Operational Business Intelligence Research Articles

MEASUREMENT OF THE EFFECTS OF BUSINESS INTELLIGENCE APPLICATIONS ON PERFORMANCE IN HOSPITALS ACCORDING TO THE MANAGERIAL LEVELS: A CHAIN HOSPITAL APPLICATION

MEASUREMENT OF THE EFFECTS OF BUSINESS INTELLIGENCE APPLICATIONS ON PERFORMANCE IN HOSPITALS ACCORDING TO THE MANAGERIAL LEVELS: A CHAIN HOSPITAL APPLICATION Mustafa IŞIK [1] Onur YARAR [2] Didem Söylemez SUR [3] ABSTRACT Problem of the Research: Hospitals are matrix and complex organizations with many areas of expertise. In hospitals, multi-modular and integrated systems that can have multiple software and databases provide support in the execution of business processes within this complex organizational structure. Getting the right data at the right time for decision support purposes is often an important problem. In overcoming these problems, the problem of the research is what the contribution of integrated business intelligence applications solutions can be in decision-making stages from all levels of management. Purpose of the research: This research was conducted to investigate the effect of business intelligence reporting practices on performance according to managerial levels in hospital management systems. The research is a study conducted to measure the effect of business intelligence practices on performance in a chain hospital group that uses business intelligence in connection with the ERP (Enterprise Resource Planning) system with the highest number of hospitals in Turkey. The questionnaire study was conducted on 383 people. In this context, BI (Business Intelligence) architecture has been examined with all its components and information has been given about the benefits it provides to the enterprises. In the application developed as an example, a data warehouse modeling in accordance with the information required by the top management in hospitals, preparation of analyses, creation of presentation layer and presentation of the prepared analyses and reports on the control table were carried out. Material &Method: The universe of the research is composed of people who have the authority and access to use BI at MLP care group for this study. The number of authorized people on the ERP system was 591 and 65% of them have filled out the requested survey. 231 was our minimum number and for increasing the reliability we have modified our number of samples to 383. So 383 samples were reached to strengthen the validity and the reliability of the survey. In this sample range, it was also aimed to compare the performance impact between senior, intermediate and operational level managers. Conclusion: As a result of the research, it was determined that corporate business intelligence application screens at all types of managerial levels have a positive and significant effect on measurable performance indicators. In this context, when businesses monitor and control their operational activities through corporate business intelligence, it has been concluded that performance indicators provide less time loss, high reliability, integrated data, quality and accurate valuation advantages in the evaluation process. In the research, it was observed that the effect of performance results of operational managers' business intelligence applications from management levels was higher than that of senior and middle level managers. Keywords: Business Intelligence, Big Data, Corporate Performance Management, Hospital Information Management Systems, Data Analysis [1] Mustafa IŞIK, Asst. Prof. İstinye University Faculty of Economics, Administrative and Social Sciences, Department of Health Management/ mustafa.isik@mlpcare.com [2] Onur YARAR, Asst. Prof. Okan University, Healthcare Management, Turkey / onur.yarar@okan.edu.tr [3] Didem Söylemez SUR, Asst. Prof. Kent University, Healthcare Management, Turkey /didem.sur@kent.edu.tr

Integrated Document-based Electronic Health Records Persistence Framework

Electronic health record systems work beyond just recording patients` health data. They have multiple secondary functionalities, such as data reporting and clinical decision support. As each of these systems` workloads has contradictory different needs, managing a multipurpose electronic health record is a challenge. This paper proposes a unified healthcare data framework that can simplify health information system infrastructure. It investigates the suitability of the document-based NoSQL persistence mechanism, storing electronic health records data as a design choice for managing varied complexity ad hoc queries used in operational business intelligence. The performance of the most popular two document-based NoSQL back-ends, Couchbase Server and MongoDB, is compared according to the size of the database and query execution time. Results showed that while MongoDB can execute simple single-document queries nearly in milliseconds. It does not provide satisfactory response time for unplanned complex queries spanning multiple documents. By utilizing its analytics services and multi-dimensional scaling architecture, Couchbase Server multi-node cluster outperforms the response times of MongoDB for both simple and complex healthcare data access patterns. The primary advantage of the proposed tightly coupled EHRs processing framework is its flexibility to manage workload according to changing requirements.

