Business Intelligence Infrastructure Research Articles

Information Quality of Business Intelligence Systems: A Maturity-based Assessment

Background: The primary role of a Business Intelligence (BI) system is to provide information to decision-makers within an organization. Moreover, it is crucial to acknowledge that the quality of this information is of greatest significance. Several studies have extensively discussed the importance of information quality in information systems, including BI. However, there is relatively little discussion on the factors influencing 'Information quality”. Objective: This study aimed to address this literature gap by investigating the determinants of BI maturity that impacted information quality. Methods: A maturity model comprising three dimensions was introduced, namely Data quality, BI infrastructure, and Data-driven culture. Data were collected from 84 companies and were analyzed using the SEM-PLS approach. Results: The analysis showed that maturity had a highly positive influence on Information Quality, validating the relevance of the three proposed determinant factors. Conclusion: This study suggested and strongly supported the importance and relevance of Data quality, BI infrastructure, and Data-driven culture as key dimensions of BI maturity. The robust statistical relationship between maturity and information quality showed the effectiveness of approaching the systems from a maturity perspective. This investigation paved the way for exploring additional dimensions that impact Information quality. Keywords: BI infrastructure, BI maturity, Data-driven culture, Data quality, Information quality.

Electric Scooter Injuries in Tampa, Florida, Are Associated With High Rates of Head Injury, Hospital Admission, and Emergency Medical Service Transport and Low Rates of Helmet Use.

Background Standing electric scooters (e-scooters) were introduced in Tampa, Florida, in 2019. We reviewed 292 e-scooter injury cases at the Tampa General Hospital Emergency Department (ED) to determine what insights we could gain. We sought to identify the characteristics of such presentations, including chief complaint (CC), patient age, day of the week, time of day, length of stay, disposition, acuity, and means of arrival to the ED. We were particularly interested in studying the rates of hospital admission, Emergency Medical Service (EMS) transport, emergent acuity presentations, and head injuries. We also sought to identify the prevalence of alcohol use prior to e-scooter accidents and its effect on the above factors. Methodology This was a retrospective chart review and was exempt from the University of South Florida's Institutional Review Board approval (STUDY004031). Data from routine clinical care in the Tampa General Hospital ED, a Level-1 Trauma Center ED in Tampa, Florida, from July 19, 2019, to May 30,2022, were collected through an operational report within the business intelligence infrastructure of the hospital's electronic medical record system. Data from patients with scooter injury-related encounter codes were extracted to an electronicdata capture form and deidentified. Narratives were reviewed to exclude uncertain cases (e.g., patients with moped, kick scooter, mobility scooter injuries, etc.) and to flag for alcohol endorsement, altered mental status, helmet usage, and head injuries that were not listed as the CC. CC, means of arrival, acuity, disposition, arrival/departure day of week, and arrival/departure hour were collected. Data analysis was completed using Microsoft Excel version 16.5 (Microsoft Corp., Redmond, WA, USA) and SPSS Statistics version 28.0 (IBM Corp., Armonk, NY, USA). Results A total of 292 of 442 collected cases remained after removing irrelevant flags. Overall, 30.8% (n = 90) of patients were between the ages of 21 and 30, and most patients presented on weekends and nights. Moreover, 40.8% (n = 119) suffered head injuries, 40.8% (n = 119) arrived via EMS, 31.5% (n = 92) were admitted to the hospital, and 18.8% (n = 55) were designated as emergent acuity. Apart from the admission rate, these rates were all higher among alcohol endorsers (39, 13.4%) than non-endorsers (253, 86.6%). Only 2.1% of patients endorsed helmet use. Conclusions We found higher rates of hospital admission and EMS transports in our ED than many previous studies in urban areas have reported. Our data suggest that alcohol use increases the risk of more serious e-scooter injuries, characterized by higher acuity, EMS transport rate, and head injuries among alcohol endorsers. These findings are highly relevant due to the rapidly growing e-scooter presence across the United States and may serve to inform hospitals and EMS systems regarding their role in injury management, as well as future policy regarding their safe use.

