Collaborative Business Intelligence: Enabling Collaborative Decision Making in Enterprises

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.

Historically, domains such as business intelligence would require a single analyst to engage with data, develop a model, answer operational questions, and predict future behaviors. However, as the problems and domains become more complex, organizations are employing teams of analysts to explore and model data to generate knowledge. Furthermore, given the rapid increase in data collection, organizations are struggling to develop practices for intelligence analysis in the era of big data. Currently, a variety of machine learning and data mining techniques are available to model data and to generate insights and predictions, and developments in the field of visual analytics have focused on how to effectively link data mining algorithms with interactive visuals to enable analysts to explore, understand, and interact with data and data models. Although studies have explored the role of single analysts in the visual analytics pipeline, little work has explored the role of teamwork and visual analytics in the analysis of big data. In this article, we present an experiment integrating statistical models, visual analytics techniques, and user experiments to study the role of teamwork in predictive analytics. We frame our experiment around the analysis of social media data for box office prediction problems and compare the prediction performance of teams, groups, and individuals. Our results indicate that a team's performance is mediated by the team's characteristics such as openness of individual members to others' positions and the type of planning that goes into the team's analysis. These findings have important implications for how organizations should create teams in order to make effective use of information from their analytic models.

In present business competition, organizations are emphasizing on optimal decision support systems (DSS) to enhance its growth-oriented decision support system process. To meet these requirements, the enterprise solutions facilitate certain robust and productive business intelligence (BI) applications. On the other hand, to retain market, organizations are emphasizing on service innovation discovery and its redesign (SIDRD) paradigm using BI applications. To accomplish SIDRD, DSS-oriented BI applications require huge feedback datasets to process where the data could be retrieved from certain feedback channel or data warehouses (DWs). The data warehouses encompass datasets from varied resources, and to ensure optimal data extraction and retrieval, these DWs need robust data mining schemes compatible with multidimensional data model (MDDM). On the other hand, to provide most precise and accurate DSS applications, the data security and its privacy preservation are inevitable. Considering all these requirements, in this paper, a robust privacy-preserved mining model called Commutative RSA (CRSA) has been developed and implemented with C5.0 decision tree algorithm to achieve SIDRD objectives with BI utilities. The developed paradigm has exhibited optimal performance for optimized accuracy, overheads, and secure rule set generation for BI. The performance for the developed SIDRD has been analyzed in terms of coverage accuracy and F1 scores, and the overall system has performed optimal.

Business Intelligence (BI) represents a pivotal advancement in leveraging information technology to enhance organizational performance. BI tools serve as crucial aids in decision-making processes by furnishing requisite insights. In higher education institutions, BI can contribute to leaders and managers in providing perspectives related to academics, learning, and management. Central to BI development is the meticulous gathering of requirements, a process pivotal in identifying organizational informational and knowledge needs. This involves employing various methods such as interviews, observation, and analysis, including leveraging learning analytics to discern data utility for enhanced learning processes. Various studies show that learning analytics contributes to improving the learning and education process. On the other hand, learning analytics requires activity data that is integrated, subject oriented, and time series which are aligned with the characteristics of the data warehouse (DWH) as the main component of BI. This research endeavors to develop BI utilizing academic and e-learning data, exemplified through a case study of Telkom University's Academic Systems and Learning Management Systems (LMS). This study aims to provide actionable insights into the intersection of BI and learning analytics, ultimately enhancing educational processes and organizational decision-making capabilities. By integrating learning analytics into BI development, the resultant BI systems can cater not only to current managerial demands but also anticipate future analytical needs. The implementation of the multidimensional schema was successfully executed. This process involved mapping data from the academic information system and the LMS as data sources to the data warehouse, the Extract, Transform, and Load (ETL) process, and development of the prototype. The testing on the prototype indicated that the prototype meets the intended requirements and provides valuable insights through its comprehensive reporting capabilities. This demonstrates the effectiveness of the implemented multidimensional schema, ETL process, and the overall design of the reporting dashboard.

This study aims to apply the concept of Business Intelligence (BI) by implementing it using Google Data Studio tools in analyzing production data. The BI approach utilizes technology and analytical methods to collect, integrate, analyze, and present the business information needed by company leaders. Google Data Studio is the latest BI platform and provides convenience in interactive data visualization. In this study, the implementation of BI was carried out using Google Data Studio to analyze cardboard box production data. This approach involves the process of collecting production data from various related sources, such as manufacturing systems, inventory systems, and sales systems. The data is then integrated and presented in the form of an interactive and easy-to-understand visualization. The research method used includes literature studies on BI concepts and technology, as well as experiments using Google Data Studio to analyze the production data that has been collected. During the experiment, various indicators of production performance were identified and analyzed, such as productivity, efficiency and error rate. The results of this analysis are interpreted to gain valuable insights in improving production processes and making better business decisions. It is hoped that this research will provide a better understanding of BI implementation using Google Data Studio in the context of production data analysis. It is hoped that the findings of this research can become the basis for companies to obtain better information and make more effective decisions in improving production performance.