The five key components for building an operational business intelligence ecosystem

The five key components for building an operational business intelligence ecosystem

A Generic Functional Architecture for Operational BI System

In recent years, Operational Business Intelligence has emerged as an important trend in the Business Intelligence (BI) market. Majority of BI application architectures are bespoke in nature which have several architectural limitations like tightly coupled, static, historic, subjective, no performance measurement of business processes, limited user access, limited analytical processing, querying and reporting features. In this article, a generic functional architecture for Operational BI systems based on software architecture principles is presented. All functional modules of the system are derived from the key features of the system and by using top down approach of software design principles. The similar functional modules are grouped into sub-systems and a set of these sub-systems constitutes overall functional architecture. The proposed architecture overcomes the limitations of traditional BI architectures.

Design and Evaluation of an Analytical Framework to Analyze and Control Production Processes

Decision makers in industry use IT systems to enrich task fulfilment with information systematically. Next to engineering-driven concepts, the application of Operational Business Intelligence (OpBI) is discussed to support production-specific decisions and to organize production processes. Currently, this discussion focusses rather on practicability studies than on certain implementations. Therefore, we introduce a framework to analyze industry processes using design science research. Evaluation is carried out with data from a rod and wire rolling process in forming industry. In conclusion, the framework approach improves capabilities of users to analyze a steel's rolling behavior during rolling experiments automatically.

Building an Efficient Data Vault for a Corrections Environment Using SCRUM and AGILE Techniques for Operational Business Intelligence

Building an Efficient Data Vault for a Corrections Environment Using SCRUM and AGILE Techniques for Operational Business Intelligence

Architectural Framework for Operational Business Intelligence System

Architectural Framework for Operational Business Intelligence System

Big Data Analytics for developing countries – Using the Cloud for Operational BI in Health

AbstractThe multi‐layered view of digital divide suggests there is inequality of access to ICT, inequality of capability to exploit ICT and inequality of outcomes after exploiting ICT. This is evidently clear in the health systems of developing countries. In this paper, we look at cloud computing being able to provide computing as a utility service that might bridge this digital divide for Health Information Systems in developing countries. We highlight the role of Operational Business Intelligence (BI) tools to be able to make better decisions in health service provisioning. Through the case of DHIS2 software and its Analytics‐as‐a‐Service (AaaS) model, we look at how tools can exploit Cloud computing capabilities to perform analytics on Big Data that is resulting from integration of health data from multiple sources. Beyond looking at purely warehousing techniques, we suggest understanding Big Data from Organizational Capabilities and expanding organizational capabilities by offloading computing as a utility to vendors through cloud computing.

Operational Business Intelligence: Processing Mixed Workloads

Modern business applications have operational business intelligence features that require processing analytical queries and business transactions together at the same time and on the same data. This results in mixed workloads, which are a big challenge for current database management systems. Cloud computing fosters the development of data management systems optimized for specific application scenarios. The authors propose an architecture for software-as-a-service business applications that helps process these mixed workloads with a special-purpose main-memory database system, while meeting strict service-level objectives with stringent response-time and throughput guarantees.

The data warehouse virtualization framework for operational business intelligence

AbstractIn order to explore the most current information and react faster to changing business conditions, organizations consider real‐time data warehousing a powerful technique to achieve operational business intelligence (BI). We propose in this paper a novel real‐time data warehouse (RTDW) framework based on the virtualization concept. Our approach introduces a conceptual modelling technique, known as ring modelling, for real‐time data management and multidimensional analysis. This technique produces a flexible semi‐structured data model that accommodates unknown business process data and relationships as they evolve, handles schema changes and aggregate‐management efficiently, and scales well with the large size of increasing data volumes. With the help of a telecommunication business example, We evaluated our proposed approach in an extensive experimental study where we compared our approach Ring Model with existing structured multidimensional conceptual models (MCMs), i.e. relational OLAP and multidimensional OLAP, and with semi‐structured MCM, i.e. XML Cubes, in terms of scalability, data storage estimations, data updates loading time, and query response times. Our performance results show that encouraging speedups are achieved.

