Data Mining and Business Intelligence in SME Customer Relationship Value Analysis

Business Intelligence (BI) systems are applied by increasing numbers of organizations in a wide range of sectors. Despite the growing numbers of BI solutions and the experience augmented in many implementation projects worldwide, as well as the value that BI systems provide to organizations, IT literature lacks a coherent set of definitions through which BI systems can be classified, categorized, and assessed. Furthermore, BI implementation projects often do not succeed or do not fully accomplish the degrees of value and performance as expected, when firms fail to complete the system implementation, to satisfy the needs of users or when the benefits BI systems produce are lower than anticipated. This paper presents an analytical framework through which BI systems are defined and classified. On the basis of this framework, possible technical, organizational, and personal factors that affect the failure, partial or full success of BI system implementations are discussed. These factors are followed by a case study and empirical data analyses that exemplify and assess the extent to which various organizational attributes and properties of users influence the success or failure of BI implementation projects. Finally, implications regarding the management of BI system implementation projects and the organizations that apply them are derived.

In the era of the information economic development of society, which is based on the digitalization of communication technologies and software for business administration algorithms, business analytics occupies an important place, a tool for obtaining high-quality, unique, and expensive information, maintaining the global competitive advantages of projects in the face of long-term force majeure risks of the viability of the economy. Since the middle of the last century, information technologies have been in constant development, gradually being introduced into all spheres and strata of society: production, accounting and analytical support, management, analysis, forecasts, risks. This paper considers the tools of digital business transformation and trends within the new knowledge paradigm based on a controlled understanding of the value properties of an information product. The successful use of business intelligence can indicate an increase in the success of the company and the country's competitiveness in the global market. Business analytics of companies is currently undergoing a stage of reengineering due to testing various programs, understanding, and analyzing the database, improving communication interaction at all levels of business processes, and making decisions in conditions of interactivity and transparency. The state of traceability and transparency of information, evolution and dynamics of conclusions and proposals over a long period of the company's activity in the sectoral and global aspect is of value. The features, characteristics, and functions of business intelligence (BI) systems as a tool for increasing the efficiency of the company's activities are analyzed. Business intelligence systems were considered as a tool for data analysis and processing, a comparative analysis of modern business intelligence systems and market trends of business intelligence systems was conducted, because of which the role of BI systems in increasing the efficiency of the company's activities was revealed. Keywords: business analytics, business data analysis, data integration, data presentation, IT sphere, company performance.

Business Intelligence (BI) systems are designed to provide information to support the decision making process in companies and governmental institutions. In this scenario, future events depend on the decisions and on the previous events. Therefore, the mathematical analysis of past data can be an important tool for the decision making process and to detect anomalies. Depending on the amount and the type of data to be analyzed, techniques from statistics, Machine Learning (ML), data mining and signal processing can be used to automate all or part of the system. In this paper, we propose to incorporate Gaussian Process for Regression (GPR) in BI systems in order to predict the data. As presented in this work, fraud detection is one important application of BI systems. We show that such application is possible with the use of GPR in the predictive stage, considering that GPR natively returns a full statistical description of the estimated variable, which can be used as a trigger measure to classify trusted and untrusted data. We validate our proposal with real world BI data provided by the Brazilian Federal Patrimony Department (SPU), regarding the monthly collection of federal taxes. In order to take into account the multidimensional structure of this specific data, we propose a pre-processing stage for reshaping the original time series into a bidimensional structure. The resulting algorithm, with GPR at its core, outperforms classical predictive schemes such as Artificial Neural Network (ANN).

