scholarly journals Data Mining: An Emerging Approach towards Business Decision Making

2021 ◽  
Vol 2 (1) ◽  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Business Decision

Research on Customer Value Evaluation Based on Data Mining and Mathematical Statistics

2014 ◽  
Vol 926-930 ◽  
pp. 3890-3893
Author(s):  
Bin Yang
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Customer Value ◽  
Customer Relationship ◽  
Management Decision ◽  
Value Assessment ◽  
Business Decision ◽  
Value Evaluation ◽  
Customer Information ◽  
Management Decision Making

In business you can get a number of data about customer information. How to find useful information for business decision-making from so many complicated, messy data and then to perform customer value assessment is a very important and complicated process. In this paper, data mining topics is identified as customer value assessment to assess customer value through data mining and statistical methods, in order to support the company's marketing decision making and customer relationship management decision making.

scholarly journals Data warehousing and data mining: A case study

2005 ◽  
Vol 15 (1) ◽  
pp. 125-145 ◽  
Author(s):  
Milija Suknovic ◽  
Milutin Cupic ◽  
Milan Martic ◽  
Darko Krulj
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Warehouse ◽  
Business Decision ◽  
Serbia And Montenegro ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Business System ◽  
Time Period ◽  
Mining Algorithms

This paper shows design and implementation of data warehouse as well as the use of data mining algorithms for the purpose of knowledge discovery as the basic resource of adequate business decision making process. The project is realized for the needs of Student's Service Department of the Faculty of Organizational Sciences (FOS), University of Belgrade, Serbia and Montenegro. This system represents a good base for analysis and predictions in the following time period for the purpose of quality business decision-making by top management. Thus, the first part of the paper shows the steps in designing and development of data warehouse of the mentioned business system. The second part of the paper shows the implementation of data mining algorithms for the purpose of deducting rules, patterns and knowledge as a resource for support in the process of decision making.

Decision-making framework with double-loop learning through interpretable black-box machine learning models

2017 ◽  
Vol 117 (7) ◽  
pp. 1389-1406 ◽  
Author(s):  
Marko Bohanec ◽  
Marko Robnik-Šikonja ◽  
Mirjana Kljajić Borštnar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Organizational Learning ◽  
Decision Making Process ◽  
Organizational Setting ◽  
Business Decision ◽  
Content Type ◽  
Industry Standard ◽  
General Explanation

Purpose The purpose of this paper is to address the problem of weak acceptance of machine learning (ML) models in business. The proposed framework of top-performing ML models coupled with general explanation methods provides additional information to the decision-making process. This builds a foundation for sustainable organizational learning. Design/methodology/approach To address user acceptance, participatory approach of action design research (ADR) was chosen. The proposed framework is demonstrated on a B2B sales forecasting process in an organizational setting, following cross-industry standard process for data mining (CRISP-DM) methodology. Findings The provided ML model explanations efficiently support business decision makers, reduce forecasting error for new sales opportunities, and facilitate discussion about the context of opportunities in the sales team. Research limitations/implications The quality and quantity of available data affect the performance of models and explanations. Practical implications The application in the real-world company demonstrates the utility of the approach and provides evidence that transparent explanations of ML models contribute to individual and organizational learning. Social implications All used methods are available as an open-source software and can improve the acceptance of ML in data-driven decision making. Originality/value The proposed framework incorporates existing ML models and general explanation methodology into a decision-making process. To the authors’ knowledge, this is the first attempt to support organizational learning with a framework combining ML explanations, ADR, and data mining methodology based on the CRISP-DM industry standard.

scholarly journals Perancangan Aplikasi Data Mining Transaksi Penjualan untuk Mengetahui Pola Beli Konsumen pada Toko Singgalang Padang Menggunakan Algoritma Apriori Berbasis Web

2018 ◽  
pp. 37-44
Author(s):  
Nelisa ◽  
Aulia Fitrul Hadi
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Analysis ◽  
Frequent Itemset ◽  
Business Decision ◽  
Transaction Data ◽  
Analysis Application ◽  
Two Parameters

It takes a method or technique that can transform mountains of data into a valuable information or knowledge (knowledge) that are useful to support business decision making. Therefore in this paper association analysis application developed for extracting and interpreting the pattern of trend of sales of goods are often sold simultaneously from the transaction data using algorithms apriori. Apriori algorithms will from a frequent itemset as predetermined based on two parameters, support and confidence, to find the rules of the association between a combination of items. Knowledge of a product can be used by companies to increase production ansd sales of a product..

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

BUSINESS INTELLIGENCE AND POWER OF KNOWLEDGE

2018 ◽  
Vol 28 (5) ◽  
pp. 1489-1496
Author(s):  
Branislav Stanisavljević
Keyword(s):  
Decision Making ◽  
Financial Performance ◽  
Competitive Advantage ◽  
Business Intelligence ◽  
National Economy ◽  
Business Performance ◽  
Medium Size ◽  
Business Decision ◽  
Economic Point ◽  
Insight Into

