Enterprise Business Intelligence Research Articles

Business intelligence systems for population health management: a scoping review

Abstract Objective Population 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 Methods PubMed, 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. Results The 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 Conclusion PHM 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.

Professionalization and knowledge management in enterprise systems adoption in Mexican manufacturing firms

ABSTRACT Advanced integrated management systems are playing a decisive role in Industry 4.0. Therefore, it is important to understand the influence and effects of knowledge management (KM) and professionalization (PR) on their development. This research aims to propose that PR and KM help firms adopt and implement enterprise resource planning (ERPs) and business intelligence (BI) systems. The study uses PLS-SEM to analyse a sample of 120 firms collected through an electronic instrument focused on decision-makers in the manufacturing industry of Coahuila, Mexico. The results show that KM and PR positively affect ERP and BI systems and that ERPs partially mediate PR, KM, and BI systems.

Identifying obstacles to evaluating business intelligence in Micro-Small Apparel Enterprises: a case study in Durban, South Africa

The escalating financial burdens faced by the Small Medium and Micro Enterprise (SMME) sector necessitate a perpetual pursuit of enhanced managerial efficacy. For enhanced financial management, client relationship management and inventory control, it is necessary for businesses to utilise various technologies and methodologies. One approach involves the utilisation of business analytical technologies, such as Business Intelligence (BI). Business Intelligence (BI) serves as a valuable tool for enterprises, aiding in the examination and interpretation of data to facilitate informed decision-making processes aimed at enhancing overall business efficacy. The eThekwini region encompasses a significant presence of micro-small enterprises within the apparel manufacturing and retail sector. The primary aim of this research was to identify and analyse the many issues that hinder the evaluation of business intelligence (BI) in micro-small apparel enterprises located in Durban, a city in Kwa-Zulu Natal, South Africa. A cross-sectional quantitative study was undertaken to examine a cohort of 161 small apparel business owners. The participants were chosen through the utilisation of a judgemental sampling technique. The data was gathered through the utilisation of anonymous questionnaires. The data was analysed by utilising multiple linear regression analysis to determine the characteristics that hinder the evaluation of business intelligence (BI) in the small apparel industry. The study employs the Technology-Organisation-Environment (TOE) framework as its conceptual framework. The researcher identified several obstacles that hinder the evaluation of business intelligence in micro-small textile enterprises in South Africa. These factors include perceived relative advantage, cost, organisational data environment, organizational decision-making culture, and external support. However, there are numerous governmental institutions that are intended to provide support for micro-small companies. Therefore, it is recommended that these institutions establish training initiatives aimed at educating apparel SMMEs on BI. The implementation of target-driven training has the potential to significantly contribute to the achievement of vision 2030 for fostering the growth of small, medium, and micro enterprises (SMMEs) to enhance the South African economy.

Business Intelligence and Decision-Making in Micro Small and Medium Enterprises in Africa

A strong business intelligence strategy can deliver accurate data and reporting capabilities faster to business operators; help them make better quicker business decisions. Holding this statement constant, the researchers investigated business intelligence and decision making in MSMEs in Africa. The study is qualitatively done with six participants who responded to an unstructured questionnaire employed to gather the needed relevant data. The collected data were meticulously transcribed and analyzed. After completion, the numerous data entry forms underwent internal validation to guarantee that the replies were accurately categorized before being harmonized and divided into two main categories- competitive advantage and operational efficiency. The Study found that business intelligence is pivotal for timely decision-making in firms especially in the MSMEs sector of the Nigeria economy as it is a strategy used by owner-managers in the organization to achieve operational efficiency and competitive advantage in a tense business environment like Nigeria. It is evident, therefore, that Africa especially Nigeria is not doing badly in the MSMEs sector of the economy and to sustain this stride, governments at all levels must factor in MSMEs and technology into their policy formulation and implications. It is only when that is done that Africa in general and Nigeria, in particular, can boost of not only registering its name in the global economic map but will have a viable economic base that will continue to attract foreign direct investment.