Business intelligence ability to enhance organizational performance and performance evaluation capabilities by improving data mining systems for competitive advantage

Improving organizational performance is a priority for the managers and experts of companies. Performance appraisal capabilities are activities that use available information to improve business processes and achieve company goals. Knowledge information entered into companies, on the one hand, is effective in learning people and, on the other hand, is used in management decisions. This study aims to improve these organizational performances by using Business intelligence (BI) and Artificial intelligence (AI) on Data mining systems (DMS) in privately held companies. This descriptive survey study is based on the purpose of applied and quantitative research. The statistical population includes managers of privately held companies. These companies use BI and AI in their data mining systems. The results revealed that BI capabilities & reliability affect performance capabilities. Further, Performance evaluation capabilities (PEC) impact competitive advantage; Since the BI capabilities are related to BI reliability, thus, Business intelligence infrastructure (BII) impresses BI reliability. Path coefficient between the two variables of integration of BII on BI capabilities is β = 0.389. Moreover, the T-statistic between these two variables is t = 5.768. Path coefficient between the two variables of BI capabilities over reliability business intelligence is β = 0.158. Moreover, the T-statistic between these two variables is t = 1.967. Path coefficient between the two variables of Integrate BII with business intelligence reliability is β = 0.344. Moreover, the T-statistic between these two variables is t = 4.452. Innovation has a significant impact on the company's performance by creating an improved market position, which indicates a competitive advantage and superior performance.

The impact of big data analytics talent capability on business intelligence infrastructure to achieve firm performance

Purpose The emerging attention in big data has led businesses to improve big data analytics talent capability to enrich firm performance. The big data capability pays off for some companies but not for all, and it appears that very few have achieved a big impact through big data. Rooted in the latest literature on the knowledge-based view, IT capability, big data talent capability and business intelligence, this study aims to examine how big data talent capability impact on business intelligence infrastructure to achieve firm performance. Design/methodology/approach The primary survey data of 272 IT managers and big data analysts from Chinese firms was analyzed by using the structural equation modeling and partial least squares (Smart PLS 3.0). The analysis uncovers a positive and significant relationship in the proposed model. Findings The finding shows that the big data analytics talent capability positively impacts on business intelligence infrastructure that in turn directs to achieve firm financial and marketing performance. Originality/value This study theorized on the multitheoretic lenses, and findings suggest the managers and industry practitioners to develop business intelligence infrastructure capabilities from big data analytics talent capability.

Effect of Business Intelligence Infrastructure Capabilities on Performance of Commercial Banks in Kenya

Besides promoting economic progress and national development, performance of commercial banks is vital in order to reward shareholders for their investment. The Kenyan banking sector has continuously invested in business intelligence capability with most banks' management justifying the investment to achieve the expected growth and improved efficiency in operations from use of data. This study focused on investigating business intelligence infrastructure capabilities on performance of commercial banks in Kenya. The study was anchored on Resource-Based View Theory. An explanatory non-experimental design was employed to conduct a census involving the 43 commercial banks in Kenya. The heads of IT, Operations Managers and Credit Managers of the commercial banks were the respondents. Both primary and secondary data were applied. A semi-structured questionnaire was used to gather primary data whereas secondary data was gathered from banks audited financial statements using data collection sheet. Descriptive and inferential statistics were applied for analysis. Means, standard deviation and frequencies were used. Ordinary least squares model was also used in analysis. The study results were relayed in charts, graphs and tables. The study concludes that business intelligence infrastructure capabilities have a positive effect on bank performance. It was recommended that the leadership of commercial banks should embrace business intelligence infrastructure capabilities to augment performance.

Business Intelligence Systems for Innovative Development of Organizations

Today, it is believed that Business Intelligence (BI) systems have become a strategic tool for economic growth, determining the competitiveness of many organizations and their innovative development. A theory as well a practice show that BI systems are used mainly to: (a) develop modern strategies and business models; (b) creative sources of competitive advantage; (c) make fundamental transformations in organizations; and (d) integrate and develop the entire ecosystem. Unfortunately, the topic of components that determinate an innovative development of organizations based on BI is still a poorly understood issue. The study investigates a topic of BI and proposes a framework to provide BI-based innovative development of organizations. The framework contains four components: (a) digital strategy and support from top management; (b) BI infrastructure and tools; (c) information and knowledge repositories; and (d) organizational culture. The framework has been subjected to initial verification by conducting 150 direct interviews in small and medium enterprises among owners, top executives, and managers.

