Data Mining and Its Contribution to Decision-Making in Business Organizations

NADIA HAMDI PACHA,1 FATMA ZOHRA KHEBAZI,2 and NACHIDA MAZOUZ3

lecturer and Researcher, Faculty of Economics, Business, and Management Sciences, University Ali Lounici-Blida 2, Route d'El Afroun, Blida, Algeria, E-mail: This email address is being protected from spam bots, you need Javascript enabled to view it

  • 2 Lecturer, Faculty of Economics, Business, and Management Sciences, Khemis Miliana University, Rue Thiniet El Had, Khemis Miliana, Ain Defla, Algeria, E-mail: This email address is being protected from spam bots, you need Javascript enabled to view it
  • 3 Lecturer, Faculty of Economics, Business, and Management Sciences, University Ali Lounici-Blida 2, Route d’El Afroun, Blida, Algeria

ABSTRACT

Data mining (DM) has emerged in response to the need for organizations to find a way to take advantage of the large data stored in then databases and repositories, from which traditional analysis methods are unable to extract useful information from them. This technology, based on intelligent deductive algorithms, allows the conversion of a huge amount of raw data into meaningful information, and into new knowledge, which is commonly used to support decision-making.

Therefore, this chapter seeks to provide a theoretical study on how this technology contributes to support decision-making in business organizations. This is to sensitize organizations and practitioners of the advantages of this technology is adopted and arouse the curiosity of scholars and researchers to enrich the subject by studies and research, especially in economic and administrative sciences.

To reach clear results, the descriptive and analytical approach was adopted in dealing with the subject by reviewing the articles, studies, and reports related to the research problem. These references were used in determining the basic theoretical concepts related to DM and their assumed role in the decision-making process. A practical case was also noted on the application of the technology by looking at the status of Dubai Airports.

This research paper concluded that this smart technology works to search for relationships, trends, and patterns hidden in databases, to be used in building models of prediction, and in exploring the behavior of individuals, and in determining their general trends.

This useful information is used in several areas, such as creating a competitive advantage that the organization focuses on, or improving the performance of its services. The systematic and orderly use of this technology also makes the organization’s information system an integrated system that discovers shares, and distributes knowledge.

This allows the provision of accurate and rapid information, which contributes to better decision-making, especially on how to increase profits or reduce costs. This research concluded that Dubai Airports Corporation has been able to benefit from the applications of this technology, not only in supporting the decision-making process but also in creating a competitive advantage, improving sendees, reducing costs, and raising its overall performance.

7.1 INTRODUCTION

DM is a new trend in the success of decision support systems in modem business organizations. The development of this technology is the inevitable result of the rapid development of information and communication technology, digitization, artificial intelligence, and machine learning, which resulted in the provision of a huge amount of data stored in the various databases and stores.

The orientation of organizations towards the knowledge economy has forced them to seek new knowledge to be used in making various decisions that will improve their competitive advantage. In response to these new variables in the business environment, DM technology has been introduced, which has allowed the organization to search within the vast volumes of raw data available to it and extract new, previously unknown, implicit information that could be utilized.

DM allowed the discoveiy of new knowledge hidden in databases, by identifying relationships that were not discovered by traditional methods of analysis, in addition, to identifying patterns and trends in these data and turning them into infonnation with specific characteristics and evidence, which can be used in the decision-making process.

Accordingly, the problem of this paper is to determine how this technology contributes to the decision-making process in the business organization. In this light, the research attempts to answer the following sub-questions:

  • • What is data mining? Why is it important?
  • • How does data mining help to make appropriate decisions that will improve the performance of business organizations?
  • • What are the applications of data mining in organizations, and specifically in Dubai airports?

Therefore, this paper seeks to achieve the following objectives:

  • • Introduce data mining technology and its role in providing knowledge to the organization;
  • • Understand how it can contribute to decision-making;
  • • Access to technology applications in some fields;
  • • View practical status by reviewing the status of Dubai airports.