Towards a Conceptual Framework for Data Management in Business Intelligence

Business intelligence (BI) refers to technologies, tools, and practices for collecting, integrating, analyzing, and presenting large volumes of information to enable improved decision-making. A modern BI architecture typically consists of a data warehouse made up of one or more data marts that consolidate data from several operational databases. BI further incorporates a combination of analytics, data management, and reporting tools, together with associated methodologies for managing and analyzing data. An important goal of BI initiatives is to improve business decision-making for organizations to increase revenue, improve operational efficiency, and gain a competitive advantage. In this article, we analyze qualitatively various prominent business intelligence (BI) frameworks in the literature and develop a comprehensive BI framework from these. Through the technique of qualitative propositions, we identify the properties, respective advantages, and possible disadvantages of the said BI frameworks to develop a comprehensive framework aimed mainly at data management, incorporating the advantages and eliminating the disadvantages of the individual frameworks. The BI landscape is vast, so as a limitation, we note that the new framework is conceptual; hence, no implementation or any quantitative measurement is performed at this stage. That said, our work exhibits originality since it combines numerous BI frameworks into a comprehensive framework, thereby contributing to conceptual BI framework development. As part of future work, the new framework will be formally specified, followed by a practical phase, namely, conducting case studies in the industry to assist companies in their BI applications.

Toward a design theory of strategic enterprise management business intelligence (SEMBI) capability maturity model

PurposeThe purpose of this study is to demonstrate that Strategic Enterprise Management (SEM) and Business Intelligence (BI) have the potential to integrate management decisions vertically through an organization’s hierarchy. This study also aims to present a design theory framework and build a model dimension using eight principles serving as mid-range theories.Design/methodology/approachThis study uses a design science perspective to posit how organizations can successfully implement SEMBI (a union of SEM and BI). This study then completes the design theory by building the method dimension using two principles. Finally, the study presents testable hypotheses for the theory and an evaluation using stakeholder attitudes and judgments as proxies for objective measures.FindingsIn the search for a prescription for SEMBI success, this study finds that the notion of the Capability Maturity Model (CMM) is a good artifact with which to organize the principles the authors are seeking. CMM has since been adapted to suit different contexts by incorporating relevant principles from those domains. Hereafter, this study refers to SEMBI–CMM as the adapted solution for SEMBI's success.Originality/valueThis study coins and uses the term SEMBI to represent the union of SEM and BI. This term retains its distinct identities and principles and forms a holistic and integrated view of SEM and BI implementation strategies. In an effort to advance this line of research, this study employs a design science perspective to address the question of how an organization can successfully implement SEMBI.

Investigating Business Intelligence as a Possible Differentiator for Administrative Competitive Edge

Business Intelligence (BI) has been recognised as a crucial component of the organisation’s success. Whilst some organisations are able to exploit and realise the benefits of BI, there are still several organisations that fail to capitalise on its potential. The aim was to investigate BI as a possible differentiator for administrative competitive edge. The study applied a deductive research approach and a quantitative research method. Data was collected using questionnaires from a population of 302 respondents. Results revealed that, the use of BI in organisation is dependent on the employees realising the strong correlation between business alignment, BI and strategy. Management support was found to be a key indicator in demonstrating leadership, commitment, and advocacy towards BI initiatives. The study found that the alignment of BI with strategy within an organisation makes it possible for the organisation to reap the benefits of BI which led to better decision making. BI was found to be useful in assisting respondents to perform and complete their tasks quicker and with ease. The conceptual framework tested in the study comprised of three constructs: strategy alignment, management support and BI utilisation as contributing components in making it a differentiator in gaining a competitive advantage.

