Using Fuzzy Logic for Optimizing Business Intelligence Success in Multiple Investment Combinations

Using Fuzzy Logic for Optimizing Business Intelligence Success in Multiple Investment Combinations

Mandana Farzaneh (Sharif University of Technology, Iran), Iman Raeesi Vanani (Department of Information Technology Management, University of Tehran, Iran) and Babak Sohrabi (Department of Information Technology Management, University of Tehran, Iran)
Copyright: © 2015 |Pages: 13
DOI: 10.4018/978-1-4666-5888-2.ch091
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Business intelligence (BI) has become a critical foundation of competition for organizations over the last several years and has a significant role in sustaining competitive advantage. However there are many problems in BI investment, which are derived from the existence of a large amount of criteria involved in its success which are not all tangible. According to the large amount of complicated quantitative and qualitative factors to be considered in the candidate BI projects and because of the vagueness of them, fuzzy logic is employed to optimize BI investment in businesses. In this regards a fuzzy inference system (FIS) is designed to specifically help in overcoming the complexity of evaluation the business intelligence investment. Proposed FIS is based on a knowledge-driven approach. The main objective is to present to readers a concise deep insight of critical success factors of BI investment model based on FIS.
Chapter Preview
Top

Introduction

Because of the markets agglomeration and economic environment evolution, the ability to collect data and convert them to useful information for the decision process can be the element which introduces value-added services ahead of the competition and makes competitive advantage. Lack of vertical integration of information systems, together with the rapidly increasing volume, velocity, and variety of data spread across the enterprise, make it extremely difficult for management to analyze, summarize and extract reliable, relevant, and easy to use information for decision making. In response to these problems, many organizations are compelled to improve their business execution and manage cross-organizational decision support needs in terms of access to relevant information through the investment of a BI system (Akhavan & Salehi, 2013; Luminita & Magdalena, 2012; Petrini & Pozzebon, 2009).

The main task of a BI system (BIS) include intelligent exploration, integration, aggregation and a multidimensional analysis of data as a highly valuable corporate resource which originate from various information resources in order to provides a better understanding of underlying trends and dependencies that affect the business. Hence, meaningful and actionable information can be delivered at the right time, at the right location, and in the right form to assist individuals, departments or divisions to facilitate effective decision making. Business intelligence system is wide spread across organizations throughout most industries and become a technological solution offering data analytical capabilities and has a significant role in business value of the firm (Azma & Mostafapour, 2012; Ramakrishnan, Jones & Sidorova, 2012). BI is comprised of technical and organizational elements which aggregate and present greater volumes of data in different ways from multiple sources. It enable management support, increase autonomy, organizational performance and flexibility of users by creating quick and simple analyses (Isıka, Jones & Sidorova, 2013; Jamaludin & Mansor, 2011).

In today's highly competitive and increasingly uncertain world, the quality and timeliness of an organization's BI can mean not only the difference between profit and loss, but also even the difference between survival and bankruptcy (Bahrami, Arabzad & Ghorbani, 2012). Companies using BI systems can achieve a single consistent new and unified insight of business information, manage and analyze structural and non-structural information and exploit it to gain knowledge about the business domain (Bonney, 2013; Lin et al., 2009). Consequently organization could comprehend hidden meanings in data, predict, solve problems, innovate and learn in ways that increase organizational knowledge, implement new business model, optimize decision making processes, and establish and achieve business goals effectively (Rouhani, Ghazanfari & Jafari, 2012). It also reduces the probability of underperformance or sudden extreme decisions due to late arrival of information and enhances efficiency and transparency in the internal affairs of the key processes and procedures (Rubin & Rubin, 2013). BI technologies integrate a large set of packages and tools for data analysis, query, and reporting such as online analytical processing1, data mining tools, report extractors, applied artificial intelligence, visualizations, statistical analysis, forecasting, dashboards, and the underlying specialized IT infrastructure (such as data warehouses, data marts and ETL tools) (Elbashir, Collier & Davern, 2008).

Key Terms in this Chapter

Business Intelligence: A broad category of applications, technologies, and processes for integrated acquisition, interpretation, collation, analysis, and exploitation of data to help business users make better decisions in order to improve business operations, reduce uncertainty & apply past experience to develop an exact understanding of business dynamics.

Fuzzy Logic: Take into account ambiguous cases or exceptions in natural language and progressively incorporate them into the expertise. It allows for vague boundaries, provides a mechanism to utilize fuzziness in subjective or imprecise determinations of preferences, constraints, and goals.

Fuzzy Inference System: Employing fuzzy if-then rules to express input–output relationships and model the qualitative inputs and reasoning process for creating the output. The fuzzy inference systems incorporate a set of antecedent and consequent fuzzy membership functions as well as a set of Fuzzy IF–THEN rules which considered a firm basis for developing the core of any system which might be used for making decisions in vague and inaccurate situations.

Success Factor: Combination of important facts that are required in order to accomplish one or more desirable business goals. When key success factors are categorized as critical success factors, they are viewed as variables that have direct impact on effectiveness of a business.

Complete Chapter List

Search this Book:
Reset