The literature review shows that business intelligence (BI) has a long history even though its systematic use in the business context is more recent. Despite the importance of systematic BI, the concept is still trying to find a footing in both academia and the business world. From the literature review, one sees that there are numerous intelligence concepts and that their categorization is ambiguous. The BI concept is also multidimensional as there is no precise or universal conception of what BI is. The article aims to provide an analysis of BI definitions and related intelligence concepts, such as the content of each key concept, what it describes and how intelligence concepts relate to each other. Another objective of the article is to increase the academic understanding and status of this recent field of research on intelligence activities.

Business intelligence (BI) is a set of technologies and strategies allowing analyzing data and providing information to help in the decision-making process. Today, BI is used in many sectors and shows marvelous results. In this paper, we are interested in supporting decision-making in higher education. BI concepts when integrated to the process of academic affairs can make significant improvement. The present paper aims at describing a BI solution to support academic affairs at Taibah University. Using BI, we can take advantages of a set of analytical tools that support decision-making for different types of users (students, faculty members, administrators and decision makers). The proposed BI solution consists essentially of three mains tasks: (1) collecting data from different sources using the three operations (Extract, Transform and Load), (2) proposing a multidimensional solution that describes the academic processes, and (3) visualizing results through a set of dashboards and reports. Experiments are made using the SQL Server Data Tools. Three steps are detailed which are: SQL Server Integration Services, SQL Server Analysis Services and SQL Server Reporting Services. The proposed solution provides many statistical and predictive indicators needed in academic tasks.

Business intelligence encompasses all activities dealing with collecting, storing/managing, and analyzing business-relevant data with the objective of generating knowledge as input to decision support. Business intelligence is often used as an umbrella term for data warehousing, reporting and OLAP (online analytical processing), MIS/DSS, and data mining, respectively.If we count all topics listed above, it is obvious that business intelligence has quite a long history also in the tourism domain. As early examples in tourism, we can identify the DINAMO system introduced by American Airlines already in 1988 or TourMIS in 1998.The widespread use of ICT, especially the uptake of the Internet and social media, led to an increase of available data on customers, competitors, and the whole market in all major business domains, including tourism. More powerful hardware and sophisticated methods to store and analyze such data turned business intelligence into one of the fastest-growing technologies and most challenging areas in the last decade.This chapter gives an overview on the topic of business intelligence and all technical components of a BI architecture (i.e., information extraction and transformation, data warehousing, and different mechanisms and tools to access and analyze data, like reporting or OLAP tools, dashboards, or data mining toolsets). Moreover, the chapter looks at the history of BI in tourism and presents and discusses typical application scenarios in tourism. Finally, we look at current trends and latest developments in the area of business intelligence and their expected implications for the tourism domain.KeywordsBusiness intelligenceData warehousingData analysisOnline analytical processingManagement information systemsDecision support systems

The Business intelligence (BI) literature has grown rapidly during the last three decades. Yet the knowledge about its theoretical underpinnings and its links to strategy research remain elusive. This state of affairs draws from two different scientific communities (informatics and business) that have generated multiple research streams, which duplicate research, neglect each other’s contributions, and overlook the relationship between BI and strategy. In response, we structure the BI scientific landscape and map its evolution to offer scholars and managers a clear view of where we stand and the way forward. For this endeavor, we conduct a systematic review of articles published in top-tier journals and identify 90 articles covering more than 30 years of scientific research on BI. We then run a co-citation analysis of selected articles and their reference lists. This yielded the structuring of BI scholarly community around six research clusters: Environmental Scanning (ES), Competitive Intelligence (CI), Market Intelligence (MI), Decision Support Systems (DSS), Intelligence Technologies (IT), and Knowing as Practice (KAP). The co-citation network exposed overlapping and divergent theoretical roots across the six clusters and permitted to map the evolution of BI following two pendulum swings. In light of the structure and evolution of the BI research, we offer a future research agenda that links BI to strategy work. Our article, thus, contributes by 1) structuring the theoretical landscape of BI research, 2) deciphering the theoretical roots of BI literature, 3) mapping the evolution of BI scholarly community, and 4) suggesting an agenda for research conducive to strategy.