The importance of the research lies in the fact that the field of DM is still a relatively recent topic, both in terms of the literature related to this field and in terms of its practical applications to organizations.

The study of the dimensions of the use and investment of big data in business organizations and in the decision-making process is still the focus of the attention of many scholars, researchers, and decision-makers. Therefore, we considered it important to shed more light on this technique by introducing it and clarifying its effects.

Therefore, we will address this topic by addressing first, the most important elements that will define the technology of DM, which will foim the theoretical framework for research. We then review the tools used in this research in order to determine the contribution of this technique to decisionmaking, in addition to the results achieved. Finally, we discuss these findings in light of the practical situation, indicating the limits of this study and its prospects in conclusion.

7.2 THEORETICAL FRAMEWORK FOR RESEARCH

The accumulation of data has become a major problem for all organizations, which requires the existence of new methods of dealing with them. In order to ensure easy access to any required information and as soon as possible, it enables the management to make rational decisions on any aspect of the organization. The accumulated data is general and comprehensive for all parts of the organization and its various activities. DM technology is an appropriate solution to this issue [1].

The term DM appeared in the United States in the mid-nineties, combining statistics and information technology. It relies on the use of modem algorithms in data processing and analysis to reach patterns and relationships in large data sets, which allows the prediction of future behavior. It is a guide to decision-making in all business applications [1].

DM was defined as “extracting useful knowledge from large amounts of data, using machine learning techniques and statistical methods” [2]. It is also defined as “an analysis of data sets that are often gr eat for discovering and summarizing unexpected relationships, in a new and understandable way” [3]. DM is the process by which the organization seeks to find new knowledge hidden in large databases, in which it is difficult to use regular statistical methods for analysis.

Some of the mam factors that have led organizations to increase their use of this technique are [4, 5]: [1]

The most important benefit of DM technology is its ability to provide accurate, collect, and fast information, which benefits the organization in many respects [1]:

  • • Access to data that would not otherwise have been possible;
  • • Find some kind of deductive data types by examining the records in the data warehouse files;
  • • Achieve a certain understanding level that contributes to the discovery of knowledge from databases;
  • • Predict future values resulting from operations and business behavior;
  • • Discover the hidden links, trends, and new patterns that exist between the vast amounts of business data.

This is done by using two exploration models [6]:

i. Prospective or Predictive Models: A model of the system results from it by described the used data used for exploration and amis to predict the value of certain characteristics, such as the probability of purchase for the customer.

ii. Descriptive Exploration models: It produces new information based on the information contained within the user data in the exploration process. In addition, it is divided into clustering models that allow individuals, events, or products to be clustered into; and correlation models that allow the identification of relationships between them.

Data can be explored using several tools; the most important of them are

[1.7]: [2]

analysis, or on relatively recent methods such as correlation forces, case-based inference, and neural networks.

> Third: Prediction: prediction is similar to classify or estimate, except that the data are classified on the basis of predicting the future or their behavior estimation of future value. Traditional tools used in forecasting include regression types and discriminatory analysis. New methods include decision trees, neural networks, and genetic algorithms.

У Fourth: Clustering analysis or fragmentation: it is a descriptive approach designed to separate homogeneous data from heterogeneous characteristics in a community of individuals. This is done based on information contained in the totals of the variables that describe them, helping develop marketing programs tailored to the customers’ own sizes in order to repeat the purchase or transform it into loyal customers. Cluster aggregation methods are assisted by statistical cluster analysis, decision tree-based methods, neural networks, and genetic algorithms.

У Fifth: Rule analysis: it refers to a set of methods that are used to link buying patterns through cross-sectorial or over time. For example, the analysis of the market basket method, by using the underlying information in the goods purchased by consumers actually, to predict the potential of goods bought them if they are offering special offers or if they are introduced to these goods.

> Sixth: Regression analysis: it is used to convert the data into an expressive explanatory value that helps in adding value to the prediction and estimation process, such as predicting sales volume or the relationship between variables.

