Real-time Business Intelligence Research Articles

Leveraging Data Analytics for Financial Stability: A Blueprint for Sustaining SMEs in Economically Distressed Regions of the U.S.

Purpose: Our research explores how data analytics can empower small- and medium-sized enterprises (SMEs) in economically distressed regions of the U.S. by improving financial decision-making, operational efficiency, and risk mitigation. The study aims to provide a blueprint for leveraging data-driven insights to ensure long-term financial stability and economic growth in underserved areas, particularly in the Midwest and Southern states. Methodology: The study employs a case-study approach to analyze the real-world applications of data analytics platforms in SMEs within economically distressed regions. It uses statistical analysis to compare the performance of businesses that have adopted data analytics tools with those relying on traditional methods. The methodology also includes the collection of data from various sources such as financial performance records, customer analytics, and predictive models. Findings: The research demonstrates that SMEs using data analytics experience significant improvements in financial performance. For instance, predictive analytics increased financial forecasting accuracy by 25%, and real-time business intelligence tools reduced operational costs by 15%. Businesses using customer analytics tools saw a 20% rise in customer retention and a 15% increase in revenue. The study shows that data-driven strategies can improve overall financial stability by 30% and enable SMEs to proactively mitigate risks in volatile markets. Unique Contribution to Theory, Policy, and Practice: The study contributes to the emerging body of literature on the transformative potential of data analytics in financially distressed regions, providing new insights into how real-time and predictive analytics can enhance financial decision-making in SMEs’ practices within the economically distressed regions. The study provides a practical blueprint for SMEs to adopt data analytics tools, demonstrating their impact on financial stability, operational efficiency, and long-term growth. It offers actionable recommendations for business leaders in distressed regions to integrate analytics equitably into their decision-making processes.

Real-time business intelligence development using machine learning to increase the potential of the dairy goat milk business

Real-time business intelligence development using machine learning to increase the potential of the dairy goat milk business

Big data analytics in Australian pharmaceutical supply chain

PurposeDrawing on information processing view (IPV) theory, the objective of this study is to explore big data analytics (BDA) in pharmaceutical supply chain (PSC) for better business intelligence. Supply chain operations reference (SCOR) model is used to identify and discuss the likely benefits of BDA adoption in five processes: plan, source, make, deliver and return.Design/methodology/approachSemi-structured interviews with managers in a triad comprising pharmaceutical manufacturers, wholesalers/distributors and public hospital pharmacies were undertaken. NVivo software was used for thematic data analysis.FindingsThe findings revealed that BDA capability would be more practical and helpful in planning, delivery and return processes within PSC. Sourcing and making processes are perceived to be less beneficial.Practical implicationsThe study informs managers about the strategic role of BDA capabilities in SCOR processes for improved business intelligence.Originality/valueAdoption of BDA in SCOR processes within PSC is a step towards resolving the challenges of drug shortages, counterfeiting and inventory optimisation through timely decision. Despite its innumerable benefits of BDA, Australian PSC is far behind in BDA investment. The study advances the IPV theory by illustrating and strengthening the fact that data sharing and analytics can generate real-time business intelligence helping in better health care support through BDA-enabled PSC.

Digitisation and system integration in the public sector – Consequences for teaching

The digitisation of public sector ICT is driven by a number of factors, which are not altogether dissimilar from the private industries: Increased self-service via the Internet, the Internet of Things (IoT), real-time business intelligence and the advent of integrated information systems as the “backbone” of organisational ICT. This fundamentally changes the landscape of public sector ICT. Particularly the smart cities concept has become a main driver of this evolution, whereby the smart cities concept heavily relies on an intelligent infrastructure that is the large-scale application of IoT.
 However, how does this translate into public sector-oriented education? This paper presents a university level teaching programme that covers the topics of integrated information systems for the environment described above. The paper deals with the research question how to embed such a programme in a conventional public sector-oriented university course programme. It details the didactic specificities and analyses the feedback from the roll out. It also analyses the prior knowledge required from students and the changes in other elements of a public administration course programme necessitated by a digitisation orientation.

