From Data to Knowledge: A Cognitive Approach to Retail Business Intelligence

Consumer-oriented companies can no longer afford to make decisions or measure results based on gut feeling. They must be able to take advantage of all available data. Advanced analytics makes it possible to capture value and benefit from big data, however, this isn´t a given. Companies must hire, develop, and retain skilled analysts, who can distinguish relevant from irrelevant data, draw the right assumptions, and translate information into insights. To lighten the burden on companies and support big data analytics, this paper presents a KID (Data-Information-Knowledge) model based on a cognitive approach which can accumulate experience and gain knowledge by continuously perceiving data, interpreting data into meaningful information, absorbing incoming information, and updating knowledge as humans do. This is a process of from data to knowledge and knowledge about correlations among attributes, making assumptions, and testing the assumptions with appropriate algorithms which are constantly updated and summarized in this data-information-knowledge cyclic process. This approach is applied to a retail business for understanding customer purchasing and product sale situations, so as to support provision of better service and timely adaptation of business strategy.