Comments on “In-Memory Databases in Business Information Systems”

In-memory databases, which keep the full data set in the main memory for reading accesses, and their application in business information systems, receive more and more attention due to the development and marketing of SAP technology HANA. In issue 6/2011 of the journal Business & Information Systems Engineering, a total of 11 authors discuss potential applications of in-memory databases in business scenarios in 6 articles (Loos et al. 2011). Thereby, questions about the capability of in-memory technology, resulting consequences for OLTP and OLAP applications as well as implications on the architecture of business information systems, amongst others, were in the focus of the discussion. After publication, the first author received feedback and suggestions. Three of these responses resulted in written statements: Strohmeier focuses on the question whether or not future OLTP and OLAP applications should be integrated. He states that concepts for both an operative decision support and for the provision of operative real-time information need to be developed. As a result, he sees a demand for more research in the area “Operational Business Intelligence”. Piller provides more examples of application scenarios, which emerge with in-memory databases. For instance, operative reporting, adaptive planning, explorative mass data analysis or the analysis of consumer data from embedded systems. Schutte explains, after a description of implicit presumptions and requirements for in-memory technology, the potential of in-memory technology using a practical example of the EDEKA group, which he sees in batch processes, initial supply, and release change. If you like to comment on this topic or another article of the journal Business & Information Systems Engineering, please send your feedback to loos@iwi.uni-sb.de.

Using Business Intelligence for Operational Decision-Making in Call Centers

This paper proposes an operational business intelligence system for call centers. Using data collected from a large U.S. insurance company, the authors demonstrate a decision tree based solution to help the company achieve excellence through improved service levels. The initial results from this study provide insight into the factors affecting this firm’s call center service levels, and the solution developed in this paper provides two distinct advantages to managers. First, it enables them to identify key factors and the role they play in determining service levels. Second, a sliding window approach is proposed which allows managers to see the effects of resource reallocation on service levels on an on-going basis.

The Importance of Process Thinking in Business Intelligence

The growing field of Operational Business Intelligence (BI) has resulted in increasing interest in BI-supported Business Processes (BPs), including their management and ongoing improvement. This has led BI practitioners to consider another field–Business Process Management (BPM)–that is closely related to business performance management. However, current approaches to the BPM and operational BI integration have been limited and reduced to the problem of technical integration of BPM and BI systems. This paper argues that by adopting process- thinking in BI, further opportunities for business value creation could be discovered through systematic analysis of the non-technical aspects of BI and BPM integration, including strategy alignment, human-centered knowledge management, and ongoing improvement of BI supported processes. The authors propose a theoretical framework founded in the related research in BPM, BI, and Knowledge Management (KM) fields, describing the ways it has been used to guide ongoing empirical research in diverse case organizations across different industry sectors.

Informatics in Radiology: Automated Web-based Graphical Dashboard for Radiology Operational Business Intelligence

Radiology departments today are faced with many challenges to improve operational efficiency, performance, and quality. Many organizations rely on antiquated, paper-based methods to review their historical performance and understand their operations. With increased workloads, geographically dispersed image acquisition and reading sites, and rapidly changing technologies, this approach is increasingly untenable. A Web-based dashboard was constructed to automate the extraction, processing, and display of indicators and thereby provide useful and current data for twice-monthly departmental operational meetings. The feasibility of extracting specific metrics from clinical information systems was evaluated as part of a longer-term effort to build a radiology business intelligence architecture. Operational data were extracted from clinical information systems and stored in a centralized data warehouse. Higher-level analytics were performed on the centralized data, a process that generated indicators in a dynamic Web-based graphical environment that proved valuable in discussion and root cause analysis. Results aggregated over a 24-month period since implementation suggest that this operational business intelligence reporting system has provided significant data for driving more effective management decisions to improve productivity, performance, and quality of service in the department.