Big data analytics capability and organizational performance measures: The mediating role of business intelligence infrastructure

The growing importance of big data has headed enterprises to advance their big data analytics capability to strengthen their firm performance. This study tests how big data capability impact on business intelligence infrastructure to achieve firm performance measures such as operational performance and marketing performance. This study is based on the recent literature on the knowledge-based view, big data capability, IT capability, and business intelligence. The primary survey of 272 responses from Chinese firms’ IT managers and big data analysts are used to uncover the relationship in the proposed model. The finding shows that the big data analytics capability significantly impacts on business intelligence infrastructure that in turn positively impact on operational performance and marketing performance. Further, the business intelligence infrastructure partially mediates between big data analytics capability and operational performance, and fully mediates between big data analytics capability and marketing performance. This research contributes to the information systems literature such as big data analytic capability, business intelligence, and firm performance measures, and thus offers grounds to extend more widespread studies in this field. This study adds to the literature on the theory and practical bases for big data capability and business intelligence infrastructure.

Leveraging business intelligence systems to enhance management control and business process performance in the public sector

PurposeRecent research and policy reports indicate public sector organizations struggle to leverage information technology-based performance measurement systems and fail to effectively evaluate performance beyond financial metrics. This study aims to focus on organizational factors that influence the assimilation of business intelligence (BI) systems into integrated management control systems and the corollary impact on improving business process performance within public sector organizations.Design/methodology/approachThe complete Australian client list was acquired from a leading BI vendor; and the authors surveyed all public sector organizations, receiving 226 individual responses representing 160 public sector organizations in Australia. Using latent construct measurement, structural equation modeling (SEM)-partial least squares is used to test the theoretical model.FindingsWhen top management promotes knowledge creation among the organization’s operational level employees and support their activities with strong BI infrastructure, the same knowledge and infrastructure capabilities that are critical to assimilation in private sector hold in the public sector. However, public sector organizations generally have difficulty retaining staff with expertise in new technologies and attracting new innovative staff that can leverage smart systems to effect major change in performance measurement. When top management effectively manages knowledge importation from external entities to counteract deficiencies, public sector organizations effectively assimilate BI knowledge into performance measurement yielding strong process performance.Research limitations/implicationsWhen top management promotes knowledge creation among the organization’s operational level employees and support their activities with strong BI infrastructure, the same knowledge and infrastructure capabilities critical to assimilation in the private sector hold in the public sector. However, public sector organizations generally have difficulty retaining staff with expertise in new technologies and attracting new innovative staff that can leverage smart systems to effect major change in performance measurement. The research extends the theory behind organizational absorptive capacity by highlighting how knowledge importation can be used as an external source facilitating internal knowledge creation. This collaborative knowledge creation leads to affective assimilation of BI technologies and associated performance gains.Practical implicationsThe results provide guidance to public sector organizations that struggle to measure and validate service outcomes under New Public Management regulations and mandates.Originality/valueThe results reveal that consistent with the philosophies behind New Public Management strategies, private sector measures for increasing organizational absorptive capacity can be applied in the public sector. However, knowledge importation appears to be a major catalyst in the public sector where the resources to retain skilled professionals with an ability to leverage contemporary technologies into service performance are often very limited. Top management team knowledge and skills are critical to effectively leveraging these internal and external knowledge creation mechanisms.

Business Intelligence and Business Value in Organisations: A Systematic Literature Review

Organisations must derive adequate business value (BV) from Business Intelligence (BI) adoption to retain their profitability and long-term sustainability. Yet, the nuances that define the realisation of BV from BI are still not understood by many organisations that have adopted BI. This paper aims to foster a deeper understanding of the relationship between Business Intelligence (BI) and business value (BV) by focusing on the theories that have been used, the critical factors of BV derivation, the inhibitors of BV, and the different forms of BV. To do this, a systematic literature review (SLR) methodology was adopted. Articles were retrieved from three scholarly databases, namely Google Scholar, Scopus, and Science Direct, based on relevant search strings. Inclusion and exclusion criteria were applied to select ninety-three (93) papers as the primary studies. We found that the most used theoretical frameworks in studies on BI and BV are the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), Technology-Organisation-Environment (TOE), and Contingency Theory (CON). The most acknowledged critical factors of BV are skilled human capital, BI Infrastructure, data quality, BI application and usage/data culture, BI alignment with organisational goals, and top management support. The most acclaimed inhibitors of BV are data quality and handling, data security and protection, lack of BI Infrastructure, and lack of skilled human resource capital, while customer intelligence is the most acknowledged form of BV. So far, many theories that are relevant to BI and BV, critical factors, inhibitors, and forms of BV were marginally mentioned in the literature, requiring more investigations. The study reveals opportunities for future research that can be explored to gain a deeper understanding of the issues of BV derivation from BI. It also offers useful insights for adopters of BI, BI researchers, and BI practitioners.