Research carried out in the last few years as the example of companies belonging to the category of medium-size enterprises has shown that, for example, typical enterprises, of the total number of data processed in information of importance for its business, seriously takes into consideration and process only 10% of the observed firms. It is justifiable to ask whether these 10% of the processed and analyzed business information can have an adequate potential or motive power to direct the organization to success that is measured by competitive advantages and on a sustainable basis? Or, the question can be formulated: what happens to the rest, mostly 90% of the information that the enterprise does not transform into a form suitable for business analysis and decision-making. It is precisely the task of business intelligence to find a way to utilize all the data collected and processed in the business decision-making process. In this regard, we can conclude that Business Intelligence is, in fact, the framework title for all tools and / or applications that will enable the collection, processing, analysis, distribution to decision-making bodies in the business system in order to derivate from this information valid business decisions - as the most important and / or most important task of the manager. Of course, from an economic point of view, the best decisions are management decisions that provide a lasting competitive advantage and achieve maximum financial performance. This means that business intelligence actually allows a more complete and / or comprehensive view of the overall business performance of all its parts and subsystems. But the system functions can be measured essential and positive economic and financial performance, as well as the position in the branch of the business to which it belongs, and wider, within the national economy. (Of course, today the boundaries of the national economy have become too crowded for many companies, bearing in mind globalization and competitiveness in the light of organization of work and business function). The advantage of business intelligence as a model, if accepted at the organization level, ensures that each subsystem in the organization receives precisely the information needed to make development decisions, but also decisions regarding operational activities. So, it should be born in mind that business intelligence does not imply that information is shared on some key words, on the contrary, the goal is to look at the context of the business, or in general, and that anyone in the further decision hierarchy can manage exactly the same information that is necessary for achieving excellent business performance. Because, if the insight into the information is not complete, the analysis is based on the description of individual parts, i.e. proving partial performance in the realization of individual information, which can certainly create a space for the loss of the expensive time and energy. Illustratively, if the view, or insight into the information, is not 100%, then all business decision-making is like the song of J.J. Zmaj "Elephant", about an elephant and a blindmen, where everyone feels and act only on the base of the experienced work, and brings judgment on what is what or what can be. As in this song for children, everyone thinks that he touches different animals and when they make claims about what they feel, everyone describes a completely different life. Therefore, business intelligence implies that information is fully considered and it is basically the basis or knowledge base, and therefore the basis of business excellence. In doing so, the main problem is how information is transformed into knowledge and based on it in business decision making. It is precisely in this segment that the main advantage of business intelligence is its contribution to the knowledge and business of the company based on power of knowledge. Therefore, for modern business conditions, it is characteristic that the management of the company is realized on the basis of partial knowledge about stakeholders (buyers, suppliers, competitors, shareholders, governments, institutional framework, legislation), and only a complete overview of managers at the highest level in all these partial interest groups allows managers to have a “boat” called the organization of labor leading a safe hand through the storm, Scile and Haribde threatens to endanger business, towards a calm sea and a safe harbor - called a sustainable competitive advantage based on power and knowledge.

scholarly journals Soil Diversity (Pedodiversity) and Ecosystem Services

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 288 ◽  
Author(s):  
Elena A. Mikhailova ◽  
Hamdi A. Zurqani ◽  
Christopher J. Post ◽  
Mark A. Schlautman ◽  
Gregory C. Post
Keyword(s):  
Decision Making ◽  
Ecosystem Services ◽  
Sustainable Use ◽  
Taxonomic Diversity ◽  
Soil Survey ◽  
Original Research ◽  
Soil Taxonomy ◽  
Extrinsic Factors ◽  
Business Decision ◽  
Soil Diversity

Soil ecosystem services (ES) (e.g., provisioning, regulation/maintenance, and cultural) and ecosystem disservices (ED) are dependent on soil diversity/pedodiversity (variability of soils), which needs to be accounted for in the economic analysis and business decision-making. The concept of pedodiversity (biotic + abiotic) is highly complex and can be broadly interpreted because it is formed from the interaction of atmospheric diversity (abiotic + biotic), biodiversity (biotic), hydrodiversity (abiotic + biotic), and lithodiversity (abiotic) within ecosphere and anthroposphere. Pedodiversity is influenced by intrinsic (within the soil) and extrinsic (outside soil) factors, which are also relevant to ES/ED. Pedodiversity concepts and measures may need to be adapted to the ES framework and business applications. Currently, there are four main approaches to analyze pedodiversity: taxonomic (diversity of soil classes), genetic (diversity of genetic horizons), parametric (diversity of soil properties), and functional (soil behavior under different uses). The objective of this article is to illustrate the application of pedodiversity concepts and measures to value ES/ED with examples based on the contiguous United States (U.S.), its administrative units, and the systems of soil classification (e.g., U.S. Department of Agriculture (USDA) Soil Taxonomy, Soil Survey Geographic (SSURGO) Database). This study is based on a combination of original research and literature review examples. Taxonomic pedodiversity in the contiguous U.S. exhibits high soil diversity, with 11 soil orders, 65 suborders, 317 great groups, 2026 subgroups, and 19,602 series. The ranking of “soil order abundance” (area of each soil order within the U.S.) expressed as the proportion of the total area is: (1) Mollisols (27%), (2) Alfisols (17%), (3) Entisols (14%), (4) Inceptisols and Aridisols (11% each), (5) Spodosols (3%), (6) Vertisols (2%), and (7) Histosols and Andisols (1% each). Taxonomic, genetic, parametric, and functional pedodiversity are an essential context for analyzing, interpreting, and reporting ES/ED within the ES framework. Although each approach can be used separately, three of these approaches (genetic, parametric, and functional) fall within the “umbrella” of taxonomic pedodiversity, which separates soils based on properties important to potential use. Extrinsic factors play a major role in pedodiversity and should be accounted for in ES/ED valuation based on various databases (e.g., National Atmospheric Deposition Program (NADP) databases). Pedodiversity is crucial in identifying soil capacity (pedocapacity) and “hotspots” of ES/ED as part of business decision making to provide more sustainable use of soil resources. Pedodiversity is not a static construct but is highly dynamic, and various human activities (e.g., agriculture, urbanization) can lead to soil degradation and even soil extinction.

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