53,349 research outputs found
Integrating Business Intelligence and Analytics in Managing Public Sector Performance: An Empirical Study
Business intelligence and analytics (BIA) is emerging as a critical area to boost organizational performance. Nowadays, data is not only important and valuable to the organization but recognized as necessary to spike the organization performance and success. As a result, many organizations spend a considerable amount of investment toward obtaining faster accurate information on a real-time basis. The previous study revealed that even though many organizations use business intelligence technologies for obtaining information, yet they still lack analytics implementation. Therefore, this study aims to discover the integrated implementation factors of business intelligence and analytics in managing organizational performance, particularly for organizations of the public sector. In achieving this, a depth literature review was carried out to identify the influential factors in the implementation of business intelligence, business analytics, and performance management. The subject matter experts in Business Intelligence (BI), Business Analytics (BA) and Organisational Performance Management (OPM) were invited to participate in this empirical study, which was conducted in Malaysia. The study was carried out through interviewing experts, in order to identify the essential factors for business intelligence and data analytics implementation. Twenty essential factors and sixty-four sub-factors were identified and analyzed to construct the integrated factors in BIA and OPM implementation. The result of the study revealed four integrated factors of the BIA and OPM implementation, such as skill, documentation, visualization, and work culture. Finance, data management, software, strategic planning, and decision-making are other factors integrated with BI, BA, and OPM respectively. Finally, this study illustrates the integrated factors in a visual form
Examining the Role of Business Intelligence and Analytics in Hospitality Revenue Management
Most hospitality revenue management forecasting systems were built prior to the business intelligence and analytics movement. Only recently these systems have been enhanced to offer contemporary business intelligence and analytics functionalities. In addition, revenue management professionals are receiving support from standalone, supplementary business intelligence and analytics platforms. The purpose of this dissertation was to produce a holistic review of and establish the role of business intelligence and analytics within hospitality revenue management. Data was collected from twenty-three interviews; all participants were employed by hospitality organizations in revenue management specific positions. Grounded theory methodology was utilized. The results show that nearly all of revenue management tasks are supported by business intelligence and analytics functionalities, irrespective of where the functionalities are housed, in revenue management systems or in business intelligence and analytics tools. Also, opportunities to integrate more advanced functionalities into revenue management systems, including those relating to interfaces, were identified. As part of this inquiry, revenue managersâ beliefs and perceptions - including relative advantage, job-fit, and trust - were examined to determine which have influence on the usage of business intelligence and analytics within revenue management systems and as standalone tools. Overall, twenty-two categories/themes were formulated across four research questions. This dissertation contributes to the examination of the role of business intelligence and analytics in hospitality revenue management, but there is still much more to investigate, particularly as compatibility of hospitality systems and data management are improved
Role and Application of Artificial Intelligence in Business Analytics: A Critical Evaluation
The commercial adoption of artificial intelligence in business analytics tools across multiple industries is being driven by the rising volume and complexity of company data. Business organizations are being helped by the widespread application of artificial intelligence and machine learning in business intelligence to glean meaningful insights from sizable and complicated datasets and provide business recommendations that are clear to any business user. Within the industry of information technology, the business analytics is used to refer the usage of computing to gain the insights from data. Such data can be acquired from the internal sources of company like from its enterprise resource planning application, warehouse and mart data, providers of third-party data, or from other public sources. Sample of 198 respondents from different business sectors were to know the role, application and impact of artificial intelligence in business analytics. It is found that there is a significant role of artificial intelligence in business analytics. This paper provides a rigorous examination of the literature in an effort to illustrate the value-creating procedures and to elucidate how organizations may employ AI technologies in their operations. In this study, the forms of AI use in the organizational setting, first- and second-order impacts, and usage typologies are highlighted along with the significant enablers and inhibitors of AI adoption and use. Our analysis synthesizes the current literature
The Challenges of Business Analytics: Successes and Failures
The successful use of business analytics is an important element of a companyâs success. Business analytics enables analysts and managers to engage in an IT-driven sense-making process in which they use the data and analysis as a means to understand the phenomena that the data represent . Not all organizations apply business analytics successfully to decision making. When used correctly, the actionable intelligence gained from a business analytics program can be utilized to improve strategic decision making. Conversely, an organization that does not utilize business analytics information appropriately will not experience optimal decision making; failing to realize the full potential of a data analytics program. This paper examines some organizations that implemented data analytics programs; both successfully and unsuccessfully, and discuss the implications for each organization. Based on the lesson learned, we present ways to implement a successful business analytics program
Healthcare Analytics Leadership: Clinical & Business Intelligence Plan Development
Future healthcare leaders require expert knowledge and practical capabilities in the evaluation, selection, application and ongoing oversight of the best types of analytics to create continuous learning healthcare systems. These systems may result in continuously improving the demonstrable quality, safety and efficiency of healthcare organizations.
Data is an asset for organizations. However, many companies do not know how to establish analytical road maps for future action.
