Business Intelligence - A Managerial Approach
For courses on Business Intelligence or Decision Support Systems.A managerial approach to understanding business intelligence systems.To help future managers use and understand analytics, Business Intelligence provides students with a solid foundation of BI that is reinforced with hands-on practice.
Business Intelligence - A Managerial Approach
See the decision-making aspects: Managerial Approach. This text takes a managerial approach to Business Intelligence (BI), emphasising the applications and implementations behind the concepts. This approach allows students to understand how BI works in a way that will help them adopt these technologies in future managerial roles. Put the concepts into action: Access to the Teradata Network. Teradata University Network (TUN) is a free learning portal sponsored by Teradata, a division of NCR, whose objective is to help faculty learn, teach, communicate, and collaborate with others in the field of BI. Business Intelligence is interconnected with TUN via various hands-on assignments provided in all chapters and is accessible to students through the portal. Understand the context: Real-world Orientation. Extensive, vivid examples from large corporations, small businesses, and government and not-for-profit agencies make the difficult concepts more accessible and relevant. International examples of global competition, partnerships, and trade are also provided throughout. These real-world case studies show students the capabilities of BI, its cost and justification, and the innovative ways real corporations are using BI in their operations.
A managerial approach to understanding business intelligence systems. To help future managers use and understand analytics, Business Intelligence provides students with a solid foundation of BI that is reinforced with hands-on practice.
Digital transformation can bring the opportunity of implementation of data-driven management approach to clinical laboratories. Data-driven clinical laboratory management depends mainly on real-time collection analysis of process data. Recent systems lack some features such as access to data, processing of data, sharing of data, real-time data analysis and presentation. As managerial decisions have to rely on information extracted from laboratory data, one of the most important skills for laboratory management is data-centric mindset.
By adopting digital transformation strategy, clinical laboratories can gain managerial insights from existing data, identify areas for internal performance improvement (utilization management, quality control practices, turnaround time, cost, errors, etc.), track quality indicators continuously, improve health-related outcomes of patients. These opportunities to change the value creation and delivery can be realized by the implementation of informatics tools like business intelligence, expert systems, decision support systems, data analytics platforms, AI/ML applications; adoption of state-of-the-art IT infrastructure, full connectivity, seamless reporting, real-time data collection and analysis [6, 7].
Business intelligence (BI) can be defined as solutions providing/facilitating to reach strategic targets, increase efficiency, improve patient-clinician satisfaction and full compliance to legal regulations. New decision-making mechanisms can be plotted with business intelligence software. BI systems can impact on the design of processes and accelerate decision making processes and may induce reduction of costs, improvement of patient outcomes, reaching quality goals, monitoring functional/dynamic structure of organization and determining required changes for the future . Features of business intelligence software such as specificity to laboratory, user-friendliness, convenience of establishment and maintenance, adaptability, cost-efficiency has to be evaluated by laboratory specialists thoroughly for functional/useful business intelligence.
Data collection and processing improves managerial insight of laboratory specialists. Data-driven managerial approach can be realized with technological innovations. Production of test results in medical laboratories is not a service that only includes the test analysis step. Services like counseling about accurate test selection before analysis, interpretation of results, and recommendations for further investigations are also included in diagnostic services provided by medical laboratories. Accurate testing and accurate assessment of outcomes are important extra-analytical steps required to make more efficient use of the laboratory. The extreme overspecialization in medicine and rapid development of new diagnostic tests highlight the role of the laboratory in interpreting diagnostic test results. Test utilization management and test interpretation can be streamlined by the help of novel digital technologies to deliver more value to patients and physicians. To support utilization management, decision support systems can be implemented.
Most of the clinical laboratories still have traditional IT artifacts consisting of laboratory information systems which are mainly client/server or desktop applications with no to very basic components like middleware, business intelligence/analytics, decision support system and monolithic software architecture, on-premise data storage facilities and limited computing abilities.
Digital transformation is a process that aims to improve an entity by triggering significant changes to its properties through combinations of information, computing, communication and connectivity technologies. Even though the concept may sound like a technical issue, it is not only about modernization of IT infrastructure. To overcome key barriers of digital transformation (Table 1), the process involves mainly managerial responsibilities like alignment of business and IT strategies, planning of DT strategy, change management, transformational and learning-oriented leadership .
This digest explores the strategies and techniques we use when sharing and discussing information in order to influence an outcome. The work began with some raw research, surveying over 200 people in the USA and Canada to understand their methods and approaches to business persuasion. In addition this issue explores the broader use of narrative as a tool for persuasion and knowledge transfer at length, and from different angles.
Abstract: This paper emphasizes the importance of statistical approach to intelligent managerial decision-making. The research was based on an original empirical survey, conducted on the basis of a random sample of large Croatian firms, aimed at estimating the extent at which Croatian managers use statistical methods. The research results are presented in this paper, and suggestions are given for the promotion of the statistical education in order to increase the level of statistical thinking in Croatian firms.
Today's manager cannot carry out great deal of modern management tools (Rigby, 2005) without knowledge of statistical methods and statistical thinking. Statistical thinking and applied statistical methods help managers to cope with modern business conditions and obtain best results for the firm and their personal carrier (Whitaker et al., 2001). Statistical methods can provide many possible solutions for solving the modern age paradox of more information but less understanding. Statistical thinking should have positive influence on managerial efficiency, but in the business practice it is rare to find a manager who thinks, in such a manner, which is a result of the traditional approach to managers' education. The aim of this study is to estimate the extent of use of statistical methods in Croatian firms and to suggest the promotion of the statistical education in order to increase the level of statistical thinking. To this end, an original empirical survey was conducted on the basis of a random sample of large Croatian firms. The results of this research, as well as suggestions how to include statistical thinking in the statistical education, are a scientific contribution of this paper to intelligent managerial decision making.
Nowadays, the new technologies for Business Intelligence as DataWarehouse, OLAP, Data Mining, emerged and are needed for the managerial process. In the area of decision support systems, a basic role is held by a data warehouse which is an online repository for decision support applications using complex star join queries. Answering such queries efficiently is often difficult due to the complex nature of both the data and the queries. One of the most challenging tasks for the data warhouse administrator (DWA) is the selection of a set of indexes to attain optimal performance for a given workload under storage constraint. The problem is shown to be NP-hard since it involves searching a vast space of possible configurations. It is very much important to extract meaningful information from the workload which represents the major step towards building relevant indexes. This paper presents an approach for selecting an optimized index configuration using association rules with Apriori algorithm which can drive to understand with more accuracy the attributes correlation. This helps to recommend an index set that closely match the requirements of the provided workload. Experimented using the ABP-1 benchmark, our proposed approach achieves good performance compared with previous studies.
This subject covers a range of issues in organisational applications of business intelligence with regard to knowledge management, enterprise/business process management and organisational decision making. It addresses the processes of generation, dissemination, retention, application and distribution of corporate information and knowledge. The subject also includes key aspects of information systems development approaches and ways of designing systems that provide business intelligence to enterprises. The techniques are explored practically in project-based assignments. 041b061a72