Comparative analysis of modern fault tolerance technologies

On-demand cloud applications is one of the rapidly developing technologies that highly demanded on the market. With the increase of computer systems and requirements for real-time business intelligence, potential customers of such services turn to providers of «computing on demand» services. In the data centers that used for such services, computer systems face a large amount of processed data, which in turn leads to an imbalance in the load and provokes system failures. With infrastructure as a service (IaaS) usage, customers expect efficient performance of their tasks at an optimal price. In situation, when a client submits a task for work, it is deployed on a computer system in the data center of the service provider and there is no a hundred percent assurance that task would be completed and not failed because of lack of resources or software failure. Fault tolerance and high availability issues are currently the most acute in that field. The modern set of software tools for providing fault tolerance is quite wide. In general, they are all called to ensure the trouble-free operation of the computer system, but in fact they have significant differences in conceptual terms. High availability solutions provide fully automated failover to a backup system so that users and applications can continue working without disruption. HA solutions must have the ability to provide an immediate recovery point. At the same time, they must provide a recovery time capability that is significantly better than the recovery time that you experience in a non-HA solution topology. Analyzed and compared modern fault tolerance and high availability technologies. Compared modern models of High Availability system and tools for its implementation. Identified the shortcomings of modern models and tools for fault tolerance implementing. Developed recommendations for the usage of approaches that would help to eliminate reviewed shortcomings in different cases.

Use of Big Data in Supply Chain Management

Abstract: The rapidly expanding interest in the application of big data analytics (BDA) in supply chain management (SCM) among academics and practitioners has prompted an assessment of current research progress in order to define a new agenda. the use of sophisticated analytics tools to improve supply chain management The apps are divided into three categories: descriptive, predictive, and prescriptive analytics, as well as the supply chain operations reference (SCOR) model domains of plan, source, make, deliver, and return. This review answers to the demand by offering a new classification scheme that provides a comprehensive picture of current literature on where and how BDA has been used in SCM. The classification system is based on Mayring's (2008) content analysis method and addresses four research questions: (1) In which aspects of SCM is BDA used? (2) To what extent is BDA employed in these SCM domains in terms of analytics? (3) What are the different types of BDA models utilised in SCM? (4) How are these models developed using BDA techniques? The consideration of these four topics reveals several research gaps, pointing to future study directions. Purpose - Rapid innovation and globalisation have created a plethora of opportunities and choices for businesses and consumers in the marketplace. Due to competitive pressures, sourcing and manufacturing are now done on a global basis, resulting in a huge increase in product availability. The purpose of this article is to determine whether real-time business intelligence (BI) is required in supply chain analytics. Design/methodology/approach – The paper argues for and analyses the benefits and drawbacks of BI. Findings – The article focuses on the need to review the classic BI notion of integrating and consolidating information in an organisation in order to help service-oriented businesses that want to keep their customers. Using a BI methodology to improve the effectiveness and efficiency of supply chain analytics is a vital component of a company's ability to establish a competitive edge. Originality/value – This study contributes to a better understanding of the difficulties surrounding the usage of business intelligence tools in supply chains. Keywords: Supply chain management, Business analytics, Information systems, Big Data, Big data analysis

TEACHING REQUIREMENTS OF A DIGITISED PUBLIC ADMINISTRATION

The digitisation of public sector ICT is driven by a number of factors: Increased self- service via the Internet, the Internet of Things (IoT), real-time business intelligence and the advent of integrated information systems as the “backbone” of organisational ICT. This is accentuated by the Covid-19 pandemic. The paper presents an implemented university level teaching programme that covers the topics of integrated information systems for the environment described above. The paper also deals with the research question of how to embed such a programme in a conventional, public sector-oriented university course programme. It details the didactic specificities and analyses the feedback from the roll out and the prior knowledge required from students and the changes in other elements of a public administration course programme necessitated by digitisation orientation. It finally summarises the experience made and illustrates the necessity for further research.

Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles

An electric vehicle with autonomous driving is a possibility provided technology innovations in multi-disciplinary approach. Electric vehicles leverage environmental conditions and are much desired in the contemporary world. Another great possibility is to strive for making the vehicle to drive itself (autonomous driving) provided instructions. When the two are combined, it leads to a different dimension of environmental safety and technology driven driving that has many pros and cons as well. It is still in its infancy and there is much research to be carried out. In this context, this paper is aimed at building an Artificial Intelligence (AI) framework that has dual goal of “monitoring and regulating power usage” and facilitating autonomous driving with technology-driven and real time knowledge required. A methodology is proposed with multiple deep learning methods. For instance, deep learning is used for localization of vehicle, path planning at high level and path planning for low level. Apart from this, there is reinforcement learning and transfer learning to speed up the process of gaining real time business intelligence. To facilitate real time knowledge discovery from given scenarios, both edge and cloud resources are appropriately exploited to benefit the vehicle as driving safety is given paramount importance. There is power management module where modular Recurrent Neural Network is used. Another module known as speed control is used to have real time control over the speed of the vehicle. The usage of AI framework makes the electronic and autonomous vehicles realize unprecedented possibilities in power management and safe autonomous driving.Key words:Artificial IntelligenceAutonomous DrivingRecurrent Neural NetworkTransfer Learning

