SSMDM: An approach of big data for semantically Master Data Management

Master data is critical for any business organization. Big organizations like Oracle, Infosys, IBM, Google, Facebook and TCS started working on Master Data Management (MDM) in early 20's. Multinational corporations spend millions of dollars for Managing their Master Data, so as to ensure quality of service and customer retention as well. Unlike big organizations, Small and Midsized Enterprises (SME's), because of their limited resources, are unable to exploit the economies of scale associated with master data management. In this paper a Synthetic Semantic Master Data Modeler (SSMDM) has been proposed, this modeler primarily uses the concept of Google's knowledge graph to identify semantics within data sets. Using SSMDM, synthetic yet realistic master data was generated to find out probable ontologies within synthetic data sets. Based on these ontologies, some rules were framed to produce synthetic facts. These synthetic facts were further used to decide services and cuisines to be offered at a newly opened eating joint. Since inception, we keep on collecting actual customer data. Once sufficient data was available we statistically analyzed the facts originated from actual data with those created synthetically. The results were promising and justify the use of SSMDM for the purpose of policy making in SMEs.