligence and the methodologies are implemented and adopted successfully the pertinent of the decision shifts to the decision-makers [8].
The main elements of the Business Intelligence System

C. Business Intelligence Capabilities

There are many researchers who say that due to the absence of fit, in many organisations, the adoption of Business Intelligence resulted in a failure. Organisational Business Intelligence consists of various criteria on which it is reviewed [9]. They are discussed below

Data Quality: The Data which is relevant and clean is considered one of the most crucial factors of the success of Business Intelligence. The organisations incorporate data that is available in various sources, will continue facing issues that surround the quality of the work [9].
Integration with various other systems: The organisation that uses multiple sources data and converting them to various systems of information, the integration performance gets affected by the communication quality among those systems [9].
Access to users: The Business Intelligence tools have various innovative capabilities that can help in the serving various purposes, not all of which fit in the Business Intelligence. It is very essential that the that the companies maintain a balance for the allowance of the way the users of Business Intelligence are given proper access to information to make a proper decision [10].
Flexibility: In order to sustain in the dynamic environment, the companies need to flexible enough to select a particular technology of Business Intelligence, to give its rivals a competitive edge [10].
Support to Risk Management: Risk management is considered one of the essential pillars in the Business Intelligence, since it provides assistance to the potential decision making. It is one such area where the circumstances are not certain, and full of high risk. Adopting Business Intelligence tools is of utmost importance it enables in the overcoming of the risks that are associated with the business [10].

D. Factors influencing Business intelligence Projects
The factors that influence the project of Business Intelligence are discussed below:
Technologies: The essential factors that influence the development of the systems of Business Intelligence in a complex enterprise are the software and hardware technologies. The empowerment of this pattern has enabled to utilise the advanced procedures that are needed for the utilization of strategies of learning that inductive to the organisation [10]. Analytics: Analytical methodologies and mathematical models play an essential role in the advancement of information and knowledge. They bring out the data which is accessible in most of the companies. The data visualization in accordance with the flexible and timely views, plays an essential role in the facilitation of the making of decision making, however, present a passive support form [11].

Human Resources: Human Resources of a company are considered the most important resources that an organisation can ever have. Every company can have access to the analytical tools that are available.

However, when a company is wanting a competitive edge over the rivals, that is when the human resources come into the picture. They enhance the decision-making capacity of the organization [12].

IV. Conclusion
From the above research, it can be concluded that adopting the system of Business Intelligence has made an important place in the market for which provides assistance in the promotion of diffusion of knowledge and has become the cornerstone of the decision-making processes in businesses. However, there must be proper understanding and linkage between the business requirements and the application of specific business intelligence tool for supporting the ongoing operations. Further, the capabilities of technologies would also be discussed. Business Intelligence refers to the framework that enables the transformation of the information to data and later into learning. It enhances the process of decision making of the company. The system of Business Intelligence is constructed by the help of a pyramid, the components of which are sources of data, data mart/data warehouse, exploration of data, data mining, optimization, and decisions.

V. References
[1] T. S. Adeyelure, B. M. Kalema and K. J. Bwalya, "Development of Mobile Business Intelligence framework for small and medium enterprises in developing countries: Case study of South Africa and Nigeria," 2016 4th International Symposium on Computational and Business Intelligence (ISCBI), Olten, 2016, pp. 11-14, doi: 10.1109/ISCBI.2016.7743252
[2] S. Atul Khedkar and S. K. Shinde, "Customer Review Analytics for Business Intelligence," 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India, 2018, pp. 1-5, doi: 10.1109/ICCIC.2018.8782305.
[3] J. Kiruthika and S. Khaddaj, "Impact and Challenges of Using of Virtual Reality & Artificial Intelligence in Businesses," 2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES), Anyang, 2017, pp. 165-168, doi: 10.1109/DCABES.2017.43.
[4] C. Siemen, N. Clever, B. Barann and J. Becker, "Requirements Elicitation for an Inter-Organizational Business Intelligence System for Small and Medium Retail Enterprises," 2018 IEEE 20th Conference on Business Informatics (CBI), Vienna, 2018, pp. 129-138, doi: 10.1109/CBI.2018.00023.
[5] P. Wazurkar, R. S. Bhadoria and D. Bajpai, "Predictive analytics in data science for business intelligence solutions," 2017 7th International Conference on Communication Systems and Network Technologies (CSNT), Nagpur, 2017, pp. 367-370, doi: 10.1109/CSNT.2017.8418568.
[6] B. Ramesh and A. Ramakrishna, "Unified Business Intelligence Ecosystem: A Project Management Approach to Address Business Intelligence Challenges," 2018 Portland International Conference on Management of Engineering and Technology (PICMET), Honolulu, HI, 2018, pp. 1-10, doi: 10.23919/PICMET.2018.8481744.
[7] R. Somya, D. Manongga and M. A. Ineke Pakereng, "Service-Oriented Business Intelligence (SoBI) for Academic and Financial Data Integration in University," 2018 International Seminar on Application for Technology of Information and Communication, Semarang, 2018, pp. 1-5, doi: 10.1109/ISEMANTIC.2018.8549762.
[8] S. Wijayanto and M. L. Khodra, "Business Intelligence According to Aspect-Based Sentiment Analysis using Double Propagation," 2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, 2018, pp. 105-109, doi: 10.1109/ICITISEE.2018.8720961.
[9] K. C. Susena, D. M. Simanjuntak, Parwito, W. Fadillah, Yulyardo and A. S. Girsang, "Business Intelligence for Evaluating Loan Collection Performance at Bank," 2018 International Conference on Orange Technologies (ICOT), Nusa Dua, BALI, Indonesia, 2018, pp. 1-6, doi: 10.1109/ICOT.2018.8705829.
[10] P. Valter, P. Lindgren and R. Prasad, "Artificial intelligence and deep learning in a world of humans and persuasive business models," 2017 Global Wireless Summit (GWS), Cape Town, 2017, pp. 209-214, doi: 10.1109/GWS.2017.8300487.
[11] B. AlArmouty and S. Fraihat, "Data Analytics and Business Intelligence Framework for Stock Market Trading," 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS), Amman, Jordan, 2019, pp. 1-6, doi: 10.1109/ICTCS.2019.8923059.
[12] S. Feng, "Research on Demand Control of Business Intelligence," 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, 2019, pp. 292-295, doi: 10.1109/MLBDBI48998.2019.00065.