IoT-Based Sensor Systems for Intelligence in Transportation, Healthcare and Natural Gas Detection

In the last decade, significant advances have been made to enhance the deployment of sensors. Everything from novel manufacturing technologies (including additive manufacturing techniques) to topologies to material systems.Here we are defining the utility of a sensor as the ability to provide intelligence. Intelligence comes from collecting data , extracting information , which comes from data analytics, and interpreting the information is what we are calling intelligence . Many tools are available, even off-the-shelf capabilities, which include machine learning, artificial intelligence, and even sets of toolboxes in MatLab that have changed the way we obtaining intelligence.In order to obtain viable data, integrity on the collection of the data becomes important. Collection can be done by assigning IP addresses to sensors, on-board data storage and/or Internet of things-type (IoT-type) platforms that have really enabled the utilization of sensors to explode. The challenge is always about obtaining intelligence.This paper introduces the concept of SensorComm’s emission monitoring solution, Wi-NOx™. Wi-NOx™ is an IoT-based real-time NOx emission monitoring system. Installed at the tailpipe, it provides key stakeholders with real-time business intelligence by capturing the pollution footprint of vehicles. Operational efficiencies of up to 15% include fuel savings, predictive maintenance, and performance optimization, all obtained through analytics. This system was developed to monitor emissions from vehicles, more recently modified to monitor patients remotely for healthcare applications, and now to monitor natural gas for oil & gas applications.This talk will discuss the systems strategy for data surety, data analytics, and examples of the intelligence extracted specifically from an application.This material is based upon work supported by the U.S. Department of Energy, National Energy Technology Office program under Award Number DE-FOA-0031864, and by the National Science Foundation under Grant No. 1632498. Figure 1

Big Data Velocity Management–From Stream to Warehouse via High Performance Memory Optimized Index Join

Efficient resource optimization is critical to manage the velocity and volume of real-time streaming data in near-real-time data warehousing and business intelligence. This article presents a memory optimisation algorithm for rapidly joining streaming data with persistent master data in order to reduce data latency. Typically during the transformation phase of ETL (Extraction, Transformation, and Loading) a stream of transactional data needs to be joined with master data stored on disk. To implement this process, a semi-stream join operator is commonly used. Most semi-stream join operators cache frequent parts of the master data to improve their performance, this process requires careful distribution of allocated memory among the components of the join operator. This article presents a cache inequality approach to optimise cache size and memory. To test this approach, we present a novel Memory Optimal Index-based Join (MOIJ) algorithm. MOIJ supports many-to-many types of joins and adapts to dynamic streaming data. We also present a cost model for MOIJ and compare the performance with existing algorithms empirically as well as analytically. We envisage the enhanced ability of processing near-real-time streaming data using minimal memory will reduce latency in processing big data and will contribute to the development of high-performance real-time business intelligence systems.

DOD-ETL: distributed on-demand ETL for near real-time business intelligence

The competitive dynamics of the globalized market demand information on the internal and external reality of corporations. Information is a precious asset and is responsible for establishing key advantages to enable companies to maintain their leadership. However, reliable, rich information is no longer the only goal. The time frame to extract information from data determines its usefulness. This work proposes DOD-ETL, a tool that addresses, in an innovative manner, the main bottleneck in Business Intelligence solutions, the Extract Transform Load process (ETL), providing it in near real-time. DOD-ETL achieves this by combining an on-demand data stream pipeline with a distributed, parallel and technology-independent architecture with in-memory caching and efficient data partitioning. We compared DOD-ETL with other Stream Processing frameworks used to perform near real-time ETL and found DOD-ETL executes workloads up to 10 times faster. We have deployed it in a large steelworks as a replacement for its previous ETL solution, enabling near real-time reports previously unavailable.

ETL Framework for Real-Time Business Intelligence over Medical Imaging Repositories

In the last decades, the amount of medical imaging studies and associated metadata has been rapidly increasing. Despite being mostly used for supporting medical diagnosis and treatment, many recent initiatives claim the use of medical imaging studies in clinical research scenarios but also to improve the business practices of medical institutions. However, the continuous production of medical imaging studies coupled with the tremendous amount of associated data, makes the real-time analysis of medical imaging repositories difficult using conventional tools and methodologies. Those archives contain not only the image data itself but also a wide range of valuable metadata describing all the stakeholders involved in the examination. The exploration of such technologies will increase the efficiency and quality of medical practice. In major centers, it represents a big data scenario where Business Intelligence (BI) and Data Analytics (DA) are rare and implemented through data warehousing approaches. This article proposes an Extract, Transform, Load (ETL) framework for medical imaging repositories able to feed, in real-time, a developed BI (Business Intelligence) application. The solution was designed to provide the necessary environment for leading research on top of live institutional repositories without requesting the creation of a data warehouse. It features an extensible dashboard with customizable charts and reports, with an intuitive web-based interface that empowers the usage of novel data mining techniques, namely, a variety of data cleansing tools, filters, and clustering functions. Therefore, the user is not required to master the programming skills commonly needed for data analysts and scientists, such as Python and R.

