Business Intelligence: Strengths, Weaknesses, and Opportunities

Business Intelligence: Strengths, Weaknesses, and Opportunities

Jakia Sultana (University of Dhaka, Bangladesh) and Ahmed Jimoh (Auchi Polytechnic, Nigeria)
DOI: 10.4018/978-1-7998-5077-9.ch011

Abstract

This chapter discussed business intelligence (BI), highlighting its general strengths, weaknesses, and opportunities in the organizational context and in the context of unstructured data. Initially, a brief background on BI was discussed, followed by the discussion on benefit and challenges in different context. Recommendations provided for the challenges were discussed. Later, the chapter further looked at business intelligence and artificial intelligence followed by the future outlook of business intelligence. The contents of this chapter will help theoretically to understand the business intelligence, its background, benefits and challenges, and how to deal with the challenges by the given recommendations. Practically, this chapter will give insight to organizations about challenges to think about earlier stage based on the discussion on challenges in the organizational context.
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Background

According to Devens (1986), the concept of business intelligence was first used by Richard Millar to describe how Sir Henry Furness, a banker, profited over his competitor by acting on the information he gathered. In 1958, an article was written by an IBM computer scientist whose name is Hans Peter Luhn, describing the potential of business intelligence gathering through the use of technology. Literature suggested that the modern version of business intelligence evolved from Decision Support Systems (DSS). It was the first database management system to be developed; following by Online Analytical Processing (OLAP), Executive Information Systems (EIS) and data warehouses tools. These were developed alongside DSS to ease access and organisation of data.

Key Terms in this Chapter

Integration: Incorporating elements together (i.e., tools, technology, or systems).

Disruptive Technology: Technology that significantly change the way of business operate or process.

Scalability: The possibility of upgrading and expanding an already existing system (information system; hardware/software/people-ware) where and when required.

Ba: Business analytics; is the use of statistical methods to explore business data to make profitable decisions in business management. Same as BI.

ROI: Return on investment is a performance metrics to determine gains in organizations.

DSS: Decision support system.

ETL: Means to extract, transform, and load information. Usually from a database system.

BI/A: Business intelligence/business analytics.

HANA: HANA is a version of SAP developed by SAP SE.

Big Data Analytics: This refers to the capability to read and analyze volume of different data sets.

OLAP: Stands for online analytical processing. It is a multi-way approach for information analysis. It is usually based on online processing.

DOMO for Business Dashboard/Apptus for Sales Enablement/Avanade for Business Insight: These are different examples of BI tools.

Adoption Rate: A rate at which the implementation and use of technology or tools occur.

Unstructured Data: Data remained in unfiltered way or in a raw way which need to be processed to make information.

SAP: This is an enterprise resource planning product developed by a German company.

BYOD: Means bring your own device. It a concept that allow users to bring their device to use in an organisation rather than using the official ones.

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