The Adoption of Enterprise Resource Planning and Business Intelligence Systems in Small and Medium Enterprises: A Conceptual Framework

The widespread globalism of services and the fast technological progress brought about by IT have made organizations today competitive in offering new products and services. One of the highlighted innovations is the enterprise resource planning and business intelligence system (ERPB), which has captivated the interest of industry analysts and policymakers due to its potential to provide more intellectual information for decision making and ensure its continuity. Firms must use ERPBI appropriately to flourish, as its misuse leads to failure. Even though research has focused on the factors influencing ERPBI adoption, most studies have ignored many factors that influenced the behavioral intention to adopt variable that significantly affects the adoption and use of technology. Ensuring the proper adoption of ERPBI is critical to corporate success. If ERPBI is misused, this will not serve its objective. Although a few studies have studied factors affecting the success of ERPBI adoption, the majority have failed to discover drivers of continuity and sustainability. This article proposed a conceptual framework for ERPBI adoption using the TOE and UTAUT models. Four main hypotheses were formulated for the three dimensions of technology, organization, and environment. The proposed framework is vital in adopting the ERPBI and could shape the direction in successfully adopting new technologies in SMEs. This study could fill in the gap in ERPBI research because previous studies have not focused on ERPBI adoption factors, ERPBI adoption, and business continuity.

Table2Vec-automated universal representation learning of enterprise data DNA for benchmarkable and explainable enterprise data science

Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. They involve enterprise DNA associated with domain-oriented transactions and master data, informational and operational metadata, and relevant external data. A critical challenge in enterprise data science is to enable an effective ‘whole-of-enterprise’ data understanding and data-driven discovery and decision-making on all-round enterprise DNA. Accordingly, here we introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes (similar to biological genomes and DNA in organisms) and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. Such automated universal enterprise representation and learning cannot be addressed by existing enterprise data warehouses (EDWs), business intelligence and corporate analytics systems, where ‘enterprise big tables’ are constructed with reporting and analytics conducted by specific analysts on respective domain subjects and goals. It addresses critical limitations and gaps of existing representation learning, enterprise analytics and cloud analytics, which are analytical subject, task and data-specific, creating analytical silos in an enterprise. We illustrate Table2Vec in characterizing all-round customer data DNA in an enterprise on complex heterogeneous multi-relational big tables to build universal customer vector representations. The learned universal representation of each customer is all-round, representative and benchmarkable to support both enterprise-wide and domain-specific learning goals and tasks in enterprise data science. Table2Vec significantly outperforms the existing shallow, boosting and deep learning methods typically used for enterprise analytics. We further discuss the research opportunities, directions and applications of automated universal enterprise representation and learning and the learned enterprise data DNA for automated, all-purpose, whole-of-enterprise and ethical machine learning and data science.

A model of business intelligence on cloud for managing SMEs in COVID-19 pandemic (Case: Iranian SMEs)

A model of business intelligence on cloud for managing SMEs in COVID-19 pandemic (Case: Iranian SMEs)

Discussion on the Application of Enterprise Business Intelligence Data Analysis System

Enterprise Business Intelligence (BI) system refers to data mining through the existing database of the enterprise, and data analysis according to customer requirements through comprehensive processing. The data analysis efficiency is high and the operation is convenient. This paper mainly analyzes the application of enterprise BI data analysis system in enterprises.

Assimilation of business intelligence: The effect of external pressures and top leaders commitment during pandemic crisis

The business intelligence (BI) has been often touted as a game-changer especially during the pandemic crisis. Although most managers are familiar with BI and agree that, it should be operationalized across their organizations. The BI is not well assimilated throughout adopting organizations. Rooted in institutional and upper echelon theories, this study proposes a theoretical model aimed toward explaining BI assimilation. We surveyed 174 respondents occupying leadership positions from174 auto-components manufacturing firms in India to gather data. The findings suggest that normative and mimetic (but not coercive) factors significantly influence top leader’s commitment to the BI initiatives. We found that the commitment of the top leaders influences the assimilation of BI via acceptance and routinization. Our study is an attempt to address the previous research calls related to BI assimilation. The findings of the study inform the information management scholars via theory-based research on phenomena related to post-adoption BI diffusion during a pandemic crisis. Practitioners can utilize the results of our study to design their policies that help assimilate BI such that forecasted benefits can be fully realized during an uncertain time.