Population Health Intelligence describes a new discipline whose role is to collect, organize, harmonize, analyze, disseminate and act upon the data available to clinicians, health system leaders, the pharmaceutical and biotechnology industry, and healthcare payers.
This webinar on Analytics Leadership will demonstrate how to create and implement Clinical & Business Intelligence Plans that transform data into actionable organizational insights.
Agenda Introduction Healthcare Analytics Leadership: Clinical & Business Intelligence Plan Development Population Health Intelligence
Presentation: 53:3
MEASURING THE ORGANIZATIONAL ANALYTICAL COMPETENCE: DEVELOPMENT OF A SCALE
The massive growth in the amount of data that companies, organizations, and society have been compelled to deal with, reinforces the need for studies on subjects such as business intelligence, business intelligence and analytics, and big data. Although certain aspects of these themes are already established in research, there is still a lack of understanding and consensus on how to combine variables to encourage better use of data. In this study, we propose a comprehensive conceptualization of a new construct -- analytical competence (ACOMP) -- comprised of three dimensions grounded in the business intelligence and analytics literature and absorptive capacity theory. To properly develop the ACOMP scale, we followed a six-step procedure and collected data from 82 organizations. We validated a nomological model where the ACOMP scale was tested as an antecedent of organizational performance regarding making decisions and learning. The results of this study provide support for ACOMP as a valid and reliable scale that is useful for both academic and managerial purposes
Healthcare Data Analytics on the Cloud
Meaningful analysis of voluminous health information has always been a challenge in most healthcare organizations. Accurate and timely information required by the management to lead a healthcare organization through the challenges found in the industry can be obtained using business intelligence (BI) or business analytics tools. However, these require large capital investments to implement and support the large volumes of data that needs to be analyzed to identify trends. They also require enormous processing power which places pressure on the business resources in addition to the dynamic changes in the digital technology. This paper evaluates the various nuances of business analytics of healthcare hosted on the cloud computing environment. The paper explores BI being offered as Software as a Service (SaaS) solution towards offering meaningful use of information for improving functions in healthcare enterprise. It also attempts to identify the challenges that healthcare enterprises face when making use of a BI SaaS solution
Integrating IoT Analytics into Marketing Decision Making: A Smart Data-Driven Approach
With the advent of the Internet of Things (IoT), businesses have gained access to vast amounts of data generated by interconnected devices. Leveraging IoT analytics and marketing intelligence, organizations can extract valuable insights from this data to enhance decision-making processes. This paper presents a comprehensive methodology for data-driven decision-making in the context of IoT analytics and marketing intelligence. A real-time example is used to illustrate the application of this methodology, followed by an inference and discussion of the results. The rise of IoT has enabled real-time data collection from a wide array of interconnected devices, offering unprecedented opportunities for businesses to gain actionable insights. This paper focuses on the intersection of IoT analytics and marketing intelligence, exploring how data-driven decision-making can empower organizations to optimize their marketing strategies, customer experiences, and overall business performance
An Integrated Model of Business Intelligence & Analytics Capabilities and Organizational Performance
Organizations can leverage business intelligence and analytics (BI&A) to transform themselves through a holistic integration process. Contrary to this proposition, many organizations implement BI&A without aligning or integrating it with organizational strategies. Some implement BI&A in a very ad hoc manner without any plans to leverage it. From a research point of view, we lack an integrated framework that can inform both academics and practitioners about adroit applications with business intelligence and analytics capabilities in organizations. We examine what significant BI&A capabilities organizations need to create value from BI&A. We conceptualize second-order constructs that affect the BI&A value-creation process: innovation infrastructure capability, customer process capability, business-to-business (B2B) process capability, and integration capability. We propose that these higher-order BI&A capabilities influence organizational performance through BI&A effectivenessâs the mediation effect. We developed a questionnaire instrument and collected data from 154 firms in India. Partial least squares analysis provides broad support for our hypotheses. Our contributions include identifying and empirically assessing key BI&A capabilities that directly impact how effectively an organization implements BI&A
Predictive Analytics â Examining the Effects on Decision Making in Organizations
Predictive analytics is a type of business analytics which enables predictions to be made, about occurrence of particular events in the future, based on data of the past. The predictive analytics is widely incorporated among the most successful organizations where it supports their decision-making process. The aim of our study is to examine the effects on decision making in organizations caused by predictive analytics. We perform a qualitative study to investigate the effects by using Simonâs model to break down the decision-making process and analyse how the predictive analytics affects each stage. Additionally we test the propositions from Huberâs theory of the effects of advanced information technology on organizational design, intelligence and decision making, in the context of predictive analytics as an advanced information technology. Our contribution to IS knowledge is derived from our findings which show that the predictive analytics offers strong support in the intelligence and design phase of the decision-making process, while having no effect on the choice phase. Furthermore, through the prism of Huberâs theory, we find that the predictive analytics generates effects on the organizational intelligence and decision making, while also having effects at subunit level, organizational level and the organizational memory
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