Cloud computing with AI for banking and e-commerce applications

PurposeIn cloud computing, banking knowledge with business intelligence (BI) will be a better option for the users, and the users do not have an overall picture of the whole thing until the entire job is completed.Design/methodology/approachAlthough the constraints are costly, complex, inflexible and integrated BI infrastructures, it helps to combine the banking and electronic commerce (EC) application data. Furthermore, this paper explores cloud computing infrastructure as a potential remedy for data processing issues.FindingsA modern cloud computing with artificial intelligence, environmental climate to shorten periods for BI delivery, rising the expense of BI programs relatively with traditional BI of EC applications, provide customers with the ability for quicker rollout and better flexibility to incorporate research and to improve the performance, accuracy and efficiency.Originality/valueIn the technological world of today, the new era of BI analytical data management has been established with certain restrictions on the adoption of BI.

Business Intelligence and Firm Performance: Assessing Value and Future Directions in Pakistani Firms

The aim of this research is to examine how Business Intelligence (BI) enhances the firm’s performance in Pakistani firms. Pakistan is a developing country and BI is supposed to be in its initial stages in Pakistan while the data about BI implementation and usage in Pakistani firms are also not much available especially in statistical form. Model and questionnaire were adopted from Peters et. al. (2016). BI system quality is based on BI infrastructure, functionality, and self-service which helps in getting a better competitive advantage and increasing firm performance by enhancing performance measurement capabilities. Data was collected from 300 employees of different firms in Karachi, where business intelligence is being implemented. Results were analyzed through SEM-PLS. Results indicated that BI system quality enhances the performance measurement capabilities, that enhances the competitive advantage and optimizing the firm performance.

Business Intelligence Infrastructure of Medical Record Data History System to help Doctorin differencing rare and dangerous disease in patient

Business Intelligence Infrastructure of Medical Record Data History System to help Doctorin differencing rare and dangerous disease in patient

An exploratory SOLAP tool for linked open data

Business intelligence (BI) technologies are being successfully applied in data analytics, including in the use of spatial data. The advent of the semantic web brought semantically rich and semi-structured data formats, such as resource description framework (RDF), which are external to the BI infrastructure. The integration of both conventional and semantic web data into a BI system results in a new category of analytical tools called exploratory OLAP. We extend exploratory OLAP with spatial capabilities, providing an exploratory SOLAP, called ExpSOLAP. ExpSOLAP is the first analytical tool that integrates semantic, spatial, semi-structured data with traditional spatial, structured data sources. Additionally, SOLAP queries can be posed in both data sources.

An exploratory SOLAP tool for linked open data

Business intelligence (BI) technologies are being successfully applied in data analytics, including in the use of spatial data. The advent of the semantic web brought semantically rich and semi-structured data formats, such as resource description framework (RDF), which are external to the BI infrastructure. The integration of both conventional and semantic web data into a BI system results in a new category of analytical tools called exploratory OLAP. We extend exploratory OLAP with spatial capabilities, providing an exploratory SOLAP, called ExpSOLAP. ExpSOLAP is the first analytical tool that integrates semantic, spatial, semi-structured data with traditional spatial, structured data sources. Additionally, SOLAP queries can be posed in both data sources.

A Feature-based Concept for Decision Support to Cope with Product Variety

Industrial companies face the situation that customers demand more and more individualized products. This results in an increasing number of product variations and in batch sizes which tend to one. The essential decisions to ensure resource efficiency in this context are part of the product development. It has to be decided if it is possible to fulfill customers’ requirements by adapting existing products and parts, or if a new design is necessary. On the one hand, this is a technical question, but on the other hand, it is important to be able to estimate the costs of such decisions reliably. State of the art is that relevant information is spread across a company in different information systems and rarely available within an appropriate time. But an important step to support such decisions is to identify similarities between products based on their technical properties and to combine this with relevant economic information. This paper proposes a concept for the aforesaid task.The applied methodology is based on the design science approach: Various interviews with experts from German industrial companies deliver the requirements for the concept. The approach uses feature-technology which is the basis of the digital product model. Each part is described completely by the total amount of all features and their corresponding parameters. Features extracted from computer aided design (CAD) systems and loaded into a Business Intelligence infrastructure offer the possibility to analyze various parts on the feature-level.The results reveal that Business Intelligence is able to provide an adequate basis for decision-making in this context. It is possible to combine the relevant manufacturing and economic information with original product data from engineering and, therefore, to deal with an increasing product variety.