Teaching Smart Production: An Insight into the Learning Factory for Cyber-Physical Production Systems (LVP)

Increasing global competition among producing companies necessitates a further specialization and continuous enhancement of products, manufacturing processes and business models. The current advances in these areas are highly influenced by the digitization, which might not only change the work environment within a factory but might also change entire value-added chains. Therefore, producing companies express their demand for learning environments, which address ongoing developments regarding digitization and Industry 4.0.This paper presents the concept for the learning factory for cyber-physical production systems (LVP). Based on a production scenario, which includes manufacturing as well as assembly, the learning environment demonstrates how recent technologies can be used to cope with high numbers of product variants and small lot sizes. The technologies utilized within the LVP include interactive assistance systems for workers, real-time business intelligence and simulation tools for single processes and for the overall material flow. The learning factory shows the technical implementation of these technologies as well as their impact on the organization of the production system. By attending one of the interactive training sessions, participants can experience the various advantages of intelligent production systems.

Healthcare hashtag index development: Identifying global impact in social media

PurposeCreate an index of global reach for healthcare hashtags and tweeters therein, filterable by topic of interest. Materials and methodsFor this proof-of-concept study we focused on the field of Primary Care and Family Medicine. Six hashtags were selected based on their importance, from the ones included in the ‘Healthcare Hashtag Project’. Hashtag Global Reach (HGR) was calculated using the additive aggregation of five weighted, normalized indicator variables: number of impressions, tweets, tweeters, user locations, and user languages. Data were obtained for the last quarter of 2014 and first quarter of 2015 using Symplur Signals. Topic-specific HGR were calculated for the top 10 terms and for sets of quotes mapped after a thematic analysis. Individual Global Reach, IGR, was calculated across hashtags as additive indexes of three indicators: replies, retweets and mentions. ResultsUsing the HGR score we were able to rank six selected hashtags and observe their performance throughout the study period. We found that #PrimaryCare and #FMRevolution had the highest HGR score in both quarters; interestingly, #FMChangeMakers experienced a marked increase in its global visibility during the study period. “Health Policy” was the commonest theme, while “Care”, “Family” and “Health” were the most common terms. DiscussionThis is the first study describing an altmetric hashtag index. Assuming analytical soundness, the Index might prove generalizable to other healthcare hashtags. If released as a real-time business intelligence tool with customizable settings, it could aid publishing and strategic decisions by netizens, organizations, and analysts. IGR could also serve to augment academic evaluation and professional development. ConclusionOur study demonstrates the feasibility of using an index on the global reach of healthcare hashtags and tweeters.

From Business Intelligence to semantic data stream management

The Semantic Web technologies are being increasingly used for exploiting relations between data. In addition, new tendencies of real-time systems, such as social networks, sensors, cameras or weather information, are continuously generating data. This implies that data and links between them are becoming extremely vast. Such huge quantity of data needs to be analyzed, processed, as well as stored if necessary. In this position paper, we will introduce recent work on Real-Time Business Intelligence combined with semantic data stream management. We will present underlying approaches such as continuous queries, data summarization and matching, and stream reasoning.

Teaching business intelligence to computing students through a simulation and evolutionary lab sequence

Business Intelligence (BI) techniques and workflow has changed significantly in the last few years. Due to the introduction of SAP HANA and other similar tools, real-time BI in support of business ...

Enable Better and Timelier Decision-Making Using Real-Time Business Intelligence System

Today's businesses need support when making decisions. Business intelligence (BI) helps businesses to make decisions based on good pre-analysis and documented data, and enables information to be presented when and where the decisions need to be made. Real time business intelligence (RTBI) presents numbers in real time, providing the decision makers at the operational and tactical layers with data as fresh as it can

Big data analytics for knowledge generation in tourism destinations – A case from Sweden