Intelligence‐led policing at the Amsterdam‐Amstelland Police Department: operationalized business intelligence with an enterprise ambition

AbstractThis article elaborates on the setup for intelligence‐led policing in support of ensuring public safety as it presented itself in the Spring of 2007 at the Amsterdam‐Amstelland Police Department in the Netherlands. The picture that is painted is the outcome of a qualitative research effort involving semi‐structured interviews triangulated with other internal data sources. The main goal of the article is to establish how an organization can leverage its operationalized business intelligence ambition by connecting it into an ambition for better enterprise management. The case ends in showcasing two popular operational business intelligence tools instrumental to capacity management. Copyright © 2009 John Wiley & Sons, Ltd.

1,000 tables under the form

The goal of operational Business Intelligence (BI) is to help organizations improve the efficiency of their business by giving every "operational worker" insights needed to make better operational decisions, and aligning day-to-day operations with strategic goals. Operational BI reporting contributes to this goal by embedding analytics and reporting information into workflow applications so that the business user has all required information (contextual and business data) in order to make good decisions. EII systems facilitate the construction of operational BI reports by enabling the creation and querying of customized virtual database schemas over a set of distributed and heterogeneous data sources with a low TCO. Queries over these virtual databases feed the operational BI reports. We describe the characteristics of operational BI reporting applications and show that they increase the complexity of the source to target mapping defined between source data and virtual databases. We show that this complexity yields the execution of "mega queries", i.e., queries with possible a 1,000 tables in their FROM clause. We present some key optimization methods that have been successfully implemented in SAP Business Objects Data Federator system to deal with mega queries.

A testbed for managing dynamic mixed workloads

Workload management for operational business intelligence (BI) databases is difficult. Queries vary widely in length and objectives. Resource contention is difficult to predict and to control as dynamically-arriving, long, analyst queries compete for resources with ongoing online-transaction processing (OLTP) queries and batch report queries. Currently, administrators struggle to choose workload management policies and set their thresholds manually. The goal of our project is a software framework to make the management of such mixed workloads easier. Our framework includes a policy controller that tunes workload management policies automatically to meet workload objectives. This demonstration of our system illustrates (1) the difficulty of managing a BI database workload and (2) the benefits of tuning policies automatically and individually for each service class of queries in a workload. In addition, our demonstrator is a useful research tool for understanding how policies and a policy controller adapt as the system state changes under a mixed workload. In our demo, the participant plays the administrator and tunes the policies for a variety of difficult-to-manage workloads as they execute. These policies include admission control, scheduling, and execution control policies. We visualize the policies, the user objectives, and the load on the system components (CPUs, memory, disks) during execution, which helps the participant see whether objectives are being met and make appropriate policy decisions. At the end of each workload, the participant is given the opportunity to compare how their policies met workload objectives versus policies determined by our automatic policy controller.

Managing operational business intelligence workloads

We explore how to manage database workloads that contain a mixture of OLTP-like queries that run for milliseconds as well as business intelligence queries and maintenance tasks that last for hours. As data warehouses grow in size to petabytes and complex analytic queries play a greater role in day-to-day business operations, factors such as inaccurate cardinality estimates, data skew, and resource contention all make it notoriously difficult to predict how such queries will behave before they start executing. However, traditional workload management assumes that accurate expectations for the resource requirements and performance characteristics of a workload are available at compile-time, and relies on such information in order to make critical workload management decisions. In this paper, we describe our approach to dealing with inaccurate predictions. First, we evaluate the ability of workload management algorithms to handle workloads that include unexpectedly long-running queries. Second, we describe a new and more accurate method for predicting the resource usage of queries before runtime. We have carried out an extensive set of experiments, and report on a few of our results.