Nowadays a lot of massive data is stored and typically, the data itself contains a lot of non-trivial but useful information. Data mining techniques can be used to discover this information which can help the companies for decision-making. However, in real life applications, data is massive and is stored over distributed sites. One of my major research topics is to protect privacy over this kind of data. Previously, the important characteristics, issues and challenges related to management of the large amount of data has been explored. Various open source data analytics frameworks that deal with large amount of Data analytics workloads have been discussed. Comparative study between the given frameworks and suitability of the same has been proposed. Digital universe is flooded with huge amount of data generated by number of users worldwide. These data are of diverse in nature, come from various sources and in many forms. To keep with the desire to store and analyze ever larger volumes of complex data, relational databases vendors have delivered specialized analytical platforms that come in many shapes and sizes from software only to analytical services that run in third party hosted environments. In addition new technologies have emerged to address exploding volumes of complex data, including web traffic, social media content and machine generated data including sensor data, global positioning system data.Big data is defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value from it by capturing and analysis process. Due to such large size of data it becomes very difficult to perform effective analysis using the existing traditional techniques. Big data has become a prominent research field, especially when it comes to decision making and data analysis. However, Big data due to its various properties like volume, velocity, variety, variability, value and complexity put forward many challenges. Since Big data is a recent upcoming technology in the market which can bring huge benefits to the business organizations, it becomes necessary that various challenges and issues associated in bringing and adapting to this technology are brought into light. Another challenge is that data collection may not have enough accuracy which will lead to a non-consistent analysis that can critically affect the decision based on this analysis. Moreover, it is clearly apparent that organizations need to employ data-driven decision making to gain competitive advantage. Processing, integrating and interacting with more data should make it better data, providing both more panoramic and more granular views to aid strategic decision making. This is made possible via Big Data exploiting affordable and usable Computational and Storage Resources. Many offerings are based on the Map-Reduce and Hadoop paradigms and most focus solely on the analytical side. Nonetheless, in many respects it remains unclear what Big Data actually is; current offerings appear as isolated silos that are difficult to integrate and/or make it difficult to better utilize existing data and systems. Since data is growing at a huge speed making it difficult to handle such large amount of data (Exabyte). The main difficulty in handling such large amount of data is because that the volume is increasing rapidly in comparison to the computing resources. The Big data term which is being used now a days is kind of misnomer as it points out only the size of the data not putting too much of attention to its other existing properties.If data is to be used to make accurate decisions in time it becomes necessary that it should be available in accurate, complete and timely manner. This makes the data management and governance process bit complex adding the necessity to make Data open and make it available to government agencies in standardized manner with standardized APIs, metadata and formats thus leading to better decision making, business intelligence and productivity improvements.This paper presents a discussion and evaluation for the most prominent techniques used in the processes of data collection and analysis in order to identify the privacy defects in them that affects the accuracy of big data. Depending on the results of this analysis, recommendations were provided for improving data collection and analysis techniques that will help to avoid if not all then most of the problems facing the use of big data in decision making. Keywords: Big Data, Big Data Challenges, Big Data Accuracy, Big Data Collection, Big Data Analytics.

The advent of information technologies brought with it the availability of huge amounts of data to be utilized by enterprises. Data mining technologies are used to search vast amounts of data for vital insight regarding business. Data mining is used to acquire business intelligence and to acquire hidden knowledge in large databases or the Internet. Business intelligence can find hidden relations, predict future outcomes, and speculate and allocate resources. This uncovered knowledge helps in gaining competitive advantages, better customer relationships, and even fraud detection. In this chapter, the authors describe how data mining is used to achieve business intelligence. Furthermore, they look into some of the challenges in achieving business intelligence.

A Business Intelligence (BI) system can provide information in a reliable, instantaneous and automatic way in order to support crucial decisions of companies as well as of governments. The first step for the implementation of a BI system is the development of the ontology, which can be performed via an institutional glossary. However, usually the link between the ontology and the BI system is not direct, and therefore the implementation of the BI system may be slow. In this paper, we show the application of concept maps for BI system implementation. We exemplify this application using the study case of the implementation of the BI system for the Federal Property Department (SPU), which belongs to the Ministry of Planning, Budget and Management (MP) in Brazil. Besides the efficient implementation via concept maps, the impacts of our proposed BI system are also highlighted in this paper.

There is currently a trend for companies to invest in business intelligence (BI) systems and integrate them with their original enterprise resource planning (ERP) systems in order to enhance their management decision-making capability. Even though, much attention has been paid to decision-making benefits of integrated systems in practice, limited academic research has investigated the issue of decision-making improvement. Therefore, the motivation for this paper is to examine the impact of integrated ERP and BI (abbreviated as ERPBI) systems on decision making performance (DMP) and introduce a comprehensive measurement instrument for assessing that impact. A survey was conducted involving 108 companies in the semiconductor industry in Taiwan. The results of the survey indicate firstly, that ERPBI system usage in organisations is positively related to DMP. Secondly, organisations using ERPBI systems achieve higher levels of decision-making performance compared to organisations which only use ERP systems.

This article was focused on establishing whether Business Intelligence (BI) systems provide sustainability to commercial banks by influencing their financial condition. As part of the search for a solution to the research problem, a hypothesis was formulated which assumes that the use of the Business Intelligence management system improves the financial condition of commercial banks. To assess this impact, a novel comparative method was used, which assumed comparing financial condition indicators in three aspects: before and after the implementation of the Business Intelligence system (comparison over time), with average indicators of a group of banks (comparison to the industry), with reference to changes in the overall economic situation. As a result of the method used, a synthetic indicator of the impact of using Business Intelligence (ABI) was calculated. This study was conducted in relation to six out of the thirteen largest commercial banks listed on the Warsaw Stock Exchange in 2020, which have implemented the Business Intelligence system since 2001. The assets of the examined banks cover 60% of the assets of commercial banks in Poland. As a result of the study, a positive impact of using the BI system on selected areas of the financial condition of commercial banks was identified. In particular, this impact relates to areas of productivity, the quality of assets and liabilities, profitability and debt. The generalized results of this study allow for the determination of cause and effect relationships between the use of the BI system in commercial banks and the improvement of the financial condition indicators as well as sustainability banking.