Abstract This paper presents a knowledge infrastructure which has recently been implemented as a genuine novelty at the leading Swedish mountain tourism destination, Are. By applying a Business Intelligence approach, the Destination Management Information System Are (DMIS-Are) drives knowledge creation and application as a precondition for organizational learning at tourism destinations. Schianetz, Kavanagh, and Lockington’s (2007) concept of the ‘ Learning Tourism Destination ’ and the ‘ Knowledge Destination Framework ’ introduced by Hopken, Fuchs, Keil, and Lexhagen (2011) build the theoretical fundament for the technical architecture of the presented Business Intelligence application. After having introduced the development process of indicators measuring destination performance as well as customer behaviour and experience, the paper highlights how DMIS-Are can be used by tourism managers to gain new knowledge about customer-based destination processes focused on pre- and post-travel phases, like “ Web-Navigation ”, “ Booking ” and “ Feedback ”. After a concluding discussion about the various components building the prototypically implemented BI-based DMIS infrastructure with data from destination stakeholders, the agenda of future research is sketched. The agenda considers, for instance, the application of real-time Business Intelligence to gain real-time knowledge on tourists’ on-site behaviour at tourism destinations.

Electronic Markets on Internet marketing

Dear readers of Electronic Markets, It has been 5 years since ElectronicMarkets published the first special issue on Internet marketing. In the preface to this section, the guest editor Christopher P. Holland noted that “The changing pattern of communication between and among customers and suppliers will [...] affect all of the marketing stages of adoption from customer awareness, attitude, lead generation and validation through to the trial of products, sales and customer retention” (Holland 2008). This was at a time when the evolution of the Web 2.0 or Social Media technologies was still in their early stages. Internet marketing mainly referred to broadcasting information via websites and electronic mailings, advertising on search and marketplace sites as well as the evaluation of customer activity and communication of customer feedback. Today, the statement is still valid and we find that the convergence of many technologies has enhanced the potentials of Internet marketing towards more real-time capabilities. The technological “enablers” are mobile and social technologies that link the points where information is created and where information is used. Mobile technologies from auto identification and positioning (e.g. NFC, RFID, GPS) to smartphones and tablet computers are equipped with individual Internet addresses and generate information whenever the status of objects or user input is changed. This includes information on changes of location, preferences and needs which may be collected automatically in an unprecedented volume. Increasingly, not only structured information, such as events in a logistics chain or likes in the social web, is available, but also unstructured information, such as opinions and feedback. Together with real-time business intelligence and in-memory systems this yields a glimpse at the future opportunities of Internet marketing where information is not only collected and analyzed, but used to enrich – also in realtime – communication with customers and other contacts. Clearly, information technology has increased the opportunities for businesses to learn from market development, the usage of products as well as customer feedback and to leverage this knowledge for product development and more immediate as well as direct customer interaction. For Electronic Markets this is an exciting field since understanding the future mechanisms of Internet marketing calls for research that links a broad range of topics from technological, marketing and business to legal issues (see also Ngai 2003). We believe that electronic markets as information systems “in the cloud”, which enable n:m networking among market participants, will become more critical in this context. This applies to the technological integration of various devices across multiple actors, the placement and pricing of advertisements as well as collecting market intelligence and the governance of data usage in line with legal constraints (see also Alt and Klein 2011). The special section on “Internet Marketing” in this issue may be conceived as a contribution to foster the understanding of marketing in electronic media. It first includes a comprehensive overview of research in this field since 1994 which may serve as a compelling reference to support and guide future research. Among the identified Internet marketing topics are future search strategies which not only refer to an earlier article published in Electronic Markets (Gauzente 2009), but also to a second article in this special issue which analyzes how product searches are influenced by brand names and recommendations of other users. Finally, a third paper reports on the effectiveness of electronic couponing for digital R. Alt (*) University of Leipzig, Grimmaische Str. 12, 04109 Leipzig, Germany e-mail: rainer.alt@uni-leipzig.de

Designing Real Time Business Intelligence Reporting for Better Business Insights

warehousing has been one of the key implementation practices which was followed in many industries. With constant innovation in hardware and enterprise software's the decision support systems were able to provide more accurate information about the business based on the available historical data. With the rapid change in customer buying habits and frequently changing markets every business needs more insight into all the available data that is in the database. A successful organization needs to take better business decision based on the most recent data. Hence if an analytical reporting tool can provide the business insights based on the real time data available it will definitely help the organizations to understand the business in real time and take better decision on the fly.. The usage of in-memory computing and data storage options like columnar database capability for business intelligence applications can be highly considered for designing the business intelligence systems that provide real time business intelligence reports. This article will analyze the effectiveness of having a real time analytical reporting tool which also use the in-memory technology for business intelligence based applications and see how it can help in making organization to take better business decisions. Keywordstime business intelligence reporting, Performance dashboards, In-Memory analytics.