Effect of Business Intelligence and Analytics on Business Performance

This article examines the relationship between extent of business intelligence and analytics (BI&A) implementation and business performance. Also, it analyzes the moderating role of technology in this relationship. The data for our model is collected from secondary data sources for 116 public companies in United States. The data is analyzed using structural equation modeling techniques. Our findings show positive relationship between extent of BI&A implementation and business performance. Furthermore, our results show that type of BI&A has significant moderating impact on the BI&A implementation and performance relationship

The Intervening Role of Readiness Factors on the Relationship between Business Intelligence and Analytics Usage and Firm Performance

The use of information technology (IT) infrastructure alongside Business Intelligence/Business Analytics (BI/BA) strategies, had initially faced a lot of barriers for implementation. Business uncertainty regarding the potential of IT infrastructure as a practical mechanism to incorporate alongside the integration of BI/BA applications, was just one of the many barriers to implementation that was presented in the literature. Although many companies have begun to realize the effectiveness of BI/BA integration on decision-making and organizational control, minimal research has been done to explore the direct effect of BI/BA investments towards fostering improved organizational performance. In this study, we propose a conceptual framework to determine to what extent the implementation of BI/BA, alongside the presence of readiness factors influences firm performance. The findings of our research will define the potential of BI/BA to improve a company\u27s organizational performance allowing for purposeful investment in BI/BA

Predict Network, Application Performance Using Machine Learning and Predictive Analytics

In this thesis, a study is performed to find the effect of applications on resource consumption in computer networks and how to make use of available technologies such as predictive analytics, machine learning and business intelligence to predict if an application can degrade the network performance or consume computer system resources. In recent years, having a healthy computer system and the network is essential for continuity of business. The study focusses on analyzing the performance metrics collected from real networks using scripts and available programs created specifically for monitoring applications and network in real-time. This work has significant importance because monitoring real-time performance doesn’t give accurate or concise information about the reasons behind any degradation in network or application performance. On the other hand, analyzing those performance metrics over a certain period and find a correlation between metrics and applications gives much more relevant information about the root cause of problems. The findings proved that there is a correlation between certain performance metrics, besides correlation found between metrics and applications which conclude the study objectives. The benefits of this study could be seen in analyzing complex networks where there is uncertainty in determining the root cause of a problem in applications or networks

Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

How do top- and bottom-performing companies differ in using business analytics?

Purpose Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA. Design/methodology/approach Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies. Findings Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment. Practical implications Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities. Originality/value This study provides useful management insights into the effective use of BA for improving organizational performance

Using big data for customer centric marketing

This chapter deliberates on “big data” and provides a short overview of business intelligence and emerging analytics. It underlines the importance of data for customer-centricity in marketing. This contribution contends that businesses ought to engage in marketing automation tools and apply them to create relevant, targeted customer experiences. Today’s business increasingly rely on digital media and mobile technologies as on-demand, real-time marketing has become more personalised than ever. Therefore, companies and brands are striving to nurture fruitful and long lasting relationships with customers. In a nutshell, this chapter explains why companies should recognise the value of data analysis and mobile applications as tools that drive consumer insights and engagement. It suggests that a strategic approach to big data could drive consumer preferences and may also help to improve the organisational performance.peer-reviewe

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment