Abstract
Many banks today are faced with the challenges how to appropriately store, process, use, and transfer data with the close integration of enterprise-level strategies and management. A key definition as lean theory is introduced to explain how it can lead and guide the companies to effectively conduct their digital knowledge transfer from multiple dimensions. This chapter first presents the overall background with the emphasis on different phases of data usage. Second, the main challenges on digital knowledge transfer for organizations are specified, which demonstrates the importance of using lean theory to manage data. Third, the key disciplines on lean theory and how they can be alleviated to transfer data are introduced. Finally, in order to present clear understandings, Bank F is used as a case to demonstrate how it has used lean theory to conduct effective and comprehensive digital knowledge transfer.
Top2. Background
With the continuous development of various technologies and the applications into business, today companies are globally accepted a digital and knowledge transformation towards Industry 4.0 and Big Data. As presented in Figure 1, the technologies have derived the world to go through four different phases of industrial revolutions from the use of power and water for production to Industry 4.0 as Internet of Things, Machine Learning, and Smart factories (Abd Rahman, Mohamad, & Abdul Rahman, 2021). The associated changes have kept influencing the fundamental landscape of the global business (Abd Rahman et al., 2021). First, the large scale of the data generated by various organizations has amplified immensely in recent decades (Gupta, Modgil, & Gunasekaran, 2020). The data generated by organizations in 2010 accounted for more than seven exabytes (Manyika et al. 2011). And it is estimated that by 2020, this figure will reach to 40 exabytes (Gantz and Reinsel 2012). It not only prompts many technology companies like SAP, Oracle, IBM, and Google for further data exploitation and management (Matthias et al. 2017; Porter and Heppelmann 2014), but also requires companies today to store, transfer, and manage their data more intelligently. Second, it also derives organizations to re-evaluate their manufacturing and management methods to be more agile, customized, responsive, flexible, and lean (Abd Rahman et al., 2021). Consequently, lean data management has been defined as one of the most popular and extended philosophies in different application domains among others (Holweg, 2007).
Figure 1. The four phases of industrial revolutions
Key Terms in this Chapter
Online Transaction Processing (OLTP): A type of data processing enables the online real-time execution of a large number of transactions by a large number of people.
Application Programming Interface (API): Mechanisms enable two or more computer software or programs to communicate with each other.
Artificial Intelligence Model: A set of programs to analyze data to find patterns and make forecasting.
Online Analytical Processing (OLAP): A technology good at processing a large amount of data from different dimensions.
Data Mart: A subset of data warehouse specializing in a particular business line, department, or subject.
Lean theory: A philosophy of manufacturing and operation integrating a set of principles, techniques, and tools into various operational processes to improve effectiveness, human resources, assets, and overall productivity and quality.
Business Intelligence: Business Intelligence is a software process for data analysis and management of business information through integrating strategies and technologies used by companies.
Data Lake: A centralized data repository to store, management, process, and secure all structured and unstructured data at any scale.
Artificial Intelligence: An advanced technique enables developing smart machines to perform tasks and think and function as human beings.