A Study of Business Intelligence Strategy Development by Large Organizations

Organizations have adopted business intelligence solutions with a mixed degree of benefits. Some businesses highlight significant outcomes, while others identify limitations or shortfalls in the benefits derived. Notably, the alignment of business strategy with the adoption of business intelligence processes has been an important predictor of firms being able to achieve organizational wide benefits. The chapter uses a case study approach to document the informational needs achieved through aligning organizational strategy and the adoption of business intelligence solution at two distinct companies. The adoption approaches used by each firm, although different, reflect the important areas in which business intelligence is most useful—strategic alignment, governance, and information presentation.

Strategy, Action Plan, and Approaches for Business Intelligence in Banking and Mining

This case study will analyze the critical success factors and key matters related to the deployment of BI deployment in different organizations. Different organizations have different approaches to making BI available for different business users, divisions, and departments. Data visualization is also one of the important factors which will provide user better reflection of data rather than make them confuse about organization data with too much information in the reports and dashboards. Data quality and diverse standards, which make BI famous in the different organizations, are also analyzed during the investigation of both organizations used in this case study. The case study analysis also shows how BI maturity, governance, and framework are key factors involved in the successful deployment of the BI in different organizations.

Opportunities and Challenges of Implementing Predictive Analytics for Competitive Advantage

Predictive analytics has become an increasingly hot topic in analytics landscape. As a result, predictive analytics deployments are gaining momentum. Yet, the adoption rate is slow, and organizations are only beginning to scratch the surface in regard to the potential applications of this technology. Implemented properly, the business benefits can be substantial. However, there are strategic pitfalls to consider. The key objective of this chapter is to propose a conceptual model for successful implementation of analytics in organizations. This chapter highlights the importance of predictive analytics, identifies determinants of implementation success, and covers some of the potential benefits of this technology. Furthermore, this study reviews key attributes of a successful predictive analytics platform and illustrates how to overcome some of the strategic pitfalls of incorporating this technology in business. Finally, this study highlights successful implementation of analytics solutions in manufacturing and service industry.

Business Intelligence Maturity Framework

This chapter builds an enterprise-level business intelligence maturity (EBIM) model that facilitates an improved view of the path in business intelligence (BI) implementation. The authors adopted a two-stage qualitative approach, namely a Delphi study followed by case studies. The findings reveal that a comprehensive EBIM model enables organizations to plan, assess, and manage their BI initiatives more effectively than previously. In addition, this study lists important key process areas (KPAs) that influence BI implementation success. The authors found that the KPAs in different levels and dimensions help organizations identify critical areas in need of maximum attention and channel their scarce resources to improve those areas. Further, the entire EBIM framework fosters better use of limited resources in critical areas, which is likely to have a greater effect at the right time.

Developing Strategic Capability Through Business Intelligence Applications

Company performance can be measured at all levels across an organization, and in the German healthcare industry, business intelligence systems play a crucial role in achieving this. For one major health insurance company (discussed here as an alias – AK Healthcare), the deployment of business intelligence applications has supported sustained growth in turnover and market share in the past five years. In this chapter, these tools are classified within an appropriate conceptual framework which encompasses the organization's information infrastructure and associated processes. Different components of the framework are identified and examples are given: systems infrastructure, data provision/access control, the BI tools and technologies, report generation, and information users. The use and integration of business intelligence tools in the strategy development process is then analyzed, and the key functions and features of these tools for strategic capability development are discussed. Research findings encompass system access, report characteristics, and end-users' capabilities.

Depicting Data Quality Issues in Business Intelligence Environment Through a Metadata Framework

Modern business intelligence systems depend highly on high quality data. The core of data quality management is to identify all possible sources of data quality problems. To achieve this goal, an extensive metadata infrastructure is the most promising solution. Through theoretical metadata model investigation, the authors identified a set of data quality dimensions by carefully examining the data quality management principles and applied those principles to current BI environment. They summarize the analysis by proposing a BI data quality framework.

Tailorable Technologies for Improving Business Intelligence Systems

The Australian farm-based businesses can be benefited from specially designed applications for cost-effective operation while maximizing profits to survive in economic and environmental crises. For decision support, existing business intelligence systems (BIS) approaches scarcely deal with specific user's provisions to adjust changing situations in decision making, without extra technical exertions. In this chapter, the authors describe a conceptual framework of tailorable BIS solution that is based on case study findings in that the highlighted requirements are relevant to address changing situations through enhancing end user's engagement. The activities of end user's engagement supported through the use of tailorable features that reinforce a shift from the traditional BIS process to a new provision where business owners can actively involve in adjusting their features to their decision support.

Culturally Sensitive Data Visualizations

Organizations are undergoing significant changes in their business environment. Competing on a global scale means that organizations must better understand cultural issues of their customers, employees, and key stakeholders. As business analytics grows in importance, managers are sharing data visualizations to a variety of different cultures and beliefs. When creating culturally sensitive data visualizations, a set of best practices is required to assist managers to make timely and accurate decisions with the least possible cultural bias. This chapter aims to develop these best practices by analyzing cultural traits, communication habits, and other differences between the western and eastern global regions.

The Rise of Embedded Analytics

The internet revolution, cloud computing, and the evolution to self-service analytics have all contributed to the changing dimensions of business intelligence. To compete effectively in a digitally driven world, business leaders must understand and address the critical shifts taking place in the field of analytics, and how these shifts impact their overall strategy. The key objective of this chapter is to propose a conceptual model for successful implementation of embedded analytics in organizations. This chapter also covers some of the potential benefits of analytics, explores the changing dimensions of analytics, and provides a guide to some of the opportunities that are available for using embedded analytics in business. Furthermore, this study reviews key attributes of a successful modern analytics platform and illustrates how to overcome some of the key challenges of incorporating embedded analytics into an analytic strategy in business. Finally, this study highlights successful implementation of analytics solutions in manufacturing and service industry.

A Framework and Architecture for Performance Management in Virtual Organizations

Virtual organizations are becoming common in the new world of work characterized by modern data exchange capabilities. These organizations face new challenges in information sharing that traditional approaches cannot address. This research proposes a framework and architecture for providing performance data to partners in virtual organizations. The framework aligns the activities of partners in a virtual organization at three different layers and defines common performance measurement indicators at each layer. It also proposes an implementation architecture that enables inter-organizational performance management in collaborative environments. The proposed architecture is validated through a prototype developed using IBM business process and business intelligence products.