Innovative Uses of OData Services in Modern SAP Solutions

OData (Open Data Protocol) has emerged as a pivotal framework in the realm of modern SAP solutions, facilitating seamless data integration and interoperability across diverse platforms. This abstract explores the innovative applications of OData services within SAP environments, highlighting their transformative impact on enterprise data management. By leveraging OData, organizations can enhance data accessibility, allowing developers to create rich, interactive applications that draw data from multiple sources. The protocol supports RESTful architecture, making it easier to consume and manipulate data via standard HTTP methods. This adaptability enables businesses to implement real-time analytics, fostering agile decision-making and improving overall operational efficiency. Furthermore, the integration of OData with SAP Fiori applications empowers users with intuitive interfaces, streamlining workflows and enhancing user experience. As companies continue to embrace digital transformation, OData services offer a robust solution for connecting cloud and on-premise applications, enabling enterprises to harness the power of data-driven insights. This paper aims to showcase various innovative use cases of OData services in modern SAP solutions, including their role in developing APIs, enhancing mobile applications, and driving business intelligence initiatives. By examining these applications, this research underscores the significance of OData as a catalyst for innovation and efficiency in the evolving landscape of enterprise resource planning (ERP) systems. Through practical examples and case studies, we demonstrate how leveraging OData services can lead to a more responsive, integrated, and data-centric approach in SAP implementations.

What Are the Critical Success Factors for Agile Analytics Projects?

ABSTRACT To get value from BI (Business Intelligence) and Big Data initiatives, organizations need to develop the capability to successfully execute their analytics projects. Via updating Chow and Cao’s list of 12 success factors for agile projects, 43 attributes of these potential critical success factors (CSFs) were identified. Data from four case studies of analytics projects suggest that the critical success factors for analytics projects may be Strong Customer Involvement and a Methodical Project Definition Process.

A model to adopt Enterprise Resource Planning (ERP) and Business Intelligence (BI) among saudi SMEs

Objective of the study: This paper touched upon the major issues that are related to intention towards the use of ERPBI in the Saudi private sector – Saudi Arabia is an example of a developing countryMethodology / approach: This paper used a qualitative approach and the sample of the study comprised of 30 experts from the Saudi private sector, who were involved in ERPBI.Originality / Relevance: According to prior studies in literature, there is no specific model that supports and explains ERPBI, and as such, this increases the chances of ineffective adoption and use of ERPBI. This study found that ERPBI adoption models are lacking, and those that exist are inapplicable as they exclude several significant factors for successful adoption.Main results: The findings indicated that for the promotion of the ERPBI model, the entire significant factors have to be considered and incorporated. Those factors are: system quality, service quality, and information quality factors, change management, effective communication, training factors, clear vision and planning, competitive pressure, and government role.Theoretical / methodological contributions: This study’s results open door for future research based on the gap in theories applied and the factors investigated in the context of developing nations. Social / management contributions: Shedding light on the factors and their relationship with ERPBI use success would assist top management in minimizing losses through system use for enhanced cash flow, and improved performance of employees and that can eventually bring about the overall performance of the organization.

International Journal of Innovation - IJI completes 7 years

International Journal of Innovation - IJI completes 7 years

Business Intelligence Capability, Complementary Resources and Performance among Public Listed Firms in Kenya

Whereas Business Intelligence initiative has been a primary focus many organisations globally for several years and accounting for billions of dollars in capital expenditure, empirical research remains sparse on the actual impact derived from this investment. Even when the benefits are established, its indirect and delayed impact on business results make it difficult to assess its value. Available literature on how insights triggered by Business Intelligence are transformed into profitable business learning is vague and fragmented. Hence, the main objective of this study was to establish the influence of complementary resources on the relationship between Business Intelligence capability and firm performance. The study used interdisciplinary theories to achieve the research objective, namely, Information systems capability theory and organisational learning. The study was performed using a mixed methods research methodology and cross-sectional approach. Data was collected from 64 public listed companies in Kenya. Findings indicated that complementary resources have a positive and significant impact on the relationship between Business Intelligence capability and performance. The study contributed to theory by building a framework for business intelligence assessment, including factors that significantly lead to improved performance. The results also provide new insights into the existing literature and suggest directions for future research with implications for academia, policymakers and management.

