Soroosh Nalchigar

Information technology project evaluation: An integrated data envelopment analysis and balanced scorecard approach

Information technology (IT) is a tool crucial for enterprises to achieve a competitive advantage and organizational innovation. A critical aspect of IT management is the decision whereby the best set of IT projects is selected from many competing proposals. The optimal selection process is a significant strategic resource allocation decision that can engage an organization in substantial long-term commitments. However, making such decisions is difficult because there are lots of quantitative and qualitative factors to be considered in evaluation process. This paper has two main contributions. Firstly, it combines two well-established managerial methodologies, balanced scorecard (BSC) and data envelopment analysis (DEA), and proposes a new approach for IT project selection. This approach uses BSC as a comprehensive framework for defining IT projects evaluation criteria and uses DEA as a nonparametric technique for ranking IT projects. Secondly, this paper introduces a new integrated DEA model which identifies most efficient IT project by considering cardinal and ordinal data. Applicability of proposed approach is illustrated by using real world data of Iran Ministry of Science, Research and Technology.

A new DEA method for supplier selection in presence of both cardinal and ordinal data

The success of a supply chain is highly dependent on selection of best suppliers. These decisions are an important component of production and logistics management for many firms. Little attention is given in the literature to the simultaneous consideration of cardinal and ordinal data in supplier selection process. This paper proposes a new integrated data envelopment analysis (DEA) model which is able to identify most efficient supplier in presence of both cardinal and ordinal data. Then, utilizing this model, an innovative method for prioritizing suppliers by considering multiple criteria is proposed. As an advantage, our method identifies best supplier by solving only one mixed integer linear programming (MILP). Applicability of proposed method is indicated by using data set includes specifications of 18 suppliers.

A new method for ranking discovered rules from data mining by DEA

Data mining techniques, extracting patterns from large databases have become widespread in business. Using these techniques, various rules may be obtained and only a small number of these rules may be selected for implementation due, at least in part, to limitations of budget and resources. Evaluating and ranking the interestingness or usefulness of association rules is important in data mining. This paper proposes a new integrated data envelopment analysis (DEA) model which is able to find most efficient association rule by solving only one mixed integer linear programming (MILP). Then, utilizing this model, a new method for prioritizing association rules by considering multiple criteria is proposed. As an advantage, the proposed method is computationally more efficient than previous works. Using an example of market basket analysis, applicability of our DEA based method for measuring the efficiency of association rules with multiple criteria is illustrated.

A Conceptual Modeling Framework for Business Analytics

Data analytics is an essential element for success in modern enterprises. Nonetheless, to effectively design and implement analytics systems is a non-trivial task. This paper proposes a modeling framework (a set of metamodels and a set of design catalogues) for requirements analysis of data analytics systems. It consists of three complementary modeling views: business view, analytics design view, and data preparation view. These views are linked together and act as a bridge between enterprise strategies, analytics algorithms, and data preparation activities. The framework comes with a set of catalogues that codify and represent an organized body of business analytics design knowledge. The framework has been applied to three real-world case studies and findings are discussed.

On Ranking Discovered Rules of Data Mining by Data Envelopment Analysis: Some Models with Wider Applications

The convergence of computing and communication has resulted in a society that feeds on information. There is exponentially increasing huge amount of information locked up in databases—information that is potentially important but has not yet been discovered or articulated (Whitten & Frank, 2005). Data mining, the extraction of implicit, previously unknown, and potentially useful information from data, can be viewed as a result of the natural evolution of Information Technology (IT). An evolutionary path has been passed in database field from data collection and database creation to data management, data analysis and understanding. According to Han & Camber (2001) the major reason that data mining has attracted a great deal of attention in information industry in recent years is due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. The information and knowledge gained can be used for applications ranging from business management, production control, and market analysis, to engineering design and science exploration. In other words, in today’s business environment, it is essential to mine vast volumes of data for extracting patterns in order to support superior decision-making. Therefore, the importance of data mining is becoming increasingly obvious. Many data mining techniques have also been presented in various applications, such as association rule mining, sequential pattern mining, classification, clustering, and other statistical methods (Chen & Weng, 2008). Association rule mining is a widely recognized data mining method that determines consumer purchasing patterns in transaction databases. Many applications have used association rule mining techniques to discover useful information, including market basket analysis, product recommendation, web page pre-fetch, gene regulation pathways identification, medical record analysis, and so on (Chen & Weng, 2009). Extracting association rules has received considerable research attention and there are several efficient algorithms that cope with popular and computationally expensive task of association rule mining (Hipp et al., 2000). Using these algorithms, various rules may be obtained and only a small number of these rules may be selected for implementation due, at