У Seventh: Sequential analysis: it seeks to find similar models in the qualities that occur during a business succession period.

DM is conducted according to the following stages [1, 8]:

  • 1. The Stage of Business Understanding: It is an essential element for the success of the DM process.
  • 2. Data Understanding Stage: It is the first stage of the exploration of data. In addition, this by knowing what is the nature of the data, in order to assist designers in the use of algorithms or tools used for specific issues with high accuracy. This leads to maximize the chances of success as well as to raise the effectiveness and efficiency of the system of knowledge discovery. The required stages of understanding the data are:
    • Data Collection: It means identifying the data source in the study process.
    • Data Description: It is the focus on configuring the contents of files or tables.
    • Data Quality and Review: It is the act of negligence of unnecessary or poor quality data that are not useful in the study in order to ensure accurate data.
    • Exploratory Analysis of Data: This stage is important because it focuses on developing hypotheses related to the problem under study. The initial analysis of the data is earned out using visualization or direct analysis OLAP.
  • 3. Data Preparation Stage: This phase aims to improve the quality of real data to explore and increase the efficiency of the process by reducing the required tune for exploration. This stage includes the following steps:
    • • Selection: The choice of expected variables and sample.
    • • Construction and transformation variables: To formulate new variables to construct effective models.
    • • Data integration: storing data sets in multipurpose databases with the aim of consolidating them into a single database.
    • • Data formatting: Rearranging the data fields by the data-mining model.
  • 4. Model Building and Validation: By testing and examining various alternatives to get the best model to solve the problem under study.
  • 5. Evaluation and Interpretation: By checking the reliability of the data sets that are glowing by the model. Since the results of this data are known, they are compared with the actual results in the stability of the running data set to check the accuracy of the form.
  • 6. Model Deployment: It includes the publication and distribution model within the organization to assist the decision-making process.
  • 7.3 SEARCH TOOLS AND RESULTS

In order to identify the problem of research, it was reported to reviewing the articles, studies, and research available on the subject. This is to obtain a clear-cut picture of the role of DM technology in providing the organization with the knowledge, how it supports decision-making, as well as its various applications and uses.

A selected practical case was also inferred in order to drop the theoretical research results on the practical reality by reviewing the different applications of this technology in one of the major business organizations in the aviation sector, represented by the Dubai Airports Corporation. After reviewing the websites of many organizations, this case was chosen in view of the tangible positive effects this technology has had on improving airport management and the sendees it provides to travelers.

By looking at the implications of adopting this technology on organizations, we have found that it leads to a range of positive effects as a result of providing extensive information and new knowledge to help better decision-making.

This technology leads to the creation of a new internal work environment, which makes the organization information system an integrated system [9], which, in addition to their traditional roles, exercise the cognitive role, which provides the organization with the knowledge through [9,10]: [3]

DM technology also provides accurate and fast information that demonstrates its importance in supporting the decision making process. In addition to providing the following features [9]:

  • • Assist decision-makers in activating the interdependence between the different divisions and actions of the organization.
  • • Facilitates the handling of advanced information technologies and helps to measure the effectiveness and productivity of different sub- information systems by providing accurate information.
  • • Assists in the effective use of available data sources and resources, and in planning and improving the used accounting and banking information systems.
  • • The information helps to increase knowledge, reduce alternatives, and eliminate the uncertainty.
  • • Enable the decision-maker to identify the problem and its elements, complete the control of the information and data files necessary for its decisions, and seek to develop accounting information systems at appropriate costs.
  • • It provides the necessary information to make decisions that contribute to expediting tasks and simplifying procedures.

As a result of these features, technology applications were used in various fields, where the first applications for DM in the field of customer relationship management, by analyzing the behavior of customers in order to maintain their loyalty and propose products according to their wishes.

Distribution organizations or large areas of distribution are the first to use them, and then moved to banks, then insurance institutions, then mobile phone companies, then water and electricity institutions, and more recently, air transport and rail transport institutions, etc. The applications of this technique have also been used in other fields [8]: [4]

forecasts, and stock performance, and in determining the risk of loans and financial fraud.