The concept of Business Intelligence (BI) was first proposed in the late 1980-s. Since then the field of BI has experienced the rapid extension and development. More than 50 billion dollars have already been invested in different BI tools and application software, such as new desktop data analysis tools, data warehousing technologies, data extraction middleware and many other tools and techniques into the market. But not all the BI initiatives are successful. This paper posits that a key antecedent of BI success is having the right BI Capabilities, and right BI Capabilities depend on the Decision Environment in which the BI is used. Research suggests that a lack of fit between an organization and its BI is one of the reasons for lack of success. The match between the Decision Environment and what a BI provides has been studied as an indicator of success. The theoretical framework is proposed in this paper to study relationship between BI Capabilities, BI Success and Decision Environment

Business Intelligence (BI) technology with Extract, Transform, and Loading process, Data Warehouse, and OLAP have demonstrated the ability of information and knowledge generation for supporting decision making. In the last decade, the advancement of the Web 2.0 technology is improving the accessibility of web of data across the cloud. Linked Open Data, Linked Open Statistical Data, and Open Government Data is increasing massively, creating a more significant computer-recognizable data available for sharing. In agricultural production analytics, data resources with high availability and accessibility is a primary requirement. However, today’s data accessibility for production analytics is limited in the 2 or 3-stars open data format and rarely has attributes for spatiotemporal analytics. The new data warehouse concept has a new approach to combine the openness of data resources with mobility or spatiotemporal data in nature. This new approach could help the decision-makers to use external data to make a crucial decision more intuitive and flexible. This paper proposed the development of a spatiotemporal data warehouse with an integration process using service-oriented architecture and open data sources. The data sources are originating from the Village and Rural Area Information System (SIDeKa) that capture the agricultural production transaction in a daily manner. This paper also describes the way to spatiotemporal analytics for agricultural production using a new spatiotemporal data warehouse approach. The experiment results, by executing six relevant spatiotemporal query samples on DW with fact table contains 324096 tuples with temporal integer/float for each tuple, 4495 tuples of field dimension with geographic data as polygons, 80 tuples of village dimension, dozens of tuples of the district, subdistrict, province dimensions. The DW time dimension contains 3653 tuples representing a date for ten years, proved that this new approach has a convenient, simple model, and expressive performance for supporting executive to make decisions on agriculture production analytics based on spatiotemporal data. This research also underlines the prospects for scaling and nurturing the spatiotemporal data warehouse initiative.

Data Warehouses (DWs) are currently considered to be the cornerstone of Business Intelligence (BI) systems. Security is a key issue in DWs since the business information that they manage is crucial and highly sensitive, and should be carefully protected. However, the increasing amount of data available on the Web signifies that more and more DW systems are considering the Web as the primary data source through which to populate their DWs. XML is therefore widely accepted as being the principal means through which to provide easier data and metadata interchange among heterogeneous data sources from the Web and the DW systems.Although security issues have been considered during the whole development process of traditional DWs, current research lacks approaches with which to consider security when the target platform is based on the Web and XML technologies. The idiosyncrasy of the unstructured and semi-structured data available on the Web definitely requires particular security rules that are specifically tailored to these systems in order to permit their particularities to be captured correctly.In order to tackle this situation, in this paper, we propose a methodological approach based on the Model Driven Architecture (MDA) for the development of Secure XML DWs. We therefore specify a set of transformation rules that are able to automatically generate not only the corresponding XML structure of the DW from secure conceptual DW models, but also the security rules specified within the DW XML structure, thus allowing us to implement both aspects simultaneously. A case study is provided at the end of the paper to show the benefits of our approach.

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.

BI (Business Intelligence) is an important discipline for companies and the challenges it faces are strategic. A central concept in BI is the data warehouse, which is a set of consolidated data from heterogeneous sources (usually databases in 3NF). To model the data warehouse, the Inmon and Kimball approaches are the most used. Both solutions monopolize the BI market However, a third modeling approach called “Data Vault” of its creator Linstedt, is gaining ground from year to year. It allows building a data warehouse of raw (unprocessed) data from heterogeneous sources. The purpose of this paper is to present a comparative study of the three precedent approaches. First, we study each approach separately and then we draw a comparison between them. Finally, we include recommendations for selecting the best approach before concluding this paper.

ObjectivePopulation health management (PHM) is a promising data-driven approach to address the challenges faced by health care systems worldwide. Although Business Intelligence (BI) systems are known to be relevant for a data-driven approach, the usage for PHM is limited in its elaboration. To explore available scientific publications, a systematic review guided by PRISMA was conducted of mature BI initiatives to investigate their decision contexts and BI capabilities.Materials and MethodsPubMed, Embase, and Web of Science were searched for articles published from January 2012 through November 2023. Articles were included if they described a (potential) BI system for PHM goals. Additional relevant publications were identified through snowballing. Technological Readiness Levels were evaluated to select mature initiatives from the 29 initiatives found. From the 11 most mature systems the decision context (eg, patient identification, risk stratification) and BI capabilities (eg, data warehouse, linked biobank) were extracted.ResultsThe initiatives found are highly fragmented in decision context and BI capabilities. Varied terminology is used and much information is missing. Impact on population’s health is currently limited for most initiatives. Care Link, CommunityRx, and Gesundes Kinzigtal currently stand out in aligning BI capabilities with their decision contexts.Discussion and ConclusionPHM is a data-driven approach that requires a coherent data strategy and understanding of decision contexts and user needs. Effective BI capabilities depend on this understanding. Designing public-private partnerships to protect intellectual property while enabling rapid knowledge development is crucial. Development of a framework is proposed for systematic knowledge building.