Possibility of Improving Efficiency within Business Intelligence Systems in Companies

The paper proposes a new approach of data warehouse minimization by fuzzy-based ETL filter for ETL processes in business intelligence (BI) systems and discusses possibilities to tune up the process. First part introduces common company systems and possible data sources in the company. Second part states the problem with interpreting information in BI systems and explains a data representation in the BI systems. Third part of the paper defines a rule base and input and output values of the expert system. Last part of the paper proposes a two ways to minimize a data - modification border of the fuzzy set and omitting useless combinations of the linguistic variables and modifiers.

Data Mining (DM) is being applied with success in Business Intelligence (BI) environments, and several examples of applications can be found. BI and DM have different roots and, as a consequence, have significantly different characteristics. DM came up from scientific environments; thus, it is not business oriented. DM tools still demand heavy work in order to obtain the intended results. On the contrary, BI is rooted in industry and business. As a result, BI tools are user-friendly. This chapter reflects on this difference from a historical perspective. Starting with a separated historical perspective of each one, BI and DM, the author then discusses how they converged into the current situation, when DM is used, and integrated, in BI environments with success.

Business intelligence system (BIS) has gained a high momentum and played a significant role in enhancing the business environment especially with the help of cloud. The BIS with cloud computing is the solution to overcome complex problems where large amounts of data (market analysis, customer’s view, feedback and organization response time) are required to be processed. The objective of this paper is to show the realistic perspective of the possibilities for maximizing benefits and minimizing risks in Indian business. The authors have highlighted the importance of cloud computing in business and tried to develop a framework for integrating BIS with cloud computing, and an attempt has also been made to analyse the efficiency of the proposed model which improves performance over the cloud. Some of the challenging issues as well as the future research directions have also been discussed.

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Huge amount of data produced in business can be processed to get useful information using B usiness intelligence (BI). Business intelligence comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current and predictive views of business operations. Mobile business intelligence (MBI) is the ability to access BI-related data such as Key Performance Indicators (KPIs), business metrics, and dashboards on mobile devices. The MBI provides users with necessary information and content, regardless of their location and time, in order to gain business insights through information analysis using applications optimized for mobile devices. Although in recent years, the business intelligence and analytics has gained an increasing amount of interest among researchers and practitioners, there is a little academic literature available on MBI. This survey is intended to study the adoption of Business Intelligence (BI) with special emphasis on mobile business intelligence in various fields during past 10 years and highlights the challenges faced by users in its implementation. The study also emphasized on relationship between MBI and big data analytics, application of MBI, and difficulties in using the MBI tools. Keywords : Mobile business intelligence, business intelligence, big data, analytic

Business Intelligence (BI) systems address the demands of large scale enterprises for operational analytics, management information and decision support tasks. Building such applications presents many challenges. They must support complex and changing data models, have fast turnarounds, present an up-to-date and accurate view of information and provide extensibility mechanisms for new analyses. Widely adopted distributed object systems, such as J2EE can be heavyweight and inflexible when applied to the described scenario. This paper presents our experience when developing a data analysis system that applies a combination of lightweight distributed component technologies available for Java. These technologies are combined in an event-based architecture that anticipates constant changes to analysis algorithms in short time frames and provides the ability to maintain correlated analyses in a consistent state. The resulting architecture is extensible, easy to deploy, highly configurable and has a very flexible data model. We compare this approach with existing distributed object systems and evaluate its suitability to provide business intelligence.

Visualization interfaces that offer multiple coordinated views on a particular set of data items are useful for navigating and exploring complex information spaces. In application areas like customer relationship management (CRM) or business intelligence (BI), for example, structured customer or company data is often associated to text-based information like telemarketing transcripts or trade press articles. Supporting the analysis of such text information and related structured data is a major challenge for current CRM and BI tools. In this paper, we present a system for visually analyzing these particular information spaces based on a multiple views paradigm. The system brings together views on text similarity, text categories, and associated relational attributes; visual overlay techniques help to keep track of navigation and selection operations in complementary views. The paper sketches an application example and summarizes results from an initial usability evaluation.