Business Intelligence Infrastructure for Academic Libraries

Objective – To describe the rationale for and development of MetriDoc, an information technology infrastructure that facilitates the collection, transport, and use of library activity data.
 
 Methods – With the help of the Institute for Museum and Library Services, the University of Pennsylvania Libraries have been working on creating a decision support system for library activity data. MetriDoc is a means of “lighting up” an array of data sources to build a comprehensive repository of quantitative information about services and user behavior. A data source can be a database, text file, Extensible Markup Language (XML), or any binary object that contains data and has business value. MetriDoc provides simple tools to extract useful information from various data sources; transform, resolve, and consolidate that data; and finally store them in a repository. 
 
 Results – The Penn Libraries completed five reference projects to prove basic concepts of the MetriDoc framework and make available a set of applications that other institutions could test in a deployment of the MetriDoc core. These reference projects are written as configurable plugins to the core framework and can be used to parse and store EZ-Proxy log data, COUNTER data, interlibrary loan transactional data from ILLIAD, fund expenditure data from the Voyager integrated library system, and transactional data from the Relais platform, which supports the BorrowDirect and EZBorrow resource sharing consortiums. The MetriDoc framework is currently undergoing test implementations at the University of Chicago and North Carolina State University, and the Kuali-OLE project is actively considering it as the basis of an analytics module.
 
 Conclusion – If libraries decide that a business intelligence infrastructure is strategically important, deep collaboration will be essential to progress, given the costs and complexity of the challenge.

A Dual-Mode Intelligent Shopping Assistant

Radio Frequency Identification” (RFID) promises to enable an automatic collection of shopping data for business intelligent purposes. Three important components of intelligence have been identified for systems; ability to predict, ability to adapt and ability to take appropriate action. While extensive research has been done on shopping assistants and the use of RFID in the grocery industry, there is still a lack of insight into the benefits of processing shopping data within a Business Intelligence infrastructure and the components of intelligence. This paper presents iShopper, an intelligent dualmode shopping assistant. The main objective of iShopper is to create new shopping incentives through exploiting the high penetration of mobile communications, as well as the opportunities that emerging automatic product identification technologies and business intelligence offer to the grocery industry. The results indicate that the role of Business Intelligence components and RFID technology in shopping should not be underestimated. Key-words: RFID, Business Intelligence, Shopping assistant, Optimization, Adaption, Recommendation.

Practicing for Business Intelligence Application with SQL Server 2008

Successful business intelligence application used by an enterprise often produces a very high return on investment. This paper introduces the application of best practices to business intelligence infrastructure with SQL Server 2008.

USING ENTERPRISE MASHUPS AS A BUSINESS INTELLIGENCE SITUATIONAL APPLICATION

A review of recent literature highlights that web 2.0 technologies are enabling a new programming approach. Business end users are able to analyze disparate data to aid in time sensitive decisionmaking that was traditionally performed by data warehouse and business intelligence infrastructures. The Internet has enabled the consumer to become aware of the possibility of situated software. Data is no longer purely controlled by enterprise IS organizations. Mashup platforms allow users to create an ad-hoc composition of data sources into a new representation to serve as a business intelligence report. By making all data available, business endusers can aggregate the data that they deem important, not just the data that was made available in existing data warehouse dimensions.

Management Support with Structured and Unstructured Data—An Integrated Business Intelligence Framework

In the course of the evolution of management support towards corporate wide Business Intelligence infrastructures, the integration of components for handling unstructured data comes into focus. In this paper, three types of approaches for tackling the respective challenges are distinguished. The approaches are mapped to a three layer BI framework and discussed regarding challenges and business potential. The application of the framework is exemplified for the domains of Competitive Intelligence and Customer Relationship Management.