INTEGRATING DATA ANALYTICS & KNOWLEDGE MANAGEMENT: A CONCEPTUAL MODEL

Abstract

Data analytics may be heavily reliant on technology such as statistical models, machine learning algorithms, big data, and cloud computing; however, its outcome depends largely on human qualities such as experience, intuition, value, and judgement. Human knowledge is at the core of data analytics and knowledge management plays a key role in the analytics process. This paper uses the Data-Information-Knowledge-Wisdom (DIKW) hierarchy as an overarching structure to examine the end-to-end process of data analytics and to illustrate a conceptual three-phase data analytics process model integrating knowledge management practices including the discovery, creation, and application of knowledge. Nonaka’s knowledge conversion theory is applied to the analytics process to shed light on the easily and often overlooked human and organizational aspects that are fundamental to the effectiveness of data analytics. The alignment and synergy between data analytics and knowledge management help foster collaboration, drive innovation, and ultimately improve outcome.