Dovetailing of Business Intelligence and Knowledge Management: An Integrative Framework

The rapid advancement in Information and Communication Technology is driving a revolutionary change in the way organizations do business. The fast growing capabilities of both generating and collecting data has generated an imperative need for new techniques and tools that can intelligently and automatically transform the processed data into valuable information and knowledge for effective decision making. Business intelligence (BI) plays an important role extracting valuable information and discovering the hidden patterns in internal as well as external sources of data. The main purpose of the BI is to improve the knowledge with information that allows managers to make effective decisions to achieve organizational objectives. However majority of organizational knowledge is in unstructured form or in the minds of its employees. On the other hand, Knowledge Management (KM) encompasses both tacit and explicit knowledge to enhance s the organizations performance by providing collaborative tools to learn, create and share the knowledge within the organization. Therefore, it is imperative for the organizations to integrate BI with KM. The purpose of this paper is to discuss the importance of integration of BI with KM and provide a framework to integrate BI and KM. Keywords: Business Intelligence (BI), Knowledge Management (KM), Scorecard, Dashboard, ETL, Data Mining, OLAP, Tacit Knowledge, Explicit Knowledg

Intelligent architecture to support second generation general accounting

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementThis study aimed to innovate the world of accounting software. After so many years, accountants are faced with an unbelievable amount of work, which is not always productive, effective and efficient for both the accountant and the company that provided him with the data required to carry out the accounting. There is already accounting software with various automation processes, from ornamentation to profitability analysis and management reporting. There is also software that is updated in accordance with the accounting laws, i.e., the platform changes its mechanisms according to the changes in the law. Despite the existence of this software, manual work remains, and the amount of information accountants are faced with is still very large. It is difficult for accountants to do a 100% reliable job with so much information and data they have. One of the most common situations in the accounting world is undoubtedly the miscalculation or forgetting of some financial or non-financial data found in accounting operations (income statements, balance sheets, etc.). To render accounting operations efficient, effective and productive, errorfree and 100% reliable, an intelligent architecture has been developed to support second generation general accounting. This architectural design was developed with a view to make the existing software smarter with the help of artificial intelligence. A study was carried out on accounting keys and concepts, on AI and main process automation techniques to build the model. With these studies it was intended to acquire all possible requirements for the creation of the architecture. Towards the end of the thesis the model was validated

Implementing a Business Intelligence System for small and medium-sized enterprises

Over the years, Business Intelligence (BI) systems have become critically important to organizations due to the increasing fast-paced competition, the vast amount of daily generated data and the complexity of how to manage collected data. Business intelligence systems empower organizations to gain insights and to understand a clearer view of their vast data, business and customers, which help to make better decisions and hence produce better results and increase profit. BI refers to a collection of an organization’s resources such as tools, technologies, applications, systems and databases which enable organizations to manage insights of their business data, activities and performance in order to make better decision. However the majority of existing BI systems, target and support large organizations, and the small and medium-sized organizations (SMEs) are mostly overlooked due to lack of substantial finance. The paper elaborates the considerations for implementing BI systems for SMEs. Some new trends such as cloud BI solutions, open BI sources solutions are reviewed. The paper finally provides for the implementation of Business Intelligence system for a SME, the purpose and constraints of the system are detailed

Big data reduction framework for value creation in sustainable enterprises

Value creation is a major sustainability factor for enterprises, in addition to profit maximization and revenue generation. Modern enterprises collect big data from various inbound and outbound data sources. The inbound data sources handle data generated from the results of business operations, such as manufacturing, supply chain management, marketing, and human resource management, among others. Outbound data sources handle customer-generated data which are acquired directly or indirectly from customers, market analysis, surveys, product reviews, and transactional histories. However, cloud service utilization costs increase because of big data analytics and value creation activities for enterprises and customers. This article presents a novel concept of big data reduction at the customer end in which early data reduction operations are performed to achieve multiple objectives, such as a) lowering the service utilization cost, b) enhancing the trust between customers and enterprises, c) preserving privacy of customers, d) enabling secure data sharing, and e) delegating data sharing control to customers. We also propose a framework for early data reduction at customer end and present a business model for end-to-end data reduction in enterprise applications. The article further presents a business model canvas and maps the future application areas with its nine components. Finally, the article discusses the technology adoption challenges for value creation through big data reduction in enterprise applications

A review and future direction of agile, business intelligence, analytics and data science

Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions

Reviews

Brian Clegg, Mining The Internet — Information Gathering and Research on the Net, Kogan Page: London, 1999. ISBN: 0–7494–3025–7. Paperback, 147 pages, £9.99

Developing Strategic Capability through Business Intelligence Applications: A case study from the German Healthcare Insurance Industry

Wynn, M. and Brinkmann, D., (2018), in Yeoh, W. and Miah, S. (eds) Business Intelligence in Organisational Settings, IGI-Global. Company performance can be measured at all levels across an organisation, 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 article, these tools are classified within an appropriate conceptual framework which encompasses the organisation’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