  • Insurance: These techniques have been widely used in this area by determining policy prices, and the expected future oscillations, and in identifying counterfeit claims.
  • Operating Management: Neural networks were used in planning, scheduling, project management, as well as quality management.

In general, business organizations can benefit from DM applications in:

  • • Writing summary reports on specific categories such as regular customers and credit cards;
  • • analysis of trade tendency by creating markets with strong or weak growth potential;
  • • Marketing for certain categories based on common characteristics that are discovered;
  • • analysis of use by finding a particular pattern for the use of services and goods;
  • • Compare campaign strategies with each other in order to find the most effective.

As for Dubai Airports, it looked forward to taking advantage of the exploration of large volumes of operational data to lead better customer service through the airport-from waiting times in the safety queue to the toilets [11], especially in light of expectations of high rates of passenger and cargo traffic [12].

It also seeks to increase the capacity of its airports, using existing resources and without resorting to the construction of any additional space, infrastructure, or new runways [13]. To overcome this problem, the corporation considered that the solution lies in the use and analysis of data in airports to increase its efficiency.

Through DM, Dubai Airports Corporation aims to monitor and collect data from all possible sources and optimally use it to increase the capacity of existing airports, improve sendees, reduce energy consumption, and reduce financial costs [11].

In order to achieve the goal set by DAC for the exploration, the sources of data to be collected from the airports were identified in: flight data, Wi-Fi data, metal detector data, baggage data, various sensors data (door sensors- bathrooms-water taps), etc.) and 3D camera data to fetch queue data [13].

Dubai Airports has entrusted the exploration to a specialized company, Splunk [13], as a result of the vast amount of data collected from previous sources, making the exploration process difficult and expensive and requires considerable resources.

7.4 PRESENTATION AND DISCUSSION OF PRACTICAL RESULTS

Dubai Airports operates and develops both Dubai International Airport and Dubai A1 Maktoum International Airport [14], currently handling over 10 million tonnes of cargo and over 160 million passengers annually, making The busiest airports in the world [15].

Following the exploration of its operational data, Dubai Airports has been able to achieve its objectives. This process has enabled decision-makers to improve the sendees provided to customers as a result of providing accurate and fast information. This technique has allowed the identification of situations requiring a particular action and then sending alerts to decision-makers in real-time to take appropriate measures and actions. This allowed the following results [11, 13, 15]: [5]

a sticker on the baggage allows the generation of information from more than 200 data points, which is conformity with the data related to the operations of the airport and forecasting the expected loads during the coming horns.

  • 4. Reduce Costs and Improve Cleaning Services and Energy Consumption: This is to provide all bathrooms and tap sensors, where data from these sensors show the bathrooms and laundries most frequently used or must be verified clean and functioning properly. Based on the information obtained from these data, the necessary number of staff is determined, and clean-up and maintenance operations are carried out in a timely maimer, which has allowed energy consumption to be reduced to 20%, which will save about $23 million in 2023.
  • 7.5 CONCLUSION

As a result of strong growth in databases and stores and intense competition in the global marketplace, business organizations are increasingly interested in looking for new applications.

In order to make the most of their data, to discover new knowledge that helps to make appropriate decisions, to improve their performance, and increase their competitiveness, a procedural phase was therefore needed to extract specifications and hidden relationships in the vast amounts of business data stored in traditional information systems and to provide new, previously unknown information on which to make new decisions.

This is what is allowed by DM technology, which is part of the discovery of knowledge, which begins with the selection of data of interest and conducts a number of pre-processing operations to be appropriate to the technology of exploration. Then apply that technology and evaluate the results obtained and make the appropriate decision accordingly.

Through this paper, we concluded that DM is a smart technology that allows the analysis, synthesis, and transformation of large data into useful and important information.