Business intelligence and organizational performance

PurposeOrganizations introduce business intelligence (BI) to increase their performance, but often, this initiative is not aligned with the business process management (BPM) initiative, which also aims to improve organizational performance. Although some findings from the literature indicate that BI implementation has a positive impact on organizational performance, the impact seems to be indirect. Therefore, the purpose of this study is to enhance the understanding of how BI maturity is translated into organizational performance. Alignment of BI and BPM initiatives seems one possible way for creating business value with BI, particularly because BI enables process performance measurement and management, which allows the BI initiative to become more business focused.Design/methodology/approachA questionnaire was prepared and used to collect data in Croatian and Slovenian organizations with more than 50 employees. A BI–BPM alignment measurement instrument was developed for the purpose of this study using the recommended process of scale development and validation. A total of 185 responses were analyzed by the structural equation modeling technique.FindingsOur results provide evidence that the effect of BI on organizational performance is fully mediated by alignment of BI and BPM initiatives, and therefore, BI business value can be generated through the use of common terminology and methodologies, as well as a strong communication between BI and BPM experts, managers and teams in order to coordinate the two initiatives.Originality/valueThis study has responded to the call for better understanding of how the impact of BI on organization performance is realized. It confirmed that BI and BPM initiatives should be aligned in order to give BI a business value.

Data Profiling Model for Assessing the Quality Traits of Master Data Management

Enterprise Resource Planning (ERP) and Business Intelligence (BI) system demand progressive rules for maintaining the valuable information about customers, products, suppliers and vendors as data captured through different sources may not be of high quality due to human errors, in many cases. The problem encounters when this information is accessible across multiple systems, within same organization. Providing adequacy to this scattered data is a top agenda for any organization as maintaining the data is complicated, as having high quality data. Master Data Management (MDM) provides a solution to these problems by maintaining “a single reference of truth” with authoritative source of master data (Customer, products, employees etc). Master Data Management (MDM) is a highlighted concern now a day as valid data is the demand for strategic, tactical and operational steering of every organization. The lane to MDM initiates with the quality of data which demands for discovery of master data, profiling and analysis. As inadequacy of data may leads to adverse effects such as wrong decision, loss of time, bad results and unnecessary risk. Thus there is a need to deal with master data and quality of this specific data in a successful and efficient manner. For ensuring this purpose, an approach is proposed in this paper. The research focuses on development of a Model for Data Profiling to assess the level of Quality Traits for Master Data Management. Results are shown by executing the defined steps on TALEND tool over collected dataset. Thus, level of quality traits processes directly correlates with an organization’s ability to make the proper decisions and better outcomes.

Comparative Analysis of Two BI Tools: Micro Strategy and Tableau

The need for enterprise business intelligence initiatives has skyrocketed due to the potential to empower businesses to make better data driven decisions. Better insights are being needed by business users into core business problems as they need access to these insights faster than ever before. Many BI vendors have provided feasible options to buyer looking to implement BI solution. It is difficult for a business person to reach any one decision when given a variety of options.BI previously had merely reporting capabilities, BI solutions have progressively acquired drastic functionality, like data mining, improved analytics tools, and online analytical processing (OLAP) services, with various company mergers and acquisitions, and the BI field has evolved to more complex and heavy BI solutions.Two widely used BI-tools for reporting and providing business insights are MicroStrategy and Tableau. MicroStrategy is well known for its software which is based on single code base, it is suitable for applications of all sizes; grips hardware, operating system, and data source infrastructure while making BI more accessible for business users. Tableau supports ad hoc analysis for the day to day user, it facilitates the business users to better visualize and understand their data. Also, for easy consumption, it allows to display the KPIs easily on a centralized analytics platform.