Simulating strategic information systems planning process using fuzzy cognitive map

Strategic information systems planning (SISP) is one of the key factors in modern information age. Proposition of different methods for strategic information system planning baffle the organisations about using which of them. The problem here is the complexity of dealing with strategic information system planning due to superabundant factors engaged in it. In this paper the applications of fuzzy cognitive maps (FCMs), as decision making and modelling tool, in SISP context have been discussed. The objective is to simulate and represent the factors affecting the planning process which is considered both a tool and a need in todays competitive society. The resulting SISP fuzzy cognitive map gives a clear perception of factors affecting the planning process and their relations which help decision-makers and planners analyse and come to their related decisions and plans.

From Business Intelligence Insights to Actions: A Methodology for Closing the Sense-and-Respond Loop in the Adaptive Enterprise

Business Intelligence (BI) and analytics play a critical role in modern businesses by assisting them to gain insights about internal operations and the external environment and to make timely data-driven decisions. Actions resulting from these insights often require changes to various parts of the enterprise. A significant challenge in these contexts is to systematically connect and coordinate the BI-driven insights with consequent enterprise decisions and actions. This paper proposes a methodology for closing the gap between what an enterprise senses from BI-driven insights and its response actions and changes. This methodology adopts and synthesizes existing modeling frameworks, mainly i * and the Business Intelligence Model (BIM), to provide a coherent step-by-step way of connecting the sensed signals of the enterprise to subsequent responses, and hence to make BI and analytics more actionable and understandable. Applicability of the proposed methodology is illustrated in a case scenario.

Conceptual Modeling for Business Analytics: A Framework and Potential Benefits

Advanced analytics solutions are becoming widespread in business organizations. While data scientists create, implement, or apply machine learning algorithms, business stakeholders need the ultimate solution to gain competitive advantage and performance improvement. How can one, systematically, elicit analytical requirements? How can one design the analytics system for addressing such requirement? How can one assure the alignment between data analytics solutions and business strategies? How can one codify and represent analytics know-how in terms of design patterns? This paper has two contributions. First, it introduces a conceptual modeling framework for addressing those challenges. Second, it assesses the potential use cases and limitations of the framework by applying it to two case studies.

Mapping and Usage of Know-How Contributions

Mapping know-how, which is knowledge of how to achieve specific goals, is important as the creation pace and amount of knowledge is tremendously increasing. Thus, such knowledge needs to be managed to better understand tradeoffs among solutions and identify knowledge gaps. Drawing from goal-oriented requirements engineering, in this paper we propose a specialized (and light weight) use of concept maps to map out contributions to problem-solving knowledge in specific domains. In particular, we leverage on the means-end relationship which plays a major role in such domains and further extend it to be able to depict alternatives and tradeoffs among possible solutions. We illustrate the approach using problems and solutions drawn from two domains and discuss the usefulness and usability of the know-how maps. The proposed mapping approach allows for a condensed representation of the knowledge within a domain including the contributions made and the open challenges.

Business-driven data analytics: A conceptual modeling framework

Abstract The effective development of advanced data analytics solutions requires tackling challenges such as eliciting analytical requirements, designing the machine learning solution, and ensuring the alignment between analytics initiatives and business strategies, among others. The use of conceptual modeling methods and techniques is seen to be of considerable value in overcoming such challenges. This paper proposes a modeling framework (including a set of metamodels and a set of design catalogues) for requirements analysis and design of data analytics systems. It consists of three complementary modeling views: business view, analytics design view, and data preparation view. These views are linked together to connect enterprise strategies to analytics algorithms and to data preparation activities. The framework includes a set of design catalogues that codify and represent an organized body of business analytics design knowledge. As the first attempt to validate the framework, three real-world data analytics case studies are used to illustrate the expressiveness and usability of the framework. Findings suggest that the framework provides an adequate set of concepts to support the design and implementation of analytics solutions.