This is by creating anonymous relationships, highlighting general trends, and showing patterns in these statements, which were not known to the organization before the exploration. DM leads to internal and external knowledge of the organization by providing sources of information and facilitating access to it by its personnel, which helps to coordinate the various activities and processes.

It also makes the organization’s information system an integrated system, which works to discover, share, and distribute knowledge, allowing accurate and rapid information to contribute to better decision-making, especially in terms of how to increase profits or reduce costs.

The best example of how DM applications can be used to support decision-making, create a competitive advantage, and improve the overall performance of the organization is Dubai Airports. With the investment of its data mine, it was able to extract important infonnation from flight data, sensor data, digital cameras, metal detectors, luggage systems, and Wi-Fi data.

In light of this new infonnation, the decision-makers took appropriate procedures and necessary measures to increase the carrying capacity of Dubai airports, improve the services provided, and reduce energy consumption and reduce costs, using only existing resources.

In spite of the important results we have reached through this paper, this research did not satisfy the subject of his right to study. This is because of the multiplicity of its competencies, on the one hand, and the fact that the main objective of this chapter is to introduce this modem technology to nonspecialists and to highlight its role to managers and decision-makers as a technology that supports decision-making in the organization, on the other.

This topic, given its importance, opens the door to numerous theoretical and practical studies related to exploring its most important uses in different sectors and fields, whether in business organizations, non-profit organizations, or public sector institutions. For example, the contribution of data exploration to reducing the cost of running government facilities, delivering public sendees, improving the performance of hospitals and universities, improving the performance of public institutions, or the limits of applying this technology in certain areas.

KEYWORDS

  • • baggage distribution system
  • • data mining
  • • decision making
  • • Dubai Airports Corporation
  • • organization information system
  • • pre-processing operations

REFERENCES

  • 1. Sabah, M. M., & Sadiq, H. A. M., (2015). The impact of data mining technology on the development of decision-making: An exploratory study in The Iraqi Ministry of Higher Education and Scientific Research. Management and Economics Magazines, 38 Years (Vol. 103, p. 87).
  • 2. Al-Fatlawi, H., (2017). Forecasting Techniques in Data Mining. Center for Research and Information Rehabilitation, University of Kufa, [Online]: http://uokufa.edu.iq (accessed on 22 October 2020).
  • 3. David, H., Heikki, M., & Padhraic, S., (2001). Data Mining (p. 222). Massachusetts Institute of Technology, USA.
  • 4. Paulraj, P., (2010). Data Warehousing Fundamentals (p. 430). John Wiley and Sons, New York. USA.
  • 5. Al-Alaq, B. A., (2005). Digital Management: Fields and Applications (pp. 89-91). Emirates Center for Strategic Studies and Research, Abu Dhabi.
  • 6. El-Deeb, К. H. M., (2012). Modem Methods of Data Analysis (Data Mining), an Analytical Study (p. 3). [Online]: http://digital.jilwan.com/digital2012/download2012. php?f=jalsa4/4_l .pdf (accessed on 22 October 2020).
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  • 8. Al-Ali, A. S., Amer, I. Q., & Al-Omari, G., (2006). Introduction to Knowledge Management (pp. 100-102). Dar Al-Massirah for Publishing, Distribution and Printing, Amman.
  • 9. Al-Shehadeh, A. R., (2013). The effect of application of data mining techniques on banking operations management. Damascus Journal of Economic and Legal Sciences, 29(2), 178.
  • 10. Turban, E., & Leidner, D., (2008). Information Technolog)’ for Management (6* edn., pp. 183-186). John Wiley & Sons, New Jersey.
  • 11. Scott, C., (2017). Dubai Airport Turns to Real-Time Data in Drive to be the World's "Biggest and Best Airport." Computer World, UK, [Online]. https://www. computerworlduk.com/data/npl-relaunches-with-new-focus-on-measurements- supporting-uks-digital-industries-3664417/ (accessed on 22 October 2020).
  • 12. Michael, I., (2014). Executive Director of Dubai Airports, said: "We Do Not Want to be Bigger, We Want to be the Best, with New Levels of Customer Service.”
  • 13. Splunk, (2018). Dubai Airports Flies into the Future with Splunk: Case Study. [Online]: https://www.splunk.com/pdfs/customer-success-stories/dubai-airports-case-study.pdf (accessed on 22 October 2020).
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  • 15. Al-Khayat, I. How Dubai Airports Analysis Data to Improve Its Setvices. Arabic site, Science [Online], https://datasciencearabi.com/dubai-aiipoi1s (accessed on 22 October 2020).

CHAPTER 8

  • [1] Increasing the data amount that is produced by organizations, and thelimited traditional analytical tools to discover new patterns. • Expansion of DM applications to many fields such as marketing,banking, insurance, transportation. • The emergence of new methods of analysis, most notably neuralnetworks, genetic algorithms systems, and induction rules. • The market competition that leads organizations to make the most ofthe data, especially with the difficulty of obtaining it and the growingneed for rapid analytical results. • The tremendous development in data storage and processing methods,such as data warehouses and markets data warehouse. • The emergence of new generations of user-friendly software: Microsoft Windows, Client-Server software. • Costs of electronic communications reduce, which eases access todatabases.
  • [2] First: Summarization: it refers to the methods of fragmentation oflarge data blocks into summary measures, which provide a generaldescription of the variables and their relationships. As summarization methods, we can include averages, totals, and descriptive statistics that include measures of central tendency such as arithmeticmean, median, and mode, and dispersion measures such as standarddeviation. У Second: Classification: it includes identifying levels within the datato be recognized on the information through correspondence with theproposed levels in advance. It helps to discover new levels and itemsof information. Classification can be accomplished according tohistorical statistical methods, such as regression and discriminatory
  • [3] Creating Knowledge: Research systems on accounting and financialinformation provide graphics, analytics, and document managementtools to those working in the field of knowledge, as well as the provision for sources of information and internal and external knowledge. • Discovering and Codifying Knowledge: DM technology providesthe possibility to develop and integrate expertise for the purposeof creating models and relationships in large amounts of data anddiscovering new knowledge. • Sharing Knowledge: Research systems for accounting and financialinformation provided by collective cooperation exploration mechanisms help employees to access and work simultaneously on the samedocument, from different locations, and then coordinate their differentactivities. • Distributing Knowledge: Research systems for DM techniques andtheir communication tools can secure documents and other formsof information and distribute them to information and knowledgeworkers in order to link offices to other business units within andoutside the organization.
  • [4] Marketing: Artificial neural networks are used in target marketingstudies, and they helped the use of customer’s approach of the allocation according to demographic realities, like sex, age groups, as wellas purchasing. • Retail: These techniques have been used effectively to predict sales,based on several variables such as market variables or customerbuying habits. • Banks: The applications of this technology in this field have beenused in an excellent way in finding guaranteed prices, future price
  • [5] Reducing the Required Time to Bypass Security Measures: Dubai Airports has been able to reduce the time required for security measures to five minutes for 95% of its passengers. This wasachieved by analyzing metal detector data and 3D camera data.For example, people traveling to cold areas wear heavy shoes thatactivate the alarm, so an automatic message appears on the screensnear the rows informing passengers to take off their shoes to speedup traffic; the results of this data analysis are also shared with thesecurity and safety section to continuously improve sendees. 2. Secoud-Improviug Internet Access: Dubai Airports has been ableto offer its passengers the fastest and the best Internet sendee in theworld and this for more than 20,000 passengers at the same time.It was done by monitoring all access in real-time and to identifyplaces where there is a lot of pressure points and resolve the problemdirectly, in addition to identifying any harmful acts carried out bytravelers and stop it. 3. Increasing the Efficiency of the Baggage Distribution System: The baggage system at Dubai airports is one of the most complexsystems in the world and works to predict the weights of baggageand cargo and delivery to the right destination, and this by placing
 
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